Remora is a monitoring utility for Apache Kafka that provides consumer lag checking as a service. An HTTP endpoint is provided to request consumer group information on demand. Combining this with a time series database like KairosDB it is possible to graph your consumer group status; see remora fetcher for an example of this.
Remora is stable and production ready. A number of production kafka clusters in Zalando are being monitored by Remora right now!
We created Remora after spending some time using Linkedin's burrow application for monitoring consumer lag and experiencing some performance problems (burrow shut down after an unknown amount with no error stack, alert or sign of error. We have no idea why but we had to keep restarting the app which was very annoying). Remora provides the Kafka consumer group command as an HTTP endpoint.
We are using Kafka 0.10.2.1 extensively. As almost all our applications depend on Kafka, we needed a way to visualise consumer data over a time period in order to discover issues with our consumers. Remora lets us do exactly this, it exposes consumer group metrics over HTTP which allow us to create alarms if a consumer has stopped or slowed consumption from a topic or even on a single partition. ~ Team Buffalo @ Zalando Dublin
We are using Kafka 0.10.2.1 along with Akka Streams. We use Remora to track, alert, and visualise any lag within any of our components ~ Team Setanta @ Zalando Dublin
We rely on Kafka for streaming DB change events on to other teams within our organisation. Remora greatly aids us in ensuring our Kafka and Kafka Connect components are functioning correctly by monitoring both the number of events been produced, and any lag present on a per consumer basis. It is proving an excellent tool in providing data which we use to trigger real time alerts ~ Team Warhol @ Zalando Dublin
We use Kafka and Kafka Streaming to orchestrate the different components of our text processing pipeline. Through data provided by Remora, we monitor lags in different topics as part of our monitoring dashboard and alerting system. Remora makes it easier for us to quickly identify and respond to bottlenecks and problems. ~ Team Sapphire @ Zalando Dublin
We are using Mirror Maker to replicate data between two Kafka brokers and Remora has been a great help to monitor the replication in real time. The metrics exposed by Remora are pushed to Datadog, on top of which we build dashboards and triggers to help us react in case of failure. ~ Sqooba Switzerland
The latest release of Remora supports Apache Kafka 3.1.0 and earlier.
To find the latest releases, please see the following examples:
$ curl https://registry.opensource.zalan.do/teams/buffalo/artifacts/remora/tags | jq ".[] | .name"
$ pierone latest buffalo remora --url registry.opensource.zalan.do # requires `$ pip3 install stups-pierone`
Images for all versions are available on Zalando opensource pierone
They can be used as follows:
docker run -it --rm -p 9000:9000 -e KAFKA_ENDPOINT=127.0.0.1:9092 registry.opensource.zalan.do/buffalo/remoraRun it with different log level:
docker run -it --rm -p 9000:9000 -e KAFKA_ENDPOINT=127.0.0.1:9092 -e 'JAVA_OPTS=-Dlogback-root-level=INFO' registry.opensource.zalan.do/buffalo/remoraFor further examples see the docker-compose.yml
docker-compose -f basic-example/docker-compose.yml upRun remora in IDE with kafka and zookeeper run by docker-compose. Note you must set -e KAFKA_ENDPOINT="kafka:9094" and --network basic-example_default for Remora to work with Kafka from docker-compose.
docker-compose -f basic-example/docker-compose.yml up --scale remora=0Remora is stateless, so test the scale of the API
docker-compose -f basic-example/docker-compose.yml up --scale remora=3For examples with broker authentication see the docker-compose.yml
docker-compose -f auth-example/docker-compose.yml up$ curl http://localhost:9000/consumers
["consumer-1", "consumer-2", "consumer-3"]$ curl http://localhost:9000/consumers/<ConsumerGroupId>
{
"state":"Empty",
"partition_assignment":[
{
"group":"console-consumer-20891",
"coordinator":{
"id":0,
"id_string":"0",
"host":"foo.company.com",
"port":9092
},
"topic":"products-in",
"partition":1,
"offset":3,
"lag":0,
"consumer_id":"-",
"host":"-",
"client_id":"-",
"log_end_offset":3
},
{
"group":"console-consumer-20891",
"coordinator":{
"id":0,
"id_string":"0",
"host":"foo.company.com",
"port":9092
},
"topic":"products-in",
"partition":0,
"offset":3,
"lag":0,
"consumer_id":"consumer-1-7baba9b9-0ec3-4241-9433-f36255dd4708",
"host":"/xx.xxx.xxx.xxx",
"client_id":"consumer-1",
"log_end_offset":3
}
],
"lag_per_topic":{
"products-in" : 0
}
}$ curl http://localhost:9000/health
{
"cluster_id": "foobar_123",
"controller": {
"host": "xx.xxx.xxx.xxx",
"id": 0,
"id_string": "0",
"port": 9092
},
"nodes": [
{
"host": "xx.xxx.xxx.xxx",
"id": 0,
"id_string": "0",
"port": 9092
}
]
}$ curl http://localhost:9000/metrics
{
"version": "3.0.0",
"gauges": {
"PS-MarkSweep.count": {
"value": 7371
},
"PS-MarkSweep.time": {
"value": 310404
},
"PS-Scavenge.count": {
"value": 476530
},
"PS-Scavenge.time": {
"value": 1234370
},
"blocked.count": {
"value": 0
},
"count": {
"value": 12
},
"daemon.count": {
"value": 3
},
"deadlock.count": {
"value": 0
},
"deadlocks": {
"value": []
},
"heap.committed": {
"value": 74448896
},
"heap.init": {
"value": 132120576
},
"heap.max": {
"value": 1860698112
},
"heap.usage": {
"value": 0.021295551247380425
},
"heap.used": {
"value": 39624592
},
"new.count": {
"value": 0
},
"non-heap.committed": {
"value": 73883648
},
"non-heap.init": {
"value": 2555904
},
"non-heap.max": {
"value": -1
},
"non-heap.usage": {
"value": -72377144
},
"non-heap.used": {
"value": 72377144
},
"pools.Code-Cache.committed": {
"value": 27525120
},
"pools.Code-Cache.init": {
"value": 2555904
},
"pools.Code-Cache.max": {
"value": 251658240
},
"pools.Code-Cache.usage": {
"value": 0.10638478597005209
},
"pools.Code-Cache.used": {
"value": 26772608
},
"pools.Compressed-Class-Space.committed": {
"value": 5242880
},
"pools.Compressed-Class-Space.init": {
"value": 0
},
"pools.Compressed-Class-Space.max": {
"value": 1073741824
},
"pools.Compressed-Class-Space.usage": {
"value": 0.004756048321723938
},
"pools.Compressed-Class-Space.used": {
"value": 5106768
},
"pools.Metaspace.committed": {
"value": 41115648
},
"pools.Metaspace.init": {
"value": 0
},
"pools.Metaspace.max": {
"value": -1
},
"pools.Metaspace.usage": {
"value": 0.984972144911835
},
"pools.Metaspace.used": {
"value": 40497768
},
"pools.PS-Eden-Space.committed": {
"value": 40894464
},
"pools.PS-Eden-Space.init": {
"value": 33554432
},
"pools.PS-Eden-Space.max": {
"value": 693108736
},
"pools.PS-Eden-Space.usage": {
"value": 0.02002515230164405
},
"pools.PS-Eden-Space.used": {
"value": 13879608
},
"pools.PS-Old-Gen.committed": {
"value": 31457280
},
"pools.PS-Old-Gen.init": {
"value": 88080384
},
"pools.PS-Old-Gen.max": {
"value": 1395654656
},
"pools.PS-Old-Gen.usage": {
"value": 0.018360885975505965
},
"pools.PS-Old-Gen.used": {
"value": 25625456
},
"pools.PS-Survivor-Space.committed": {
"value": 2097152
},
"pools.PS-Survivor-Space.init": {
"value": 5242880
},
"pools.PS-Survivor-Space.max": {
"value": 2097152
},
"pools.PS-Survivor-Space.usage": {
"value": 0.0625
},
"pools.PS-Survivor-Space.used": {
"value": 131072
},
"runnable.count": {
"value": 4
},
"terminated.count": {
"value": 0
},
"timed_waiting.count": {
"value": 1
},
"total.committed": {
"value": 148332544
},
"total.init": {
"value": 134676480
},
"total.max": {
"value": 1860698111
},
"total.used": {
"value": 112001672
},
"waiting.count": {
"value": 7
}
},
"counters": {
"KafkaClientActor.receiveCounter": {
"count": 1443078
},
"foo.3.bar.GET-rejections": {
"count": 1
},
"foo.3bar.GET-rejections": {
"count": 1
},
"foo.4.bar.GET-rejections": {
"count": 1
},
"health.GET-2xx": {
"count": 1
},
"metrics.GET-2xx": {
"count": 5
}
},
"histograms": {},
"meters": {
"KafkaClientActor.receiveExceptionMeter": {
"count": 0,
"m15_rate": 0,
"m1_rate": 0,
"m5_rate": 0,
"mean_rate": 0,
"units": "events/second"
}
},
"timers": {
"KafkaClientActor.receiveTimer": {
"count": 1443078,
"max": 0.496106,
"mean": 0.023955427605185976,
"min": 0.00855,
"p50": 0.013158,
"p75": 0.015818,
"p95": 0.069989,
"p98": 0.18145599999999998,
"p99": 0.193686,
"p999": 0.47478499999999996,
"stddev": 0.04561406607191679,
"m15_rate": 0.8672873098267513,
"m1_rate": 0.8576046718431439,
"m5_rate": 0.8704903354041494,
"mean_rate": 0.34074311090084636,
"duration_units": "milliseconds",
"rate_units": "calls/second"
},
"RemoraKafkaConsumerGroupService.describe-timer": {
"count": 1372542,
"max": 3953.5592429999997,
"mean": 165.67620936478744,
"min": 4.631377,
"p50": 22.125121,
"p75": 124.258938,
"p95": 527.534084,
"p98": 800.1686119999999,
"p99": 3316.226616,
"p999": 3611.7097409999997,
"stddev": 473.995637636751,
"m15_rate": 0.8508541627113339,
"m1_rate": 0.8450436821406069,
"m5_rate": 0.8545541048945428,
"mean_rate": 0.324087977369598,
"duration_units": "milliseconds",
"rate_units": "calls/second"
},
"RemoraKafkaConsumerGroupService.list-timer": {
"count": 70536,
"max": 2167.1663869999998,
"mean": 163.13534839326368,
"min": 56.275192999999994,
"p50": 162.584495,
"p75": 162.584495,
"p95": 162.584495,
"p98": 200.345285,
"p99": 200.345285,
"p999": 437.69862,
"stddev": 23.321317038931596,
"m15_rate": 0.016617378383700615,
"m1_rate": 0.015343754688965648,
"m5_rate": 0.016501030706405084,
"mean_rate": 0.016655133007592124,
"duration_units": "milliseconds",
"rate_units": "calls/second"
},
"metrics.GET": {
"count": 2,
"max": 174.712404,
"mean": 88.26670169568574,
"min": 4.375856,
"p50": 4.375856,
"p75": 174.712404,
"p95": 174.712404,
"p98": 174.712404,
"p99": 174.712404,
"p999": 174.712404,
"stddev": 85.15869346735195,
"m15_rate": 0,
"m1_rate": 0,
"m5_rate": 0,
"mean_rate": 0.6714371986436051,
"duration_units": "milliseconds",
"rate_units": "calls/second"
}
}
}Additional configuration can be passed via the following environment variables:
- SERVER_PORT - default
9000 - KAFKA_ENDPOINT - default
localhost:9092 - ACTOR_TIMEOUT - default
60 seconds - AKKA_HTTP_SERVER_REQUEST_TIMEOUT -
default 60 seconds - AKKA_HTTP_SERVER_IDLE_TIMEOUT -
default 60 seconds - TO_REGISTRY -
default falsereports lag/offset/end to metricsRegistry - EXPORT_METRICS_INTERVAL_SECONDS -
default 20interval to report lag/offset/end to metricsRegistry
The following environment variables can be used to configure reporting to Cloudwatch:
- CLOUDWATCH_ON -
default falsereports metricsRegistry to cloudwatch, TO_REGISTRY will need to be switched on! - CLOUDWATCH_NAME -
default 'remora'name to appear on cloudwatch - CLOUDWATCH_METRIC_FILTER -
default ''metric names to filter on cloudwatch. Set the CLOUDWATCH_METRIC_FILTER variable to a regex string to filter out metric names that DO NOT match the regex.
The following environment variables can be used to configure reporting to Datadog:
- DATADOG_ON -
default falsereports metricsRegistry to Datadog, TO_REGISTRY will need to be switched on! - DATADOG_NAME -
default 'remora'name to appear on datadog - DATADOG_INTERVAL_MINUTES -
default '1'The reporting interval, in minutes. - DATADOG_AGENT_HOST -
default 'localhost'The host on which a Datadog agent is running. - DATADOG_AGENT_PORT -
default '8125'The port of the Datadog agent. - DATADOG_CONSUMER_GROUPS -
default '[]'List of consumer groups for which metrics will be sent to Datadog. An empty list means that all metrics will be sent.
Reporting to datadog agent:
Reporting to Datadog is done via DogStatsD, which is usually running on the same host as remora. However, as Remora is running inside a docker container, some steps are required to make the integration:
- Set DATADOG_AGENT_HOST as the address of the host on your machine
- In the datadog agent configuration, set
non_local_traffic: yes
This way, a docker container running Remora will be able to communicate with a Datadog agent on the host machine.
- Scala
- SBT
Create docker image locally. The image will be built to remora:0.1.0-SNAPSHOT by default.
$ sbt docker:publishLocalWe are happy to accept contributions. First, take a look at our contributing guidelines.
Please check the Issues Page for contribution ideas.
Feel free to contact one of the maintainers.
MIT