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Playground for measuring performance of functional programming tools in Scala. Gathers statistics about videos.
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README.md

Influencer Stats

This application gathers and aggregates stats for your influencer social media campaigns.

Setup

YouTube mock server

You need YouTube mock server to be able to test performance without going over YouTube API limits. To build the image, run docker build -t miciek/influencer-stats-youtube youtube. To run it, execute docker run -d --rm --name youtube -p 8081:80 miciek/influencer-stats-youtube.

Running the application

After executing sbt run, you need to configure the first collection:

curl -XPUT -H "Content-Type: application/json" localhost:8080/collections/99757a95-f758-499f-a170-bea93b2d8bcf -d '{ "videos": [ "-4lB5EKS5Uk", "-jlLkTtgWUk", "1FEFpk-uIYo" ] }'

Then, you will be able to fetch the stats for videos in this collection:

curl localhost:8080/collections/99757a95-f758-499f-a170-bea93b2d8bcf/stats

Performance tests

To run performance tests, you will need wrk. To analyse what's going on inside the application, please install async-profiler.

Before starting, let's first establish the performance of our YouTube mock server:

> wrk -t2 -c256 -d30s --latency http://localhost:8081/youtube/v3/videos
  Running 30s test @ http://localhost:8081/youtube/v3/videos
    2 threads and 256 connections
    Thread Stats   Avg      Stdev     Max   +/- Stdev
      Latency    11.86ms    2.10ms  44.67ms   95.00%
      Req/Sec    10.87k   614.63    12.14k    79.17%
    Latency Distribution
       50%   11.72ms
       75%   11.98ms
       90%   12.36ms
       99%   23.59ms
    649268 requests in 30.01s, 486.65MB read
    Socket errors: connect 0, read 124, write 0, timeout 0
  Requests/sec:  21634.72
  Transfer/sec:     16.22MB

Additionally, let's see what is the performance of collections with no videos (no additional requests to YouTube server are made):

wrk -t1 -c1 -d30s --latency http://localhost:8080/collections/39757a95-e758-499f-a170-bea93b2d8bca/stats
Running 30s test @ http://localhost:8080/collections/39757a95-e758-499f-a170-bea93b2d8bca/stats
  1 threads and 1 connections
  Thread Stats   Avg      Stdev     Max   +/- Stdev
    Latency   682.73us    3.35ms  47.60ms   97.16%
    Req/Sec     5.83k   776.99     6.53k    83.67%
  Latency Distribution
     50%  155.00us
     75%  164.00us
     90%  196.00us
     99%   21.81ms
  174201 requests in 30.01s, 29.41MB read
Requests/sec:   5804.37
Transfer/sec:      0.98MB

Remember that each test should be run several times to warm up JVM.

Flamegraph generation

To compare different versions, we will use flamegraphs. The command below generates flamegraph for the load-tested application (should be started after around 10s of wrk):

jps # to get the <PID> of the application
cd async-profiler
./profiler.sh -d 10 -f /tmp/flamegraph.svg <PID>

Generated flamegraphs are stored in flamegraphs directory. Please view the flamegraphs as raw files in the browser, because only then they become interactive.

Version 1 (DefaultLogger/InMemListState/AkkaHttpVideoClient/AkkaHttpServer)

> wrk -t1 -c16 -d30s --latency http://localhost:8080/collections/99757a95-f758-499f-a170-bea93b2d8bcf/stats
  Running 30s test @ http://localhost:8080/collections/99757a95-f758-499f-a170-bea93b2d8bcf/stats
    1 threads and 16 connections
    Thread Stats   Avg      Stdev     Max   +/- Stdev
      Latency    56.47ms    9.05ms 128.51ms   72.73%
      Req/Sec   284.33     31.34   353.00     72.33%
    Latency Distribution
       50%   56.03ms
       75%   61.61ms
       90%   66.89ms
       99%   82.41ms
    8510 requests in 30.06s, 1.54MB read
  Requests/sec:    283.12
  Transfer/sec:     52.53KB

Version 1 Flame Graph

According to the flame graph, the most performance can be gained from optimizing DefaultLogger, which took 70.29% of CPU time.

Version 2 (DroppingLogger/InMemListState/AkkaHttpVideoClient/AkkaHttpServer)

> wrk -t1 -c16 -d30s --latency http://localhost:8080/collections/99757a95-f758-499f-a170-bea93b2d8bcf/stats
  Running 30s test @ http://localhost:8080/collections/99757a95-f758-499f-a170-bea93b2d8bcf/stats
    1 threads and 16 connections
    Thread Stats   Avg      Stdev     Max   +/- Stdev
      Latency     6.94ms   10.49ms 151.46ms   95.37%
      Req/Sec     3.00k   565.78     3.79k    82.00%
    Latency Distribution
       50%    4.67ms
       75%    5.47ms
       90%    9.61ms
       99%   62.53ms
    89757 requests in 30.06s, 16.26MB read
  Requests/sec:   2986.26
  Transfer/sec:    554.09KB

Version 2 Flame Graph

According to the flame graph, the most performance can be gained from optimizing fetchVideoListResponse, which took 19.92% of CPU time.

Version 3 (DroppingLogger/InMemListState/HammockVideoClient/AkkaHttpServer)

> wrk -t1 -c16 -d30s --latency http://localhost:8080/collections/99757a95-f758-499f-a170-bea93b2d8bcf/stats
  Running 30s test @ http://localhost:8080/collections/99757a95-f758-499f-a170-bea93b2d8bcf/stats
    1 threads and 16 connections
    Thread Stats   Avg      Stdev     Max   +/- Stdev
      Latency     5.06ms    2.44ms  26.40ms   74.12%
      Req/Sec     3.24k   285.48     3.72k    62.00%
    Latency Distribution
       50%    4.55ms
       75%    6.22ms
       90%    8.25ms
       99%   13.05ms
    96564 requests in 30.00s, 17.50MB read
  Requests/sec:   3218.48
  Transfer/sec:    597.18KB

Version 3 Flame Graph

According to the flame graph, the most performance can be gained from optimizing hammock/jvm/Interpreter, which took 54.98% of CPU time.

Version 4 (DroppingLogger/InMemListState/HammockVideoClient/AkkaHttpServer/StatisticsCaching)

> wrk -t1 -c16 -d30s --latency http://localhost:8080/collections/99757a95-f758-499f-a170-bea93b2d8bcf/stats
  Running 30s test @ http://localhost:8080/collections/99757a95-f758-499f-a170-bea93b2d8bcf/stats
    1 threads and 16 connections
    Thread Stats   Avg      Stdev     Max   +/- Stdev
      Latency     2.87ms   11.63ms 144.63ms   94.76%
      Req/Sec    31.59k     7.02k   39.26k    84.35%
    Latency Distribution
       50%  346.00us
       75%  439.00us
       90%    1.74ms
       99%   62.86ms
    933303 requests in 30.03s, 169.11MB read
  Requests/sec:  31081.81
  Transfer/sec:      5.63MB

Version 4 Flame Graph

According to the flame graph, the most performance can be gained from optimizing the server which takes all the CPU time.

Version 5 (DroppingLogger/InMemListState/HammockVideoClient/Http4sServer/StatisticsCaching)

> wrk -t1 -c16 -d30s --latency http://localhost:8080/collections/99757a95-f758-499f-a170-bea93b2d8bcf/stats
  Running 30s test @ http://localhost:8080/collections/99757a95-f758-499f-a170-bea93b2d8bcf/stats
    1 threads and 16 connections
    Thread Stats   Avg      Stdev     Max   +/- Stdev
      Latency   555.80us    1.38ms  17.53ms   96.68%
      Req/Sec    38.79k     3.46k   48.66k    68.67%
    Latency Distribution
       50%  314.00us
       75%  396.00us
       90%  485.00us
       99%    9.05ms
    1158664 requests in 30.02s, 181.22MB read
  Requests/sec:  38596.24
  Transfer/sec:      6.04MB

Version 5 Flame Graph

Version 1 with real YouTube server

I run the first version (the worst performant one) with real YouTube API and here are the results. We can compare them to get a network overhead:

> wrk -t1 -c16 -d30s --latency http://localhost:8080/collections/99757a95-f758-499f-a170-bea93b2d8bcf/stats
  Running 30s test @ http://localhost:8080/collections/99757a95-f758-499f-a170-bea93b2d8bcf/stats
    1 threads and 16 connections
    Thread Stats   Avg      Stdev     Max   +/- Stdev
      Latency   393.60ms  173.80ms   1.24s    85.57%
      Req/Sec    44.59     24.01   125.00     57.66%
    Latency Distribution
       50%  362.83ms
       75%  419.28ms
       90%  579.63ms
       99%    1.09s
    1239 requests in 30.04s, 219.00KB read
    Socket errors: connect 0, read 0, write 0, timeout 1
  Requests/sec:     41.25
  Transfer/sec:      7.29KB
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