From 5949e6c4477fd3cb07a6962dbee48b4416ea65dd Mon Sep 17 00:00:00 2001 From: Kazuaki Ishizaki Date: Thu, 9 Mar 2017 22:58:52 -0800 Subject: [SPARK-19008][SQL] Improve performance of Dataset.map by eliminating boxing/unboxing ## What changes were proposed in this pull request? This PR improve performance of Dataset.map() for primitive types by removing boxing/unbox operations. This is based on [the discussion](https://github.com/apache/spark/pull/16391#discussion_r93788919) with cloud-fan. Current Catalyst generates a method call to a `apply()` method of an anonymous function written in Scala. The types of an argument and return value are `java.lang.Object`. As a result, each method call for a primitive value involves a pair of unboxing and boxing for calling this `apply()` method and a pair of boxing and unboxing for returning from this `apply()` method. This PR directly calls a specialized version of a `apply()` method without boxing and unboxing. For example, if types of an arguments ant return value is `int`, this PR generates a method call to `apply$mcII$sp`. This PR supports any combination of `Int`, `Long`, `Float`, and `Double`. The following is a benchmark result using [this program](https://github.com/apache/spark/pull/16391/files) with 4.7x. Here is a Dataset part of this program. Without this PR ``` OpenJDK 64-Bit Server VM 1.8.0_111-8u111-b14-2ubuntu0.16.04.2-b14 on Linux 4.4.0-47-generic Intel(R) Xeon(R) CPU E5-2667 v3 3.20GHz back-to-back map: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative ------------------------------------------------------------------------------------------------ RDD 1923 / 1952 52.0 19.2 1.0X DataFrame 526 / 548 190.2 5.3 3.7X Dataset 3094 / 3154 32.3 30.9 0.6X ``` With this PR ``` OpenJDK 64-Bit Server VM 1.8.0_111-8u111-b14-2ubuntu0.16.04.2-b14 on Linux 4.4.0-47-generic Intel(R) Xeon(R) CPU E5-2667 v3 3.20GHz back-to-back map: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative ------------------------------------------------------------------------------------------------ RDD 1883 / 1892 53.1 18.8 1.0X DataFrame 502 / 642 199.1 5.0 3.7X Dataset 657 / 784 152.2 6.6 2.9X ``` ```java def backToBackMap(spark: SparkSession, numRows: Long, numChains: Int): Benchmark = { import spark.implicits._ val rdd = spark.sparkContext.range(0, numRows) val ds = spark.range(0, numRows) val func = (l: Long) => l + 1 val benchmark = new Benchmark("back-to-back map", numRows) ... benchmark.addCase("Dataset") { iter => var res = ds.as[Long] var i = 0 while (i < numChains) { res = res.map(func) i += 1 } res.queryExecution.toRdd.foreach(_ => Unit) } benchmark } ``` A motivating example ```java Seq(1, 2, 3).toDS.map(i => i * 7).show ``` Generated code without this PR ```java /* 005 */ final class GeneratedIterator extends org.apache.spark.sql.execution.BufferedRowIterator { /* 006 */ private Object[] references; /* 007 */ private scala.collection.Iterator[] inputs; /* 008 */ private scala.collection.Iterator inputadapter_input; /* 009 */ private UnsafeRow deserializetoobject_result; /* 010 */ private org.apache.spark.sql.catalyst.expressions.codegen.BufferHolder deserializetoobject_holder; /* 011 */ private org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter deserializetoobject_rowWriter; /* 012 */ private int mapelements_argValue; /* 013 */ private UnsafeRow mapelements_result; /* 014 */ private org.apache.spark.sql.catalyst.expressions.codegen.BufferHolder mapelements_holder; /* 015 */ private org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter mapelements_rowWriter; /* 016 */ private UnsafeRow serializefromobject_result; /* 017 */ private org.apache.spark.sql.catalyst.expressions.codegen.BufferHolder serializefromobject_holder; /* 018 */ private org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter serializefromobject_rowWriter; /* 019 */ /* 020 */ public GeneratedIterator(Object[] references) { /* 021 */ this.references = references; /* 022 */ } /* 023 */ /* 024 */ public void init(int index, scala.collection.Iterator[] inputs) { /* 025 */ partitionIndex = index; /* 026 */ this.inputs = inputs; /* 027 */ inputadapter_input = inputs[0]; /* 028 */ deserializetoobject_result = new UnsafeRow(1); /* 029 */ this.deserializetoobject_holder = new org.apache.spark.sql.catalyst.expressions.codegen.BufferHolder(deserializetoobject_result, 0); /* 030 */ this.deserializetoobject_rowWriter = new org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter(deserializetoobject_holder, 1); /* 031 */ /* 032 */ mapelements_result = new UnsafeRow(1); /* 033 */ this.mapelements_holder = new org.apache.spark.sql.catalyst.expressions.codegen.BufferHolder(mapelements_result, 0); /* 034 */ this.mapelements_rowWriter = new org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter(mapelements_holder, 1); /* 035 */ serializefromobject_result = new UnsafeRow(1); /* 036 */ this.serializefromobject_holder = new org.apache.spark.sql.catalyst.expressions.codegen.BufferHolder(serializefromobject_result, 0); /* 037 */ this.serializefromobject_rowWriter = new org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter(serializefromobject_holder, 1); /* 038 */ /* 039 */ } /* 040 */ /* 041 */ protected void processNext() throws java.io.IOException { /* 042 */ while (inputadapter_input.hasNext() && !stopEarly()) { /* 043 */ InternalRow inputadapter_row = (InternalRow) inputadapter_input.next(); /* 044 */ int inputadapter_value = inputadapter_row.getInt(0); /* 045 */ /* 046 */ boolean mapelements_isNull = true; /* 047 */ int mapelements_value = -1; /* 048 */ if (!false) { /* 049 */ mapelements_argValue = inputadapter_value; /* 050 */ /* 051 */ mapelements_isNull = false; /* 052 */ if (!mapelements_isNull) { /* 053 */ Object mapelements_funcResult = null; /* 054 */ mapelements_funcResult = ((scala.Function1) references[0]).apply(mapelements_argValue); /* 055 */ if (mapelements_funcResult == null) { /* 056 */ mapelements_isNull = true; /* 057 */ } else { /* 058 */ mapelements_value = (Integer) mapelements_funcResult; /* 059 */ } /* 060 */ /* 061 */ } /* 062 */ /* 063 */ } /* 064 */ /* 065 */ serializefromobject_rowWriter.zeroOutNullBytes(); /* 066 */ /* 067 */ if (mapelements_isNull) { /* 068 */ serializefromobject_rowWriter.setNullAt(0); /* 069 */ } else { /* 070 */ serializefromobject_rowWriter.write(0, mapelements_value); /* 071 */ } /* 072 */ append(serializefromobject_result); /* 073 */ if (shouldStop()) return; /* 074 */ } /* 075 */ } /* 076 */ } ``` Generated code with this PR (lines 48-56 are changed) ```java /* 005 */ final class GeneratedIterator extends org.apache.spark.sql.execution.BufferedRowIterator { /* 006 */ private Object[] references; /* 007 */ private scala.collection.Iterator[] inputs; /* 008 */ private scala.collection.Iterator inputadapter_input; /* 009 */ private UnsafeRow deserializetoobject_result; /* 010 */ private org.apache.spark.sql.catalyst.expressions.codegen.BufferHolder deserializetoobject_holder; /* 011 */ private org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter deserializetoobject_rowWriter; /* 012 */ private int mapelements_argValue; /* 013 */ private UnsafeRow mapelements_result; /* 014 */ private org.apache.spark.sql.catalyst.expressions.codegen.BufferHolder mapelements_holder; /* 015 */ private org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter mapelements_rowWriter; /* 016 */ private UnsafeRow serializefromobject_result; /* 017 */ private org.apache.spark.sql.catalyst.expressions.codegen.BufferHolder serializefromobject_holder; /* 018 */ private org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter serializefromobject_rowWriter; /* 019 */ /* 020 */ public GeneratedIterator(Object[] references) { /* 021 */ this.references = references; /* 022 */ } /* 023 */ /* 024 */ public void init(int index, scala.collection.Iterator[] inputs) { /* 025 */ partitionIndex = index; /* 026 */ this.inputs = inputs; /* 027 */ inputadapter_input = inputs[0]; /* 028 */ deserializetoobject_result = new UnsafeRow(1); /* 029 */ this.deserializetoobject_holder = new org.apache.spark.sql.catalyst.expressions.codegen.BufferHolder(deserializetoobject_result, 0); /* 030 */ this.deserializetoobject_rowWriter = new org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter(deserializetoobject_holder, 1); /* 031 */ /* 032 */ mapelements_result = new UnsafeRow(1); /* 033 */ this.mapelements_holder = new org.apache.spark.sql.catalyst.expressions.codegen.BufferHolder(mapelements_result, 0); /* 034 */ this.mapelements_rowWriter = new org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter(mapelements_holder, 1); /* 035 */ serializefromobject_result = new UnsafeRow(1); /* 036 */ this.serializefromobject_holder = new org.apache.spark.sql.catalyst.expressions.codegen.BufferHolder(serializefromobject_result, 0); /* 037 */ this.serializefromobject_rowWriter = new org.apache.spark.sql.catalyst.expressions.codegen.UnsafeRowWriter(serializefromobject_holder, 1); /* 038 */ /* 039 */ } /* 040 */ /* 041 */ protected void processNext() throws java.io.IOException { /* 042 */ while (inputadapter_input.hasNext() && !stopEarly()) { /* 043 */ InternalRow inputadapter_row = (InternalRow) inputadapter_input.next(); /* 044 */ int inputadapter_value = inputadapter_row.getInt(0); /* 045 */ /* 046 */ boolean mapelements_isNull = true; /* 047 */ int mapelements_value = -1; /* 048 */ if (!false) { /* 049 */ mapelements_argValue = inputadapter_value; /* 050 */ /* 051 */ mapelements_isNull = false; /* 052 */ if (!mapelements_isNull) { /* 053 */ mapelements_value = ((scala.Function1) references[0]).apply$mcII$sp(mapelements_argValue); /* 054 */ } /* 055 */ /* 056 */ } /* 057 */ /* 058 */ serializefromobject_rowWriter.zeroOutNullBytes(); /* 059 */ /* 060 */ if (mapelements_isNull) { /* 061 */ serializefromobject_rowWriter.setNullAt(0); /* 062 */ } else { /* 063 */ serializefromobject_rowWriter.write(0, mapelements_value); /* 064 */ } /* 065 */ append(serializefromobject_result); /* 066 */ if (shouldStop()) return; /* 067 */ } /* 068 */ } /* 069 */ } ``` Java bytecode for methods for `i => i * 7` ```java $ javap -c Test\$\$anonfun\$5\$\$anonfun\$apply\$mcV\$sp\$1.class Compiled from "Test.scala" public final class org.apache.spark.sql.Test$$anonfun$5$$anonfun$apply$mcV$sp$1 extends scala.runtime.AbstractFunction1$mcII$sp implements scala.Serializable { public static final long serialVersionUID; public final int apply(int); Code: 0: aload_0 1: iload_1 2: invokevirtual #18 // Method apply$mcII$sp:(I)I 5: ireturn public int apply$mcII$sp(int); Code: 0: iload_1 1: bipush 7 3: imul 4: ireturn public final java.lang.Object apply(java.lang.Object); Code: 0: aload_0 1: aload_1 2: invokestatic #29 // Method scala/runtime/BoxesRunTime.unboxToInt:(Ljava/lang/Object;)I 5: invokevirtual #31 // Method apply:(I)I 8: invokestatic #35 // Method scala/runtime/BoxesRunTime.boxToInteger:(I)Ljava/lang/Integer; 11: areturn public org.apache.spark.sql.Test$$anonfun$5$$anonfun$apply$mcV$sp$1(org.apache.spark.sql.Test$$anonfun$5); Code: 0: aload_0 1: invokespecial #42 // Method scala/runtime/AbstractFunction1$mcII$sp."":()V 4: return } ``` ## How was this patch tested? Added new test suites to `DatasetPrimitiveSuite`. Author: Kazuaki Ishizaki Closes #17172 from kiszk/SPARK-19008. --- .../org/apache/spark/sql/execution/objects.scala | 6 +- .../org/apache/spark/sql/DatasetBenchmark.scala | 122 ++++++++++++++++++++- .../apache/spark/sql/DatasetPrimitiveSuite.scala | 51 +++++++++ 3 files changed, 171 insertions(+), 8 deletions(-) (limited to 'sql/core/src') diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/objects.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/objects.scala index 199ba5ce69..fdd1bcc94b 100644 --- a/sql/core/src/main/scala/org/apache/spark/sql/execution/objects.scala +++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/objects.scala @@ -28,11 +28,13 @@ import org.apache.spark.sql.catalyst.InternalRow import org.apache.spark.sql.catalyst.expressions._ import org.apache.spark.sql.catalyst.expressions.codegen._ import org.apache.spark.sql.catalyst.expressions.objects.Invoke +import org.apache.spark.sql.catalyst.plans.logical.FunctionUtils import org.apache.spark.sql.catalyst.plans.physical._ import org.apache.spark.sql.Row import org.apache.spark.sql.catalyst.plans.logical.LogicalKeyedState import org.apache.spark.sql.execution.streaming.KeyedStateImpl -import org.apache.spark.sql.types.{DataType, ObjectType, StructType} +import org.apache.spark.sql.types._ +import org.apache.spark.util.Utils /** @@ -219,7 +221,7 @@ case class MapElementsExec( override def doConsume(ctx: CodegenContext, input: Seq[ExprCode], row: ExprCode): String = { val (funcClass, methodName) = func match { case m: MapFunction[_, _] => classOf[MapFunction[_, _]] -> "call" - case _ => classOf[Any => Any] -> "apply" + case _ => FunctionUtils.getFunctionOneName(outputObjAttr.dataType, child.output(0).dataType) } val funcObj = Literal.create(func, ObjectType(funcClass)) val callFunc = Invoke(funcObj, methodName, outputObjAttr.dataType, child.output) diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DatasetBenchmark.scala b/sql/core/src/test/scala/org/apache/spark/sql/DatasetBenchmark.scala index 66d94d6016..1a0672b887 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/DatasetBenchmark.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/DatasetBenchmark.scala @@ -31,6 +31,49 @@ object DatasetBenchmark { case class Data(l: Long, s: String) + def backToBackMapLong(spark: SparkSession, numRows: Long, numChains: Int): Benchmark = { + import spark.implicits._ + + val rdd = spark.sparkContext.range(0, numRows) + val ds = spark.range(0, numRows) + val df = ds.toDF("l") + val func = (l: Long) => l + 1 + + val benchmark = new Benchmark("back-to-back map long", numRows) + + benchmark.addCase("RDD") { iter => + var res = rdd + var i = 0 + while (i < numChains) { + res = res.map(func) + i += 1 + } + res.foreach(_ => Unit) + } + + benchmark.addCase("DataFrame") { iter => + var res = df + var i = 0 + while (i < numChains) { + res = res.select($"l" + 1 as "l") + i += 1 + } + res.queryExecution.toRdd.foreach(_ => Unit) + } + + benchmark.addCase("Dataset") { iter => + var res = ds.as[Long] + var i = 0 + while (i < numChains) { + res = res.map(func) + i += 1 + } + res.queryExecution.toRdd.foreach(_ => Unit) + } + + benchmark + } + def backToBackMap(spark: SparkSession, numRows: Long, numChains: Int): Benchmark = { import spark.implicits._ @@ -72,6 +115,49 @@ object DatasetBenchmark { benchmark } + def backToBackFilterLong(spark: SparkSession, numRows: Long, numChains: Int): Benchmark = { + import spark.implicits._ + + val rdd = spark.sparkContext.range(1, numRows) + val ds = spark.range(1, numRows) + val df = ds.toDF("l") + val func = (l: Long) => l % 2L == 0L + + val benchmark = new Benchmark("back-to-back filter Long", numRows) + + benchmark.addCase("RDD") { iter => + var res = rdd + var i = 0 + while (i < numChains) { + res = res.filter(func) + i += 1 + } + res.foreach(_ => Unit) + } + + benchmark.addCase("DataFrame") { iter => + var res = df + var i = 0 + while (i < numChains) { + res = res.filter($"l" % 2L === 0L) + i += 1 + } + res.queryExecution.toRdd.foreach(_ => Unit) + } + + benchmark.addCase("Dataset") { iter => + var res = ds.as[Long] + var i = 0 + while (i < numChains) { + res = res.filter(func) + i += 1 + } + res.queryExecution.toRdd.foreach(_ => Unit) + } + + benchmark + } + def backToBackFilter(spark: SparkSession, numRows: Long, numChains: Int): Benchmark = { import spark.implicits._ @@ -165,9 +251,22 @@ object DatasetBenchmark { val numRows = 100000000 val numChains = 10 - val benchmark = backToBackMap(spark, numRows, numChains) - val benchmark2 = backToBackFilter(spark, numRows, numChains) - val benchmark3 = aggregate(spark, numRows) + val benchmark0 = backToBackMapLong(spark, numRows, numChains) + val benchmark1 = backToBackMap(spark, numRows, numChains) + val benchmark2 = backToBackFilterLong(spark, numRows, numChains) + val benchmark3 = backToBackFilter(spark, numRows, numChains) + val benchmark4 = aggregate(spark, numRows) + + /* + OpenJDK 64-Bit Server VM 1.8.0_111-8u111-b14-2ubuntu0.16.04.2-b14 on Linux 4.4.0-47-generic + Intel(R) Xeon(R) CPU E5-2667 v3 @ 3.20GHz + back-to-back map long: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative + ------------------------------------------------------------------------------------------------ + RDD 1883 / 1892 53.1 18.8 1.0X + DataFrame 502 / 642 199.1 5.0 3.7X + Dataset 657 / 784 152.2 6.6 2.9X + */ + benchmark0.run() /* OpenJDK 64-Bit Server VM 1.8.0_91-b14 on Linux 3.10.0-327.18.2.el7.x86_64 @@ -178,7 +277,18 @@ object DatasetBenchmark { DataFrame 2647 / 3116 37.8 26.5 1.3X Dataset 4781 / 5155 20.9 47.8 0.7X */ - benchmark.run() + benchmark1.run() + + /* + OpenJDK 64-Bit Server VM 1.8.0_121-8u121-b13-0ubuntu1.16.04.2-b13 on Linux 4.4.0-47-generic + Intel(R) Xeon(R) CPU E5-2667 v3 @ 3.20GHz + back-to-back filter Long: Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative + ------------------------------------------------------------------------------------------------ + RDD 846 / 1120 118.1 8.5 1.0X + DataFrame 270 / 329 370.9 2.7 3.1X + Dataset 545 / 789 183.5 5.4 1.6X + */ + benchmark2.run() /* OpenJDK 64-Bit Server VM 1.8.0_91-b14 on Linux 3.10.0-327.18.2.el7.x86_64 @@ -189,7 +299,7 @@ object DatasetBenchmark { DataFrame 59 / 72 1695.4 0.6 22.8X Dataset 2777 / 2805 36.0 27.8 0.5X */ - benchmark2.run() + benchmark3.run() /* Java HotSpot(TM) 64-Bit Server VM 1.8.0_60-b27 on Mac OS X 10.12.1 @@ -201,6 +311,6 @@ object DatasetBenchmark { Dataset sum using Aggregator 4656 / 4758 21.5 46.6 0.4X Dataset complex Aggregator 6636 / 7039 15.1 66.4 0.3X */ - benchmark3.run() + benchmark4.run() } } diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DatasetPrimitiveSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DatasetPrimitiveSuite.scala index 6b50cb3e48..82b707537e 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/DatasetPrimitiveSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/DatasetPrimitiveSuite.scala @@ -62,6 +62,40 @@ class DatasetPrimitiveSuite extends QueryTest with SharedSQLContext { 2, 3, 4) } + test("mapPrimitive") { + val dsInt = Seq(1, 2, 3).toDS() + checkDataset(dsInt.map(_ > 1), false, true, true) + checkDataset(dsInt.map(_ + 1), 2, 3, 4) + checkDataset(dsInt.map(_ + 8589934592L), 8589934593L, 8589934594L, 8589934595L) + checkDataset(dsInt.map(_ + 1.1F), 2.1F, 3.1F, 4.1F) + checkDataset(dsInt.map(_ + 1.23D), 2.23D, 3.23D, 4.23D) + + val dsLong = Seq(1L, 2L, 3L).toDS() + checkDataset(dsLong.map(_ > 1), false, true, true) + checkDataset(dsLong.map(e => (e + 1).toInt), 2, 3, 4) + checkDataset(dsLong.map(_ + 8589934592L), 8589934593L, 8589934594L, 8589934595L) + checkDataset(dsLong.map(_ + 1.1F), 2.1F, 3.1F, 4.1F) + checkDataset(dsLong.map(_ + 1.23D), 2.23D, 3.23D, 4.23D) + + val dsFloat = Seq(1F, 2F, 3F).toDS() + checkDataset(dsFloat.map(_ > 1), false, true, true) + checkDataset(dsFloat.map(e => (e + 1).toInt), 2, 3, 4) + checkDataset(dsFloat.map(e => (e + 123456L).toLong), 123457L, 123458L, 123459L) + checkDataset(dsFloat.map(_ + 1.1F), 2.1F, 3.1F, 4.1F) + checkDataset(dsFloat.map(_ + 1.23D), 2.23D, 3.23D, 4.23D) + + val dsDouble = Seq(1D, 2D, 3D).toDS() + checkDataset(dsDouble.map(_ > 1), false, true, true) + checkDataset(dsDouble.map(e => (e + 1).toInt), 2, 3, 4) + checkDataset(dsDouble.map(e => (e + 8589934592L).toLong), + 8589934593L, 8589934594L, 8589934595L) + checkDataset(dsDouble.map(e => (e + 1.1F).toFloat), 2.1F, 3.1F, 4.1F) + checkDataset(dsDouble.map(_ + 1.23D), 2.23D, 3.23D, 4.23D) + + val dsBoolean = Seq(true, false).toDS() + checkDataset(dsBoolean.map(e => !e), false, true) + } + test("filter") { val ds = Seq(1, 2, 3, 4).toDS() checkDataset( @@ -69,6 +103,23 @@ class DatasetPrimitiveSuite extends QueryTest with SharedSQLContext { 2, 4) } + test("filterPrimitive") { + val dsInt = Seq(1, 2, 3).toDS() + checkDataset(dsInt.filter(_ > 1), 2, 3) + + val dsLong = Seq(1L, 2L, 3L).toDS() + checkDataset(dsLong.filter(_ > 1), 2L, 3L) + + val dsFloat = Seq(1F, 2F, 3F).toDS() + checkDataset(dsFloat.filter(_ > 1), 2F, 3F) + + val dsDouble = Seq(1D, 2D, 3D).toDS() + checkDataset(dsDouble.filter(_ > 1), 2D, 3D) + + val dsBoolean = Seq(true, false).toDS() + checkDataset(dsBoolean.filter(e => !e), false) + } + test("foreach") { val ds = Seq(1, 2, 3).toDS() val acc = sparkContext.longAccumulator -- cgit v1.2.3