/**
* Copyright (C) 2014-2016 LinkedIn Corp. (pinot-core@linkedin.com)
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package com.linkedin.pinot.query.aggregation;
import com.linkedin.pinot.common.data.FieldSpec;
import com.linkedin.pinot.common.data.MetricFieldSpec;
import com.linkedin.pinot.common.data.Schema;
import com.linkedin.pinot.common.request.AggregationInfo;
import com.linkedin.pinot.common.request.transform.TransformExpressionTree;
import com.linkedin.pinot.common.segment.ReadMode;
import com.linkedin.pinot.core.data.GenericRow;
import com.linkedin.pinot.core.data.readers.RecordReader;
import com.linkedin.pinot.core.indexsegment.IndexSegment;
import com.linkedin.pinot.core.indexsegment.generator.SegmentGeneratorConfig;
import com.linkedin.pinot.core.operator.BReusableFilteredDocIdSetOperator;
import com.linkedin.pinot.core.operator.BaseOperator;
import com.linkedin.pinot.core.operator.MProjectionOperator;
import com.linkedin.pinot.core.operator.blocks.TransformBlock;
import com.linkedin.pinot.core.operator.filter.MatchEntireSegmentOperator;
import com.linkedin.pinot.core.operator.transform.TransformExpressionOperator;
import com.linkedin.pinot.core.plan.AggregationFunctionInitializer;
import com.linkedin.pinot.core.query.aggregation.AggregationExecutor;
import com.linkedin.pinot.core.query.aggregation.AggregationFunctionContext;
import com.linkedin.pinot.core.query.aggregation.DefaultAggregationExecutor;
import com.linkedin.pinot.core.segment.creator.impl.SegmentIndexCreationDriverImpl;
import com.linkedin.pinot.core.segment.index.loader.Loaders;
import com.linkedin.pinot.util.TestUtils;
import java.io.File;
import java.util.ArrayList;
import java.util.Collections;
import java.util.HashMap;
import java.util.List;
import java.util.Map;
import java.util.Random;
import org.apache.commons.io.FileUtils;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import org.testng.Assert;
import org.testng.annotations.AfterSuite;
import org.testng.annotations.BeforeSuite;
import org.testng.annotations.Test;
/**
* Unit test for DefaultAggregationExecutor class.
* - Builds a segment with random data.
* - Uses DefaultAggregationExecutor class to perform various aggregations on the
* data.
* - Also computes those aggregations itself.
* - Asserts that the aggregation results returned by the class are the same as
* returned by the local computations.
*
* Currently tests 'sum', 'min' & 'max' functions, and can be easily extended to
* test other functions as well.
* Asserts that aggregation results returned by the executor are as expected.
*/
@Test
public class DefaultAggregationExecutorTest {
protected static Logger LOGGER = LoggerFactory.getLogger(DefaultAggregationExecutorTest.class);
private static File INDEX_DIR = new File(FileUtils.getTempDirectory() + File.separator + "AggregationExecutorTest");
private static final String SEGMENT_NAME = "TestAggregation";
private static final String METRIC_PREFIX = "metric_";
private static final String[] AGGREGATION_FUNCTIONS = {"sum", "max", "min"};
private static final int NUM_METRIC_COLUMNS = AGGREGATION_FUNCTIONS.length;
private static final double MAX_VALUE = Integer.MAX_VALUE;
private static final int NUM_ROWS = 1000;
public static IndexSegment _indexSegment;
private Random _random;
private List<AggregationInfo> _aggregationInfoList;
private String[] _columns;
private int[] _docIdSet;
private double[][] _inputData;
/**
* Initializations prior to the test:
* - Build a segment with metric columns (that will be aggregated) containing
* randomly generated data.
*
* @throws Exception
*/
@BeforeSuite
void init()
throws Exception {
_random = new Random(System.currentTimeMillis());
_docIdSet = new int[NUM_ROWS];
int numColumns = AGGREGATION_FUNCTIONS.length;
_inputData = new double[numColumns][NUM_ROWS];
_columns = new String[numColumns];
setupSegment();
_aggregationInfoList = new ArrayList<>();
for (int i = 0; i < _columns.length; i++) {
AggregationInfo aggregationInfo = new AggregationInfo();
aggregationInfo.setAggregationType(AGGREGATION_FUNCTIONS[i]);
Map<String, String> params = new HashMap<String, String>();
params.put("column", _columns[i]);
aggregationInfo.setAggregationParams(params);
_aggregationInfoList.add(aggregationInfo);
}
}
/**
* Runs 'sum', 'min' & 'max' aggregation functions on the DefaultAggregationExecutor.
* Asserts that the aggregation results returned by the executor are as expected.
*/
@Test
void testAggregation() {
Map<String, BaseOperator> dataSourceMap = new HashMap<>();
for (String column : _indexSegment.getColumnNames()) {
dataSourceMap.put(column, _indexSegment.getDataSource(column));
}
int totalRawDocs = _indexSegment.getSegmentMetadata().getTotalRawDocs();
MatchEntireSegmentOperator matchEntireSegmentOperator = new MatchEntireSegmentOperator(totalRawDocs);
BReusableFilteredDocIdSetOperator docIdSetOperator =
new BReusableFilteredDocIdSetOperator(matchEntireSegmentOperator, totalRawDocs, 10000);
MProjectionOperator projectionOperator = new MProjectionOperator(dataSourceMap, docIdSetOperator);
TransformExpressionOperator transformOperator =
new TransformExpressionOperator(projectionOperator, Collections.<TransformExpressionTree>emptyList());
TransformBlock transformBlock = (TransformBlock) transformOperator.nextBlock();
int numAggFuncs = _aggregationInfoList.size();
AggregationFunctionContext[] aggrFuncContextArray = new AggregationFunctionContext[numAggFuncs];
AggregationFunctionInitializer aggFuncInitializer =
new AggregationFunctionInitializer(_indexSegment.getSegmentMetadata());
for (int i = 0; i < numAggFuncs; i++) {
AggregationInfo aggregationInfo = _aggregationInfoList.get(i);
aggrFuncContextArray[i] = AggregationFunctionContext.instantiate(aggregationInfo);
aggrFuncContextArray[i].getAggregationFunction().accept(aggFuncInitializer);
}
AggregationExecutor aggregationExecutor = new DefaultAggregationExecutor(aggrFuncContextArray);
aggregationExecutor.init();
aggregationExecutor.aggregate(transformBlock);
aggregationExecutor.finish();
List<Object> result = aggregationExecutor.getResult();
for (int i = 0; i < result.size(); i++) {
double actual = (double) result.get(i);
double expected = computeAggregation(AGGREGATION_FUNCTIONS[i], _inputData[i]);
Assert.assertEquals(actual, expected,
"Aggregation mis-match for function " + AGGREGATION_FUNCTIONS[i] + ", Expected: " + expected + " Actual: "
+ actual);
}
}
/**
* Clean up the temporary data (segment).
*
*/
@AfterSuite
void tearDown() {
if (INDEX_DIR.exists()) {
FileUtils.deleteQuietly(INDEX_DIR);
}
if (_indexSegment != null) {
_indexSegment.destroy();
}
}
/**
* Helper method to setup the index segment on which to perform aggregation tests.
* - Generates a segment with {@link #NUM_METRIC_COLUMNS} and {@link #NUM_ROWS}
* - Random 'double' data filled in the metric columns. The data is also populated
* into the _inputData[], so it can be used to test the results.
*
* @throws Exception
*/
private void setupSegment()
throws Exception {
if (INDEX_DIR.exists()) {
FileUtils.deleteQuietly(INDEX_DIR);
}
SegmentGeneratorConfig config = new SegmentGeneratorConfig();
config.setSegmentName(SEGMENT_NAME);
config.setOutDir(INDEX_DIR.getAbsolutePath());
Schema schema = buildSchema();
config.setSchema(schema);
final List<GenericRow> data = new ArrayList<>();
for (int i = 0; i < NUM_ROWS; i++) {
Map<String, Object> map = new HashMap<String, Object>();
for (int j = 0; j < _columns.length; j++) {
String metricName = _columns[j];
double value = _random.nextDouble() * MAX_VALUE;
_inputData[j][i] = value;
map.put(metricName, value);
}
GenericRow genericRow = new GenericRow();
genericRow.init(map);
data.add(genericRow);
_docIdSet[i] = i;
}
SegmentIndexCreationDriverImpl driver = new SegmentIndexCreationDriverImpl();
RecordReader reader = new TestUtils.GenericRowRecordReader(schema, data);
driver.init(config, reader);
driver.build();
_indexSegment = Loaders.IndexSegment.load(new File(INDEX_DIR, driver.getSegmentName()), ReadMode.heap);
}
/**
* Helper method to build schema for the segment on which aggregation tests will be run.
*
* @return
*/
private Schema buildSchema() {
Schema schema = new Schema();
for (int i = 0; i < NUM_METRIC_COLUMNS; i++) {
String metricName = METRIC_PREFIX + i;
MetricFieldSpec metricFieldSpec = new MetricFieldSpec(metricName, FieldSpec.DataType.DOUBLE);
schema.addField(metricFieldSpec);
_columns[i] = metricName;
}
return schema;
}
/**
* Helper method to compute aggregation on a given array of values.
* @param functionName
* @param values
* @return
*/
private double computeAggregation(String functionName, double[] values) {
switch (functionName.toLowerCase()) {
case "sum":
return computeSum(values);
case "max":
return computeMax(values);
case "min":
return computeMin(values);
default:
throw new RuntimeException("Unsupported function " + functionName);
}
}
/**
* Helper method to compute sum of a given array of values.
* @param values
* @return
*/
private double computeSum(double[] values) {
double sum = 0.0;
for (int i = 0; i < values.length; i++) {
sum += values[i];
}
return sum;
}
/**
* Helper method to compute max of a given array of values.
* @param values
* @return
*/
private double computeMax(double[] values) {
double max = Double.NEGATIVE_INFINITY;
for (int i = 0; i < values.length; i++) {
max = Math.max(max, values[i]);
}
return max;
}
/**
* Helper method to compute min of a given array of values.
* @param values
* @return
*/
private double computeMin(double[] values) {
double min = Double.POSITIVE_INFINITY;
for (int i = 0; i < values.length; i++) {
min = Math.min(min, values[i]);
}
return min;
}
}