From b9d42bf13577dac027f5b8afa9b37e66b3402e14 Mon Sep 17 00:00:00 2001 From: YANGDB Date: Wed, 5 Apr 2023 11:19:39 -0700 Subject: [PATCH 1/5] Integration for aws.alb -> s3 -> MaterializedView using opensearch SQL plugin Signed-off-by: YANGDB --- .../alb/assets/datasource/datasources.json | 9 ++ .../alb/assets/display/alb-dashboard.ndjson | 84 +++++++++++ .../alb/assets/index/alb_logs_metrics.json | 30 ++++ .../alb/assets/index/alb_logs_raw.json | 42 ++++++ .../alb/assets/table/alb_logs_temp.sql | 38 +++++ .../alb/assets/view/alb_logs_metrics.sql | 14 ++ integrations/alb/assets/view/alb_logs_raw.sql | 11 ++ integrations/alb/config.json | 35 +++++ integrations/alb/info/README.md | 137 ++++++++++++++++++ integrations/alb/info/alb_logo.png | Bin 0 -> 27513 bytes integrations/alb/samples/alb_log_signal.json | 66 +++++++++ .../alb/samples/alb_metric_signal.json | 59 ++++++++ integrations/alb/samples/log_entries.txt | 13 ++ .../alb/test/add_alb_logs_temp_logs_DML.sql | 115 +++++++++++++++ .../alb/test/alb_logs_metrics_index.json | 30 ++++ integrations/alb/test/alb_logs_raw_index.json | 42 ++++++ integrations/alb/test/alb_logs_temp_DDL.sql | 44 ++++++ 17 files changed, 769 insertions(+) create mode 100644 integrations/alb/assets/datasource/datasources.json create mode 100644 integrations/alb/assets/display/alb-dashboard.ndjson create mode 100644 integrations/alb/assets/index/alb_logs_metrics.json create mode 100644 integrations/alb/assets/index/alb_logs_raw.json create mode 100644 integrations/alb/assets/table/alb_logs_temp.sql create mode 100644 integrations/alb/assets/view/alb_logs_metrics.sql create mode 100644 integrations/alb/assets/view/alb_logs_raw.sql create mode 100644 integrations/alb/config.json create mode 100644 integrations/alb/info/README.md create mode 100644 integrations/alb/info/alb_logo.png create mode 100644 integrations/alb/samples/alb_log_signal.json create mode 100644 integrations/alb/samples/alb_metric_signal.json create mode 100644 integrations/alb/samples/log_entries.txt create mode 100644 integrations/alb/test/add_alb_logs_temp_logs_DML.sql create mode 100644 integrations/alb/test/alb_logs_metrics_index.json create mode 100644 integrations/alb/test/alb_logs_raw_index.json create mode 100644 integrations/alb/test/alb_logs_temp_DDL.sql diff --git a/integrations/alb/assets/datasource/datasources.json b/integrations/alb/assets/datasource/datasources.json new file mode 100644 index 000000000..0b82996df --- /dev/null +++ b/integrations/alb/assets/datasource/datasources.json @@ -0,0 +1,9 @@ +[ + { + "name" : "myspark", + "connector": "jdbc", + "properties" : { + "url" : "jdbc:hive2://spark-thrift:10000/default" + } + } +] \ No newline at end of file diff --git a/integrations/alb/assets/display/alb-dashboard.ndjson b/integrations/alb/assets/display/alb-dashboard.ndjson new file mode 100644 index 000000000..5ff6402d3 --- /dev/null +++ b/integrations/alb/assets/display/alb-dashboard.ndjson @@ -0,0 +1,84 @@ +{ + "attributes": { + "title": "AWS ALB Logs Dashboard", + "hits": 0, + "description": "", + "panelsJSON": [ + { + "gridData": { + "x": 0, + "y": 0, + "w": 24, + "h": 15, + "i": "1" + }, + "version": "7.14.0", + "panelIndex": "1", + "embeddableConfig": {}, + "panelRefName": "panel_0" + }, + { + "gridData": { + "x": 24, + "y": 0, + "w": 24, + "h": 15, + "i": "2" + }, + "version": "7.14.0", + "panelIndex": "2", + "embeddableConfig": {}, + "panelRefName": "panel_1" + }, + { + "gridData": { + "x": 0, + "y": 15, + "w": 48, + "h": 15, + "i": "3" + }, + "version": "7.14.0", + "panelIndex": "3", + "embeddableConfig": {}, + "panelRefName": "panel_2" + } + ], + "optionsJSON": { + "hidePanelTitles": false, + "useMargins": true + }, + "version": 1, + "timeRestore": false, + "kibanaSavedObjectMeta": { + "searchSourceJSON": { + "query": { + "language": "kuery", + "query": "" + }, + "filter": [] + } + } + }, + "references": [ + { + "name": "panel_0", + "type": "visualization", + "id": "visualization_id_1" + }, + { + "name": "panel_1", + "type": "visualization", + "id": "visualization_id_2" + }, + { + "name": "panel_2", + "type": "visualization", + "id": "visualization_id_3" + } + ], + "id": "dashboard_id", + "type": "dashboard", + "updated_at": "2023-03-27T00:00:00.000Z", + "version": "WzAsMV0=" +} diff --git a/integrations/alb/assets/index/alb_logs_metrics.json b/integrations/alb/assets/index/alb_logs_metrics.json new file mode 100644 index 000000000..c15c8b0c1 --- /dev/null +++ b/integrations/alb/assets/index/alb_logs_metrics.json @@ -0,0 +1,30 @@ +{ + "mappings" : { + "properties" : { + "count2xx" : { + "type" : "long" + }, + "count4xx" : { + "type" : "long" + }, + "count5xx" : { + "type" : "long" + }, + "latencyInSec" : { + "type" : "float" + }, + "timestamp" : { + "type" : "date" + }, + "totalCount" : { + "type" : "long" + }, + "totalReceivedBytes" : { + "type" : "long" + }, + "totalSentBytes" : { + "type" : "long" + } + } + } +} \ No newline at end of file diff --git a/integrations/alb/assets/index/alb_logs_raw.json b/integrations/alb/assets/index/alb_logs_raw.json new file mode 100644 index 000000000..2a93da976 --- /dev/null +++ b/integrations/alb/assets/index/alb_logs_raw.json @@ -0,0 +1,42 @@ +{ + "mappings" : { + "properties" : { + "receivedBytes" : { + "type" : "long" + }, + "requestUrl" : { + "type" : "text", + "fields" : { + "keyword" : { + "type" : "keyword", + "ignore_above" : 256 + } + } + }, + "requestVerb" : { + "type" : "text", + "fields" : { + "keyword" : { + "type" : "keyword", + "ignore_above" : 256 + } + } + }, + "sentBytes" : { + "type" : "long" + }, + "statusCode" : { + "type" : "text", + "fields" : { + "keyword" : { + "type" : "keyword", + "ignore_above" : 256 + } + } + }, + "timestamp" : { + "type" : "date" + } + } + } +} \ No newline at end of file diff --git a/integrations/alb/assets/table/alb_logs_temp.sql b/integrations/alb/assets/table/alb_logs_temp.sql new file mode 100644 index 000000000..f2cca1057 --- /dev/null +++ b/integrations/alb/assets/table/alb_logs_temp.sql @@ -0,0 +1,38 @@ +CREATE TABLE IF NOT EXISTS alb_logs_temp +( + type string, + time timestamp, + elb string, + client_ip string, + client_port int, + target_ip string, + target_port int, + request_processing_time double, + target_processing_time double, + response_processing_time double, + elb_status_code int, + target_status_code string, + received_bytes bigint, + sent_bytes bigint, + request_verb string, + request_url string, + request_proto string, + user_agent string, + ssl_cipher string, + ssl_protocol string, + target_group_arn string, + trace_id string, + domain_name string, + chosen_cert_arn string, + matched_rule_priority string, + request_creation_time string, + actions_executed string, + redirect_url string, + lambda_error_reason string, + target_port_list string, + target_status_code_list string, + classification string, + classification_reason string +) + USING PARQUET + LOCATION 's3a://xxx/' diff --git a/integrations/alb/assets/view/alb_logs_metrics.sql b/integrations/alb/assets/view/alb_logs_metrics.sql new file mode 100644 index 000000000..d4385e61c --- /dev/null +++ b/integrations/alb/assets/view/alb_logs_metrics.sql @@ -0,0 +1,14 @@ +CREATE MATERIALIZED VIEW alb_logs_metrics + AS +SELECT + UNIX_MILLIS(window.start) AS timestamp, + COUNT(*) AS totalCount, + AVG(target_processing_time) FILTER(WHERE target_processing_time != -1) AS latencyInSec, + COUNT(*) FILTER(WHERE target_status_code LIKE '2__') AS count2xx, + COUNT(*) FILTER(WHERE target_status_code LIKE '4__') AS count4xx, + COUNT(*) FILTER(WHERE target_status_code LIKE '5__') AS count5xx, + SUM(received_bytes) AS totalReceivedBytes, + SUM(sent_bytes) AS totalSentBytes +FROM alb_logs_temp +WHERE client_ip = '10.212.10.101' +GROUP BY TUMBLE(time, '1 Minute'); \ No newline at end of file diff --git a/integrations/alb/assets/view/alb_logs_raw.sql b/integrations/alb/assets/view/alb_logs_raw.sql new file mode 100644 index 000000000..7b4a32a5d --- /dev/null +++ b/integrations/alb/assets/view/alb_logs_raw.sql @@ -0,0 +1,11 @@ +CREATE MATERIALIZED VIEW alb_logs_raw + AS +SELECT + UNIX_MILLIS(time) AS timestamp, + request_verb AS requestVerb, + request_url AS requestUrl, + target_status_code AS statusCode, + received_bytes AS receivedBytes, + sent_bytes AS sentBytes +FROM alb_logs_temp +WHERE client_ip = '10.212.10.101' \ No newline at end of file diff --git a/integrations/alb/config.json b/integrations/alb/config.json new file mode 100644 index 000000000..ec3fc9232 --- /dev/null +++ b/integrations/alb/config.json @@ -0,0 +1,35 @@ +{ + "name": "alb_logs", + "version": { + "integ": "0.1.0", + "schema": "1.0.0", + "resource": "^2.6.0" + }, + "description": "AWS ALB Integration", + "catalog": "observability", + "components": [ + "http,communication,cloud,container,aws_elb" + ], + "collection":[ + { + "logs": [{ + "info": "ALB logs", + "input_type":"logfile", + "dataset":"aws.alb", + "labels" :["aws","ALB"] + }] + }, + { + "metrics": [{ + "info": "ALB metrics signals ", + "input_type": "metrics", + "dataset": "alb.status", + "labels": ["metrics",""] + }] + } + ], + "repo": { + "github": "https://github.com/opensearch-project/observability/tree/main/integrarions/alb" + } +} + diff --git a/integrations/alb/info/README.md b/integrations/alb/info/README.md new file mode 100644 index 000000000..a3f9a29f1 --- /dev/null +++ b/integrations/alb/info/README.md @@ -0,0 +1,137 @@ +![Alb logs](alb_logo.png) + +# What is [ALB](https://docs.aws.amazon.com/elasticloadbalancing/latest/application/load-balancer-access-logs.html) + +AWS Application Load Balancer (ALB) is a service offered by Amazon Web Services (AWS) that helps distribute incoming application traffic across multiple targets, such as Amazon EC2 instances, containers, or IP addresses. It is a part of AWS Elastic Load Balancing (ELB) services, which also include the Classic Load Balancer (CLB) and the Network Load Balancer (NLB). + +The ALB operates at the application layer (Layer 7) of the Open Systems Interconnection (OSI) model and is designed to route HTTP/HTTPS traffic. It can make routing decisions based on the content of the request, allowing more advanced load distribution compared to the Classic Load Balancer. + + +## What is ALB Integration + +ALB integration is concerned with the following aspects + +- Allow simple and automatic generation of all schematic structured + - logs (using the standard SS4O logs schema including specific cloud components logs & custom AWS Elb logs) + - metrics (using the standard SS4O schema) + +- Add Dashboard Assets for both logs / metrics + +- Add correlation queries to investigate logs based metrics + +## ALB logs fields + +The following table describes the fields of an access log entry. + +## Logs reference + +The `elb` dataset collects logs from AWS ELBs. + +**Exported fields** + +| Field | Description | Type | +|---|---|---| +| @timestamp | timestamp. | date | +| aws.elb.backend.http.response.status_code | The status code from the backend (status code sent to the client from ELB is stored in `http.response.status_code` | long | +| aws.elb.backend.ip | The IP address of the backend processing this connection. | keyword | +| aws.elb.backend.port | The port in the backend processing this connection. | keyword | +| aws.elb.backend_processing_time.sec | The total time in seconds since the connection is sent to the backend till the backend starts responding. | float | +| aws.elb.chosen_cert.arn | The ARN of the chosen certificate presented to the client in TLS/SSL connections. | keyword | +| aws.elb.chosen_cert.serial | The serial number of the chosen certificate presented to the client in TLS/SSL connections. | keyword | +| aws.elb.classification | The classification for desync mitigation. | keyword | +| aws.elb.classification_reason | The classification reason code. | keyword | +| aws.elb.connection_time.ms | The total time of the connection in milliseconds, since it is opened till it is closed. | long | +| aws.elb.error.reason | The error reason if the executed action failed. | keyword | +| aws.elb.incoming_tls_alert | The integer value of TLS alerts received by the load balancer from the client, if present. | keyword | +| aws.elb.listener | The ELB listener that received the connection. | keyword | +| aws.elb.matched_rule_priority | The priority value of the rule that matched the request, if a rule matched. | keyword | +| aws.elb.name | The name of the load balancer. | keyword | +| aws.elb.protocol | The protocol of the load balancer (http or tcp). | keyword | +| aws.elb.redirect_url | The URL used if a redirection action was executed. | keyword | +| aws.elb.request_processing_time.sec | The total time in seconds since the connection or request is received until it is sent to a registered backend. | float | +| aws.elb.response_processing_time.sec | The total time in seconds since the response is received from the backend till it is sent to the client. | float | +| aws.elb.ssl_cipher | The SSL cipher used in TLS/SSL connections. | keyword | +| aws.elb.ssl_protocol | The SSL protocol used in TLS/SSL connections. | keyword | +| aws.elb.target_group.arn | The ARN of the target group handling the request. | keyword | +| aws.elb.target_port | List of IP addresses and ports for the targets that processed this request. | keyword | +| aws.elb.target_status_code | List of status codes from the responses of the targets. | keyword | +| aws.elb.tls_handshake_time.ms | The total time for the TLS handshake to complete in milliseconds once the connection has been established. | long | +| aws.elb.tls_named_group | The TLS named group. | keyword | +| aws.elb.trace_id | The contents of the `X-Amzn-Trace-Id` header. | keyword | +| aws.elb.type | The type of the load balancer for v2 Load Balancers. | keyword | +| cloud.account.id | The cloud account or organization id used to identify different entities in a multi-tenant environment. Examples: AWS account id, Google Cloud ORG Id, or other unique identifier. | keyword | +| cloud.availability_zone | Availability zone in which this host, resource, or service is located. | keyword | +| cloud.image.id | Image ID for the cloud instance. | keyword | +| cloud.instance.id | Instance ID of the host machine. | keyword | +| cloud.instance.name | Instance name of the host machine. | keyword | +| cloud.machine.type | Machine type of the host machine. | keyword | +| cloud.project.id | The cloud project identifier. Examples: Google Cloud Project id, Azure Project id. | keyword | +| cloud.provider | Name of the cloud provider. Example values are aws, azure, gcp, or digitalocean. | keyword | +| cloud.region | Region in which this host, resource, or service is located. | keyword | +| container.id | Unique container id. | keyword | +| container.image.name | Name of the image the container was built on. | keyword | +| container.labels | Image labels. | object | +| container.name | Container name. | keyword | +| data_stream.dataset | Data stream dataset. | constant_keyword | +| data_stream.namespace | Data stream namespace. | constant_keyword | +| data_stream.type | Data stream type. | constant_keyword | +| destination.bytes | Bytes sent from the destination to the source. | long | +| destination.domain | The domain name of the destination system. This value may be a host name, a fully qualified domain name, or another host naming format. The value may derive from the original event or be added from enrichment. | keyword | +| ecs.version | ECS version this event conforms to. `ecs.version` is a required field and must exist in all events. When querying across multiple indices -- which may conform to slightly different ECS versions -- this field lets integrations adjust to the schema version of the events. | keyword | +| error.message | Error message. | match_only_text | +| event.category | This is one of four ECS Categorization Fields, and indicates the second level in the ECS category hierarchy. `event.category` represents the "big buckets" of ECS categories. For example, filtering on `event.category:process` yields all events relating to process activity. This field is closely related to `event.type`, which is used as a subcategory. This field is an array. This will allow proper categorization of some events that fall in multiple categories. | keyword | +| event.dataset | Event dataset | constant_keyword | +| event.end | event.end contains the date when the event ended or when the activity was last observed. | date | +| event.kind | This is one of four ECS Categorization Fields, and indicates the highest level in the ECS category hierarchy. `event.kind` gives high-level information about what type of information the event contains, without being specific to the contents of the event. For example, values of this field distinguish alert events from metric events. The value of this field can be used to inform how these kinds of events should be handled. They may warrant different retention, different access control, it may also help understand whether the data coming in at a regular interval or not. | keyword | +| event.module | Event module | constant_keyword | +| event.outcome | This is one of four ECS Categorization Fields, and indicates the lowest level in the ECS category hierarchy. `event.outcome` simply denotes whether the event represents a success or a failure from the perspective of the entity that produced the event. Note that when a single transaction is described in multiple events, each event may populate different values of `event.outcome`, according to their perspective. Also note that in the case of a compound event (a single event that contains multiple logical events), this field should be populated with the value that best captures the overall success or failure from the perspective of the event producer. Further note that not all events will have an associated outcome. For example, this field is generally not populated for metric events, events with `event.type:info`, or any events for which an outcome does not make logical sense. | keyword | +| event.start | event.start contains the date when the event started or when the activity was first observed. | date | +| host.architecture | Operating system architecture. | keyword | +| host.containerized | If the host is a container. | boolean | +| host.domain | Name of the domain of which the host is a member. For example, on Windows this could be the host's Active Directory domain or NetBIOS domain name. For Linux this could be the domain of the host's LDAP provider. | keyword | +| host.hostname | Hostname of the host. It normally contains what the `hostname` command returns on the host machine. | keyword | +| host.id | Unique host id. As hostname is not always unique, use values that are meaningful in your environment. Example: The current usage of `beat.name`. | keyword | +| host.ip | Host ip addresses. | ip | +| host.mac | Host MAC addresses. The notation format from RFC 7042 is suggested: Each octet (that is, 8-bit byte) is represented by two [uppercase] hexadecimal digits giving the value of the octet as an unsigned integer. Successive octets are separated by a hyphen. | keyword | +| host.name | Name of the host. It can contain what `hostname` returns on Unix systems, the fully qualified domain name, or a name specified by the user. The sender decides which value to use. | keyword | +| host.os.build | OS build information. | keyword | +| host.os.codename | OS codename, if any. | keyword | +| host.os.family | OS family (such as redhat, debian, freebsd, windows). | keyword | +| host.os.kernel | Operating system kernel version as a raw string. | keyword | +| host.os.name | Operating system name, without the version. | keyword | +| host.os.name.text | Multi-field of `host.os.name`. | match_only_text | +| host.os.platform | Operating system platform (such centos, ubuntu, windows). | keyword | +| host.os.version | Operating system version as a raw string. | keyword | +| host.type | Type of host. For Cloud providers this can be the machine type like `t2.medium`. If vm, this could be the container, for example, or other information meaningful in your environment. | keyword | +| http.request.body.bytes | Size in bytes of the request body. | long | +| http.request.method | HTTP request method. The value should retain its casing from the original event. For example, `GET`, `get`, and `GeT` are all considered valid values for this field. | keyword | +| http.request.referrer | Referrer for this HTTP request. | keyword | +| http.response.body.bytes | Size in bytes of the response body. | long | +| http.response.status_code | HTTP response status code. | long | +| http.version | HTTP version. | keyword | +| source.address | Some event source addresses are defined ambiguously. The event will sometimes list an IP, a domain or a unix socket. You should always store the raw address in the `.address` field. Then it should be duplicated to `.ip` or `.domain`, depending on which one it is. | keyword | +| source.as.number | Unique number allocated to the autonomous system. The autonomous system number (ASN) uniquely identifies each network on the Internet. | long | +| source.as.organization.name | Organization name. | keyword | +| source.as.organization.name.text | Multi-field of `source.as.organization.name`. | match_only_text | +| source.geo.city_name | City name. | keyword | +| source.geo.continent_name | Name of the continent. | keyword | +| source.geo.country_iso_code | Country ISO code. | keyword | +| source.geo.country_name | Country name. | keyword | +| source.geo.location | Longitude and latitude. | geo_point | +| source.geo.region_name | Region name. | keyword | +| source.ip | IP address of the source (IPv4 or IPv6). | ip | +| source.port | Port of the source. | long | +| tags | List of keywords used to tag each event. | keyword | +| trace.id | Unique identifier of the trace. A trace groups multiple events like transactions that belong together. For example, a user request handled by multiple inter-connected services. | keyword | +| url.domain | Domain of the url, such as "www.elastic.co". In some cases a URL may refer to an IP and/or port directly, without a domain name. In this case, the IP address would go to the `domain` field. If the URL contains a literal IPv6 address enclosed by `[` and `]` (IETF RFC 2732), the `[` and `]` characters should also be captured in the `domain` field. | keyword | +| url.original | Unmodified original url as seen in the event source. Note that in network monitoring, the observed URL may be a full URL, whereas in access logs, the URL is often just represented as a path. This field is meant to represent the URL as it was observed, complete or not. | wildcard | +| url.original.text | Multi-field of `url.original`. | match_only_text | +| url.path | Path of the request, such as "/search". | wildcard | +| url.port | Port of the request, such as 443. | long | +| url.query | The query field describes the query string of the request, such as "q=elasticsearch". The `?` is excluded from the query string. If a URL contains no `?`, there is no query field. If there is a `?` but no query, the query field exists with an empty string. The `exists` query can be used to differentiate between the two cases. | keyword | +| url.scheme | Scheme of the request, such as "https". 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Subject: [PATCH 2/5] fix broken markdown documents links and remove redundant docs (#1495) Signed-off-by: YANGDB --- docs/Integration-plugin-tasks.md | 10 +- docs/Integration-structure.md | 6 +- docs/Integrations.md | 2 +- docs/Simple-schema.md | 4 +- docs/schema/README.md | 59 ++++- docs/schema/observability/README.md | 66 ++++- docs/schema/observability/logs/Usage.md | 6 +- .../schema/observability/traces/Usage.md | 4 +- integrations/README.md | 4 +- src/main/resources/schema/README.md | 136 ---------- .../resources/schema/observability/README.md | 163 ------------ .../schema/observability/logs/README.md | 234 ------------------ .../schema/observability/logs/Usage.md | 117 --------- .../schema/observability/metrics/README.md | 105 -------- .../schema/observability/traces/README.md | 153 ------------ src/main/resources/schema/security/README.md | 9 - src/main/resources/schema/system/README.md | 57 ----- 17 files changed, 137 insertions(+), 998 deletions(-) rename {src/main/resources => docs}/schema/observability/traces/Usage.md (93%) delete mode 100644 src/main/resources/schema/README.md delete mode 100644 src/main/resources/schema/observability/README.md delete mode 100644 src/main/resources/schema/observability/logs/README.md delete mode 100644 src/main/resources/schema/observability/logs/Usage.md delete mode 100644 src/main/resources/schema/observability/metrics/README.md delete mode 100644 src/main/resources/schema/observability/traces/README.md delete mode 100644 src/main/resources/schema/security/README.md delete mode 100644 src/main/resources/schema/system/README.md diff --git a/docs/Integration-plugin-tasks.md b/docs/Integration-plugin-tasks.md index 9c362b803..7130272c7 100644 --- a/docs/Integration-plugin-tasks.md +++ b/docs/Integration-plugin-tasks.md @@ -9,13 +9,13 @@ The integration plugins is currently responsible for the next tasks: ### Catalog Registration -During the loading of the Integration Plugin it will go over all the [catalog schemas](../../schema/README.md) and creates the appropriate +During the loading of the Integration Plugin it will go over all the [catalog schemas](schema/README.md) and creates the appropriate template mapping for each catalog schema entity. This will allow the future Integration to be validated to the schema catalog they are associated with. -**For example** - the [Observability](../../schema/observability) catalog will eventually be registered with the next mapping templates - - [Traces](../../schema/observability/traces/traces.mapping) - - [Logs](../../schema/observability/logs/logs.mapping) - - [Metrics](../../schema/observability/metrics/metrics.mapping) +**For example** - the [Observability](schema/observability) catalog will eventually be registered with the next mapping templates + - [Traces](../src/main/resources/schema/observability/traces/traces.mapping) + - [Logs](../src/main/resources/schema/observability/logs/logs.mapping) + - [Metrics](../src/main/resources/schema/observability/metrics/metrics.mapping) These mapping specify a backed `data-stream` index pattern they conform with [Naming Pattern](observability/Naming-convention.md). diff --git a/docs/Integration-structure.md b/docs/Integration-structure.md index d00a6245b..949eeb043 100644 --- a/docs/Integration-structure.md +++ b/docs/Integration-structure.md @@ -183,16 +183,16 @@ In this case the field resides in the `instrumentationScope.attributes.identific ``` `Catalog:` -This defines the catalog source from which the Integration is associated with. This is based on the `catalog` API which is part of the integration support.[Sample Observability catalog](../../schema/system/samples/catalog.json) +This defines the catalog source from which the Integration is associated with. This is based on the `catalog` API which is part of the integration support.[Sample Observability catalog](schema/system/samples/catalog.json) `Components:` -This section defines the classification components associated to this Integration according to [ECS specification](https://www.elastic.co/guide/en/ecs/current/ecs-allowed-values-event-category.html) and expresses in the [Sample Observability catalog](../../schema/system/samples/catalog.json) +This section defines the classification components associated to this Integration according to [ECS specification](https://www.elastic.co/guide/en/ecs/current/ecs-allowed-values-event-category.html) and expresses in the [Sample Observability catalog](schema/system/samples/catalog.json) `collection:` This references the different types of collection this integration if offering. It can be one of the following Observability catalog's element: { *`Traces, Logs, Metrics`* }. -The collection **name** (`logs`,`traces`,`metrics`) reflects the catalog's `category` as it appears in the [Sample Observability catalog](../../schema/system/samples/catalog.json) +The collection **name** (`logs`,`traces`,`metrics`) reflects the catalog's `category` as it appears in the [Sample Observability catalog](schema/system/samples/catalog.json) **Collections** diff --git a/docs/Integrations.md b/docs/Integrations.md index 87a8bf101..93652d8a6 100644 --- a/docs/Integrations.md +++ b/docs/Integrations.md @@ -46,7 +46,7 @@ Observability telemetry schema defines the following components - **logs**, **tr In many occasions, correlate between the logs, traces and metrics is mandatory to be able to monitor and understand how the system is behaving. In addition, the distributed nature of the application produces multiple formats of telemetry signals arriving from different components ( network router, web server, database) -For such correlation to be possible the industry has formulated several protocols ([OTEL](https://github.com/open-telemetry), [ECS](https://github.com/elastic/ecs), [OpenMetrics](https://github.com/OpenObservability/OpenMetrics), [Alerts](https://github.com/MicrosoftDocs/azure-docs/blob/main/articles/azure-monitor/alerts/alerts-common-schema.md)) for communicating these signals - the **Observability schemas**. +For such correlation to be possible the industry has formulated several protocols: ([OTEL](https://github.com/open-telemetry), [ECS](https://github.com/elastic/ecs), [OpenMetrics](https://github.com/OpenObservability/OpenMetrics), [Alerts]()) for communicating these signals - the **Observability schemas**. ## Problem definition Today in OpenSearch, Observability and its dashboards are only partially aware (traces only) of the schematic structure of these signal types. In addition, the actual schema mapping is not present internally in the Observability plugin and has to be imported externally. diff --git a/docs/Simple-schema.md b/docs/Simple-schema.md index fec47463e..9d70f622c 100644 --- a/docs/Simple-schema.md +++ b/docs/Simple-schema.md @@ -84,8 +84,8 @@ The data model is highly coupled with the visual components, for example - the A ## Ingestion Pipeline A mandatory part of an Observability solution is its ability to ingest data at scale, currently, OpenSearch Observability support the following out of the box schematized data providers: -- Data prepper - https://github.com/opensearch-project/data-prepper -- Jaeger - https://opensearch.org/docs/latest/observing-your-data/trace/trace-analytics-jaeger/ +- [Data prepper](https://github.com/opensearch-project/data-prepper) +- [Jaeger](https://opensearch.org/docs/latest/observing-your-data/trace/trace-analytics-jaeger) ### **Data Prepper:** diff --git a/docs/schema/README.md b/docs/schema/README.md index c8202c693..2262d489f 100644 --- a/docs/schema/README.md +++ b/docs/schema/README.md @@ -57,7 +57,7 @@ Each component has two flavours: A component may be classified as a `container` which has the ability to group / combine multiple components inside. -For example, we can examine the [`logs`](observability/logs/logs.mapping) component that has the capacity to combine additional components (such as `http`, `communication` and more) +For example, we can examine the [`logs`](../../src/main/resources/schema/observability/logs/logs.mapping) component that has the capacity to combine additional components (such as `http`, `communication` and more) ```json5 ... "composed_of": [ @@ -78,4 +78,59 @@ A component also has a list of `tags` which are aliases for the component name w "container": false } ... -``` \ No newline at end of file +``` + +## Data Correlation + +In order to be able to correlate information across different signal (represented in different indices) we introduced the notion of correlation into the schema. +This information is represented explicitly in both the declarative schema file and the physical mapping file + +This information will enable the knowledge to be projected and allow for analytic engine to produce a join query that will take advantage of these relationships. +The correlation metadata info is exported in the following way: + +### Observability Correlation Example: + +### Schema related +In JSON Schema, there is no built-in way to represent relationships directly between multiple schemas, like you would find in a relational database. However, you can establish relationships indirectly by using a combination of `$id`, `$ref`, and consistent property naming across your schemas. +For example the [`logs.schema`](../../src/main/resources/schema/observability/logs/logs.schema) file contains the next `$ref` references for the `traceId` & `spanId` fields that belong to the `traces.schema`. + +```json5 + ... + "traceId": { + "$ref": "https://opensearch.org/schemas/observability/Span#/properties/traceId" + }, + "spanId": { + "$ref": "https://opensearch.org/schemas/observability/Span#/properties/spanId" + }, + ... +``` + +We can observe that the `traceId` field is defined by referencing to the [Span](../../src/main/resources/schema/observability/traces/traceGroups.schema) schema and explicitly to the `#/properties/spanId` field reference location. + +### Mapping related +Each mapping template will contain the foreign schemas that are referenced to in that specific mapping file. For example the [`logs.mapping`](../../src/main/resources/schema/observability/logs/logs.schema) file will contain the next correlation object in the mapping `_meta` section: + +```json5 + "_meta": { + "description": "Simple Schema For Observability", + "catalog": "observability", + "type": "logs", + "correlations": [ + { + "field": "spanId", + "foreign-schema": "traces", + "foreign-field": "spanId" + }, + { + "field": "traceId", + "foreign-schema": "traces", + "foreign-field": "traceId" + } + ] + } + +``` + +Each `correlations` field contains the F.K field name - `spanId` , the referenced schema - `traces` and the source field name in that schema `spanId` + +This information can be used to generate the correct join queries on a contextual basis. diff --git a/docs/schema/observability/README.md b/docs/schema/observability/README.md index 3089a6381..9a718234b 100644 --- a/docs/schema/observability/README.md +++ b/docs/schema/observability/README.md @@ -107,9 +107,14 @@ The Observability indices would follow the recommended for immutable data stream Index pattern will follow the next naming template `sso_{type}`-`{dataset}`-`{namespace}` - - **type** - indicated the observability high level types "logs", "metrics", "traces" (prefixed by the `sso_` schema convention ) - - **dataset** - The field can contain anything that classify the source of the data - such as `nginx.access` - - **namespace** - A user defined namespace - mainly useful to allow grouping of data such as production grade, geography classification +**type** +- indicated the observability high level types "logs", "metrics", "traces" (prefixed by the `sso_` schema convention ) + +**dataset** +- The field can contain anything that classify the source of the data - such as `nginx.access` + +**namespace** +- A user defined namespace - mainly useful to allow grouping of data such as production grade, geography classification This strategy allows two degrees of naming freedom: dataset and namespace. For example a customer may want to route the nginx logs from two geographical areas into two different indices: @@ -129,7 +134,7 @@ The `sso_{type}-{dataset}-{namespace}` combination dictates the target index, `{ - Logs - `sso_logs` For example if within the ingested log contains the following section: -```json +```json5 { ... "attributes": { @@ -157,5 +162,58 @@ This `data_stream` will use one of the Observability ready-made index templates **If the ingesting party has a need to update the template default index setting (shards, replicas ) it may do so before the actual creation of the data_stream.** +## Observability Signals Correlation + +In order to be able to correlate information across different signal (represented in different indices) we introduced the notion of correlation into the schema. +This information is represented explicitly in both the declarative schema file (for example [`logs.schema`](../../../src/main/resources/schema/observability/logs/logs.schema)) and the physical mapping file ([`logs.mapping`](../../../src/main/resources/schema/observability/logs/logs.mapping)) + +This information will enable the knowledge to be projected and allow for analytic engine to produce a join query that will take advantage of these relationships. +The correlation metadata info is exported in the following way: + +### Schema related +In JSON Schema, there is no built-in way to represent relationships directly between multiple schemas, like you would find in a relational database. However, you can establish relationships indirectly by using a combination of `$id`, `$ref`, and consistent property naming across your schemas. +For example the [`logs.schema`](../../../src/main/resources/schema/observability/logs/logs.schema) file contains the next `$ref` references for the `traceId` & `spanId` fields that belong to the [`traces.schema`](../../../src/main/resources/schema/observability/traces/traceGroups.schema). + +```json5 + ... + "traceId": { + "$ref": "https://opensearch.org/schemas/observability/Span#/properties/traceId" + }, + "spanId": { + "$ref": "https://opensearch.org/schemas/observability/Span#/properties/spanId" + }, + ... +``` + +We can observe that the `traceId` field is defined by referencing to the [Span](../../../src/main/resources/schema/observability/traces/traceGroups.schema) schema and explicitly to the `#/properties/spanId` field reference location. + +### Mapping related +Each mapping template will contain the foreign schemas that are referenced to in that specific mapping file. For example the [`logs.mapping`](../../../src/main/resources/schema/observability/logs/logs.mapping) file will contain the next correlation object in the mapping `_meta` section: + +```json5 + "_meta": { + "description": "Simple Schema For Observability", + "catalog": "observability", + "type": "logs", + "correlations": [ + { + "field": "spanId", + "foreign-schema": "traces", + "foreign-field": "spanId" + }, + { + "field": "traceId", + "foreign-schema": "traces", + "foreign-field": "traceId" + } + ] + } + +``` + +Each `correlations` field contains the F.K (foreign-key) field name - `spanId` , the referenced schema - `traces` and the source field name in that schema `spanId` + +This information can be used to generate the correct join queries on a contextual basis. + ### Note It is important to mention that these new capabilities would not change or prevent existing customer usage of the system and continue to allow proprietary usage. diff --git a/docs/schema/observability/logs/Usage.md b/docs/schema/observability/logs/Usage.md index 03fa0c5bb..9210261ba 100644 --- a/docs/schema/observability/logs/Usage.md +++ b/docs/schema/observability/logs/Usage.md @@ -7,15 +7,15 @@ Start the OpenSearch cluster and follow the next steps for manually setup of the `>> PUT _component_template/http_template` -Copy the http.mapping content [here](http.mapping) +Copy the http.mapping content [here](../../../../src/main/resources/schema/observability/logs/http.mapping) `>> PUT _component_template/communication_template` -Copy the communication.mapping content [here](communication.mapping) +Copy the communication.mapping content [here](../../../../src/main/resources/schema/observability/logs/communication.mapping) `>> PUT _index_template/logs` -Copy the logs.mapping content [here](logs.mapping) +Copy the logs.mapping content [here](../../../../src/main/resources/schema/observability/logs/logs.mapping) Now you can create an data-stream index (following the logs index pattern) that has the supported schema: diff --git a/src/main/resources/schema/observability/traces/Usage.md b/docs/schema/observability/traces/Usage.md similarity index 93% rename from src/main/resources/schema/observability/traces/Usage.md rename to docs/schema/observability/traces/Usage.md index 31fe0b9b3..04a644414 100644 --- a/src/main/resources/schema/observability/traces/Usage.md +++ b/docs/schema/observability/traces/Usage.md @@ -7,11 +7,11 @@ Start the OpenSearch cluster and follow the next steps for manually setup of the `>> PUT _component_template/tracegroups_template` -Copy the [traceGroups.mapping](traceGroups.mapping) +Copy the [traceGroups.mapping](../../../../src/main/resources/schema/observability/traces/traceGroups.mapping) `>> PUT _index_template/traces` -Copy the [traces.mapping](traces.mapping) +Copy the [traces.mapping](../../../../src/main/resources/schema/observability/traces/traces.mapping) Now you can create an data-stream index (following the logs index pattern) that has the supported schema: diff --git a/integrations/README.md b/integrations/README.md index 8916dc79b..5aabb1e2c 100644 --- a/integrations/README.md +++ b/integrations/README.md @@ -1,4 +1,4 @@ -# Definitions +# Integration Definitions ## Bundle @@ -98,7 +98,7 @@ integration-template-name `Config.json` file includes the following Integration configuration see [NginX config](nginx/config.json) - Additional information on the config structure see [Structure](docs/Integration-structure.md) + Additional information on the config structure see [Structure](../docs/Integration-structure.md) #### Display: diff --git a/src/main/resources/schema/README.md b/src/main/resources/schema/README.md deleted file mode 100644 index cdebae05a..000000000 --- a/src/main/resources/schema/README.md +++ /dev/null @@ -1,136 +0,0 @@ -# Simple Schema for Open Search - -## Background -Simple Schema for OpenSearch brings the concept of organized and structured catalog data. -A catalog of schemas is a comprehensive collection of all the possible data schemas or structures that can be used to represent information. - -It provides a standardized way of organizing and describing the structure of data, making it easier to analyze, compare, and share data across different systems and applications. -Structured data refers to any data that is organized in a specific format or schema - for example Observability data or security data... - -By using a catalog of schemas, data analysts and scientists can easily identify and understand the different structures of data, allowing them to correlate and analyze information more effectively. -One of the key benefits of a catalog of schemas is that it promotes interoperability between different systems and applications. -By using standardized schema descriptions, data can be shared and exchanged more easily, regardless of the system or application being used. - -The use of a catalog of schemas can also improve data quality by ensuring that data is consistent and accurate. This is because the schema provides a clear definition of the data structure and the rules for how data should be entered, validated, and stored. - -## OpenSearch Schemas - -Opensearch supports out of the box the following schemas - - [Observability](observability/README.md) - - [Security](security/README.md) - - [System](system/README.md) - -### Observability -Simple Schema for [Observability](https://github.com/opensearch-project/observability) allows ingestion of both (OTEL/ECS) formats and internally consolidate them to best of its capabilities for presenting a unified Observability platform. - -### Security -OpenSearch Security is a [plugin](https://github.com/opensearch-project/security) for OpenSearch that offers encryption, authentication and authorization. When combined with OpenSearch Security-Advanced Modules, it supports authentication via Active Directory, LDAP, Kerberos, JSON web tokens, SAML, OpenID and more. It includes fine grained role-based access control to indices, documents and fields. It also provides multi-tenancy support in OpenSearch Dashboards. - -### System -System represents the internal structure of Opensearch's `.dashboard` entities such as `dashboard`, `notebook`, `save-query` `integration`. Every saved object may declare itself in the system -catalog folder and publish a corresponding schema. This capability will allow validation of these objects and also simplify structure evolution using this schema. - -### Catalog Loading -During Integration plugin loading, it will go over all the Opensearch's schema supported catalogs and generate the appropriate templates representing them. -This will allow any future Integration using these catalogs without the need to explicitly defining them thus maintaining a unified common schema. - -Each catalog may support semantic versioning so that it may evolve its schema as needed. -In the future, the catalog will enable to associate domains with catalogs and allow externally importing catalogs into Opensearch for additional collaboration. - -### Catalog Structure -A catalog is structured in the following way: - - - Catalog named folder: `Observability` - - Categories named folder : `Logs`, `Traces`, `Metrics` - - Component named file : `http` , `communication` , `traces` , `metrics` - -Each level encapsulates additional internal structure that allows a greater level of composability and agility. -The details of each catalog structure is described in the [catalog.json](system/samples/catalog.json) file that resides in the root level of each catalog folder. - -**Component** -The component is the leaf level definition of the catalog hierarchy, it details the actual building blocks of the catalog's types and fields. - -Each component has two flavours: - - - `$component.mapping` - describes how the type is physically stored in the underlying index - - `$component.mapping` - describing the actual json schema for this component type - -A component may be classified as a `container` which has the ability to group / combine multiple components inside. - -For example, we can examine the [`logs`](observability/logs/logs.mapping) component that has the capacity to combine additional components (such as `http`, `communication` and more) -```json5 - ... - "composed_of": [ - "http_template", - "communication_template" - ], - ... -``` -A component also has a list of `tags` which are aliases for the component name which can be used to reference it directly by an integration components list. - -```json5 - ... - { - "component": "communication", - "version": "1.0", - "url": "https://github.com/opensearch-project/observability/tree/2.x/schema/observability/logs/communication", - "tags": ["web"], - "container": false - } - ... -``` - -## Data Correlation - -In order to be able to correlate information across different signal (represented in different indices) we introduced the notion of correlation into the schema. -This information is represented explicitly in both the declarative schema file and the physical mapping file - -This information will enable the knowledge to be projected and allow for analytic engine to produce a join query that will take advantage of these relationships. -The correlation metadata info is exported in the following way: - -### Observability Correlation Example: - -### Schema related -In JSON Schema, there is no built-in way to represent relationships directly between multiple schemas, like you would find in a relational database. However, you can establish relationships indirectly by using a combination of `$id`, `$ref`, and consistent property naming across your schemas. -For example the [`logs.schema`](observability/logs/logs.schema) file contains the next `$ref` references for the `traceId` & `spanId` fields that belong to the `traces.schema`. - -```json5 - ... - "traceId": { - "$ref": "https://opensearch.org/schemas/observability/Span#/properties/traceId" - }, - "spanId": { - "$ref": "https://opensearch.org/schemas/observability/Span#/properties/spanId" - }, - ... -``` - -We can observe that the `traceId` field is defined by referencing to the [Span](observability/traces/traceGroups.schema) schema and explicitly to the `#/properties/spanId` field reference location. - -### Mapping related -Each mapping template will contain the foreign schemas that are referenced to in that specific mapping file. For example the [`logs.mapping`](observability/logs/logs.schema) file will contain the next correlation object in the mapping `_meta` section: - -```json5 - "_meta": { - "description": "Simple Schema For Observability", - "catalog": "observability", - "type": "logs", - "correlations": [ - { - "field": "spanId", - "foreign-schema": "traces", - "foreign-field": "spanId" - }, - { - "field": "traceId", - "foreign-schema": "traces", - "foreign-field": "traceId" - } - ] - } - -``` - -Each `correlations` field contains the F.K field name - `spanId` , the referenced schema - `traces` and the source field name in that schema `spanId` - -This information can be used to generate the correct join queries on a contextual basis. diff --git a/src/main/resources/schema/observability/README.md b/src/main/resources/schema/observability/README.md deleted file mode 100644 index 51c941db1..000000000 --- a/src/main/resources/schema/observability/README.md +++ /dev/null @@ -1,163 +0,0 @@ -# Simple Schema for Observability - -## Background -Observability is the ability to measure a system’s current state based on the data it generates, such as logs, metrics, and traces. Observability relies on telemetry derived from instrumentation that comes from the endpoints and services. - -Observability telemetry signals (logs, metrics, traces) arriving from the system would contain all the necessary information needed to observe and monitor. - -Modern application can have a complicated distributed architecture that combines cloud native and microservices layers. Each layer produces telemetry signals that may have different structure and information. - -Using Simple Schema's Observability telemetry schema we can organize, correlate and investigate system behavior in a standard and well-defined manner. - -Observability telemetry schema defines the following components - **logs, traces and metrics**. - -**Logs** provide comprehensive system details, such as a fault and the specific time when the fault occurred. By analyzing the logs, one can troubleshoot code and identify where and why the error occurred. - -**Traces** represent the entire journey of a request or action as it moves through all the layers of a distributed system. Traces allow you to profile and observe systems, especially containerized applications, serverless architectures, or microservices architecture. - -**Metrics** provide a numerical representation of data that can be used to determine a service or component’s overall behaviour over time. - - -In many occasions, correlation between the logs, traces and metrics is mandatory to be able to monitor and understand how the system is behaving. In addition, the distributed nature of the application produces multiple formats of telemetry signals arriving from different components ( network router, web server, database) - -For such correlation to be possible the industry has formulated several protocols ([OTEL](https://github.com/open-telemetry), [ECS](https://github.com/elastic/ecs), [OpenMetrics](https://github.com/OpenObservability/OpenMetrics)) for communicating these signals - the Observability schemas. - ---- -## Schema Aware Components - -The role of the Observability [plugin](https://github.com/opensearch-project/observability) is intended to allow maximum flexibility and not imposing a strict Index structure of the data source. Nevertheless, the modern nature of distributed application and the vast amount of telemetry producers is changing this perception. - -Today many of the Observability solutions (splunk, datadog, dynatrace) recommend using a consolidated schema to represent the entire variance of log/trace/metrics producers. - -This allows monitoring, incidents investigation and corrections process to become simpler, maintainable and reproducible. - - -A Schema-Aware visualization component is a component which assumes the existence of specific index/indices name patterns and expects these indices to have a specific structure - a schema. - -As an example we can see that **Trace-Analytics** is a schema-aware visual component since it directly assumes the traces & serviceMap indices exist and expects them to follow a specific structure. - -This definition doesn’t change the existing status of visualization components which are not “Schema Aware” but it only regulates which Visual components would benefit using a schema and which will be agnostic of its content. - -Operation Panel for example, are not “Schema Aware” since they don’t assume in advanced the existence of a specific index nor do they expect the index they display to have a specific structure. - -## Data Model - -Simple Schema for Observability allows ingestion of both (OTEL/ECS) formats and internally consolidate them to best of its capabilities for presenting a unified Observability platform. - -## Observability index naming - -The Observability indices would follow the recommended for immutable data stream ingestion pattern using the [data_stream concepts](https://opensearch.org/docs/latest/opensearch/data-streams/) - -Index pattern will follow the next naming template `sso_{type}`-`{dataset}`-`{namespace}` - -**type** -- indicated the observability high level types "logs", "metrics", "traces" (prefixed by the `sso_` schema convention ) - -**dataset** -- The field can contain anything that classify the source of the data - such as `nginx.access` - -**namespace** -- A user defined namespace - mainly useful to allow grouping of data such as production grade, geography classification - -This strategy allows two degrees of naming freedom: dataset and namespace. For example a customer may want to route the nginx logs from two geographical areas into two different indices: - - - `sso_logs-nginx-us` - - `sso_logs-nginx-eu` - -This type of distinction also allows for creation of crosscutting queries by setting the next index query pattern `sso_logs-nginx-*` or by using a geographic based crosscutting query `sso_logs-*-eu`. - -## Data index routing - -The [ingestion component](https://github.com/opensearch-project/data-prepper) which is responsible for ingesting the Observability signals is responsible to route the data into the relevant indices. - -The `sso_{type}-{dataset}-{namespace}` combination dictates the target index, `{type}` is prefixed with the `sso_` prefix into one of the supported type: - - - Traces - `sso_traces` - - Metrics - `sso_metrics` - - Logs - `sso_logs` - -For example if within the ingested log contains the following section: -```json5 -{ - ... - "attributes": { - "data_stream": { - "type": "span", - "dataset": "mysql", - "namespace": "prod" - } - } -} -``` -This indicates that the target index for this observability signal should be `sso_traces`-`mysql`-`prod` index that follows uses the traces schema mapping. - -## Observability Index templates - -With the expectation of multiple Observability data providers and the need to consolidate all to a single common schema - the Observability plugin will take the following responsibilities : - - - Define and create all the signals **index templates** upon loading - - Publish a versioned schema file (Json Schema) for each signal type for general validation usage by any 3rd party - -## Observability Ingestion pipeline -The responsibility on an **Observability-ingestion-pipeline** is to create the actual `data_stream` in which it is expecting to ingest into. - -This `data_stream` will use one of the Observability ready-made index templates (Metrics,Traces and Logs) and conform with the above naming pattern (`sso_{type}`-`{dataset}`-`{namespace}`) - -**If the ingesting party has a need to update the template default index setting (shards, replicas ) it may do so before the actual creation of the data_stream.** - -## Observability Signals Correlation - -In order to be able to correlate information across different signal (represented in different indices) we introduced the notion of correlation into the schema. -This information is represented explicitly in both the declarative schema file (for example [`logs.schema`](logs/logs.schema)) and the physical mapping file ([`logs.mapping`](logs/logs.mapping)) - -This information will enable the knowledge to be projected and allow for analytic engine to produce a join query that will take advantage of these relationships. -The correlation metadata info is exported in the following way: - -### Schema related -In JSON Schema, there is no built-in way to represent relationships directly between multiple schemas, like you would find in a relational database. However, you can establish relationships indirectly by using a combination of `$id`, `$ref`, and consistent property naming across your schemas. -For example the [`logs.schema`](logs/logs.schema) file contains the next `$ref` references for the `traceId` & `spanId` fields that belong to the [`traces.schema`](traces/traceGroups.schema). - -```json5 - ... - "traceId": { - "$ref": "https://opensearch.org/schemas/observability/Span#/properties/traceId" - }, - "spanId": { - "$ref": "https://opensearch.org/schemas/observability/Span#/properties/spanId" - }, - ... -``` - -We can observe that the `traceId` field is defined by referencing to the [Span](traces/traceGroups.schema) schema and explicitly to the `#/properties/spanId` field reference location. - -### Mapping related -Each mapping template will contain the foreign schemas that are referenced to in that specific mapping file. For example the [`logs.mapping`](logs/logs.mapping) file will contain the next correlation object in the mapping `_meta` section: - -```json5 - "_meta": { - "description": "Simple Schema For Observability", - "catalog": "observability", - "type": "logs", - "correlations": [ - { - "field": "spanId", - "foreign-schema": "traces", - "foreign-field": "spanId" - }, - { - "field": "traceId", - "foreign-schema": "traces", - "foreign-field": "traceId" - } - ] - } - -``` - -Each `correlations` field contains the F.K (foreign-key) field name - `spanId` , the referenced schema - `traces` and the source field name in that schema `spanId` - -This information can be used to generate the correct join queries on a contextual basis. - -### Note -It is important to mention that these new capabilities would not change or prevent existing customer usage of the system and continue to allow proprietary usage. - diff --git a/src/main/resources/schema/observability/logs/README.md b/src/main/resources/schema/observability/logs/README.md deleted file mode 100644 index 9fcaad705..000000000 --- a/src/main/resources/schema/observability/logs/README.md +++ /dev/null @@ -1,234 +0,0 @@ -# Logs Schema Support - -Observability refers to the ability to monitor and diagnose systems and applications in real-time, in order to understand how they are behaving and identify potential issues. -logs serve as a primary source of information for understanding and debugging complex systems. -Logs provide a record of events, errors, and performance that help developers and administrators identify and resolve issues, monitor system behavior, and improve reliability. - -Logs can also be used to gain insight into user behavior and facilitate auditing and compliance. -By analyzing logs, one can detect patterns, anomalies, and correlations that can inform decisions and facilitate problem-solving. -Logs help to ensure the visibility, reliability, and stability of systems. - -## Details -The next section provides the Simple Schema for Observability support which conforms with the OTEL specification. - -- logs.mapping presents the template mapping for creating the Simple Schema for Observability index -- logs.schema presents the json schema validation for verification of a metrics document conforms to the mapping structure - -## Logs -See [OTEL Logs convention](https://github.com/open-telemetry/opentelemetry-specification/blob/main/specification/logs/data-model.md) -See [OTEL logs protobuf](https://github.com/open-telemetry/opentelemetry-proto/tree/main/opentelemetry/proto/logs/v1) -See [ECS logs](https://github.com/elastic/ecs) - -Simple Schema for Observability conforms with OTEL logs protocol and also was greatly inspired from the Elastic-Common-Schema schema. - -Simple Schema for Observability defines the next data model: - -## General Fields - -### Event Fields -Within a particular domain, the ```event.name``` attribute identifies the event. Events with same domain and name are structurally similar to one another. -For example, some domains could have well-defined schema for their events based on event names. (OTEL driven) - -`event.kind` gives high-level information about what type of information the event contains, without being specific to the contents of the event.(ECS driven) -**Possible values** - - **alert** - indicates an alerting type event which can be triggered by any alerting mechanism - - **enrichment** - indicates an enriched typed event that adds additional context to the original event - - **event** - the default type of the event - - **metric** - this indicated the event describes a numeric measurement - -The `event.domain` attribute is used to logically separate events from different systems. For example, to record Events from `browser` apps, `mobile` apps and `Kubernetes`, we could use browser, device and k8s as the domain for their Events. -This provides a clean separation of semantics for events in each of the domains. (OTEL driven) - -`event.category` gives categorical-level information about what type of information the event contains, this field is an array. (ECS driven) -**Possible values** - - authentication - events are of a challenge and response process by any system that has such responsibilities - - configuration - events related to the configuration of a system or an application - - database - events that are generated as part of the storage system (SQL RDBMS and such) - - driver - events related to the O/S device driver - - email - events related to email messages, email attachments and such - - file - events related to the fact that it has been created on, or has existed on a filesystem - - host - events related to host inventory or lifecycle events - - iam - Identity & access Management logs types - - network - events relating of network activities (connection / traffic and such) - - package - events indication of software packages installation of hosts - - process - events related to O/S process information - - registry - events related to O/S registry events - - session - events related to a persistent connection between different network components - - web - events related to web server activity - -`event.category` corresponds with `event.domain` - -`event.type` gives a fine grain details of the event's category including the phase in-which the field is part of. -This will allow proper categorization of some events that fall in multiple event types, this field is an array. (ECS driven) - -**Possible values** - - access - indication that this event has accesses some resource - - admin - indication that this event is related to the admin context - - allowed - indication that this event was subsequently allowed by some authority system - - change - indication that this event is related to something that has changed - - connection - indication that this event is related to network traffic with indication of connection activity - - creation - indication that this event is related to resources being created - - deletion - indication that this event is related to resources being deleted - - denied - indication that this event is related to resources being denied access - - error - indication of an error related event - - group - indication of events that are related to group objects - - info - indication of events which are informative without other distinct classification - - installation - indication that this event is related to resources being installed - - protocol - indicate that the event is related to specific knowledge of protocol info - - end - indicate that te event is related to some termination state - - start - indicate that te event is related to some initiation state - - user - indicate that the event is related to specific knowledge a user resource - - -`event.result` gives a success or a failure indication from the perspective of the entity that produced the event. (ECS driven) - -**Possible values** - - - failure - - success - - pending - - undetermined - - -#### Exception Fields -This field encapsulated the exception that should appear under the event section `event.exception` (OTEL driven) - - - `message`: The exception message. - - `stacktrace`: A stacktrace as a string in the natural representation for the language runtime. The representation is to be determined and documented by each language SIG. - - `type`: The type of the exception (its fully-qualified class name, if applicable). The dynamic type of the exception should be preferred over the static type in languages that support it. - ---- - -### Overview - -In observability, Logs are typically unstructured data that is generated by applications or systems as a record of events or messages. -Logs can contain any information that the emitting application or system wants to include, and they often contain free-form text rather than structured data. -This makes logs difficult to process and analyze automatically, but it also provides a lot of flexibility and versatility in terms of what information can be captured. - -According to [ECS](https://github.com/elastic/ecs) and the most recent (experimental) [OTEL](https://github.com/open-telemetry/opentelemetry-specification/tree/main/specification/trace/semantic_conventions) definitions, we formalized a unified -log schema. This schema can be used for working with a well-structured set of typed logs arriving from categorical sources. -These sources are expected to report information in a specific way that will simplify future correlations and consolidate similar concerns. - -### data-stream -[data-stream](https://opensearch.org/docs/latest/opensearch/data-streams/) Data streams simplify this process and enforce a setup that best suits time-series data, such as being designed primarily for append-only data and ensuring that each document has a timestamp field. -A data stream is internally composed of multiple backing indices. Search requests are routed to all the backing indices, while indexing requests are routed to the latest write index. - -As part of the Observability naming scheme, the value of the data stream fields combine to the name of the actual data stream : - -`{data_stream.type}-{data_stream.dataset}-{data_stream.namespace}`. -This means the fields can only contain characters that are valid as part of names of data streams. - - - **type** conforms to one of the supported Observability signals (Traces, Logs, Metrics, Alerts) - - **dataset** user defined field that can mainly be utilized for describing the origin of the signal - - **namespace** user custom field that can be used to describe any customer domain specific classification - -#### Timestamp field -As part of the data-stream definition the `@timestamp` is mandatory, if the field is not present to begin with use `ObservedTimestamp` as value for this field - -### Instrumentation scope -This is a logical unit of the application with which the emitted telemetry can be associated. It is typically the developer’s choice to decide what denotes a reasonable instrumentation scope. -The most common approach is to use the instrumentation library as the scope, however other scopes are also common, e.g. a module, a package, or a class can be chosen as the instrumentation scope. - -The instrumentation scope may have zero or more additional attributes that provide additional information about the scope. As an example the field -`instrumentationScope.attributes.identification` is presented will be used to determine the resource origin of the signal and can be used to filter accordingly - -For Example - in the sample [nginx_access-log.json](sample/nginx_access-log.json) this value equals to `nginx` indicating the signal source. - -This field is expected to appear in any future integration or Observability resources into OpenSearch. - - -### Logs Classifications - -#### HTTP -_Inspired by [ECS - http](https://www.elastic.co/guide/en/ecs/current/ecs-http.html), [OTEL - http](https://github.com/open-telemetry/opentelemetry-specification/blob/main/specification/trace/semantic_conventions/http.md)_ - - - `method` - HTTP request method. `GET; POST; HEAD` (OTEL driven) Correspond with `request.method` (ECS driven) - - `status_code` - [Http response code](https://tools.ietf.org/html/rfc7231#section-6) (OTEL driven) Correspond with `response.status_code` (ECS driven) - - `flavor` - Kind of HTTP protocol used. (OTEL driven) Correspond with `version` (ECS driven) - - `user_agent` - Value of the HTTP User-Agent header sent by the client. (OTEL driven) - - - `request.id` - A unique identifier for each HTTP request to correlate logs between clients and servers in transactions. (ECS driven) - - `request_content_length` - The size of the request payload body in bytes. (OTEL driven) Correspond with `request.bytes` (ECS driven) - - `request.body.content` - The full HTTP request body. (ECS driven) - - `request.referrer` - Referrer for this HTTP request. (ECS driven) - - `request.header` - HTTP request headers key/value object (OTEL driven) - - `request.mime_type` - Mime type of the body of the response. (ECS driven) - - - `response_content_length` - The size of the response payload body in bytes. (OTEL driven) Correspond with `response.bytes` (ECS driven) - - `response.body.content` - The full HTTP response body. (ECS driven) - - `response.header` - HTTP response headers key/value object (OTEL driven) - - - `url` - Full HTTP request URL in the form scheme://host[:port]/path?query[#fragment] (OTEL driven) - - `resend_count` - The ordinal number of request resending attempt (OTEL driven) - - - `scheme` - The URI scheme identifying the used protocol. (OTEL driven) - - `target` - The full request target as passed in a HTTP request line or equivalent. (OTEL driven) - - `route` - The matched route (path template in the format used by the respective server framework) (OTEL driven) - - `client_ip` - The IP address of the original client behind all proxies (OTEL driven) `client.ip` - IP address of the client (IPv4 or IPv6). (ECS driven) - -#### Communication -Includes client / server part of the communication - - **_Inspired by_** : - - [ECS - client](https://www.elastic.co/guide/en/ecs/8.6/ecs-client.html) - - [ECS - Server](https://www.elastic.co/guide/en/ecs/8.6/ecs-server.html#ecs-server) - - [ECS - Source](https://www.elastic.co/guide/en/ecs/8.6/ecs-source.html) - - [ECS - Destination](https://www.elastic.co/guide/en/ecs/8.6/ecs-destination.html) - - [OTEL - network](https://github.com/open-telemetry/opentelemetry-specification/blob/main/specification/trace/semantic_conventions/http.md) - - - - `sock.family` - Protocol address family which is used for communication. (OTEL driven) - - - `client.domain` - The domain name of the client system. (ECS driven) Correspond with `source.domain`, (ECS driven) - - `client.bytes` - Bytes sent from the client to the server. (ECS driven) Correspond with `source.bytes` (ECS driven) - - `client.ip` - IP address of the client (IPv4 or IPv6). (ECS driven) Correspond with `source.ip` (ECS driven) - - `client.mac` - MAC address of the client. (IPv4 or IPv6). (ECS driven) Correspond with `source.mac` (ECS driven) - - `client.packets` - Packets sent from the client to the server. (ECS driven) Correspond with `source.packets` (ECS driven) - - - `sock.host.addr` - Local socket address. (OTEL driven) Correspond with `client.address`,`source.address` (ECS driven) - - `sock.host.port` - Local socket port number. (OTEL driven) Corresponds with `client.port`,`source.port` (ECS driven) - - - `server.domain` - The domain name of the server system. (ECS driven) Correspond with `destination.domain` (ECS driven) - - `server.bytes` - Bytes sent from the server to the client. (ECS driven) Correspond with `destination.bytes` (ECS driven) - - `server.ip` - IP address of the server (IPv4 or IPv6). (ECS driven) Correspond with `destination.ip` (ECS driven) - - `server.mac` - MAC address of the server. (IPv4 or IPv6). (ECS driven) Correspond with `destination.mac` (ECS driven) - - `server.packets` - Packets sent from the server to the client.. (ECS driven) Correspond with `destination.packets` (ECS driven) - - - `sock.peer.addr` - Remote socket peer address: IPv4 or IPv6 (OTEL driven) Correspond with `server.address`, `destination.address` (ECS driven) - - `sock.peer.name` - Remote socket peer name. (OTEL driven) Correspond with `destination.domain` (ECS driven) - - `sock.peer.port` - Remote socket peer port. (OTEL driven) (OTEL driven) Corresponds with `server.port`,`destination.port` (ECS driven) - ---- - -```text - - ___________________ - | _______________ | - | |XXXXXXXXXXXXX| | - | |XXXXXXXXXXXXX| | - | |XXXXXXXXXXXXX| | - | |XXXXXXXXXXXXX| | - | |XXXXXXXXXXXXX| | - |_________________| - _[_______]_ - ___[___________]___ - | [_____] []|__ - | [_____] []| \__ - L___________________J \ \___\/ - ___________________ /\ - /###################\ (__) - -``` - ---- - -### References - - https://github.com/opensearch-project/observability/issues/1413 - - https://github.com/opensearch-project/observability/issues/1405 - - https://github.com/opensearch-project/observability/issues/1411 - - https://github.com/open-telemetry/opentelemetry-specification/blob/main/specification/logs/semantic_conventions/events.md - - https://github.com/open-telemetry/opentelemetry-specification/blob/main/specification/trace/semantic_conventions/http.md - - https://github.com/open-telemetry/opentelemetry-specification/blob/main/specification/trace/semantic_conventions/http.md - - https://www.elastic.co/guide/en/ecs/8.6/ecs-destination.html - - https://www.elastic.co/guide/en/ecs/8.6/ecs-client.html - - https://www.elastic.co/guide/en/ecs/8.6/ecs-server.html \ No newline at end of file diff --git a/src/main/resources/schema/observability/logs/Usage.md b/src/main/resources/schema/observability/logs/Usage.md deleted file mode 100644 index 03fa0c5bb..000000000 --- a/src/main/resources/schema/observability/logs/Usage.md +++ /dev/null @@ -1,117 +0,0 @@ -# Actual Index setup and usage -The next document describes the practicality of manually defining and initiating of the observability schema indices, it assumes the OpenSearch cluster -is up and running. - -### Setting Up the Logs Mapping -Start the OpenSearch cluster and follow the next steps for manually setup of the Log mapping template: - -`>> PUT _component_template/http_template` - -Copy the http.mapping content [here](http.mapping) - -`>> PUT _component_template/communication_template` - -Copy the communication.mapping content [here](communication.mapping) - -`>> PUT _index_template/logs` - -Copy the logs.mapping content [here](logs.mapping) - -Now you can create an data-stream index (following the logs index pattern) that has the supported schema: - -`>> PUT _data_stream/sso_logs-dataset-test1` - -You can also directly start ingesting data without creating a data stream. -Because we have a matching index template with a data_stream object, OpenSearch automatically creates the data stream: - -`POST sso_logs-dataset-test1/_doc` -```json -{ - "body": "login attempt failed", - "@timestamp": "2013-03-01T00:00:00", - ... -} - -``` - -To see information about a that data stream: -`GET _data_stream/sso_logs-dataset-test1` - -Would respond the following: -```json -{ - "data_streams" : [ - { - "name" : "sso_logs-dataset-test1", - "timestamp_field" : { - "name" : "@timestamp" - }, - "indices" : [ - { - "index_name" : ".ds-sso_logs-dataset-test1-000001", - "index_uuid" : "-VhmuhrQQ6ipYCmBhn6vLw" - } - ], - "generation" : 1, - "status" : "GREEN", - "template" : "sso_logs-*-*" - } - ] -} -``` - -To see more insights about the data stream, use the `_stats` endpoint: -`GET _data_stream/sso_logs-dataset-test1/_stats` -Would respond the following: -```json -{ - "_shards" : { - "total" : 1, - "successful" : 1, - "failed" : 0 - }, - "data_stream_count" : 1, - "backing_indices" : 1, - "total_store_size_bytes" : 208, - "data_streams" : [ - { - "data_stream" : "sso_logs-dataset-test1", - "backing_indices" : 1, - "store_size_bytes" : 208, - "maximum_timestamp" : 0 - } - ] -} -``` -### Ingestion -To ingest data into a data stream, you can use the regular indexing APIs. Make sure every document that you index has a timestamp field. - -`POST sso_logs-dataset-test1/_doc` -```json -{ - "body": "login attempt failed", - "@timestamp": "2013-03-01T00:00:00", - ... -} - -``` -You can search a data stream just like you search a regular index or an index alias. The search operation applies to all of the backing indices (all data present in the stream). - -`GET sso_logs-dataset-test1/_search` -```json -{ - "query": { - "match": { - ... - } - } -} -``` - -### Manage data-streams in OpenSearh - -To manage data streams from OpenSearch Dashboards, open OpenSearch Dashboards, choose Index Management, select Indices or Policy managed indices. - -see additional information: - - https://opensearch.org/docs/latest/opensearch/data-streams/#step-6-manage-data-streams-in-opensearch-dashboards - - https://opensearch.org/docs/latest/opensearch/data-streams/#step-5-rollover-a-data-stream \ No newline at end of file diff --git a/src/main/resources/schema/observability/metrics/README.md b/src/main/resources/schema/observability/metrics/README.md deleted file mode 100644 index 78e940f51..000000000 --- a/src/main/resources/schema/observability/metrics/README.md +++ /dev/null @@ -1,105 +0,0 @@ -# Metrics Schema Support - -Observability refers to the ability to monitor and diagnose systems and applications in real-time, in order to understand how they are behaving and identify potential issues. -Metrics present a critical component of observability, providing quantifiable data about the performance and behavior of systems and applications. -The importance of supporting metrics structured schema lies in the fact that it enables better analysis and understanding of system behavior. - -A structured schema provides a clear, consistent format, making it easier for observability tools to process and aggregate the data. -This in turn makes it easier for engineers to understand the performance and behavior of their systems, and quickly identify potential issues. - -When metrics are unstructured, it can be difficult for observability tools to extract meaningful information from them. -For example, if the data for a particular metric is not consistently recorded in the same format, it can be difficult to compare and analyze performance data over time. -Similarly, if metrics are not consistently named or categorized, it can be difficult to understand their context and significance. - -With a structured schema in place, observability tools can automatically extract and aggregate data, making it easier to understand system behavior at a high level. -This can help teams quickly identify performance bottlenecks, track changes in system behavior over time, and make informed decisions about system performance optimization. - -## Details -The next section provides the Simple Schema for Observability support which conforms with the OTEL specification. - -- metrics.mapping presents the template mapping for creating the Simple Schema for Observability index -- metrics.schema presents the json schema validation for verification of a metrics document conforms to the mapping structure - -## Metrics -see [OTEL metrics convention](https://opentelemetry.io/docs/reference/specification/metrics/) -see [OTEL metrics protobuf](https://github.com/open-telemetry/opentelemetry-proto/tree/main/opentelemetry/proto/metrics/v1) - -Simple Schema for Observability conforms with OTEL metrics protocol which defines the next data model: - -#### Timestamp field -As part of the data-stream definition the `@timestamp` is mandatory, if the field is not present in the original signal populate this field using `ObservedTimestamp` as value. - -### Instrumentation scope -This is a logical unit of the application with which the emitted telemetry can be associated. It is typically the developer’s choice to decide what denotes a reasonable instrumentation scope. -The most common approach is to use the instrumentation library as the scope, however other scopes are also common, e.g. a module, a package, or a class can be chosen as the instrumentation scope. - -The instrumentation scope may have zero or more additional attributes that provide additional information about the scope. As an example the field -`instrumentationScope.attributes.identification` is presented will be used to determine the resource origin of the signal and can be used to filter accordingly - -### Overview -Metrics are a specific kind of telemetry data. They represent a snapshot of the current state for a set of data. -Metrics are distinct from logs or events, which focus on records or information about individual events. - -Metrics expresses all system states as numerical values; counts, current values and such. -Metrics tend to aggregate data temporally, while this can lose information, the reduction in overhead is an engineering trade-off commonly chosen in many modern monitoring systems. - -Time series are a record of changing information over time. While time series can support arbitrary strings or binary data, only numeric data is in our scope. -Common examples of metric time series would be network interface counters, device temperatures, BGP connection states, and alert states. - -### Metric streams -In a similar way to the data_stream attribute field representing the category of a trace, the metric streams are grouped into individual Metric objects, identified by: - - - The originating Resource attributes - - The instrumentation Scope (e.g., instrumentation library name, version) - - The metric stream’s name - -### Metrics -Metric object is defined by the following properties: - - - The data point type (e.g. Sum, Gauge, Histogram ExponentialHistogram, Summary) - - The metric stream’s unit - - The data point properties, where applicable: AggregationTemporality, Monotonic - -The description is also present in the metrics object but is not part of the identification fields -_- The metric stream’s description_ - - -### Data Types - -**Values:** Metric values in MUST be either floating points or integers. - -**Attributes:** Labels are key-value pairs consisting of string as keys and Any type as values (strings, object, array) - -**MetricPoint:** Each MetricPoint consists of a set of values, depending on the MetricFamily type. - -**Metric** Metrics are defined by a unique attributes (dimensions) within a MetricFamily. - ---- - -Metrics MUST contain a list of one or more MetricPoints. Metrics with the same name for a given MetricFamily SHOULD have the same set of label names in their LabelSet. - -* Metrics.name: String value representation of the matrix purpose -* Metrics.type: Valid values are "gauge", "counter","histogram", and "summary". -* Metrics.Unit: specifies MetricFamily units. - -## Metric Types - -### Gauge -Gauges are current measurements, such as bytes of memory currently used or the number of items in a queue. For gauges the absolute value is what is of interest to a user. -**_A MetricPoint in a Metric with the type gauge MUST have a single value._** -Gauges MAY increase, decrease, or stay constant over time. Even if they only ever go in one direction, they might still be gauges and not counters. - -### Counter -Counters measure discrete events. Common examples are the number of HTTP requests received, CPU seconds spent, or bytes sent. For counters how quickly they are increasing over time is what is of interest to a user. -**_A MetricPoint in a Metric with the type Counter MUST have one value called Total._** - -### Histogram / Exponential-Histogram -Histograms measure distributions of discrete events. Common examples are the latency of HTTP requests, function runtimes, or I/O request sizes. -**_A Histogram MetricPoint MUST contain at least one bucket_**, and SHOULD contain Sum, and Created values. Every bucket MUST have a threshold and a value. - -### Summary -Summaries also measure distributions of discrete events and MAY be used when Histograms are too expensive and/or an average event size is sufficient. -**_A Summary MetricPoint MAY consist of a Count, Sum, Created, and a set of quantiles._** -Semantically, Count and Sum values are counters & MUST be an integer. - - diff --git a/src/main/resources/schema/observability/traces/README.md b/src/main/resources/schema/observability/traces/README.md deleted file mode 100644 index dcf16adcb..000000000 --- a/src/main/resources/schema/observability/traces/README.md +++ /dev/null @@ -1,153 +0,0 @@ -# Traces Schema Support -Observability in the software industry is the ability to monitor and diagnose systems and applications in real-time, in order to understand how they are behaving and identify potential issues. -Traces are a critical component of observability, providing detailed information about the flow of requests through a system, including timing information and any relevant contextual data. - -The importance of supporting traces schema lies in the fact that it enables better analysis and understanding of system behavior. -A structured schema provides a clear, consistent format for traces, making it easier for observability tools to process and aggregate the data. -This in turn makes it easier for engineers to understand the performance and behavior of their systems, and quickly identify potential issues. - -When traces are unstructured, it can be difficult for observability tools to extract meaningful information from them - For example, if the timing information for a particular request is not consistently represented in the same format, -it can be difficult to compare and analyze performance data over time. Similarly, if contextual data is not consistently recorded, it can be difficult to understand the context in which a particular request was executed. - -With a structured schema in place, observability tools can automatically extract and aggregate data, making it easier to understand system behavior at a high level. -This can help teams quickly identify performance bottlenecks, track the root cause of errors, and resolve issues more efficiently. - -## Details -The next section provides the Simple Schema for Observability support which conforms with the OTEL specification. - -- traces.mapping presents the template mapping for creating the Simple Schema for Observability index -- traces.schema presents the json schema validation for verification of a trace document conforms to the mapping structure - -### data-stream -[data-stream](https://opensearch.org/docs/latest/opensearch/data-streams/) Data streams simplify this process and enforce a setup that best suits time-series data, such as being designed primarily for append-only data and ensuring that each document has a timestamp field. -A data stream is internally composed of multiple backing indices. Search requests are routed to all the backing indices, while indexing requests are routed to the latest write index. - -As part of the Observability naming scheme, the value of the data stream fields combine to the name of the actual data stream : - -`{data_stream.type}-{data_stream.dataset}-{data_stream.namespace}`. -This means the fields can only contain characters that are valid as part of names of data streams. - -- **type** conforms to one of the supported Observability signals (Traces, Logs, Metrics, Alerts) -- **dataset** user defined field that can mainly be utilized for describing the origin of the signal -- **namespace** user custom field that can be used to describe any customer domain specific classification - -#### Timestamp field -As part of the data-stream definition the `@timestamp` is mandatory, if the field is not present to begin with use `ObservedTimestamp` as value for this field -**Note** - `@timestamp` value is the actual signal happening time and `observedTimestamp` is the time the exporter reads the actual event record. - -### Instrumentation scope -This is a logical unit of the application with which the emitted telemetry can be associated. It is typically the developer’s choice to decide what denotes a reasonable instrumentation scope. -The most common approach is to use the instrumentation library as the scope, however other scopes are also common, e.g. a module, a package, or a class can be chosen as the instrumentation scope. - -The instrumentation scope may have zero or more additional attributes that provide additional information about the scope. As an example the field -`instrumentationScope.attributes.identification` is presented will be used to determine the resource origin of the signal and can be used to filter accordingly - -## Traces -see [OTEL traces convention](https://github.com/open-telemetry/opentelemetry-specification/tree/main/semantic_conventions/trace) - -Traces are defined implicitly by their Spans - In particular, a Trace can be thought of as a directed acyclic graph (DAG) of Spans, where the edges between Spans are defined as parent/child relationship. - -## Spans -A span represents an operation within a transaction. Each Span encapsulates the following state: -Observability in the software industry is the ability to monitor and diagnose systems and applications in real-time, in order to understand how they are behaving and identify potential issues. -Traces are a critical component of observability, providing detailed information about the flow of requests through a system, including timing information and any relevant contextual data. - -The importance of supporting traces schema lies in the fact that it enables better analysis and understanding of system behavior. -A structured schema provides a clear, consistent format for traces, making it easier for observability tools to process and aggregate the data. -This in turn makes it easier for engineers to understand the performance and behavior of their systems, and quickly identify potential issues. - -When traces are unstructured, it can be difficult for observability tools to extract meaningful information from them - For example, if the timing information for a particular request is not consistently represented in the same format, -it can be difficult to compare and analyze performance data over time. Similarly, if contextual data is not consistently recorded, it can be difficult to understand the context in which a particular request was executed. - -With a structured schema in place, observability tools can automatically extract and aggregate data, making it easier to understand system behavior at a high level. -This can help teams quickly identify performance bottlenecks, track the root cause of errors, and resolve issues more efficiently. - -## Details -The next section provides the Simple Schema for Observability support which conforms with the OTEL specification. - -- traces.mapping presents the template mapping for creating the Simple Schema for Observability index -- traces.schema presents the json schema validation for verification of a trace document conforms to the mapping structure - -### data-stream -[data-stream](https://opensearch.org/docs/latest/opensearch/data-streams/) Data streams simplify this process and enforce a setup that best suits time-series data, such as being designed primarily for append-only data and ensuring that each document has a timestamp field. -A data stream is internally composed of multiple backing indices. Search requests are routed to all the backing indices, while indexing requests are routed to the latest write index. - -As part of the Observability naming scheme, the value of the data stream fields combine to the name of the actual data stream : - -`{data_stream.type}-{data_stream.dataset}-{data_stream.namespace}`. -This means the fields can only contain characters that are valid as part of names of data streams. - -- **type** conforms to one of the supported Observability signals (Traces, Logs, Metrics, Alerts) -- **dataset** user defined field that can mainly be utilized for describing the origin of the signal -- **namespace** user custom field that can be used to describe any customer domain specific classification - -#### Timestamp field -As part of the data-stream definition the `@timestamp` is mandatory, if the field is not present to begin with use `ObservedTimestamp` as value for this field -**Note** - `@timestamp` value is the actual signal happening time and `observedTimestamp` is the time the exporter reads the actual event record. - -### Instrumentation scope -This is a logical unit of the application with which the emitted telemetry can be associated. It is typically the developer’s choice to decide what denotes a reasonable instrumentation scope. -The most common approach is to use the instrumentation library as the scope, however other scopes are also common, e.g. a module, a package, or a class can be chosen as the instrumentation scope. - -The instrumentation scope may have zero or more additional attributes that provide additional information about the scope. As an example the field -`instrumentationScope.attributes.identification` is presented will be used to determine the resource origin of the signal and can be used to filter accordingly - -## Traces -see [OTEL traces convention](https://github.com/open-telemetry/opentelemetry-specification/tree/main/semantic_conventions/trace) - -Traces are defined implicitly by their Spans - In particular, a Trace can be thought of as a directed acyclic graph (DAG) of Spans, where the edges between Spans are defined as parent/child relationship. - -## Spans -A span represents an operation within a transaction. Each Span encapsulates the following state: - -* An operation name -* start and finish timestamp -* Attributes list of key-value pairs. -* Set of Events, each of which is itself a tuple (timestamp, name, Attributes) -* Parent's Span identifier. -* Links to causally-related Spans (via the SpanContext of those related Spans). -* SpanContext information required to reference a Span. - -### SpanContext -Represents all the information that identifies Span in the Trace and is propagated to child Spans and across process boundaries. -A **SpanContext** contains the tracing identifiers and the options that are propagated from parent to child Spans. - -* `TraceId` - It is worldwide unique with practically sufficient probability by being made as 16 randomly generated bytes - used to group all spans for a specific trace together across all processes. -* `SpanId` - It is the identifier for a span, globally unique with practically sufficient probability by being made as 8 randomly generated bytes. When passed to a child Span this identifier becomes the parent span id for the child Span. -* `Tracestate` - carries tracing-system specific context in a list of key value pairs . Trace-state allows different vendors propagate additional information and inter-operate with their legacy Id formats. For more details see this. - -Additional fields can be supported via the Attributes key/value store see [traces](https://github.com/open-telemetry/opentelemetry-specification/blob/main/specification/trace/semantic_conventions/README.md) - -### Structure -The default fields that are supported by the traces are -- **TraceId** : It is worldwide unique with practically sufficient probability by being made as 16 randomly generated bytes - used to group all spans for a specific trace together across all processes. -- **SpanId** : It is the identifier for a span, globally unique with practically sufficient probability by being made as 8 randomly generated bytes. When passed to a child Span this identifier becomes the parent span id for the child Span. -- **ParentId** : It is the identifier for a span's parent span. -- **TraceState** : carries tracing-system specific context in a list of key value pairs. Tracestate allows different vendors propagate additional information and inter-operate with their legacy Id formats. - -- **Name** : String representing the span's name -- **Kind** - - SpanKind.CLIENT - - SpanKind.SERVER - - SpanKind.CONSUMER - - SpanKind.PRODUCER - - SpanKind.INTERNAL - -- **StartTime** : Start time of the event -- **EndTime** : End time of the event -- **Attributes** - - An Attribute is a key-value pair, which has the following structure [Attributes](https://github.com/open-telemetry/opentelemetry-specification/blob/b00980832b4b823155001df56dbf9203d4e53f98/specification/common/README.md#attribute) - -- **DroppedAttributesCount** : Integer counting the dropped attributes -- **Events** : A set of the next tuples (timestamp, name, Attributes) -- **DroppedEventsCount** : Integer counting the dropped events -- **Links** : links to causally-related Spans -- **DroppedLinksCount** : Integer counting the dropped links -- **Status** - - - _status code is the int value - status message is the text representation_ - - - `UNSET = 0` : The default status. - - `OK = 1` : The operation has been validated by an Application developer or Operator to have completed successfully. - - `ERROR = 2` : The operation contains an error. \ No newline at end of file diff --git a/src/main/resources/schema/security/README.md b/src/main/resources/schema/security/README.md deleted file mode 100644 index f5f87bf52..000000000 --- a/src/main/resources/schema/security/README.md +++ /dev/null @@ -1,9 +0,0 @@ -# Security Domain Schema - -OpenSearch Security is a [plugin](https://github.com/opensearch-project/security) for OpenSearch that offers encryption, authentication and authorization. When combined with OpenSearch Security-Advanced Modules, it supports authentication via Active Directory, LDAP, Kerberos, JSON web tokens, SAML, OpenID and more. It includes fine grained role-based access control to indices, documents and fields. It also provides multi-tenancy support in OpenSearch Dashboards. - -Another leading security specification if the [Open Cybersecurity Schema Framework](https://github.com/ocsf/ocsf-schema) -OCSF is a framework for creating schemas and it also delivers a cybersecurity event schema built with the framework. - -OCSF framework is made up of a set of categories, event classes, data types, and an attribute dictionary. The framework is not restricted to cybersecurity nor to events, however the initial focus of the framework has been a schema for cybersecurity events. A schema browser for the cybersecurity schema can be found at schema.ocsf.io. This is the recommended way to explore the schema. - diff --git a/src/main/resources/schema/system/README.md b/src/main/resources/schema/system/README.md deleted file mode 100644 index 20bf9b48a..000000000 --- a/src/main/resources/schema/system/README.md +++ /dev/null @@ -1,57 +0,0 @@ -# Internal System Schema - -This folder contains internal representation of assets that are stored in the system indices of dashboard and integration. - - Application - - Datasource - - Index-Pattern - - Integration - - Notebook - - Operational-Panel - - SavedQuery - - Visualization - -### Application -[Application](https://opensearch.org/docs/2.5/observing-your-data/app-analytics/) enables creation of custom observability display to view the availability status of your systems, where you can combine log events with trace and metric data into a single view of overall system health. -This lets you quickly pivot between logs, traces, and metrics to dig into the source of any issues. - - - [Schema](application.schema) - -### Datasource -[Data-source](https://opensearch.org/docs/2.4/dashboards/discover/multi-data-sources/) Enables adding multiple data sources to a single dashboard. -OpenSearch Dashboards allows you to dynamically manage data sources, create index patterns based on those data sources, and execute queries against a specific data source and then combine visualizations in one dashboard. - - - [Schema](datasource.schema) - -### Index-Pattern -An Index Pattern allows to access data that you want to explore. An index pattern selects the data to use. An index pattern may point to multiple indices, data stream, or index aliases. - - - [Schema](index-pattern.schema) - -### Integration -Integration is a schematized and categorized bundle of assets grouped together to allow simple and coherent way to view, analyze and investigate different aspects of your data. -Integrations allow pre-defining dashboards, visualizations, index-templates, saved-queries and additional assets so that they provide a complete meaningful user experience. - - - [Schema](integration.schema) - -### Notebook -[Notebook](https://opensearch.org/docs/2.5/observing-your-data/notebooks/) A notebook is a document composed of two elements: code blocks (Markdown/SQL/PPL) and visualizations. -Choose multiple timelines to compare and contrast visualizations. -You can also generate reports directly from your notebooks. Common use cases include creating postmortem reports, designing runbooks, building live infrastructure reports, and writing documentation. - - - [Schema](notebook.schema) - -### Operational-Panel -[Operational Panels](https://opensearch.org/docs/2.5/observing-your-data/operational-panels/) in OpenSearch Dashboards are collections of visualizations generated using Piped Processing Language (PPL) queries. - - - [Schema](operational-panel.schema) - -### Saved-Query -A saved query (saved search) allows to reuse a search created in a dashboard for other dashboards. - - - [Schema](saved-query.schema) - -### Visualization -[Visualization](https://opensearch.org/docs/2.5/dashboards/visualize/viz-index/) allows translation of complex, high-volume, or numerical data into a visual representation that is easier to process. -OpenSearch Dashboards gives you data visualization tools to improve and automate the visual communication process. By using visual elements like charts, graphs, or maps to represent data, you can advance business intelligence and support data-driven decision-making and strategic planning. - - - [Schema](visualization.schema) From c13f7a65bbfb7e819b640232427dc97eb2ae8151 Mon Sep 17 00:00:00 2001 From: YANGDB Date: Wed, 5 Apr 2023 11:40:59 -0700 Subject: [PATCH 3/5] add additional information of the steps and assets dependencies with one another Signed-off-by: YANGDB --- docs/Integration-plugin-tasks.md | 10 +- docs/Integration-structure.md | 6 +- docs/Integrations.md | 2 +- docs/Simple-schema.md | 4 +- docs/schema/README.md | 59 ++++- docs/schema/observability/README.md | 66 ++++- docs/schema/observability/logs/Usage.md | 6 +- .../schema/observability/traces/Usage.md | 4 +- integrations/README.md | 4 +- integrations/alb/info/README.md | 34 ++- .../alb/info/alb-integration-load-assets.png | Bin 0 -> 686644 bytes src/main/resources/schema/README.md | 136 ---------- .../resources/schema/observability/README.md | 163 ------------ .../schema/observability/logs/README.md | 234 ------------------ .../schema/observability/logs/Usage.md | 117 --------- .../schema/observability/metrics/README.md | 105 -------- .../schema/observability/traces/README.md | 153 ------------ src/main/resources/schema/security/README.md | 9 - src/main/resources/schema/system/README.md | 57 ----- 19 files changed, 170 insertions(+), 999 deletions(-) rename {src/main/resources => docs}/schema/observability/traces/Usage.md (93%) create mode 100644 integrations/alb/info/alb-integration-load-assets.png delete mode 100644 src/main/resources/schema/README.md delete mode 100644 src/main/resources/schema/observability/README.md delete mode 100644 src/main/resources/schema/observability/logs/README.md delete mode 100644 src/main/resources/schema/observability/logs/Usage.md delete mode 100644 src/main/resources/schema/observability/metrics/README.md delete mode 100644 src/main/resources/schema/observability/traces/README.md delete mode 100644 src/main/resources/schema/security/README.md delete mode 100644 src/main/resources/schema/system/README.md diff --git a/docs/Integration-plugin-tasks.md b/docs/Integration-plugin-tasks.md index 9c362b803..7130272c7 100644 --- a/docs/Integration-plugin-tasks.md +++ b/docs/Integration-plugin-tasks.md @@ -9,13 +9,13 @@ The integration plugins is currently responsible for the next tasks: ### Catalog Registration -During the loading of the Integration Plugin it will go over all the [catalog schemas](../../schema/README.md) and creates the appropriate +During the loading of the Integration Plugin it will go over all the [catalog schemas](schema/README.md) and creates the appropriate template mapping for each catalog schema entity. This will allow the future Integration to be validated to the schema catalog they are associated with. -**For example** - the [Observability](../../schema/observability) catalog will eventually be registered with the next mapping templates - - [Traces](../../schema/observability/traces/traces.mapping) - - [Logs](../../schema/observability/logs/logs.mapping) - - [Metrics](../../schema/observability/metrics/metrics.mapping) +**For example** - the [Observability](schema/observability) catalog will eventually be registered with the next mapping templates + - [Traces](../src/main/resources/schema/observability/traces/traces.mapping) + - [Logs](../src/main/resources/schema/observability/logs/logs.mapping) + - [Metrics](../src/main/resources/schema/observability/metrics/metrics.mapping) These mapping specify a backed `data-stream` index pattern they conform with [Naming Pattern](observability/Naming-convention.md). diff --git a/docs/Integration-structure.md b/docs/Integration-structure.md index d00a6245b..949eeb043 100644 --- a/docs/Integration-structure.md +++ b/docs/Integration-structure.md @@ -183,16 +183,16 @@ In this case the field resides in the `instrumentationScope.attributes.identific ``` `Catalog:` -This defines the catalog source from which the Integration is associated with. This is based on the `catalog` API which is part of the integration support.[Sample Observability catalog](../../schema/system/samples/catalog.json) +This defines the catalog source from which the Integration is associated with. This is based on the `catalog` API which is part of the integration support.[Sample Observability catalog](schema/system/samples/catalog.json) `Components:` -This section defines the classification components associated to this Integration according to [ECS specification](https://www.elastic.co/guide/en/ecs/current/ecs-allowed-values-event-category.html) and expresses in the [Sample Observability catalog](../../schema/system/samples/catalog.json) +This section defines the classification components associated to this Integration according to [ECS specification](https://www.elastic.co/guide/en/ecs/current/ecs-allowed-values-event-category.html) and expresses in the [Sample Observability catalog](schema/system/samples/catalog.json) `collection:` This references the different types of collection this integration if offering. It can be one of the following Observability catalog's element: { *`Traces, Logs, Metrics`* }. -The collection **name** (`logs`,`traces`,`metrics`) reflects the catalog's `category` as it appears in the [Sample Observability catalog](../../schema/system/samples/catalog.json) +The collection **name** (`logs`,`traces`,`metrics`) reflects the catalog's `category` as it appears in the [Sample Observability catalog](schema/system/samples/catalog.json) **Collections** diff --git a/docs/Integrations.md b/docs/Integrations.md index 87a8bf101..93652d8a6 100644 --- a/docs/Integrations.md +++ b/docs/Integrations.md @@ -46,7 +46,7 @@ Observability telemetry schema defines the following components - **logs**, **tr In many occasions, correlate between the logs, traces and metrics is mandatory to be able to monitor and understand how the system is behaving. In addition, the distributed nature of the application produces multiple formats of telemetry signals arriving from different components ( network router, web server, database) -For such correlation to be possible the industry has formulated several protocols ([OTEL](https://github.com/open-telemetry), [ECS](https://github.com/elastic/ecs), [OpenMetrics](https://github.com/OpenObservability/OpenMetrics), [Alerts](https://github.com/MicrosoftDocs/azure-docs/blob/main/articles/azure-monitor/alerts/alerts-common-schema.md)) for communicating these signals - the **Observability schemas**. +For such correlation to be possible the industry has formulated several protocols: ([OTEL](https://github.com/open-telemetry), [ECS](https://github.com/elastic/ecs), [OpenMetrics](https://github.com/OpenObservability/OpenMetrics), [Alerts]()) for communicating these signals - the **Observability schemas**. ## Problem definition Today in OpenSearch, Observability and its dashboards are only partially aware (traces only) of the schematic structure of these signal types. In addition, the actual schema mapping is not present internally in the Observability plugin and has to be imported externally. diff --git a/docs/Simple-schema.md b/docs/Simple-schema.md index fec47463e..9d70f622c 100644 --- a/docs/Simple-schema.md +++ b/docs/Simple-schema.md @@ -84,8 +84,8 @@ The data model is highly coupled with the visual components, for example - the A ## Ingestion Pipeline A mandatory part of an Observability solution is its ability to ingest data at scale, currently, OpenSearch Observability support the following out of the box schematized data providers: -- Data prepper - https://github.com/opensearch-project/data-prepper -- Jaeger - https://opensearch.org/docs/latest/observing-your-data/trace/trace-analytics-jaeger/ +- [Data prepper](https://github.com/opensearch-project/data-prepper) +- [Jaeger](https://opensearch.org/docs/latest/observing-your-data/trace/trace-analytics-jaeger) ### **Data Prepper:** diff --git a/docs/schema/README.md b/docs/schema/README.md index c8202c693..2262d489f 100644 --- a/docs/schema/README.md +++ b/docs/schema/README.md @@ -57,7 +57,7 @@ Each component has two flavours: A component may be classified as a `container` which has the ability to group / combine multiple components inside. -For example, we can examine the [`logs`](observability/logs/logs.mapping) component that has the capacity to combine additional components (such as `http`, `communication` and more) +For example, we can examine the [`logs`](../../src/main/resources/schema/observability/logs/logs.mapping) component that has the capacity to combine additional components (such as `http`, `communication` and more) ```json5 ... "composed_of": [ @@ -78,4 +78,59 @@ A component also has a list of `tags` which are aliases for the component name w "container": false } ... -``` \ No newline at end of file +``` + +## Data Correlation + +In order to be able to correlate information across different signal (represented in different indices) we introduced the notion of correlation into the schema. +This information is represented explicitly in both the declarative schema file and the physical mapping file + +This information will enable the knowledge to be projected and allow for analytic engine to produce a join query that will take advantage of these relationships. +The correlation metadata info is exported in the following way: + +### Observability Correlation Example: + +### Schema related +In JSON Schema, there is no built-in way to represent relationships directly between multiple schemas, like you would find in a relational database. However, you can establish relationships indirectly by using a combination of `$id`, `$ref`, and consistent property naming across your schemas. +For example the [`logs.schema`](../../src/main/resources/schema/observability/logs/logs.schema) file contains the next `$ref` references for the `traceId` & `spanId` fields that belong to the `traces.schema`. + +```json5 + ... + "traceId": { + "$ref": "https://opensearch.org/schemas/observability/Span#/properties/traceId" + }, + "spanId": { + "$ref": "https://opensearch.org/schemas/observability/Span#/properties/spanId" + }, + ... +``` + +We can observe that the `traceId` field is defined by referencing to the [Span](../../src/main/resources/schema/observability/traces/traceGroups.schema) schema and explicitly to the `#/properties/spanId` field reference location. + +### Mapping related +Each mapping template will contain the foreign schemas that are referenced to in that specific mapping file. For example the [`logs.mapping`](../../src/main/resources/schema/observability/logs/logs.schema) file will contain the next correlation object in the mapping `_meta` section: + +```json5 + "_meta": { + "description": "Simple Schema For Observability", + "catalog": "observability", + "type": "logs", + "correlations": [ + { + "field": "spanId", + "foreign-schema": "traces", + "foreign-field": "spanId" + }, + { + "field": "traceId", + "foreign-schema": "traces", + "foreign-field": "traceId" + } + ] + } + +``` + +Each `correlations` field contains the F.K field name - `spanId` , the referenced schema - `traces` and the source field name in that schema `spanId` + +This information can be used to generate the correct join queries on a contextual basis. diff --git a/docs/schema/observability/README.md b/docs/schema/observability/README.md index 3089a6381..9a718234b 100644 --- a/docs/schema/observability/README.md +++ b/docs/schema/observability/README.md @@ -107,9 +107,14 @@ The Observability indices would follow the recommended for immutable data stream Index pattern will follow the next naming template `sso_{type}`-`{dataset}`-`{namespace}` - - **type** - indicated the observability high level types "logs", "metrics", "traces" (prefixed by the `sso_` schema convention ) - - **dataset** - The field can contain anything that classify the source of the data - such as `nginx.access` - - **namespace** - A user defined namespace - mainly useful to allow grouping of data such as production grade, geography classification +**type** +- indicated the observability high level types "logs", "metrics", "traces" (prefixed by the `sso_` schema convention ) + +**dataset** +- The field can contain anything that classify the source of the data - such as `nginx.access` + +**namespace** +- A user defined namespace - mainly useful to allow grouping of data such as production grade, geography classification This strategy allows two degrees of naming freedom: dataset and namespace. For example a customer may want to route the nginx logs from two geographical areas into two different indices: @@ -129,7 +134,7 @@ The `sso_{type}-{dataset}-{namespace}` combination dictates the target index, `{ - Logs - `sso_logs` For example if within the ingested log contains the following section: -```json +```json5 { ... "attributes": { @@ -157,5 +162,58 @@ This `data_stream` will use one of the Observability ready-made index templates **If the ingesting party has a need to update the template default index setting (shards, replicas ) it may do so before the actual creation of the data_stream.** +## Observability Signals Correlation + +In order to be able to correlate information across different signal (represented in different indices) we introduced the notion of correlation into the schema. +This information is represented explicitly in both the declarative schema file (for example [`logs.schema`](../../../src/main/resources/schema/observability/logs/logs.schema)) and the physical mapping file ([`logs.mapping`](../../../src/main/resources/schema/observability/logs/logs.mapping)) + +This information will enable the knowledge to be projected and allow for analytic engine to produce a join query that will take advantage of these relationships. +The correlation metadata info is exported in the following way: + +### Schema related +In JSON Schema, there is no built-in way to represent relationships directly between multiple schemas, like you would find in a relational database. However, you can establish relationships indirectly by using a combination of `$id`, `$ref`, and consistent property naming across your schemas. +For example the [`logs.schema`](../../../src/main/resources/schema/observability/logs/logs.schema) file contains the next `$ref` references for the `traceId` & `spanId` fields that belong to the [`traces.schema`](../../../src/main/resources/schema/observability/traces/traceGroups.schema). + +```json5 + ... + "traceId": { + "$ref": "https://opensearch.org/schemas/observability/Span#/properties/traceId" + }, + "spanId": { + "$ref": "https://opensearch.org/schemas/observability/Span#/properties/spanId" + }, + ... +``` + +We can observe that the `traceId` field is defined by referencing to the [Span](../../../src/main/resources/schema/observability/traces/traceGroups.schema) schema and explicitly to the `#/properties/spanId` field reference location. + +### Mapping related +Each mapping template will contain the foreign schemas that are referenced to in that specific mapping file. For example the [`logs.mapping`](../../../src/main/resources/schema/observability/logs/logs.mapping) file will contain the next correlation object in the mapping `_meta` section: + +```json5 + "_meta": { + "description": "Simple Schema For Observability", + "catalog": "observability", + "type": "logs", + "correlations": [ + { + "field": "spanId", + "foreign-schema": "traces", + "foreign-field": "spanId" + }, + { + "field": "traceId", + "foreign-schema": "traces", + "foreign-field": "traceId" + } + ] + } + +``` + +Each `correlations` field contains the F.K (foreign-key) field name - `spanId` , the referenced schema - `traces` and the source field name in that schema `spanId` + +This information can be used to generate the correct join queries on a contextual basis. + ### Note It is important to mention that these new capabilities would not change or prevent existing customer usage of the system and continue to allow proprietary usage. diff --git a/docs/schema/observability/logs/Usage.md b/docs/schema/observability/logs/Usage.md index 03fa0c5bb..9210261ba 100644 --- a/docs/schema/observability/logs/Usage.md +++ b/docs/schema/observability/logs/Usage.md @@ -7,15 +7,15 @@ Start the OpenSearch cluster and follow the next steps for manually setup of the `>> PUT _component_template/http_template` -Copy the http.mapping content [here](http.mapping) +Copy the http.mapping content [here](../../../../src/main/resources/schema/observability/logs/http.mapping) `>> PUT _component_template/communication_template` -Copy the communication.mapping content [here](communication.mapping) +Copy the communication.mapping content [here](../../../../src/main/resources/schema/observability/logs/communication.mapping) `>> PUT _index_template/logs` -Copy the logs.mapping content [here](logs.mapping) +Copy the logs.mapping content [here](../../../../src/main/resources/schema/observability/logs/logs.mapping) Now you can create an data-stream index (following the logs index pattern) that has the supported schema: diff --git a/src/main/resources/schema/observability/traces/Usage.md b/docs/schema/observability/traces/Usage.md similarity index 93% rename from src/main/resources/schema/observability/traces/Usage.md rename to docs/schema/observability/traces/Usage.md index 31fe0b9b3..04a644414 100644 --- a/src/main/resources/schema/observability/traces/Usage.md +++ b/docs/schema/observability/traces/Usage.md @@ -7,11 +7,11 @@ Start the OpenSearch cluster and follow the next steps for manually setup of the `>> PUT _component_template/tracegroups_template` -Copy the [traceGroups.mapping](traceGroups.mapping) +Copy the [traceGroups.mapping](../../../../src/main/resources/schema/observability/traces/traceGroups.mapping) `>> PUT _index_template/traces` -Copy the [traces.mapping](traces.mapping) +Copy the [traces.mapping](../../../../src/main/resources/schema/observability/traces/traces.mapping) Now you can create an data-stream index (following the logs index pattern) that has the supported schema: diff --git a/integrations/README.md b/integrations/README.md index 8916dc79b..5aabb1e2c 100644 --- a/integrations/README.md +++ b/integrations/README.md @@ -1,4 +1,4 @@ -# Definitions +# Integration Definitions ## Bundle @@ -98,7 +98,7 @@ integration-template-name `Config.json` file includes the following Integration configuration see [NginX config](nginx/config.json) - Additional information on the config structure see [Structure](docs/Integration-structure.md) + Additional information on the config structure see [Structure](../docs/Integration-structure.md) #### Display: diff --git a/integrations/alb/info/README.md b/integrations/alb/info/README.md index a3f9a29f1..60a39c6f8 100644 --- a/integrations/alb/info/README.md +++ b/integrations/alb/info/README.md @@ -134,4 +134,36 @@ The `elb` dataset collects logs from AWS ELBs. | user_agent.name | Name of the user agent. | keyword | | user_agent.original | Unparsed user_agent string. | keyword | | user_agent.original.text | Multi-field of `user_agent.original`. | match_only_text | -| user_agent.version | Version of the user agent. | keyword | \ No newline at end of file +| user_agent.version | Version of the user agent. | keyword | + + +### Integration Loading Process +The AWS ALB logs integration loading process includes the following assets + - **Connectivity** + - S3 Datasource connectivity + - Spark compute engine connectivity + - **Tables** + - alb logs external table (definition including fields mapping to observability logs template) + - alb logs index (definition based on observability logs template) + - **Views** + - alb log materialized view (view is a pre-calculated query based on a specific given dimension) + - alb metrics materialized view (view is a pre-calculated query based on a specific given dimension) + - **Display** + - alb dashboard for viewing all the given information in a meaningful manner + + +#### Assets Loading Order +The general order for which the assets are needed to be loaded is dictated by the next concepts: + +1) Connectivity - First the connection related configuration are required to be validated for existence and correctness. + +2) Mapping - Next the schema specific instruction dictated by the integration's config must be verified for existence or be created + +3) Tables - Next the external / internal tables / indices will be verified for existence or be created - (they are based on the mapping phase and connectivity phase ) + +4) Views - Next the views search templates need to be created (they are based on the tables and connectivity phase ) + +5) Display - Last the dashboards and visual assets are uploaded (they are based on the views, tables and connectivity phase) + + +![](alb-integration-load-assets.png) diff --git a/integrations/alb/info/alb-integration-load-assets.png b/integrations/alb/info/alb-integration-load-assets.png new file mode 100644 index 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z9qns~e?lgGq>EU`-Ju;q~|%&W$r$m31swaV97Pvk)HA*EQ8*Aw}cQ$;vDs7wfr$D_8&KV#J`Gnv19-7cmKN- z_`m#$5vrpZ^1c4f<-4u;xffqRK`XyINh5iPPz4-+Cf>IK#m|lfGz$4H>~Qp+|92|( opHYne-^krRKGER|fuw+c|1wDVy>Y}I@Sm#E^((oTjeq<90EPzjWB>pF literal 0 HcmV?d00001 diff --git a/src/main/resources/schema/README.md b/src/main/resources/schema/README.md deleted file mode 100644 index cdebae05a..000000000 --- a/src/main/resources/schema/README.md +++ /dev/null @@ -1,136 +0,0 @@ -# Simple Schema for Open Search - -## Background -Simple Schema for OpenSearch brings the concept of organized and structured catalog data. -A catalog of schemas is a comprehensive collection of all the possible data schemas or structures that can be used to represent information. - -It provides a standardized way of organizing and describing the structure of data, making it easier to analyze, compare, and share data across different systems and applications. -Structured data refers to any data that is organized in a specific format or schema - for example Observability data or security data... - -By using a catalog of schemas, data analysts and scientists can easily identify and understand the different structures of data, allowing them to correlate and analyze information more effectively. -One of the key benefits of a catalog of schemas is that it promotes interoperability between different systems and applications. -By using standardized schema descriptions, data can be shared and exchanged more easily, regardless of the system or application being used. - -The use of a catalog of schemas can also improve data quality by ensuring that data is consistent and accurate. This is because the schema provides a clear definition of the data structure and the rules for how data should be entered, validated, and stored. - -## OpenSearch Schemas - -Opensearch supports out of the box the following schemas - - [Observability](observability/README.md) - - [Security](security/README.md) - - [System](system/README.md) - -### Observability -Simple Schema for [Observability](https://github.com/opensearch-project/observability) allows ingestion of both (OTEL/ECS) formats and internally consolidate them to best of its capabilities for presenting a unified Observability platform. - -### Security -OpenSearch Security is a [plugin](https://github.com/opensearch-project/security) for OpenSearch that offers encryption, authentication and authorization. When combined with OpenSearch Security-Advanced Modules, it supports authentication via Active Directory, LDAP, Kerberos, JSON web tokens, SAML, OpenID and more. It includes fine grained role-based access control to indices, documents and fields. It also provides multi-tenancy support in OpenSearch Dashboards. - -### System -System represents the internal structure of Opensearch's `.dashboard` entities such as `dashboard`, `notebook`, `save-query` `integration`. Every saved object may declare itself in the system -catalog folder and publish a corresponding schema. This capability will allow validation of these objects and also simplify structure evolution using this schema. - -### Catalog Loading -During Integration plugin loading, it will go over all the Opensearch's schema supported catalogs and generate the appropriate templates representing them. -This will allow any future Integration using these catalogs without the need to explicitly defining them thus maintaining a unified common schema. - -Each catalog may support semantic versioning so that it may evolve its schema as needed. -In the future, the catalog will enable to associate domains with catalogs and allow externally importing catalogs into Opensearch for additional collaboration. - -### Catalog Structure -A catalog is structured in the following way: - - - Catalog named folder: `Observability` - - Categories named folder : `Logs`, `Traces`, `Metrics` - - Component named file : `http` , `communication` , `traces` , `metrics` - -Each level encapsulates additional internal structure that allows a greater level of composability and agility. -The details of each catalog structure is described in the [catalog.json](system/samples/catalog.json) file that resides in the root level of each catalog folder. - -**Component** -The component is the leaf level definition of the catalog hierarchy, it details the actual building blocks of the catalog's types and fields. - -Each component has two flavours: - - - `$component.mapping` - describes how the type is physically stored in the underlying index - - `$component.mapping` - describing the actual json schema for this component type - -A component may be classified as a `container` which has the ability to group / combine multiple components inside. - -For example, we can examine the [`logs`](observability/logs/logs.mapping) component that has the capacity to combine additional components (such as `http`, `communication` and more) -```json5 - ... - "composed_of": [ - "http_template", - "communication_template" - ], - ... -``` -A component also has a list of `tags` which are aliases for the component name which can be used to reference it directly by an integration components list. - -```json5 - ... - { - "component": "communication", - "version": "1.0", - "url": "https://github.com/opensearch-project/observability/tree/2.x/schema/observability/logs/communication", - "tags": ["web"], - "container": false - } - ... -``` - -## Data Correlation - -In order to be able to correlate information across different signal (represented in different indices) we introduced the notion of correlation into the schema. -This information is represented explicitly in both the declarative schema file and the physical mapping file - -This information will enable the knowledge to be projected and allow for analytic engine to produce a join query that will take advantage of these relationships. -The correlation metadata info is exported in the following way: - -### Observability Correlation Example: - -### Schema related -In JSON Schema, there is no built-in way to represent relationships directly between multiple schemas, like you would find in a relational database. However, you can establish relationships indirectly by using a combination of `$id`, `$ref`, and consistent property naming across your schemas. -For example the [`logs.schema`](observability/logs/logs.schema) file contains the next `$ref` references for the `traceId` & `spanId` fields that belong to the `traces.schema`. - -```json5 - ... - "traceId": { - "$ref": "https://opensearch.org/schemas/observability/Span#/properties/traceId" - }, - "spanId": { - "$ref": "https://opensearch.org/schemas/observability/Span#/properties/spanId" - }, - ... -``` - -We can observe that the `traceId` field is defined by referencing to the [Span](observability/traces/traceGroups.schema) schema and explicitly to the `#/properties/spanId` field reference location. - -### Mapping related -Each mapping template will contain the foreign schemas that are referenced to in that specific mapping file. For example the [`logs.mapping`](observability/logs/logs.schema) file will contain the next correlation object in the mapping `_meta` section: - -```json5 - "_meta": { - "description": "Simple Schema For Observability", - "catalog": "observability", - "type": "logs", - "correlations": [ - { - "field": "spanId", - "foreign-schema": "traces", - "foreign-field": "spanId" - }, - { - "field": "traceId", - "foreign-schema": "traces", - "foreign-field": "traceId" - } - ] - } - -``` - -Each `correlations` field contains the F.K field name - `spanId` , the referenced schema - `traces` and the source field name in that schema `spanId` - -This information can be used to generate the correct join queries on a contextual basis. diff --git a/src/main/resources/schema/observability/README.md b/src/main/resources/schema/observability/README.md deleted file mode 100644 index 51c941db1..000000000 --- a/src/main/resources/schema/observability/README.md +++ /dev/null @@ -1,163 +0,0 @@ -# Simple Schema for Observability - -## Background -Observability is the ability to measure a system’s current state based on the data it generates, such as logs, metrics, and traces. Observability relies on telemetry derived from instrumentation that comes from the endpoints and services. - -Observability telemetry signals (logs, metrics, traces) arriving from the system would contain all the necessary information needed to observe and monitor. - -Modern application can have a complicated distributed architecture that combines cloud native and microservices layers. Each layer produces telemetry signals that may have different structure and information. - -Using Simple Schema's Observability telemetry schema we can organize, correlate and investigate system behavior in a standard and well-defined manner. - -Observability telemetry schema defines the following components - **logs, traces and metrics**. - -**Logs** provide comprehensive system details, such as a fault and the specific time when the fault occurred. By analyzing the logs, one can troubleshoot code and identify where and why the error occurred. - -**Traces** represent the entire journey of a request or action as it moves through all the layers of a distributed system. Traces allow you to profile and observe systems, especially containerized applications, serverless architectures, or microservices architecture. - -**Metrics** provide a numerical representation of data that can be used to determine a service or component’s overall behaviour over time. - - -In many occasions, correlation between the logs, traces and metrics is mandatory to be able to monitor and understand how the system is behaving. In addition, the distributed nature of the application produces multiple formats of telemetry signals arriving from different components ( network router, web server, database) - -For such correlation to be possible the industry has formulated several protocols ([OTEL](https://github.com/open-telemetry), [ECS](https://github.com/elastic/ecs), [OpenMetrics](https://github.com/OpenObservability/OpenMetrics)) for communicating these signals - the Observability schemas. - ---- -## Schema Aware Components - -The role of the Observability [plugin](https://github.com/opensearch-project/observability) is intended to allow maximum flexibility and not imposing a strict Index structure of the data source. Nevertheless, the modern nature of distributed application and the vast amount of telemetry producers is changing this perception. - -Today many of the Observability solutions (splunk, datadog, dynatrace) recommend using a consolidated schema to represent the entire variance of log/trace/metrics producers. - -This allows monitoring, incidents investigation and corrections process to become simpler, maintainable and reproducible. - - -A Schema-Aware visualization component is a component which assumes the existence of specific index/indices name patterns and expects these indices to have a specific structure - a schema. - -As an example we can see that **Trace-Analytics** is a schema-aware visual component since it directly assumes the traces & serviceMap indices exist and expects them to follow a specific structure. - -This definition doesn’t change the existing status of visualization components which are not “Schema Aware” but it only regulates which Visual components would benefit using a schema and which will be agnostic of its content. - -Operation Panel for example, are not “Schema Aware” since they don’t assume in advanced the existence of a specific index nor do they expect the index they display to have a specific structure. - -## Data Model - -Simple Schema for Observability allows ingestion of both (OTEL/ECS) formats and internally consolidate them to best of its capabilities for presenting a unified Observability platform. - -## Observability index naming - -The Observability indices would follow the recommended for immutable data stream ingestion pattern using the [data_stream concepts](https://opensearch.org/docs/latest/opensearch/data-streams/) - -Index pattern will follow the next naming template `sso_{type}`-`{dataset}`-`{namespace}` - -**type** -- indicated the observability high level types "logs", "metrics", "traces" (prefixed by the `sso_` schema convention ) - -**dataset** -- The field can contain anything that classify the source of the data - such as `nginx.access` - -**namespace** -- A user defined namespace - mainly useful to allow grouping of data such as production grade, geography classification - -This strategy allows two degrees of naming freedom: dataset and namespace. For example a customer may want to route the nginx logs from two geographical areas into two different indices: - - - `sso_logs-nginx-us` - - `sso_logs-nginx-eu` - -This type of distinction also allows for creation of crosscutting queries by setting the next index query pattern `sso_logs-nginx-*` or by using a geographic based crosscutting query `sso_logs-*-eu`. - -## Data index routing - -The [ingestion component](https://github.com/opensearch-project/data-prepper) which is responsible for ingesting the Observability signals is responsible to route the data into the relevant indices. - -The `sso_{type}-{dataset}-{namespace}` combination dictates the target index, `{type}` is prefixed with the `sso_` prefix into one of the supported type: - - - Traces - `sso_traces` - - Metrics - `sso_metrics` - - Logs - `sso_logs` - -For example if within the ingested log contains the following section: -```json5 -{ - ... - "attributes": { - "data_stream": { - "type": "span", - "dataset": "mysql", - "namespace": "prod" - } - } -} -``` -This indicates that the target index for this observability signal should be `sso_traces`-`mysql`-`prod` index that follows uses the traces schema mapping. - -## Observability Index templates - -With the expectation of multiple Observability data providers and the need to consolidate all to a single common schema - the Observability plugin will take the following responsibilities : - - - Define and create all the signals **index templates** upon loading - - Publish a versioned schema file (Json Schema) for each signal type for general validation usage by any 3rd party - -## Observability Ingestion pipeline -The responsibility on an **Observability-ingestion-pipeline** is to create the actual `data_stream` in which it is expecting to ingest into. - -This `data_stream` will use one of the Observability ready-made index templates (Metrics,Traces and Logs) and conform with the above naming pattern (`sso_{type}`-`{dataset}`-`{namespace}`) - -**If the ingesting party has a need to update the template default index setting (shards, replicas ) it may do so before the actual creation of the data_stream.** - -## Observability Signals Correlation - -In order to be able to correlate information across different signal (represented in different indices) we introduced the notion of correlation into the schema. -This information is represented explicitly in both the declarative schema file (for example [`logs.schema`](logs/logs.schema)) and the physical mapping file ([`logs.mapping`](logs/logs.mapping)) - -This information will enable the knowledge to be projected and allow for analytic engine to produce a join query that will take advantage of these relationships. -The correlation metadata info is exported in the following way: - -### Schema related -In JSON Schema, there is no built-in way to represent relationships directly between multiple schemas, like you would find in a relational database. However, you can establish relationships indirectly by using a combination of `$id`, `$ref`, and consistent property naming across your schemas. -For example the [`logs.schema`](logs/logs.schema) file contains the next `$ref` references for the `traceId` & `spanId` fields that belong to the [`traces.schema`](traces/traceGroups.schema). - -```json5 - ... - "traceId": { - "$ref": "https://opensearch.org/schemas/observability/Span#/properties/traceId" - }, - "spanId": { - "$ref": "https://opensearch.org/schemas/observability/Span#/properties/spanId" - }, - ... -``` - -We can observe that the `traceId` field is defined by referencing to the [Span](traces/traceGroups.schema) schema and explicitly to the `#/properties/spanId` field reference location. - -### Mapping related -Each mapping template will contain the foreign schemas that are referenced to in that specific mapping file. For example the [`logs.mapping`](logs/logs.mapping) file will contain the next correlation object in the mapping `_meta` section: - -```json5 - "_meta": { - "description": "Simple Schema For Observability", - "catalog": "observability", - "type": "logs", - "correlations": [ - { - "field": "spanId", - "foreign-schema": "traces", - "foreign-field": "spanId" - }, - { - "field": "traceId", - "foreign-schema": "traces", - "foreign-field": "traceId" - } - ] - } - -``` - -Each `correlations` field contains the F.K (foreign-key) field name - `spanId` , the referenced schema - `traces` and the source field name in that schema `spanId` - -This information can be used to generate the correct join queries on a contextual basis. - -### Note -It is important to mention that these new capabilities would not change or prevent existing customer usage of the system and continue to allow proprietary usage. - diff --git a/src/main/resources/schema/observability/logs/README.md b/src/main/resources/schema/observability/logs/README.md deleted file mode 100644 index 9fcaad705..000000000 --- a/src/main/resources/schema/observability/logs/README.md +++ /dev/null @@ -1,234 +0,0 @@ -# Logs Schema Support - -Observability refers to the ability to monitor and diagnose systems and applications in real-time, in order to understand how they are behaving and identify potential issues. -logs serve as a primary source of information for understanding and debugging complex systems. -Logs provide a record of events, errors, and performance that help developers and administrators identify and resolve issues, monitor system behavior, and improve reliability. - -Logs can also be used to gain insight into user behavior and facilitate auditing and compliance. -By analyzing logs, one can detect patterns, anomalies, and correlations that can inform decisions and facilitate problem-solving. -Logs help to ensure the visibility, reliability, and stability of systems. - -## Details -The next section provides the Simple Schema for Observability support which conforms with the OTEL specification. - -- logs.mapping presents the template mapping for creating the Simple Schema for Observability index -- logs.schema presents the json schema validation for verification of a metrics document conforms to the mapping structure - -## Logs -See [OTEL Logs convention](https://github.com/open-telemetry/opentelemetry-specification/blob/main/specification/logs/data-model.md) -See [OTEL logs protobuf](https://github.com/open-telemetry/opentelemetry-proto/tree/main/opentelemetry/proto/logs/v1) -See [ECS logs](https://github.com/elastic/ecs) - -Simple Schema for Observability conforms with OTEL logs protocol and also was greatly inspired from the Elastic-Common-Schema schema. - -Simple Schema for Observability defines the next data model: - -## General Fields - -### Event Fields -Within a particular domain, the ```event.name``` attribute identifies the event. Events with same domain and name are structurally similar to one another. -For example, some domains could have well-defined schema for their events based on event names. (OTEL driven) - -`event.kind` gives high-level information about what type of information the event contains, without being specific to the contents of the event.(ECS driven) -**Possible values** - - **alert** - indicates an alerting type event which can be triggered by any alerting mechanism - - **enrichment** - indicates an enriched typed event that adds additional context to the original event - - **event** - the default type of the event - - **metric** - this indicated the event describes a numeric measurement - -The `event.domain` attribute is used to logically separate events from different systems. For example, to record Events from `browser` apps, `mobile` apps and `Kubernetes`, we could use browser, device and k8s as the domain for their Events. -This provides a clean separation of semantics for events in each of the domains. (OTEL driven) - -`event.category` gives categorical-level information about what type of information the event contains, this field is an array. (ECS driven) -**Possible values** - - authentication - events are of a challenge and response process by any system that has such responsibilities - - configuration - events related to the configuration of a system or an application - - database - events that are generated as part of the storage system (SQL RDBMS and such) - - driver - events related to the O/S device driver - - email - events related to email messages, email attachments and such - - file - events related to the fact that it has been created on, or has existed on a filesystem - - host - events related to host inventory or lifecycle events - - iam - Identity & access Management logs types - - network - events relating of network activities (connection / traffic and such) - - package - events indication of software packages installation of hosts - - process - events related to O/S process information - - registry - events related to O/S registry events - - session - events related to a persistent connection between different network components - - web - events related to web server activity - -`event.category` corresponds with `event.domain` - -`event.type` gives a fine grain details of the event's category including the phase in-which the field is part of. -This will allow proper categorization of some events that fall in multiple event types, this field is an array. (ECS driven) - -**Possible values** - - access - indication that this event has accesses some resource - - admin - indication that this event is related to the admin context - - allowed - indication that this event was subsequently allowed by some authority system - - change - indication that this event is related to something that has changed - - connection - indication that this event is related to network traffic with indication of connection activity - - creation - indication that this event is related to resources being created - - deletion - indication that this event is related to resources being deleted - - denied - indication that this event is related to resources being denied access - - error - indication of an error related event - - group - indication of events that are related to group objects - - info - indication of events which are informative without other distinct classification - - installation - indication that this event is related to resources being installed - - protocol - indicate that the event is related to specific knowledge of protocol info - - end - indicate that te event is related to some termination state - - start - indicate that te event is related to some initiation state - - user - indicate that the event is related to specific knowledge a user resource - - -`event.result` gives a success or a failure indication from the perspective of the entity that produced the event. (ECS driven) - -**Possible values** - - - failure - - success - - pending - - undetermined - - -#### Exception Fields -This field encapsulated the exception that should appear under the event section `event.exception` (OTEL driven) - - - `message`: The exception message. - - `stacktrace`: A stacktrace as a string in the natural representation for the language runtime. The representation is to be determined and documented by each language SIG. - - `type`: The type of the exception (its fully-qualified class name, if applicable). The dynamic type of the exception should be preferred over the static type in languages that support it. - ---- - -### Overview - -In observability, Logs are typically unstructured data that is generated by applications or systems as a record of events or messages. -Logs can contain any information that the emitting application or system wants to include, and they often contain free-form text rather than structured data. -This makes logs difficult to process and analyze automatically, but it also provides a lot of flexibility and versatility in terms of what information can be captured. - -According to [ECS](https://github.com/elastic/ecs) and the most recent (experimental) [OTEL](https://github.com/open-telemetry/opentelemetry-specification/tree/main/specification/trace/semantic_conventions) definitions, we formalized a unified -log schema. This schema can be used for working with a well-structured set of typed logs arriving from categorical sources. -These sources are expected to report information in a specific way that will simplify future correlations and consolidate similar concerns. - -### data-stream -[data-stream](https://opensearch.org/docs/latest/opensearch/data-streams/) Data streams simplify this process and enforce a setup that best suits time-series data, such as being designed primarily for append-only data and ensuring that each document has a timestamp field. -A data stream is internally composed of multiple backing indices. Search requests are routed to all the backing indices, while indexing requests are routed to the latest write index. - -As part of the Observability naming scheme, the value of the data stream fields combine to the name of the actual data stream : - -`{data_stream.type}-{data_stream.dataset}-{data_stream.namespace}`. -This means the fields can only contain characters that are valid as part of names of data streams. - - - **type** conforms to one of the supported Observability signals (Traces, Logs, Metrics, Alerts) - - **dataset** user defined field that can mainly be utilized for describing the origin of the signal - - **namespace** user custom field that can be used to describe any customer domain specific classification - -#### Timestamp field -As part of the data-stream definition the `@timestamp` is mandatory, if the field is not present to begin with use `ObservedTimestamp` as value for this field - -### Instrumentation scope -This is a logical unit of the application with which the emitted telemetry can be associated. It is typically the developer’s choice to decide what denotes a reasonable instrumentation scope. -The most common approach is to use the instrumentation library as the scope, however other scopes are also common, e.g. a module, a package, or a class can be chosen as the instrumentation scope. - -The instrumentation scope may have zero or more additional attributes that provide additional information about the scope. As an example the field -`instrumentationScope.attributes.identification` is presented will be used to determine the resource origin of the signal and can be used to filter accordingly - -For Example - in the sample [nginx_access-log.json](sample/nginx_access-log.json) this value equals to `nginx` indicating the signal source. - -This field is expected to appear in any future integration or Observability resources into OpenSearch. - - -### Logs Classifications - -#### HTTP -_Inspired by [ECS - http](https://www.elastic.co/guide/en/ecs/current/ecs-http.html), [OTEL - http](https://github.com/open-telemetry/opentelemetry-specification/blob/main/specification/trace/semantic_conventions/http.md)_ - - - `method` - HTTP request method. `GET; POST; HEAD` (OTEL driven) Correspond with `request.method` (ECS driven) - - `status_code` - [Http response code](https://tools.ietf.org/html/rfc7231#section-6) (OTEL driven) Correspond with `response.status_code` (ECS driven) - - `flavor` - Kind of HTTP protocol used. (OTEL driven) Correspond with `version` (ECS driven) - - `user_agent` - Value of the HTTP User-Agent header sent by the client. (OTEL driven) - - - `request.id` - A unique identifier for each HTTP request to correlate logs between clients and servers in transactions. (ECS driven) - - `request_content_length` - The size of the request payload body in bytes. (OTEL driven) Correspond with `request.bytes` (ECS driven) - - `request.body.content` - The full HTTP request body. (ECS driven) - - `request.referrer` - Referrer for this HTTP request. (ECS driven) - - `request.header` - HTTP request headers key/value object (OTEL driven) - - `request.mime_type` - Mime type of the body of the response. (ECS driven) - - - `response_content_length` - The size of the response payload body in bytes. (OTEL driven) Correspond with `response.bytes` (ECS driven) - - `response.body.content` - The full HTTP response body. (ECS driven) - - `response.header` - HTTP response headers key/value object (OTEL driven) - - - `url` - Full HTTP request URL in the form scheme://host[:port]/path?query[#fragment] (OTEL driven) - - `resend_count` - The ordinal number of request resending attempt (OTEL driven) - - - `scheme` - The URI scheme identifying the used protocol. (OTEL driven) - - `target` - The full request target as passed in a HTTP request line or equivalent. (OTEL driven) - - `route` - The matched route (path template in the format used by the respective server framework) (OTEL driven) - - `client_ip` - The IP address of the original client behind all proxies (OTEL driven) `client.ip` - IP address of the client (IPv4 or IPv6). (ECS driven) - -#### Communication -Includes client / server part of the communication - - **_Inspired by_** : - - [ECS - client](https://www.elastic.co/guide/en/ecs/8.6/ecs-client.html) - - [ECS - Server](https://www.elastic.co/guide/en/ecs/8.6/ecs-server.html#ecs-server) - - [ECS - Source](https://www.elastic.co/guide/en/ecs/8.6/ecs-source.html) - - [ECS - Destination](https://www.elastic.co/guide/en/ecs/8.6/ecs-destination.html) - - [OTEL - network](https://github.com/open-telemetry/opentelemetry-specification/blob/main/specification/trace/semantic_conventions/http.md) - - - - `sock.family` - Protocol address family which is used for communication. (OTEL driven) - - - `client.domain` - The domain name of the client system. (ECS driven) Correspond with `source.domain`, (ECS driven) - - `client.bytes` - Bytes sent from the client to the server. (ECS driven) Correspond with `source.bytes` (ECS driven) - - `client.ip` - IP address of the client (IPv4 or IPv6). (ECS driven) Correspond with `source.ip` (ECS driven) - - `client.mac` - MAC address of the client. (IPv4 or IPv6). (ECS driven) Correspond with `source.mac` (ECS driven) - - `client.packets` - Packets sent from the client to the server. (ECS driven) Correspond with `source.packets` (ECS driven) - - - `sock.host.addr` - Local socket address. (OTEL driven) Correspond with `client.address`,`source.address` (ECS driven) - - `sock.host.port` - Local socket port number. (OTEL driven) Corresponds with `client.port`,`source.port` (ECS driven) - - - `server.domain` - The domain name of the server system. (ECS driven) Correspond with `destination.domain` (ECS driven) - - `server.bytes` - Bytes sent from the server to the client. (ECS driven) Correspond with `destination.bytes` (ECS driven) - - `server.ip` - IP address of the server (IPv4 or IPv6). (ECS driven) Correspond with `destination.ip` (ECS driven) - - `server.mac` - MAC address of the server. (IPv4 or IPv6). (ECS driven) Correspond with `destination.mac` (ECS driven) - - `server.packets` - Packets sent from the server to the client.. (ECS driven) Correspond with `destination.packets` (ECS driven) - - - `sock.peer.addr` - Remote socket peer address: IPv4 or IPv6 (OTEL driven) Correspond with `server.address`, `destination.address` (ECS driven) - - `sock.peer.name` - Remote socket peer name. (OTEL driven) Correspond with `destination.domain` (ECS driven) - - `sock.peer.port` - Remote socket peer port. (OTEL driven) (OTEL driven) Corresponds with `server.port`,`destination.port` (ECS driven) - ---- - -```text - - ___________________ - | _______________ | - | |XXXXXXXXXXXXX| | - | |XXXXXXXXXXXXX| | - | |XXXXXXXXXXXXX| | - | |XXXXXXXXXXXXX| | - | |XXXXXXXXXXXXX| | - |_________________| - _[_______]_ - ___[___________]___ - | [_____] []|__ - | [_____] []| \__ - L___________________J \ \___\/ - ___________________ /\ - /###################\ (__) - -``` - ---- - -### References - - https://github.com/opensearch-project/observability/issues/1413 - - https://github.com/opensearch-project/observability/issues/1405 - - https://github.com/opensearch-project/observability/issues/1411 - - https://github.com/open-telemetry/opentelemetry-specification/blob/main/specification/logs/semantic_conventions/events.md - - https://github.com/open-telemetry/opentelemetry-specification/blob/main/specification/trace/semantic_conventions/http.md - - https://github.com/open-telemetry/opentelemetry-specification/blob/main/specification/trace/semantic_conventions/http.md - - https://www.elastic.co/guide/en/ecs/8.6/ecs-destination.html - - https://www.elastic.co/guide/en/ecs/8.6/ecs-client.html - - https://www.elastic.co/guide/en/ecs/8.6/ecs-server.html \ No newline at end of file diff --git a/src/main/resources/schema/observability/logs/Usage.md b/src/main/resources/schema/observability/logs/Usage.md deleted file mode 100644 index 03fa0c5bb..000000000 --- a/src/main/resources/schema/observability/logs/Usage.md +++ /dev/null @@ -1,117 +0,0 @@ -# Actual Index setup and usage -The next document describes the practicality of manually defining and initiating of the observability schema indices, it assumes the OpenSearch cluster -is up and running. - -### Setting Up the Logs Mapping -Start the OpenSearch cluster and follow the next steps for manually setup of the Log mapping template: - -`>> PUT _component_template/http_template` - -Copy the http.mapping content [here](http.mapping) - -`>> PUT _component_template/communication_template` - -Copy the communication.mapping content [here](communication.mapping) - -`>> PUT _index_template/logs` - -Copy the logs.mapping content [here](logs.mapping) - -Now you can create an data-stream index (following the logs index pattern) that has the supported schema: - -`>> PUT _data_stream/sso_logs-dataset-test1` - -You can also directly start ingesting data without creating a data stream. -Because we have a matching index template with a data_stream object, OpenSearch automatically creates the data stream: - -`POST sso_logs-dataset-test1/_doc` -```json -{ - "body": "login attempt failed", - "@timestamp": "2013-03-01T00:00:00", - ... -} - -``` - -To see information about a that data stream: -`GET _data_stream/sso_logs-dataset-test1` - -Would respond the following: -```json -{ - "data_streams" : [ - { - "name" : "sso_logs-dataset-test1", - "timestamp_field" : { - "name" : "@timestamp" - }, - "indices" : [ - { - "index_name" : ".ds-sso_logs-dataset-test1-000001", - "index_uuid" : "-VhmuhrQQ6ipYCmBhn6vLw" - } - ], - "generation" : 1, - "status" : "GREEN", - "template" : "sso_logs-*-*" - } - ] -} -``` - -To see more insights about the data stream, use the `_stats` endpoint: -`GET _data_stream/sso_logs-dataset-test1/_stats` -Would respond the following: -```json -{ - "_shards" : { - "total" : 1, - "successful" : 1, - "failed" : 0 - }, - "data_stream_count" : 1, - "backing_indices" : 1, - "total_store_size_bytes" : 208, - "data_streams" : [ - { - "data_stream" : "sso_logs-dataset-test1", - "backing_indices" : 1, - "store_size_bytes" : 208, - "maximum_timestamp" : 0 - } - ] -} -``` -### Ingestion -To ingest data into a data stream, you can use the regular indexing APIs. Make sure every document that you index has a timestamp field. - -`POST sso_logs-dataset-test1/_doc` -```json -{ - "body": "login attempt failed", - "@timestamp": "2013-03-01T00:00:00", - ... -} - -``` -You can search a data stream just like you search a regular index or an index alias. The search operation applies to all of the backing indices (all data present in the stream). - -`GET sso_logs-dataset-test1/_search` -```json -{ - "query": { - "match": { - ... - } - } -} -``` - -### Manage data-streams in OpenSearh - -To manage data streams from OpenSearch Dashboards, open OpenSearch Dashboards, choose Index Management, select Indices or Policy managed indices. - -see additional information: - - https://opensearch.org/docs/latest/opensearch/data-streams/#step-6-manage-data-streams-in-opensearch-dashboards - - https://opensearch.org/docs/latest/opensearch/data-streams/#step-5-rollover-a-data-stream \ No newline at end of file diff --git a/src/main/resources/schema/observability/metrics/README.md b/src/main/resources/schema/observability/metrics/README.md deleted file mode 100644 index 78e940f51..000000000 --- a/src/main/resources/schema/observability/metrics/README.md +++ /dev/null @@ -1,105 +0,0 @@ -# Metrics Schema Support - -Observability refers to the ability to monitor and diagnose systems and applications in real-time, in order to understand how they are behaving and identify potential issues. -Metrics present a critical component of observability, providing quantifiable data about the performance and behavior of systems and applications. -The importance of supporting metrics structured schema lies in the fact that it enables better analysis and understanding of system behavior. - -A structured schema provides a clear, consistent format, making it easier for observability tools to process and aggregate the data. -This in turn makes it easier for engineers to understand the performance and behavior of their systems, and quickly identify potential issues. - -When metrics are unstructured, it can be difficult for observability tools to extract meaningful information from them. -For example, if the data for a particular metric is not consistently recorded in the same format, it can be difficult to compare and analyze performance data over time. -Similarly, if metrics are not consistently named or categorized, it can be difficult to understand their context and significance. - -With a structured schema in place, observability tools can automatically extract and aggregate data, making it easier to understand system behavior at a high level. -This can help teams quickly identify performance bottlenecks, track changes in system behavior over time, and make informed decisions about system performance optimization. - -## Details -The next section provides the Simple Schema for Observability support which conforms with the OTEL specification. - -- metrics.mapping presents the template mapping for creating the Simple Schema for Observability index -- metrics.schema presents the json schema validation for verification of a metrics document conforms to the mapping structure - -## Metrics -see [OTEL metrics convention](https://opentelemetry.io/docs/reference/specification/metrics/) -see [OTEL metrics protobuf](https://github.com/open-telemetry/opentelemetry-proto/tree/main/opentelemetry/proto/metrics/v1) - -Simple Schema for Observability conforms with OTEL metrics protocol which defines the next data model: - -#### Timestamp field -As part of the data-stream definition the `@timestamp` is mandatory, if the field is not present in the original signal populate this field using `ObservedTimestamp` as value. - -### Instrumentation scope -This is a logical unit of the application with which the emitted telemetry can be associated. It is typically the developer’s choice to decide what denotes a reasonable instrumentation scope. -The most common approach is to use the instrumentation library as the scope, however other scopes are also common, e.g. a module, a package, or a class can be chosen as the instrumentation scope. - -The instrumentation scope may have zero or more additional attributes that provide additional information about the scope. As an example the field -`instrumentationScope.attributes.identification` is presented will be used to determine the resource origin of the signal and can be used to filter accordingly - -### Overview -Metrics are a specific kind of telemetry data. They represent a snapshot of the current state for a set of data. -Metrics are distinct from logs or events, which focus on records or information about individual events. - -Metrics expresses all system states as numerical values; counts, current values and such. -Metrics tend to aggregate data temporally, while this can lose information, the reduction in overhead is an engineering trade-off commonly chosen in many modern monitoring systems. - -Time series are a record of changing information over time. While time series can support arbitrary strings or binary data, only numeric data is in our scope. -Common examples of metric time series would be network interface counters, device temperatures, BGP connection states, and alert states. - -### Metric streams -In a similar way to the data_stream attribute field representing the category of a trace, the metric streams are grouped into individual Metric objects, identified by: - - - The originating Resource attributes - - The instrumentation Scope (e.g., instrumentation library name, version) - - The metric stream’s name - -### Metrics -Metric object is defined by the following properties: - - - The data point type (e.g. Sum, Gauge, Histogram ExponentialHistogram, Summary) - - The metric stream’s unit - - The data point properties, where applicable: AggregationTemporality, Monotonic - -The description is also present in the metrics object but is not part of the identification fields -_- The metric stream’s description_ - - -### Data Types - -**Values:** Metric values in MUST be either floating points or integers. - -**Attributes:** Labels are key-value pairs consisting of string as keys and Any type as values (strings, object, array) - -**MetricPoint:** Each MetricPoint consists of a set of values, depending on the MetricFamily type. - -**Metric** Metrics are defined by a unique attributes (dimensions) within a MetricFamily. - ---- - -Metrics MUST contain a list of one or more MetricPoints. Metrics with the same name for a given MetricFamily SHOULD have the same set of label names in their LabelSet. - -* Metrics.name: String value representation of the matrix purpose -* Metrics.type: Valid values are "gauge", "counter","histogram", and "summary". -* Metrics.Unit: specifies MetricFamily units. - -## Metric Types - -### Gauge -Gauges are current measurements, such as bytes of memory currently used or the number of items in a queue. For gauges the absolute value is what is of interest to a user. -**_A MetricPoint in a Metric with the type gauge MUST have a single value._** -Gauges MAY increase, decrease, or stay constant over time. Even if they only ever go in one direction, they might still be gauges and not counters. - -### Counter -Counters measure discrete events. Common examples are the number of HTTP requests received, CPU seconds spent, or bytes sent. For counters how quickly they are increasing over time is what is of interest to a user. -**_A MetricPoint in a Metric with the type Counter MUST have one value called Total._** - -### Histogram / Exponential-Histogram -Histograms measure distributions of discrete events. Common examples are the latency of HTTP requests, function runtimes, or I/O request sizes. -**_A Histogram MetricPoint MUST contain at least one bucket_**, and SHOULD contain Sum, and Created values. Every bucket MUST have a threshold and a value. - -### Summary -Summaries also measure distributions of discrete events and MAY be used when Histograms are too expensive and/or an average event size is sufficient. -**_A Summary MetricPoint MAY consist of a Count, Sum, Created, and a set of quantiles._** -Semantically, Count and Sum values are counters & MUST be an integer. - - diff --git a/src/main/resources/schema/observability/traces/README.md b/src/main/resources/schema/observability/traces/README.md deleted file mode 100644 index dcf16adcb..000000000 --- a/src/main/resources/schema/observability/traces/README.md +++ /dev/null @@ -1,153 +0,0 @@ -# Traces Schema Support -Observability in the software industry is the ability to monitor and diagnose systems and applications in real-time, in order to understand how they are behaving and identify potential issues. -Traces are a critical component of observability, providing detailed information about the flow of requests through a system, including timing information and any relevant contextual data. - -The importance of supporting traces schema lies in the fact that it enables better analysis and understanding of system behavior. -A structured schema provides a clear, consistent format for traces, making it easier for observability tools to process and aggregate the data. -This in turn makes it easier for engineers to understand the performance and behavior of their systems, and quickly identify potential issues. - -When traces are unstructured, it can be difficult for observability tools to extract meaningful information from them - For example, if the timing information for a particular request is not consistently represented in the same format, -it can be difficult to compare and analyze performance data over time. Similarly, if contextual data is not consistently recorded, it can be difficult to understand the context in which a particular request was executed. - -With a structured schema in place, observability tools can automatically extract and aggregate data, making it easier to understand system behavior at a high level. -This can help teams quickly identify performance bottlenecks, track the root cause of errors, and resolve issues more efficiently. - -## Details -The next section provides the Simple Schema for Observability support which conforms with the OTEL specification. - -- traces.mapping presents the template mapping for creating the Simple Schema for Observability index -- traces.schema presents the json schema validation for verification of a trace document conforms to the mapping structure - -### data-stream -[data-stream](https://opensearch.org/docs/latest/opensearch/data-streams/) Data streams simplify this process and enforce a setup that best suits time-series data, such as being designed primarily for append-only data and ensuring that each document has a timestamp field. -A data stream is internally composed of multiple backing indices. Search requests are routed to all the backing indices, while indexing requests are routed to the latest write index. - -As part of the Observability naming scheme, the value of the data stream fields combine to the name of the actual data stream : - -`{data_stream.type}-{data_stream.dataset}-{data_stream.namespace}`. -This means the fields can only contain characters that are valid as part of names of data streams. - -- **type** conforms to one of the supported Observability signals (Traces, Logs, Metrics, Alerts) -- **dataset** user defined field that can mainly be utilized for describing the origin of the signal -- **namespace** user custom field that can be used to describe any customer domain specific classification - -#### Timestamp field -As part of the data-stream definition the `@timestamp` is mandatory, if the field is not present to begin with use `ObservedTimestamp` as value for this field -**Note** - `@timestamp` value is the actual signal happening time and `observedTimestamp` is the time the exporter reads the actual event record. - -### Instrumentation scope -This is a logical unit of the application with which the emitted telemetry can be associated. It is typically the developer’s choice to decide what denotes a reasonable instrumentation scope. -The most common approach is to use the instrumentation library as the scope, however other scopes are also common, e.g. a module, a package, or a class can be chosen as the instrumentation scope. - -The instrumentation scope may have zero or more additional attributes that provide additional information about the scope. As an example the field -`instrumentationScope.attributes.identification` is presented will be used to determine the resource origin of the signal and can be used to filter accordingly - -## Traces -see [OTEL traces convention](https://github.com/open-telemetry/opentelemetry-specification/tree/main/semantic_conventions/trace) - -Traces are defined implicitly by their Spans - In particular, a Trace can be thought of as a directed acyclic graph (DAG) of Spans, where the edges between Spans are defined as parent/child relationship. - -## Spans -A span represents an operation within a transaction. Each Span encapsulates the following state: -Observability in the software industry is the ability to monitor and diagnose systems and applications in real-time, in order to understand how they are behaving and identify potential issues. -Traces are a critical component of observability, providing detailed information about the flow of requests through a system, including timing information and any relevant contextual data. - -The importance of supporting traces schema lies in the fact that it enables better analysis and understanding of system behavior. -A structured schema provides a clear, consistent format for traces, making it easier for observability tools to process and aggregate the data. -This in turn makes it easier for engineers to understand the performance and behavior of their systems, and quickly identify potential issues. - -When traces are unstructured, it can be difficult for observability tools to extract meaningful information from them - For example, if the timing information for a particular request is not consistently represented in the same format, -it can be difficult to compare and analyze performance data over time. Similarly, if contextual data is not consistently recorded, it can be difficult to understand the context in which a particular request was executed. - -With a structured schema in place, observability tools can automatically extract and aggregate data, making it easier to understand system behavior at a high level. -This can help teams quickly identify performance bottlenecks, track the root cause of errors, and resolve issues more efficiently. - -## Details -The next section provides the Simple Schema for Observability support which conforms with the OTEL specification. - -- traces.mapping presents the template mapping for creating the Simple Schema for Observability index -- traces.schema presents the json schema validation for verification of a trace document conforms to the mapping structure - -### data-stream -[data-stream](https://opensearch.org/docs/latest/opensearch/data-streams/) Data streams simplify this process and enforce a setup that best suits time-series data, such as being designed primarily for append-only data and ensuring that each document has a timestamp field. -A data stream is internally composed of multiple backing indices. Search requests are routed to all the backing indices, while indexing requests are routed to the latest write index. - -As part of the Observability naming scheme, the value of the data stream fields combine to the name of the actual data stream : - -`{data_stream.type}-{data_stream.dataset}-{data_stream.namespace}`. -This means the fields can only contain characters that are valid as part of names of data streams. - -- **type** conforms to one of the supported Observability signals (Traces, Logs, Metrics, Alerts) -- **dataset** user defined field that can mainly be utilized for describing the origin of the signal -- **namespace** user custom field that can be used to describe any customer domain specific classification - -#### Timestamp field -As part of the data-stream definition the `@timestamp` is mandatory, if the field is not present to begin with use `ObservedTimestamp` as value for this field -**Note** - `@timestamp` value is the actual signal happening time and `observedTimestamp` is the time the exporter reads the actual event record. - -### Instrumentation scope -This is a logical unit of the application with which the emitted telemetry can be associated. It is typically the developer’s choice to decide what denotes a reasonable instrumentation scope. -The most common approach is to use the instrumentation library as the scope, however other scopes are also common, e.g. a module, a package, or a class can be chosen as the instrumentation scope. - -The instrumentation scope may have zero or more additional attributes that provide additional information about the scope. As an example the field -`instrumentationScope.attributes.identification` is presented will be used to determine the resource origin of the signal and can be used to filter accordingly - -## Traces -see [OTEL traces convention](https://github.com/open-telemetry/opentelemetry-specification/tree/main/semantic_conventions/trace) - -Traces are defined implicitly by their Spans - In particular, a Trace can be thought of as a directed acyclic graph (DAG) of Spans, where the edges between Spans are defined as parent/child relationship. - -## Spans -A span represents an operation within a transaction. Each Span encapsulates the following state: - -* An operation name -* start and finish timestamp -* Attributes list of key-value pairs. -* Set of Events, each of which is itself a tuple (timestamp, name, Attributes) -* Parent's Span identifier. -* Links to causally-related Spans (via the SpanContext of those related Spans). -* SpanContext information required to reference a Span. - -### SpanContext -Represents all the information that identifies Span in the Trace and is propagated to child Spans and across process boundaries. -A **SpanContext** contains the tracing identifiers and the options that are propagated from parent to child Spans. - -* `TraceId` - It is worldwide unique with practically sufficient probability by being made as 16 randomly generated bytes - used to group all spans for a specific trace together across all processes. -* `SpanId` - It is the identifier for a span, globally unique with practically sufficient probability by being made as 8 randomly generated bytes. When passed to a child Span this identifier becomes the parent span id for the child Span. -* `Tracestate` - carries tracing-system specific context in a list of key value pairs . Trace-state allows different vendors propagate additional information and inter-operate with their legacy Id formats. For more details see this. - -Additional fields can be supported via the Attributes key/value store see [traces](https://github.com/open-telemetry/opentelemetry-specification/blob/main/specification/trace/semantic_conventions/README.md) - -### Structure -The default fields that are supported by the traces are -- **TraceId** : It is worldwide unique with practically sufficient probability by being made as 16 randomly generated bytes - used to group all spans for a specific trace together across all processes. -- **SpanId** : It is the identifier for a span, globally unique with practically sufficient probability by being made as 8 randomly generated bytes. When passed to a child Span this identifier becomes the parent span id for the child Span. -- **ParentId** : It is the identifier for a span's parent span. -- **TraceState** : carries tracing-system specific context in a list of key value pairs. Tracestate allows different vendors propagate additional information and inter-operate with their legacy Id formats. - -- **Name** : String representing the span's name -- **Kind** - - SpanKind.CLIENT - - SpanKind.SERVER - - SpanKind.CONSUMER - - SpanKind.PRODUCER - - SpanKind.INTERNAL - -- **StartTime** : Start time of the event -- **EndTime** : End time of the event -- **Attributes** - - An Attribute is a key-value pair, which has the following structure [Attributes](https://github.com/open-telemetry/opentelemetry-specification/blob/b00980832b4b823155001df56dbf9203d4e53f98/specification/common/README.md#attribute) - -- **DroppedAttributesCount** : Integer counting the dropped attributes -- **Events** : A set of the next tuples (timestamp, name, Attributes) -- **DroppedEventsCount** : Integer counting the dropped events -- **Links** : links to causally-related Spans -- **DroppedLinksCount** : Integer counting the dropped links -- **Status** - - - _status code is the int value - status message is the text representation_ - - - `UNSET = 0` : The default status. - - `OK = 1` : The operation has been validated by an Application developer or Operator to have completed successfully. - - `ERROR = 2` : The operation contains an error. \ No newline at end of file diff --git a/src/main/resources/schema/security/README.md b/src/main/resources/schema/security/README.md deleted file mode 100644 index f5f87bf52..000000000 --- a/src/main/resources/schema/security/README.md +++ /dev/null @@ -1,9 +0,0 @@ -# Security Domain Schema - -OpenSearch Security is a [plugin](https://github.com/opensearch-project/security) for OpenSearch that offers encryption, authentication and authorization. When combined with OpenSearch Security-Advanced Modules, it supports authentication via Active Directory, LDAP, Kerberos, JSON web tokens, SAML, OpenID and more. It includes fine grained role-based access control to indices, documents and fields. It also provides multi-tenancy support in OpenSearch Dashboards. - -Another leading security specification if the [Open Cybersecurity Schema Framework](https://github.com/ocsf/ocsf-schema) -OCSF is a framework for creating schemas and it also delivers a cybersecurity event schema built with the framework. - -OCSF framework is made up of a set of categories, event classes, data types, and an attribute dictionary. The framework is not restricted to cybersecurity nor to events, however the initial focus of the framework has been a schema for cybersecurity events. A schema browser for the cybersecurity schema can be found at schema.ocsf.io. This is the recommended way to explore the schema. - diff --git a/src/main/resources/schema/system/README.md b/src/main/resources/schema/system/README.md deleted file mode 100644 index 20bf9b48a..000000000 --- a/src/main/resources/schema/system/README.md +++ /dev/null @@ -1,57 +0,0 @@ -# Internal System Schema - -This folder contains internal representation of assets that are stored in the system indices of dashboard and integration. - - Application - - Datasource - - Index-Pattern - - Integration - - Notebook - - Operational-Panel - - SavedQuery - - Visualization - -### Application -[Application](https://opensearch.org/docs/2.5/observing-your-data/app-analytics/) enables creation of custom observability display to view the availability status of your systems, where you can combine log events with trace and metric data into a single view of overall system health. -This lets you quickly pivot between logs, traces, and metrics to dig into the source of any issues. - - - [Schema](application.schema) - -### Datasource -[Data-source](https://opensearch.org/docs/2.4/dashboards/discover/multi-data-sources/) Enables adding multiple data sources to a single dashboard. -OpenSearch Dashboards allows you to dynamically manage data sources, create index patterns based on those data sources, and execute queries against a specific data source and then combine visualizations in one dashboard. - - - [Schema](datasource.schema) - -### Index-Pattern -An Index Pattern allows to access data that you want to explore. An index pattern selects the data to use. An index pattern may point to multiple indices, data stream, or index aliases. - - - [Schema](index-pattern.schema) - -### Integration -Integration is a schematized and categorized bundle of assets grouped together to allow simple and coherent way to view, analyze and investigate different aspects of your data. -Integrations allow pre-defining dashboards, visualizations, index-templates, saved-queries and additional assets so that they provide a complete meaningful user experience. - - - [Schema](integration.schema) - -### Notebook -[Notebook](https://opensearch.org/docs/2.5/observing-your-data/notebooks/) A notebook is a document composed of two elements: code blocks (Markdown/SQL/PPL) and visualizations. -Choose multiple timelines to compare and contrast visualizations. -You can also generate reports directly from your notebooks. Common use cases include creating postmortem reports, designing runbooks, building live infrastructure reports, and writing documentation. - - - [Schema](notebook.schema) - -### Operational-Panel -[Operational Panels](https://opensearch.org/docs/2.5/observing-your-data/operational-panels/) in OpenSearch Dashboards are collections of visualizations generated using Piped Processing Language (PPL) queries. - - - [Schema](operational-panel.schema) - -### Saved-Query -A saved query (saved search) allows to reuse a search created in a dashboard for other dashboards. - - - [Schema](saved-query.schema) - -### Visualization -[Visualization](https://opensearch.org/docs/2.5/dashboards/visualize/viz-index/) allows translation of complex, high-volume, or numerical data into a visual representation that is easier to process. -OpenSearch Dashboards gives you data visualization tools to improve and automate the visual communication process. By using visual elements like charts, graphs, or maps to represent data, you can advance business intelligence and support data-driven decision-making and strategic planning. - - - [Schema](visualization.schema) From 3689a4654b671407c71a4aa0dd81691ce31c560a Mon Sep 17 00:00:00 2001 From: YANGDB Date: Thu, 20 Apr 2023 23:19:05 +0200 Subject: [PATCH 4/5] add dashboard preview images Signed-off-by: YANGDB --- integrations/aws/elb/info/README.md | 6 ++++++ integrations/aws/elb/info/dashboard-elb.jpg | Bin 0 -> 650492 bytes integrations/nginx/info/README.md | 7 ++++++- .../info/preview/nginx-dashboard-preview.png | Bin 0 -> 518335 bytes 4 files changed, 12 insertions(+), 1 deletion(-) create mode 100644 integrations/aws/elb/info/dashboard-elb.jpg create mode 100644 integrations/nginx/info/preview/nginx-dashboard-preview.png diff --git a/integrations/aws/elb/info/README.md b/integrations/aws/elb/info/README.md index dc0eff0f3..7065ca399 100644 --- a/integrations/aws/elb/info/README.md +++ b/integrations/aws/elb/info/README.md @@ -86,3 +86,9 @@ The next diagram describes the ALB-Logs Integration assets loading and validatio This diagram details different aspects of the interaction between the visual dashboard and panels to the backing SQL queries that would be executed. ![](alb-integration-load-assets.png) + + +### Dashboards +The following dashboard preview shows the summarized information collected from the load balancer log index + +![dashboard-elb.jpg](dashboard-elb.jpg) \ No newline at end of file diff --git a/integrations/aws/elb/info/dashboard-elb.jpg b/integrations/aws/elb/info/dashboard-elb.jpg new file mode 100644 index 0000000000000000000000000000000000000000..e2c4c30fec0ff333cca6e2dc27ee07eef64b3983 GIT binary patch literal 650492 zcmeFa2V7K3)-T!!h=_tHK{Apvl9kk=0wN-!l9S}n0+KVWC|N*2KtVvV#0JT+C4+!S zmL@lnB{Z?2)7^Mx=IG2fXU@!bzjxn#&#k+E_1k+_TD9uGR;{(FAbcUrfX>`hQBwgC z5rIHAfPWyuEa-}|mz^~Tq@e*40D(X!Ktv&SAQB)2d;($wZV>3$+hc!yLi*?}@n6#v 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The in_tail plugin reads log files line by line and sends them to Fluent-bit engine to be processed. -See additional details [here](fluet-bit/README.md). \ No newline at end of file +See additional details [here](fluet-bit/README.md). + +### Dashboards +The following dashboard preview shows the summarized information collected from the access log index + +![nginx-dashboard-preview.png](preview%2Fnginx-dashboard-preview.png) \ No newline at end of file diff --git a/integrations/nginx/info/preview/nginx-dashboard-preview.png b/integrations/nginx/info/preview/nginx-dashboard-preview.png new file mode 100644 index 0000000000000000000000000000000000000000..a74558296bd013f581844d3f6dc60328dc4b6258 GIT binary patch literal 518335 zcmb@u1yEeex(12{cS~>$k^sSda0u@15Fofa10e+0;6Vp>2=0*J?(ROg4sLJu+56mk z>b%S&USy37Tl^7KU1_ndsql7B-0)c@+B1V1%y%Vhp z7X$-?T4p6Kt}G)iPO0qdU}0rz4g>QsEHN2LSxtxN%c;*kRW>cvBuX2~Bnl(QyYsDN#g6HQQAo2FXb4+9=_6%NN}s7L}hw`4_-eJet8-5 zw-`-7&G0yB0IQhEY*a 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zna)0^)(Ju6waq-=d_%Csf&7pt<|MYjknpAFvIIHthn1DCbUNJ}{XvyQtkrFqV%~5% zmap@P>ux97TIjv|QqY6Jt?^jgKIPzH&QNF+P|{ST!LLf%o_r~y-oVxRONbJ7c}k=H zUG_@m9iU;?#jo*01i*&=8fmlHq5 z>vdVLbu4deQbx~FH{vk?ombRZ3Wg55dC2 Date: Thu, 18 May 2023 13:31:28 -0700 Subject: [PATCH 5/5] append service & traces analytics docs & info Signed-off-by: YANGDB --- docs/schema/README.md | 2 +- docs/schema/observability/README.md | 6 +- docs/schema/observability/traces/Usage.md | 2 +- explain.json | 10979 ++++++++++++++++ .../display/sso-logs-dashboard-new.ndjson | 276 +- integrations/nginx/test/nginx-otel/Dockerfile | 0 .../service-map/info/ServiceMapQueries.md | 116 + .../info/TraceAnalyticsServiceMap.md | 403 + .../info/providers/data-prepper/README.md | 2 +- .../info/providers/jaeger/README.md | 2 +- .../service-map/samples/preloaded/README.md | 4 +- ...raceGroups.mapping => tracegroups.mapping} | 0 ...{traceGroups.schema => tracegroups.schema} | 0 13 files changed, 11517 insertions(+), 275 deletions(-) create mode 100644 explain.json delete mode 100644 integrations/nginx/test/nginx-otel/Dockerfile create mode 100644 integrations/service-map/info/ServiceMapQueries.md create mode 100644 integrations/service-map/info/TraceAnalyticsServiceMap.md rename src/main/resources/schema/observability/traces/{traceGroups.mapping => tracegroups.mapping} (100%) rename src/main/resources/schema/observability/traces/{traceGroups.schema => tracegroups.schema} (100%) diff --git a/docs/schema/README.md b/docs/schema/README.md index 2262d489f..86c31eddd 100644 --- a/docs/schema/README.md +++ b/docs/schema/README.md @@ -105,7 +105,7 @@ For example the [`logs.schema`](../../src/main/resources/schema/observability/lo ... ``` -We can observe that the `traceId` field is defined by referencing to the [Span](../../src/main/resources/schema/observability/traces/traceGroups.schema) schema and explicitly to the `#/properties/spanId` field reference location. +We can observe that the `traceId` field is defined by referencing to the [Span](../../src/main/resources/schema/observability/traces/tracegroups.schema) schema and explicitly to the `#/properties/spanId` field reference location. ### Mapping related Each mapping template will contain the foreign schemas that are referenced to in that specific mapping file. For example the [`logs.mapping`](../../src/main/resources/schema/observability/logs/logs.schema) file will contain the next correlation object in the mapping `_meta` section: diff --git a/docs/schema/observability/README.md b/docs/schema/observability/README.md index 9a718234b..7db438109 100644 --- a/docs/schema/observability/README.md +++ b/docs/schema/observability/README.md @@ -47,7 +47,7 @@ For example the [`logs.schema`](../../../src/main/resources/schema/observability ... ``` -We can observe that the `traceId` field is defined by referencing to the [Span](../../../src/main/resources/schema/observability/traces/traceGroups.schema) schema and explicitly to the `#/properties/spanId` field reference location. +We can observe that the `traceId` field is defined by referencing to the [Span](../../../src/main/resources/schema/observability/traces/tracegroups.schema) schema and explicitly to the `#/properties/spanId` field reference location. ### Mapping related Each mapping template will contain the foreign schemas that are referenced to in that specific mapping file. For example the [`logs.mapping`](../../../src/main/resources/schema/observability/logs/logs.schema) file will contain the next correlation object in the mapping `_meta` section: @@ -172,7 +172,7 @@ The correlation metadata info is exported in the following way: ### Schema related In JSON Schema, there is no built-in way to represent relationships directly between multiple schemas, like you would find in a relational database. However, you can establish relationships indirectly by using a combination of `$id`, `$ref`, and consistent property naming across your schemas. -For example the [`logs.schema`](../../../src/main/resources/schema/observability/logs/logs.schema) file contains the next `$ref` references for the `traceId` & `spanId` fields that belong to the [`traces.schema`](../../../src/main/resources/schema/observability/traces/traceGroups.schema). +For example the [`logs.schema`](../../../src/main/resources/schema/observability/logs/logs.schema) file contains the next `$ref` references for the `traceId` & `spanId` fields that belong to the [`traces.schema`](../../../src/main/resources/schema/observability/traces/tracegroups.schema). ```json5 ... @@ -185,7 +185,7 @@ For example the [`logs.schema`](../../../src/main/resources/schema/observability ... ``` -We can observe that the `traceId` field is defined by referencing to the [Span](../../../src/main/resources/schema/observability/traces/traceGroups.schema) schema and explicitly to the `#/properties/spanId` field reference location. +We can observe that the `traceId` field is defined by referencing to the [Span](../../../src/main/resources/schema/observability/traces/tracegroups.schema) schema and explicitly to the `#/properties/spanId` field reference location. ### Mapping related Each mapping template will contain the foreign schemas that are referenced to in that specific mapping file. For example the [`logs.mapping`](../../../src/main/resources/schema/observability/logs/logs.mapping) file will contain the next correlation object in the mapping `_meta` section: diff --git a/docs/schema/observability/traces/Usage.md b/docs/schema/observability/traces/Usage.md index 04a644414..acf69bf6e 100644 --- a/docs/schema/observability/traces/Usage.md +++ b/docs/schema/observability/traces/Usage.md @@ -7,7 +7,7 @@ Start the OpenSearch cluster and follow the next steps for manually setup of the `>> PUT _component_template/tracegroups_template` -Copy the [traceGroups.mapping](../../../../src/main/resources/schema/observability/traces/traceGroups.mapping) +Copy the [traceGroups.mapping](../../../../src/main/resources/schema/observability/traces/tracegroups.mapping) `>> PUT _index_template/traces` diff --git a/explain.json b/explain.json new file mode 100644 index 000000000..636fa8614 --- /dev/null +++ b/explain.json @@ -0,0 +1,10979 @@ +{ + "took" : 106403, + "timed_out" : false, + "_shards" : { + "total" : 80, + "successful" : 80, + "skipped" : 0, + "failed" : 0 + }, + "hits" : { + "total" : { + "value" : 10000, + "relation" : "gte" + }, + "max_score" : 0.9999999, + "hits" : [ + { + "_shard" : "[titleindex_2023_nmslib][66]", + "_node" : "Rs__KDmORxCRdXLCJWvNmQ", + "_index" : "titleindex_2023_nmslib", + "_type" : "_doc", + "_id" : "GTI:amzn1.dv.gti.a3649f58-ab5f-4446-a94e-b7e89516b1f8:c90c7f96-83b6-3819-849f-1c2aa3aa51f0", + "_score" : 0.9999999, + "_routing" : "GTI:amzn1.dv.gti.a3649f58-ab5f-4446-a94e-b7e89516b1f8", + "_source" : { + "type" : "Image Embedding", + "subjectId" : "amzn1.dv.gti.a3649f58-ab5f-4446-a94e-b7e89516b1f8", + "subjectIdType" : "GTI", + "source" : { + "provider" : "DAPS" + }, + "startTime" : 752000, + "endTime" : 752000, + "frameEmbedding" : [ + 0.465576171875, + -0.364501953125, + -0.394287109375, + -0.084228515625, + 0.2239990234375, + 0.394775390625, + 0.2110595703125, + -0.287353515625, + 0.35693359375, + 0.1817626953125, + 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b/integrations/service-map/info/ServiceMapQueries.md new file mode 100644 index 000000000..a20ecea4a --- /dev/null +++ b/integrations/service-map/info/ServiceMapQueries.md @@ -0,0 +1,116 @@ +# Service-map Queries + + +`index_service_map` = Graph Connection metadata +`raw_index = trace metadata` + + +* Get all nodes(services), + +`select distinct(serviceName) from index_service_map +` + +* Get all edges + +`select t1.serviceName, t2.serviceName from index_service_map t1, index_service_map t2 where t2.destination.domain == t1.target.domain && t2.destination.resource == t1.target.resource +` + +## Metric Queries + +* Get Latency Metrics (for each service) + +for each X in nodes: + +``` +select avg(durationInNanos) from raw_index t1 +where t1.serviceName = X +and +( +(t1.name in (select target.resource from index_service_map where serviceName=X) +and t1.parentSpanId exists) +or +t1.parentSpanId does not exists +) +``` + + + +* GetThroughput → GetRequestCount + +for each X in nodes: + +``` +select count(distinct(spanId)) from raw_index t1 +where t1.serviceName = X +and +( +(t1.name in (select target.resource from index_service_map where serviceName=X) +and t1.parentSpanId exists) +or +t1.parentSpanId does not exists +) +``` + +* Get Error Rate + +for each X in nodes: +errorCount = + +``` +select count(distinct(spanId)) from raw_index t1 +where t1.serviceName = X +and +( +(t1.name in (select target.resource from index_service_map where serviceName=X) +and t1.parentSpanId exists) +or +t1.parentSpanId does not exists +) +and t1.status is Error +``` + +errorRate = errorCount/throughput + +## Filtering Queries + +* Filter by TRACE_GROUP + +for each node X, + + +``` +select avg(durationInNanos) from raw_index t1 +where t1.serviceName = X +and +( +(t1.name in (select target.resource from index_service_map where serviceName=X and traceGroupName = TRACE_GROUP) +and t1.parentSpanId exists) +or +(t1.parentSpanId does not exists and t1.name = TRACE_GROUP) +) +and +t1.traceId in (list of TraceIds which are part of the TraceGroup) + +``` + + + +* Filter by Trace Percentile + +for each X in nodes: + + +``` +select avg(durationInNanos) from raw_index t1 +where t1.serviceName = X +and +( +(t1.name in (select target.resource from index_service_map where serviceName=X) +and t1.parentSpanId exists) +or +t1.parentSpanId does not exists +) +and t1.TraceId in (list of TraceIds that are part of the percentile graph) + +``` + diff --git a/integrations/service-map/info/TraceAnalyticsServiceMap.md b/integrations/service-map/info/TraceAnalyticsServiceMap.md new file mode 100644 index 000000000..bec73fc25 --- /dev/null +++ b/integrations/service-map/info/TraceAnalyticsServiceMap.md @@ -0,0 +1,403 @@ +# Trace Analytics Service Map - UI + +## Node Map Construct + +Construct the map from service_map index and get a Map of service and list of target + +* service, has one attribute **serviceName**. +* target, has one attribute **resource** + +In the following example, we assume user select the following service denoted as **service_list** + +* database, **database_resource_list** +* analytics-service, **analytics-service_list** +* inventory, **inventory_list** + +The unin of all the resource list **database_resource_list + analytics-service_list + inventory_list** denoated as **resource_list** + +## Metric Queries - Enrich + +### Reference + +* [Service-map Queries](ServiceMapQueries.md) + +### Get Latency Metrics (for each service) + +The filter condition is: + +``` +resource.attributes.service.name in service_list +and (name in resource_list or parentSpanId not exist) +``` + +Then we can use the single DSL query to get the metrics of each service. + +``` +GET otel-v1-apm-span-000001/_search +{ + "from": 0, + "size": 0, + "query": { + "bool": { + "filter": [ + { + "terms": { + "resource.attributes.service.name.keyword": [ + "analytics-service", + "database", + "inventory" + ] + } + }, + { + "bool": { + "should": [ + { + "bool": { + "must_not": { + "exists": { + "field": "parentSpanId.keyword", + "boost": 1 + } + } + } + }, + { + "terms": { + "name.keyword": [ + "analytics-service", + "database", + "inventory", + "authentication", + "recommendation", + "payment" + ] + } + } + ], + "adjust_pure_negative": true, + "boost": 1 + } + } + ] + } + }, + "aggregations" : { + "name.keyword" : { + "terms" : { + "field" : "resource.attributes.service.name.keyword", + "size" : 200, + "min_doc_count" : 1, + "shard_min_doc_count" : 0, + "show_term_doc_count_error" : false, + "order" : [ + { + "_count" : "desc" + }, + { + "_key" : "asc" + } + ] + }, + "aggregations" : { + "AVG_0" : { + "avg" : { + "field" : "durationInNanos" + } + } + } + } + } +} + +# Result +https://paste.amazon.com/show/penghuo/1604538583 +``` + +### GetThroughput - GetRequestCount + +``` +GET otel-v1-apm-span-000001/_search +{ + "from": 0, + "size": 0, + "query": { + "bool": { + "filter": [ + { + "terms": { + "resource.attributes.service.name.keyword": [ + "analytics-service", + "database", + "inventory" + ] + } + }, + { + "bool": { + "should": [ + { + "bool": { + "must_not": { + "exists": { + "field": "parentSpanId.keyword", + "boost": 1 + } + } + } + }, + { + "terms": { + "name.keyword": [ + "analytics-service", + "database", + "inventory", + "authentication", + "recommendation", + "payment" + ] + } + } + ], + "adjust_pure_negative": true, + "boost": 1 + } + } + ] + } + }, + "aggregations" : { + "name.keyword" : { + "terms" : { + "field" : "resource.attributes.service.name.keyword", + "size" : 200, + "min_doc_count" : 1, + "shard_min_doc_count" : 0, + "show_term_doc_count_error" : false, + "order" : [ + { + "_count" : "desc" + }, + { + "_key" : "asc" + } + ] + }, + "aggregations" : { + "COUNT_0" : { + "cardinality" : { + "field" : "spanId.keyword" + } + } + } + } + } +} + +# Result +https://paste.amazon.com/show/penghuo/1604446208 +``` + +### GetErrorRate + +* solution I, caculate error rate with two queries. The first query get the total request for each service(denoted as **request**), the second query get the total error count for each service (denoted as **error**). Then the **error_rate = error / request**. + +``` +GET otel-v1-apm-span-000001/_search +{ + "from": 0, + "size": 0, + "query": { + "bool": { + "filter": [ + { + "term": { + "status.code.keyword": { + "value": "UnknownError" + } + } + }, + { + "terms": { + "resource.attributes.service.name.keyword": [ + "analytics-service", + "database", + "inventory" + ] + } + }, + { + "bool": { + "should": [ + { + "bool": { + "must_not": { + "exists": { + "field": "parentSpanId.keyword", + "boost": 1 + } + } + } + }, + { + "terms": { + "name.keyword": [ + "analytics-service", + "database", + "inventory", + "authentication", + "recommendation", + "payment" + ] + } + } + ] + } + } + ] + } + }, + "aggregations" : { + "name.keyword" : { + "terms" : { + "field" : "resource.attributes.service.name.keyword", + "size" : 200, + "min_doc_count" : 1, + "shard_min_doc_count" : 0, + "show_term_doc_count_error" : false, + "order" : [ + { + "_count" : "desc" + }, + { + "_key" : "asc" + } + ] + }, + "aggregations" : { + "COUNT_0" : { + "cardinality" : { + "field" : "spanId.keyword" + } + } + } + } + } +} +``` + +## + +## Average Latency for each Trace group of selected Service + +``` +SELECT T1.traceGroup, avg(T1.durationInNanos) +FROM otel-v1-apm-span-000001 T1 JOIN otel-v1-apm-span-000001 T2 +ON T1.traceId = T2.traceId +WHERE T1.traceGroup IS NOT NULL AND T2.serviceName = 'mysql' +GROUP BY T1.traceGroup +``` + + + +``` +# 1. GET TraceIds for mysql service +GET /otel-v1-apm-span-000001/_search +{ + "from" : 0, + "size" : 1000, + "query" : { + "bool" : { + "filter" : [ + { + "bool" : { + "must" : [ + { + "term" : { + "serviceName" : { + "value" : "mysql", + "boost" : 1.0 + } + } + } + ], + "adjust_pure_negative" : true, + "boost" : 1.0 + } + } + ], + "adjust_pure_negative" : true, + "boost" : 1.0 + } + }, + "_source" : { + "includes" : [ + "traceId" + ], + "excludes" : [ ] + } +} + +# 2. Calculate the average latency of each Trace Group +GET otel-v1-apm-span-000001/_search +{ + "from": 0, + "size": 0, + "query": { + "bool": { + "filter": [ + { + "bool": { + "must": { + "exists": { + "field": "parentSpanId", + "boost": 1 + } + } + } + }, + { + "terms": { + "traceId": [ + "000000000000000010d182dd2dc19b43", + "00000000000000003fbaec3b30b81597" + ] + } + + } + ] + } + }, + "aggregations" : { + "name.keyword" : { + "terms" : { + "field" : "traceGroup", + "size" : 200, + "min_doc_count" : 1, + "shard_min_doc_count" : 0, + "show_term_doc_count_error" : false, + "order" : [ + { + "_count" : "desc" + }, + { + "_key" : "asc" + } + ] + }, + "aggregations" : { + "AVG_0" : { + "avg" : { + "field" : "durationInNanos" + } + } + } + } + } +} +``` + + + + + diff --git a/integrations/service-map/info/providers/data-prepper/README.md b/integrations/service-map/info/providers/data-prepper/README.md index c28dbeca7..435a9c867 100644 --- a/integrations/service-map/info/providers/data-prepper/README.md +++ b/integrations/service-map/info/providers/data-prepper/README.md @@ -72,7 +72,7 @@ We will define the next mapping alias that will be part of the [config.json](../ ``` -To address this difference, the `trace` signal can be augmented with additional trace-component - in this case it will be the [traceGroup](../../../schema/observability/traces/traceGroup). +To address this difference, the `trace` signal can be augmented with additional trace-component - in this case it will be the [traceGroup](../../../../../src/main/resources/schema/observability/traces/tracegroups.mapping). ## Service dashboard Service will have a dashboard comprised of the following visualizations: diff --git a/integrations/service-map/info/providers/jaeger/README.md b/integrations/service-map/info/providers/jaeger/README.md index 926736ab1..a86adb8cc 100644 --- a/integrations/service-map/info/providers/jaeger/README.md +++ b/integrations/service-map/info/providers/jaeger/README.md @@ -81,7 +81,7 @@ We will define the next mapping alias that will be part of the [config.json](../ -To address this difference, the `trace` signal can be augmented with additional trace-component - in this case it will be the [traceGroup](../../../schema/observability/traces/traceGroup). +To address this difference, the `trace` signal can be augmented with additional trace-component - in this case it will be the [traceGroup](../../../../../src/main/resources/schema/observability/traces/tracegroups.mapping). ## Service dashboard Service will have a dashboard comprised of the following visualizations: diff --git a/integrations/service-map/samples/preloaded/README.md b/integrations/service-map/samples/preloaded/README.md index ab3b821f2..963bdf31e 100644 --- a/integrations/service-map/samples/preloaded/README.md +++ b/integrations/service-map/samples/preloaded/README.md @@ -13,7 +13,7 @@ This will load both opensearch server & dashboards - `$ docker compose up` - Ensure vm.max_map_count has been set to 262144 or higher (`sudo sysctl -w vm.max_map_count=262144`). -2. Load the Simple Schema traces / services / metrics index templates [Loading Traces](../../../../schema/observability/traces/Usage.md) +2. Load the Simple Schema traces / services / metrics index templates [Loading Traces](../../../../docs/schema/observability/traces/Usage.md) - `curl -XPUT localhost:9200/_component_template/tracegroups_template -H "Content-Type: application/json" --data-binary @traceGroups.mapping` @@ -24,7 +24,7 @@ This will load both opensearch server & dashboards - `curl -XPUT localhost:9200/_index_template/metrics -H "Content-Type: application/json" --data-binary @metrics.mapping` -3. Load the (proprietary) `data-prepper` traces / services [traces mapping template](../../schema/data-prepper-traces.mapping) , [Service mapping templates](../../schema/data-prepper-services.mapping) +3. Load the (proprietary) `data-prepper` traces / services [traces mapping template](../../assets/mapping/data-prepper-traces.mapping) , [Service mapping templates](../../assets/mapping/data-prepper-services.mapping) - `curl -XPUT localhost:9200/_index_template/otel-v1-apm-span -H "Content-Type: application/json" --data-binary @data-prepper-traces.mapping` - `curl -XPUT localhost:9200/_template/otel-v1-apm-service -H "Content-Type: application/json" --data-binary @data-prepper-services.mapping` diff --git a/src/main/resources/schema/observability/traces/traceGroups.mapping b/src/main/resources/schema/observability/traces/tracegroups.mapping similarity index 100% rename from src/main/resources/schema/observability/traces/traceGroups.mapping rename to src/main/resources/schema/observability/traces/tracegroups.mapping diff --git a/src/main/resources/schema/observability/traces/traceGroups.schema b/src/main/resources/schema/observability/traces/tracegroups.schema similarity index 100% rename from src/main/resources/schema/observability/traces/traceGroups.schema rename to src/main/resources/schema/observability/traces/tracegroups.schema