Implementation of BackboneImbalanceScenario

[garyparrot]

這個 PR 實作一個 Scenario 他可以建立 #1424 的情境。

Scenario 現在會回傳一個物件，內容包含他的生成情境的一些描述和統計資訊。這個 PR 完成後，會再另外開一個 PR，給 WebService 新增一個 ScenarioHandler，他可以透過 Web API 的形式來對叢集套用特定的情境。然後將 Scenario 回傳的資訊以 JSON 回覆給使用者。

回覆的資訊包含：

• 節點的網路輸入
• 節點的網路輸出
• 平均 topic consumer fanout
• Performance Tool 的指令
• 每個 topic 的 consumer fanout
• 每個 topic 的 data rate
• topic 的 data rate 統計成的直方圖
• 總叢集承受的 consume data rate
• 總叢集承受的 produce data rate
• 總叢集 partiiton 數量
• 總叢集 topic 數量

詳細回傳如下：

CLICK ME

{
  "brokerEgress": {
    "0": "535.10 MB/seconds",
    "1": "523.16 MB/seconds",
    "2": "1.03 GB/seconds",
    "3": "541.52 MB/seconds",
    "4": "504.48 MB/seconds",
    "5": "507.36 MB/seconds"
  },
  "brokerIngress": {
    "0": "494.61 MB/seconds",
    "1": "496.46 MB/seconds",
    "2": "1.00 GB/seconds",
    "3": "502.34 MB/seconds",
    "4": "486.16 MB/seconds",
    "5": "486.10 MB/seconds"
  },
  "consumerFanoutAverage": 1,
  "perfCommands": [
    {
      "args": "--topics topic_528,topic_927,topic_804,topic_409,topic_366,topic_641,topic_364,topic_881,topic_127,topic_248,topic_887,topic_126,topic_764,topic_522,topic_490,topic_252,topic_71,topic_76,topic_879,topic_913,topic_515,topic_639,topic_750,topic_233,topic_871,topic_630,topic_198,topic_595,topic_870,topic_116,topic_512,topic_876,topic_634,topic_995,topic_236,topic_874,topic_241,topic_480,topic_919,topic_6,topic_65,topic_549,topic_824,topic_946,topic_704,topic_429,topic_825,topic_305,topic_943,topic_669,topic_781,topic_540,topic_787,topic_666,topic_304,topic_942,topic_268,topic_664,topic_148,topic_391,topic_273,topic_791,topic_395,topic_150,topic_52,topic_417,topic_46,topic_812,topic_818,topic_936,topic_530,topic_651,topic_773,topic_498,topic_495,topic_253,topic_375,topic_931,topic_774,topic_532,topic_137,topic_258,topic_262,topic_383,topic_780,topic_140,topic_726,topic_206,topic_602,topic_328,topic_848,topic_288,topic_561,topic_562,topic_683,topic_446,topic_204,topic_600,topic_689,topic_843,topic_169,topic_324,topic_445,topic_171,topic_290,topic_174,topic_571,topic_293,topic_319,topic_836,topic_437,topic_713,topic_959,topic_278,topic_793,topic_397,topic_556,topic_952,topic_435,topic_678,topic_796,topic_158,topic_313,topic_680,topic_285,topic_164,topic_161,topic_21,topic_748,topic_869,topic_228,topic_624,topic_13,topic_504,topic_867,topic_628,topic_904,topic_18,topic_222,topic_982,topic_341,topic_100,topic_743,topic_501,topic_226,topic_348,topic_587,topic_741,topic_466,topic_862,topic_983,topic_863,topic_190,topic_351,topic_907,topic_95,topic_736,topic_88,topic_219,topic_615,topic_858,topic_977,topic_739,topic_618,topic_850,topic_451,topic_452,topic_455,topic_456,topic_581,topic_186,topic_87 --throttle topic_528-0:211993Byte/second,topic_528-1:557429Byte/second,topic_528-2:509815Byte/second,topic_528-3:726966Byte/second,topic_528-4:59155Byte/second,topic_528-5:591654Byte/second,topic_927-0:132141Byte/second,topic_927-1:106657Byte/second,topic_927-2:78738Byte/second,topic_927-3:363299Byte/second,topic_927-4:240320Byte/second,topic_927-5:243224Byte/second,topic_927-6:325646Byte/second,topic_804-0:454732Byte/second,topic_804-1:498303Byte/second,topic_804-2:32398Byte/second,topic_804-3:181627Byte/second,topic_804-4:490678Byte/second,topic_804-5:312826Byte/second,topic_804-6:167089Byte/second,topic_804-7:109307Byte/second,topic_409-0:38829Byte/second,topic_409-1:946522Byte/second,topic_409-2:926910Byte/second,topic_409-3:658937Byte/second,topic_409-4:86168Byte/second,topic_409-5:664454Byte/second,topic_366-0:84088Byte/second,topic_366-1:55603Byte/second,topic_366-2:188656Byte/second,topic_366-3:112423Byte/second,topic_366-4:242167Byte/second,topic_641-0:428857Byte/second,topic_641-1:402110Byte/second,topic_641-2:418841Byte/second,topic_641-3:421123Byte/second,topic_641-4:50177Byte/second,topic_641-5:199204Byte/second,topic_641-6:191346Byte/second,topic_641-7:80378Byte/second,topic_364-0:578624Byte/second,topic_364-1:536308Byte/second,topic_364-2:728271Byte/second,topic_364-3:275714Byte/second,topic_364-4:150689Byte/second,topic_881-0:94196Byte/second,topic_881-1:138591Byte/second,topic_881-2:30133Byte/second,topic_881-3:181619Byte/second,topic_881-4:88390Byte/second,topic_881-5:82785Byte/second,topic_881-6:138198Byte/second,topic_881-7:31353Byte/second,topic_881-8:144354Byte/second,topic_881-9:27491Byte/second,topic_881-10:85787Byte/second,topic_881-11:173701Byte/second,topic_881-12:11704Byte/second,topic_881-13:23233Byte/second,topic_881-14:82775Byte/second,topic_127-0:50421Byte/second,topic_127-1:427796Byte/second,topic_127-2:98081Byte/second,topic_127-3:81355Byte/second,topic_127-4:176574Byte/second,topic_127-5:282549Byte/second,topic_127-6:70928Byte/second,topic_127-7:10453Byte/second,topic_127-8:48666Byte/second,topic_248-0:447569Byte/second,topic_248-1:667618Byte/second,topic_248-2:140281Byte/second,topic_248-3:455525Byte/second,topic_248-4:621307Byte/second,topic_887-0:217684Byte/second,topic_887-1:776723Byte/second,topic_887-2:193469Byte/second,topic_887-3:632350Byte/second,topic_887-4:443339Byte/second,topic_887-5:120336Byte/second,topic_126-0:698037Byte/second,topic_126-1:495009Byte/second,topic_126-2:859774Byte/second,topic_126-3:24019Byte/second,topic_126-4:1115743Byte/second,topic_764-0:64193Byte/second,topic_764-1:138759Byte/second,topic_764-2:746404Byte/second,topic_764-3:557607Byte/second,topic_764-4:742077Byte/second,topic_764-5:120203Byte/second,topic_764-6:324036Byte/second,topic_764-7:698563Byte/second,topic_522-0:199393Byte/second,topic_522-1:96099Byte/second,topic_522-2:289760Byte/second,topic_522-3:2998Byte/second,topic_522-4:47405Byte/second,topic_522-5:180234Byte/second,topic_522-6:187842Byte/second,topic_522-7:119530Byte/second,topic_522-8:257015Byte/second,topic_522-9:118631Byte/second,topic_522-10:150033Byte/second,topic_522-11:82382Byte/second,topic_522-12:217647Byte/second,topic_522-13:59542Byte/second,topic_490-0:432495Byte/second,topic_490-1:371091Byte/second,topic_490-2:334090Byte/second,topic_490-3:501079Byte/second,topic_490-4:539487Byte/second,topic_490-5:442671Byte/second,topic_490-6:482609Byte/second,topic_490-7:198281Byte/second,topic_490-8:226547Byte/second,topic_490-9:426008Byte/second,topic_252-0:34637Byte/second,topic_252-1:104619Byte/second,topic_252-2:74038Byte/second,topic_252-3:111352Byte/second,topic_252-4:70240Byte/second,topic_252-5:25673Byte/second,topic_252-6:93096Byte/second,topic_252-7:49304Byte/second,topic_252-8:139328Byte/second,topic_71-0:634850Byte/second,topic_71-1:619374Byte/second,topic_71-2:412077Byte/second,topic_71-3:31698Byte/second,topic_71-4:409400Byte/second,topic_76-0:188349Byte/second,topic_76-1:389516Byte/second,topic_76-2:135067Byte/second,topic_76-3:286846Byte/second,topic_76-4:515321Byte/second,topic_879-0:54890Byte/second,topic_879-1:137692Byte/second,topic_879-2:114724Byte/second,topic_879-3:117836Byte/second,topic_879-4:147352Byte/second,topic_879-5:150524Byte/second,topic_913-0:671603Byte/second,topic_913-1:197680Byte/second,topic_913-2:130990Byte/second,topic_913-3:284240Byte/second,topic_913-4:377220Byte/second,topic_913-5:105230Byte/second,topic_913-6:391331Byte/second,topic_913-7:268175Byte/second,topic_515-0:93851Byte/second,topic_515-1:146831Byte/second,topic_515-2:412453Byte/second,topic_515-3:184772Byte/second,topic_515-4:206641Byte/second,topic_515-5:286629Byte/second,topic_515-6:231922Byte/second,topic_515-7:160695Byte/second,topic_515-8:40339Byte/second,topic_515-9:219041Byte/second,topic_515-10:2288Byte/second,topic_515-11:369430Byte/second,topic_515-12:45115Byte/second,topic_639-0:203793Byte/second,topic_639-1:8018Byte/second,topic_639-2:163028Byte/second,topic_639-3:177515Byte/second,topic_639-4:252294Byte/second,topic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--producers 16 --consumers 24 --run.until 1day --key.size 10KiB --key.distribution zipfian --bootstrap.servers 192.168.103.177:25655,192.168.103.178:25655,192.168.103.179:25655,192.168.103.180:25655,192.168.103.181:25655,192.168.103.182:25655",
      "egress": "500.88 MB/seconds",
      "ingress": "359.95 MB/seconds"
    },
    { ... },
    { ... },
    { ... },
    { ... },
    { ... },
    { ... },
  ],
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    "backbone": 1,
    "topic_0": 0,
    "topic_1": 1,
    "topic_10": 1,
    "topic_100": 1,
    "topic_101": 1,
    // ...
    "topic_970": 1,
    "topic_971": 1,
    "topic_972": 1,
    "topic_973": 0,
    "topic_974": 0,
    "topic_975": 1,
    "topic_976": 0,
    "topic_977": 3,
    "topic_978": 0,
    "topic_979": 0,
    "topic_98": 0,
    "topic_980": 1,
    "topic_981": 1,
    "topic_982": 1,
    "topic_983": 1,
    "topic_984": 2,
    "topic_985": 2,
    "topic_986": 2,
    "topic_987": 1,
    "topic_988": 0,
    "topic_989": 2,
    "topic_99": 3,
    "topic_990": 0,
    "topic_991": 1,
    "topic_992": 1,
    "topic_993": 1,
    "topic_994": 0,
    "topic_995": 1,
    "topic_996": 1,
    "topic_997": 1,
    "topic_998": 2,
    "topic_999": 0
  },
  "topicDataRate": {
    "backbone": "504.18 MB/seconds",
    "topic_0": "2.65 MB/seconds",
    "topic_1": "2.21 MB/seconds",
    "topic_10": "2.35 MB/seconds",
    "topic_100": "3.90 MB/seconds",
    "topic_101": "2.74 MB/seconds",
    "topic_102": "4.30 MB/seconds",
    "topic_103": "2.25 MB/seconds",
    // ...
    "topic_984": "2.31 MB/seconds",
    "topic_985": "3.39 MB/seconds",
    "topic_986": "2.13 MB/seconds",
    "topic_987": "2.16 MB/seconds",
    "topic_988": "2.02 MB/seconds",
    "topic_989": "5.36 MB/seconds",
    "topic_99": "7.99 MB/seconds",
    "topic_990": "7.31 MB/seconds",
    "topic_991": "2.33 MB/seconds",
    "topic_992": "2.20 MB/seconds",
    "topic_993": "2.43 MB/seconds",
    "topic_994": "2.21 MB/seconds",
    "topic_995": "5.58 MB/seconds",
    "topic_996": "3.54 MB/seconds",
    "topic_997": "5.34 MB/seconds",
    "topic_998": "2.10 MB/seconds",
    "topic_999": "2.51 MB/seconds"
  },
  "topicDataRateHistogram": {
    "[2.00 MB/seconds, 2.19 MB/seconds]": "250",
    "[2.19 MB/seconds, 2.41 MB/seconds]": "187",
    "[2.41 MB/seconds, 2.64 MB/seconds]": "141",
    "[2.65 MB/seconds, 2.92 MB/seconds]": "105",
    "[2.93 MB/seconds, 3.24 MB/seconds]": "79",
    "[3.24 MB/seconds, 3.62 MB/seconds]": "59",
    "[3.62 MB/seconds, 4.00 MB/seconds]": "45",
    "[4.00 MB/seconds, 4.37 MB/seconds]": "33",
    "[4.38 MB/seconds, 4.86 MB/seconds]": "25",
    "[4.87 MB/seconds, 5.27 MB/seconds]": "19",
    "[5.34 MB/seconds, 5.58 MB/seconds]": "14",
    "[5.65 MB/seconds, 6.04 MB/seconds]": "11",
    "[6.15 MB/seconds, 6.77 MB/seconds]": "8",
    "[6.81 MB/seconds, 7.31 MB/seconds]": "6",
    "[7.42 MB/seconds, 8.22 MB/seconds]": "4",
    "[8.24 MB/seconds, 8.36 MB/seconds]": "3",
    "[8.74 MB/seconds, 9.06 MB/seconds]": "3",
    "[9.82 MB/seconds, 9.91 MB/seconds]": "2",
    "[9.94 MB/seconds, 9.94 MB/seconds]": "1",
    "[10.19 MB/seconds, 10.19 MB/seconds]": "1",
    "[10.29 MB/seconds, 10.29 MB/seconds]": "1",
    "[11.43 MB/seconds, 11.43 MB/seconds]": "1",
    "[11.66 MB/seconds, 11.66 MB/seconds]": "1",
    "[11.99 MB/seconds, 11.99 MB/seconds]": "1",
    "[504.18 MB/seconds, 504.18 MB/seconds]": "1"
  },
  "totalConsumeRate": "3.64 GB/seconds",
  "totalPartitions": 10060,
  "totalProduceRate": "3.47 GB/seconds",
  "totalTopics": 1001
}

[garyparrot]

目前 Performance Tool 生成的指令在執行上會有一些效能的問題

比如這個指令

--topics topic_924,topic_407,topic_129,topic_525,topic_405,topic_807,topic_124,topic_245,topic_485,topic_881,topic_644,topic_367,topic_521,topic_642,topic_247,topic_401,topic_372,topic_373,topic_252,topic_75,topic_910,topic_239,topic_911,topic_636,topic_112,topic_751,topic_359,topic_117,topic_513,topic_477,topic_240,topic_918,topic_2,topic_5,topic_307,topic_946,topic_56,topic_789,topic_943,topic_701,topic_669,topic_949,topic_387,topic_542,topic_663,topic_149,topic_545,topic_941,topic_666,topic_820,topic_425,topic_268,topic_273,topic_152,topic_54,topic_935,topic_814,topic_899,topic_415,topic_658,topic_419,topic_816,topic_893,topic_531,topic_374,topic_253,topic_375,topic_496,topic_650,topic_776,topic_535,topic_775,topic_258,topic_412,topic_260,topic_382,topic_967,topic_208,topic_34,topic_847,topic_844,topic_602,topic_328,topic_36,topic_729,topic_442,topic_289,topic_165,topic_326,topic_722,topic_447,topic_601,topic_686,topic_566,topic_291,topic_571,topic_172,topic_294,topic_173,topic_30,topic_32,topic_319,topic_957,topic_715,topic_712,topic_27,topic_277,topic_156,topic_671,topic_832,topic_433,topic_280,topic_284,topic_680,topic_681,topic_162,topic_109,topic_989,topic_747,topic_505,topic_99,topic_509,topic_903,topic_628,topic_585,topic_860,topic_344,topic_223,topic_584,topic_188,topic_468,topic_226,topic_985,topic_623,topic_986,topic_103,topic_500,topic_467,topic_193,topic_191,topic_472,topic_90,topic_470,topic_92,topic_96,topic_616,topic_977,topic_178,topic_971,topic_179,topic_333,topic_215,topic_699,topic_579,topic_731,topic_182,topic_460,topic_582,topic_184 --throttle 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--producers 16 --consumers 24 --run.until 1day --key.size 10KiB --key.distribution zipfian --bootstrap.servers 192.168.103.177:25655,192.168.103.178:25655,192.168.103.179:25655,192.168.103.180:25655,192.168.103.181:25655,192.168.103.182:25655

他嘗試對 1627 個 Partition 進行對應速度的 throttle，該生成結果預期 Performance Client 會輸出 359 MB/s 的資料量，但實際上 Performance Client 只有輸出 30 MB/s 上下的吞吐量，然後跑 Performance 的設備有 CPU 資源用盡的跡象。

[garyparrot]

目前 Performance Tool 生成的指令在執行上會有一些效能的問題

我後面會去追看看這個問題

[chia7712]

他嘗試對 1627 個 Partition 進行對應速度的 throttle，該生成結果預期 Performance Client 會輸出 359 MB/s 的資料量，但實際上 Performance Client 只有輸出 30 MB/s 上下的吞吐量，然後跑 Performance 的設備有 CPU 資源用盡的跡象。

大概有大量的collection操作太操，如果這是嚴重的技術問題（例如單機上操作的瓶頸），我們就要逐步移動到使用分散式版本的perf (see https://github.com/skiptests/astraea/blob/main/docs/connector/performance_banchmark_connector.md)

不過該分散式版本功能還很陽春 @@

[chia7712]

我後面會去追看看這個問題

可否先開議題？

[garyparrot]

其他普通 topic 的流量問題解決了，現在剩 backbone topic 沒辦法打到 500 MB/s 的速度，目前專門分一個 performance client 來做這件事情也沒辦法做到那麼快速的傳輸，我這邊能做到的最大速度是 300 MB/s 的寫入和 100 MB/s 的讀取。backbone topic 和其他 topic 的主要差異除了流量高之外還有他只有一個 partition，我猜可能是撞到 partition 速度的物理限制?

晚點會嘗試看看其他方法，看能不能做到 500 MB/s 的傳輸

[garyparrot]

晚點會嘗試看看其他方法，看能不能做到 500 MB/s 的傳輸

剛剛確認過了，應該是卡到底下硬碟讀寫的頻寬，我記得那幾臺設備的 SSD 寫入速度最高可以到 500 MB/s 附近，但查核點的測試環境下，backbone topic 自己就會佔據掉 500 MB/s 的讀寫，其中一個 SSD 還要承受同個 broker 中另外 500 個小 partition 的流量，這樣前後加總起來，目前的設備應該是很難在沒有人為介入的情況下重現查核點的情境。

目前可能的做法有

• 直接改查核點的內容，設計成目前的 SSD 可以容納的情境。
• 人為介入，確保 backbone topic 只會佔用一個 broker，但同時查核點可能背後會多一個要負載平衡的項目：Data Folder 間的流量。
• 替換成更頂的 SSD （需要買 6 * 3 = 18 個寫入速度更好的 SSD）

@chia7712 你覺得這些解決方案如何，我覺得第一個應該是最簡單的做法，但我不知道文件出去了沒還能不能改。

[chia7712]

直接改查核點的內容，設計成目前的 SSD 可以容納的情境。

文件還沒出去，你們的報告改到二月底了（等其他人）

不過可以先提一下你打算改成哪樣的數字

[garyparrot]

不過可以先提一下你打算改成哪樣的數字

變更有3項：

• Backbone Topic 的 Partition 數量由 1 增長到 24 個
• Backbone Topic 的 Partition 流量會不一，且其分佈會導致負載不平衡
• 普通 Topic 的 Consumer Fanout 從介於 0 ~ 3 變成 1 ~ 3

比較有爭議的改變是第二項，第二項是說肯定會有負載不平衡的情況發生，這個是透過在 Scenario 內刻意調整 Partition 在某些節點的流量來達成的，之所以要這樣做是因為 Kafka 的設計不一定會負載不平衡，這邊透過腳本刻意的方式，確保負載不平衡的情境會發生。這種流量分佈在生產環境是真的有可能會發生，只是發生的可能性是一個機率，然後這邊就是假設這個運氣不好的機率真的成真。在這個假設下去做負載平衡。

第三項是因為 Performance Tool 對無 consumer 的情境支援有些困難，所以這邊直接改成 topic 一定會有 consumer。

目前測試 BackboneImbalanceScenario，他能夠打出想要的情境，只是有些許的誤差。

• 節點應承受的 Producer 流量會有大約 2 MB/s 的誤差
• 節點應承受的 Consumer 流量會有大約 8 MB/s 的誤差

[garyparrot]

嘗試使用 Zipfian 來製造負載不平衡的情境

目前發現到的問題有

1. Zipfian 的輸出的流量不太穩定 (Performance NIC Produce 圖中的綠色線段)。
2. Zipfian 營造出來的負載不平衡結果變化很大，比如 A 最大的差距約 200 MB，但 C 最大的差距約 100 MB。
3. Zipfian 有機會撞到 Disk 讀寫的瓶頸

關於第一點的部分

我嘗試把其他 Perf 關掉，然後 Zipfian 那臺的流量有變穩定的趨勢，我覺得是遇到 SSD 讀寫上的瓶頸，有些時候 SSD 讀寫到達 500 MB/s 附近。

如果用 Zipfian 做實驗的時候可能意外會比較多（太多變數），我在想能不能在實驗之前先用 Zipfian 分佈生成流量，確定流量不會造成實驗環境的瓶頸之後，把對應的值用 throttle 去限制（類似上面 #1476 (comment) 提出的做法，只是換個故事說這件事情）

[garyparrot]

另外一個做法是把整體流量壓下去，不要打到現在 500 MB 到 700 MB 那麼高，可能把流量控制在比如 200 MB 到 500 MB 之間，這樣實驗的執行會能夠比較穩定

[chia7712]

我嘗試把其他 Perf 關掉，然後 Zipfian 那臺的流量有變穩定的趨勢，我覺得是遇到 SSD 讀寫上的瓶頸，

撞到硬體的上限並不是什麼意外（這很正常），反之，這更是一個好的情境，可以說在這種狀況下有大量資源浪費（撞到上限可能導致 retry or 其他 overhead），而透過你設計的 balancer 可以有效平衡負載讓各個資料流避免撞到硬體上限

如果用 Zipfian 做實驗的時候可能意外會比較多（太多變數

同上

[garyparrot]

撞到硬體的上限並不是什麼意外（這很正常），反之，這更是一個好的情境

不過這樣負載平衡演算法會很難做，我們是基於過去的效能指標來判斷 Partition 背後的負載量，可能因為撞到硬體上限而難以估計一個 Partition 背後真正的負載量是多少（比如他的值一直彈跳，誤會真正的負載值，或是以為 Partition A 的負載量是 100 MB/s，但搬移完成後硬體上限問題不存在，Partition A 的負載量長到原本應該輸入的 150 MB/s，造成最後的結果依然負載不平衡）

[chia7712]

不過這樣負載平衡演算法會很難做，我們是基於過去的效能指標來判斷 Partition 背後的負載量，可能因為撞到硬體上限而難以估計一個 Partition 背後真正的負載量是多少（比如他的值一直彈跳，誤會真正的負載值，或是以為 Partition A 的負載量是 100 MB/s，但搬移完成後硬體上限問題不存在，Partition A 的負載量長到原本應該輸入的 150 MB/s，造成最後的結果依然負載不平衡）

這就是真實有意義且有挑戰感的題目了不是嗎 :) 我們的 cost function 的目標就是要能克服這些真實且會發生的狀況，像你現在說到的這個現象，你能否找到一個數學的方式來減輕“誤判”，這樣就會是既有理論又有實際意義的題目

但如果是為了查核項目方便，我會建議可以考慮用壓縮來減緩 I/O

[garyparrot]

我會建議可以考慮用壓縮來減緩 I/O

我使用 kafka server side compression 壓縮，不過好像對 zipfian 的流量有影響

我可能會改用減少其他 topic 的流量的方式來確保穩定性

查核情境的修改:

• 每個普通 topic 的流量平均: 3MB/s -> 1.5MB/s
• 骨幹 Topic 的流量 950 MB/s ± 20%
• 骨幹 Topic 的 Partition 數量 24
• 骨幹 Topic 的 key 存在分佈不平衡的趨勢
• 普通 Topic 的 Consumer Fanout： 0 ~ 3 -> 1 ~ 6

[chia7712]

查核情境的修改:

從上次討論的結果來看，情境已經有些變化，我們回原本的議題討論，也麻煩你試著修改 #1424 的描述，把情境的部分也重寫一次。

[garyparrot]

從上次討論的結果來看，情境已經有些變化，我們回原本的議題討論，也麻煩你試著修改 #1424 的描述，把情境的部分也重寫一次。

已修改，這隻 PR 的情境生成程式碼我需要再做一下修正，後面測試穩定後大致上就完成了

[garyparrot]

Metrics 量太大導致計算時間增加嗎？

Network Cost 現在只單看一個時間點的 metrics，還沒把整個時間軸的數據都攤開。

是你說的那句“不管是看一個或整個時間軸 跟計算所需的時間有關聯嗎"嗎？我可能沒有看懂可以多解釋壹下嗎

NetworkCost 現在只衡量 t 一個時間點的 metrics，還沒有做到像未來 NetworkHotspotCost 要衡量 [t, t + l] 時間的 metrics 這種等級的計算複雜度，所以我覺得 NetworkCost 會慢的原因不是因為 "一個 Partition 涉及的 metrics 的計算太多"，而是 "Partition 數量太多" 沒辦法在我們常用的計劃生成時間(30秒)下得出一個好的優化計劃。

從火焰圖來看算成本是花很多時間，那麼花很多時間的關鍵點是什麼

• NetworkCost#updateCurrentClsuter 佔據大約整體 8% 時間，這邊在目前輸入的 ClusterInfo 是不是 inital cluster
• NetworkCost#estimateRate 這個部分在預估每個 Partition 的負載大小，佔據整體 26.71%
  • 其中 20% 的時間用來總統計預估的計算
  • 其中 6% 的時間用來一些負載關係的整理
• NetworkCost#clusterCost 裡面的 統整每個節點的 DataRate 這個部分佔據整體 13.47%

上面這些工作量都和 Partition 數量有關。用 #1502 的程式碼對不同 Partition 數量的叢集做優化，可以看出這個 Partition 數量和執行時間之間的趨勢。他們有著非常高的正相關。

叢集 Partitions 數量	每次衡量 Cluster Cost 耗費的平均時間	在 30秒優化中衡量 Cluster Cost Func 的次數
10000	136ms	118
5000	58ms	263
1000	11ms	1296
100	~0ms	16447

所以花太多時間的關鍵點應該是 Partition 數量太多沒辦法在現行的實作 & 30 秒內得到好的計劃。

[chia7712]

所以花太多時間的關鍵點應該是 Partition 數量太多沒辦法在現行的實作 & 30 秒內得到好的計劃。

ok，不過這一段應該會在 #1508 後有一個明顯的改動，我們可以先不急著追求既有程式碼的優化。或者說應該先完成 #1508 後再來看是否有一樣的效能議題，然後在 #1508 的基礎下進行調整

[garyparrot]

#1508 的修正似乎讓速度變更慢

• 原本 [BALANCER] Implementation of BalancerBenchmark #1502 的進度測試: 4cac070
  • Total Run time: 30073 ms
  • Total ClusterCost Evaluation: 597
  • Average ClusterCost Processing: 26ms

• [BALANCER] Implementation of BalancerBenchmark #1502 和套用 ClusterInfoSensor 的 NetworkCost 速度測試: e8a360d
  • Total Run time: 33382 ms
  • Total ClusterCost Evaluation: 12
  • Average ClusterCost Processing: 2751ms

套用 ClusterInfoSensor 後，算法進步的速度慢了大約 100 倍。火焰圖指出有 3/4 比例的時間都在計算 ClusterInfoSensor#metricViewCluster。

[chia7712]

@garyparrot 這回到我們上次討論的，量和值有時候就是trade off，看你想要往哪邊用力

[garyparrot]

效能問題解得差不多後嘗試進行實驗，目前發現的幾個問題

Zipfian 的 Consumer 吞吐量追不上

Zipfian 分佈下的 Producer/Consumer 有非常顯著的 "Produce 寫入流量 > Consume 讀取流量" 的問題，這個現象導致目前查核點的情境沒辦法被準確呈現。

上圖所有 parititon 的 consumer fanout 都是 1，理論上節點的輸入和輸出流量要長得一致，但負責 zipfian 最熱門的那個 partition 的 broker 的 egress 流量特別低 (右邊紅色)。去 performance tool 輸出也能看到 consumer 的總消費吞吐量輸 produce 約 100 MB/s 附近。

透過連線進去看 Consumer 的 Lag 資訊，可以看到有些比較熱門的 Partition 有非常顯著的 lag：

上圖的 Consumer Partition 大約堆積了 76 GB 的資料還沒消費（10KB per record）

Zipfian 的 Performance Tool 在負載優化後速度降低

這個應該也是 consumer lag 造成的，目前還不確定 Consumer 吞吐的下降原因。

NetworkCost 流量預估不準確

Initial Cost: WeightCompositeClusterCost[{"NetworkIngressCost" cost 0.03792682892185111 weight 3.0 description {417.18 MB/seconds, 434.34 MB/seconds, 433.15 MB/seconds, 429.81 MB/seconds, 546.48 MB/seconds, 466.01 MB/seconds} }, {"ReplicaNumberCost" cost 0.0023148148148148147 weight 1.0 description LongSummaryStatistics{count=6, sum=9936, min=1646, average=1656.000000, max=1669} }, {"NetworkEgressCost" cost 0.006618972192553504 weight 0.0 description {552.49 MB/seconds, 573.62 MB/seconds, 575.05 MB/seconds, 568.12 MB/seconds, 569.87 MB/seconds, 570.08 MB/seconds} }] = 0.029023825395092035

ClusterCost 裡面某些節點的流量值和監控上看到的情況不一致，裡面的計算可能有些誤差存在 (約 50MB/s)

[chia7712]

回頭看了一下這隻PR，幾個疑問：

他可以透過 Web API 的形式來對叢集套用特定的情境。然後將 Scenario 回傳的資訊以 JSON 回覆給使用者。

請問這邊的套用是指什麼？

回覆的資訊包含

這些功能除了查核驗收以外， production 上有機會使用嗎？

[garyparrot]

請問這邊的套用是指什麼？

把 #1424 的情境建立在叢集上，比如那 10000 個 Partition

回覆的資訊包含

這些功能除了查核驗收以外， production 上有機會使用嗎？

我想沒有辦法在 production 上用，這些 scenario 都是實驗用途。

這 PR 主要是實驗的程式碼和工具要推上來方便重現。另外除了這個 PR 的建立情境之外，還有一段是把情境的 Performance Tool 開起來，因為那個部分會牽涉很多東西，所以沒有打算推上，我想這部分可以靠其他人閱讀 scenario output 來手動建起來。

如果說這些功能要避免出現在 production 的話，那 SkewedPartitionScenario 可以要撤下來，他現在有出現在建立 topic 的 WEB API 上面

[chia7712]

這 PR 主要是實驗的程式碼和工具要推上來方便重現。另外除了這個 PR 的建立情境之外，還有一段是把情境的 Performance Tool 開起來，因為那個部分會牽涉很多東西，所以沒有打算推上，我想這部分可以靠其他人閱讀 scenario output 來手動建起來。如果說這些功能要避免出現在 production 的話，那 SkewedPartitionScenario 可以要撤下來，他現在有出現在建立 topic 的 WEB API 上面

我們還是稍微區分一下 production code 和 驗收的項目。

驗收的項目可以先用獨立的專案放著，然後其中涉及到production 變更 或是 需求的部分再推到官方專案上

[garyparrot]

驗收的項目可以先用獨立的專案放著，然後其中涉及到production 變更 或是 需求的部分再推到官方專案上

ok 那這個 PR 的內容就不考慮合併了