[
https://issues.apache.org/jira/browse/HIVE-23716?focusedWorklogId=461637&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-461637
]
ASF GitHub Bot logged work on HIVE-23716:
-----------------------------------------
Author: ASF GitHub Bot
Created on: 21/Jul/20 15:19
Start Date: 21/Jul/20 15:19
Worklog Time Spent: 10m
Work Description: pgaref commented on a change in pull request #1147:
URL: https://github.com/apache/hive/pull/1147#discussion_r458180264
##########
File path:
ql/src/java/org/apache/hadoop/hive/ql/exec/vector/mapjoin/VectorMapJoinAntiJoinStringOperator.java
##########
@@ -0,0 +1,371 @@
+/*
+ * 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.ql.exec.vector.mapjoin;
+
+import org.apache.hadoop.hive.ql.CompilationOpContext;
+import org.apache.hadoop.hive.ql.exec.JoinUtil;
+import org.apache.hadoop.hive.ql.exec.vector.BytesColumnVector;
+import org.apache.hadoop.hive.ql.exec.vector.VectorizationContext;
+import org.apache.hadoop.hive.ql.exec.vector.VectorizedRowBatch;
+import org.apache.hadoop.hive.ql.exec.vector.expressions.StringExpr;
+import org.apache.hadoop.hive.ql.exec.vector.expressions.VectorExpression;
+import
org.apache.hadoop.hive.ql.exec.vector.mapjoin.hashtable.VectorMapJoinBytesHashSet;
+import org.apache.hadoop.hive.ql.metadata.HiveException;
+import org.apache.hadoop.hive.ql.plan.OperatorDesc;
+import org.apache.hadoop.hive.ql.plan.VectorDesc;
+import org.slf4j.Logger;
+import org.slf4j.LoggerFactory;
+
+import java.util.Arrays;
+
+// Single-Column String hash table import.
+// Single-Column String specific imports.
+
+// TODO : Duplicate codes need to merge with semi join.
+/*
+ * Specialized class for doing a vectorized map join that is an anti join on a
Single-Column String
+ * using a hash set.
+ */
+public class VectorMapJoinAntiJoinStringOperator extends
VectorMapJoinAntiJoinGenerateResultOperator {
+
+ private static final long serialVersionUID = 1L;
+
+
//------------------------------------------------------------------------------------------------
+
+ private static final String CLASS_NAME =
VectorMapJoinAntiJoinStringOperator.class.getName();
+ private static final Logger LOG = LoggerFactory.getLogger(CLASS_NAME);
+
+ protected String getLoggingPrefix() {
+ return super.getLoggingPrefix(CLASS_NAME);
+ }
+
+
//------------------------------------------------------------------------------------------------
+
+ // (none)
+
+ // The above members are initialized by the constructor and must not be
+ // transient.
+ //---------------------------------------------------------------------------
+
+ // The hash map for this specialized class.
+ private transient VectorMapJoinBytesHashSet hashSet;
+
+ //---------------------------------------------------------------------------
+ // Single-Column String specific members.
+ //
+
+ // The column number for this one column join specialization.
+ private transient int singleJoinColumn;
+
+ //---------------------------------------------------------------------------
+ // Pass-thru constructors.
+ //
+
+ /** Kryo ctor. */
+ protected VectorMapJoinAntiJoinStringOperator() {
+ super();
+ }
+
+ public VectorMapJoinAntiJoinStringOperator(CompilationOpContext ctx) {
+ super(ctx);
+ }
+
+ public VectorMapJoinAntiJoinStringOperator(CompilationOpContext ctx,
OperatorDesc conf,
+ VectorizationContext vContext,
VectorDesc vectorDesc) throws HiveException {
+ super(ctx, conf, vContext, vectorDesc);
+ }
+
+ //---------------------------------------------------------------------------
+ // Process Single-Column String anti Join on a vectorized row batch.
+ //
+
+ @Override
+ protected void commonSetup() throws HiveException {
+ super.commonSetup();
+
+ /*
+ * Initialize Single-Column String members for this specialized class.
+ */
+
+ singleJoinColumn = bigTableKeyColumnMap[0];
+ }
+
+ @Override
+ public void hashTableSetup() throws HiveException {
+ super.hashTableSetup();
+
+ /*
+ * Get our Single-Column String hash set information for this specialized
class.
+ */
+
+ hashSet = (VectorMapJoinBytesHashSet) vectorMapJoinHashTable;
+ }
+
+ @Override
+ public void processBatch(VectorizedRowBatch batch) throws HiveException {
+
+ try {
+
+ // Do the per-batch setup for an anti join.
+
+ // (Currently none)
+ // antiPerBatchSetup(batch);
+
+ // For anti joins, we may apply the filter(s) now.
+ for(VectorExpression ve : bigTableFilterExpressions) {
+ ve.evaluate(batch);
+ }
+
+ final int inputLogicalSize = batch.size;
+ if (inputLogicalSize == 0) {
+ return;
+ }
+
+ // Perform any key expressions. Results will go into scratch columns.
+ if (bigTableKeyExpressions != null) {
+ for (VectorExpression ve : bigTableKeyExpressions) {
+ ve.evaluate(batch);
+ }
+ }
+
+ /*
+ * Single-Column String specific declarations.
+ */
+
+ // The one join column for this specialized class.
+ BytesColumnVector joinColVector = (BytesColumnVector)
batch.cols[singleJoinColumn];
+ byte[][] vector = joinColVector.vector;
+ int[] start = joinColVector.start;
+ int[] length = joinColVector.length;
+
+ /*
+ * Single-Column Long check for repeating.
+ */
+
+ // Check single column for repeating.
+ boolean allKeyInputColumnsRepeating = joinColVector.isRepeating;
+
+ if (allKeyInputColumnsRepeating) {
+
+ /*
+ * Repeating.
+ */
+
+ // All key input columns are repeating. Generate key once. Lookup
once.
+ // Since the key is repeated, we must use entry 0 regardless of
selectedInUse.
+
+ /*
+ * Single-Column String specific repeated lookup.
+ */
+
+ JoinUtil.JoinResult joinResult;
+ if (!joinColVector.noNulls && joinColVector.isNull[0]) {
+ joinResult = JoinUtil.JoinResult.MATCH;
+ } else {
+ byte[] keyBytes = vector[0];
+ int keyStart = start[0];
+ int keyLength = length[0];
+ joinResult = hashSet.contains(keyBytes, keyStart, keyLength,
hashSetResults[0]);
+ if (joinResult == JoinUtil.JoinResult.NOMATCH) {
+ joinResult = JoinUtil.JoinResult.MATCH;
+ } else if (joinResult == JoinUtil.JoinResult.MATCH) {
+ joinResult = JoinUtil.JoinResult.NOMATCH;
+ }
+ }
+
+ /*
+ * Common repeated join result processing.
+ */
+
+ if (LOG.isDebugEnabled()) {
+ LOG.debug(CLASS_NAME + " batch #" + batchCounter + " repeated
joinResult " + joinResult.name());
+ }
+ finishAntiRepeated(batch, joinResult, hashSetResults[0]);
+ } else {
+
+ /*
+ * NOT Repeating.
+ */
+
+ if (LOG.isDebugEnabled()) {
+ LOG.debug(CLASS_NAME + " batch #" + batchCounter + " non-repeated");
+ }
+
+ // We remember any matching rows in matchs / matchSize. At the end of
the loop,
+ // selected / batch.size will represent both matching and non-matching
rows for outer join.
+ // Only deferred rows will have been removed from selected.
+ int selected[] = batch.selected;
+ boolean selectedInUse = batch.selectedInUse;
+
+ int hashSetResultCount = 0;
+ int allMatchCount = 0;
+ int spillCount = 0;
+
+ /*
+ * Single-Column String specific variables.
+ */
+
+ int saveKeyBatchIndex = -1;
+
+ // We optimize performance by only looking up the first key in a
series of equal keys.
+ boolean haveSaveKey = false;
+ JoinUtil.JoinResult saveJoinResult = JoinUtil.JoinResult.NOMATCH;
+
+ // Logical loop over the rows in the batch since the batch may have
selected in use.
+ for (int logical = 0; logical < inputLogicalSize; logical++) {
+ int batchIndex = (selectedInUse ? selected[logical] : logical);
+
+ /*
+ * Single-Column String get key.
+ */
+
+ // Implicit -- use batchIndex.
+ boolean isNull = !joinColVector.noNulls &&
joinColVector.isNull[batchIndex];
+
+ /*
+ * Equal key series checking.
+ */
+
+ if (isNull || !haveSaveKey ||
+ !StringExpr.equal(vector[saveKeyBatchIndex],
start[saveKeyBatchIndex], length[saveKeyBatchIndex],
+ vector[batchIndex], start[batchIndex],
length[batchIndex])) {
+
+ // New key.
+
+ if (haveSaveKey) {
+ // Move on with our counts.
+ switch (saveJoinResult) {
+ case MATCH:
+ // We have extracted the existence from the hash set result,
so we don't keep it.
+ break;
+ case SPILL:
+ // We keep the hash set result for its spill information.
+ hashSetResultCount++;
+ break;
+ case NOMATCH:
+ break;
+ }
+ }
+
+ if (isNull) {
+ saveJoinResult = JoinUtil.JoinResult.NOMATCH;
+ haveSaveKey = false;
+ } else {
+ // Regardless of our matching result, we keep that information
to make multiple use
+ // of it for a possible series of equal keys.
+ haveSaveKey = true;
+
+ /*
+ * Single-Column String specific save key and lookup.
+ */
+
+ saveKeyBatchIndex = batchIndex;
+
+ /*
+ * Single-Column String specific lookup key.
+ */
+
+ byte[] keyBytes = vector[batchIndex];
+ int keyStart = start[batchIndex];
+ int keyLength = length[batchIndex];
+ saveJoinResult = hashSet.contains(keyBytes, keyStart, keyLength,
hashSetResults[hashSetResultCount]);
+ if (saveJoinResult == JoinUtil.JoinResult.NOMATCH) {
Review comment:
Inversion func
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Issue Time Tracking
-------------------
Worklog Id: (was: 461637)
Time Spent: 4h 40m (was: 4.5h)
> Support Anti Join in Hive
> --------------------------
>
> Key: HIVE-23716
> URL: https://issues.apache.org/jira/browse/HIVE-23716
> Project: Hive
> Issue Type: Bug
> Reporter: mahesh kumar behera
> Assignee: mahesh kumar behera
> Priority: Major
> Labels: pull-request-available
> Attachments: HIVE-23716.01.patch
>
> Time Spent: 4h 40m
> Remaining Estimate: 0h
>
> Currently hive does not support Anti join. The query for anti join is
> converted to left outer join and null filter on right side join key is added
> to get the desired result. This is causing
> # Extra computation — The left outer join projects the redundant columns
> from right side. Along with that, filtering is done to remove the redundant
> rows. This is can be avoided in case of anti join as anti join will project
> only the required columns and rows from the left side table.
> # Extra shuffle — In case of anti join the duplicate records moved to join
> node can be avoided from the child node. This can reduce significant amount
> of data movement if the number of distinct rows( join keys) is significant.
> # Extra Memory Usage - In case of map based anti join , hash set is
> sufficient as just the key is required to check if the records matches the
> join condition. In case of left join, we need the key and the non key columns
> also and thus a hash table will be required.
> For a query like
> {code:java}
> select wr_order_number FROM web_returns LEFT JOIN web_sales ON
> wr_order_number = ws_order_number WHERE ws_order_number IS NULL;{code}
> The number of distinct ws_order_number in web_sales table in a typical 10TB
> TPCDS set up is just 10% of total records. So when we convert this query to
> anti join, instead of 7 billion rows, only 600 million rows are moved to join
> node.
> In the current patch, just one conversion is done. The pattern of
> project->filter->left-join is converted to project->anti-join. This will take
> care of sub queries with “not exists” clause. The queries with “not exists”
> are converted first to filter + left-join and then its converted to anti
> join. The queries with “not in” are not handled in the current patch.
> From execution side, both merge join and map join with vectorized execution
> is supported for anti join.
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