Flow.flattenMerge benchmark

benchmarks/build.gradle

@@ -74,4 +74,6 @@ dependencies {
     compile "org.openjdk.jmh:jmh-core:1.21"
     compile 'com.typesafe.akka:akka-actor_2.12:2.5.0'
     compile project(':kotlinx-coroutines-core')
+    // add jmh dependency on main
+    jmh sourceSets.main.runtimeClasspath
 }

benchmarks/scripts/generate_plots_flow_flatten_merge.py

@@ -0,0 +1,57 @@
+import pandas as pd
+import sys
+import matplotlib.pyplot as plt
+from matplotlib.ticker import FormatStrFormatter
+
+input_file = "build/reports/jmh/results.csv"
+output_file = "flow-flatten-merge.svg"
+elements = 100000
+benchmark_name = "benchmarks.flow.FlowFlattenMergeBenchmark.flattenMerge"
+csv_columns = ["Benchmark", "Score", "Score Error (99.9%)", "Unit", "Param: concurrency", "Param: flows"]
+rename_columns = {"Benchmark": "benchmark", "Score" : "score", "Score Error (99.9%)" : "score_error", "Unit" : "unit",
+                  "Param: concurrency" : "concurrency", "Param: flows" : "flows"}
+
+markers = ['.', 'v', '^', '1', '2', '8', 'p', 'P', 'x', 'D', 'd', 's']
+colours = ['red', 'gold', 'sienna', 'olivedrab', 'lightseagreen', 'navy', 'blue', 'm', 'crimson', 'yellow', 'orangered', 'slateblue', 'aqua', 'black', 'silver']
+
+def next_colour():
+    i = 0
+    while True:
+        yield colours[i % len(colours)]
+        i += 1
+
+def next_marker():
+    i = 0
+    while True:
+        yield markers[i % len(markers)]
+        i += 1
+
+def draw(data, plt):
+    plt.xscale('log', basex=2)
+    plt.gca().xaxis.set_major_formatter(FormatStrFormatter('%0.f'))
+    plt.grid(linewidth='0.5', color='lightgray')
+    if data.unit.unique()[0] != "ops/s":
+        print("Unexpected time unit: " + data.unit.unique()[0])
+        sys.exit(1)
+    plt.ylabel("elements / ms")
+    plt.xlabel('concurrency')
+    plt.xticks(data.concurrency.unique())
+
+    colour_gen = next_colour()
+    marker_gen = next_marker()
+    for flows in data.flows.unique():
+        gen_colour = next(colour_gen)
+        gen_marker = next(marker_gen)
+        res = data[(data.flows == flows)]
+#         plt.plot(res.concurrency, res.score*elements/1000, label="flows={}".format(flows), color=gen_colour, marker=gen_marker)
+        plt.errorbar(x=res.concurrency, y=res.score*elements/1000, yerr=res.score_error*elements/1000, solid_capstyle='projecting',
+                     label="flows={}".format(flows), capsize=4, color=gen_colour)
+
+data = pd.read_table(input_file, sep=",")
+data = data[csv_columns].rename(columns=rename_columns)
+data = data[(data.benchmark == benchmark_name)]
+plt.figure(figsize=(8, 6.5))
+draw(data, plt)
+plt.legend(loc='upper center', borderpad=0, ncol=4, frameon=False, borderaxespad=4, prop={'size': 8})
+plt.tight_layout(pad=12, w_pad=2, h_pad=1)
+plt.savefig(output_file, bbox_inches='tight')

benchmarks/src/jmh/kotlin/benchmarks/SemaphoreBenchmark.kt

@@ -1,13 +1,13 @@
 package benchmarks
 
+import doGeomDistrWork
 import kotlinx.coroutines.*
 import kotlinx.coroutines.channels.Channel
 import kotlinx.coroutines.scheduling.ExperimentalCoroutineDispatcher
 import kotlinx.coroutines.sync.Semaphore
 import kotlinx.coroutines.sync.withPermit
 import org.openjdk.jmh.annotations.*
 import java.util.concurrent.ForkJoinPool
-import java.util.concurrent.ThreadLocalRandom
 import java.util.concurrent.TimeUnit
 
 @Warmup(iterations = 3, time = 500, timeUnit = TimeUnit.MICROSECONDS)
@@ -50,9 +50,9 @@ open class SemaphoreBenchmark {
             jobs += GlobalScope.launch {
                 repeat(n) {
                     semaphore.withPermit {
-                        doWork(WORK_INSIDE)
+                        doGeomDistrWork(WORK_INSIDE)
                     }
-                    doWork(WORK_OUTSIDE)
+                    doGeomDistrWork(WORK_OUTSIDE)
                 }
             }
         }
@@ -68,9 +68,9 @@ open class SemaphoreBenchmark {
             jobs += GlobalScope.launch {
                 repeat(n) {
                     semaphore.send(Unit) // acquire
-                    doWork(WORK_INSIDE)
+                    doGeomDistrWork(WORK_INSIDE)
                     semaphore.receive() // release
-                    doWork(WORK_OUTSIDE)
+                    doGeomDistrWork(WORK_OUTSIDE)
                 }
             }
         }
@@ -83,15 +83,6 @@ enum class SemaphoreBenchDispatcherCreator(val create: (parallelism: Int) -> Cor
     EXPERIMENTAL({ parallelism -> ExperimentalCoroutineDispatcher(corePoolSize = parallelism, maxPoolSize = parallelism) })
 }
 
-private fun doWork(work: Int) {
-    // We use geometric distribution here
-    val p = 1.0 / work
-    val r = ThreadLocalRandom.current()
-    while (true) {
-        if (r.nextDouble() < p) break
-    }
-}
-
 private const val WORK_INSIDE = 80
 private const val WORK_OUTSIDE = 40
 private const val BATCH_SIZE = 1000000

benchmarks/src/jmh/kotlin/benchmarks/flow/FlowFlattenMergeBenchmark.kt

@@ -0,0 +1,61 @@
+/*
+ * Copyright 2016-2019 JetBrains s.r.o. Use of this source code is governed by the Apache 2.0 license.
+ */
+package benchmarks.flow
+
+import doGeomDistrWork
+import kotlinx.coroutines.Dispatchers
+import kotlinx.coroutines.flow.*
+import kotlinx.coroutines.runBlocking
+import org.openjdk.jmh.annotations.*
+import java.util.concurrent.TimeUnit
+
+/**
+ * Benchmark to measure performance of [kotlinx.coroutines.flow.FlowKt.flattenMerge].
+ * In addition to that, it can be considered as a macro benchmark for the [kotlinx.coroutines.sync.Semaphore]
+ */
+@Warmup(iterations = 3, time = 1)
+@Measurement(iterations = 10, time = 1)
+@BenchmarkMode(Mode.Throughput)
+@OutputTimeUnit(TimeUnit.SECONDS)
+@State(Scope.Benchmark)
+@Fork(1)
+open class FlowFlattenMergeBenchmark {
+
+    /**
+     * Number of flows that are merged in this benchmark. Negative number means that number of flows
+     * will be computed as -([flows] * [concurrency]), positive number will be chosen as number of flows.
+     */
+    @Param("-10", "-1", "100", "500")
+    private var flows: Int = 0
+
+    @Param("1", "2", "4") // local machine
+//    @Param("1", "2", "4", "8", "16", "32", "64", "96") // Google Cloud
+    private var concurrency: Int = 0
+
+    private lateinit var flow: Flow<Flow<Int>>
+
+    @Setup
+    fun setup() {
+        val n = if (flows >= 0) flows else -(flows * concurrency)
+        val flowElementsToProcess = ELEMENTS / n
+
+        flow = (1..n).asFlow().map {
+            flow {
+                repeat(flowElementsToProcess) {
+                    doGeomDistrWork(WORK)
+                    emit(it)
+                }
+            }
+        }
+    }
+
+    @Benchmark
+    fun flattenMerge() = runBlocking(Dispatchers.Default) {
+        flow.flattenMerge(concurrency = concurrency).collect()
+    }
+}
+
+// If you change this variable please be sure that you change variable elements in the corresponding python script as well
+private const val ELEMENTS = 100_000
+private const val WORK = 100

benchmarks/src/main/kotlin/BenchmarkUtils.kt

@@ -0,0 +1,14 @@
+/*
+* Copyright 2016-2019 JetBrains s.r.o. Use of this source code is governed by the Apache 2.0 license.
+*/
+
+import java.util.concurrent.ThreadLocalRandom
+
+fun doGeomDistrWork(work: Int) {
+    // We use geometric distribution here
+    val p = 1.0 / work
+    val r = ThreadLocalRandom.current()
+    while (true) {
+        if (r.nextDouble() < p) break
+    }
+}