Kafka ingest performance issues

Elastic Stack Logstash
1

Im sending all my beat* data into kafka and reading it from there via logstash (running on kubernetes). I use kafka for lots of other purposes and I know we can read 1M records a second using a simple consumer. When I look at the stats for my cluster it appears we're getting ~50K records / sec.

I suspect I have something misconfigured in my logstash setup. Not sure where to look to find tips/tricks for optimizing this path.

System: all 7.3.0
5 data nodes running on kubernetes hosts with 56 cores/64G RAM
5 ingest nodes
3 master nodes

file/metric beat kafka topics have 20 partitions and 20 consumers for each - also running in kubernetes.

2

Have you verified that Elasticsearch is not the bottleneck? What type of storage does your data nodes have?

3

Data nodes storage is from a SAN via 10G network.

How would I (dis)prove that elastic is the limiting factor?

Additional: yesterday I split out some of the higher traffic items from file/metric beat into their own kafka topics. Where my slower topics have 20 partitions / consumers these new ones only have 5/5 and seem to be performing much better.

Is this something I could solve by (properly) using pipelines?

Looking at the pipeline ui in Kibana.. a few facts surface:

  1. The UI is only letting me see one (of 5? more?) filter-chains
  2. For some outputs Im seeing > 25ms/event of latency. That's going to add up quickly. How do I fix this?
4

If you got improved throughput by changing Logstash config it isquite likely that Elasticsearch is not the bottleneck. It may be something related to the Kafka input plugin design, but I do not know the internals.

5

Im pretty certain now that elasticsearch is the bottleneck.

Seeing an increasing number of errors msgs like this:

[logstash.outputs.elasticsearch] retrying failed action with response code: 429 ({"type"=>"es_rejected_execution_exception", "reason"=>"rejected execution of processing of [7142251][indices:data/write/bulk[s][p]]: request: BulkShardRequest [[filebeat-2019.08.05][0]] containing [124] requests, target allocation id: aSctrLvyTpSZkzPqVIvE0A, primary term: 2 on EsThreadPoolExecutor[name = elasticsearch-data-1/write, queue capacity = 200, org.elasticsearch.common.util.concurrent.EsThreadPoolExecutor@8085181[Running, pool size = 1, active threads = 1, queued tasks = 200, completed tasks = 5601415]]"})
1 Like
6

Looking at thread pools on these data nodes I found this entry:

    "write" : {
      "type" : "fixed",
      "size" : 1,
      "queue_size" : 200
    },

Is this correct? I checked the available cores on the pod - 56.

Found some info here: https://www.elastic.co/blog/why-am-i-seeing-bulk-rejections-in-my-elasticsearch-cluster

Not an answer - but reasons not to monkey with thread pool size.

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