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commit 1cebabcd617e4c78daaa7a60a457db71a8a2a175 Author: Jon Malkin <[email protected]> AuthorDate: Fri Dec 20 12:45:05 2024 -0800 Initial commit --- .asf.yaml | 8 + .gitignore | 73 +++++++ LICENSE | 210 +++++++++++++++++++ NOTICE | 7 + README.md | 24 +++ build.sbt | 57 ++++++ .../apache/spark/sql/aggregate/KllAggregate.scala | 147 ++++++++++++++ .../org/apache/spark/sql/aggregate/KllMerge.scala | 154 ++++++++++++++ .../spark/sql/expressions/KllExpressions.scala | 224 +++++++++++++++++++++ .../scala/org/apache/spark/sql/functions_ds.scala | 155 ++++++++++++++ .../registrar/DatasketchesFunctionRegistry.scala | 85 ++++++++ .../spark/sql/types/KllDoublesSketchType.scala | 37 ++++ .../spark/sql/types/KllDoublesSketchWrapper.scala | 47 +++++ src/test/scala/org/apache/spark/sql/KllTest.scala | 210 +++++++++++++++++++ .../org/apache/spark/sql/SparkSessionManager.scala | 41 ++++ 15 files changed, 1479 insertions(+) diff --git a/.asf.yaml b/.asf.yaml new file mode 100644 index 0000000..daa5a02 --- /dev/null +++ b/.asf.yaml @@ -0,0 +1,8 @@ +github: + homepage: https://datasketches.apache.org + ghp_branch: gh-pages + ghp_path: /docs + + features: + # Enable issue management + issues: true diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..fe02d20 --- /dev/null +++ b/.gitignore @@ -0,0 +1,73 @@ +# VSCode +.vscode/ + +# Eclipse project files +.classpath +.project +.settings/ +.checkstyle + +# IntelliJ project files +*.idea +**/*.iml +*.ipr +*.iws + +# Scala project files and related plugins +target/ +project/ +.bsp/ +.bloop/ +.metals/ +.scala-build/ + +# OSX files +**/.DS_Store + +# Compiler output, class files +*.class +bin/ + +# Log file +*.log + +# BlueJ files +*.ctxt + +# Mobile Tools for Java (J2ME) +.mtj.tmp/ + +# Package Files # +*.jar +*.war +*.ear +*.zip +*.tar.gz +*.rar + +# virtual machine crash logs, see http://www.java.com/en/download/help/error_hotspot.xml +hs_err_pid* + +#Test config and output +test-output/ +local/ +reports/ +.pmd +tmp/ + +# Build artifacts +target/ +serialization_test_data/ +out/ +build/ +jarsIn/ +build.xml +*.properties +*.releaseBackup +*.next +*.tag +doc/ + + +# Sketch binary test files +*.sk \ No newline at end of file diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000..51e907d --- /dev/null +++ b/LICENSE @@ -0,0 +1,210 @@ + + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. 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See the NOTICE file + distributed with this work for additional information + regarding copyright ownership. The ASF licenses this file + to you 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. +--> + +# Apache<sup>®</sup> DataSketches™ Spark Library + +This repo is still an early-stage work in progress. + +There have been multiple attempts to help integrate Apache DataSketches into Apache Spark, including one built into Spark itself as of v3.5. All are useful work, but in comparing them, there are various limitations to each library. Whether limitng the type of sketches available (e.g. native Spark provides only HLL) or limiting flexibility and functionality (e.g. forcing HLL and Theta to use a common interface which precludes set operations HLL cannot support, or using global parameters t [...] diff --git a/build.sbt b/build.sbt new file mode 100644 index 0000000..daa4c63 --- /dev/null +++ b/build.sbt @@ -0,0 +1,57 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You 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. + */ + +name := "datasketches-spark" +version := "1.0-SNAPSHOT" +scalaVersion := "2.12.20" + +organization := "org.apache.datasketches" +description := "The Apache DataSketches package for Spark" + +licenses += ("Apache-2.0", url("http://www.apache.org/licenses/LICENSE-2.0";)) + +// these do not impact code generation in spark +javacOptions ++= Seq("-source", "17", "-target", "17") +scalacOptions ++= Seq("-encoding", "UTF-8", "-release", "11") +Test / javacOptions ++= Seq("-source", "17", "-target", "17") +Test / scalacOptions ++= Seq("-encoding", "UTF-8", "-release", "11") + +libraryDependencies ++= Seq( + "org.scala-lang" % "scala-library" % "2.12.6", + "org.apache.spark" %% "spark-sql" % "3.4.4" % "provided", + "org.apache.datasketches" % "datasketches-java" % "6.1.1" % "compile", + "org.scalatest" %% "scalatest" % "3.2.19" % "test", + "org.scalatestplus" %% "junit-4-13" % "3.2.19.0" % "test" +) + +Test / testOptions += Tests.Argument(TestFrameworks.ScalaTest, "-oD") + +scalacOptions ++= Seq( + "-deprecation", + "-feature", + "-unchecked", + "-Xlint" +) + +Test / logBuffered := false + +// Only show warnings and errors on the screen for compilations. +// This applies to both test:compile and compile and is Info by default +Compile / logLevel := Level.Warn + +// Level.INFO is needed to see detailed output when running tests +Test / logLevel := Level.Info diff --git a/src/main/scala/org/apache/spark/sql/aggregate/KllAggregate.scala b/src/main/scala/org/apache/spark/sql/aggregate/KllAggregate.scala new file mode 100644 index 0000000..1cea930 --- /dev/null +++ b/src/main/scala/org/apache/spark/sql/aggregate/KllAggregate.scala @@ -0,0 +1,147 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You 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 org.apache.spark.sql.aggregate + +import org.apache.datasketches.kll.{KllSketch, KllDoublesSketch} +import org.apache.spark.SparkUnsupportedOperationException +import org.apache.spark.sql.catalyst.InternalRow +import org.apache.spark.sql.catalyst.expressions.{ExpectsInputTypes, Expression, ExpressionDescription, Literal} +import org.apache.spark.sql.catalyst.expressions.aggregate.TypedImperativeAggregate +import org.apache.spark.sql.catalyst.trees.BinaryLike +import org.apache.spark.sql.types.{AbstractDataType, DataType, IntegerType, LongType, NumericType, FloatType, DoubleType, KllDoublesSketchWrapper, KllDoublesSketchType} + +/** + * The KllDoublesSketchAgg function utilizes a Datasketches KllDoublesSketch instance + * to create a sketch from a column of values which can be used to estimate quantiles + * and histograms. + * + * @param child child expression against which unique counting will occur + * @param k the size-accraucy trade-off parameter for the sketch + */ +// scalastyle:off line.size.limit +@ExpressionDescription( + usage = """ + _FUNC_(expr, k) - Creates a KllDoublesSketch and returns the binary representation. + `k` (optional, default: 200) the size-accuracy trade-off parameter.""", + examples = """ + Examples: + > SELECT kll_get_quantile(_FUNC_(col, 200), 0.5) FROM VALUES (1.0), (1.0), (2.0), (2.0), (3.0) tab(col); + 2.0 + """, +) +// scalastyle:on line.size.limit +case class KllDoublesSketchAgg( + left: Expression, + right: Expression, + mutableAggBufferOffset: Int = 0, + inputAggBufferOffset: Int = 0) + extends TypedImperativeAggregate[KllDoublesSketchWrapper] + with BinaryLike[Expression] + with ExpectsInputTypes { + + lazy val k: Int = { + right.eval() match { + case null => KllSketch.DEFAULT_K + case k: Int => k + case _ => throw new SparkUnsupportedOperationException( + s"Unsupported input type ${right.dataType.catalogString}", + Map("dataType" -> dataType.toString)) + } + } + + // Constructors + + def this(child: Expression) = { + this(child, Literal(KllSketch.DEFAULT_K), 0, 0) + } + + def this(child: Expression, k: Expression) = { + this(child, k, 0, 0) + } + + def this(child: Expression, k: Int) = { + this(child, Literal(k), 0, 0) + } + + // Copy constructors + + override def withNewMutableAggBufferOffset(newMutableAggBufferOffset: Int): KllDoublesSketchAgg = + copy(mutableAggBufferOffset = newMutableAggBufferOffset) + + override def withNewInputAggBufferOffset(newInputAggBufferOffset: Int): KllDoublesSketchAgg = + copy(inputAggBufferOffset = newInputAggBufferOffset) + + override protected def withNewChildrenInternal(newLeft: Expression, + newRight: Expression): KllDoublesSketchAgg = { + copy(left = newLeft, right = newRight) + } + + // overrides for TypedImperativeAggregate + override def prettyName: String = "kll_sketch_agg" + + override def dataType: DataType = KllDoublesSketchType + + override def nullable: Boolean = false + + override def inputTypes: Seq[AbstractDataType] = Seq(NumericType, IntegerType, LongType, FloatType, DoubleType) + + // create buffer + override def createAggregationBuffer(): KllDoublesSketchWrapper = new KllDoublesSketchWrapper(KllDoublesSketch.newHeapInstance(k)) + + // update + override def update(wrapper: KllDoublesSketchWrapper, input: InternalRow): KllDoublesSketchWrapper = { + val value = left.eval(input) + if (value != null) { + left.dataType match { + case DoubleType => wrapper.sketch.update(value.asInstanceOf[Double]) + case FloatType => wrapper.sketch.update(value.asInstanceOf[Float].toDouble) + case IntegerType => wrapper.sketch.update(value.asInstanceOf[Int].toDouble) + case LongType => wrapper.sketch.update(value.asInstanceOf[Long].toDouble) + case _ => throw new SparkUnsupportedOperationException( + s"Unsupported input type ${left.dataType.catalogString}", + Map("dataType" -> dataType.toString)) + } + } + wrapper + } + + // union (merge) + override def merge(wrapper: KllDoublesSketchWrapper, other: KllDoublesSketchWrapper): KllDoublesSketchWrapper = { + if (other != null && !other.sketch.isEmpty) { + wrapper.sketch.merge(other.sketch) + } + wrapper + } + + // eval + override def eval(wrapper: KllDoublesSketchWrapper): Any = { + if (wrapper == null || wrapper.sketch.isEmpty) { + null + } else { + wrapper.toByteArray + } + } + + override def serialize(wrapper: KllDoublesSketchWrapper): Array[Byte] = { + KllDoublesSketchType.serialize(wrapper) + } + + override def deserialize(bytes: Array[Byte]): KllDoublesSketchWrapper = { + KllDoublesSketchType.deserialize(bytes) + } +} diff --git a/src/main/scala/org/apache/spark/sql/aggregate/KllMerge.scala b/src/main/scala/org/apache/spark/sql/aggregate/KllMerge.scala new file mode 100644 index 0000000..67db2cc --- /dev/null +++ b/src/main/scala/org/apache/spark/sql/aggregate/KllMerge.scala @@ -0,0 +1,154 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You 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 org.apache.spark.sql.aggregate + +import org.apache.datasketches.kll.{KllSketch, KllDoublesSketch} + +import org.apache.spark.SparkUnsupportedOperationException +import org.apache.spark.sql.catalyst.InternalRow +import org.apache.spark.sql.catalyst.expressions.{ExpectsInputTypes, Expression, ExpressionDescription} +import org.apache.spark.sql.catalyst.expressions.aggregate.TypedImperativeAggregate +import org.apache.spark.sql.catalyst.trees.UnaryLike +import org.apache.spark.sql.types.{AbstractDataType, DataType, KllDoublesSketchWrapper, KllDoublesSketchType} +import org.apache.datasketches.memory.Memory + +/** + * The KllDoublesMergeAgg function utilizes a Datasketches KllDoublesSketch instance to + * combine multiple sketches into a single sketch. + * + * See [[https://datasketches.apache.org/docs/HLL/HLL.html]] for more information. + * + * @param child child expression against which unique counting will occur + */ +// scalastyle:off line.size.limit +@ExpressionDescription( + usage = """ + _FUNC_(expr, k) - Merges multiple KllDoublesSketch images and returns the binary representation + """, + examples = """ + Examples: + > SELECT kll_get_quantile(_FUNC_(sketch), 0.5) FROM (SELECT kll_sketch_agg(col) as sketch FROM VALUES (1.0), (2.0) tab(col) UNION ALL SELECT kll_sketch_agg(col) as sketch FROM VALUES (2.0), (3.0) tab(col)); + 2.0 + """, + //group = "agg_funcs", +) +// scalastyle:on line.size.limit +case class KllDoublesMergeAgg( + child: Expression, + mutableAggBufferOffset: Int = 0, + inputAggBufferOffset: Int = 0) + extends TypedImperativeAggregate[Option[KllDoublesSketchWrapper]] + with UnaryLike[Expression] + with ExpectsInputTypes { + + // Constructors + + // this seems redundant given the constructor? + def this(child: Expression) = this(child, 0, 0) + + // Copy constructors + + override def withNewMutableAggBufferOffset(newMutableAggBufferOffset: Int): KllDoublesMergeAgg = + copy(mutableAggBufferOffset = newMutableAggBufferOffset) + + override def withNewInputAggBufferOffset(newInputAggBufferOffset: Int): KllDoublesMergeAgg = + copy(inputAggBufferOffset = newInputAggBufferOffset) + + override protected def withNewChildInternal(newChild: Expression): KllDoublesMergeAgg = + copy(child = newChild) + + + // overrides for TypedImperativeAggregate + override def prettyName: String = "kll_merge_agg" + + override def dataType: DataType = KllDoublesSketchType + + override def nullable: Boolean = false + + // TODO: refine this? + override def inputTypes: Seq[AbstractDataType] = Seq(KllDoublesSketchType) + + // create buffer + override def createAggregationBuffer(): Option[KllDoublesSketchWrapper] = { + None + } + + // update + override def update(unionOption: Option[KllDoublesSketchWrapper], input: InternalRow): Option[KllDoublesSketchWrapper] = { + val value = child.eval(input) + if (value != null && value != None) { + child.dataType match { + case KllDoublesSketchType => + if (unionOption == None || unionOption.get.sketch.isEmpty) { + // if union is empty, just return a copy of the input sketch + // TODO: is this serialized or already as a sketch object? + //Some(KllDoublesSketch.heapify(Memory.wrap(value.asInstanceOf[Array[Byte]]))) + Some(KllDoublesSketchWrapper.deserialize(value.asInstanceOf[Array[Byte]])) + } else { + unionOption.get.sketch.merge(KllDoublesSketch.wrap(Memory.wrap(value.asInstanceOf[Array[Byte]]))) + unionOption + } + case _ => throw new SparkUnsupportedOperationException( + s"Unsupported input type ${child.dataType.catalogString}", + Map("dataType" -> dataType.toString)) + } + } else { + unionOption + } + } + + // union (merge) + override def merge(unionOption: Option[KllDoublesSketchWrapper], otherOption: Option[KllDoublesSketchWrapper]): Option[KllDoublesSketchWrapper] = { + (unionOption, otherOption) match { + case (Some(union), Some(other)) => + union.sketch.merge(other.sketch) + Some(union) + + // for these others, we'll return the input even if degenerate + case (Some(union), None) => + unionOption + case (None, Some(other)) => + otherOption + case (None, None) => + unionOption + } + } + + // eval + override def eval(unionOption: Option[KllDoublesSketchWrapper]): Any = { + unionOption match { + case Some(wrapper) => wrapper.sketch.toByteArray + case None => None // can this happen in practice? If so, what should we return? + } + } + + override def serialize(sketchOption: Option[KllDoublesSketchWrapper]): Array[Byte] = { + sketchOption match { + case Some(wrapper) => wrapper.sketch.toByteArray + case None => KllDoublesSketch.newHeapInstance(KllSketch.DEFAULT_K).toByteArray + } + } + + override def deserialize(bytes: Array[Byte]): Option[KllDoublesSketchWrapper] = { + if (bytes.length > 0) { + Some(KllDoublesSketchType.deserialize(bytes)) + } else { + None + } + } +} diff --git a/src/main/scala/org/apache/spark/sql/expressions/KllExpressions.scala b/src/main/scala/org/apache/spark/sql/expressions/KllExpressions.scala new file mode 100644 index 0000000..df2aa2f --- /dev/null +++ b/src/main/scala/org/apache/spark/sql/expressions/KllExpressions.scala @@ -0,0 +1,224 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You 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 org.apache.spark.sql.expressions + +import org.apache.datasketches.memory.Memory +import org.apache.datasketches.kll.KllDoublesSketch +import org.apache.spark.sql.catalyst.expressions.{Expression, ExpectsInputTypes, UnaryExpression, BinaryExpression} +import org.apache.spark.sql.types.{AbstractDataType, DataType, ArrayType, DoubleType, KllDoublesSketchType} +import org.apache.spark.sql.catalyst.expressions.NullIntolerant +import org.apache.spark.sql.catalyst.expressions.codegen.{CodeBlock, CodegenContext, ExprCode} +import org.apache.datasketches.quantilescommon.QuantileSearchCriteria +import org.apache.spark.sql.catalyst.util.GenericArrayData +import org.apache.spark.sql.catalyst.expressions.ExpressionDescription +import org.apache.spark.sql.catalyst.expressions.ImplicitCastInputTypes + +@ExpressionDescription( + usage = """ + _FUNC_(expr) - Returns the minimum value seem by the sketch given the binary representation + of a Datasketches KllDoublesSketch. """, + examples = """ + Examples: + > SELECT _FUNC_(kll_sketch_agg(col)) FROM VALUES (1.0), (2.0), (3.0) tab(col); + 1.0 + """ + //group = "misc_funcs", +) +case class KllGetMin(child: Expression) + extends UnaryExpression + with ExpectsInputTypes + with NullIntolerant { + + override protected def withNewChildInternal(newChild: Expression): KllGetMin = { + copy(child = newChild) + } + + override def prettyName: String = "kll_get_min" + + override def inputTypes: Seq[AbstractDataType] = Seq(KllDoublesSketchType) + + override def dataType: DataType = DoubleType + + override def nullSafeEval(input: Any): Any = { + val bytes = input.asInstanceOf[Array[Byte]] + val sketch = KllDoublesSketch.wrap(Memory.wrap(bytes)) + sketch.getMinItem + } + + override protected def nullSafeCodeGen(ctx: CodegenContext, ev: ExprCode, f: String => String): ExprCode = { + val childEval = child.genCode(ctx) + val sketch = ctx.freshName("sketch") + + val code = + s""" + |${childEval.code} + |final org.apache.datasketches.kll.KllDoublesSketch $sketch = org.apache.spark.sql.types.KllDoublesSketchWrapper.wrapAsReadOnlySketch(${childEval.value}); + |final double ${ev.value} = $sketch.getMinItem(); + """.stripMargin + ev.copy(code = CodeBlock(Seq(code), Seq.empty), isNull = childEval.isNull) + } + + override def doGenCode(ctx: CodegenContext, ev: ExprCode): ExprCode = { + nullSafeCodeGen(ctx, ev, c => s"($c)") + } +} + +@ExpressionDescription( + usage = """ + _FUNC_(expr) - Returns the maximum value seem by the sketch given the binary representation + of a Datasketches KllDoublesSketch. """, + examples = """ + Examples: + > SELECT _FUNC_(kll_sketch_agg(col)) FROM VALUES (1.0), (2.0), (3.0) tab(col); + 3.0 + """ + //group = "misc_funcs", +) +case class KllGetMax(child: Expression) + extends UnaryExpression + with ExpectsInputTypes + with NullIntolerant { + + override protected def withNewChildInternal(newChild: Expression): KllGetMax = { + copy(child = newChild) + } + + override def prettyName: String = "kll_get_max" + + override def inputTypes: Seq[AbstractDataType] = Seq(KllDoublesSketchType) + + override def dataType: DataType = DoubleType + + override def nullSafeEval(input: Any): Any = { + val bytes = input.asInstanceOf[Array[Byte]] + val sketch = KllDoublesSketch.wrap(Memory.wrap(bytes)) + sketch.getMaxItem + } + + override protected def nullSafeCodeGen(ctx: CodegenContext, ev: ExprCode, f: String => String): ExprCode = { + val childEval = child.genCode(ctx) + val sketch = ctx.freshName("sketch") + + val code = + s""" + |${childEval.code} + |final org.apache.datasketches.kll.KllDoublesSketch $sketch = org.apache.spark.sql.types.KllDoublesSketchWrapper.wrapAsReadOnlySketch(${childEval.value}); + |final double ${ev.value} = $sketch.getMaxItem(); + """.stripMargin + ev.copy(code = CodeBlock(Seq(code), Seq.empty), isNull = childEval.isNull) + } + + override def doGenCode(ctx: CodegenContext, ev: ExprCode): ExprCode = { + nullSafeCodeGen(ctx, ev, c => s"($c)") + } +} + + +/** + * Returns the PMF and CDF of the given quantile search criteria. + * + * @param left A KllDoublesSketch sketch, in serialized form + * @param right An array of split points, as doubles + * @param isInclusive If true, use INCLUSIVE else EXCLUSIVE + * @param isPmf Whether to return the PMF (true) or CDF (false) + */ +@ExpressionDescription( + usage = """ + _FUNC_(expr, expr, isInclusive, isPmf) - Returns an approximation to the PMF or CDF (default: isPmf = false) + of the given KllDoublesSketch using the specified search criteria (default: inclusive, isInclusive = true) + or exclusive using the given split points. + """, + examples = """ + Examples: + > SELECT _FUNC_(kll_sketch_agg(col), array(1.5, 3.5), true, true) FROM VALUES (1.0), (2.0), (3.0) tab(col); + [0.3333333333333333, 0.6666666666666666, 0.0] + """ +) +case class KllGetPmfCdf(left: Expression, + right: Expression, + isInclusive: Boolean = true, + isPmf: Boolean = false) + extends BinaryExpression + with ExpectsInputTypes + with NullIntolerant + with ImplicitCastInputTypes { + + override protected def withNewChildrenInternal(newLeft: Expression, + newRight: Expression) = { + copy(left = newLeft, right = newRight, isInclusive = isInclusive, isPmf = isPmf) + } + + override def prettyName: String = "kll_get_pmf_cdf" + + override def inputTypes: Seq[AbstractDataType] = Seq(KllDoublesSketchType, ArrayType(DoubleType)) + + override def dataType: DataType = ArrayType(DoubleType, containsNull = false) + + override def nullSafeEval(leftInput: Any, rightInput: Any): Any = { + val sketchBytes = leftInput.asInstanceOf[Array[Byte]] + val splitPoints = rightInput.asInstanceOf[GenericArrayData].toDoubleArray + val sketch = KllDoublesSketch.wrap(Memory.wrap(sketchBytes)) + + val result: Array[Double] = + if (isPmf) { + sketch.getPMF(splitPoints, if (isInclusive) QuantileSearchCriteria.INCLUSIVE else QuantileSearchCriteria.EXCLUSIVE) + } else { + sketch.getCDF(splitPoints, if (isInclusive) QuantileSearchCriteria.INCLUSIVE else QuantileSearchCriteria.EXCLUSIVE) + } + new GenericArrayData(result) + } + + override protected def nullSafeCodeGen(ctx: CodegenContext, ev: ExprCode, f: (String, String) => String): ExprCode = { + val sketchEval = left.genCode(ctx) + val sketch = ctx.freshName("sketch") + val splitPointsEval = right.genCode(ctx) + val code = + s""" + |${sketchEval.code} + |${splitPointsEval.code} + |if (${sketchEval.isNull} || ${splitPointsEval.isNull}) { + | ${ev.isNull} = true; + |} else { + | QuantileSearchCriteria searchCriteria = ${if (isInclusive) "QuantileSearchCriteria.INCLUSIVE" else "QuantileSearchCriteria.EXCLUSIVE"}; + | final org.apache.datasketches.kll.KllDoublesSketch $sketch = org.apache.spark.sql.types.KllDoublesSketchWrapper.wrapAsReadOnlySketch(${sketchEval.value}); + | final double[] splitPoints = ((org.apache.spark.sql.catalyst.util.GenericArrayData)${splitPointsEval.value}).toDoubleArray(); + | final double[] result = ${if (isPmf) s"$sketch.getPMF(splitPoints, searchCriteria)" else s"$sketch.getCDF(splitPoints, searchCriteria)"}; + | GenericArrayData ${ev.value} = new GenericArrayData(result); + | ${ev.isNull} = false; + |} + """.stripMargin + ev.copy(code = CodeBlock(Seq(code), Seq.empty)) + } + + override def doGenCode(ctx: CodegenContext, ev: ExprCode): ExprCode = { + nullSafeCodeGen(ctx, ev, (arg1, arg2) => s"($arg1, $arg2)") + } +} + +// default search criteria = inclusive +// getQuantile(rank, QuantileSearchCriteria) +// getQuantileLowerBound(rank) +// getQuantileUpperBound(rank) +// getQuantiles(double ranks[], QuantileSearchCriteria) +// getRank(quantile, QuantileSearchCriteria) +// getRanks(quantile[]), QuantileSearchCriteria) +// getNormalizedRankError(bool isPmf) +// isEstimationMode() +// toString(bool, bool) -- already part of the wrapper +// getK() ? +// getNumRetained() ? diff --git a/src/main/scala/org/apache/spark/sql/functions_ds.scala b/src/main/scala/org/apache/spark/sql/functions_ds.scala new file mode 100644 index 0000000..7c2a96e --- /dev/null +++ b/src/main/scala/org/apache/spark/sql/functions_ds.scala @@ -0,0 +1,155 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You 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 org.apache.spark.sql + +import org.apache.spark.sql.functions.lit +import org.apache.spark.sql.catalyst.expressions.aggregate.AggregateFunction + +import org.apache.spark.sql.catalyst.expressions.Expression +import org.apache.spark.sql.aggregate.{KllDoublesSketchAgg, KllDoublesMergeAgg} +import org.apache.spark.sql.expressions._ +import org.apache.spark.sql.catalyst.expressions.Literal +import org.apache.spark.sql.types.ArrayType +import org.apache.spark.sql.types.DoubleType + +// this class defines and maps all the variants of each function invocation, analagous +// to the functions object in org.apache.spark.sql.functions +object functions_ds { + + private def withExpr(expr: => Expression): Column = Column(expr) + + private def withAggregateFunction(func: AggregateFunction): Column = { + Column(func.toAggregateExpression()) + } + + // get min + def kll_get_min(expr: Column): Column = withExpr { + new KllGetMin(expr.expr) + } + + def kll_get_min(columnName: String): Column = { + kll_get_min(Column(columnName)) + } + + // get max + def kll_get_max(expr: Column): Column = withExpr { + new KllGetMax(expr.expr) + } + + def kll_get_max(columnName: String): Column = { + kll_get_max(Column(columnName)) + } + + // build sketch + def kll_sketch_agg(expr: Column, k: Column): Column = withAggregateFunction { + new KllDoublesSketchAgg(expr.expr, k.expr) + } + + def kll_sketch_agg(expr: Column, k: Int): Column = { + kll_sketch_agg(expr, lit(k)) + } + + def kll_sketch_agg(columnName: String, k: Int): Column = { + kll_sketch_agg(Column(columnName), k) + } + + def kll_sketch_agg(expr: Column): Column = withAggregateFunction { + new KllDoublesSketchAgg(expr.expr) + } + + def kll_sketch_agg(columnName: String): Column = { + kll_sketch_agg(Column(columnName)) + } + + // merge sketches + def kll_merge_agg(expr: Column): Column = withAggregateFunction { + new KllDoublesMergeAgg(expr.expr) + } + + def kll_merge_agg(columnName: String): Column = { + kll_merge_agg(Column(columnName)) + } + + // get PMF + def kll_get_pmf(sketch: Column, splitPoints: Column, isInclusive: Boolean): Column = withExpr { + new KllGetPmfCdf(sketch.expr, splitPoints.expr, isInclusive, true) + } + + def kll_get_pmf(sketch: Column, splitPoints: Column): Column = withExpr { + new KllGetPmfCdf(sketch.expr, splitPoints.expr, true, true) + } + + def kll_get_pmf(columnName: String, splitPoints: Column, isInclusive: Boolean): Column = { + kll_get_pmf(Column(columnName), splitPoints, isInclusive) + } + + def kll_get_pmf(columnName: String, splitPoints: Column): Column = { + kll_get_pmf(Column(columnName), splitPoints) + } + + def kll_get_pmf(sketch: Column, splitPoints: Array[Double], isInclusive: Boolean): Column = { + kll_get_pmf(sketch, Column(Literal.create(splitPoints, ArrayType(DoubleType))), isInclusive) + } + + def kll_get_pmf(sketch: Column, splitPoints: Array[Double]): Column = { + kll_get_pmf(sketch, Column(Literal.create(splitPoints, ArrayType(DoubleType)))) + } + + def kll_get_pmf(columnName: String, splitPoints: Array[Double], isInclusive: Boolean): Column = { + kll_get_pmf(Column(columnName), splitPoints, isInclusive) + } + + def kll_get_pmf(columnName: String, splitPoints: Array[Double]): Column = { + kll_get_pmf(Column(columnName), splitPoints) + } + + + // get CDF + def kll_get_cdf(sketch: Column, splitPoints: Column, isInclusive: Boolean): Column = withExpr { + new KllGetPmfCdf(sketch.expr, splitPoints.expr, isInclusive, false) + } + + def kll_get_cdf(sketch: Column, splitPoints: Column): Column = withExpr { + new KllGetPmfCdf(sketch.expr, splitPoints.expr, true, false) + } + + def kll_get_cdf(columnName: String, splitPoints: Column, isInclusive: Boolean): Column = { + kll_get_cdf(Column(columnName), splitPoints, isInclusive) + } + + def kll_get_cdf(columnName: String, splitPoints: Column): Column = { + kll_get_cdf(Column(columnName), splitPoints) + } + + def kll_get_cdf(sketch: Column, splitPoints: Array[Double], isInclusive: Boolean): Column = { + kll_get_cdf(sketch, Column(Literal.create(splitPoints, ArrayType(DoubleType))), isInclusive) + } + + def kll_get_cdf(sketch: Column, splitPoints: Array[Double]): Column = { + kll_get_cdf(sketch, Column(Literal.create(splitPoints, ArrayType(DoubleType)))) + } + + def kll_get_cdf(columnName: String, splitPoints: Array[Double], isInclusive: Boolean): Column = { + kll_get_cdf(Column(columnName), splitPoints, isInclusive) + } + + def kll_get_cdf(columnName: String, splitPoints: Array[Double]): Column = { + kll_get_cdf(Column(columnName), splitPoints) + } + +} diff --git a/src/main/scala/org/apache/spark/sql/registrar/DatasketchesFunctionRegistry.scala b/src/main/scala/org/apache/spark/sql/registrar/DatasketchesFunctionRegistry.scala new file mode 100644 index 0000000..5ab1738 --- /dev/null +++ b/src/main/scala/org/apache/spark/sql/registrar/DatasketchesFunctionRegistry.scala @@ -0,0 +1,85 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You 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 org.apache.spark.registrar + +import org.apache.spark.sql.SparkSession +import org.apache.spark.sql.catalyst.FunctionIdentifier +import org.apache.spark.sql.catalyst.analysis.FunctionRegistryBase +import org.apache.spark.sql.catalyst.analysis.FunctionRegistry.FunctionBuilder +import org.apache.spark.sql.catalyst.expressions.{Expression, ExpressionInfo} + +import scala.reflect.ClassTag + +// DataSketches imports +import org.apache.spark.sql.aggregate.{KllDoublesSketchAgg, KllDoublesMergeAgg} +import org.apache.spark.sql.expressions.{KllGetMin, KllGetMax} +import org.apache.spark.sql.expressions.KllGetPmfCdf + +// based on org.apache.spark.sql.catalyst.FunctionRegistry +trait DatasketchesFunctionRegistry { + // override this to define the actual functions + val expressions: Map[String, (ExpressionInfo, FunctionBuilder)] + + // registers all the functions in the expressions Map + def registerFunctions(spark: SparkSession): Unit = { + expressions.foreach { case (name, (info, builder)) => + spark.sessionState.functionRegistry.registerFunction(FunctionIdentifier(name), info, builder) + } + } + + // simplifies defining the expression (ignoring "since" as a stand-alone library) + protected def expression[T <: Expression : ClassTag](name: String): (String, (ExpressionInfo, FunctionBuilder)) = { + val (expressionInfo, builder) = FunctionRegistryBase.build[T](name, None) + (name, (expressionInfo, builder)) + } + + // some functions throw a query compile-time exception around the wrong + // number of parameters when using expression(). This function allows + // explicit argument handling by providing a lambda to use for the bulder. + protected def complexExpression[T <: Expression : ClassTag](name: String)(f: (Seq[Expression]) => T): (String, (ExpressionInfo, FunctionBuilder)) = { + val expressionInfo = FunctionRegistryBase.expressionInfo[T](name, None) + val builder: FunctionBuilder = (args: Seq[Expression]) => f(args) + (name, (expressionInfo, builder)) + } +} + +// defines the Map for the Datasketches functions +object DatasketchesFunctionRegistry extends DatasketchesFunctionRegistry { + override val expressions: Map[String, (ExpressionInfo, FunctionBuilder)] = Map( + expression[KllDoublesSketchAgg]("kll_sketch_agg"), + expression[KllDoublesMergeAgg]("kll_merge_agg"), + expression[KllGetMin]("kll_get_min"), + expression[KllGetMax]("kll_get_max"), + + // TODO: it seems like there's got to be a way to simplify this, but + // perhaps not with the optional isInclusive parameter? + // Spark uses ExprssionBuilder, extending that class via a builder class + // and overriding build() to handle the lambda. + // It allows for a cleaner registry here, so we can look at where to put + // the builder classes in the future. + // See org.apache.spark.sql.catalyst.expressions.variant.variantExpressions.scala + complexExpression[KllGetPmfCdf]("kll_get_pmf") { args: Seq[Expression] => + val isInclusive = if (args.length > 2) args(2).eval().asInstanceOf[Boolean] else true + new KllGetPmfCdf(args(0), args(1), isInclusive = isInclusive, isPmf = true) + }, + complexExpression[KllGetPmfCdf]("kll_get_cdf") { args: Seq[Expression] => + val isInclusive = if (args.length > 2) args(2).eval().asInstanceOf[Boolean] else true + new KllGetPmfCdf(args(0), args(1), isInclusive = isInclusive, isPmf = false) + } + ) +} diff --git a/src/main/scala/org/apache/spark/sql/types/KllDoublesSketchType.scala b/src/main/scala/org/apache/spark/sql/types/KllDoublesSketchType.scala new file mode 100644 index 0000000..65a3635 --- /dev/null +++ b/src/main/scala/org/apache/spark/sql/types/KllDoublesSketchType.scala @@ -0,0 +1,37 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You 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 org.apache.spark.sql.types + +class KllDoublesSketchType extends UserDefinedType[KllDoublesSketchWrapper] { + override def sqlType: DataType = DataTypes.BinaryType + + override def serialize(wrapper: KllDoublesSketchWrapper): Array[Byte] = { + wrapper.toByteArray + } + + override def deserialize(data: Any): KllDoublesSketchWrapper = { + val bytes = data.asInstanceOf[Array[Byte]] + KllDoublesSketchWrapper.deserialize(bytes) + } + + override def userClass: Class[KllDoublesSketchWrapper] = classOf[KllDoublesSketchWrapper] + + override def catalogString: String = "KllDoublesSketch" +} + +case object KllDoublesSketchType extends KllDoublesSketchType diff --git a/src/main/scala/org/apache/spark/sql/types/KllDoublesSketchWrapper.scala b/src/main/scala/org/apache/spark/sql/types/KllDoublesSketchWrapper.scala new file mode 100644 index 0000000..774d4c8 --- /dev/null +++ b/src/main/scala/org/apache/spark/sql/types/KllDoublesSketchWrapper.scala @@ -0,0 +1,47 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You 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 org.apache.spark.sql.types + +import org.apache.datasketches.kll.KllDoublesSketch +import org.apache.datasketches.memory.Memory + +@SQLUserDefinedType(udt = classOf[KllDoublesSketchType]) +case class KllDoublesSketchWrapper(sketch: KllDoublesSketch) { + + def toByteArray: Array[Byte] = sketch.toByteArray + + def serialize: Array[Byte] = sketch.toByteArray + + def toString(withLevels: Boolean = false, withData: Boolean = false ): String = { + sketch.toString(withLevels, withData) + } +} + +object KllDoublesSketchWrapper { + // TODO: determine if wrap or writableWrap is a better option + def deserialize(bytes: Array[Byte]): KllDoublesSketchWrapper = { + val sketch = KllDoublesSketch.heapify(Memory.wrap(bytes)) + KllDoublesSketchWrapper(sketch) + } + + // this can go away in favor of directly calling the KllDoublesSketch.wrap + // from codegen once janino can generate java 8+ code + def wrapAsReadOnlySketch(bytes: Array[Byte]): KllDoublesSketch = { + KllDoublesSketch.wrap(Memory.wrap(bytes)) + } +} diff --git a/src/test/scala/org/apache/spark/sql/KllTest.scala b/src/test/scala/org/apache/spark/sql/KllTest.scala new file mode 100644 index 0000000..49e0a18 --- /dev/null +++ b/src/test/scala/org/apache/spark/sql/KllTest.scala @@ -0,0 +1,210 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You 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 org.apache.spark.sql + +import scala.util.Random +import org.apache.spark.sql.functions._ +import org.apache.spark.sql.functions_ds._ +import org.apache.spark.registrar.DatasketchesFunctionRegistry +import scala.collection.mutable.WrappedArray + +class KllTest extends SparkSessionManager { + import spark.implicits._ + + // helper method to check if two arrays are equal + private def compareArrays(ref: Array[Double], tst: WrappedArray[Double]) { + val tstArr = tst.toArray + if (ref.length != tstArr.length) + throw new AssertionError("Array lengths do not match: " + ref.length + " != " + tstArr.length) + (ref zip tstArr).foreach { case (v1, v2) => if (v1 != v2) throw new AssertionError("Values do not match: " + v1 + " != " + v2) } + } + + test("KLL Doubles Sketch via scala") { + val n = 100 + val data = (for (i <- 1 to n) yield i.toDouble).toDF("value") + + val sketchDf = data.agg(kll_sketch_agg("value").as("sketch")) + val result: Row = sketchDf.select(kll_get_min($"sketch").as("min"), + kll_get_max($"sketch").as("max") + ).head + + val minValue = result.getAs[Double]("min") + val maxValue = result.getAs[Double]("max") + assert(minValue == 1.0) + assert(maxValue == n.toDouble) + + val splitPoints = Array[Double](20.5, 50, 102) + val pmfCdfResult = sketchDf.select( + kll_get_pmf($"sketch", splitPoints).as("pmf_inclusive"), + kll_get_pmf($"sketch", splitPoints, false).as("pmf_exclusive"), + kll_get_cdf($"sketch", splitPoints).as("cdf_inclusive"), + kll_get_cdf($"sketch", splitPoints, false).as("cdf_exclusive") + ).head + + val pmf_incl = Array[Double](0.2, 0.3, 0.5, 0.0) + compareArrays(pmf_incl, pmfCdfResult.getAs[WrappedArray[Double]]("pmf_inclusive")) + + val pmf_excl = Array[Double](0.2, 0.29, 0.51, 0.0) + compareArrays(pmf_excl, pmfCdfResult.getAs[WrappedArray[Double]]("pmf_exclusive")) + + val cdf_incl = Array[Double](0.2, 0.5, 1.0, 1.0) + compareArrays(cdf_incl, pmfCdfResult.getAs[WrappedArray[Double]]("cdf_inclusive")) + + val cdf_excl = Array[Double](0.2, 0.49, 1.0, 1.0) + compareArrays(cdf_excl, pmfCdfResult.getAs[WrappedArray[Double]]("cdf_exclusive")) + } + + test("Kll Doubles Sketch via SQL") { + // register Datasketches functions + DatasketchesFunctionRegistry.registerFunctions(spark) + + val n = 100 + val data = (for (i <- 1 to n) yield i.toDouble).toDF("value") + data.createOrReplaceTempView("data_table") + + val kllDf = spark.sql( + s""" + |SELECT + | kll_get_min(kll_sketch_agg(value, 200)) AS min, + | kll_get_max(kll_sketch_agg(value, 200)) AS max + |FROM + | data_table + """.stripMargin + ) + val minValue = kllDf.head.getAs[Double]("min") + val maxValue = kllDf.head.getAs[Double]("max") + assert(minValue == 1.0) + assert(maxValue == n.toDouble) + + val splitPoints = "array(20.5, 50, 102)" + val pmfCdfResult = spark.sql( + s""" + |SELECT + | kll_get_pmf(t.sketch, ${splitPoints}) AS pmf_inclusive, + | kll_get_pmf(t.sketch, ${splitPoints}, false) AS pmf_exclusive, + | kll_get_cdf(t.sketch, ${splitPoints}) AS cdf_inclusive, + | kll_get_cdf(t.sketch, ${splitPoints}, false) AS cdf_exclusive + |FROM + | (SELECT + | kll_sketch_agg(value, 200) sketch + | FROM + | data_table) t + """.stripMargin + ).head + + val pmf_incl = Array[Double](0.2, 0.3, 0.5, 0.0) + compareArrays(pmf_incl, pmfCdfResult.getAs[WrappedArray[Double]]("pmf_inclusive")) + + val pmf_excl = Array[Double](0.2, 0.29, 0.51, 0.0) + compareArrays(pmf_excl, pmfCdfResult.getAs[WrappedArray[Double]]("pmf_exclusive")) + + val cdf_incl = Array[Double](0.2, 0.5, 1.0, 1.0) + compareArrays(cdf_incl, pmfCdfResult.getAs[WrappedArray[Double]]("cdf_inclusive")) + + val cdf_excl = Array[Double](0.2, 0.49, 1.0, 1.0) + compareArrays(cdf_excl, pmfCdfResult.getAs[WrappedArray[Double]]("cdf_exclusive")) + } + + test("KLL Doubles Merge via Scala") { + val data = generateData().toDF("id", "value") + + // compute global min and max + val minMax: Row = data.agg(min("value").as("min"), max("value").as("max")).collect.head + val globalMin = minMax.getAs[Double]("min") + val globalMax = minMax.getAs[Double]("max") + + // create a sketch for each id value + val idSketchDf = data.groupBy($"id").agg(kll_sketch_agg($"value").as("sketch")) + + // merge into an aggregate sketch + val mergedSketchDf = idSketchDf.agg(kll_merge_agg($"sketch").as("sketch")) + + // check min and max + val result: Row = mergedSketchDf.select(kll_get_min($"sketch").as("min"), + kll_get_max($"sketch").as("max")) + .head + + val sketchMin = result.getAs[Double]("min") + val sketchMax = result.getAs[Double]("max") + + assert(globalMin == sketchMin) + assert(globalMax == sketchMax) + } + + test("KLL Doubles Merge via SQL") { + // register Datasketches functions + DatasketchesFunctionRegistry.registerFunctions(spark) + + val data = generateData().toDF() + data.createOrReplaceTempView("data_table") + + // compute global min and max from dataframe + val minMax: Row = data.agg(min("value").as("min"), max("value").as("max")).head + val globalMin = minMax.getAs[Double]("min") + val globalMax = minMax.getAs[Double]("max") + + // cannot do nested aggregations so need + // need a first pass to generate sketches + val idSketchDf = spark.sql( + s""" + |SELECT + | id, + | kll_sketch_agg(value, 200) AS sketch + |FROM + | data_table + |GROUP BY + | id + """.stripMargin + ) + idSketchDf.createOrReplaceTempView("sketch_table") + + // now merge the sketches + val mergedSketchDf = spark.sql( + s""" + |SELECT + | kll_get_min(sub.sketch) AS min, + | kll_get_max(sub.sketch) AS max + |FROM + | (SELECT + | kll_merge_agg(sketch) AS sketch + | FROM + | sketch_table + | ) sub + """.stripMargin + ) + + // check min and max + val result: Row = mergedSketchDf.head + val sketchMin = result.getAs[Double]("min") + val sketchMax = result.getAs[Double]("max") + + assert(globalMin == sketchMin) + assert(globalMax == sketchMax) + } + + // a simple data generator + private def generateData(numRecords: Int = 25000, numClasses: Int = 20): Seq[DataRow] = { + val maxId = numClasses + for (i <- 0 until numRecords) yield { + val id = i % maxId + DataRow(id, id + Random.nextDouble()) + } + } +} + +case class DataRow(id: Integer, value: Double) diff --git a/src/test/scala/org/apache/spark/sql/SparkSessionManager.scala b/src/test/scala/org/apache/spark/sql/SparkSessionManager.scala new file mode 100644 index 0000000..3675620 --- /dev/null +++ b/src/test/scala/org/apache/spark/sql/SparkSessionManager.scala @@ -0,0 +1,41 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You 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 org.apache.spark.sql + +import org.apache.log4j.{Level, Logger} +import org.scalatest.BeforeAndAfterAll +import org.scalatest.funsuite.AnyFunSuite + +/** + * This class provides a common base for tests. It can perhaps + * be simplified or eliminated but was useful for very early-stage + * testing. + */ +trait SparkSessionManager extends AnyFunSuite with BeforeAndAfterAll { + Logger.getRootLogger().setLevel(Level.OFF) + + lazy val spark: SparkSession = SparkSession + .builder() + .appName("datasketches-spark-tests") + .master("local[3]") + .getOrCreate() + + override def beforeAll(): Unit = { + spark.sparkContext.setLogLevel("OFF") + } +} --------------------------------------------------------------------- To unsubscribe, e-mail: [email protected] For additional commands, e-mail: [email protected]
