Add jmh benchmarks for bloom filters (#1913)

Author: anish749

scio-jmh/README.md

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+# JMH benchmarks
+
+JMH based benchmarks for certain specific components in Scio.
+
+Getting started and running JMH benchmarks via sbt-shell:
+```
+$ sbt
+...
+sbt:scio> project scio-jmh
+sbt:scio-jmh> jmh:run -f1 -wi 2 -i 3 .*BloomFilter.*Benchmark.*
+```
+
+The options for `jmh:run`
+ - `-f1` Run with 1 fork
+ - `-wi 2` Run 2 warm up iterations
+ - `-i 3` Run 3 iterations
+ - `.*BloomFilter.*Benchmark.*` RegExp for Benchmark

scio-jmh/src/test/scala/com/spotify/scio/jmh/BloomFilterCreateBenchmark.scala

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+/*
+ * Copyright 2019 Spotify AB.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+package com.spotify.scio.jmh
+
+import com.spotify.scio.util.{BloomFilter, BloomFilterAggregator, MutableBF}
+import org.openjdk.jmh.annotations._
+
+import scala.util.Random
+
+/**
+ * Benchmarks for com.spotify.scio.util.BloomFilter
+ *
+ * Creating a BF from a collection
+ */
+object BloomFilterCreateBenchmark {
+  def createRandomString(nbrOfStrings: Int, lengthOfStrings: Int): Seq[String] =
+    Seq.fill(nbrOfStrings)(Random.nextString(lengthOfStrings))
+
+  @State(Scope.Benchmark)
+  class BloomFilterState {
+    @Param(Array("100", "1000", "10000"))
+    var nbrOfElements: Int = 0
+
+    @Param(Array("0.01", "0.001"))
+    var falsePositiveRate: Double = 0
+
+    var randomStrings: Seq[String] = _
+
+    @Setup(Level.Trial)
+    def setup(): Unit =
+      randomStrings = createRandomString(nbrOfElements, 10)
+
+  }
+}
+
+@State(Scope.Benchmark)
+class BloomFilterCreateBenchmark {
+
+  import BloomFilterCreateBenchmark._
+
+  /**
+   * Create a bloom filter by aggregating on a monoid.
+   * This is the most efficient way to create the bloom filter.
+   */
+  @Benchmark
+  def scioMutableBF(bloomFilterState: BloomFilterState): MutableBF[String] = {
+    val bfMonoid =
+      BloomFilter[String](bloomFilterState.nbrOfElements, bloomFilterState.falsePositiveRate)
+    val bfAggregator = BloomFilterAggregator(bfMonoid)
+
+    val sBf = bloomFilterState.randomStrings.aggregate(bfAggregator.monoid.zero)(_ += _, _ ++= _)
+    sBf
+  }
+
+}

scio-jmh/src/test/scala/com/spotify/scio/jmh/BloomFilterQueryBenchmark.scala

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+/*
+ * Copyright 2019 Spotify AB.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+package com.spotify.scio.jmh
+
+import com.spotify.scio.util.{BloomFilter, MutableBF}
+import com.twitter.algebird.ApproximateBoolean
+import org.openjdk.jmh.annotations._
+
+/**
+ * Benchmarks for com.spotify.scio.util.BloomFilter
+ *
+ * Querying for elements from a BloomFilter.
+ */
+object BloomFilterQueryBenchmark {
+
+  @State(Scope.Benchmark)
+  class BloomFilterState {
+
+    @Param(Array("100", "1000", "10000"))
+    var nbrOfElements: Int = 0
+
+    @Param(Array("0.001", "0.01"))
+    var falsePositiveRate: Double = 0
+
+    private[scio] var bf: MutableBF[String] = _
+
+    @Setup(Level.Trial)
+    def setup(): Unit = {
+      val randomStrings =
+        BloomFilterCreateBenchmark.createRandomString(nbrOfElements, 10)
+      bf = BloomFilter[String](nbrOfElements, falsePositiveRate)
+        .create(randomStrings: _*)
+    }
+  }
+}
+
+@State(Scope.Benchmark)
+class BloomFilterQueryBenchmark {
+  import BloomFilterQueryBenchmark._
+
+  @Benchmark
+  def scioBloomFilter(bloomFilterState: BloomFilterState): ApproximateBoolean =
+    bloomFilterState.bf.contains("1")
+}