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https://issues.apache.org/jira/browse/HIVE-22239?focusedWorklogId=325676&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-325676
]
ASF GitHub Bot logged work on HIVE-22239:
-----------------------------------------
Author: ASF GitHub Bot
Created on: 09/Oct/19 12:51
Start Date: 09/Oct/19 12:51
Worklog Time Spent: 10m
Work Description: jcamachor commented on pull request #787: HIVE-22239
URL: https://github.com/apache/hive/pull/787#discussion_r332994124
##########
File path:
standalone-metastore/metastore-server/src/main/java/org/apache/hadoop/hive/metastore/columnstats/aggr/TimestampColumnStatsAggregator.java
##########
@@ -0,0 +1,358 @@
+/*
+ * 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.hadoop.hive.metastore.columnstats.aggr;
+
+import java.util.Collections;
+import java.util.Comparator;
+import java.util.HashMap;
+import java.util.LinkedList;
+import java.util.List;
+import java.util.Map;
+import org.apache.hadoop.hive.common.ndv.NumDistinctValueEstimator;
+import org.apache.hadoop.hive.common.ndv.NumDistinctValueEstimatorFactory;
+import org.apache.hadoop.hive.metastore.api.ColumnStatisticsData;
+import org.apache.hadoop.hive.metastore.api.ColumnStatisticsObj;
+import org.apache.hadoop.hive.metastore.api.Timestamp;
+import org.apache.hadoop.hive.metastore.api.TimestampColumnStatsData;
+import org.apache.hadoop.hive.metastore.api.MetaException;
+import
org.apache.hadoop.hive.metastore.columnstats.cache.TimestampColumnStatsDataInspector;
+import
org.apache.hadoop.hive.metastore.utils.MetaStoreServerUtils.ColStatsObjWithSourceInfo;
+import org.slf4j.Logger;
+import org.slf4j.LoggerFactory;
+
+import static
org.apache.hadoop.hive.metastore.columnstats.ColumnsStatsUtils.timestampInspectorFromStats;
+
+public class TimestampColumnStatsAggregator extends ColumnStatsAggregator
implements
+ IExtrapolatePartStatus {
+
+ private static final Logger LOG =
LoggerFactory.getLogger(TimestampColumnStatsAggregator.class);
+
+ @Override
+ public ColumnStatisticsObj aggregate(List<ColStatsObjWithSourceInfo>
colStatsWithSourceInfo,
+ List<String> partNames, boolean areAllPartsFound) throws
MetaException {
+ ColumnStatisticsObj statsObj = null;
+ String colType = null;
+ String colName = null;
+ // check if all the ColumnStatisticsObjs contain stats and all the ndv are
+ // bitvectors
+ boolean doAllPartitionContainStats = partNames.size() ==
colStatsWithSourceInfo.size();
+ NumDistinctValueEstimator ndvEstimator = null;
+ for (ColStatsObjWithSourceInfo csp : colStatsWithSourceInfo) {
+ ColumnStatisticsObj cso = csp.getColStatsObj();
+ if (statsObj == null) {
+ colName = cso.getColName();
+ colType = cso.getColType();
+ statsObj = ColumnStatsAggregatorFactory.newColumnStaticsObj(colName,
colType,
+ cso.getStatsData().getSetField());
+ LOG.trace("doAllPartitionContainStats for column: {} is: {}", colName,
doAllPartitionContainStats);
+ }
+ TimestampColumnStatsDataInspector timestampColumnStats =
timestampInspectorFromStats(cso);
+
+ if (timestampColumnStats.getNdvEstimator() == null) {
+ ndvEstimator = null;
+ break;
+ } else {
+ // check if all of the bit vectors can merge
+ NumDistinctValueEstimator estimator =
timestampColumnStats.getNdvEstimator();
+ if (ndvEstimator == null) {
+ ndvEstimator = estimator;
+ } else {
+ if (ndvEstimator.canMerge(estimator)) {
+ continue;
+ } else {
+ ndvEstimator = null;
+ break;
+ }
+ }
+ }
+ }
+ if (ndvEstimator != null) {
+ ndvEstimator = NumDistinctValueEstimatorFactory
+ .getEmptyNumDistinctValueEstimator(ndvEstimator);
+ }
+ LOG.debug("all of the bit vectors can merge for " + colName + " is " +
(ndvEstimator != null));
+ ColumnStatisticsData columnStatisticsData = new ColumnStatisticsData();
+ if (doAllPartitionContainStats || colStatsWithSourceInfo.size() < 2) {
+ TimestampColumnStatsDataInspector aggregateData = null;
+ long lowerBound = 0;
+ long higherBound = 0;
+ double densityAvgSum = 0.0;
+ for (ColStatsObjWithSourceInfo csp : colStatsWithSourceInfo) {
+ ColumnStatisticsObj cso = csp.getColStatsObj();
+ TimestampColumnStatsDataInspector newData =
timestampInspectorFromStats(cso);
+ higherBound += newData.getNumDVs();
+ densityAvgSum += (diff(newData.getHighValue(), newData.getLowValue()))
+ / newData.getNumDVs();
+ if (ndvEstimator != null) {
+ ndvEstimator.mergeEstimators(newData.getNdvEstimator());
+ }
+ if (aggregateData == null) {
+ aggregateData = newData.deepCopy();
+ } else {
+ aggregateData.setLowValue(min(aggregateData.getLowValue(),
newData.getLowValue()));
+ aggregateData
+ .setHighValue(max(aggregateData.getHighValue(),
newData.getHighValue()));
+ aggregateData.setNumNulls(aggregateData.getNumNulls() +
newData.getNumNulls());
+ aggregateData.setNumDVs(Math.max(aggregateData.getNumDVs(),
newData.getNumDVs()));
+ }
+ }
+ if (ndvEstimator != null) {
+ // if all the ColumnStatisticsObjs contain bitvectors, we do not need
to
+ // use uniform distribution assumption because we can merge bitvectors
+ // to get a good estimation.
+ aggregateData.setNumDVs(ndvEstimator.estimateNumDistinctValues());
+ } else {
+ long estimation;
+ if (useDensityFunctionForNDVEstimation) {
+ // We have estimation, lowerbound and higherbound. We use estimation
+ // if it is between lowerbound and higherbound.
+ double densityAvg = densityAvgSum / partNames.size();
+ estimation = (long) (diff(aggregateData.getHighValue(),
aggregateData.getLowValue()) / densityAvg);
Review comment:
I agree with you... Much of this logic could be simplified. There is an
abstract class `ColumnStatsAggregator` but it is basically empty. We should
move some of the common logic there or to a utility class. I have created
another JIRA: https://issues.apache.org/jira/browse/HIVE-22310.
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Issue Time Tracking
-------------------
Worklog Id: (was: 325676)
Time Spent: 3h 40m (was: 3.5h)
> Scale data size using column value ranges
> -----------------------------------------
>
> Key: HIVE-22239
> URL: https://issues.apache.org/jira/browse/HIVE-22239
> Project: Hive
> Issue Type: Improvement
> Components: Physical Optimizer
> Reporter: Jesus Camacho Rodriguez
> Assignee: Jesus Camacho Rodriguez
> Priority: Major
> Labels: pull-request-available
> Attachments: HIVE-22239.01.patch, HIVE-22239.02.patch,
> HIVE-22239.patch
>
> Time Spent: 3h 40m
> Remaining Estimate: 0h
>
> Currently, min/max values for columns are only used to determine whether a
> certain range filter falls out of range and thus filters all rows or none at
> all. If it does not, we just use a heuristic that the condition will filter
> 1/3 of the input rows. Instead of using that heuristic, we can use another
> one that assumes that data will be uniformly distributed across that range,
> and calculate the selectivity for the condition accordingly.
> This patch also includes the propagation of min/max column values from
> statistics to the optimizer for timestamp type.
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