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Posted to commits@pinot.apache.org by GitBox <gi...@apache.org> on 2022/12/05 21:15:02 UTC
[GitHub] [pinot] snleee opened a new pull request, #9910: [WIP] Add Variance and Standard Deviation Aggregation Functions
snleee opened a new pull request, #9910:
URL: https://github.com/apache/pinot/pull/9910
This PR adds `VAR_POP()`, `VAR_SAMP()`, `STDDEV_POP()`, `STDDEV_SAMP()` aggregation functions.
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[GitHub] [pinot] snleee commented on a diff in pull request #9910: Add Variance and Standard Deviation Aggregation Functions
Posted by GitBox <gi...@apache.org>.
snleee commented on code in PR #9910:
URL: https://github.com/apache/pinot/pull/9910#discussion_r1041809590
##########
pinot-core/src/main/java/org/apache/pinot/core/common/ObjectSerDeUtils.java:
##########
@@ -123,7 +124,9 @@ public enum ObjectType {
FloatLongPair(29),
DoubleLongPair(30),
StringLongPair(31),
- CovarianceTuple(32);
+ CovarianceTuple(32),
Review Comment:
If I reuse the covariance implementation, then it will become `naive algorithm` on https://en.wikipedia.org/wiki/Algorithms_for_calculating_variance#Na%C3%AFve_algorithm
According to the documentation, directly computing `Var = (SumSq − (Sum × Sum) / n) / (n − 1)` is not numerically stable.
The existing implementation is being used by Hive and Presto and they look to have separate implementation for covariance and variance.
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[GitHub] [pinot] snleee commented on a diff in pull request #9910: Add Variance and Standard Deviation Aggregation Functions
Posted by GitBox <gi...@apache.org>.
snleee commented on code in PR #9910:
URL: https://github.com/apache/pinot/pull/9910#discussion_r1041821415
##########
pinot-core/src/test/java/org/apache/pinot/queries/VarianceQueriesTest.java:
##########
@@ -0,0 +1,446 @@
+/**
+ * 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.pinot.queries;
+
+import java.io.File;
+import java.util.ArrayList;
+import java.util.Arrays;
+import java.util.List;
+import java.util.Random;
+import org.apache.commons.io.FileUtils;
+import org.apache.commons.math3.stat.descriptive.moment.StandardDeviation;
+import org.apache.commons.math3.stat.descriptive.moment.Variance;
+import org.apache.commons.math3.util.Precision;
+import org.apache.pinot.common.response.broker.BrokerResponseNative;
+import org.apache.pinot.core.operator.blocks.results.AggregationResultsBlock;
+import org.apache.pinot.core.operator.blocks.results.GroupByResultsBlock;
+import org.apache.pinot.core.operator.query.AggregationOperator;
+import org.apache.pinot.core.operator.query.GroupByOperator;
+import org.apache.pinot.core.query.aggregation.groupby.AggregationGroupByResult;
+import org.apache.pinot.segment.local.customobject.VarianceTuple;
+import org.apache.pinot.segment.local.indexsegment.immutable.ImmutableSegmentLoader;
+import org.apache.pinot.segment.local.segment.creator.impl.SegmentIndexCreationDriverImpl;
+import org.apache.pinot.segment.local.segment.readers.GenericRowRecordReader;
+import org.apache.pinot.segment.spi.ImmutableSegment;
+import org.apache.pinot.segment.spi.IndexSegment;
+import org.apache.pinot.segment.spi.creator.SegmentGeneratorConfig;
+import org.apache.pinot.spi.config.table.TableConfig;
+import org.apache.pinot.spi.config.table.TableType;
+import org.apache.pinot.spi.data.FieldSpec;
+import org.apache.pinot.spi.data.Schema;
+import org.apache.pinot.spi.data.readers.GenericRow;
+import org.apache.pinot.spi.utils.ReadMode;
+import org.apache.pinot.spi.utils.builder.TableConfigBuilder;
+import org.testng.annotations.BeforeClass;
+import org.testng.annotations.Test;
+
+import static org.testng.Assert.assertEquals;
+import static org.testng.Assert.assertNotNull;
+import static org.testng.Assert.assertTrue;
+
+
+public class VarianceQueriesTest extends BaseQueriesTest {
+
+ private static final File INDEX_DIR = new File(FileUtils.getTempDirectory(), "VarianceQueriesTest");
+
+ private static final String RAW_TABLE_NAME = "testTable";
+ private static final String SEGMENT_NAME = "testSegment";
+
+ private static final int NUM_RECORDS = 2000;
+ private static final int NUM_GROUPS = 10;
+ private static final int MAX_VALUE = 500;
+ private static final double RELATIVE_EPSILON = 0.0001;
+ private static final double DELTA = 0.0001;
+
+ private static final String INT_COLUMN = "intColumn";
+ private static final String LONG_COLUMN = "longColumn";
+ private static final String FLOAT_COLUMN = "floatColumn";
+ private static final String DOUBLE_COLUMN = "doubleColumn";
+ private static final String GROUP_BY_COLUMN = "groupByColumn";
+
+ private static final Schema SCHEMA =
+ new Schema.SchemaBuilder().addSingleValueDimension(INT_COLUMN, FieldSpec.DataType.INT)
+ .addSingleValueDimension(LONG_COLUMN, FieldSpec.DataType.LONG)
+ .addSingleValueDimension(FLOAT_COLUMN, FieldSpec.DataType.FLOAT)
+ .addSingleValueDimension(DOUBLE_COLUMN, FieldSpec.DataType.DOUBLE)
+ .addSingleValueDimension(GROUP_BY_COLUMN, FieldSpec.DataType.DOUBLE).build();
+
+ private static final TableConfig TABLE_CONFIG =
+ new TableConfigBuilder(TableType.OFFLINE).setTableName(RAW_TABLE_NAME).build();
+
+ private IndexSegment _indexSegment;
+ private List<IndexSegment> _indexSegments;
+
+ int[] _intValues = new int[NUM_RECORDS];
+ long[] _longValues = new long[NUM_RECORDS];
+ float[] _floatValues = new float[NUM_RECORDS];
+ double[] _doubleValues = new double[NUM_RECORDS];
+
+ @Override
+ protected String getFilter() {
+ // filter out half of the rows based on group id
+ return " WHERE groupByColumn < " + (NUM_GROUPS / 2);
+ }
+
+ @Override
+ protected IndexSegment getIndexSegment() {
+ return _indexSegment;
+ }
+
+ @Override
+ protected List<IndexSegment> getIndexSegments() {
+ return _indexSegments;
+ }
+
+ @BeforeClass
+ public void setUp()
+ throws Exception {
+ FileUtils.deleteDirectory(INDEX_DIR);
+ Random random = new Random();
+ List<GenericRow> records = new ArrayList<>(NUM_RECORDS);
+
+ for (int i = 0; i < NUM_RECORDS; i++) {
+ GenericRow record = new GenericRow();
+ int intValue = -MAX_VALUE + random.nextInt() * 2 * MAX_VALUE;
+ long longValue = -MAX_VALUE + random.nextLong() * 2 * MAX_VALUE;
+ float floatValue = -MAX_VALUE + random.nextFloat() * 2 * MAX_VALUE;
+ double doubleValue = -MAX_VALUE + random.nextDouble() * 2 * MAX_VALUE;
+
+ _intValues[i] = intValue;
+ _longValues[i] = longValue;
+ _floatValues[i] = floatValue;
+ _doubleValues[i] = doubleValue;
+
+ record.putValue(INT_COLUMN, _intValues[i]);
+ record.putValue(LONG_COLUMN, _longValues[i]);
+ record.putValue(FLOAT_COLUMN, _floatValues[i]);
+ record.putValue(DOUBLE_COLUMN, _doubleValues[i]);
+ record.putValue(GROUP_BY_COLUMN, Math.floor(i / (NUM_RECORDS / NUM_GROUPS)));
+ records.add(record);
+ }
+
+ SegmentGeneratorConfig segmentGeneratorConfig = new SegmentGeneratorConfig(TABLE_CONFIG, SCHEMA);
+ segmentGeneratorConfig.setTableName(RAW_TABLE_NAME);
+ segmentGeneratorConfig.setSegmentName(SEGMENT_NAME);
+ segmentGeneratorConfig.setOutDir(INDEX_DIR.getPath());
+
+ SegmentIndexCreationDriverImpl driver = new SegmentIndexCreationDriverImpl();
+ driver.init(segmentGeneratorConfig, new GenericRowRecordReader(records));
+ driver.build();
+
+ ImmutableSegment immutableSegment = ImmutableSegmentLoader.load(new File(INDEX_DIR, SEGMENT_NAME), ReadMode.mmap);
+ _indexSegment = immutableSegment;
+ _indexSegments = Arrays.asList(immutableSegment, immutableSegment);
+ }
+
+ @Test
+ public void testVarianceAggregationOnly() {
+ // Compute the expected values
+ Variance[] expectedVariances = new Variance[8];
+ for (int i = 0; i < 8; i++) {
+ if (i < 4) {
+ expectedVariances[i] = new Variance(false);
+ } else {
+ expectedVariances[i] = new Variance(true);
+ }
+ }
+ for (int i = 0; i < NUM_RECORDS; i++) {
+ expectedVariances[0].increment(_intValues[i]);
+ expectedVariances[1].increment(_longValues[i]);
+ expectedVariances[2].increment(_floatValues[i]);
+ expectedVariances[3].increment(_doubleValues[i]);
+ expectedVariances[4].increment(_intValues[i]);
+ expectedVariances[5].increment(_longValues[i]);
+ expectedVariances[6].increment(_floatValues[i]);
+ expectedVariances[7].increment(_doubleValues[i]);
+ }
+ double expectedIntSum = Arrays.stream(_intValues).asDoubleStream().sum();
+ double expectedLongSum = Arrays.stream(_longValues).asDoubleStream().sum();
+ double expectedFloatSum = 0.0;
+ for (int i = 0; i < _floatValues.length; i++) {
+ expectedFloatSum += _floatValues[i];
+ }
+ double expectedDoubleSum = Arrays.stream(_doubleValues).sum();
+
+ // Compute the query
+ String query = "SELECT VAR_POP(intColumn), VAR_POP(longColumn), VAR_POP(floatColumn), VAR_POP(doubleColumn),"
+ + "VAR_SAMP(intColumn), VAR_SAMP(longColumn), VAR_SAMP(floatColumn), VAR_SAMP(doubleColumn) FROM testTable";
+ AggregationOperator aggregationOperator = getOperator(query);
+ AggregationResultsBlock resultsBlock = aggregationOperator.nextBlock();
+ QueriesTestUtils.testInnerSegmentExecutionStatistics(aggregationOperator.getExecutionStatistics(), NUM_RECORDS, 0,
+ NUM_RECORDS * 4, NUM_RECORDS);
+ List<Object> aggregationResult = resultsBlock.getResults();
+
+ // Validate the aggregation results
+ checkWithPrecisionForVariance((VarianceTuple) aggregationResult.get(0), NUM_RECORDS, expectedIntSum,
+ expectedVariances[0].getResult(), false);
+ checkWithPrecisionForVariance((VarianceTuple) aggregationResult.get(1), NUM_RECORDS, expectedLongSum,
+ expectedVariances[1].getResult(), false);
+ checkWithPrecisionForVariance((VarianceTuple) aggregationResult.get(2), NUM_RECORDS, expectedFloatSum,
+ expectedVariances[2].getResult(), false);
+ checkWithPrecisionForVariance((VarianceTuple) aggregationResult.get(3), NUM_RECORDS, expectedDoubleSum,
+ expectedVariances[3].getResult(), false);
+ checkWithPrecisionForVariance((VarianceTuple) aggregationResult.get(4), NUM_RECORDS, expectedIntSum,
+ expectedVariances[4].getResult(), true);
+ checkWithPrecisionForVariance((VarianceTuple) aggregationResult.get(5), NUM_RECORDS, expectedLongSum,
+ expectedVariances[5].getResult(), true);
+ checkWithPrecisionForVariance((VarianceTuple) aggregationResult.get(6), NUM_RECORDS, expectedFloatSum,
+ expectedVariances[6].getResult(), true);
+ checkWithPrecisionForVariance((VarianceTuple) aggregationResult.get(7), NUM_RECORDS, expectedDoubleSum,
+ expectedVariances[7].getResult(), true);
+
+ // Update the expected result by 3 more times (broker query will compute 4 identical segments)
+ for (int i = 0; i < NUM_RECORDS * 3; i++) {
+ int pos = i % NUM_RECORDS;
+ expectedVariances[0].increment(_intValues[pos]);
+ expectedVariances[1].increment(_longValues[pos]);
+ expectedVariances[2].increment(_floatValues[pos]);
+ expectedVariances[3].increment(_doubleValues[pos]);
+ expectedVariances[4].increment(_intValues[pos]);
+ expectedVariances[5].increment(_longValues[pos]);
+ expectedVariances[6].increment(_floatValues[pos]);
+ expectedVariances[7].increment(_doubleValues[pos]);
+ }
+
+ // Validate the response
+ BrokerResponseNative brokerResponse = getBrokerResponse(query);
+ brokerResponse.getResultTable();
+ Object[] results = brokerResponse.getResultTable().getRows().get(0);
+ assertTrue(
+ Precision.equalsWithRelativeTolerance((double) results[0], expectedVariances[0].getResult(), RELATIVE_EPSILON));
+ assertTrue(
+ Precision.equalsWithRelativeTolerance((double) results[1], expectedVariances[1].getResult(), RELATIVE_EPSILON));
+ assertTrue(
+ Precision.equalsWithRelativeTolerance((double) results[2], expectedVariances[2].getResult(), RELATIVE_EPSILON));
+ assertTrue(
+ Precision.equalsWithRelativeTolerance((double) results[3], expectedVariances[3].getResult(), RELATIVE_EPSILON));
+ assertTrue(
+ Precision.equalsWithRelativeTolerance((double) results[4], expectedVariances[4].getResult(), RELATIVE_EPSILON));
+ assertTrue(
+ Precision.equalsWithRelativeTolerance((double) results[5], expectedVariances[5].getResult(), RELATIVE_EPSILON));
+ assertTrue(
+ Precision.equalsWithRelativeTolerance((double) results[6], expectedVariances[6].getResult(), RELATIVE_EPSILON));
+ assertTrue(
+ Precision.equalsWithRelativeTolerance((double) results[7], expectedVariances[7].getResult(), RELATIVE_EPSILON));
+
+ // Validate the response for a query with a filter
+ query = "SELECT VAR_POP(intColumn) from testTable" + getFilter();
+ brokerResponse = getBrokerResponse(query);
+ brokerResponse.getResultTable();
+ results = brokerResponse.getResultTable().getRows().get(0);
+ Variance filterExpectedVariance = new Variance(false);
+ for (int i = 0; i < NUM_RECORDS / 2; i++) {
+ filterExpectedVariance.increment(_intValues[i]);
+ }
+ assertTrue(Precision.equalsWithRelativeTolerance((double) results[0], filterExpectedVariance.getResult(),
+ RELATIVE_EPSILON));
+ }
+
+ @Test
+ public void testVarianceAggregationGroupBy() {
+ // Compute expected group results
+ Variance[] expectedGroupByResult = new Variance[NUM_GROUPS];
+ double[] expectedSum = new double[NUM_GROUPS];
+
+ for (int i = 0; i < NUM_GROUPS; i++) {
+ expectedGroupByResult[i] = new Variance(false);
+ }
+ for (int j = 0; j < NUM_RECORDS; j++) {
+ int pos = j / (NUM_RECORDS / NUM_GROUPS);
+ expectedGroupByResult[pos].increment(_intValues[j]);
+ expectedSum[pos] += _intValues[j];
+ }
+
+ String query = "SELECT VAR_POP(intColumn) FROM testTable GROUP BY groupByColumn ORDER BY groupByColumn";
+ GroupByOperator groupByOperator = getOperator(query);
+ GroupByResultsBlock resultsBlock = groupByOperator.nextBlock();
+ QueriesTestUtils.testInnerSegmentExecutionStatistics(groupByOperator.getExecutionStatistics(), NUM_RECORDS, 0,
+ NUM_RECORDS * 2, NUM_RECORDS);
+ AggregationGroupByResult aggregationGroupByResult = resultsBlock.getAggregationGroupByResult();
+ assertNotNull(aggregationGroupByResult);
+ for (int i = 0; i < NUM_GROUPS; i++) {
+
+ VarianceTuple actualVarianceTuple = (VarianceTuple) aggregationGroupByResult.getResultForGroupId(0, i);
+ checkWithPrecisionForVariance(actualVarianceTuple, NUM_RECORDS / NUM_GROUPS, expectedSum[i],
+ expectedGroupByResult[i].getResult(), false);
+ }
+ }
+
+ @Test
+ public void testStandardDeviationAggregationOnly() {
+ // Compute the expected values
+ StandardDeviation[] expectedStdDevs = new StandardDeviation[8];
+ for (int i = 0; i < 8; i++) {
+ if (i < 4) {
+ expectedStdDevs[i] = new StandardDeviation(false);
+ } else {
+ expectedStdDevs[i] = new StandardDeviation(true);
+ }
+ }
+ for (int i = 0; i < NUM_RECORDS; i++) {
+ expectedStdDevs[0].increment(_intValues[i]);
+ expectedStdDevs[1].increment(_longValues[i]);
+ expectedStdDevs[2].increment(_floatValues[i]);
+ expectedStdDevs[3].increment(_doubleValues[i]);
+ expectedStdDevs[4].increment(_intValues[i]);
+ expectedStdDevs[5].increment(_longValues[i]);
+ expectedStdDevs[6].increment(_floatValues[i]);
+ expectedStdDevs[7].increment(_doubleValues[i]);
+ }
+
+ double expectedIntSum = Arrays.stream(_intValues).asDoubleStream().sum();
+ double expectedLongSum = Arrays.stream(_longValues).asDoubleStream().sum();
+ double expectedFloatSum = 0.0;
+ for (int i = 0; i < _floatValues.length; i++) {
+ expectedFloatSum += _floatValues[i];
+ }
+ double expectedDoubleSum = Arrays.stream(_doubleValues).sum();
+
+ // Compute the query
+ String query =
+ "SELECT STDDEV_POP(intColumn), STDDEV_POP(longColumn), STDDEV_POP(floatColumn), STDDEV_POP(doubleColumn),"
+ + "STDDEV_SAMP(intColumn), STDDEV_SAMP(longColumn), STDDEV_SAMP(floatColumn), STDDEV_SAMP(doubleColumn) "
+ + "FROM testTable";
+ AggregationOperator aggregationOperator = getOperator(query);
+ AggregationResultsBlock resultsBlock = aggregationOperator.nextBlock();
+ QueriesTestUtils.testInnerSegmentExecutionStatistics(aggregationOperator.getExecutionStatistics(), NUM_RECORDS, 0,
+ NUM_RECORDS * 4, NUM_RECORDS);
+ List<Object> aggregationResult = resultsBlock.getResults();
+
+ // Validate the aggregation results
+ checkWithPrecisionForStandardDeviation((VarianceTuple) aggregationResult.get(0), NUM_RECORDS, expectedIntSum,
+ expectedStdDevs[0].getResult(), false);
+ checkWithPrecisionForStandardDeviation((VarianceTuple) aggregationResult.get(1), NUM_RECORDS, expectedLongSum,
+ expectedStdDevs[1].getResult(), false);
+ checkWithPrecisionForStandardDeviation((VarianceTuple) aggregationResult.get(2), NUM_RECORDS, expectedFloatSum,
+ expectedStdDevs[2].getResult(), false);
+ checkWithPrecisionForStandardDeviation((VarianceTuple) aggregationResult.get(3), NUM_RECORDS, expectedDoubleSum,
+ expectedStdDevs[3].getResult(), false);
+ checkWithPrecisionForStandardDeviation((VarianceTuple) aggregationResult.get(4), NUM_RECORDS, expectedIntSum,
+ expectedStdDevs[4].getResult(), true);
+ checkWithPrecisionForStandardDeviation((VarianceTuple) aggregationResult.get(5), NUM_RECORDS, expectedLongSum,
+ expectedStdDevs[5].getResult(), true);
+ checkWithPrecisionForStandardDeviation((VarianceTuple) aggregationResult.get(6), NUM_RECORDS, expectedFloatSum,
+ expectedStdDevs[6].getResult(), true);
+ checkWithPrecisionForStandardDeviation((VarianceTuple) aggregationResult.get(7), NUM_RECORDS, expectedDoubleSum,
+ expectedStdDevs[7].getResult(), true);
+
+ // Update the expected result by 3 more times (broker query will compute 4 identical segments)
Review Comment:
@jasperjiaguo Can you elaborate on your suggestion?
I referred covariance test and it also uses `getBrokerResponse()` to get the result. According to the documentation, `getBrokerResponse()` seems to compute the result from 4 identical segments. FYI,`getBrokerResponse()` internally calls `getDistinctInstances()`.
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[GitHub] [pinot] snleee commented on a diff in pull request #9910: Add Variance and Standard Deviation Aggregation Functions
Posted by GitBox <gi...@apache.org>.
snleee commented on code in PR #9910:
URL: https://github.com/apache/pinot/pull/9910#discussion_r1042826090
##########
pinot-core/src/test/java/org/apache/pinot/queries/VarianceQueriesTest.java:
##########
@@ -0,0 +1,446 @@
+/**
+ * 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.pinot.queries;
+
+import java.io.File;
+import java.util.ArrayList;
+import java.util.Arrays;
+import java.util.List;
+import java.util.Random;
+import org.apache.commons.io.FileUtils;
+import org.apache.commons.math3.stat.descriptive.moment.StandardDeviation;
+import org.apache.commons.math3.stat.descriptive.moment.Variance;
+import org.apache.commons.math3.util.Precision;
+import org.apache.pinot.common.response.broker.BrokerResponseNative;
+import org.apache.pinot.core.operator.blocks.results.AggregationResultsBlock;
+import org.apache.pinot.core.operator.blocks.results.GroupByResultsBlock;
+import org.apache.pinot.core.operator.query.AggregationOperator;
+import org.apache.pinot.core.operator.query.GroupByOperator;
+import org.apache.pinot.core.query.aggregation.groupby.AggregationGroupByResult;
+import org.apache.pinot.segment.local.customobject.VarianceTuple;
+import org.apache.pinot.segment.local.indexsegment.immutable.ImmutableSegmentLoader;
+import org.apache.pinot.segment.local.segment.creator.impl.SegmentIndexCreationDriverImpl;
+import org.apache.pinot.segment.local.segment.readers.GenericRowRecordReader;
+import org.apache.pinot.segment.spi.ImmutableSegment;
+import org.apache.pinot.segment.spi.IndexSegment;
+import org.apache.pinot.segment.spi.creator.SegmentGeneratorConfig;
+import org.apache.pinot.spi.config.table.TableConfig;
+import org.apache.pinot.spi.config.table.TableType;
+import org.apache.pinot.spi.data.FieldSpec;
+import org.apache.pinot.spi.data.Schema;
+import org.apache.pinot.spi.data.readers.GenericRow;
+import org.apache.pinot.spi.utils.ReadMode;
+import org.apache.pinot.spi.utils.builder.TableConfigBuilder;
+import org.testng.annotations.BeforeClass;
+import org.testng.annotations.Test;
+
+import static org.testng.Assert.assertEquals;
+import static org.testng.Assert.assertNotNull;
+import static org.testng.Assert.assertTrue;
+
+
+public class VarianceQueriesTest extends BaseQueriesTest {
+
+ private static final File INDEX_DIR = new File(FileUtils.getTempDirectory(), "VarianceQueriesTest");
+
+ private static final String RAW_TABLE_NAME = "testTable";
+ private static final String SEGMENT_NAME = "testSegment";
+
+ private static final int NUM_RECORDS = 2000;
+ private static final int NUM_GROUPS = 10;
+ private static final int MAX_VALUE = 500;
+ private static final double RELATIVE_EPSILON = 0.0001;
+ private static final double DELTA = 0.0001;
+
+ private static final String INT_COLUMN = "intColumn";
+ private static final String LONG_COLUMN = "longColumn";
+ private static final String FLOAT_COLUMN = "floatColumn";
+ private static final String DOUBLE_COLUMN = "doubleColumn";
+ private static final String GROUP_BY_COLUMN = "groupByColumn";
+
+ private static final Schema SCHEMA =
+ new Schema.SchemaBuilder().addSingleValueDimension(INT_COLUMN, FieldSpec.DataType.INT)
+ .addSingleValueDimension(LONG_COLUMN, FieldSpec.DataType.LONG)
+ .addSingleValueDimension(FLOAT_COLUMN, FieldSpec.DataType.FLOAT)
+ .addSingleValueDimension(DOUBLE_COLUMN, FieldSpec.DataType.DOUBLE)
+ .addSingleValueDimension(GROUP_BY_COLUMN, FieldSpec.DataType.DOUBLE).build();
+
+ private static final TableConfig TABLE_CONFIG =
+ new TableConfigBuilder(TableType.OFFLINE).setTableName(RAW_TABLE_NAME).build();
+
+ private IndexSegment _indexSegment;
+ private List<IndexSegment> _indexSegments;
+
+ int[] _intValues = new int[NUM_RECORDS];
+ long[] _longValues = new long[NUM_RECORDS];
+ float[] _floatValues = new float[NUM_RECORDS];
+ double[] _doubleValues = new double[NUM_RECORDS];
+
+ @Override
+ protected String getFilter() {
+ // filter out half of the rows based on group id
+ return " WHERE groupByColumn < " + (NUM_GROUPS / 2);
+ }
+
+ @Override
+ protected IndexSegment getIndexSegment() {
+ return _indexSegment;
+ }
+
+ @Override
+ protected List<IndexSegment> getIndexSegments() {
+ return _indexSegments;
+ }
+
+ @BeforeClass
+ public void setUp()
+ throws Exception {
+ FileUtils.deleteDirectory(INDEX_DIR);
+ Random random = new Random();
+ List<GenericRow> records = new ArrayList<>(NUM_RECORDS);
+
+ for (int i = 0; i < NUM_RECORDS; i++) {
+ GenericRow record = new GenericRow();
+ int intValue = -MAX_VALUE + random.nextInt() * 2 * MAX_VALUE;
+ long longValue = -MAX_VALUE + random.nextLong() * 2 * MAX_VALUE;
+ float floatValue = -MAX_VALUE + random.nextFloat() * 2 * MAX_VALUE;
+ double doubleValue = -MAX_VALUE + random.nextDouble() * 2 * MAX_VALUE;
+
+ _intValues[i] = intValue;
+ _longValues[i] = longValue;
+ _floatValues[i] = floatValue;
+ _doubleValues[i] = doubleValue;
+
+ record.putValue(INT_COLUMN, _intValues[i]);
+ record.putValue(LONG_COLUMN, _longValues[i]);
+ record.putValue(FLOAT_COLUMN, _floatValues[i]);
+ record.putValue(DOUBLE_COLUMN, _doubleValues[i]);
+ record.putValue(GROUP_BY_COLUMN, Math.floor(i / (NUM_RECORDS / NUM_GROUPS)));
+ records.add(record);
+ }
+
+ SegmentGeneratorConfig segmentGeneratorConfig = new SegmentGeneratorConfig(TABLE_CONFIG, SCHEMA);
+ segmentGeneratorConfig.setTableName(RAW_TABLE_NAME);
+ segmentGeneratorConfig.setSegmentName(SEGMENT_NAME);
+ segmentGeneratorConfig.setOutDir(INDEX_DIR.getPath());
+
+ SegmentIndexCreationDriverImpl driver = new SegmentIndexCreationDriverImpl();
+ driver.init(segmentGeneratorConfig, new GenericRowRecordReader(records));
+ driver.build();
+
+ ImmutableSegment immutableSegment = ImmutableSegmentLoader.load(new File(INDEX_DIR, SEGMENT_NAME), ReadMode.mmap);
+ _indexSegment = immutableSegment;
+ _indexSegments = Arrays.asList(immutableSegment, immutableSegment);
+ }
+
+ @Test
+ public void testVarianceAggregationOnly() {
+ // Compute the expected values
+ Variance[] expectedVariances = new Variance[8];
+ for (int i = 0; i < 8; i++) {
+ if (i < 4) {
+ expectedVariances[i] = new Variance(false);
+ } else {
+ expectedVariances[i] = new Variance(true);
+ }
+ }
+ for (int i = 0; i < NUM_RECORDS; i++) {
+ expectedVariances[0].increment(_intValues[i]);
+ expectedVariances[1].increment(_longValues[i]);
+ expectedVariances[2].increment(_floatValues[i]);
+ expectedVariances[3].increment(_doubleValues[i]);
+ expectedVariances[4].increment(_intValues[i]);
+ expectedVariances[5].increment(_longValues[i]);
+ expectedVariances[6].increment(_floatValues[i]);
+ expectedVariances[7].increment(_doubleValues[i]);
+ }
+ double expectedIntSum = Arrays.stream(_intValues).asDoubleStream().sum();
+ double expectedLongSum = Arrays.stream(_longValues).asDoubleStream().sum();
+ double expectedFloatSum = 0.0;
+ for (int i = 0; i < _floatValues.length; i++) {
+ expectedFloatSum += _floatValues[i];
+ }
+ double expectedDoubleSum = Arrays.stream(_doubleValues).sum();
+
+ // Compute the query
+ String query = "SELECT VAR_POP(intColumn), VAR_POP(longColumn), VAR_POP(floatColumn), VAR_POP(doubleColumn),"
+ + "VAR_SAMP(intColumn), VAR_SAMP(longColumn), VAR_SAMP(floatColumn), VAR_SAMP(doubleColumn) FROM testTable";
+ AggregationOperator aggregationOperator = getOperator(query);
+ AggregationResultsBlock resultsBlock = aggregationOperator.nextBlock();
+ QueriesTestUtils.testInnerSegmentExecutionStatistics(aggregationOperator.getExecutionStatistics(), NUM_RECORDS, 0,
+ NUM_RECORDS * 4, NUM_RECORDS);
+ List<Object> aggregationResult = resultsBlock.getResults();
+
+ // Validate the aggregation results
+ checkWithPrecisionForVariance((VarianceTuple) aggregationResult.get(0), NUM_RECORDS, expectedIntSum,
+ expectedVariances[0].getResult(), false);
+ checkWithPrecisionForVariance((VarianceTuple) aggregationResult.get(1), NUM_RECORDS, expectedLongSum,
+ expectedVariances[1].getResult(), false);
+ checkWithPrecisionForVariance((VarianceTuple) aggregationResult.get(2), NUM_RECORDS, expectedFloatSum,
+ expectedVariances[2].getResult(), false);
+ checkWithPrecisionForVariance((VarianceTuple) aggregationResult.get(3), NUM_RECORDS, expectedDoubleSum,
+ expectedVariances[3].getResult(), false);
+ checkWithPrecisionForVariance((VarianceTuple) aggregationResult.get(4), NUM_RECORDS, expectedIntSum,
+ expectedVariances[4].getResult(), true);
+ checkWithPrecisionForVariance((VarianceTuple) aggregationResult.get(5), NUM_RECORDS, expectedLongSum,
+ expectedVariances[5].getResult(), true);
+ checkWithPrecisionForVariance((VarianceTuple) aggregationResult.get(6), NUM_RECORDS, expectedFloatSum,
+ expectedVariances[6].getResult(), true);
+ checkWithPrecisionForVariance((VarianceTuple) aggregationResult.get(7), NUM_RECORDS, expectedDoubleSum,
+ expectedVariances[7].getResult(), true);
+
+ // Update the expected result by 3 more times (broker query will compute 4 identical segments)
+ for (int i = 0; i < NUM_RECORDS * 3; i++) {
+ int pos = i % NUM_RECORDS;
+ expectedVariances[0].increment(_intValues[pos]);
+ expectedVariances[1].increment(_longValues[pos]);
+ expectedVariances[2].increment(_floatValues[pos]);
+ expectedVariances[3].increment(_doubleValues[pos]);
+ expectedVariances[4].increment(_intValues[pos]);
+ expectedVariances[5].increment(_longValues[pos]);
+ expectedVariances[6].increment(_floatValues[pos]);
+ expectedVariances[7].increment(_doubleValues[pos]);
+ }
+
+ // Validate the response
+ BrokerResponseNative brokerResponse = getBrokerResponse(query);
+ brokerResponse.getResultTable();
+ Object[] results = brokerResponse.getResultTable().getRows().get(0);
+ assertTrue(
+ Precision.equalsWithRelativeTolerance((double) results[0], expectedVariances[0].getResult(), RELATIVE_EPSILON));
+ assertTrue(
+ Precision.equalsWithRelativeTolerance((double) results[1], expectedVariances[1].getResult(), RELATIVE_EPSILON));
+ assertTrue(
+ Precision.equalsWithRelativeTolerance((double) results[2], expectedVariances[2].getResult(), RELATIVE_EPSILON));
+ assertTrue(
+ Precision.equalsWithRelativeTolerance((double) results[3], expectedVariances[3].getResult(), RELATIVE_EPSILON));
+ assertTrue(
+ Precision.equalsWithRelativeTolerance((double) results[4], expectedVariances[4].getResult(), RELATIVE_EPSILON));
+ assertTrue(
+ Precision.equalsWithRelativeTolerance((double) results[5], expectedVariances[5].getResult(), RELATIVE_EPSILON));
+ assertTrue(
+ Precision.equalsWithRelativeTolerance((double) results[6], expectedVariances[6].getResult(), RELATIVE_EPSILON));
+ assertTrue(
+ Precision.equalsWithRelativeTolerance((double) results[7], expectedVariances[7].getResult(), RELATIVE_EPSILON));
+
+ // Validate the response for a query with a filter
+ query = "SELECT VAR_POP(intColumn) from testTable" + getFilter();
+ brokerResponse = getBrokerResponse(query);
+ brokerResponse.getResultTable();
+ results = brokerResponse.getResultTable().getRows().get(0);
+ Variance filterExpectedVariance = new Variance(false);
+ for (int i = 0; i < NUM_RECORDS / 2; i++) {
+ filterExpectedVariance.increment(_intValues[i]);
+ }
+ assertTrue(Precision.equalsWithRelativeTolerance((double) results[0], filterExpectedVariance.getResult(),
+ RELATIVE_EPSILON));
+ }
+
+ @Test
+ public void testVarianceAggregationGroupBy() {
+ // Compute expected group results
+ Variance[] expectedGroupByResult = new Variance[NUM_GROUPS];
+ double[] expectedSum = new double[NUM_GROUPS];
+
+ for (int i = 0; i < NUM_GROUPS; i++) {
+ expectedGroupByResult[i] = new Variance(false);
+ }
+ for (int j = 0; j < NUM_RECORDS; j++) {
+ int pos = j / (NUM_RECORDS / NUM_GROUPS);
+ expectedGroupByResult[pos].increment(_intValues[j]);
+ expectedSum[pos] += _intValues[j];
+ }
+
+ String query = "SELECT VAR_POP(intColumn) FROM testTable GROUP BY groupByColumn ORDER BY groupByColumn";
+ GroupByOperator groupByOperator = getOperator(query);
+ GroupByResultsBlock resultsBlock = groupByOperator.nextBlock();
+ QueriesTestUtils.testInnerSegmentExecutionStatistics(groupByOperator.getExecutionStatistics(), NUM_RECORDS, 0,
+ NUM_RECORDS * 2, NUM_RECORDS);
+ AggregationGroupByResult aggregationGroupByResult = resultsBlock.getAggregationGroupByResult();
+ assertNotNull(aggregationGroupByResult);
+ for (int i = 0; i < NUM_GROUPS; i++) {
+
+ VarianceTuple actualVarianceTuple = (VarianceTuple) aggregationGroupByResult.getResultForGroupId(0, i);
+ checkWithPrecisionForVariance(actualVarianceTuple, NUM_RECORDS / NUM_GROUPS, expectedSum[i],
+ expectedGroupByResult[i].getResult(), false);
+ }
+ }
+
+ @Test
+ public void testStandardDeviationAggregationOnly() {
+ // Compute the expected values
+ StandardDeviation[] expectedStdDevs = new StandardDeviation[8];
+ for (int i = 0; i < 8; i++) {
+ if (i < 4) {
+ expectedStdDevs[i] = new StandardDeviation(false);
+ } else {
+ expectedStdDevs[i] = new StandardDeviation(true);
+ }
+ }
+ for (int i = 0; i < NUM_RECORDS; i++) {
+ expectedStdDevs[0].increment(_intValues[i]);
+ expectedStdDevs[1].increment(_longValues[i]);
+ expectedStdDevs[2].increment(_floatValues[i]);
+ expectedStdDevs[3].increment(_doubleValues[i]);
+ expectedStdDevs[4].increment(_intValues[i]);
+ expectedStdDevs[5].increment(_longValues[i]);
+ expectedStdDevs[6].increment(_floatValues[i]);
+ expectedStdDevs[7].increment(_doubleValues[i]);
+ }
+
+ double expectedIntSum = Arrays.stream(_intValues).asDoubleStream().sum();
+ double expectedLongSum = Arrays.stream(_longValues).asDoubleStream().sum();
+ double expectedFloatSum = 0.0;
+ for (int i = 0; i < _floatValues.length; i++) {
+ expectedFloatSum += _floatValues[i];
+ }
+ double expectedDoubleSum = Arrays.stream(_doubleValues).sum();
+
+ // Compute the query
+ String query =
+ "SELECT STDDEV_POP(intColumn), STDDEV_POP(longColumn), STDDEV_POP(floatColumn), STDDEV_POP(doubleColumn),"
+ + "STDDEV_SAMP(intColumn), STDDEV_SAMP(longColumn), STDDEV_SAMP(floatColumn), STDDEV_SAMP(doubleColumn) "
+ + "FROM testTable";
+ AggregationOperator aggregationOperator = getOperator(query);
+ AggregationResultsBlock resultsBlock = aggregationOperator.nextBlock();
+ QueriesTestUtils.testInnerSegmentExecutionStatistics(aggregationOperator.getExecutionStatistics(), NUM_RECORDS, 0,
+ NUM_RECORDS * 4, NUM_RECORDS);
+ List<Object> aggregationResult = resultsBlock.getResults();
+
+ // Validate the aggregation results
+ checkWithPrecisionForStandardDeviation((VarianceTuple) aggregationResult.get(0), NUM_RECORDS, expectedIntSum,
+ expectedStdDevs[0].getResult(), false);
+ checkWithPrecisionForStandardDeviation((VarianceTuple) aggregationResult.get(1), NUM_RECORDS, expectedLongSum,
+ expectedStdDevs[1].getResult(), false);
+ checkWithPrecisionForStandardDeviation((VarianceTuple) aggregationResult.get(2), NUM_RECORDS, expectedFloatSum,
+ expectedStdDevs[2].getResult(), false);
+ checkWithPrecisionForStandardDeviation((VarianceTuple) aggregationResult.get(3), NUM_RECORDS, expectedDoubleSum,
+ expectedStdDevs[3].getResult(), false);
+ checkWithPrecisionForStandardDeviation((VarianceTuple) aggregationResult.get(4), NUM_RECORDS, expectedIntSum,
+ expectedStdDevs[4].getResult(), true);
+ checkWithPrecisionForStandardDeviation((VarianceTuple) aggregationResult.get(5), NUM_RECORDS, expectedLongSum,
+ expectedStdDevs[5].getResult(), true);
+ checkWithPrecisionForStandardDeviation((VarianceTuple) aggregationResult.get(6), NUM_RECORDS, expectedFloatSum,
+ expectedStdDevs[6].getResult(), true);
+ checkWithPrecisionForStandardDeviation((VarianceTuple) aggregationResult.get(7), NUM_RECORDS, expectedDoubleSum,
+ expectedStdDevs[7].getResult(), true);
+
+ // Update the expected result by 3 more times (broker query will compute 4 identical segments)
Review Comment:
@jasperjiaguo Thanks for pointing out. I actually merged `Covariance & Variance tests`. This actually helped me to identify a bug when we merging empty `VarianceTuple` with non empty `VarianceTuple`!
Can you go over the pr once more?
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[GitHub] [pinot] jasperjiaguo commented on a diff in pull request #9910: Add Variance and Standard Deviation Aggregation Functions
Posted by GitBox <gi...@apache.org>.
jasperjiaguo commented on code in PR #9910:
URL: https://github.com/apache/pinot/pull/9910#discussion_r1042533813
##########
pinot-core/src/main/java/org/apache/pinot/core/common/ObjectSerDeUtils.java:
##########
@@ -123,7 +124,9 @@ public enum ObjectType {
FloatLongPair(29),
DoubleLongPair(30),
StringLongPair(31),
- CovarianceTuple(32);
+ CovarianceTuple(32),
Review Comment:
we can indeed change the covar algo to numerical stable version: similar to
https://github.com/trinodb/trino/blob/1866a23e3b0377144c1820de892c0de2762351a8/core/trino-main/src/main/java/io/trino/operator/aggregation/state/CovarianceState.java
##########
pinot-core/src/main/java/org/apache/pinot/core/common/ObjectSerDeUtils.java:
##########
@@ -123,7 +124,9 @@ public enum ObjectType {
FloatLongPair(29),
DoubleLongPair(30),
StringLongPair(31),
- CovarianceTuple(32);
+ CovarianceTuple(32),
Review Comment:
we can indeed change the covar algo to numerical stable version, similar to
https://github.com/trinodb/trino/blob/1866a23e3b0377144c1820de892c0de2762351a8/core/trino-main/src/main/java/io/trino/operator/aggregation/state/CovarianceState.java
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[GitHub] [pinot] snleee commented on a diff in pull request #9910: Add Variance and Standard Deviation Aggregation Functions
Posted by GitBox <gi...@apache.org>.
snleee commented on code in PR #9910:
URL: https://github.com/apache/pinot/pull/9910#discussion_r1041827118
##########
pinot-core/src/main/java/org/apache/pinot/core/query/aggregation/function/VarianceAggregationFunction.java:
##########
@@ -0,0 +1,204 @@
+/**
+ * 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.pinot.core.query.aggregation.function;
+
+import com.google.common.base.Preconditions;
+import java.util.Map;
+import org.apache.pinot.common.request.context.ExpressionContext;
+import org.apache.pinot.common.utils.DataSchema;
+import org.apache.pinot.core.common.BlockValSet;
+import org.apache.pinot.core.query.aggregation.AggregationResultHolder;
+import org.apache.pinot.core.query.aggregation.ObjectAggregationResultHolder;
+import org.apache.pinot.core.query.aggregation.groupby.GroupByResultHolder;
+import org.apache.pinot.core.query.aggregation.groupby.ObjectGroupByResultHolder;
+import org.apache.pinot.segment.local.customobject.VarianceTuple;
+import org.apache.pinot.segment.spi.AggregationFunctionType;
+
+
+/**
+ * Aggregation function which computes Variance and Standard Deviation
+ *
+ * The algorithm to compute variance is based on "Updating Formulae and a Pairwise Algorithm for Computing
+ * Sample Variances" by Chan et al. Please refer to the "Parallel Algorithm" section from the following wiki:
+ * - https://en.wikipedia.org/wiki/Algorithms_for_calculating_variance#Parallel_algorithm
+ */
+public class VarianceAggregationFunction extends BaseSingleInputAggregationFunction<VarianceTuple, Double> {
Review Comment:
I took out a common function that can be shared and created a util class. Please take a look
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[GitHub] [pinot] walterddr commented on a diff in pull request #9910: Add Variance and Standard Deviation Aggregation Functions
Posted by GitBox <gi...@apache.org>.
walterddr commented on code in PR #9910:
URL: https://github.com/apache/pinot/pull/9910#discussion_r1043941051
##########
pinot-core/src/test/java/org/apache/pinot/queries/StatisticalQueriesTest.java:
##########
@@ -0,0 +1,749 @@
+/**
+ * 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.pinot.queries;
+
+import java.io.File;
+import java.io.IOException;
+import java.util.ArrayList;
+import java.util.Arrays;
+import java.util.Collections;
+import java.util.List;
+import java.util.Random;
+import org.apache.commons.io.FileUtils;
+import org.apache.commons.math3.stat.correlation.Covariance;
+import org.apache.commons.math3.stat.descriptive.moment.StandardDeviation;
+import org.apache.commons.math3.stat.descriptive.moment.Variance;
+import org.apache.commons.math3.util.Precision;
+import org.apache.pinot.common.response.broker.BrokerResponseNative;
+import org.apache.pinot.common.response.broker.ResultTable;
+import org.apache.pinot.core.operator.blocks.results.AggregationResultsBlock;
+import org.apache.pinot.core.operator.blocks.results.GroupByResultsBlock;
+import org.apache.pinot.core.operator.query.AggregationOperator;
+import org.apache.pinot.core.operator.query.GroupByOperator;
+import org.apache.pinot.core.query.aggregation.groupby.AggregationGroupByResult;
+import org.apache.pinot.segment.local.customobject.CovarianceTuple;
+import org.apache.pinot.segment.local.customobject.VarianceTuple;
+import org.apache.pinot.segment.local.indexsegment.immutable.ImmutableSegmentLoader;
+import org.apache.pinot.segment.local.segment.creator.impl.SegmentIndexCreationDriverImpl;
+import org.apache.pinot.segment.local.segment.readers.GenericRowRecordReader;
+import org.apache.pinot.segment.spi.ImmutableSegment;
+import org.apache.pinot.segment.spi.IndexSegment;
+import org.apache.pinot.segment.spi.creator.SegmentGeneratorConfig;
+import org.apache.pinot.spi.config.table.TableConfig;
+import org.apache.pinot.spi.config.table.TableType;
+import org.apache.pinot.spi.data.FieldSpec;
+import org.apache.pinot.spi.data.Schema;
+import org.apache.pinot.spi.data.readers.GenericRow;
+import org.apache.pinot.spi.utils.ReadMode;
+import org.apache.pinot.spi.utils.builder.TableConfigBuilder;
+import org.testng.annotations.AfterClass;
+import org.testng.annotations.BeforeClass;
+import org.testng.annotations.Test;
+
+import static org.testng.Assert.assertEquals;
+import static org.testng.Assert.assertNotNull;
+import static org.testng.Assert.assertTrue;
+
+
+/**
+ * Queries test for statistical queries (i.e Variance, Covariance, Standard Deviation etc)
+ */
+public class StatisticalQueriesTest extends BaseQueriesTest {
+ private static final File INDEX_DIR = new File(FileUtils.getTempDirectory(), "CovarianceQueriesTest");
+ private static final String RAW_TABLE_NAME = "testTable";
+ private static final String SEGMENT_NAME = "testSegment";
+
+ // test segments 1-4 evenly divide testSegment into 4 distinct segments
+ private static final String SEGMENT_NAME_1 = "testSegment1";
+ private static final String SEGMENT_NAME_2 = "testSegment2";
+ private static final String SEGMENT_NAME_3 = "testSegment3";
+ private static final String SEGMENT_NAME_4 = "testSegment4";
+
+ private static final int NUM_RECORDS = 2000;
+ private static final int NUM_GROUPS = 10;
+ private static final int MAX_VALUE = 500;
+ private static final double RELATIVE_EPSILON = 0.0001;
+ private static final double DELTA = 0.0001;
+
+ private static final String INT_COLUMN_X = "intColumnX";
+ private static final String INT_COLUMN_Y = "intColumnY";
+ private static final String DOUBLE_COLUMN_X = "doubleColumnX";
+ private static final String DOUBLE_COLUMN_Y = "doubleColumnY";
+ private static final String LONG_COLUMN = "longColumn";
+ private static final String FLOAT_COLUMN = "floatColumn";
+ private static final String GROUP_BY_COLUMN = "groupByColumn";
+
+ private static final Schema SCHEMA =
+ new Schema.SchemaBuilder().addSingleValueDimension(INT_COLUMN_X, FieldSpec.DataType.INT)
+ .addSingleValueDimension(INT_COLUMN_Y, FieldSpec.DataType.INT)
+ .addSingleValueDimension(DOUBLE_COLUMN_X, FieldSpec.DataType.DOUBLE)
+ .addSingleValueDimension(DOUBLE_COLUMN_Y, FieldSpec.DataType.DOUBLE)
+ .addSingleValueDimension(LONG_COLUMN, FieldSpec.DataType.LONG)
+ .addSingleValueDimension(FLOAT_COLUMN, FieldSpec.DataType.FLOAT)
+ .addSingleValueDimension(GROUP_BY_COLUMN, FieldSpec.DataType.DOUBLE).build();
+ private static final TableConfig TABLE_CONFIG =
+ new TableConfigBuilder(TableType.OFFLINE).setTableName(RAW_TABLE_NAME).build();
+
+ private IndexSegment _indexSegment;
+ private List<IndexSegment> _indexSegments;
+ private List<List<IndexSegment>> _distinctInstances;
+ private int _sumIntX = 0;
+ private int _sumIntY = 0;
+ private int _sumIntXY = 0;
+
+ private double _sumDoubleX = 0;
+ private double _sumDoubleY = 0;
+ private double _sumDoubleXY = 0;
+
+ private long _sumLong = 0L;
+ private double _sumFloat = 0;
+
+ private double _sumIntDouble = 0;
+ private long _sumIntLong = 0L;
+ private double _sumIntFloat = 0;
+ private double _sumDoubleLong = 0;
+ private double _sumDoubleFloat = 0;
+ private double _sumLongFloat = 0;
+
+ private double _expectedCovIntXY;
+ private double _expectedCovDoubleXY;
+ private double _expectedCovIntDouble;
+ private double _expectedCovIntLong;
+ private double _expectedCovIntFloat;
+ private double _expectedCovDoubleLong;
+ private double _expectedCovDoubleFloat;
+ private double _expectedCovLongFloat;
+
+ private double _expectedCovWithFilter;
+
+ private final CovarianceTuple[] _expectedGroupByResultVer1 = new CovarianceTuple[NUM_GROUPS];
+ private final CovarianceTuple[] _expectedGroupByResultVer2 = new CovarianceTuple[NUM_GROUPS];
+ private final double[] _expectedFinalResultVer1 = new double[NUM_GROUPS];
+ private final double[] _expectedFinalResultVer2 = new double[NUM_GROUPS];
+
+ private boolean _useIdenticalSegment = false;
+
+ int[] _intColX = new int[NUM_RECORDS];
+ int[] _intColY = new int[NUM_RECORDS];
+ long[] _longCol = new long[NUM_RECORDS];
+ double[] _floatCol = new double[NUM_RECORDS];
+ double[] _doubleColX = new double[NUM_RECORDS];
+ double[] _doubleColY = new double[NUM_RECORDS];
+ double[] _groupByCol = new double[NUM_RECORDS];
+
+ @Override
+ protected String getFilter() {
+ // filter out half of the rows based on group id
+ return " WHERE groupByColumn < " + (NUM_GROUPS / 2);
+ }
+
+ @Override
+ protected IndexSegment getIndexSegment() {
+ return _indexSegment;
+ }
+
+ @Override
+ protected List<IndexSegment> getIndexSegments() {
+ return _indexSegments;
+ }
+
+ @Override
+ protected List<List<IndexSegment>> getDistinctInstances() {
+ if (_useIdenticalSegment) {
+ return Collections.singletonList(_indexSegments);
+ }
+ return _distinctInstances;
+ }
+
+ @BeforeClass
+ public void setUp()
+ throws Exception {
+ FileUtils.deleteDirectory(INDEX_DIR);
+
+ List<GenericRow> records = new ArrayList<>(NUM_RECORDS);
+
+ Random rand = new Random();
+ _intColX = rand.ints(NUM_RECORDS, -MAX_VALUE, MAX_VALUE).toArray();
+ _intColY = rand.ints(NUM_RECORDS, -MAX_VALUE, MAX_VALUE).toArray();
+ _doubleColX = rand.doubles(NUM_RECORDS, -MAX_VALUE, MAX_VALUE).toArray();
+ _doubleColY = rand.doubles(NUM_RECORDS, -MAX_VALUE, MAX_VALUE).toArray();
+ _longCol = rand.longs(NUM_RECORDS, -MAX_VALUE, MAX_VALUE).toArray();
+
+ int groupSize = NUM_RECORDS / NUM_GROUPS;
+ double sumX = 0;
+ double sumY = 0;
+ double sumGroupBy = 0;
+ double sumXY = 0;
+ double sumXGroupBy = 0;
+ int groupByVal = 0;
+
+ for (int i = 0; i < NUM_RECORDS; i++) {
+ GenericRow record = new GenericRow();
+ int intX = _intColX[i];
+ int intY = _intColY[i];
+ double doubleX = _doubleColX[i];
+ double doubleY = _doubleColY[i];
+ long longVal = _longCol[i];
+ float floatVal = -MAX_VALUE + rand.nextFloat() * 2 * MAX_VALUE;
+
+ // set up inner segment group by results
+ groupByVal = (int) Math.floor(i / groupSize);
+ if (i % groupSize == 0 && groupByVal > 0) {
+ _expectedGroupByResultVer1[groupByVal - 1] = new CovarianceTuple(sumX, sumGroupBy, sumXGroupBy, groupSize);
+ _expectedGroupByResultVer2[groupByVal - 1] = new CovarianceTuple(sumX, sumY, sumXY, groupSize);
+ sumX = 0;
+ sumY = 0;
+ sumGroupBy = 0;
+ sumXY = 0;
+ sumXGroupBy = 0;
+ }
+
+ sumX += doubleX;
+ sumY += doubleY;
+ sumGroupBy += groupByVal;
+ sumXY += doubleX * doubleY;
+ sumXGroupBy += doubleX * groupByVal;
+
+ _floatCol[i] = floatVal;
+ _groupByCol[i] = groupByVal;
+
+ // calculate inner segment results
+ _sumIntX += intX;
+ _sumIntY += intY;
+ _sumDoubleX += doubleX;
+ _sumDoubleY += doubleY;
+ _sumLong += longVal;
+ _sumFloat += floatVal;
+ _sumIntXY += intX * intY;
+ _sumDoubleXY += doubleX * doubleY;
+ _sumIntDouble += intX * doubleX;
+ _sumIntLong += intX * longVal;
+ _sumIntFloat += intX * _floatCol[i];
+ _sumDoubleLong += doubleX * longVal;
+ _sumDoubleFloat += doubleX * _floatCol[i];
+ _sumLongFloat += longVal * _floatCol[i];
+
+ record.putValue(INT_COLUMN_X, intX);
+ record.putValue(INT_COLUMN_Y, intY);
+ record.putValue(DOUBLE_COLUMN_X, doubleX);
+ record.putValue(DOUBLE_COLUMN_Y, doubleY);
+ record.putValue(LONG_COLUMN, longVal);
+ record.putValue(FLOAT_COLUMN, floatVal);
+ record.putValue(GROUP_BY_COLUMN, groupByVal);
+ records.add(record);
+ }
+ _expectedGroupByResultVer1[groupByVal] = new CovarianceTuple(sumX, sumGroupBy, sumXGroupBy, groupSize);
+ _expectedGroupByResultVer2[groupByVal] = new CovarianceTuple(sumX, sumY, sumXY, groupSize);
+
+ // calculate inter segment result
+ Covariance cov = new Covariance();
+ double[] newIntColX = Arrays.stream(_intColX).asDoubleStream().toArray();
+ double[] newIntColY = Arrays.stream(_intColY).asDoubleStream().toArray();
+ double[] newLongCol = Arrays.stream(_longCol).asDoubleStream().toArray();
+ _expectedCovIntXY = cov.covariance(newIntColX, newIntColY, false);
+ _expectedCovDoubleXY = cov.covariance(_doubleColX, _doubleColY, false);
+ _expectedCovIntDouble = cov.covariance(newIntColX, _doubleColX, false);
+ _expectedCovIntLong = cov.covariance(newIntColX, newLongCol, false);
+ _expectedCovIntFloat = cov.covariance(newIntColX, _floatCol, false);
+ _expectedCovDoubleLong = cov.covariance(_doubleColX, newLongCol, false);
+ _expectedCovDoubleFloat = cov.covariance(_doubleColX, _floatCol, false);
+ _expectedCovLongFloat = cov.covariance(newLongCol, _floatCol, false);
+
+ double[] filteredX = Arrays.copyOfRange(_doubleColX, 0, NUM_RECORDS / 2);
+ double[] filteredY = Arrays.copyOfRange(_doubleColY, 0, NUM_RECORDS / 2);
+ _expectedCovWithFilter = cov.covariance(filteredX, filteredY, false);
+
+ // calculate inter segment group by results
+ for (int i = 0; i < NUM_GROUPS; i++) {
+ double[] colX = Arrays.copyOfRange(_doubleColX, i * groupSize, (i + 1) * groupSize);
+ double[] colGroupBy = Arrays.copyOfRange(_groupByCol, i * groupSize, (i + 1) * groupSize);
+ double[] colY = Arrays.copyOfRange(_doubleColY, i * groupSize, (i + 1) * groupSize);
+ _expectedFinalResultVer1[i] = cov.covariance(colX, colGroupBy, false);
+ _expectedFinalResultVer2[i] = cov.covariance(colX, colY, false);
+ }
+
+ // generate testSegment
+ ImmutableSegment immutableSegment = setUpSingleSegment(records, SEGMENT_NAME);
+ _indexSegment = immutableSegment;
+ _indexSegments = Arrays.asList(immutableSegment, immutableSegment);
+
+ // divide testSegment into 4 distinct segments for distinct inter segment tests
+ // by doing so, we can avoid calculating global covariance again
+ _distinctInstances = new ArrayList<>();
+ int segmentSize = NUM_RECORDS / 4;
+ ImmutableSegment immutableSegment1 = setUpSingleSegment(records.subList(0, segmentSize), SEGMENT_NAME_1);
+ ImmutableSegment immutableSegment2 =
+ setUpSingleSegment(records.subList(segmentSize, segmentSize * 2), SEGMENT_NAME_2);
+ ImmutableSegment immutableSegment3 =
+ setUpSingleSegment(records.subList(segmentSize * 2, segmentSize * 3), SEGMENT_NAME_3);
+ ImmutableSegment immutableSegment4 =
+ setUpSingleSegment(records.subList(segmentSize * 3, NUM_RECORDS), SEGMENT_NAME_4);
+ // generate 2 instances each with 2 distinct segments
+ _distinctInstances.add(Arrays.asList(immutableSegment1, immutableSegment2));
+ _distinctInstances.add(Arrays.asList(immutableSegment3, immutableSegment4));
+ }
+
+ private ImmutableSegment setUpSingleSegment(List<GenericRow> recordSet, String segmentName)
+ throws Exception {
+ SegmentGeneratorConfig segmentGeneratorConfig = new SegmentGeneratorConfig(TABLE_CONFIG, SCHEMA);
+ segmentGeneratorConfig.setTableName(RAW_TABLE_NAME);
+ segmentGeneratorConfig.setSegmentName(segmentName);
+ segmentGeneratorConfig.setOutDir(INDEX_DIR.getPath());
+
+ SegmentIndexCreationDriverImpl driver = new SegmentIndexCreationDriverImpl();
+ driver.init(segmentGeneratorConfig, new GenericRowRecordReader(recordSet));
+ driver.build();
+
+ return ImmutableSegmentLoader.load(new File(INDEX_DIR, segmentName), ReadMode.mmap);
+ }
+
+ @Test
+ public void testCovarianceAggregationOnly() {
+ // Inner Segment
+ String query =
+ "SELECT COVAR_POP(intColumnX, intColumnY), COVAR_POP(doubleColumnX, doubleColumnY), COVAR_POP(intColumnX, "
+ + "doubleColumnX), " + "COVAR_POP(intColumnX, longColumn), COVAR_POP(intColumnX, floatColumn), "
+ + "COVAR_POP(doubleColumnX, longColumn), COVAR_POP(doubleColumnX, floatColumn), COVAR_POP(longColumn, "
+ + "floatColumn) FROM testTable";
+ AggregationOperator aggregationOperator = getOperator(query);
+ AggregationResultsBlock resultsBlock = aggregationOperator.nextBlock();
+ QueriesTestUtils.testInnerSegmentExecutionStatistics(aggregationOperator.getExecutionStatistics(), NUM_RECORDS, 0,
+ NUM_RECORDS * 6, NUM_RECORDS);
+ List<Object> aggregationResult = resultsBlock.getResults();
+ assertNotNull(aggregationResult);
+ checkWithPrecisionForCovariance((CovarianceTuple) aggregationResult.get(0), _sumIntX, _sumIntY, _sumIntXY,
+ NUM_RECORDS);
+ checkWithPrecisionForCovariance((CovarianceTuple) aggregationResult.get(1), _sumDoubleX, _sumDoubleY, _sumDoubleXY,
+ NUM_RECORDS);
+ checkWithPrecisionForCovariance((CovarianceTuple) aggregationResult.get(2), _sumIntX, _sumDoubleX, _sumIntDouble,
+ NUM_RECORDS);
+ checkWithPrecisionForCovariance((CovarianceTuple) aggregationResult.get(3), _sumIntX, _sumLong, _sumIntLong,
+ NUM_RECORDS);
+ checkWithPrecisionForCovariance((CovarianceTuple) aggregationResult.get(4), _sumIntX, _sumFloat, _sumIntFloat,
+ NUM_RECORDS);
+ checkWithPrecisionForCovariance((CovarianceTuple) aggregationResult.get(5), _sumDoubleX, _sumLong, _sumDoubleLong,
+ NUM_RECORDS);
+ checkWithPrecisionForCovariance((CovarianceTuple) aggregationResult.get(6), _sumDoubleX, _sumFloat, _sumDoubleFloat,
+ NUM_RECORDS);
+ checkWithPrecisionForCovariance((CovarianceTuple) aggregationResult.get(7), _sumLong, _sumFloat, _sumLongFloat,
+ NUM_RECORDS);
+
+ // Inter segments with 4 identical segments (2 instances each having 2 identical segments)
+ _useIdenticalSegment = true;
+ BrokerResponseNative brokerResponse = getBrokerResponse(query);
+ _useIdenticalSegment = false;
+ assertEquals(brokerResponse.getNumDocsScanned(), 4 * NUM_RECORDS);
+ assertEquals(brokerResponse.getNumEntriesScannedInFilter(), 0);
+ assertEquals(brokerResponse.getNumEntriesScannedPostFilter(), 4 * 6 * NUM_RECORDS);
+ assertEquals(brokerResponse.getTotalDocs(), 4 * NUM_RECORDS);
+ checkResultTableWithPrecisionForCovariance(brokerResponse);
+
+ // Inter segments with 4 distinct segments (2 instances each having 2 distinct segments)
+ brokerResponse = getBrokerResponse(query);
+ assertEquals(brokerResponse.getNumDocsScanned(), NUM_RECORDS);
+ assertEquals(brokerResponse.getNumEntriesScannedInFilter(), 0);
+ assertEquals(brokerResponse.getNumEntriesScannedPostFilter(), 6 * NUM_RECORDS);
+ assertEquals(brokerResponse.getTotalDocs(), NUM_RECORDS);
+ checkResultTableWithPrecisionForCovariance(brokerResponse);
+
+ // Inter segments with 4 identical segments with filter
+ _useIdenticalSegment = true;
+ query = "SELECT COVAR_POP(doubleColumnX, doubleColumnY) FROM testTable" + getFilter();
+ brokerResponse = getBrokerResponse(query);
+ _useIdenticalSegment = false;
+ assertEquals(brokerResponse.getNumDocsScanned(), 2 * NUM_RECORDS);
+ assertEquals(brokerResponse.getNumEntriesScannedInFilter(), 0);
+ assertEquals(brokerResponse.getNumEntriesScannedPostFilter(), 4 * NUM_RECORDS);
+ assertEquals(brokerResponse.getTotalDocs(), 4 * NUM_RECORDS);
+ Object[] results = brokerResponse.getResultTable().getRows().get(0);
+ assertTrue(Precision.equalsWithRelativeTolerance((double) results[0], _expectedCovWithFilter, RELATIVE_EPSILON));
+ }
+
+ @Test
+ public void testCovarianceAggregationGroupBy() {
+ // Inner Segment
+ // case 1: (col1, groupByCol) group by groupByCol => all covariances are 0's
+ String query =
+ "SELECT COVAR_POP(doubleColumnX, groupByColumn) FROM testTable GROUP BY groupByColumn ORDER BY groupByColumn";
+ GroupByOperator groupByOperator = getOperator(query);
+ GroupByResultsBlock resultsBlock = groupByOperator.nextBlock();
+ QueriesTestUtils.testInnerSegmentExecutionStatistics(groupByOperator.getExecutionStatistics(), NUM_RECORDS, 0,
+ NUM_RECORDS * 2, NUM_RECORDS);
+ AggregationGroupByResult aggregationGroupByResult = resultsBlock.getAggregationGroupByResult();
+ assertNotNull(aggregationGroupByResult);
+ for (int i = 0; i < NUM_GROUPS; i++) {
+ CovarianceTuple actualCovTuple = (CovarianceTuple) aggregationGroupByResult.getResultForGroupId(0, i);
+ CovarianceTuple expectedCovTuple = _expectedGroupByResultVer1[i];
+ checkWithPrecisionForCovariance(actualCovTuple, expectedCovTuple);
+ }
+
+ // Inter Segment with 4 identical segments
+ _useIdenticalSegment = true;
+ BrokerResponseNative brokerResponse = getBrokerResponse(query);
+ checkGroupByResultsForCovariance(brokerResponse, _expectedFinalResultVer1);
+ _useIdenticalSegment = false;
+ // Inter Segment with 4 distinct segments
+ brokerResponse = getBrokerResponse(query);
+ checkGroupByResultsForCovariance(brokerResponse, _expectedFinalResultVer1);
+
+ // Inner Segment
+ // case 2: COVAR_POP(col1, col2) group by groupByCol => nondeterministic cov
+ query =
+ "SELECT COVAR_POP(doubleColumnX, doubleColumnY) FROM testTable GROUP BY groupByColumn ORDER BY groupByColumn";
+ groupByOperator = getOperator(query);
+ resultsBlock = groupByOperator.nextBlock();
+ QueriesTestUtils.testInnerSegmentExecutionStatistics(groupByOperator.getExecutionStatistics(), NUM_RECORDS, 0,
+ NUM_RECORDS * 3, NUM_RECORDS);
+ aggregationGroupByResult = resultsBlock.getAggregationGroupByResult();
+ assertNotNull(aggregationGroupByResult);
+
+ for (int i = 0; i < NUM_GROUPS; i++) {
+ CovarianceTuple actualCovTuple = (CovarianceTuple) aggregationGroupByResult.getResultForGroupId(0, i);
+ CovarianceTuple expectedCovTuple = _expectedGroupByResultVer2[i];
+ checkWithPrecisionForCovariance(actualCovTuple, expectedCovTuple);
+ }
+
+ // Inter Segment with 4 identical segments
+ _useIdenticalSegment = true;
+ brokerResponse = getBrokerResponse(query);
+ checkGroupByResultsForCovariance(brokerResponse, _expectedFinalResultVer2);
+ _useIdenticalSegment = false;
+ // Inter Segment with 4 distinct segments
+ brokerResponse = getBrokerResponse(query);
+ checkGroupByResultsForCovariance(brokerResponse, _expectedFinalResultVer2);
+ }
+
+ @Test
+ public void testVarianceAggregationOnly() {
+ // Compute the expected values
+ Variance[] expectedVariances = new Variance[8];
+ for (int i = 0; i < 8; i++) {
+ if (i < 4) {
+ expectedVariances[i] = new Variance(false);
+ } else {
+ expectedVariances[i] = new Variance(true);
+ }
+ }
+ for (int i = 0; i < NUM_RECORDS; i++) {
+ expectedVariances[0].increment(_intColX[i]);
+ expectedVariances[1].increment(_longCol[i]);
+ expectedVariances[2].increment(_floatCol[i]);
+ expectedVariances[3].increment(_doubleColX[i]);
+ expectedVariances[4].increment(_intColX[i]);
+ expectedVariances[5].increment(_longCol[i]);
+ expectedVariances[6].increment(_floatCol[i]);
+ expectedVariances[7].increment(_doubleColX[i]);
+ }
+ double expectedIntSum = Arrays.stream(_intColX).asDoubleStream().sum();
+ double expectedLongSum = Arrays.stream(_longCol).asDoubleStream().sum();
+ double expectedFloatSum = 0.0;
+ for (int i = 0; i < _floatCol.length; i++) {
+ expectedFloatSum += _floatCol[i];
+ }
+ double expectedDoubleSum = Arrays.stream(_doubleColX).sum();
+
+ // Compute the query
+ String query = "SELECT VAR_POP(intColumnX), VAR_POP(longColumn), VAR_POP(floatColumn), VAR_POP(doubleColumnX),"
+ + "VAR_SAMP(intColumnX), VAR_SAMP(longColumn), VAR_SAMP(floatColumn), VAR_SAMP(doubleColumnX) FROM testTable";
+ AggregationOperator aggregationOperator = getOperator(query);
+ AggregationResultsBlock resultsBlock = aggregationOperator.nextBlock();
+ QueriesTestUtils.testInnerSegmentExecutionStatistics(aggregationOperator.getExecutionStatistics(), NUM_RECORDS, 0,
+ NUM_RECORDS * 4, NUM_RECORDS);
+ List<Object> aggregationResult = resultsBlock.getResults();
+
+ // Validate the aggregation results
+ checkWithPrecisionForVariance((VarianceTuple) aggregationResult.get(0), NUM_RECORDS, expectedIntSum,
+ expectedVariances[0].getResult(), false);
+ checkWithPrecisionForVariance((VarianceTuple) aggregationResult.get(1), NUM_RECORDS, expectedLongSum,
+ expectedVariances[1].getResult(), false);
+ checkWithPrecisionForVariance((VarianceTuple) aggregationResult.get(2), NUM_RECORDS, expectedFloatSum,
+ expectedVariances[2].getResult(), false);
+ checkWithPrecisionForVariance((VarianceTuple) aggregationResult.get(3), NUM_RECORDS, expectedDoubleSum,
+ expectedVariances[3].getResult(), false);
+ checkWithPrecisionForVariance((VarianceTuple) aggregationResult.get(4), NUM_RECORDS, expectedIntSum,
+ expectedVariances[4].getResult(), true);
+ checkWithPrecisionForVariance((VarianceTuple) aggregationResult.get(5), NUM_RECORDS, expectedLongSum,
+ expectedVariances[5].getResult(), true);
+ checkWithPrecisionForVariance((VarianceTuple) aggregationResult.get(6), NUM_RECORDS, expectedFloatSum,
+ expectedVariances[6].getResult(), true);
+ checkWithPrecisionForVariance((VarianceTuple) aggregationResult.get(7), NUM_RECORDS, expectedDoubleSum,
+ expectedVariances[7].getResult(), true);
+
+ // Validate the response
+ BrokerResponseNative brokerResponse = getBrokerResponse(query);
+ Object[] results = brokerResponse.getResultTable().getRows().get(0);
+ assertTrue(
+ Precision.equalsWithRelativeTolerance((double) results[0], expectedVariances[0].getResult(), RELATIVE_EPSILON));
+ assertTrue(
+ Precision.equalsWithRelativeTolerance((double) results[1], expectedVariances[1].getResult(), RELATIVE_EPSILON));
+ assertTrue(
+ Precision.equalsWithRelativeTolerance((double) results[2], expectedVariances[2].getResult(), RELATIVE_EPSILON));
+ assertTrue(
+ Precision.equalsWithRelativeTolerance((double) results[3], expectedVariances[3].getResult(), RELATIVE_EPSILON));
+ assertTrue(
+ Precision.equalsWithRelativeTolerance((double) results[4], expectedVariances[4].getResult(), RELATIVE_EPSILON));
+ assertTrue(
+ Precision.equalsWithRelativeTolerance((double) results[5], expectedVariances[5].getResult(), RELATIVE_EPSILON));
+ assertTrue(
+ Precision.equalsWithRelativeTolerance((double) results[6], expectedVariances[6].getResult(), RELATIVE_EPSILON));
+ assertTrue(
+ Precision.equalsWithRelativeTolerance((double) results[7], expectedVariances[7].getResult(), RELATIVE_EPSILON));
+
+ VarianceTuple test = ((VarianceTuple) aggregationResult.get(0));
+ test.apply((new VarianceTuple(0, 0, 0.0d)));
+ System.out.println(test.getM2());
+ // Validate the response for a query with a filter
+ query = "SELECT VAR_POP(intColumnX) from testTable" + getFilter();
+ brokerResponse = getBrokerResponse(query);
+ brokerResponse.getResultTable();
+ results = brokerResponse.getResultTable().getRows().get(0);
+ Variance filterExpectedVariance = new Variance(false);
+ for (int i = 0; i < NUM_RECORDS / 2; i++) {
+ filterExpectedVariance.increment(_intColX[i]);
+ }
+ assertTrue(Precision.equalsWithRelativeTolerance((double) results[0], filterExpectedVariance.getResult(),
+ RELATIVE_EPSILON));
+ }
+
+ @Test
+ public void testVarianceAggregationGroupBy() {
+ // Compute expected group results
+ Variance[] expectedGroupByResult = new Variance[NUM_GROUPS];
+ double[] expectedSum = new double[NUM_GROUPS];
+
+ for (int i = 0; i < NUM_GROUPS; i++) {
+ expectedGroupByResult[i] = new Variance(false);
+ }
+ for (int j = 0; j < NUM_RECORDS; j++) {
+ int pos = j / (NUM_RECORDS / NUM_GROUPS);
+ expectedGroupByResult[pos].increment(_intColX[j]);
+ expectedSum[pos] += _intColX[j];
+ }
+
+ String query = "SELECT VAR_POP(intColumnX) FROM testTable GROUP BY groupByColumn ORDER BY groupByColumn";
+ GroupByOperator groupByOperator = getOperator(query);
+ GroupByResultsBlock resultsBlock = groupByOperator.nextBlock();
+ QueriesTestUtils.testInnerSegmentExecutionStatistics(groupByOperator.getExecutionStatistics(), NUM_RECORDS, 0,
+ NUM_RECORDS * 2, NUM_RECORDS);
+ AggregationGroupByResult aggregationGroupByResult = resultsBlock.getAggregationGroupByResult();
+ assertNotNull(aggregationGroupByResult);
+ for (int i = 0; i < NUM_GROUPS; i++) {
+
+ VarianceTuple actualVarianceTuple = (VarianceTuple) aggregationGroupByResult.getResultForGroupId(0, i);
+ checkWithPrecisionForVariance(actualVarianceTuple, NUM_RECORDS / NUM_GROUPS, expectedSum[i],
+ expectedGroupByResult[i].getResult(), false);
+ }
+ }
+
+ @Test
+ public void testStandardDeviationAggregationOnly() {
Review Comment:
saw some instability on https://github.com/apache/pinot/actions/runs/3652424011/jobs/6170799903.
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[GitHub] [pinot] codecov-commenter commented on pull request #9910: Add Variance and Standard Deviation Aggregation Functions
Posted by GitBox <gi...@apache.org>.
codecov-commenter commented on PR #9910:
URL: https://github.com/apache/pinot/pull/9910#issuecomment-1338981611
# [Codecov](https://codecov.io/gh/apache/pinot/pull/9910?src=pr&el=h1&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=The+Apache+Software+Foundation) Report
> Merging [#9910](https://codecov.io/gh/apache/pinot/pull/9910?src=pr&el=desc&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=The+Apache+Software+Foundation) (de8dcc4) into [master](https://codecov.io/gh/apache/pinot/commit/ecf41be2ecd007853c2db19e1c6a038cf356cb9e?el=desc&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=The+Apache+Software+Foundation) (ecf41be) will **decrease** coverage by `48.50%`.
> The diff coverage is `0.00%`.
```diff
@@ Coverage Diff @@
## master #9910 +/- ##
=============================================
- Coverage 64.30% 15.79% -48.51%
+ Complexity 5034 175 -4859
=============================================
Files 1928 1930 +2
Lines 103874 104001 +127
Branches 15823 15846 +23
=============================================
- Hits 66793 16426 -50367
- Misses 32254 86373 +54119
+ Partials 4827 1202 -3625
```
| Flag | Coverage Δ | |
|---|---|---|
| unittests1 | `?` | |
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[GitHub] [pinot] jasperjiaguo commented on a diff in pull request #9910: Add Variance and Standard Deviation Aggregation Functions
Posted by GitBox <gi...@apache.org>.
jasperjiaguo commented on code in PR #9910:
URL: https://github.com/apache/pinot/pull/9910#discussion_r1042545235
##########
pinot-core/src/test/java/org/apache/pinot/queries/VarianceQueriesTest.java:
##########
@@ -0,0 +1,446 @@
+/**
+ * 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.pinot.queries;
+
+import java.io.File;
+import java.util.ArrayList;
+import java.util.Arrays;
+import java.util.List;
+import java.util.Random;
+import org.apache.commons.io.FileUtils;
+import org.apache.commons.math3.stat.descriptive.moment.StandardDeviation;
+import org.apache.commons.math3.stat.descriptive.moment.Variance;
+import org.apache.commons.math3.util.Precision;
+import org.apache.pinot.common.response.broker.BrokerResponseNative;
+import org.apache.pinot.core.operator.blocks.results.AggregationResultsBlock;
+import org.apache.pinot.core.operator.blocks.results.GroupByResultsBlock;
+import org.apache.pinot.core.operator.query.AggregationOperator;
+import org.apache.pinot.core.operator.query.GroupByOperator;
+import org.apache.pinot.core.query.aggregation.groupby.AggregationGroupByResult;
+import org.apache.pinot.segment.local.customobject.VarianceTuple;
+import org.apache.pinot.segment.local.indexsegment.immutable.ImmutableSegmentLoader;
+import org.apache.pinot.segment.local.segment.creator.impl.SegmentIndexCreationDriverImpl;
+import org.apache.pinot.segment.local.segment.readers.GenericRowRecordReader;
+import org.apache.pinot.segment.spi.ImmutableSegment;
+import org.apache.pinot.segment.spi.IndexSegment;
+import org.apache.pinot.segment.spi.creator.SegmentGeneratorConfig;
+import org.apache.pinot.spi.config.table.TableConfig;
+import org.apache.pinot.spi.config.table.TableType;
+import org.apache.pinot.spi.data.FieldSpec;
+import org.apache.pinot.spi.data.Schema;
+import org.apache.pinot.spi.data.readers.GenericRow;
+import org.apache.pinot.spi.utils.ReadMode;
+import org.apache.pinot.spi.utils.builder.TableConfigBuilder;
+import org.testng.annotations.BeforeClass;
+import org.testng.annotations.Test;
+
+import static org.testng.Assert.assertEquals;
+import static org.testng.Assert.assertNotNull;
+import static org.testng.Assert.assertTrue;
+
+
+public class VarianceQueriesTest extends BaseQueriesTest {
+
+ private static final File INDEX_DIR = new File(FileUtils.getTempDirectory(), "VarianceQueriesTest");
+
+ private static final String RAW_TABLE_NAME = "testTable";
+ private static final String SEGMENT_NAME = "testSegment";
+
+ private static final int NUM_RECORDS = 2000;
+ private static final int NUM_GROUPS = 10;
+ private static final int MAX_VALUE = 500;
+ private static final double RELATIVE_EPSILON = 0.0001;
+ private static final double DELTA = 0.0001;
+
+ private static final String INT_COLUMN = "intColumn";
+ private static final String LONG_COLUMN = "longColumn";
+ private static final String FLOAT_COLUMN = "floatColumn";
+ private static final String DOUBLE_COLUMN = "doubleColumn";
+ private static final String GROUP_BY_COLUMN = "groupByColumn";
+
+ private static final Schema SCHEMA =
+ new Schema.SchemaBuilder().addSingleValueDimension(INT_COLUMN, FieldSpec.DataType.INT)
+ .addSingleValueDimension(LONG_COLUMN, FieldSpec.DataType.LONG)
+ .addSingleValueDimension(FLOAT_COLUMN, FieldSpec.DataType.FLOAT)
+ .addSingleValueDimension(DOUBLE_COLUMN, FieldSpec.DataType.DOUBLE)
+ .addSingleValueDimension(GROUP_BY_COLUMN, FieldSpec.DataType.DOUBLE).build();
+
+ private static final TableConfig TABLE_CONFIG =
+ new TableConfigBuilder(TableType.OFFLINE).setTableName(RAW_TABLE_NAME).build();
+
+ private IndexSegment _indexSegment;
+ private List<IndexSegment> _indexSegments;
+
+ int[] _intValues = new int[NUM_RECORDS];
+ long[] _longValues = new long[NUM_RECORDS];
+ float[] _floatValues = new float[NUM_RECORDS];
+ double[] _doubleValues = new double[NUM_RECORDS];
+
+ @Override
+ protected String getFilter() {
+ // filter out half of the rows based on group id
+ return " WHERE groupByColumn < " + (NUM_GROUPS / 2);
+ }
+
+ @Override
+ protected IndexSegment getIndexSegment() {
+ return _indexSegment;
+ }
+
+ @Override
+ protected List<IndexSegment> getIndexSegments() {
+ return _indexSegments;
+ }
+
+ @BeforeClass
+ public void setUp()
+ throws Exception {
+ FileUtils.deleteDirectory(INDEX_DIR);
+ Random random = new Random();
+ List<GenericRow> records = new ArrayList<>(NUM_RECORDS);
+
+ for (int i = 0; i < NUM_RECORDS; i++) {
+ GenericRow record = new GenericRow();
+ int intValue = -MAX_VALUE + random.nextInt() * 2 * MAX_VALUE;
+ long longValue = -MAX_VALUE + random.nextLong() * 2 * MAX_VALUE;
+ float floatValue = -MAX_VALUE + random.nextFloat() * 2 * MAX_VALUE;
+ double doubleValue = -MAX_VALUE + random.nextDouble() * 2 * MAX_VALUE;
+
+ _intValues[i] = intValue;
+ _longValues[i] = longValue;
+ _floatValues[i] = floatValue;
+ _doubleValues[i] = doubleValue;
+
+ record.putValue(INT_COLUMN, _intValues[i]);
+ record.putValue(LONG_COLUMN, _longValues[i]);
+ record.putValue(FLOAT_COLUMN, _floatValues[i]);
+ record.putValue(DOUBLE_COLUMN, _doubleValues[i]);
+ record.putValue(GROUP_BY_COLUMN, Math.floor(i / (NUM_RECORDS / NUM_GROUPS)));
+ records.add(record);
+ }
+
+ SegmentGeneratorConfig segmentGeneratorConfig = new SegmentGeneratorConfig(TABLE_CONFIG, SCHEMA);
+ segmentGeneratorConfig.setTableName(RAW_TABLE_NAME);
+ segmentGeneratorConfig.setSegmentName(SEGMENT_NAME);
+ segmentGeneratorConfig.setOutDir(INDEX_DIR.getPath());
+
+ SegmentIndexCreationDriverImpl driver = new SegmentIndexCreationDriverImpl();
+ driver.init(segmentGeneratorConfig, new GenericRowRecordReader(records));
+ driver.build();
+
+ ImmutableSegment immutableSegment = ImmutableSegmentLoader.load(new File(INDEX_DIR, SEGMENT_NAME), ReadMode.mmap);
+ _indexSegment = immutableSegment;
+ _indexSegments = Arrays.asList(immutableSegment, immutableSegment);
+ }
+
+ @Test
+ public void testVarianceAggregationOnly() {
+ // Compute the expected values
+ Variance[] expectedVariances = new Variance[8];
+ for (int i = 0; i < 8; i++) {
+ if (i < 4) {
+ expectedVariances[i] = new Variance(false);
+ } else {
+ expectedVariances[i] = new Variance(true);
+ }
+ }
+ for (int i = 0; i < NUM_RECORDS; i++) {
+ expectedVariances[0].increment(_intValues[i]);
+ expectedVariances[1].increment(_longValues[i]);
+ expectedVariances[2].increment(_floatValues[i]);
+ expectedVariances[3].increment(_doubleValues[i]);
+ expectedVariances[4].increment(_intValues[i]);
+ expectedVariances[5].increment(_longValues[i]);
+ expectedVariances[6].increment(_floatValues[i]);
+ expectedVariances[7].increment(_doubleValues[i]);
+ }
+ double expectedIntSum = Arrays.stream(_intValues).asDoubleStream().sum();
+ double expectedLongSum = Arrays.stream(_longValues).asDoubleStream().sum();
+ double expectedFloatSum = 0.0;
+ for (int i = 0; i < _floatValues.length; i++) {
+ expectedFloatSum += _floatValues[i];
+ }
+ double expectedDoubleSum = Arrays.stream(_doubleValues).sum();
+
+ // Compute the query
+ String query = "SELECT VAR_POP(intColumn), VAR_POP(longColumn), VAR_POP(floatColumn), VAR_POP(doubleColumn),"
+ + "VAR_SAMP(intColumn), VAR_SAMP(longColumn), VAR_SAMP(floatColumn), VAR_SAMP(doubleColumn) FROM testTable";
+ AggregationOperator aggregationOperator = getOperator(query);
+ AggregationResultsBlock resultsBlock = aggregationOperator.nextBlock();
+ QueriesTestUtils.testInnerSegmentExecutionStatistics(aggregationOperator.getExecutionStatistics(), NUM_RECORDS, 0,
+ NUM_RECORDS * 4, NUM_RECORDS);
+ List<Object> aggregationResult = resultsBlock.getResults();
+
+ // Validate the aggregation results
+ checkWithPrecisionForVariance((VarianceTuple) aggregationResult.get(0), NUM_RECORDS, expectedIntSum,
+ expectedVariances[0].getResult(), false);
+ checkWithPrecisionForVariance((VarianceTuple) aggregationResult.get(1), NUM_RECORDS, expectedLongSum,
+ expectedVariances[1].getResult(), false);
+ checkWithPrecisionForVariance((VarianceTuple) aggregationResult.get(2), NUM_RECORDS, expectedFloatSum,
+ expectedVariances[2].getResult(), false);
+ checkWithPrecisionForVariance((VarianceTuple) aggregationResult.get(3), NUM_RECORDS, expectedDoubleSum,
+ expectedVariances[3].getResult(), false);
+ checkWithPrecisionForVariance((VarianceTuple) aggregationResult.get(4), NUM_RECORDS, expectedIntSum,
+ expectedVariances[4].getResult(), true);
+ checkWithPrecisionForVariance((VarianceTuple) aggregationResult.get(5), NUM_RECORDS, expectedLongSum,
+ expectedVariances[5].getResult(), true);
+ checkWithPrecisionForVariance((VarianceTuple) aggregationResult.get(6), NUM_RECORDS, expectedFloatSum,
+ expectedVariances[6].getResult(), true);
+ checkWithPrecisionForVariance((VarianceTuple) aggregationResult.get(7), NUM_RECORDS, expectedDoubleSum,
+ expectedVariances[7].getResult(), true);
+
+ // Update the expected result by 3 more times (broker query will compute 4 identical segments)
+ for (int i = 0; i < NUM_RECORDS * 3; i++) {
+ int pos = i % NUM_RECORDS;
+ expectedVariances[0].increment(_intValues[pos]);
+ expectedVariances[1].increment(_longValues[pos]);
+ expectedVariances[2].increment(_floatValues[pos]);
+ expectedVariances[3].increment(_doubleValues[pos]);
+ expectedVariances[4].increment(_intValues[pos]);
+ expectedVariances[5].increment(_longValues[pos]);
+ expectedVariances[6].increment(_floatValues[pos]);
+ expectedVariances[7].increment(_doubleValues[pos]);
+ }
+
+ // Validate the response
+ BrokerResponseNative brokerResponse = getBrokerResponse(query);
+ brokerResponse.getResultTable();
+ Object[] results = brokerResponse.getResultTable().getRows().get(0);
+ assertTrue(
+ Precision.equalsWithRelativeTolerance((double) results[0], expectedVariances[0].getResult(), RELATIVE_EPSILON));
+ assertTrue(
+ Precision.equalsWithRelativeTolerance((double) results[1], expectedVariances[1].getResult(), RELATIVE_EPSILON));
+ assertTrue(
+ Precision.equalsWithRelativeTolerance((double) results[2], expectedVariances[2].getResult(), RELATIVE_EPSILON));
+ assertTrue(
+ Precision.equalsWithRelativeTolerance((double) results[3], expectedVariances[3].getResult(), RELATIVE_EPSILON));
+ assertTrue(
+ Precision.equalsWithRelativeTolerance((double) results[4], expectedVariances[4].getResult(), RELATIVE_EPSILON));
+ assertTrue(
+ Precision.equalsWithRelativeTolerance((double) results[5], expectedVariances[5].getResult(), RELATIVE_EPSILON));
+ assertTrue(
+ Precision.equalsWithRelativeTolerance((double) results[6], expectedVariances[6].getResult(), RELATIVE_EPSILON));
+ assertTrue(
+ Precision.equalsWithRelativeTolerance((double) results[7], expectedVariances[7].getResult(), RELATIVE_EPSILON));
+
+ // Validate the response for a query with a filter
+ query = "SELECT VAR_POP(intColumn) from testTable" + getFilter();
+ brokerResponse = getBrokerResponse(query);
+ brokerResponse.getResultTable();
+ results = brokerResponse.getResultTable().getRows().get(0);
+ Variance filterExpectedVariance = new Variance(false);
+ for (int i = 0; i < NUM_RECORDS / 2; i++) {
+ filterExpectedVariance.increment(_intValues[i]);
+ }
+ assertTrue(Precision.equalsWithRelativeTolerance((double) results[0], filterExpectedVariance.getResult(),
+ RELATIVE_EPSILON));
+ }
+
+ @Test
+ public void testVarianceAggregationGroupBy() {
+ // Compute expected group results
+ Variance[] expectedGroupByResult = new Variance[NUM_GROUPS];
+ double[] expectedSum = new double[NUM_GROUPS];
+
+ for (int i = 0; i < NUM_GROUPS; i++) {
+ expectedGroupByResult[i] = new Variance(false);
+ }
+ for (int j = 0; j < NUM_RECORDS; j++) {
+ int pos = j / (NUM_RECORDS / NUM_GROUPS);
+ expectedGroupByResult[pos].increment(_intValues[j]);
+ expectedSum[pos] += _intValues[j];
+ }
+
+ String query = "SELECT VAR_POP(intColumn) FROM testTable GROUP BY groupByColumn ORDER BY groupByColumn";
+ GroupByOperator groupByOperator = getOperator(query);
+ GroupByResultsBlock resultsBlock = groupByOperator.nextBlock();
+ QueriesTestUtils.testInnerSegmentExecutionStatistics(groupByOperator.getExecutionStatistics(), NUM_RECORDS, 0,
+ NUM_RECORDS * 2, NUM_RECORDS);
+ AggregationGroupByResult aggregationGroupByResult = resultsBlock.getAggregationGroupByResult();
+ assertNotNull(aggregationGroupByResult);
+ for (int i = 0; i < NUM_GROUPS; i++) {
+
+ VarianceTuple actualVarianceTuple = (VarianceTuple) aggregationGroupByResult.getResultForGroupId(0, i);
+ checkWithPrecisionForVariance(actualVarianceTuple, NUM_RECORDS / NUM_GROUPS, expectedSum[i],
+ expectedGroupByResult[i].getResult(), false);
+ }
+ }
+
+ @Test
+ public void testStandardDeviationAggregationOnly() {
+ // Compute the expected values
+ StandardDeviation[] expectedStdDevs = new StandardDeviation[8];
+ for (int i = 0; i < 8; i++) {
+ if (i < 4) {
+ expectedStdDevs[i] = new StandardDeviation(false);
+ } else {
+ expectedStdDevs[i] = new StandardDeviation(true);
+ }
+ }
+ for (int i = 0; i < NUM_RECORDS; i++) {
+ expectedStdDevs[0].increment(_intValues[i]);
+ expectedStdDevs[1].increment(_longValues[i]);
+ expectedStdDevs[2].increment(_floatValues[i]);
+ expectedStdDevs[3].increment(_doubleValues[i]);
+ expectedStdDevs[4].increment(_intValues[i]);
+ expectedStdDevs[5].increment(_longValues[i]);
+ expectedStdDevs[6].increment(_floatValues[i]);
+ expectedStdDevs[7].increment(_doubleValues[i]);
+ }
+
+ double expectedIntSum = Arrays.stream(_intValues).asDoubleStream().sum();
+ double expectedLongSum = Arrays.stream(_longValues).asDoubleStream().sum();
+ double expectedFloatSum = 0.0;
+ for (int i = 0; i < _floatValues.length; i++) {
+ expectedFloatSum += _floatValues[i];
+ }
+ double expectedDoubleSum = Arrays.stream(_doubleValues).sum();
+
+ // Compute the query
+ String query =
+ "SELECT STDDEV_POP(intColumn), STDDEV_POP(longColumn), STDDEV_POP(floatColumn), STDDEV_POP(doubleColumn),"
+ + "STDDEV_SAMP(intColumn), STDDEV_SAMP(longColumn), STDDEV_SAMP(floatColumn), STDDEV_SAMP(doubleColumn) "
+ + "FROM testTable";
+ AggregationOperator aggregationOperator = getOperator(query);
+ AggregationResultsBlock resultsBlock = aggregationOperator.nextBlock();
+ QueriesTestUtils.testInnerSegmentExecutionStatistics(aggregationOperator.getExecutionStatistics(), NUM_RECORDS, 0,
+ NUM_RECORDS * 4, NUM_RECORDS);
+ List<Object> aggregationResult = resultsBlock.getResults();
+
+ // Validate the aggregation results
+ checkWithPrecisionForStandardDeviation((VarianceTuple) aggregationResult.get(0), NUM_RECORDS, expectedIntSum,
+ expectedStdDevs[0].getResult(), false);
+ checkWithPrecisionForStandardDeviation((VarianceTuple) aggregationResult.get(1), NUM_RECORDS, expectedLongSum,
+ expectedStdDevs[1].getResult(), false);
+ checkWithPrecisionForStandardDeviation((VarianceTuple) aggregationResult.get(2), NUM_RECORDS, expectedFloatSum,
+ expectedStdDevs[2].getResult(), false);
+ checkWithPrecisionForStandardDeviation((VarianceTuple) aggregationResult.get(3), NUM_RECORDS, expectedDoubleSum,
+ expectedStdDevs[3].getResult(), false);
+ checkWithPrecisionForStandardDeviation((VarianceTuple) aggregationResult.get(4), NUM_RECORDS, expectedIntSum,
+ expectedStdDevs[4].getResult(), true);
+ checkWithPrecisionForStandardDeviation((VarianceTuple) aggregationResult.get(5), NUM_RECORDS, expectedLongSum,
+ expectedStdDevs[5].getResult(), true);
+ checkWithPrecisionForStandardDeviation((VarianceTuple) aggregationResult.get(6), NUM_RECORDS, expectedFloatSum,
+ expectedStdDevs[6].getResult(), true);
+ checkWithPrecisionForStandardDeviation((VarianceTuple) aggregationResult.get(7), NUM_RECORDS, expectedDoubleSum,
+ expectedStdDevs[7].getResult(), true);
+
+ // Update the expected result by 3 more times (broker query will compute 4 identical segments)
Review Comment:
Yes, in `CovarianceQueriesTest`, @SabrinaZhaozyf has added the [getDistinctInstances()](https://github.com/apache/pinot/blob/270315a13f457376df04cd47bc62f84d1c1e558e/pinot-core/src/test/java/org/apache/pinot/queries/CovarianceQueriesTest.java#L157) to use 4 distinct segments for these statistical functions because Covar on 4 identical segments is the same as Covar on one of each segment, as kind of defeats the purpose of testing merge logic over multiple segments. The `getDistinctInstances` in base class is still returning 4 identical segments for backward compat. We can probably inherit `CovarianceQueriesTest` in `VarianceQueriesTest`.
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[GitHub] [pinot] snleee commented on a diff in pull request #9910: Add Variance and Standard Deviation Aggregation Functions
Posted by GitBox <gi...@apache.org>.
snleee commented on code in PR #9910:
URL: https://github.com/apache/pinot/pull/9910#discussion_r1041815451
##########
pinot-core/src/main/java/org/apache/pinot/core/query/aggregation/function/VarianceAggregationFunction.java:
##########
@@ -0,0 +1,204 @@
+/**
+ * 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.pinot.core.query.aggregation.function;
+
+import com.google.common.base.Preconditions;
+import java.util.Map;
+import org.apache.pinot.common.request.context.ExpressionContext;
+import org.apache.pinot.common.utils.DataSchema;
+import org.apache.pinot.core.common.BlockValSet;
+import org.apache.pinot.core.query.aggregation.AggregationResultHolder;
+import org.apache.pinot.core.query.aggregation.ObjectAggregationResultHolder;
+import org.apache.pinot.core.query.aggregation.groupby.GroupByResultHolder;
+import org.apache.pinot.core.query.aggregation.groupby.ObjectGroupByResultHolder;
+import org.apache.pinot.segment.local.customobject.VarianceTuple;
+import org.apache.pinot.segment.spi.AggregationFunctionType;
+
+
+/**
+ * Aggregation function which computes Variance and Standard Deviation
+ *
+ * The algorithm to compute variance is based on "Updating Formulae and a Pairwise Algorithm for Computing
+ * Sample Variances" by Chan et al. Please refer to the "Parallel Algorithm" section from the following wiki:
+ * - https://en.wikipedia.org/wiki/Algorithms_for_calculating_variance#Parallel_algorithm
+ */
+public class VarianceAggregationFunction extends BaseSingleInputAggregationFunction<VarianceTuple, Double> {
Review Comment:
Interestingly, they look very similar but the implementations of all functions are slightly different due to the algorithm difference. Also, we use a different intermediate class `VarianceTuple` instead of `CovarianceTuple`.
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[GitHub] [pinot] snleee commented on a diff in pull request #9910: Add Variance and Standard Deviation Aggregation Functions
Posted by GitBox <gi...@apache.org>.
snleee commented on code in PR #9910:
URL: https://github.com/apache/pinot/pull/9910#discussion_r1041816310
##########
pinot-core/src/main/java/org/apache/pinot/core/common/ObjectSerDeUtils.java:
##########
@@ -123,7 +124,9 @@ public enum ObjectType {
FloatLongPair(29),
DoubleLongPair(30),
StringLongPair(31),
- CovarianceTuple(32);
+ CovarianceTuple(32),
+
Review Comment:
fixed
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[GitHub] [pinot] walterddr commented on a diff in pull request #9910: Add Variance and Standard Deviation Aggregation Functions
Posted by GitBox <gi...@apache.org>.
walterddr commented on code in PR #9910:
URL: https://github.com/apache/pinot/pull/9910#discussion_r1042370739
##########
pinot-core/src/main/java/org/apache/pinot/core/common/ObjectSerDeUtils.java:
##########
@@ -123,7 +124,9 @@ public enum ObjectType {
FloatLongPair(29),
DoubleLongPair(30),
StringLongPair(31),
- CovarianceTuple(32);
+ CovarianceTuple(32),
Review Comment:
sweet! thanks for the clarification. this also means there's numeric stability for covariance. probably we should also document that. (or change the cover algo).
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[GitHub] [pinot] jasperjiaguo commented on a diff in pull request #9910: Add Variance and Standard Deviation Aggregation Functions
Posted by GitBox <gi...@apache.org>.
jasperjiaguo commented on code in PR #9910:
URL: https://github.com/apache/pinot/pull/9910#discussion_r1042533813
##########
pinot-core/src/main/java/org/apache/pinot/core/common/ObjectSerDeUtils.java:
##########
@@ -123,7 +124,9 @@ public enum ObjectType {
FloatLongPair(29),
DoubleLongPair(30),
StringLongPair(31),
- CovarianceTuple(32);
+ CovarianceTuple(32),
Review Comment:
we can indeed change the covar algo to numerical stable version, similar to
https://github.com/trinodb/trino/blob/1866a23e3b0377144c1820de892c0de2762351a8/core/trino-main/src/main/java/io/trino/operator/aggregation/state/CovarianceState.java
created issue: https://github.com/apache/pinot/issues/9936
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[GitHub] [pinot] jasperjiaguo commented on a diff in pull request #9910: Add Variance and Standard Deviation Aggregation Functions
Posted by GitBox <gi...@apache.org>.
jasperjiaguo commented on code in PR #9910:
URL: https://github.com/apache/pinot/pull/9910#discussion_r1042545235
##########
pinot-core/src/test/java/org/apache/pinot/queries/VarianceQueriesTest.java:
##########
@@ -0,0 +1,446 @@
+/**
+ * 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.pinot.queries;
+
+import java.io.File;
+import java.util.ArrayList;
+import java.util.Arrays;
+import java.util.List;
+import java.util.Random;
+import org.apache.commons.io.FileUtils;
+import org.apache.commons.math3.stat.descriptive.moment.StandardDeviation;
+import org.apache.commons.math3.stat.descriptive.moment.Variance;
+import org.apache.commons.math3.util.Precision;
+import org.apache.pinot.common.response.broker.BrokerResponseNative;
+import org.apache.pinot.core.operator.blocks.results.AggregationResultsBlock;
+import org.apache.pinot.core.operator.blocks.results.GroupByResultsBlock;
+import org.apache.pinot.core.operator.query.AggregationOperator;
+import org.apache.pinot.core.operator.query.GroupByOperator;
+import org.apache.pinot.core.query.aggregation.groupby.AggregationGroupByResult;
+import org.apache.pinot.segment.local.customobject.VarianceTuple;
+import org.apache.pinot.segment.local.indexsegment.immutable.ImmutableSegmentLoader;
+import org.apache.pinot.segment.local.segment.creator.impl.SegmentIndexCreationDriverImpl;
+import org.apache.pinot.segment.local.segment.readers.GenericRowRecordReader;
+import org.apache.pinot.segment.spi.ImmutableSegment;
+import org.apache.pinot.segment.spi.IndexSegment;
+import org.apache.pinot.segment.spi.creator.SegmentGeneratorConfig;
+import org.apache.pinot.spi.config.table.TableConfig;
+import org.apache.pinot.spi.config.table.TableType;
+import org.apache.pinot.spi.data.FieldSpec;
+import org.apache.pinot.spi.data.Schema;
+import org.apache.pinot.spi.data.readers.GenericRow;
+import org.apache.pinot.spi.utils.ReadMode;
+import org.apache.pinot.spi.utils.builder.TableConfigBuilder;
+import org.testng.annotations.BeforeClass;
+import org.testng.annotations.Test;
+
+import static org.testng.Assert.assertEquals;
+import static org.testng.Assert.assertNotNull;
+import static org.testng.Assert.assertTrue;
+
+
+public class VarianceQueriesTest extends BaseQueriesTest {
+
+ private static final File INDEX_DIR = new File(FileUtils.getTempDirectory(), "VarianceQueriesTest");
+
+ private static final String RAW_TABLE_NAME = "testTable";
+ private static final String SEGMENT_NAME = "testSegment";
+
+ private static final int NUM_RECORDS = 2000;
+ private static final int NUM_GROUPS = 10;
+ private static final int MAX_VALUE = 500;
+ private static final double RELATIVE_EPSILON = 0.0001;
+ private static final double DELTA = 0.0001;
+
+ private static final String INT_COLUMN = "intColumn";
+ private static final String LONG_COLUMN = "longColumn";
+ private static final String FLOAT_COLUMN = "floatColumn";
+ private static final String DOUBLE_COLUMN = "doubleColumn";
+ private static final String GROUP_BY_COLUMN = "groupByColumn";
+
+ private static final Schema SCHEMA =
+ new Schema.SchemaBuilder().addSingleValueDimension(INT_COLUMN, FieldSpec.DataType.INT)
+ .addSingleValueDimension(LONG_COLUMN, FieldSpec.DataType.LONG)
+ .addSingleValueDimension(FLOAT_COLUMN, FieldSpec.DataType.FLOAT)
+ .addSingleValueDimension(DOUBLE_COLUMN, FieldSpec.DataType.DOUBLE)
+ .addSingleValueDimension(GROUP_BY_COLUMN, FieldSpec.DataType.DOUBLE).build();
+
+ private static final TableConfig TABLE_CONFIG =
+ new TableConfigBuilder(TableType.OFFLINE).setTableName(RAW_TABLE_NAME).build();
+
+ private IndexSegment _indexSegment;
+ private List<IndexSegment> _indexSegments;
+
+ int[] _intValues = new int[NUM_RECORDS];
+ long[] _longValues = new long[NUM_RECORDS];
+ float[] _floatValues = new float[NUM_RECORDS];
+ double[] _doubleValues = new double[NUM_RECORDS];
+
+ @Override
+ protected String getFilter() {
+ // filter out half of the rows based on group id
+ return " WHERE groupByColumn < " + (NUM_GROUPS / 2);
+ }
+
+ @Override
+ protected IndexSegment getIndexSegment() {
+ return _indexSegment;
+ }
+
+ @Override
+ protected List<IndexSegment> getIndexSegments() {
+ return _indexSegments;
+ }
+
+ @BeforeClass
+ public void setUp()
+ throws Exception {
+ FileUtils.deleteDirectory(INDEX_DIR);
+ Random random = new Random();
+ List<GenericRow> records = new ArrayList<>(NUM_RECORDS);
+
+ for (int i = 0; i < NUM_RECORDS; i++) {
+ GenericRow record = new GenericRow();
+ int intValue = -MAX_VALUE + random.nextInt() * 2 * MAX_VALUE;
+ long longValue = -MAX_VALUE + random.nextLong() * 2 * MAX_VALUE;
+ float floatValue = -MAX_VALUE + random.nextFloat() * 2 * MAX_VALUE;
+ double doubleValue = -MAX_VALUE + random.nextDouble() * 2 * MAX_VALUE;
+
+ _intValues[i] = intValue;
+ _longValues[i] = longValue;
+ _floatValues[i] = floatValue;
+ _doubleValues[i] = doubleValue;
+
+ record.putValue(INT_COLUMN, _intValues[i]);
+ record.putValue(LONG_COLUMN, _longValues[i]);
+ record.putValue(FLOAT_COLUMN, _floatValues[i]);
+ record.putValue(DOUBLE_COLUMN, _doubleValues[i]);
+ record.putValue(GROUP_BY_COLUMN, Math.floor(i / (NUM_RECORDS / NUM_GROUPS)));
+ records.add(record);
+ }
+
+ SegmentGeneratorConfig segmentGeneratorConfig = new SegmentGeneratorConfig(TABLE_CONFIG, SCHEMA);
+ segmentGeneratorConfig.setTableName(RAW_TABLE_NAME);
+ segmentGeneratorConfig.setSegmentName(SEGMENT_NAME);
+ segmentGeneratorConfig.setOutDir(INDEX_DIR.getPath());
+
+ SegmentIndexCreationDriverImpl driver = new SegmentIndexCreationDriverImpl();
+ driver.init(segmentGeneratorConfig, new GenericRowRecordReader(records));
+ driver.build();
+
+ ImmutableSegment immutableSegment = ImmutableSegmentLoader.load(new File(INDEX_DIR, SEGMENT_NAME), ReadMode.mmap);
+ _indexSegment = immutableSegment;
+ _indexSegments = Arrays.asList(immutableSegment, immutableSegment);
+ }
+
+ @Test
+ public void testVarianceAggregationOnly() {
+ // Compute the expected values
+ Variance[] expectedVariances = new Variance[8];
+ for (int i = 0; i < 8; i++) {
+ if (i < 4) {
+ expectedVariances[i] = new Variance(false);
+ } else {
+ expectedVariances[i] = new Variance(true);
+ }
+ }
+ for (int i = 0; i < NUM_RECORDS; i++) {
+ expectedVariances[0].increment(_intValues[i]);
+ expectedVariances[1].increment(_longValues[i]);
+ expectedVariances[2].increment(_floatValues[i]);
+ expectedVariances[3].increment(_doubleValues[i]);
+ expectedVariances[4].increment(_intValues[i]);
+ expectedVariances[5].increment(_longValues[i]);
+ expectedVariances[6].increment(_floatValues[i]);
+ expectedVariances[7].increment(_doubleValues[i]);
+ }
+ double expectedIntSum = Arrays.stream(_intValues).asDoubleStream().sum();
+ double expectedLongSum = Arrays.stream(_longValues).asDoubleStream().sum();
+ double expectedFloatSum = 0.0;
+ for (int i = 0; i < _floatValues.length; i++) {
+ expectedFloatSum += _floatValues[i];
+ }
+ double expectedDoubleSum = Arrays.stream(_doubleValues).sum();
+
+ // Compute the query
+ String query = "SELECT VAR_POP(intColumn), VAR_POP(longColumn), VAR_POP(floatColumn), VAR_POP(doubleColumn),"
+ + "VAR_SAMP(intColumn), VAR_SAMP(longColumn), VAR_SAMP(floatColumn), VAR_SAMP(doubleColumn) FROM testTable";
+ AggregationOperator aggregationOperator = getOperator(query);
+ AggregationResultsBlock resultsBlock = aggregationOperator.nextBlock();
+ QueriesTestUtils.testInnerSegmentExecutionStatistics(aggregationOperator.getExecutionStatistics(), NUM_RECORDS, 0,
+ NUM_RECORDS * 4, NUM_RECORDS);
+ List<Object> aggregationResult = resultsBlock.getResults();
+
+ // Validate the aggregation results
+ checkWithPrecisionForVariance((VarianceTuple) aggregationResult.get(0), NUM_RECORDS, expectedIntSum,
+ expectedVariances[0].getResult(), false);
+ checkWithPrecisionForVariance((VarianceTuple) aggregationResult.get(1), NUM_RECORDS, expectedLongSum,
+ expectedVariances[1].getResult(), false);
+ checkWithPrecisionForVariance((VarianceTuple) aggregationResult.get(2), NUM_RECORDS, expectedFloatSum,
+ expectedVariances[2].getResult(), false);
+ checkWithPrecisionForVariance((VarianceTuple) aggregationResult.get(3), NUM_RECORDS, expectedDoubleSum,
+ expectedVariances[3].getResult(), false);
+ checkWithPrecisionForVariance((VarianceTuple) aggregationResult.get(4), NUM_RECORDS, expectedIntSum,
+ expectedVariances[4].getResult(), true);
+ checkWithPrecisionForVariance((VarianceTuple) aggregationResult.get(5), NUM_RECORDS, expectedLongSum,
+ expectedVariances[5].getResult(), true);
+ checkWithPrecisionForVariance((VarianceTuple) aggregationResult.get(6), NUM_RECORDS, expectedFloatSum,
+ expectedVariances[6].getResult(), true);
+ checkWithPrecisionForVariance((VarianceTuple) aggregationResult.get(7), NUM_RECORDS, expectedDoubleSum,
+ expectedVariances[7].getResult(), true);
+
+ // Update the expected result by 3 more times (broker query will compute 4 identical segments)
+ for (int i = 0; i < NUM_RECORDS * 3; i++) {
+ int pos = i % NUM_RECORDS;
+ expectedVariances[0].increment(_intValues[pos]);
+ expectedVariances[1].increment(_longValues[pos]);
+ expectedVariances[2].increment(_floatValues[pos]);
+ expectedVariances[3].increment(_doubleValues[pos]);
+ expectedVariances[4].increment(_intValues[pos]);
+ expectedVariances[5].increment(_longValues[pos]);
+ expectedVariances[6].increment(_floatValues[pos]);
+ expectedVariances[7].increment(_doubleValues[pos]);
+ }
+
+ // Validate the response
+ BrokerResponseNative brokerResponse = getBrokerResponse(query);
+ brokerResponse.getResultTable();
+ Object[] results = brokerResponse.getResultTable().getRows().get(0);
+ assertTrue(
+ Precision.equalsWithRelativeTolerance((double) results[0], expectedVariances[0].getResult(), RELATIVE_EPSILON));
+ assertTrue(
+ Precision.equalsWithRelativeTolerance((double) results[1], expectedVariances[1].getResult(), RELATIVE_EPSILON));
+ assertTrue(
+ Precision.equalsWithRelativeTolerance((double) results[2], expectedVariances[2].getResult(), RELATIVE_EPSILON));
+ assertTrue(
+ Precision.equalsWithRelativeTolerance((double) results[3], expectedVariances[3].getResult(), RELATIVE_EPSILON));
+ assertTrue(
+ Precision.equalsWithRelativeTolerance((double) results[4], expectedVariances[4].getResult(), RELATIVE_EPSILON));
+ assertTrue(
+ Precision.equalsWithRelativeTolerance((double) results[5], expectedVariances[5].getResult(), RELATIVE_EPSILON));
+ assertTrue(
+ Precision.equalsWithRelativeTolerance((double) results[6], expectedVariances[6].getResult(), RELATIVE_EPSILON));
+ assertTrue(
+ Precision.equalsWithRelativeTolerance((double) results[7], expectedVariances[7].getResult(), RELATIVE_EPSILON));
+
+ // Validate the response for a query with a filter
+ query = "SELECT VAR_POP(intColumn) from testTable" + getFilter();
+ brokerResponse = getBrokerResponse(query);
+ brokerResponse.getResultTable();
+ results = brokerResponse.getResultTable().getRows().get(0);
+ Variance filterExpectedVariance = new Variance(false);
+ for (int i = 0; i < NUM_RECORDS / 2; i++) {
+ filterExpectedVariance.increment(_intValues[i]);
+ }
+ assertTrue(Precision.equalsWithRelativeTolerance((double) results[0], filterExpectedVariance.getResult(),
+ RELATIVE_EPSILON));
+ }
+
+ @Test
+ public void testVarianceAggregationGroupBy() {
+ // Compute expected group results
+ Variance[] expectedGroupByResult = new Variance[NUM_GROUPS];
+ double[] expectedSum = new double[NUM_GROUPS];
+
+ for (int i = 0; i < NUM_GROUPS; i++) {
+ expectedGroupByResult[i] = new Variance(false);
+ }
+ for (int j = 0; j < NUM_RECORDS; j++) {
+ int pos = j / (NUM_RECORDS / NUM_GROUPS);
+ expectedGroupByResult[pos].increment(_intValues[j]);
+ expectedSum[pos] += _intValues[j];
+ }
+
+ String query = "SELECT VAR_POP(intColumn) FROM testTable GROUP BY groupByColumn ORDER BY groupByColumn";
+ GroupByOperator groupByOperator = getOperator(query);
+ GroupByResultsBlock resultsBlock = groupByOperator.nextBlock();
+ QueriesTestUtils.testInnerSegmentExecutionStatistics(groupByOperator.getExecutionStatistics(), NUM_RECORDS, 0,
+ NUM_RECORDS * 2, NUM_RECORDS);
+ AggregationGroupByResult aggregationGroupByResult = resultsBlock.getAggregationGroupByResult();
+ assertNotNull(aggregationGroupByResult);
+ for (int i = 0; i < NUM_GROUPS; i++) {
+
+ VarianceTuple actualVarianceTuple = (VarianceTuple) aggregationGroupByResult.getResultForGroupId(0, i);
+ checkWithPrecisionForVariance(actualVarianceTuple, NUM_RECORDS / NUM_GROUPS, expectedSum[i],
+ expectedGroupByResult[i].getResult(), false);
+ }
+ }
+
+ @Test
+ public void testStandardDeviationAggregationOnly() {
+ // Compute the expected values
+ StandardDeviation[] expectedStdDevs = new StandardDeviation[8];
+ for (int i = 0; i < 8; i++) {
+ if (i < 4) {
+ expectedStdDevs[i] = new StandardDeviation(false);
+ } else {
+ expectedStdDevs[i] = new StandardDeviation(true);
+ }
+ }
+ for (int i = 0; i < NUM_RECORDS; i++) {
+ expectedStdDevs[0].increment(_intValues[i]);
+ expectedStdDevs[1].increment(_longValues[i]);
+ expectedStdDevs[2].increment(_floatValues[i]);
+ expectedStdDevs[3].increment(_doubleValues[i]);
+ expectedStdDevs[4].increment(_intValues[i]);
+ expectedStdDevs[5].increment(_longValues[i]);
+ expectedStdDevs[6].increment(_floatValues[i]);
+ expectedStdDevs[7].increment(_doubleValues[i]);
+ }
+
+ double expectedIntSum = Arrays.stream(_intValues).asDoubleStream().sum();
+ double expectedLongSum = Arrays.stream(_longValues).asDoubleStream().sum();
+ double expectedFloatSum = 0.0;
+ for (int i = 0; i < _floatValues.length; i++) {
+ expectedFloatSum += _floatValues[i];
+ }
+ double expectedDoubleSum = Arrays.stream(_doubleValues).sum();
+
+ // Compute the query
+ String query =
+ "SELECT STDDEV_POP(intColumn), STDDEV_POP(longColumn), STDDEV_POP(floatColumn), STDDEV_POP(doubleColumn),"
+ + "STDDEV_SAMP(intColumn), STDDEV_SAMP(longColumn), STDDEV_SAMP(floatColumn), STDDEV_SAMP(doubleColumn) "
+ + "FROM testTable";
+ AggregationOperator aggregationOperator = getOperator(query);
+ AggregationResultsBlock resultsBlock = aggregationOperator.nextBlock();
+ QueriesTestUtils.testInnerSegmentExecutionStatistics(aggregationOperator.getExecutionStatistics(), NUM_RECORDS, 0,
+ NUM_RECORDS * 4, NUM_RECORDS);
+ List<Object> aggregationResult = resultsBlock.getResults();
+
+ // Validate the aggregation results
+ checkWithPrecisionForStandardDeviation((VarianceTuple) aggregationResult.get(0), NUM_RECORDS, expectedIntSum,
+ expectedStdDevs[0].getResult(), false);
+ checkWithPrecisionForStandardDeviation((VarianceTuple) aggregationResult.get(1), NUM_RECORDS, expectedLongSum,
+ expectedStdDevs[1].getResult(), false);
+ checkWithPrecisionForStandardDeviation((VarianceTuple) aggregationResult.get(2), NUM_RECORDS, expectedFloatSum,
+ expectedStdDevs[2].getResult(), false);
+ checkWithPrecisionForStandardDeviation((VarianceTuple) aggregationResult.get(3), NUM_RECORDS, expectedDoubleSum,
+ expectedStdDevs[3].getResult(), false);
+ checkWithPrecisionForStandardDeviation((VarianceTuple) aggregationResult.get(4), NUM_RECORDS, expectedIntSum,
+ expectedStdDevs[4].getResult(), true);
+ checkWithPrecisionForStandardDeviation((VarianceTuple) aggregationResult.get(5), NUM_RECORDS, expectedLongSum,
+ expectedStdDevs[5].getResult(), true);
+ checkWithPrecisionForStandardDeviation((VarianceTuple) aggregationResult.get(6), NUM_RECORDS, expectedFloatSum,
+ expectedStdDevs[6].getResult(), true);
+ checkWithPrecisionForStandardDeviation((VarianceTuple) aggregationResult.get(7), NUM_RECORDS, expectedDoubleSum,
+ expectedStdDevs[7].getResult(), true);
+
+ // Update the expected result by 3 more times (broker query will compute 4 identical segments)
Review Comment:
Yes, in `CovarianceQueriesTest`, @SabrinaZhaozyf has added the [getDistinctInstances()](https://github.com/apache/pinot/blob/270315a13f457376df04cd47bc62f84d1c1e558e/pinot-core/src/test/java/org/apache/pinot/queries/CovarianceQueriesTest.java#L157) to use 4 distinct segments for these statistical functions, because Covar on 4 identical segments is the same as Covar on one of each segment, as kind of defeats the purpose of testing merge logic over multiple segments. The `getDistinctInstances` in base class is still returning 4 identical segments though for backward compat. We can probably inherit `CovarianceQueriesTest` in `VarianceQueriesTest`.
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[GitHub] [pinot] jasperjiaguo commented on a diff in pull request #9910: Add Variance and Standard Deviation Aggregation Functions
Posted by GitBox <gi...@apache.org>.
jasperjiaguo commented on code in PR #9910:
URL: https://github.com/apache/pinot/pull/9910#discussion_r1042545235
##########
pinot-core/src/test/java/org/apache/pinot/queries/VarianceQueriesTest.java:
##########
@@ -0,0 +1,446 @@
+/**
+ * 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.pinot.queries;
+
+import java.io.File;
+import java.util.ArrayList;
+import java.util.Arrays;
+import java.util.List;
+import java.util.Random;
+import org.apache.commons.io.FileUtils;
+import org.apache.commons.math3.stat.descriptive.moment.StandardDeviation;
+import org.apache.commons.math3.stat.descriptive.moment.Variance;
+import org.apache.commons.math3.util.Precision;
+import org.apache.pinot.common.response.broker.BrokerResponseNative;
+import org.apache.pinot.core.operator.blocks.results.AggregationResultsBlock;
+import org.apache.pinot.core.operator.blocks.results.GroupByResultsBlock;
+import org.apache.pinot.core.operator.query.AggregationOperator;
+import org.apache.pinot.core.operator.query.GroupByOperator;
+import org.apache.pinot.core.query.aggregation.groupby.AggregationGroupByResult;
+import org.apache.pinot.segment.local.customobject.VarianceTuple;
+import org.apache.pinot.segment.local.indexsegment.immutable.ImmutableSegmentLoader;
+import org.apache.pinot.segment.local.segment.creator.impl.SegmentIndexCreationDriverImpl;
+import org.apache.pinot.segment.local.segment.readers.GenericRowRecordReader;
+import org.apache.pinot.segment.spi.ImmutableSegment;
+import org.apache.pinot.segment.spi.IndexSegment;
+import org.apache.pinot.segment.spi.creator.SegmentGeneratorConfig;
+import org.apache.pinot.spi.config.table.TableConfig;
+import org.apache.pinot.spi.config.table.TableType;
+import org.apache.pinot.spi.data.FieldSpec;
+import org.apache.pinot.spi.data.Schema;
+import org.apache.pinot.spi.data.readers.GenericRow;
+import org.apache.pinot.spi.utils.ReadMode;
+import org.apache.pinot.spi.utils.builder.TableConfigBuilder;
+import org.testng.annotations.BeforeClass;
+import org.testng.annotations.Test;
+
+import static org.testng.Assert.assertEquals;
+import static org.testng.Assert.assertNotNull;
+import static org.testng.Assert.assertTrue;
+
+
+public class VarianceQueriesTest extends BaseQueriesTest {
+
+ private static final File INDEX_DIR = new File(FileUtils.getTempDirectory(), "VarianceQueriesTest");
+
+ private static final String RAW_TABLE_NAME = "testTable";
+ private static final String SEGMENT_NAME = "testSegment";
+
+ private static final int NUM_RECORDS = 2000;
+ private static final int NUM_GROUPS = 10;
+ private static final int MAX_VALUE = 500;
+ private static final double RELATIVE_EPSILON = 0.0001;
+ private static final double DELTA = 0.0001;
+
+ private static final String INT_COLUMN = "intColumn";
+ private static final String LONG_COLUMN = "longColumn";
+ private static final String FLOAT_COLUMN = "floatColumn";
+ private static final String DOUBLE_COLUMN = "doubleColumn";
+ private static final String GROUP_BY_COLUMN = "groupByColumn";
+
+ private static final Schema SCHEMA =
+ new Schema.SchemaBuilder().addSingleValueDimension(INT_COLUMN, FieldSpec.DataType.INT)
+ .addSingleValueDimension(LONG_COLUMN, FieldSpec.DataType.LONG)
+ .addSingleValueDimension(FLOAT_COLUMN, FieldSpec.DataType.FLOAT)
+ .addSingleValueDimension(DOUBLE_COLUMN, FieldSpec.DataType.DOUBLE)
+ .addSingleValueDimension(GROUP_BY_COLUMN, FieldSpec.DataType.DOUBLE).build();
+
+ private static final TableConfig TABLE_CONFIG =
+ new TableConfigBuilder(TableType.OFFLINE).setTableName(RAW_TABLE_NAME).build();
+
+ private IndexSegment _indexSegment;
+ private List<IndexSegment> _indexSegments;
+
+ int[] _intValues = new int[NUM_RECORDS];
+ long[] _longValues = new long[NUM_RECORDS];
+ float[] _floatValues = new float[NUM_RECORDS];
+ double[] _doubleValues = new double[NUM_RECORDS];
+
+ @Override
+ protected String getFilter() {
+ // filter out half of the rows based on group id
+ return " WHERE groupByColumn < " + (NUM_GROUPS / 2);
+ }
+
+ @Override
+ protected IndexSegment getIndexSegment() {
+ return _indexSegment;
+ }
+
+ @Override
+ protected List<IndexSegment> getIndexSegments() {
+ return _indexSegments;
+ }
+
+ @BeforeClass
+ public void setUp()
+ throws Exception {
+ FileUtils.deleteDirectory(INDEX_DIR);
+ Random random = new Random();
+ List<GenericRow> records = new ArrayList<>(NUM_RECORDS);
+
+ for (int i = 0; i < NUM_RECORDS; i++) {
+ GenericRow record = new GenericRow();
+ int intValue = -MAX_VALUE + random.nextInt() * 2 * MAX_VALUE;
+ long longValue = -MAX_VALUE + random.nextLong() * 2 * MAX_VALUE;
+ float floatValue = -MAX_VALUE + random.nextFloat() * 2 * MAX_VALUE;
+ double doubleValue = -MAX_VALUE + random.nextDouble() * 2 * MAX_VALUE;
+
+ _intValues[i] = intValue;
+ _longValues[i] = longValue;
+ _floatValues[i] = floatValue;
+ _doubleValues[i] = doubleValue;
+
+ record.putValue(INT_COLUMN, _intValues[i]);
+ record.putValue(LONG_COLUMN, _longValues[i]);
+ record.putValue(FLOAT_COLUMN, _floatValues[i]);
+ record.putValue(DOUBLE_COLUMN, _doubleValues[i]);
+ record.putValue(GROUP_BY_COLUMN, Math.floor(i / (NUM_RECORDS / NUM_GROUPS)));
+ records.add(record);
+ }
+
+ SegmentGeneratorConfig segmentGeneratorConfig = new SegmentGeneratorConfig(TABLE_CONFIG, SCHEMA);
+ segmentGeneratorConfig.setTableName(RAW_TABLE_NAME);
+ segmentGeneratorConfig.setSegmentName(SEGMENT_NAME);
+ segmentGeneratorConfig.setOutDir(INDEX_DIR.getPath());
+
+ SegmentIndexCreationDriverImpl driver = new SegmentIndexCreationDriverImpl();
+ driver.init(segmentGeneratorConfig, new GenericRowRecordReader(records));
+ driver.build();
+
+ ImmutableSegment immutableSegment = ImmutableSegmentLoader.load(new File(INDEX_DIR, SEGMENT_NAME), ReadMode.mmap);
+ _indexSegment = immutableSegment;
+ _indexSegments = Arrays.asList(immutableSegment, immutableSegment);
+ }
+
+ @Test
+ public void testVarianceAggregationOnly() {
+ // Compute the expected values
+ Variance[] expectedVariances = new Variance[8];
+ for (int i = 0; i < 8; i++) {
+ if (i < 4) {
+ expectedVariances[i] = new Variance(false);
+ } else {
+ expectedVariances[i] = new Variance(true);
+ }
+ }
+ for (int i = 0; i < NUM_RECORDS; i++) {
+ expectedVariances[0].increment(_intValues[i]);
+ expectedVariances[1].increment(_longValues[i]);
+ expectedVariances[2].increment(_floatValues[i]);
+ expectedVariances[3].increment(_doubleValues[i]);
+ expectedVariances[4].increment(_intValues[i]);
+ expectedVariances[5].increment(_longValues[i]);
+ expectedVariances[6].increment(_floatValues[i]);
+ expectedVariances[7].increment(_doubleValues[i]);
+ }
+ double expectedIntSum = Arrays.stream(_intValues).asDoubleStream().sum();
+ double expectedLongSum = Arrays.stream(_longValues).asDoubleStream().sum();
+ double expectedFloatSum = 0.0;
+ for (int i = 0; i < _floatValues.length; i++) {
+ expectedFloatSum += _floatValues[i];
+ }
+ double expectedDoubleSum = Arrays.stream(_doubleValues).sum();
+
+ // Compute the query
+ String query = "SELECT VAR_POP(intColumn), VAR_POP(longColumn), VAR_POP(floatColumn), VAR_POP(doubleColumn),"
+ + "VAR_SAMP(intColumn), VAR_SAMP(longColumn), VAR_SAMP(floatColumn), VAR_SAMP(doubleColumn) FROM testTable";
+ AggregationOperator aggregationOperator = getOperator(query);
+ AggregationResultsBlock resultsBlock = aggregationOperator.nextBlock();
+ QueriesTestUtils.testInnerSegmentExecutionStatistics(aggregationOperator.getExecutionStatistics(), NUM_RECORDS, 0,
+ NUM_RECORDS * 4, NUM_RECORDS);
+ List<Object> aggregationResult = resultsBlock.getResults();
+
+ // Validate the aggregation results
+ checkWithPrecisionForVariance((VarianceTuple) aggregationResult.get(0), NUM_RECORDS, expectedIntSum,
+ expectedVariances[0].getResult(), false);
+ checkWithPrecisionForVariance((VarianceTuple) aggregationResult.get(1), NUM_RECORDS, expectedLongSum,
+ expectedVariances[1].getResult(), false);
+ checkWithPrecisionForVariance((VarianceTuple) aggregationResult.get(2), NUM_RECORDS, expectedFloatSum,
+ expectedVariances[2].getResult(), false);
+ checkWithPrecisionForVariance((VarianceTuple) aggregationResult.get(3), NUM_RECORDS, expectedDoubleSum,
+ expectedVariances[3].getResult(), false);
+ checkWithPrecisionForVariance((VarianceTuple) aggregationResult.get(4), NUM_RECORDS, expectedIntSum,
+ expectedVariances[4].getResult(), true);
+ checkWithPrecisionForVariance((VarianceTuple) aggregationResult.get(5), NUM_RECORDS, expectedLongSum,
+ expectedVariances[5].getResult(), true);
+ checkWithPrecisionForVariance((VarianceTuple) aggregationResult.get(6), NUM_RECORDS, expectedFloatSum,
+ expectedVariances[6].getResult(), true);
+ checkWithPrecisionForVariance((VarianceTuple) aggregationResult.get(7), NUM_RECORDS, expectedDoubleSum,
+ expectedVariances[7].getResult(), true);
+
+ // Update the expected result by 3 more times (broker query will compute 4 identical segments)
+ for (int i = 0; i < NUM_RECORDS * 3; i++) {
+ int pos = i % NUM_RECORDS;
+ expectedVariances[0].increment(_intValues[pos]);
+ expectedVariances[1].increment(_longValues[pos]);
+ expectedVariances[2].increment(_floatValues[pos]);
+ expectedVariances[3].increment(_doubleValues[pos]);
+ expectedVariances[4].increment(_intValues[pos]);
+ expectedVariances[5].increment(_longValues[pos]);
+ expectedVariances[6].increment(_floatValues[pos]);
+ expectedVariances[7].increment(_doubleValues[pos]);
+ }
+
+ // Validate the response
+ BrokerResponseNative brokerResponse = getBrokerResponse(query);
+ brokerResponse.getResultTable();
+ Object[] results = brokerResponse.getResultTable().getRows().get(0);
+ assertTrue(
+ Precision.equalsWithRelativeTolerance((double) results[0], expectedVariances[0].getResult(), RELATIVE_EPSILON));
+ assertTrue(
+ Precision.equalsWithRelativeTolerance((double) results[1], expectedVariances[1].getResult(), RELATIVE_EPSILON));
+ assertTrue(
+ Precision.equalsWithRelativeTolerance((double) results[2], expectedVariances[2].getResult(), RELATIVE_EPSILON));
+ assertTrue(
+ Precision.equalsWithRelativeTolerance((double) results[3], expectedVariances[3].getResult(), RELATIVE_EPSILON));
+ assertTrue(
+ Precision.equalsWithRelativeTolerance((double) results[4], expectedVariances[4].getResult(), RELATIVE_EPSILON));
+ assertTrue(
+ Precision.equalsWithRelativeTolerance((double) results[5], expectedVariances[5].getResult(), RELATIVE_EPSILON));
+ assertTrue(
+ Precision.equalsWithRelativeTolerance((double) results[6], expectedVariances[6].getResult(), RELATIVE_EPSILON));
+ assertTrue(
+ Precision.equalsWithRelativeTolerance((double) results[7], expectedVariances[7].getResult(), RELATIVE_EPSILON));
+
+ // Validate the response for a query with a filter
+ query = "SELECT VAR_POP(intColumn) from testTable" + getFilter();
+ brokerResponse = getBrokerResponse(query);
+ brokerResponse.getResultTable();
+ results = brokerResponse.getResultTable().getRows().get(0);
+ Variance filterExpectedVariance = new Variance(false);
+ for (int i = 0; i < NUM_RECORDS / 2; i++) {
+ filterExpectedVariance.increment(_intValues[i]);
+ }
+ assertTrue(Precision.equalsWithRelativeTolerance((double) results[0], filterExpectedVariance.getResult(),
+ RELATIVE_EPSILON));
+ }
+
+ @Test
+ public void testVarianceAggregationGroupBy() {
+ // Compute expected group results
+ Variance[] expectedGroupByResult = new Variance[NUM_GROUPS];
+ double[] expectedSum = new double[NUM_GROUPS];
+
+ for (int i = 0; i < NUM_GROUPS; i++) {
+ expectedGroupByResult[i] = new Variance(false);
+ }
+ for (int j = 0; j < NUM_RECORDS; j++) {
+ int pos = j / (NUM_RECORDS / NUM_GROUPS);
+ expectedGroupByResult[pos].increment(_intValues[j]);
+ expectedSum[pos] += _intValues[j];
+ }
+
+ String query = "SELECT VAR_POP(intColumn) FROM testTable GROUP BY groupByColumn ORDER BY groupByColumn";
+ GroupByOperator groupByOperator = getOperator(query);
+ GroupByResultsBlock resultsBlock = groupByOperator.nextBlock();
+ QueriesTestUtils.testInnerSegmentExecutionStatistics(groupByOperator.getExecutionStatistics(), NUM_RECORDS, 0,
+ NUM_RECORDS * 2, NUM_RECORDS);
+ AggregationGroupByResult aggregationGroupByResult = resultsBlock.getAggregationGroupByResult();
+ assertNotNull(aggregationGroupByResult);
+ for (int i = 0; i < NUM_GROUPS; i++) {
+
+ VarianceTuple actualVarianceTuple = (VarianceTuple) aggregationGroupByResult.getResultForGroupId(0, i);
+ checkWithPrecisionForVariance(actualVarianceTuple, NUM_RECORDS / NUM_GROUPS, expectedSum[i],
+ expectedGroupByResult[i].getResult(), false);
+ }
+ }
+
+ @Test
+ public void testStandardDeviationAggregationOnly() {
+ // Compute the expected values
+ StandardDeviation[] expectedStdDevs = new StandardDeviation[8];
+ for (int i = 0; i < 8; i++) {
+ if (i < 4) {
+ expectedStdDevs[i] = new StandardDeviation(false);
+ } else {
+ expectedStdDevs[i] = new StandardDeviation(true);
+ }
+ }
+ for (int i = 0; i < NUM_RECORDS; i++) {
+ expectedStdDevs[0].increment(_intValues[i]);
+ expectedStdDevs[1].increment(_longValues[i]);
+ expectedStdDevs[2].increment(_floatValues[i]);
+ expectedStdDevs[3].increment(_doubleValues[i]);
+ expectedStdDevs[4].increment(_intValues[i]);
+ expectedStdDevs[5].increment(_longValues[i]);
+ expectedStdDevs[6].increment(_floatValues[i]);
+ expectedStdDevs[7].increment(_doubleValues[i]);
+ }
+
+ double expectedIntSum = Arrays.stream(_intValues).asDoubleStream().sum();
+ double expectedLongSum = Arrays.stream(_longValues).asDoubleStream().sum();
+ double expectedFloatSum = 0.0;
+ for (int i = 0; i < _floatValues.length; i++) {
+ expectedFloatSum += _floatValues[i];
+ }
+ double expectedDoubleSum = Arrays.stream(_doubleValues).sum();
+
+ // Compute the query
+ String query =
+ "SELECT STDDEV_POP(intColumn), STDDEV_POP(longColumn), STDDEV_POP(floatColumn), STDDEV_POP(doubleColumn),"
+ + "STDDEV_SAMP(intColumn), STDDEV_SAMP(longColumn), STDDEV_SAMP(floatColumn), STDDEV_SAMP(doubleColumn) "
+ + "FROM testTable";
+ AggregationOperator aggregationOperator = getOperator(query);
+ AggregationResultsBlock resultsBlock = aggregationOperator.nextBlock();
+ QueriesTestUtils.testInnerSegmentExecutionStatistics(aggregationOperator.getExecutionStatistics(), NUM_RECORDS, 0,
+ NUM_RECORDS * 4, NUM_RECORDS);
+ List<Object> aggregationResult = resultsBlock.getResults();
+
+ // Validate the aggregation results
+ checkWithPrecisionForStandardDeviation((VarianceTuple) aggregationResult.get(0), NUM_RECORDS, expectedIntSum,
+ expectedStdDevs[0].getResult(), false);
+ checkWithPrecisionForStandardDeviation((VarianceTuple) aggregationResult.get(1), NUM_RECORDS, expectedLongSum,
+ expectedStdDevs[1].getResult(), false);
+ checkWithPrecisionForStandardDeviation((VarianceTuple) aggregationResult.get(2), NUM_RECORDS, expectedFloatSum,
+ expectedStdDevs[2].getResult(), false);
+ checkWithPrecisionForStandardDeviation((VarianceTuple) aggregationResult.get(3), NUM_RECORDS, expectedDoubleSum,
+ expectedStdDevs[3].getResult(), false);
+ checkWithPrecisionForStandardDeviation((VarianceTuple) aggregationResult.get(4), NUM_RECORDS, expectedIntSum,
+ expectedStdDevs[4].getResult(), true);
+ checkWithPrecisionForStandardDeviation((VarianceTuple) aggregationResult.get(5), NUM_RECORDS, expectedLongSum,
+ expectedStdDevs[5].getResult(), true);
+ checkWithPrecisionForStandardDeviation((VarianceTuple) aggregationResult.get(6), NUM_RECORDS, expectedFloatSum,
+ expectedStdDevs[6].getResult(), true);
+ checkWithPrecisionForStandardDeviation((VarianceTuple) aggregationResult.get(7), NUM_RECORDS, expectedDoubleSum,
+ expectedStdDevs[7].getResult(), true);
+
+ // Update the expected result by 3 more times (broker query will compute 4 identical segments)
Review Comment:
Yes, in `CovarianceQueriesTest`, @SabrinaZhaozyf has added the [getDistinctInstances()](https://github.com/apache/pinot/blob/270315a13f457376df04cd47bc62f84d1c1e558e/pinot-core/src/test/java/org/apache/pinot/queries/CovarianceQueriesTest.java#L157) to use 4 distinct segments for these statistical functions, because Covar on 4 identical segments is the same as Covar on one of each segment, as kind of defeats the purpose of testing merge logic over multiple segments. The `getDistinctInstances` in base class is still returning 4 identical segments for backward compat. We can probably inherit `CovarianceQueriesTest` in `VarianceQueriesTest`.
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[GitHub] [pinot] Jackie-Jiang commented on a diff in pull request #9910: Add Variance and Standard Deviation Aggregation Functions
Posted by GitBox <gi...@apache.org>.
Jackie-Jiang commented on code in PR #9910:
URL: https://github.com/apache/pinot/pull/9910#discussion_r1043947115
##########
pinot-core/src/test/java/org/apache/pinot/queries/StatisticalQueriesTest.java:
##########
@@ -0,0 +1,749 @@
+/**
+ * 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.pinot.queries;
+
+import java.io.File;
+import java.io.IOException;
+import java.util.ArrayList;
+import java.util.Arrays;
+import java.util.Collections;
+import java.util.List;
+import java.util.Random;
+import org.apache.commons.io.FileUtils;
+import org.apache.commons.math3.stat.correlation.Covariance;
+import org.apache.commons.math3.stat.descriptive.moment.StandardDeviation;
+import org.apache.commons.math3.stat.descriptive.moment.Variance;
+import org.apache.commons.math3.util.Precision;
+import org.apache.pinot.common.response.broker.BrokerResponseNative;
+import org.apache.pinot.common.response.broker.ResultTable;
+import org.apache.pinot.core.operator.blocks.results.AggregationResultsBlock;
+import org.apache.pinot.core.operator.blocks.results.GroupByResultsBlock;
+import org.apache.pinot.core.operator.query.AggregationOperator;
+import org.apache.pinot.core.operator.query.GroupByOperator;
+import org.apache.pinot.core.query.aggregation.groupby.AggregationGroupByResult;
+import org.apache.pinot.segment.local.customobject.CovarianceTuple;
+import org.apache.pinot.segment.local.customobject.VarianceTuple;
+import org.apache.pinot.segment.local.indexsegment.immutable.ImmutableSegmentLoader;
+import org.apache.pinot.segment.local.segment.creator.impl.SegmentIndexCreationDriverImpl;
+import org.apache.pinot.segment.local.segment.readers.GenericRowRecordReader;
+import org.apache.pinot.segment.spi.ImmutableSegment;
+import org.apache.pinot.segment.spi.IndexSegment;
+import org.apache.pinot.segment.spi.creator.SegmentGeneratorConfig;
+import org.apache.pinot.spi.config.table.TableConfig;
+import org.apache.pinot.spi.config.table.TableType;
+import org.apache.pinot.spi.data.FieldSpec;
+import org.apache.pinot.spi.data.Schema;
+import org.apache.pinot.spi.data.readers.GenericRow;
+import org.apache.pinot.spi.utils.ReadMode;
+import org.apache.pinot.spi.utils.builder.TableConfigBuilder;
+import org.testng.annotations.AfterClass;
+import org.testng.annotations.BeforeClass;
+import org.testng.annotations.Test;
+
+import static org.testng.Assert.assertEquals;
+import static org.testng.Assert.assertNotNull;
+import static org.testng.Assert.assertTrue;
+
+
+/**
+ * Queries test for statistical queries (i.e Variance, Covariance, Standard Deviation etc)
+ */
+public class StatisticalQueriesTest extends BaseQueriesTest {
+ private static final File INDEX_DIR = new File(FileUtils.getTempDirectory(), "CovarianceQueriesTest");
+ private static final String RAW_TABLE_NAME = "testTable";
+ private static final String SEGMENT_NAME = "testSegment";
+
+ // test segments 1-4 evenly divide testSegment into 4 distinct segments
+ private static final String SEGMENT_NAME_1 = "testSegment1";
+ private static final String SEGMENT_NAME_2 = "testSegment2";
+ private static final String SEGMENT_NAME_3 = "testSegment3";
+ private static final String SEGMENT_NAME_4 = "testSegment4";
+
+ private static final int NUM_RECORDS = 2000;
+ private static final int NUM_GROUPS = 10;
+ private static final int MAX_VALUE = 500;
+ private static final double RELATIVE_EPSILON = 0.0001;
+ private static final double DELTA = 0.0001;
+
+ private static final String INT_COLUMN_X = "intColumnX";
+ private static final String INT_COLUMN_Y = "intColumnY";
+ private static final String DOUBLE_COLUMN_X = "doubleColumnX";
+ private static final String DOUBLE_COLUMN_Y = "doubleColumnY";
+ private static final String LONG_COLUMN = "longColumn";
+ private static final String FLOAT_COLUMN = "floatColumn";
+ private static final String GROUP_BY_COLUMN = "groupByColumn";
+
+ private static final Schema SCHEMA =
+ new Schema.SchemaBuilder().addSingleValueDimension(INT_COLUMN_X, FieldSpec.DataType.INT)
+ .addSingleValueDimension(INT_COLUMN_Y, FieldSpec.DataType.INT)
+ .addSingleValueDimension(DOUBLE_COLUMN_X, FieldSpec.DataType.DOUBLE)
+ .addSingleValueDimension(DOUBLE_COLUMN_Y, FieldSpec.DataType.DOUBLE)
+ .addSingleValueDimension(LONG_COLUMN, FieldSpec.DataType.LONG)
+ .addSingleValueDimension(FLOAT_COLUMN, FieldSpec.DataType.FLOAT)
+ .addSingleValueDimension(GROUP_BY_COLUMN, FieldSpec.DataType.DOUBLE).build();
+ private static final TableConfig TABLE_CONFIG =
+ new TableConfigBuilder(TableType.OFFLINE).setTableName(RAW_TABLE_NAME).build();
+
+ private IndexSegment _indexSegment;
+ private List<IndexSegment> _indexSegments;
+ private List<List<IndexSegment>> _distinctInstances;
+ private int _sumIntX = 0;
+ private int _sumIntY = 0;
+ private int _sumIntXY = 0;
+
+ private double _sumDoubleX = 0;
+ private double _sumDoubleY = 0;
+ private double _sumDoubleXY = 0;
+
+ private long _sumLong = 0L;
+ private double _sumFloat = 0;
+
+ private double _sumIntDouble = 0;
+ private long _sumIntLong = 0L;
+ private double _sumIntFloat = 0;
+ private double _sumDoubleLong = 0;
+ private double _sumDoubleFloat = 0;
+ private double _sumLongFloat = 0;
+
+ private double _expectedCovIntXY;
+ private double _expectedCovDoubleXY;
+ private double _expectedCovIntDouble;
+ private double _expectedCovIntLong;
+ private double _expectedCovIntFloat;
+ private double _expectedCovDoubleLong;
+ private double _expectedCovDoubleFloat;
+ private double _expectedCovLongFloat;
+
+ private double _expectedCovWithFilter;
+
+ private final CovarianceTuple[] _expectedGroupByResultVer1 = new CovarianceTuple[NUM_GROUPS];
+ private final CovarianceTuple[] _expectedGroupByResultVer2 = new CovarianceTuple[NUM_GROUPS];
+ private final double[] _expectedFinalResultVer1 = new double[NUM_GROUPS];
+ private final double[] _expectedFinalResultVer2 = new double[NUM_GROUPS];
+
+ private boolean _useIdenticalSegment = false;
+
+ int[] _intColX = new int[NUM_RECORDS];
+ int[] _intColY = new int[NUM_RECORDS];
+ long[] _longCol = new long[NUM_RECORDS];
+ double[] _floatCol = new double[NUM_RECORDS];
+ double[] _doubleColX = new double[NUM_RECORDS];
+ double[] _doubleColY = new double[NUM_RECORDS];
+ double[] _groupByCol = new double[NUM_RECORDS];
+
+ @Override
+ protected String getFilter() {
+ // filter out half of the rows based on group id
+ return " WHERE groupByColumn < " + (NUM_GROUPS / 2);
+ }
+
+ @Override
+ protected IndexSegment getIndexSegment() {
+ return _indexSegment;
+ }
+
+ @Override
+ protected List<IndexSegment> getIndexSegments() {
+ return _indexSegments;
+ }
+
+ @Override
+ protected List<List<IndexSegment>> getDistinctInstances() {
+ if (_useIdenticalSegment) {
+ return Collections.singletonList(_indexSegments);
+ }
+ return _distinctInstances;
+ }
+
+ @BeforeClass
+ public void setUp()
+ throws Exception {
+ FileUtils.deleteDirectory(INDEX_DIR);
+
+ List<GenericRow> records = new ArrayList<>(NUM_RECORDS);
+
+ Random rand = new Random();
+ _intColX = rand.ints(NUM_RECORDS, -MAX_VALUE, MAX_VALUE).toArray();
+ _intColY = rand.ints(NUM_RECORDS, -MAX_VALUE, MAX_VALUE).toArray();
+ _doubleColX = rand.doubles(NUM_RECORDS, -MAX_VALUE, MAX_VALUE).toArray();
+ _doubleColY = rand.doubles(NUM_RECORDS, -MAX_VALUE, MAX_VALUE).toArray();
+ _longCol = rand.longs(NUM_RECORDS, -MAX_VALUE, MAX_VALUE).toArray();
+
+ int groupSize = NUM_RECORDS / NUM_GROUPS;
+ double sumX = 0;
+ double sumY = 0;
+ double sumGroupBy = 0;
+ double sumXY = 0;
+ double sumXGroupBy = 0;
+ int groupByVal = 0;
+
+ for (int i = 0; i < NUM_RECORDS; i++) {
+ GenericRow record = new GenericRow();
+ int intX = _intColX[i];
+ int intY = _intColY[i];
+ double doubleX = _doubleColX[i];
+ double doubleY = _doubleColY[i];
+ long longVal = _longCol[i];
+ float floatVal = -MAX_VALUE + rand.nextFloat() * 2 * MAX_VALUE;
+
+ // set up inner segment group by results
+ groupByVal = (int) Math.floor(i / groupSize);
+ if (i % groupSize == 0 && groupByVal > 0) {
+ _expectedGroupByResultVer1[groupByVal - 1] = new CovarianceTuple(sumX, sumGroupBy, sumXGroupBy, groupSize);
+ _expectedGroupByResultVer2[groupByVal - 1] = new CovarianceTuple(sumX, sumY, sumXY, groupSize);
+ sumX = 0;
+ sumY = 0;
+ sumGroupBy = 0;
+ sumXY = 0;
+ sumXGroupBy = 0;
+ }
+
+ sumX += doubleX;
+ sumY += doubleY;
+ sumGroupBy += groupByVal;
+ sumXY += doubleX * doubleY;
+ sumXGroupBy += doubleX * groupByVal;
+
+ _floatCol[i] = floatVal;
+ _groupByCol[i] = groupByVal;
+
+ // calculate inner segment results
+ _sumIntX += intX;
+ _sumIntY += intY;
+ _sumDoubleX += doubleX;
+ _sumDoubleY += doubleY;
+ _sumLong += longVal;
+ _sumFloat += floatVal;
+ _sumIntXY += intX * intY;
+ _sumDoubleXY += doubleX * doubleY;
+ _sumIntDouble += intX * doubleX;
+ _sumIntLong += intX * longVal;
+ _sumIntFloat += intX * _floatCol[i];
+ _sumDoubleLong += doubleX * longVal;
+ _sumDoubleFloat += doubleX * _floatCol[i];
+ _sumLongFloat += longVal * _floatCol[i];
+
+ record.putValue(INT_COLUMN_X, intX);
+ record.putValue(INT_COLUMN_Y, intY);
+ record.putValue(DOUBLE_COLUMN_X, doubleX);
+ record.putValue(DOUBLE_COLUMN_Y, doubleY);
+ record.putValue(LONG_COLUMN, longVal);
+ record.putValue(FLOAT_COLUMN, floatVal);
+ record.putValue(GROUP_BY_COLUMN, groupByVal);
+ records.add(record);
+ }
+ _expectedGroupByResultVer1[groupByVal] = new CovarianceTuple(sumX, sumGroupBy, sumXGroupBy, groupSize);
+ _expectedGroupByResultVer2[groupByVal] = new CovarianceTuple(sumX, sumY, sumXY, groupSize);
+
+ // calculate inter segment result
+ Covariance cov = new Covariance();
+ double[] newIntColX = Arrays.stream(_intColX).asDoubleStream().toArray();
+ double[] newIntColY = Arrays.stream(_intColY).asDoubleStream().toArray();
+ double[] newLongCol = Arrays.stream(_longCol).asDoubleStream().toArray();
+ _expectedCovIntXY = cov.covariance(newIntColX, newIntColY, false);
+ _expectedCovDoubleXY = cov.covariance(_doubleColX, _doubleColY, false);
+ _expectedCovIntDouble = cov.covariance(newIntColX, _doubleColX, false);
+ _expectedCovIntLong = cov.covariance(newIntColX, newLongCol, false);
+ _expectedCovIntFloat = cov.covariance(newIntColX, _floatCol, false);
+ _expectedCovDoubleLong = cov.covariance(_doubleColX, newLongCol, false);
+ _expectedCovDoubleFloat = cov.covariance(_doubleColX, _floatCol, false);
+ _expectedCovLongFloat = cov.covariance(newLongCol, _floatCol, false);
+
+ double[] filteredX = Arrays.copyOfRange(_doubleColX, 0, NUM_RECORDS / 2);
+ double[] filteredY = Arrays.copyOfRange(_doubleColY, 0, NUM_RECORDS / 2);
+ _expectedCovWithFilter = cov.covariance(filteredX, filteredY, false);
+
+ // calculate inter segment group by results
+ for (int i = 0; i < NUM_GROUPS; i++) {
+ double[] colX = Arrays.copyOfRange(_doubleColX, i * groupSize, (i + 1) * groupSize);
+ double[] colGroupBy = Arrays.copyOfRange(_groupByCol, i * groupSize, (i + 1) * groupSize);
+ double[] colY = Arrays.copyOfRange(_doubleColY, i * groupSize, (i + 1) * groupSize);
+ _expectedFinalResultVer1[i] = cov.covariance(colX, colGroupBy, false);
+ _expectedFinalResultVer2[i] = cov.covariance(colX, colY, false);
+ }
+
+ // generate testSegment
+ ImmutableSegment immutableSegment = setUpSingleSegment(records, SEGMENT_NAME);
+ _indexSegment = immutableSegment;
+ _indexSegments = Arrays.asList(immutableSegment, immutableSegment);
+
+ // divide testSegment into 4 distinct segments for distinct inter segment tests
+ // by doing so, we can avoid calculating global covariance again
+ _distinctInstances = new ArrayList<>();
+ int segmentSize = NUM_RECORDS / 4;
+ ImmutableSegment immutableSegment1 = setUpSingleSegment(records.subList(0, segmentSize), SEGMENT_NAME_1);
+ ImmutableSegment immutableSegment2 =
+ setUpSingleSegment(records.subList(segmentSize, segmentSize * 2), SEGMENT_NAME_2);
+ ImmutableSegment immutableSegment3 =
+ setUpSingleSegment(records.subList(segmentSize * 2, segmentSize * 3), SEGMENT_NAME_3);
+ ImmutableSegment immutableSegment4 =
+ setUpSingleSegment(records.subList(segmentSize * 3, NUM_RECORDS), SEGMENT_NAME_4);
+ // generate 2 instances each with 2 distinct segments
+ _distinctInstances.add(Arrays.asList(immutableSegment1, immutableSegment2));
+ _distinctInstances.add(Arrays.asList(immutableSegment3, immutableSegment4));
+ }
+
+ private ImmutableSegment setUpSingleSegment(List<GenericRow> recordSet, String segmentName)
+ throws Exception {
+ SegmentGeneratorConfig segmentGeneratorConfig = new SegmentGeneratorConfig(TABLE_CONFIG, SCHEMA);
+ segmentGeneratorConfig.setTableName(RAW_TABLE_NAME);
+ segmentGeneratorConfig.setSegmentName(segmentName);
+ segmentGeneratorConfig.setOutDir(INDEX_DIR.getPath());
+
+ SegmentIndexCreationDriverImpl driver = new SegmentIndexCreationDriverImpl();
+ driver.init(segmentGeneratorConfig, new GenericRowRecordReader(recordSet));
+ driver.build();
+
+ return ImmutableSegmentLoader.load(new File(INDEX_DIR, segmentName), ReadMode.mmap);
+ }
+
+ @Test
+ public void testCovarianceAggregationOnly() {
+ // Inner Segment
+ String query =
+ "SELECT COVAR_POP(intColumnX, intColumnY), COVAR_POP(doubleColumnX, doubleColumnY), COVAR_POP(intColumnX, "
+ + "doubleColumnX), " + "COVAR_POP(intColumnX, longColumn), COVAR_POP(intColumnX, floatColumn), "
+ + "COVAR_POP(doubleColumnX, longColumn), COVAR_POP(doubleColumnX, floatColumn), COVAR_POP(longColumn, "
+ + "floatColumn) FROM testTable";
+ AggregationOperator aggregationOperator = getOperator(query);
+ AggregationResultsBlock resultsBlock = aggregationOperator.nextBlock();
+ QueriesTestUtils.testInnerSegmentExecutionStatistics(aggregationOperator.getExecutionStatistics(), NUM_RECORDS, 0,
+ NUM_RECORDS * 6, NUM_RECORDS);
+ List<Object> aggregationResult = resultsBlock.getResults();
+ assertNotNull(aggregationResult);
+ checkWithPrecisionForCovariance((CovarianceTuple) aggregationResult.get(0), _sumIntX, _sumIntY, _sumIntXY,
+ NUM_RECORDS);
+ checkWithPrecisionForCovariance((CovarianceTuple) aggregationResult.get(1), _sumDoubleX, _sumDoubleY, _sumDoubleXY,
+ NUM_RECORDS);
+ checkWithPrecisionForCovariance((CovarianceTuple) aggregationResult.get(2), _sumIntX, _sumDoubleX, _sumIntDouble,
+ NUM_RECORDS);
+ checkWithPrecisionForCovariance((CovarianceTuple) aggregationResult.get(3), _sumIntX, _sumLong, _sumIntLong,
+ NUM_RECORDS);
+ checkWithPrecisionForCovariance((CovarianceTuple) aggregationResult.get(4), _sumIntX, _sumFloat, _sumIntFloat,
+ NUM_RECORDS);
+ checkWithPrecisionForCovariance((CovarianceTuple) aggregationResult.get(5), _sumDoubleX, _sumLong, _sumDoubleLong,
+ NUM_RECORDS);
+ checkWithPrecisionForCovariance((CovarianceTuple) aggregationResult.get(6), _sumDoubleX, _sumFloat, _sumDoubleFloat,
+ NUM_RECORDS);
+ checkWithPrecisionForCovariance((CovarianceTuple) aggregationResult.get(7), _sumLong, _sumFloat, _sumLongFloat,
+ NUM_RECORDS);
+
+ // Inter segments with 4 identical segments (2 instances each having 2 identical segments)
+ _useIdenticalSegment = true;
+ BrokerResponseNative brokerResponse = getBrokerResponse(query);
+ _useIdenticalSegment = false;
+ assertEquals(brokerResponse.getNumDocsScanned(), 4 * NUM_RECORDS);
+ assertEquals(brokerResponse.getNumEntriesScannedInFilter(), 0);
+ assertEquals(brokerResponse.getNumEntriesScannedPostFilter(), 4 * 6 * NUM_RECORDS);
+ assertEquals(brokerResponse.getTotalDocs(), 4 * NUM_RECORDS);
+ checkResultTableWithPrecisionForCovariance(brokerResponse);
+
+ // Inter segments with 4 distinct segments (2 instances each having 2 distinct segments)
+ brokerResponse = getBrokerResponse(query);
+ assertEquals(brokerResponse.getNumDocsScanned(), NUM_RECORDS);
+ assertEquals(brokerResponse.getNumEntriesScannedInFilter(), 0);
+ assertEquals(brokerResponse.getNumEntriesScannedPostFilter(), 6 * NUM_RECORDS);
+ assertEquals(brokerResponse.getTotalDocs(), NUM_RECORDS);
+ checkResultTableWithPrecisionForCovariance(brokerResponse);
+
+ // Inter segments with 4 identical segments with filter
+ _useIdenticalSegment = true;
+ query = "SELECT COVAR_POP(doubleColumnX, doubleColumnY) FROM testTable" + getFilter();
+ brokerResponse = getBrokerResponse(query);
+ _useIdenticalSegment = false;
+ assertEquals(brokerResponse.getNumDocsScanned(), 2 * NUM_RECORDS);
+ assertEquals(brokerResponse.getNumEntriesScannedInFilter(), 0);
+ assertEquals(brokerResponse.getNumEntriesScannedPostFilter(), 4 * NUM_RECORDS);
+ assertEquals(brokerResponse.getTotalDocs(), 4 * NUM_RECORDS);
+ Object[] results = brokerResponse.getResultTable().getRows().get(0);
+ assertTrue(Precision.equalsWithRelativeTolerance((double) results[0], _expectedCovWithFilter, RELATIVE_EPSILON));
+ }
+
+ @Test
+ public void testCovarianceAggregationGroupBy() {
+ // Inner Segment
+ // case 1: (col1, groupByCol) group by groupByCol => all covariances are 0's
+ String query =
+ "SELECT COVAR_POP(doubleColumnX, groupByColumn) FROM testTable GROUP BY groupByColumn ORDER BY groupByColumn";
+ GroupByOperator groupByOperator = getOperator(query);
+ GroupByResultsBlock resultsBlock = groupByOperator.nextBlock();
+ QueriesTestUtils.testInnerSegmentExecutionStatistics(groupByOperator.getExecutionStatistics(), NUM_RECORDS, 0,
+ NUM_RECORDS * 2, NUM_RECORDS);
+ AggregationGroupByResult aggregationGroupByResult = resultsBlock.getAggregationGroupByResult();
+ assertNotNull(aggregationGroupByResult);
+ for (int i = 0; i < NUM_GROUPS; i++) {
+ CovarianceTuple actualCovTuple = (CovarianceTuple) aggregationGroupByResult.getResultForGroupId(0, i);
+ CovarianceTuple expectedCovTuple = _expectedGroupByResultVer1[i];
+ checkWithPrecisionForCovariance(actualCovTuple, expectedCovTuple);
+ }
+
+ // Inter Segment with 4 identical segments
+ _useIdenticalSegment = true;
+ BrokerResponseNative brokerResponse = getBrokerResponse(query);
+ checkGroupByResultsForCovariance(brokerResponse, _expectedFinalResultVer1);
+ _useIdenticalSegment = false;
+ // Inter Segment with 4 distinct segments
+ brokerResponse = getBrokerResponse(query);
+ checkGroupByResultsForCovariance(brokerResponse, _expectedFinalResultVer1);
+
+ // Inner Segment
+ // case 2: COVAR_POP(col1, col2) group by groupByCol => nondeterministic cov
+ query =
+ "SELECT COVAR_POP(doubleColumnX, doubleColumnY) FROM testTable GROUP BY groupByColumn ORDER BY groupByColumn";
+ groupByOperator = getOperator(query);
+ resultsBlock = groupByOperator.nextBlock();
+ QueriesTestUtils.testInnerSegmentExecutionStatistics(groupByOperator.getExecutionStatistics(), NUM_RECORDS, 0,
+ NUM_RECORDS * 3, NUM_RECORDS);
+ aggregationGroupByResult = resultsBlock.getAggregationGroupByResult();
+ assertNotNull(aggregationGroupByResult);
+
+ for (int i = 0; i < NUM_GROUPS; i++) {
+ CovarianceTuple actualCovTuple = (CovarianceTuple) aggregationGroupByResult.getResultForGroupId(0, i);
+ CovarianceTuple expectedCovTuple = _expectedGroupByResultVer2[i];
+ checkWithPrecisionForCovariance(actualCovTuple, expectedCovTuple);
+ }
+
+ // Inter Segment with 4 identical segments
+ _useIdenticalSegment = true;
+ brokerResponse = getBrokerResponse(query);
+ checkGroupByResultsForCovariance(brokerResponse, _expectedFinalResultVer2);
+ _useIdenticalSegment = false;
+ // Inter Segment with 4 distinct segments
+ brokerResponse = getBrokerResponse(query);
+ checkGroupByResultsForCovariance(brokerResponse, _expectedFinalResultVer2);
+ }
+
+ @Test
+ public void testVarianceAggregationOnly() {
+ // Compute the expected values
+ Variance[] expectedVariances = new Variance[8];
+ for (int i = 0; i < 8; i++) {
+ if (i < 4) {
+ expectedVariances[i] = new Variance(false);
+ } else {
+ expectedVariances[i] = new Variance(true);
+ }
+ }
+ for (int i = 0; i < NUM_RECORDS; i++) {
+ expectedVariances[0].increment(_intColX[i]);
+ expectedVariances[1].increment(_longCol[i]);
+ expectedVariances[2].increment(_floatCol[i]);
+ expectedVariances[3].increment(_doubleColX[i]);
+ expectedVariances[4].increment(_intColX[i]);
+ expectedVariances[5].increment(_longCol[i]);
+ expectedVariances[6].increment(_floatCol[i]);
+ expectedVariances[7].increment(_doubleColX[i]);
+ }
+ double expectedIntSum = Arrays.stream(_intColX).asDoubleStream().sum();
+ double expectedLongSum = Arrays.stream(_longCol).asDoubleStream().sum();
+ double expectedFloatSum = 0.0;
+ for (int i = 0; i < _floatCol.length; i++) {
+ expectedFloatSum += _floatCol[i];
+ }
+ double expectedDoubleSum = Arrays.stream(_doubleColX).sum();
+
+ // Compute the query
+ String query = "SELECT VAR_POP(intColumnX), VAR_POP(longColumn), VAR_POP(floatColumn), VAR_POP(doubleColumnX),"
+ + "VAR_SAMP(intColumnX), VAR_SAMP(longColumn), VAR_SAMP(floatColumn), VAR_SAMP(doubleColumnX) FROM testTable";
+ AggregationOperator aggregationOperator = getOperator(query);
+ AggregationResultsBlock resultsBlock = aggregationOperator.nextBlock();
+ QueriesTestUtils.testInnerSegmentExecutionStatistics(aggregationOperator.getExecutionStatistics(), NUM_RECORDS, 0,
+ NUM_RECORDS * 4, NUM_RECORDS);
+ List<Object> aggregationResult = resultsBlock.getResults();
+
+ // Validate the aggregation results
+ checkWithPrecisionForVariance((VarianceTuple) aggregationResult.get(0), NUM_RECORDS, expectedIntSum,
+ expectedVariances[0].getResult(), false);
+ checkWithPrecisionForVariance((VarianceTuple) aggregationResult.get(1), NUM_RECORDS, expectedLongSum,
+ expectedVariances[1].getResult(), false);
+ checkWithPrecisionForVariance((VarianceTuple) aggregationResult.get(2), NUM_RECORDS, expectedFloatSum,
+ expectedVariances[2].getResult(), false);
+ checkWithPrecisionForVariance((VarianceTuple) aggregationResult.get(3), NUM_RECORDS, expectedDoubleSum,
+ expectedVariances[3].getResult(), false);
+ checkWithPrecisionForVariance((VarianceTuple) aggregationResult.get(4), NUM_RECORDS, expectedIntSum,
+ expectedVariances[4].getResult(), true);
+ checkWithPrecisionForVariance((VarianceTuple) aggregationResult.get(5), NUM_RECORDS, expectedLongSum,
+ expectedVariances[5].getResult(), true);
+ checkWithPrecisionForVariance((VarianceTuple) aggregationResult.get(6), NUM_RECORDS, expectedFloatSum,
+ expectedVariances[6].getResult(), true);
+ checkWithPrecisionForVariance((VarianceTuple) aggregationResult.get(7), NUM_RECORDS, expectedDoubleSum,
+ expectedVariances[7].getResult(), true);
+
+ // Validate the response
+ BrokerResponseNative brokerResponse = getBrokerResponse(query);
+ Object[] results = brokerResponse.getResultTable().getRows().get(0);
+ assertTrue(
+ Precision.equalsWithRelativeTolerance((double) results[0], expectedVariances[0].getResult(), RELATIVE_EPSILON));
+ assertTrue(
+ Precision.equalsWithRelativeTolerance((double) results[1], expectedVariances[1].getResult(), RELATIVE_EPSILON));
+ assertTrue(
+ Precision.equalsWithRelativeTolerance((double) results[2], expectedVariances[2].getResult(), RELATIVE_EPSILON));
+ assertTrue(
+ Precision.equalsWithRelativeTolerance((double) results[3], expectedVariances[3].getResult(), RELATIVE_EPSILON));
+ assertTrue(
+ Precision.equalsWithRelativeTolerance((double) results[4], expectedVariances[4].getResult(), RELATIVE_EPSILON));
+ assertTrue(
+ Precision.equalsWithRelativeTolerance((double) results[5], expectedVariances[5].getResult(), RELATIVE_EPSILON));
+ assertTrue(
+ Precision.equalsWithRelativeTolerance((double) results[6], expectedVariances[6].getResult(), RELATIVE_EPSILON));
+ assertTrue(
+ Precision.equalsWithRelativeTolerance((double) results[7], expectedVariances[7].getResult(), RELATIVE_EPSILON));
+
+ VarianceTuple test = ((VarianceTuple) aggregationResult.get(0));
+ test.apply((new VarianceTuple(0, 0, 0.0d)));
+ System.out.println(test.getM2());
+ // Validate the response for a query with a filter
+ query = "SELECT VAR_POP(intColumnX) from testTable" + getFilter();
+ brokerResponse = getBrokerResponse(query);
+ brokerResponse.getResultTable();
+ results = brokerResponse.getResultTable().getRows().get(0);
+ Variance filterExpectedVariance = new Variance(false);
+ for (int i = 0; i < NUM_RECORDS / 2; i++) {
+ filterExpectedVariance.increment(_intColX[i]);
+ }
+ assertTrue(Precision.equalsWithRelativeTolerance((double) results[0], filterExpectedVariance.getResult(),
+ RELATIVE_EPSILON));
+ }
+
+ @Test
+ public void testVarianceAggregationGroupBy() {
+ // Compute expected group results
+ Variance[] expectedGroupByResult = new Variance[NUM_GROUPS];
+ double[] expectedSum = new double[NUM_GROUPS];
+
+ for (int i = 0; i < NUM_GROUPS; i++) {
+ expectedGroupByResult[i] = new Variance(false);
+ }
+ for (int j = 0; j < NUM_RECORDS; j++) {
+ int pos = j / (NUM_RECORDS / NUM_GROUPS);
+ expectedGroupByResult[pos].increment(_intColX[j]);
+ expectedSum[pos] += _intColX[j];
+ }
+
+ String query = "SELECT VAR_POP(intColumnX) FROM testTable GROUP BY groupByColumn ORDER BY groupByColumn";
+ GroupByOperator groupByOperator = getOperator(query);
+ GroupByResultsBlock resultsBlock = groupByOperator.nextBlock();
+ QueriesTestUtils.testInnerSegmentExecutionStatistics(groupByOperator.getExecutionStatistics(), NUM_RECORDS, 0,
+ NUM_RECORDS * 2, NUM_RECORDS);
+ AggregationGroupByResult aggregationGroupByResult = resultsBlock.getAggregationGroupByResult();
+ assertNotNull(aggregationGroupByResult);
+ for (int i = 0; i < NUM_GROUPS; i++) {
+
+ VarianceTuple actualVarianceTuple = (VarianceTuple) aggregationGroupByResult.getResultForGroupId(0, i);
+ checkWithPrecisionForVariance(actualVarianceTuple, NUM_RECORDS / NUM_GROUPS, expectedSum[i],
+ expectedGroupByResult[i].getResult(), false);
+ }
+ }
+
+ @Test
+ public void testStandardDeviationAggregationOnly() {
Review Comment:
Should be fixed with #9948
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[GitHub] [pinot] jasperjiaguo commented on a diff in pull request #9910: Add Variance and Standard Deviation Aggregation Functions
Posted by GitBox <gi...@apache.org>.
jasperjiaguo commented on code in PR #9910:
URL: https://github.com/apache/pinot/pull/9910#discussion_r1041611525
##########
pinot-core/src/test/java/org/apache/pinot/queries/VarianceQueriesTest.java:
##########
@@ -0,0 +1,446 @@
+/**
+ * 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.pinot.queries;
+
+import java.io.File;
+import java.util.ArrayList;
+import java.util.Arrays;
+import java.util.List;
+import java.util.Random;
+import org.apache.commons.io.FileUtils;
+import org.apache.commons.math3.stat.descriptive.moment.StandardDeviation;
+import org.apache.commons.math3.stat.descriptive.moment.Variance;
+import org.apache.commons.math3.util.Precision;
+import org.apache.pinot.common.response.broker.BrokerResponseNative;
+import org.apache.pinot.core.operator.blocks.results.AggregationResultsBlock;
+import org.apache.pinot.core.operator.blocks.results.GroupByResultsBlock;
+import org.apache.pinot.core.operator.query.AggregationOperator;
+import org.apache.pinot.core.operator.query.GroupByOperator;
+import org.apache.pinot.core.query.aggregation.groupby.AggregationGroupByResult;
+import org.apache.pinot.segment.local.customobject.VarianceTuple;
+import org.apache.pinot.segment.local.indexsegment.immutable.ImmutableSegmentLoader;
+import org.apache.pinot.segment.local.segment.creator.impl.SegmentIndexCreationDriverImpl;
+import org.apache.pinot.segment.local.segment.readers.GenericRowRecordReader;
+import org.apache.pinot.segment.spi.ImmutableSegment;
+import org.apache.pinot.segment.spi.IndexSegment;
+import org.apache.pinot.segment.spi.creator.SegmentGeneratorConfig;
+import org.apache.pinot.spi.config.table.TableConfig;
+import org.apache.pinot.spi.config.table.TableType;
+import org.apache.pinot.spi.data.FieldSpec;
+import org.apache.pinot.spi.data.Schema;
+import org.apache.pinot.spi.data.readers.GenericRow;
+import org.apache.pinot.spi.utils.ReadMode;
+import org.apache.pinot.spi.utils.builder.TableConfigBuilder;
+import org.testng.annotations.BeforeClass;
+import org.testng.annotations.Test;
+
+import static org.testng.Assert.assertEquals;
+import static org.testng.Assert.assertNotNull;
+import static org.testng.Assert.assertTrue;
+
+
+public class VarianceQueriesTest extends BaseQueriesTest {
+
+ private static final File INDEX_DIR = new File(FileUtils.getTempDirectory(), "VarianceQueriesTest");
+
+ private static final String RAW_TABLE_NAME = "testTable";
+ private static final String SEGMENT_NAME = "testSegment";
+
+ private static final int NUM_RECORDS = 2000;
+ private static final int NUM_GROUPS = 10;
+ private static final int MAX_VALUE = 500;
+ private static final double RELATIVE_EPSILON = 0.0001;
+ private static final double DELTA = 0.0001;
+
+ private static final String INT_COLUMN = "intColumn";
+ private static final String LONG_COLUMN = "longColumn";
+ private static final String FLOAT_COLUMN = "floatColumn";
+ private static final String DOUBLE_COLUMN = "doubleColumn";
+ private static final String GROUP_BY_COLUMN = "groupByColumn";
+
+ private static final Schema SCHEMA =
+ new Schema.SchemaBuilder().addSingleValueDimension(INT_COLUMN, FieldSpec.DataType.INT)
+ .addSingleValueDimension(LONG_COLUMN, FieldSpec.DataType.LONG)
+ .addSingleValueDimension(FLOAT_COLUMN, FieldSpec.DataType.FLOAT)
+ .addSingleValueDimension(DOUBLE_COLUMN, FieldSpec.DataType.DOUBLE)
+ .addSingleValueDimension(GROUP_BY_COLUMN, FieldSpec.DataType.DOUBLE).build();
+
+ private static final TableConfig TABLE_CONFIG =
+ new TableConfigBuilder(TableType.OFFLINE).setTableName(RAW_TABLE_NAME).build();
+
+ private IndexSegment _indexSegment;
+ private List<IndexSegment> _indexSegments;
+
+ int[] _intValues = new int[NUM_RECORDS];
+ long[] _longValues = new long[NUM_RECORDS];
+ float[] _floatValues = new float[NUM_RECORDS];
+ double[] _doubleValues = new double[NUM_RECORDS];
+
+ @Override
+ protected String getFilter() {
+ // filter out half of the rows based on group id
+ return " WHERE groupByColumn < " + (NUM_GROUPS / 2);
+ }
+
+ @Override
+ protected IndexSegment getIndexSegment() {
+ return _indexSegment;
+ }
+
+ @Override
+ protected List<IndexSegment> getIndexSegments() {
+ return _indexSegments;
+ }
+
+ @BeforeClass
+ public void setUp()
+ throws Exception {
+ FileUtils.deleteDirectory(INDEX_DIR);
+ Random random = new Random();
+ List<GenericRow> records = new ArrayList<>(NUM_RECORDS);
+
+ for (int i = 0; i < NUM_RECORDS; i++) {
+ GenericRow record = new GenericRow();
+ int intValue = -MAX_VALUE + random.nextInt() * 2 * MAX_VALUE;
+ long longValue = -MAX_VALUE + random.nextLong() * 2 * MAX_VALUE;
+ float floatValue = -MAX_VALUE + random.nextFloat() * 2 * MAX_VALUE;
+ double doubleValue = -MAX_VALUE + random.nextDouble() * 2 * MAX_VALUE;
+
+ _intValues[i] = intValue;
+ _longValues[i] = longValue;
+ _floatValues[i] = floatValue;
+ _doubleValues[i] = doubleValue;
+
+ record.putValue(INT_COLUMN, _intValues[i]);
+ record.putValue(LONG_COLUMN, _longValues[i]);
+ record.putValue(FLOAT_COLUMN, _floatValues[i]);
+ record.putValue(DOUBLE_COLUMN, _doubleValues[i]);
+ record.putValue(GROUP_BY_COLUMN, Math.floor(i / (NUM_RECORDS / NUM_GROUPS)));
+ records.add(record);
+ }
+
+ SegmentGeneratorConfig segmentGeneratorConfig = new SegmentGeneratorConfig(TABLE_CONFIG, SCHEMA);
+ segmentGeneratorConfig.setTableName(RAW_TABLE_NAME);
+ segmentGeneratorConfig.setSegmentName(SEGMENT_NAME);
+ segmentGeneratorConfig.setOutDir(INDEX_DIR.getPath());
+
+ SegmentIndexCreationDriverImpl driver = new SegmentIndexCreationDriverImpl();
+ driver.init(segmentGeneratorConfig, new GenericRowRecordReader(records));
+ driver.build();
+
+ ImmutableSegment immutableSegment = ImmutableSegmentLoader.load(new File(INDEX_DIR, SEGMENT_NAME), ReadMode.mmap);
+ _indexSegment = immutableSegment;
+ _indexSegments = Arrays.asList(immutableSegment, immutableSegment);
+ }
+
+ @Test
+ public void testVarianceAggregationOnly() {
+ // Compute the expected values
+ Variance[] expectedVariances = new Variance[8];
+ for (int i = 0; i < 8; i++) {
+ if (i < 4) {
+ expectedVariances[i] = new Variance(false);
+ } else {
+ expectedVariances[i] = new Variance(true);
+ }
+ }
+ for (int i = 0; i < NUM_RECORDS; i++) {
+ expectedVariances[0].increment(_intValues[i]);
+ expectedVariances[1].increment(_longValues[i]);
+ expectedVariances[2].increment(_floatValues[i]);
+ expectedVariances[3].increment(_doubleValues[i]);
+ expectedVariances[4].increment(_intValues[i]);
+ expectedVariances[5].increment(_longValues[i]);
+ expectedVariances[6].increment(_floatValues[i]);
+ expectedVariances[7].increment(_doubleValues[i]);
+ }
+ double expectedIntSum = Arrays.stream(_intValues).asDoubleStream().sum();
+ double expectedLongSum = Arrays.stream(_longValues).asDoubleStream().sum();
+ double expectedFloatSum = 0.0;
+ for (int i = 0; i < _floatValues.length; i++) {
+ expectedFloatSum += _floatValues[i];
+ }
+ double expectedDoubleSum = Arrays.stream(_doubleValues).sum();
+
+ // Compute the query
+ String query = "SELECT VAR_POP(intColumn), VAR_POP(longColumn), VAR_POP(floatColumn), VAR_POP(doubleColumn),"
+ + "VAR_SAMP(intColumn), VAR_SAMP(longColumn), VAR_SAMP(floatColumn), VAR_SAMP(doubleColumn) FROM testTable";
+ AggregationOperator aggregationOperator = getOperator(query);
+ AggregationResultsBlock resultsBlock = aggregationOperator.nextBlock();
+ QueriesTestUtils.testInnerSegmentExecutionStatistics(aggregationOperator.getExecutionStatistics(), NUM_RECORDS, 0,
+ NUM_RECORDS * 4, NUM_RECORDS);
+ List<Object> aggregationResult = resultsBlock.getResults();
+
+ // Validate the aggregation results
+ checkWithPrecisionForVariance((VarianceTuple) aggregationResult.get(0), NUM_RECORDS, expectedIntSum,
+ expectedVariances[0].getResult(), false);
+ checkWithPrecisionForVariance((VarianceTuple) aggregationResult.get(1), NUM_RECORDS, expectedLongSum,
+ expectedVariances[1].getResult(), false);
+ checkWithPrecisionForVariance((VarianceTuple) aggregationResult.get(2), NUM_RECORDS, expectedFloatSum,
+ expectedVariances[2].getResult(), false);
+ checkWithPrecisionForVariance((VarianceTuple) aggregationResult.get(3), NUM_RECORDS, expectedDoubleSum,
+ expectedVariances[3].getResult(), false);
+ checkWithPrecisionForVariance((VarianceTuple) aggregationResult.get(4), NUM_RECORDS, expectedIntSum,
+ expectedVariances[4].getResult(), true);
+ checkWithPrecisionForVariance((VarianceTuple) aggregationResult.get(5), NUM_RECORDS, expectedLongSum,
+ expectedVariances[5].getResult(), true);
+ checkWithPrecisionForVariance((VarianceTuple) aggregationResult.get(6), NUM_RECORDS, expectedFloatSum,
+ expectedVariances[6].getResult(), true);
+ checkWithPrecisionForVariance((VarianceTuple) aggregationResult.get(7), NUM_RECORDS, expectedDoubleSum,
+ expectedVariances[7].getResult(), true);
+
+ // Update the expected result by 3 more times (broker query will compute 4 identical segments)
+ for (int i = 0; i < NUM_RECORDS * 3; i++) {
+ int pos = i % NUM_RECORDS;
+ expectedVariances[0].increment(_intValues[pos]);
+ expectedVariances[1].increment(_longValues[pos]);
+ expectedVariances[2].increment(_floatValues[pos]);
+ expectedVariances[3].increment(_doubleValues[pos]);
+ expectedVariances[4].increment(_intValues[pos]);
+ expectedVariances[5].increment(_longValues[pos]);
+ expectedVariances[6].increment(_floatValues[pos]);
+ expectedVariances[7].increment(_doubleValues[pos]);
+ }
+
+ // Validate the response
+ BrokerResponseNative brokerResponse = getBrokerResponse(query);
+ brokerResponse.getResultTable();
+ Object[] results = brokerResponse.getResultTable().getRows().get(0);
+ assertTrue(
+ Precision.equalsWithRelativeTolerance((double) results[0], expectedVariances[0].getResult(), RELATIVE_EPSILON));
+ assertTrue(
+ Precision.equalsWithRelativeTolerance((double) results[1], expectedVariances[1].getResult(), RELATIVE_EPSILON));
+ assertTrue(
+ Precision.equalsWithRelativeTolerance((double) results[2], expectedVariances[2].getResult(), RELATIVE_EPSILON));
+ assertTrue(
+ Precision.equalsWithRelativeTolerance((double) results[3], expectedVariances[3].getResult(), RELATIVE_EPSILON));
+ assertTrue(
+ Precision.equalsWithRelativeTolerance((double) results[4], expectedVariances[4].getResult(), RELATIVE_EPSILON));
+ assertTrue(
+ Precision.equalsWithRelativeTolerance((double) results[5], expectedVariances[5].getResult(), RELATIVE_EPSILON));
+ assertTrue(
+ Precision.equalsWithRelativeTolerance((double) results[6], expectedVariances[6].getResult(), RELATIVE_EPSILON));
+ assertTrue(
+ Precision.equalsWithRelativeTolerance((double) results[7], expectedVariances[7].getResult(), RELATIVE_EPSILON));
+
+ // Validate the response for a query with a filter
+ query = "SELECT VAR_POP(intColumn) from testTable" + getFilter();
+ brokerResponse = getBrokerResponse(query);
+ brokerResponse.getResultTable();
+ results = brokerResponse.getResultTable().getRows().get(0);
+ Variance filterExpectedVariance = new Variance(false);
+ for (int i = 0; i < NUM_RECORDS / 2; i++) {
+ filterExpectedVariance.increment(_intValues[i]);
+ }
+ assertTrue(Precision.equalsWithRelativeTolerance((double) results[0], filterExpectedVariance.getResult(),
+ RELATIVE_EPSILON));
+ }
+
+ @Test
+ public void testVarianceAggregationGroupBy() {
+ // Compute expected group results
+ Variance[] expectedGroupByResult = new Variance[NUM_GROUPS];
+ double[] expectedSum = new double[NUM_GROUPS];
+
+ for (int i = 0; i < NUM_GROUPS; i++) {
+ expectedGroupByResult[i] = new Variance(false);
+ }
+ for (int j = 0; j < NUM_RECORDS; j++) {
+ int pos = j / (NUM_RECORDS / NUM_GROUPS);
+ expectedGroupByResult[pos].increment(_intValues[j]);
+ expectedSum[pos] += _intValues[j];
+ }
+
+ String query = "SELECT VAR_POP(intColumn) FROM testTable GROUP BY groupByColumn ORDER BY groupByColumn";
+ GroupByOperator groupByOperator = getOperator(query);
+ GroupByResultsBlock resultsBlock = groupByOperator.nextBlock();
+ QueriesTestUtils.testInnerSegmentExecutionStatistics(groupByOperator.getExecutionStatistics(), NUM_RECORDS, 0,
+ NUM_RECORDS * 2, NUM_RECORDS);
+ AggregationGroupByResult aggregationGroupByResult = resultsBlock.getAggregationGroupByResult();
+ assertNotNull(aggregationGroupByResult);
+ for (int i = 0; i < NUM_GROUPS; i++) {
+
+ VarianceTuple actualVarianceTuple = (VarianceTuple) aggregationGroupByResult.getResultForGroupId(0, i);
+ checkWithPrecisionForVariance(actualVarianceTuple, NUM_RECORDS / NUM_GROUPS, expectedSum[i],
+ expectedGroupByResult[i].getResult(), false);
+ }
+ }
+
+ @Test
+ public void testStandardDeviationAggregationOnly() {
+ // Compute the expected values
+ StandardDeviation[] expectedStdDevs = new StandardDeviation[8];
+ for (int i = 0; i < 8; i++) {
+ if (i < 4) {
+ expectedStdDevs[i] = new StandardDeviation(false);
+ } else {
+ expectedStdDevs[i] = new StandardDeviation(true);
+ }
+ }
+ for (int i = 0; i < NUM_RECORDS; i++) {
+ expectedStdDevs[0].increment(_intValues[i]);
+ expectedStdDevs[1].increment(_longValues[i]);
+ expectedStdDevs[2].increment(_floatValues[i]);
+ expectedStdDevs[3].increment(_doubleValues[i]);
+ expectedStdDevs[4].increment(_intValues[i]);
+ expectedStdDevs[5].increment(_longValues[i]);
+ expectedStdDevs[6].increment(_floatValues[i]);
+ expectedStdDevs[7].increment(_doubleValues[i]);
+ }
+
+ double expectedIntSum = Arrays.stream(_intValues).asDoubleStream().sum();
+ double expectedLongSum = Arrays.stream(_longValues).asDoubleStream().sum();
+ double expectedFloatSum = 0.0;
+ for (int i = 0; i < _floatValues.length; i++) {
+ expectedFloatSum += _floatValues[i];
+ }
+ double expectedDoubleSum = Arrays.stream(_doubleValues).sum();
+
+ // Compute the query
+ String query =
+ "SELECT STDDEV_POP(intColumn), STDDEV_POP(longColumn), STDDEV_POP(floatColumn), STDDEV_POP(doubleColumn),"
+ + "STDDEV_SAMP(intColumn), STDDEV_SAMP(longColumn), STDDEV_SAMP(floatColumn), STDDEV_SAMP(doubleColumn) "
+ + "FROM testTable";
+ AggregationOperator aggregationOperator = getOperator(query);
+ AggregationResultsBlock resultsBlock = aggregationOperator.nextBlock();
+ QueriesTestUtils.testInnerSegmentExecutionStatistics(aggregationOperator.getExecutionStatistics(), NUM_RECORDS, 0,
+ NUM_RECORDS * 4, NUM_RECORDS);
+ List<Object> aggregationResult = resultsBlock.getResults();
+
+ // Validate the aggregation results
+ checkWithPrecisionForStandardDeviation((VarianceTuple) aggregationResult.get(0), NUM_RECORDS, expectedIntSum,
+ expectedStdDevs[0].getResult(), false);
+ checkWithPrecisionForStandardDeviation((VarianceTuple) aggregationResult.get(1), NUM_RECORDS, expectedLongSum,
+ expectedStdDevs[1].getResult(), false);
+ checkWithPrecisionForStandardDeviation((VarianceTuple) aggregationResult.get(2), NUM_RECORDS, expectedFloatSum,
+ expectedStdDevs[2].getResult(), false);
+ checkWithPrecisionForStandardDeviation((VarianceTuple) aggregationResult.get(3), NUM_RECORDS, expectedDoubleSum,
+ expectedStdDevs[3].getResult(), false);
+ checkWithPrecisionForStandardDeviation((VarianceTuple) aggregationResult.get(4), NUM_RECORDS, expectedIntSum,
+ expectedStdDevs[4].getResult(), true);
+ checkWithPrecisionForStandardDeviation((VarianceTuple) aggregationResult.get(5), NUM_RECORDS, expectedLongSum,
+ expectedStdDevs[5].getResult(), true);
+ checkWithPrecisionForStandardDeviation((VarianceTuple) aggregationResult.get(6), NUM_RECORDS, expectedFloatSum,
+ expectedStdDevs[6].getResult(), true);
+ checkWithPrecisionForStandardDeviation((VarianceTuple) aggregationResult.get(7), NUM_RECORDS, expectedDoubleSum,
+ expectedStdDevs[7].getResult(), true);
+
+ // Update the expected result by 3 more times (broker query will compute 4 identical segments)
Review Comment:
The variance on 4 identical segments is the same as variance on one of each segment. Should probably to consider using `getDistinctInstances` in `BaseQueriesTest`
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[GitHub] [pinot] walterddr commented on a diff in pull request #9910: Add Variance and Standard Deviation Aggregation Functions
Posted by GitBox <gi...@apache.org>.
walterddr commented on code in PR #9910:
URL: https://github.com/apache/pinot/pull/9910#discussion_r1041640617
##########
pinot-core/src/main/java/org/apache/pinot/core/query/aggregation/function/VarianceAggregationFunction.java:
##########
@@ -0,0 +1,204 @@
+/**
+ * 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.pinot.core.query.aggregation.function;
+
+import com.google.common.base.Preconditions;
+import java.util.Map;
+import org.apache.pinot.common.request.context.ExpressionContext;
+import org.apache.pinot.common.utils.DataSchema;
+import org.apache.pinot.core.common.BlockValSet;
+import org.apache.pinot.core.query.aggregation.AggregationResultHolder;
+import org.apache.pinot.core.query.aggregation.ObjectAggregationResultHolder;
+import org.apache.pinot.core.query.aggregation.groupby.GroupByResultHolder;
+import org.apache.pinot.core.query.aggregation.groupby.ObjectGroupByResultHolder;
+import org.apache.pinot.segment.local.customobject.VarianceTuple;
+import org.apache.pinot.segment.spi.AggregationFunctionType;
+
+
+/**
+ * Aggregation function which computes Variance and Standard Deviation
+ *
+ * The algorithm to compute variance is based on "Updating Formulae and a Pairwise Algorithm for Computing
+ * Sample Variances" by Chan et al. Please refer to the "Parallel Algorithm" section from the following wiki:
+ * - https://en.wikipedia.org/wiki/Algorithms_for_calculating_variance#Parallel_algorithm
+ */
+public class VarianceAggregationFunction extends BaseSingleInputAggregationFunction<VarianceTuple, Double> {
Review Comment:
majority of this code is shared with CoVariance agg. can we abstract out some comment utilities?
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[GitHub] [pinot] snleee merged pull request #9910: Add Variance and Standard Deviation Aggregation Functions
Posted by GitBox <gi...@apache.org>.
snleee merged PR #9910:
URL: https://github.com/apache/pinot/pull/9910
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[GitHub] [pinot] jasperjiaguo commented on a diff in pull request #9910: Add Variance and Standard Deviation Aggregation Functions
Posted by GitBox <gi...@apache.org>.
jasperjiaguo commented on code in PR #9910:
URL: https://github.com/apache/pinot/pull/9910#discussion_r1043888203
##########
pinot-core/src/test/java/org/apache/pinot/queries/VarianceQueriesTest.java:
##########
@@ -0,0 +1,446 @@
+/**
+ * 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.pinot.queries;
+
+import java.io.File;
+import java.util.ArrayList;
+import java.util.Arrays;
+import java.util.List;
+import java.util.Random;
+import org.apache.commons.io.FileUtils;
+import org.apache.commons.math3.stat.descriptive.moment.StandardDeviation;
+import org.apache.commons.math3.stat.descriptive.moment.Variance;
+import org.apache.commons.math3.util.Precision;
+import org.apache.pinot.common.response.broker.BrokerResponseNative;
+import org.apache.pinot.core.operator.blocks.results.AggregationResultsBlock;
+import org.apache.pinot.core.operator.blocks.results.GroupByResultsBlock;
+import org.apache.pinot.core.operator.query.AggregationOperator;
+import org.apache.pinot.core.operator.query.GroupByOperator;
+import org.apache.pinot.core.query.aggregation.groupby.AggregationGroupByResult;
+import org.apache.pinot.segment.local.customobject.VarianceTuple;
+import org.apache.pinot.segment.local.indexsegment.immutable.ImmutableSegmentLoader;
+import org.apache.pinot.segment.local.segment.creator.impl.SegmentIndexCreationDriverImpl;
+import org.apache.pinot.segment.local.segment.readers.GenericRowRecordReader;
+import org.apache.pinot.segment.spi.ImmutableSegment;
+import org.apache.pinot.segment.spi.IndexSegment;
+import org.apache.pinot.segment.spi.creator.SegmentGeneratorConfig;
+import org.apache.pinot.spi.config.table.TableConfig;
+import org.apache.pinot.spi.config.table.TableType;
+import org.apache.pinot.spi.data.FieldSpec;
+import org.apache.pinot.spi.data.Schema;
+import org.apache.pinot.spi.data.readers.GenericRow;
+import org.apache.pinot.spi.utils.ReadMode;
+import org.apache.pinot.spi.utils.builder.TableConfigBuilder;
+import org.testng.annotations.BeforeClass;
+import org.testng.annotations.Test;
+
+import static org.testng.Assert.assertEquals;
+import static org.testng.Assert.assertNotNull;
+import static org.testng.Assert.assertTrue;
+
+
+public class VarianceQueriesTest extends BaseQueriesTest {
+
+ private static final File INDEX_DIR = new File(FileUtils.getTempDirectory(), "VarianceQueriesTest");
+
+ private static final String RAW_TABLE_NAME = "testTable";
+ private static final String SEGMENT_NAME = "testSegment";
+
+ private static final int NUM_RECORDS = 2000;
+ private static final int NUM_GROUPS = 10;
+ private static final int MAX_VALUE = 500;
+ private static final double RELATIVE_EPSILON = 0.0001;
+ private static final double DELTA = 0.0001;
+
+ private static final String INT_COLUMN = "intColumn";
+ private static final String LONG_COLUMN = "longColumn";
+ private static final String FLOAT_COLUMN = "floatColumn";
+ private static final String DOUBLE_COLUMN = "doubleColumn";
+ private static final String GROUP_BY_COLUMN = "groupByColumn";
+
+ private static final Schema SCHEMA =
+ new Schema.SchemaBuilder().addSingleValueDimension(INT_COLUMN, FieldSpec.DataType.INT)
+ .addSingleValueDimension(LONG_COLUMN, FieldSpec.DataType.LONG)
+ .addSingleValueDimension(FLOAT_COLUMN, FieldSpec.DataType.FLOAT)
+ .addSingleValueDimension(DOUBLE_COLUMN, FieldSpec.DataType.DOUBLE)
+ .addSingleValueDimension(GROUP_BY_COLUMN, FieldSpec.DataType.DOUBLE).build();
+
+ private static final TableConfig TABLE_CONFIG =
+ new TableConfigBuilder(TableType.OFFLINE).setTableName(RAW_TABLE_NAME).build();
+
+ private IndexSegment _indexSegment;
+ private List<IndexSegment> _indexSegments;
+
+ int[] _intValues = new int[NUM_RECORDS];
+ long[] _longValues = new long[NUM_RECORDS];
+ float[] _floatValues = new float[NUM_RECORDS];
+ double[] _doubleValues = new double[NUM_RECORDS];
+
+ @Override
+ protected String getFilter() {
+ // filter out half of the rows based on group id
+ return " WHERE groupByColumn < " + (NUM_GROUPS / 2);
+ }
+
+ @Override
+ protected IndexSegment getIndexSegment() {
+ return _indexSegment;
+ }
+
+ @Override
+ protected List<IndexSegment> getIndexSegments() {
+ return _indexSegments;
+ }
+
+ @BeforeClass
+ public void setUp()
+ throws Exception {
+ FileUtils.deleteDirectory(INDEX_DIR);
+ Random random = new Random();
+ List<GenericRow> records = new ArrayList<>(NUM_RECORDS);
+
+ for (int i = 0; i < NUM_RECORDS; i++) {
+ GenericRow record = new GenericRow();
+ int intValue = -MAX_VALUE + random.nextInt() * 2 * MAX_VALUE;
+ long longValue = -MAX_VALUE + random.nextLong() * 2 * MAX_VALUE;
+ float floatValue = -MAX_VALUE + random.nextFloat() * 2 * MAX_VALUE;
+ double doubleValue = -MAX_VALUE + random.nextDouble() * 2 * MAX_VALUE;
+
+ _intValues[i] = intValue;
+ _longValues[i] = longValue;
+ _floatValues[i] = floatValue;
+ _doubleValues[i] = doubleValue;
+
+ record.putValue(INT_COLUMN, _intValues[i]);
+ record.putValue(LONG_COLUMN, _longValues[i]);
+ record.putValue(FLOAT_COLUMN, _floatValues[i]);
+ record.putValue(DOUBLE_COLUMN, _doubleValues[i]);
+ record.putValue(GROUP_BY_COLUMN, Math.floor(i / (NUM_RECORDS / NUM_GROUPS)));
+ records.add(record);
+ }
+
+ SegmentGeneratorConfig segmentGeneratorConfig = new SegmentGeneratorConfig(TABLE_CONFIG, SCHEMA);
+ segmentGeneratorConfig.setTableName(RAW_TABLE_NAME);
+ segmentGeneratorConfig.setSegmentName(SEGMENT_NAME);
+ segmentGeneratorConfig.setOutDir(INDEX_DIR.getPath());
+
+ SegmentIndexCreationDriverImpl driver = new SegmentIndexCreationDriverImpl();
+ driver.init(segmentGeneratorConfig, new GenericRowRecordReader(records));
+ driver.build();
+
+ ImmutableSegment immutableSegment = ImmutableSegmentLoader.load(new File(INDEX_DIR, SEGMENT_NAME), ReadMode.mmap);
+ _indexSegment = immutableSegment;
+ _indexSegments = Arrays.asList(immutableSegment, immutableSegment);
+ }
+
+ @Test
+ public void testVarianceAggregationOnly() {
+ // Compute the expected values
+ Variance[] expectedVariances = new Variance[8];
+ for (int i = 0; i < 8; i++) {
+ if (i < 4) {
+ expectedVariances[i] = new Variance(false);
+ } else {
+ expectedVariances[i] = new Variance(true);
+ }
+ }
+ for (int i = 0; i < NUM_RECORDS; i++) {
+ expectedVariances[0].increment(_intValues[i]);
+ expectedVariances[1].increment(_longValues[i]);
+ expectedVariances[2].increment(_floatValues[i]);
+ expectedVariances[3].increment(_doubleValues[i]);
+ expectedVariances[4].increment(_intValues[i]);
+ expectedVariances[5].increment(_longValues[i]);
+ expectedVariances[6].increment(_floatValues[i]);
+ expectedVariances[7].increment(_doubleValues[i]);
+ }
+ double expectedIntSum = Arrays.stream(_intValues).asDoubleStream().sum();
+ double expectedLongSum = Arrays.stream(_longValues).asDoubleStream().sum();
+ double expectedFloatSum = 0.0;
+ for (int i = 0; i < _floatValues.length; i++) {
+ expectedFloatSum += _floatValues[i];
+ }
+ double expectedDoubleSum = Arrays.stream(_doubleValues).sum();
+
+ // Compute the query
+ String query = "SELECT VAR_POP(intColumn), VAR_POP(longColumn), VAR_POP(floatColumn), VAR_POP(doubleColumn),"
+ + "VAR_SAMP(intColumn), VAR_SAMP(longColumn), VAR_SAMP(floatColumn), VAR_SAMP(doubleColumn) FROM testTable";
+ AggregationOperator aggregationOperator = getOperator(query);
+ AggregationResultsBlock resultsBlock = aggregationOperator.nextBlock();
+ QueriesTestUtils.testInnerSegmentExecutionStatistics(aggregationOperator.getExecutionStatistics(), NUM_RECORDS, 0,
+ NUM_RECORDS * 4, NUM_RECORDS);
+ List<Object> aggregationResult = resultsBlock.getResults();
+
+ // Validate the aggregation results
+ checkWithPrecisionForVariance((VarianceTuple) aggregationResult.get(0), NUM_RECORDS, expectedIntSum,
+ expectedVariances[0].getResult(), false);
+ checkWithPrecisionForVariance((VarianceTuple) aggregationResult.get(1), NUM_RECORDS, expectedLongSum,
+ expectedVariances[1].getResult(), false);
+ checkWithPrecisionForVariance((VarianceTuple) aggregationResult.get(2), NUM_RECORDS, expectedFloatSum,
+ expectedVariances[2].getResult(), false);
+ checkWithPrecisionForVariance((VarianceTuple) aggregationResult.get(3), NUM_RECORDS, expectedDoubleSum,
+ expectedVariances[3].getResult(), false);
+ checkWithPrecisionForVariance((VarianceTuple) aggregationResult.get(4), NUM_RECORDS, expectedIntSum,
+ expectedVariances[4].getResult(), true);
+ checkWithPrecisionForVariance((VarianceTuple) aggregationResult.get(5), NUM_RECORDS, expectedLongSum,
+ expectedVariances[5].getResult(), true);
+ checkWithPrecisionForVariance((VarianceTuple) aggregationResult.get(6), NUM_RECORDS, expectedFloatSum,
+ expectedVariances[6].getResult(), true);
+ checkWithPrecisionForVariance((VarianceTuple) aggregationResult.get(7), NUM_RECORDS, expectedDoubleSum,
+ expectedVariances[7].getResult(), true);
+
+ // Update the expected result by 3 more times (broker query will compute 4 identical segments)
+ for (int i = 0; i < NUM_RECORDS * 3; i++) {
+ int pos = i % NUM_RECORDS;
+ expectedVariances[0].increment(_intValues[pos]);
+ expectedVariances[1].increment(_longValues[pos]);
+ expectedVariances[2].increment(_floatValues[pos]);
+ expectedVariances[3].increment(_doubleValues[pos]);
+ expectedVariances[4].increment(_intValues[pos]);
+ expectedVariances[5].increment(_longValues[pos]);
+ expectedVariances[6].increment(_floatValues[pos]);
+ expectedVariances[7].increment(_doubleValues[pos]);
+ }
+
+ // Validate the response
+ BrokerResponseNative brokerResponse = getBrokerResponse(query);
+ brokerResponse.getResultTable();
+ Object[] results = brokerResponse.getResultTable().getRows().get(0);
+ assertTrue(
+ Precision.equalsWithRelativeTolerance((double) results[0], expectedVariances[0].getResult(), RELATIVE_EPSILON));
+ assertTrue(
+ Precision.equalsWithRelativeTolerance((double) results[1], expectedVariances[1].getResult(), RELATIVE_EPSILON));
+ assertTrue(
+ Precision.equalsWithRelativeTolerance((double) results[2], expectedVariances[2].getResult(), RELATIVE_EPSILON));
+ assertTrue(
+ Precision.equalsWithRelativeTolerance((double) results[3], expectedVariances[3].getResult(), RELATIVE_EPSILON));
+ assertTrue(
+ Precision.equalsWithRelativeTolerance((double) results[4], expectedVariances[4].getResult(), RELATIVE_EPSILON));
+ assertTrue(
+ Precision.equalsWithRelativeTolerance((double) results[5], expectedVariances[5].getResult(), RELATIVE_EPSILON));
+ assertTrue(
+ Precision.equalsWithRelativeTolerance((double) results[6], expectedVariances[6].getResult(), RELATIVE_EPSILON));
+ assertTrue(
+ Precision.equalsWithRelativeTolerance((double) results[7], expectedVariances[7].getResult(), RELATIVE_EPSILON));
+
+ // Validate the response for a query with a filter
+ query = "SELECT VAR_POP(intColumn) from testTable" + getFilter();
+ brokerResponse = getBrokerResponse(query);
+ brokerResponse.getResultTable();
+ results = brokerResponse.getResultTable().getRows().get(0);
+ Variance filterExpectedVariance = new Variance(false);
+ for (int i = 0; i < NUM_RECORDS / 2; i++) {
+ filterExpectedVariance.increment(_intValues[i]);
+ }
+ assertTrue(Precision.equalsWithRelativeTolerance((double) results[0], filterExpectedVariance.getResult(),
+ RELATIVE_EPSILON));
+ }
+
+ @Test
+ public void testVarianceAggregationGroupBy() {
+ // Compute expected group results
+ Variance[] expectedGroupByResult = new Variance[NUM_GROUPS];
+ double[] expectedSum = new double[NUM_GROUPS];
+
+ for (int i = 0; i < NUM_GROUPS; i++) {
+ expectedGroupByResult[i] = new Variance(false);
+ }
+ for (int j = 0; j < NUM_RECORDS; j++) {
+ int pos = j / (NUM_RECORDS / NUM_GROUPS);
+ expectedGroupByResult[pos].increment(_intValues[j]);
+ expectedSum[pos] += _intValues[j];
+ }
+
+ String query = "SELECT VAR_POP(intColumn) FROM testTable GROUP BY groupByColumn ORDER BY groupByColumn";
+ GroupByOperator groupByOperator = getOperator(query);
+ GroupByResultsBlock resultsBlock = groupByOperator.nextBlock();
+ QueriesTestUtils.testInnerSegmentExecutionStatistics(groupByOperator.getExecutionStatistics(), NUM_RECORDS, 0,
+ NUM_RECORDS * 2, NUM_RECORDS);
+ AggregationGroupByResult aggregationGroupByResult = resultsBlock.getAggregationGroupByResult();
+ assertNotNull(aggregationGroupByResult);
+ for (int i = 0; i < NUM_GROUPS; i++) {
+
+ VarianceTuple actualVarianceTuple = (VarianceTuple) aggregationGroupByResult.getResultForGroupId(0, i);
+ checkWithPrecisionForVariance(actualVarianceTuple, NUM_RECORDS / NUM_GROUPS, expectedSum[i],
+ expectedGroupByResult[i].getResult(), false);
+ }
+ }
+
+ @Test
+ public void testStandardDeviationAggregationOnly() {
+ // Compute the expected values
+ StandardDeviation[] expectedStdDevs = new StandardDeviation[8];
+ for (int i = 0; i < 8; i++) {
+ if (i < 4) {
+ expectedStdDevs[i] = new StandardDeviation(false);
+ } else {
+ expectedStdDevs[i] = new StandardDeviation(true);
+ }
+ }
+ for (int i = 0; i < NUM_RECORDS; i++) {
+ expectedStdDevs[0].increment(_intValues[i]);
+ expectedStdDevs[1].increment(_longValues[i]);
+ expectedStdDevs[2].increment(_floatValues[i]);
+ expectedStdDevs[3].increment(_doubleValues[i]);
+ expectedStdDevs[4].increment(_intValues[i]);
+ expectedStdDevs[5].increment(_longValues[i]);
+ expectedStdDevs[6].increment(_floatValues[i]);
+ expectedStdDevs[7].increment(_doubleValues[i]);
+ }
+
+ double expectedIntSum = Arrays.stream(_intValues).asDoubleStream().sum();
+ double expectedLongSum = Arrays.stream(_longValues).asDoubleStream().sum();
+ double expectedFloatSum = 0.0;
+ for (int i = 0; i < _floatValues.length; i++) {
+ expectedFloatSum += _floatValues[i];
+ }
+ double expectedDoubleSum = Arrays.stream(_doubleValues).sum();
+
+ // Compute the query
+ String query =
+ "SELECT STDDEV_POP(intColumn), STDDEV_POP(longColumn), STDDEV_POP(floatColumn), STDDEV_POP(doubleColumn),"
+ + "STDDEV_SAMP(intColumn), STDDEV_SAMP(longColumn), STDDEV_SAMP(floatColumn), STDDEV_SAMP(doubleColumn) "
+ + "FROM testTable";
+ AggregationOperator aggregationOperator = getOperator(query);
+ AggregationResultsBlock resultsBlock = aggregationOperator.nextBlock();
+ QueriesTestUtils.testInnerSegmentExecutionStatistics(aggregationOperator.getExecutionStatistics(), NUM_RECORDS, 0,
+ NUM_RECORDS * 4, NUM_RECORDS);
+ List<Object> aggregationResult = resultsBlock.getResults();
+
+ // Validate the aggregation results
+ checkWithPrecisionForStandardDeviation((VarianceTuple) aggregationResult.get(0), NUM_RECORDS, expectedIntSum,
+ expectedStdDevs[0].getResult(), false);
+ checkWithPrecisionForStandardDeviation((VarianceTuple) aggregationResult.get(1), NUM_RECORDS, expectedLongSum,
+ expectedStdDevs[1].getResult(), false);
+ checkWithPrecisionForStandardDeviation((VarianceTuple) aggregationResult.get(2), NUM_RECORDS, expectedFloatSum,
+ expectedStdDevs[2].getResult(), false);
+ checkWithPrecisionForStandardDeviation((VarianceTuple) aggregationResult.get(3), NUM_RECORDS, expectedDoubleSum,
+ expectedStdDevs[3].getResult(), false);
+ checkWithPrecisionForStandardDeviation((VarianceTuple) aggregationResult.get(4), NUM_RECORDS, expectedIntSum,
+ expectedStdDevs[4].getResult(), true);
+ checkWithPrecisionForStandardDeviation((VarianceTuple) aggregationResult.get(5), NUM_RECORDS, expectedLongSum,
+ expectedStdDevs[5].getResult(), true);
+ checkWithPrecisionForStandardDeviation((VarianceTuple) aggregationResult.get(6), NUM_RECORDS, expectedFloatSum,
+ expectedStdDevs[6].getResult(), true);
+ checkWithPrecisionForStandardDeviation((VarianceTuple) aggregationResult.get(7), NUM_RECORDS, expectedDoubleSum,
+ expectedStdDevs[7].getResult(), true);
+
+ // Update the expected result by 3 more times (broker query will compute 4 identical segments)
Review Comment:
LGTM, thanks!
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[GitHub] [pinot] jasperjiaguo commented on a diff in pull request #9910: Add Variance and Standard Deviation Aggregation Functions
Posted by GitBox <gi...@apache.org>.
jasperjiaguo commented on code in PR #9910:
URL: https://github.com/apache/pinot/pull/9910#discussion_r1041477398
##########
pinot-core/src/main/java/org/apache/pinot/core/common/ObjectSerDeUtils.java:
##########
@@ -123,7 +124,9 @@ public enum ObjectType {
FloatLongPair(29),
DoubleLongPair(30),
StringLongPair(31),
- CovarianceTuple(32);
+ CovarianceTuple(32),
+
Review Comment:
(nit) remove blank line?
##########
pinot-core/src/test/java/org/apache/pinot/queries/VarianceQueriesTest.java:
##########
@@ -0,0 +1,446 @@
+/**
+ * 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.pinot.queries;
+
+import java.io.File;
+import java.util.ArrayList;
+import java.util.Arrays;
+import java.util.List;
+import java.util.Random;
+import org.apache.commons.io.FileUtils;
+import org.apache.commons.math3.stat.descriptive.moment.StandardDeviation;
+import org.apache.commons.math3.stat.descriptive.moment.Variance;
+import org.apache.commons.math3.util.Precision;
+import org.apache.pinot.common.response.broker.BrokerResponseNative;
+import org.apache.pinot.core.operator.blocks.results.AggregationResultsBlock;
+import org.apache.pinot.core.operator.blocks.results.GroupByResultsBlock;
+import org.apache.pinot.core.operator.query.AggregationOperator;
+import org.apache.pinot.core.operator.query.GroupByOperator;
+import org.apache.pinot.core.query.aggregation.groupby.AggregationGroupByResult;
+import org.apache.pinot.segment.local.customobject.VarianceTuple;
+import org.apache.pinot.segment.local.indexsegment.immutable.ImmutableSegmentLoader;
+import org.apache.pinot.segment.local.segment.creator.impl.SegmentIndexCreationDriverImpl;
+import org.apache.pinot.segment.local.segment.readers.GenericRowRecordReader;
+import org.apache.pinot.segment.spi.ImmutableSegment;
+import org.apache.pinot.segment.spi.IndexSegment;
+import org.apache.pinot.segment.spi.creator.SegmentGeneratorConfig;
+import org.apache.pinot.spi.config.table.TableConfig;
+import org.apache.pinot.spi.config.table.TableType;
+import org.apache.pinot.spi.data.FieldSpec;
+import org.apache.pinot.spi.data.Schema;
+import org.apache.pinot.spi.data.readers.GenericRow;
+import org.apache.pinot.spi.utils.ReadMode;
+import org.apache.pinot.spi.utils.builder.TableConfigBuilder;
+import org.testng.annotations.BeforeClass;
+import org.testng.annotations.Test;
+
+import static org.testng.Assert.assertEquals;
+import static org.testng.Assert.assertNotNull;
+import static org.testng.Assert.assertTrue;
+
+
+public class VarianceQueriesTest extends BaseQueriesTest {
+
+ private static final File INDEX_DIR = new File(FileUtils.getTempDirectory(), "VarianceQueriesTest");
+
+ private static final String RAW_TABLE_NAME = "testTable";
+ private static final String SEGMENT_NAME = "testSegment";
+
+ private static final int NUM_RECORDS = 2000;
+ private static final int NUM_GROUPS = 10;
+ private static final int MAX_VALUE = 500;
+ private static final double RELATIVE_EPSILON = 0.0001;
+ private static final double DELTA = 0.0001;
+
+ private static final String INT_COLUMN = "intColumn";
+ private static final String LONG_COLUMN = "longColumn";
+ private static final String FLOAT_COLUMN = "floatColumn";
+ private static final String DOUBLE_COLUMN = "doubleColumn";
+ private static final String GROUP_BY_COLUMN = "groupByColumn";
+
+ private static final Schema SCHEMA =
+ new Schema.SchemaBuilder().addSingleValueDimension(INT_COLUMN, FieldSpec.DataType.INT)
+ .addSingleValueDimension(LONG_COLUMN, FieldSpec.DataType.LONG)
+ .addSingleValueDimension(FLOAT_COLUMN, FieldSpec.DataType.FLOAT)
+ .addSingleValueDimension(DOUBLE_COLUMN, FieldSpec.DataType.DOUBLE)
+ .addSingleValueDimension(GROUP_BY_COLUMN, FieldSpec.DataType.DOUBLE).build();
+
+ private static final TableConfig TABLE_CONFIG =
+ new TableConfigBuilder(TableType.OFFLINE).setTableName(RAW_TABLE_NAME).build();
+
+ private IndexSegment _indexSegment;
+ private List<IndexSegment> _indexSegments;
+
+ int[] _intValues = new int[NUM_RECORDS];
+ long[] _longValues = new long[NUM_RECORDS];
+ float[] _floatValues = new float[NUM_RECORDS];
+ double[] _doubleValues = new double[NUM_RECORDS];
+
+ @Override
+ protected String getFilter() {
+ // filter out half of the rows based on group id
+ return " WHERE groupByColumn < " + (NUM_GROUPS / 2);
+ }
+
+ @Override
+ protected IndexSegment getIndexSegment() {
+ return _indexSegment;
+ }
+
+ @Override
+ protected List<IndexSegment> getIndexSegments() {
+ return _indexSegments;
+ }
+
+ @BeforeClass
+ public void setUp()
+ throws Exception {
+ FileUtils.deleteDirectory(INDEX_DIR);
+ Random random = new Random();
+ List<GenericRow> records = new ArrayList<>(NUM_RECORDS);
+
+ for (int i = 0; i < NUM_RECORDS; i++) {
+ GenericRow record = new GenericRow();
+ int intValue = -MAX_VALUE + random.nextInt() * 2 * MAX_VALUE;
+ long longValue = -MAX_VALUE + random.nextLong() * 2 * MAX_VALUE;
+ float floatValue = -MAX_VALUE + random.nextFloat() * 2 * MAX_VALUE;
+ double doubleValue = -MAX_VALUE + random.nextDouble() * 2 * MAX_VALUE;
+
+ _intValues[i] = intValue;
+ _longValues[i] = longValue;
+ _floatValues[i] = floatValue;
+ _doubleValues[i] = doubleValue;
+
+ record.putValue(INT_COLUMN, _intValues[i]);
+ record.putValue(LONG_COLUMN, _longValues[i]);
+ record.putValue(FLOAT_COLUMN, _floatValues[i]);
+ record.putValue(DOUBLE_COLUMN, _doubleValues[i]);
+ record.putValue(GROUP_BY_COLUMN, Math.floor(i / (NUM_RECORDS / NUM_GROUPS)));
+ records.add(record);
+ }
+
+ SegmentGeneratorConfig segmentGeneratorConfig = new SegmentGeneratorConfig(TABLE_CONFIG, SCHEMA);
+ segmentGeneratorConfig.setTableName(RAW_TABLE_NAME);
+ segmentGeneratorConfig.setSegmentName(SEGMENT_NAME);
+ segmentGeneratorConfig.setOutDir(INDEX_DIR.getPath());
+
+ SegmentIndexCreationDriverImpl driver = new SegmentIndexCreationDriverImpl();
+ driver.init(segmentGeneratorConfig, new GenericRowRecordReader(records));
+ driver.build();
+
+ ImmutableSegment immutableSegment = ImmutableSegmentLoader.load(new File(INDEX_DIR, SEGMENT_NAME), ReadMode.mmap);
+ _indexSegment = immutableSegment;
+ _indexSegments = Arrays.asList(immutableSegment, immutableSegment);
+ }
+
+ @Test
+ public void testVarianceAggregationOnly() {
+ // Compute the expected values
+ Variance[] expectedVariances = new Variance[8];
+ for (int i = 0; i < 8; i++) {
+ if (i < 4) {
+ expectedVariances[i] = new Variance(false);
+ } else {
+ expectedVariances[i] = new Variance(true);
+ }
+ }
+ for (int i = 0; i < NUM_RECORDS; i++) {
+ expectedVariances[0].increment(_intValues[i]);
+ expectedVariances[1].increment(_longValues[i]);
+ expectedVariances[2].increment(_floatValues[i]);
+ expectedVariances[3].increment(_doubleValues[i]);
+ expectedVariances[4].increment(_intValues[i]);
+ expectedVariances[5].increment(_longValues[i]);
+ expectedVariances[6].increment(_floatValues[i]);
+ expectedVariances[7].increment(_doubleValues[i]);
+ }
+ double expectedIntSum = Arrays.stream(_intValues).asDoubleStream().sum();
+ double expectedLongSum = Arrays.stream(_longValues).asDoubleStream().sum();
+ double expectedFloatSum = 0.0;
+ for (int i = 0; i < _floatValues.length; i++) {
+ expectedFloatSum += _floatValues[i];
+ }
+ double expectedDoubleSum = Arrays.stream(_doubleValues).sum();
+
+ // Compute the query
+ String query = "SELECT VAR_POP(intColumn), VAR_POP(longColumn), VAR_POP(floatColumn), VAR_POP(doubleColumn),"
+ + "VAR_SAMP(intColumn), VAR_SAMP(longColumn), VAR_SAMP(floatColumn), VAR_SAMP(doubleColumn) FROM testTable";
+ AggregationOperator aggregationOperator = getOperator(query);
+ AggregationResultsBlock resultsBlock = aggregationOperator.nextBlock();
+ QueriesTestUtils.testInnerSegmentExecutionStatistics(aggregationOperator.getExecutionStatistics(), NUM_RECORDS, 0,
+ NUM_RECORDS * 4, NUM_RECORDS);
+ List<Object> aggregationResult = resultsBlock.getResults();
+
+ // Validate the aggregation results
+ checkWithPrecisionForVariance((VarianceTuple) aggregationResult.get(0), NUM_RECORDS, expectedIntSum,
+ expectedVariances[0].getResult(), false);
+ checkWithPrecisionForVariance((VarianceTuple) aggregationResult.get(1), NUM_RECORDS, expectedLongSum,
+ expectedVariances[1].getResult(), false);
+ checkWithPrecisionForVariance((VarianceTuple) aggregationResult.get(2), NUM_RECORDS, expectedFloatSum,
+ expectedVariances[2].getResult(), false);
+ checkWithPrecisionForVariance((VarianceTuple) aggregationResult.get(3), NUM_RECORDS, expectedDoubleSum,
+ expectedVariances[3].getResult(), false);
+ checkWithPrecisionForVariance((VarianceTuple) aggregationResult.get(4), NUM_RECORDS, expectedIntSum,
+ expectedVariances[4].getResult(), true);
+ checkWithPrecisionForVariance((VarianceTuple) aggregationResult.get(5), NUM_RECORDS, expectedLongSum,
+ expectedVariances[5].getResult(), true);
+ checkWithPrecisionForVariance((VarianceTuple) aggregationResult.get(6), NUM_RECORDS, expectedFloatSum,
+ expectedVariances[6].getResult(), true);
+ checkWithPrecisionForVariance((VarianceTuple) aggregationResult.get(7), NUM_RECORDS, expectedDoubleSum,
+ expectedVariances[7].getResult(), true);
+
+ // Update the expected result by 3 more times (broker query will compute 4 identical segments)
+ for (int i = 0; i < NUM_RECORDS * 3; i++) {
+ int pos = i % NUM_RECORDS;
+ expectedVariances[0].increment(_intValues[pos]);
+ expectedVariances[1].increment(_longValues[pos]);
+ expectedVariances[2].increment(_floatValues[pos]);
+ expectedVariances[3].increment(_doubleValues[pos]);
+ expectedVariances[4].increment(_intValues[pos]);
+ expectedVariances[5].increment(_longValues[pos]);
+ expectedVariances[6].increment(_floatValues[pos]);
+ expectedVariances[7].increment(_doubleValues[pos]);
+ }
+
+ // Validate the response
+ BrokerResponseNative brokerResponse = getBrokerResponse(query);
+ brokerResponse.getResultTable();
+ Object[] results = brokerResponse.getResultTable().getRows().get(0);
+ assertTrue(
+ Precision.equalsWithRelativeTolerance((double) results[0], expectedVariances[0].getResult(), RELATIVE_EPSILON));
+ assertTrue(
+ Precision.equalsWithRelativeTolerance((double) results[1], expectedVariances[1].getResult(), RELATIVE_EPSILON));
+ assertTrue(
+ Precision.equalsWithRelativeTolerance((double) results[2], expectedVariances[2].getResult(), RELATIVE_EPSILON));
+ assertTrue(
+ Precision.equalsWithRelativeTolerance((double) results[3], expectedVariances[3].getResult(), RELATIVE_EPSILON));
+ assertTrue(
+ Precision.equalsWithRelativeTolerance((double) results[4], expectedVariances[4].getResult(), RELATIVE_EPSILON));
+ assertTrue(
+ Precision.equalsWithRelativeTolerance((double) results[5], expectedVariances[5].getResult(), RELATIVE_EPSILON));
+ assertTrue(
+ Precision.equalsWithRelativeTolerance((double) results[6], expectedVariances[6].getResult(), RELATIVE_EPSILON));
+ assertTrue(
+ Precision.equalsWithRelativeTolerance((double) results[7], expectedVariances[7].getResult(), RELATIVE_EPSILON));
+
+ // Validate the response for a query with a filter
+ query = "SELECT VAR_POP(intColumn) from testTable" + getFilter();
+ brokerResponse = getBrokerResponse(query);
+ brokerResponse.getResultTable();
+ results = brokerResponse.getResultTable().getRows().get(0);
+ Variance filterExpectedVariance = new Variance(false);
+ for (int i = 0; i < NUM_RECORDS / 2; i++) {
+ filterExpectedVariance.increment(_intValues[i]);
+ }
+ assertTrue(Precision.equalsWithRelativeTolerance((double) results[0], filterExpectedVariance.getResult(),
+ RELATIVE_EPSILON));
+ }
+
+ @Test
+ public void testVarianceAggregationGroupBy() {
+ // Compute expected group results
+ Variance[] expectedGroupByResult = new Variance[NUM_GROUPS];
+ double[] expectedSum = new double[NUM_GROUPS];
+
+ for (int i = 0; i < NUM_GROUPS; i++) {
+ expectedGroupByResult[i] = new Variance(false);
+ }
+ for (int j = 0; j < NUM_RECORDS; j++) {
+ int pos = j / (NUM_RECORDS / NUM_GROUPS);
+ expectedGroupByResult[pos].increment(_intValues[j]);
+ expectedSum[pos] += _intValues[j];
+ }
+
+ String query = "SELECT VAR_POP(intColumn) FROM testTable GROUP BY groupByColumn ORDER BY groupByColumn";
+ GroupByOperator groupByOperator = getOperator(query);
+ GroupByResultsBlock resultsBlock = groupByOperator.nextBlock();
+ QueriesTestUtils.testInnerSegmentExecutionStatistics(groupByOperator.getExecutionStatistics(), NUM_RECORDS, 0,
+ NUM_RECORDS * 2, NUM_RECORDS);
+ AggregationGroupByResult aggregationGroupByResult = resultsBlock.getAggregationGroupByResult();
+ assertNotNull(aggregationGroupByResult);
+ for (int i = 0; i < NUM_GROUPS; i++) {
+
+ VarianceTuple actualVarianceTuple = (VarianceTuple) aggregationGroupByResult.getResultForGroupId(0, i);
+ checkWithPrecisionForVariance(actualVarianceTuple, NUM_RECORDS / NUM_GROUPS, expectedSum[i],
+ expectedGroupByResult[i].getResult(), false);
+ }
+ }
+
+ @Test
+ public void testStandardDeviationAggregationOnly() {
+ // Compute the expected values
+ StandardDeviation[] expectedStdDevs = new StandardDeviation[8];
+ for (int i = 0; i < 8; i++) {
+ if (i < 4) {
+ expectedStdDevs[i] = new StandardDeviation(false);
+ } else {
+ expectedStdDevs[i] = new StandardDeviation(true);
+ }
+ }
+ for (int i = 0; i < NUM_RECORDS; i++) {
+ expectedStdDevs[0].increment(_intValues[i]);
+ expectedStdDevs[1].increment(_longValues[i]);
+ expectedStdDevs[2].increment(_floatValues[i]);
+ expectedStdDevs[3].increment(_doubleValues[i]);
+ expectedStdDevs[4].increment(_intValues[i]);
+ expectedStdDevs[5].increment(_longValues[i]);
+ expectedStdDevs[6].increment(_floatValues[i]);
+ expectedStdDevs[7].increment(_doubleValues[i]);
+ }
+
+ double expectedIntSum = Arrays.stream(_intValues).asDoubleStream().sum();
+ double expectedLongSum = Arrays.stream(_longValues).asDoubleStream().sum();
+ double expectedFloatSum = 0.0;
+ for (int i = 0; i < _floatValues.length; i++) {
+ expectedFloatSum += _floatValues[i];
+ }
+ double expectedDoubleSum = Arrays.stream(_doubleValues).sum();
+
+ // Compute the query
+ String query =
+ "SELECT STDDEV_POP(intColumn), STDDEV_POP(longColumn), STDDEV_POP(floatColumn), STDDEV_POP(doubleColumn),"
+ + "STDDEV_SAMP(intColumn), STDDEV_SAMP(longColumn), STDDEV_SAMP(floatColumn), STDDEV_SAMP(doubleColumn) "
+ + "FROM testTable";
+ AggregationOperator aggregationOperator = getOperator(query);
+ AggregationResultsBlock resultsBlock = aggregationOperator.nextBlock();
+ QueriesTestUtils.testInnerSegmentExecutionStatistics(aggregationOperator.getExecutionStatistics(), NUM_RECORDS, 0,
+ NUM_RECORDS * 4, NUM_RECORDS);
+ List<Object> aggregationResult = resultsBlock.getResults();
+
+ // Validate the aggregation results
+ checkWithPrecisionForStandardDeviation((VarianceTuple) aggregationResult.get(0), NUM_RECORDS, expectedIntSum,
+ expectedStdDevs[0].getResult(), false);
+ checkWithPrecisionForStandardDeviation((VarianceTuple) aggregationResult.get(1), NUM_RECORDS, expectedLongSum,
+ expectedStdDevs[1].getResult(), false);
+ checkWithPrecisionForStandardDeviation((VarianceTuple) aggregationResult.get(2), NUM_RECORDS, expectedFloatSum,
+ expectedStdDevs[2].getResult(), false);
+ checkWithPrecisionForStandardDeviation((VarianceTuple) aggregationResult.get(3), NUM_RECORDS, expectedDoubleSum,
+ expectedStdDevs[3].getResult(), false);
+ checkWithPrecisionForStandardDeviation((VarianceTuple) aggregationResult.get(4), NUM_RECORDS, expectedIntSum,
+ expectedStdDevs[4].getResult(), true);
+ checkWithPrecisionForStandardDeviation((VarianceTuple) aggregationResult.get(5), NUM_RECORDS, expectedLongSum,
+ expectedStdDevs[5].getResult(), true);
+ checkWithPrecisionForStandardDeviation((VarianceTuple) aggregationResult.get(6), NUM_RECORDS, expectedFloatSum,
+ expectedStdDevs[6].getResult(), true);
+ checkWithPrecisionForStandardDeviation((VarianceTuple) aggregationResult.get(7), NUM_RECORDS, expectedDoubleSum,
+ expectedStdDevs[7].getResult(), true);
+
+ // Update the expected result by 3 more times (broker query will compute 4 identical segments)
Review Comment:
The variance on 4 identical segments is the same as variance on one of these segments. Should probably to consider using `getDistinctInstances` in `BaseQueriesTest`
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[GitHub] [pinot] walterddr commented on a diff in pull request #9910: Add Variance and Standard Deviation Aggregation Functions
Posted by GitBox <gi...@apache.org>.
walterddr commented on code in PR #9910:
URL: https://github.com/apache/pinot/pull/9910#discussion_r1041639288
##########
pinot-core/src/main/java/org/apache/pinot/core/common/ObjectSerDeUtils.java:
##########
@@ -123,7 +124,9 @@ public enum ObjectType {
FloatLongPair(29),
DoubleLongPair(30),
StringLongPair(31),
- CovarianceTuple(32);
+ CovarianceTuple(32),
Review Comment:
i was wondering if we can simply reuse CovarianceTuple and logic here.
basically Variance(x) = Covarance(x, x)
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