Examples with VectorMapJoinHashTableResult org.apache.hadoop.hive.ql.exec.vector.mapjoin.hashtable.VectorMapJoinHashTableResult used on opensource projects

Example 6 with VectorMapJoinHashTableResult

use of org.apache.hadoop.hive.ql.exec.vector.mapjoin.hashtable.VectorMapJoinHashTableResult in project hive by apache.

the class VectorMapJoinInnerBigOnlyStringOperator method process.

//---------------------------------------------------------------------------
// Process Single-Column String Inner Big-Only Join on a vectorized row batch.
//
@Override
public void process(Object row, int tag) throws HiveException {
    try {
        VectorizedRowBatch batch = (VectorizedRowBatch) row;
        alias = (byte) tag;
        if (needCommonSetup) {
            // Our one time process method initialization.
            commonSetup(batch);
            /*
         * Initialize Single-Column String members for this specialized class.
         */
            singleJoinColumn = bigTableKeyColumnMap[0];
            needCommonSetup = false;
        }
        if (needHashTableSetup) {
            // Setup our hash table specialization.  It will be the first time the process
            // method is called, or after a Hybrid Grace reload.
            /*
         * Get our Single-Column String hash multi-set information for this specialized class.
         */
            hashMultiSet = (VectorMapJoinBytesHashMultiSet) vectorMapJoinHashTable;
            needHashTableSetup = false;
        }
        batchCounter++;
        // For inner joins, we may apply the filter(s) now.
        for (VectorExpression ve : bigTableFilterExpressions) {
            ve.evaluate(batch);
        }
        final int inputLogicalSize = batch.size;
        if (inputLogicalSize == 0) {
            if (isLogDebugEnabled) {
                LOG.debug(CLASS_NAME + " batch #" + batchCounter + " empty");
            }
            return;
        }
        // Perform any key expressions.  Results will go into scratch columns.
        if (bigTableKeyExpressions != null) {
            for (VectorExpression ve : bigTableKeyExpressions) {
                ve.evaluate(batch);
            }
        }
        // We rebuild in-place the selected array with rows destine to be forwarded.
        int numSel = 0;
        /*
       * 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 String 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.NOMATCH;
            } else {
                byte[] keyBytes = vector[0];
                int keyStart = start[0];
                int keyLength = length[0];
                joinResult = hashMultiSet.contains(keyBytes, keyStart, keyLength, hashMultiSetResults[0]);
            }
            if (isLogDebugEnabled) {
                LOG.debug(CLASS_NAME + " batch #" + batchCounter + " repeated joinResult " + joinResult.name());
            }
            finishInnerBigOnlyRepeated(batch, joinResult, hashMultiSetResults[0]);
        } else {
            if (isLogDebugEnabled) {
                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 hashMultiSetResultCount = 0;
            int allMatchCount = 0;
            int equalKeySeriesCount = 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];
                if (isNull || !haveSaveKey || StringExpr.equal(vector[saveKeyBatchIndex], start[saveKeyBatchIndex], length[saveKeyBatchIndex], vector[batchIndex], start[batchIndex], length[batchIndex]) == false) {
                    if (haveSaveKey) {
                        // Move on with our counts.
                        switch(saveJoinResult) {
                            case MATCH:
                                // We have extracted the count from the hash multi-set result, so we don't keep it.
                                equalKeySeriesCount++;
                                break;
                            case SPILL:
                                // We keep the hash multi-set result for its spill information.
                                hashMultiSetResultCount++;
                                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.
               */
                        saveKeyBatchIndex = batchIndex;
                        /*
               * Single-Column String specific lookup key.
               */
                        byte[] keyBytes = vector[batchIndex];
                        int keyStart = start[batchIndex];
                        int keyLength = length[batchIndex];
                        saveJoinResult = hashMultiSet.contains(keyBytes, keyStart, keyLength, hashMultiSetResults[hashMultiSetResultCount]);
                    }
                    switch(saveJoinResult) {
                        case MATCH:
                            equalKeySeriesValueCounts[equalKeySeriesCount] = hashMultiSetResults[hashMultiSetResultCount].count();
                            equalKeySeriesAllMatchIndices[equalKeySeriesCount] = allMatchCount;
                            equalKeySeriesDuplicateCounts[equalKeySeriesCount] = 1;
                            allMatchs[allMatchCount++] = batchIndex;
                            // VectorizedBatchUtil.debugDisplayOneRow(batch, batchIndex, CLASS_NAME + " MATCH isSingleValue " + equalKeySeriesIsSingleValue[equalKeySeriesCount] + " currentKey " + currentKey);
                            break;
                        case SPILL:
                            spills[spillCount] = batchIndex;
                            spillHashMapResultIndices[spillCount] = hashMultiSetResultCount;
                            spillCount++;
                            break;
                        case NOMATCH:
                            // VectorizedBatchUtil.debugDisplayOneRow(batch, batchIndex, CLASS_NAME + " NOMATCH" + " currentKey " + currentKey);
                            break;
                    }
                } else {
                    switch(saveJoinResult) {
                        case MATCH:
                            equalKeySeriesDuplicateCounts[equalKeySeriesCount]++;
                            allMatchs[allMatchCount++] = batchIndex;
                            // VectorizedBatchUtil.debugDisplayOneRow(batch, batchIndex, CLASS_NAME + " MATCH duplicate");
                            break;
                        case SPILL:
                            spills[spillCount] = batchIndex;
                            spillHashMapResultIndices[spillCount] = hashMultiSetResultCount;
                            spillCount++;
                            break;
                        case NOMATCH:
                            // VectorizedBatchUtil.debugDisplayOneRow(batch, batchIndex, CLASS_NAME + " NOMATCH duplicate");
                            break;
                    }
                }
            }
            if (haveSaveKey) {
                // Update our counts for the last key.
                switch(saveJoinResult) {
                    case MATCH:
                        // We have extracted the count from the hash multi-set result, so we don't keep it.
                        equalKeySeriesCount++;
                        break;
                    case SPILL:
                        // We keep the hash multi-set result for its spill information.
                        hashMultiSetResultCount++;
                        break;
                    case NOMATCH:
                        break;
                }
            }
            if (isLogDebugEnabled) {
                LOG.debug(CLASS_NAME + " allMatchs " + intArrayToRangesString(allMatchs, allMatchCount) + " equalKeySeriesValueCounts " + longArrayToRangesString(equalKeySeriesValueCounts, equalKeySeriesCount) + " equalKeySeriesAllMatchIndices " + intArrayToRangesString(equalKeySeriesAllMatchIndices, equalKeySeriesCount) + " equalKeySeriesDuplicateCounts " + intArrayToRangesString(equalKeySeriesDuplicateCounts, equalKeySeriesCount) + " spills " + intArrayToRangesString(spills, spillCount) + " spillHashMapResultIndices " + intArrayToRangesString(spillHashMapResultIndices, spillCount) + " hashMapResults " + Arrays.toString(Arrays.copyOfRange(hashMultiSetResults, 0, hashMultiSetResultCount)));
            }
            finishInnerBigOnly(batch, allMatchCount, equalKeySeriesCount, spillCount, (VectorMapJoinHashTableResult[]) hashMultiSetResults, hashMultiSetResultCount);
        }
        if (batch.size > 0) {
            // Forward any remaining selected rows.
            forwardBigTableBatch(batch);
        }
    } catch (IOException e) {
        throw new HiveException(e);
    } catch (Exception e) {
        throw new HiveException(e);
    }
}

Example 7 with VectorMapJoinHashTableResult

use of org.apache.hadoop.hive.ql.exec.vector.mapjoin.hashtable.VectorMapJoinHashTableResult in project hive by apache.

the class VectorMapJoinInnerGenerateResultOperator method finishInner.

/**
   * Generate the inner join output results for one vectorized row batch.
   *
   * @param batch
   *          The big table batch with any matching and any non matching rows both as
   *          selected in use.
   * @param allMatchCount
   *          Number of matches in allMatchs.
   * @param equalKeySeriesCount
   *          Number of single value matches.
   * @param spillCount
   *          Number of spills in spills.
   * @param hashMapResultCount
   *          Number of entries in hashMapResults.
   */
protected void finishInner(VectorizedRowBatch batch, int allMatchCount, int equalKeySeriesCount, int spillCount, int hashMapResultCount) throws HiveException, IOException {
    int numSel = 0;
    /*
     * Optimize by running value expressions only over the matched rows.
     */
    if (allMatchCount > 0 && bigTableValueExpressions != null) {
        performValueExpressions(batch, allMatchs, allMatchCount);
    }
    for (int i = 0; i < equalKeySeriesCount; i++) {
        int hashMapResultIndex = equalKeySeriesHashMapResultIndices[i];
        VectorMapJoinHashMapResult hashMapResult = hashMapResults[hashMapResultIndex];
        int allMatchesIndex = equalKeySeriesAllMatchIndices[i];
        boolean isSingleValue = equalKeySeriesIsSingleValue[i];
        int duplicateCount = equalKeySeriesDuplicateCounts[i];
        if (isSingleValue) {
            numSel = generateHashMapResultSingleValue(batch, hashMapResult, allMatchs, allMatchesIndex, duplicateCount, numSel);
        } else {
            generateHashMapResultMultiValue(batch, hashMapResult, allMatchs, allMatchesIndex, duplicateCount);
        }
    }
    if (spillCount > 0) {
        spillHashMapBatch(batch, (VectorMapJoinHashTableResult[]) hashMapResults, spills, spillHashMapResultIndices, spillCount);
    }
    batch.size = numSel;
    batch.selectedInUse = true;
}

Example 8 with VectorMapJoinHashTableResult

use of org.apache.hadoop.hive.ql.exec.vector.mapjoin.hashtable.VectorMapJoinHashTableResult in project hive by apache.

the class VectorMapJoinLeftSemiLongOperator method process.

//---------------------------------------------------------------------------
// Process Single-Column Long Left-Semi Join on a vectorized row batch.
//
@Override
public void process(Object row, int tag) throws HiveException {
    try {
        VectorizedRowBatch batch = (VectorizedRowBatch) row;
        alias = (byte) tag;
        if (needCommonSetup) {
            // Our one time process method initialization.
            commonSetup(batch);
            /*
         * Initialize Single-Column Long members for this specialized class.
         */
            singleJoinColumn = bigTableKeyColumnMap[0];
            needCommonSetup = false;
        }
        if (needHashTableSetup) {
            // Setup our hash table specialization.  It will be the first time the process
            // method is called, or after a Hybrid Grace reload.
            /*
         * Get our Single-Column Long hash set information for this specialized class.
         */
            hashSet = (VectorMapJoinLongHashSet) vectorMapJoinHashTable;
            useMinMax = hashSet.useMinMax();
            if (useMinMax) {
                min = hashSet.min();
                max = hashSet.max();
            }
            needHashTableSetup = false;
        }
        batchCounter++;
        // For left semi joins, we may apply the filter(s) now.
        for (VectorExpression ve : bigTableFilterExpressions) {
            ve.evaluate(batch);
        }
        final int inputLogicalSize = batch.size;
        if (inputLogicalSize == 0) {
            if (isLogDebugEnabled) {
                LOG.debug(CLASS_NAME + " batch #" + batchCounter + " empty");
            }
            return;
        }
        // Perform any key expressions.  Results will go into scratch columns.
        if (bigTableKeyExpressions != null) {
            for (VectorExpression ve : bigTableKeyExpressions) {
                ve.evaluate(batch);
            }
        }
        /*
       * Single-Column Long specific declarations.
       */
        // The one join column for this specialized class.
        LongColumnVector joinColVector = (LongColumnVector) batch.cols[singleJoinColumn];
        long[] vector = joinColVector.vector;
        /*
       * 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 Long specific repeated lookup.
         */
            JoinUtil.JoinResult joinResult;
            if (!joinColVector.noNulls && joinColVector.isNull[0]) {
                joinResult = JoinUtil.JoinResult.NOMATCH;
            } else {
                long key = vector[0];
                if (useMinMax && (key < min || key > max)) {
                    // Out of range for whole batch.
                    joinResult = JoinUtil.JoinResult.NOMATCH;
                } else {
                    joinResult = hashSet.contains(key, hashSetResults[0]);
                }
            }
            if (isLogDebugEnabled) {
                LOG.debug(CLASS_NAME + " batch #" + batchCounter + " repeated joinResult " + joinResult.name());
            }
            finishLeftSemiRepeated(batch, joinResult, hashSetResults[0]);
        } else {
            if (isLogDebugEnabled) {
                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 Long specific variables.
         */
            long saveKey = 0;
            // 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 Long get key.
           */
                long currentKey;
                boolean isNull;
                if (!joinColVector.noNulls && joinColVector.isNull[batchIndex]) {
                    currentKey = 0;
                    isNull = true;
                } else {
                    currentKey = vector[batchIndex];
                    isNull = false;
                }
                if (isNull || !haveSaveKey || currentKey != saveKey) {
                    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 Long specific save key.
               */
                        saveKey = currentKey;
                        if (useMinMax && (currentKey < min || currentKey > max)) {
                            // Key out of range for whole hash table.
                            saveJoinResult = JoinUtil.JoinResult.NOMATCH;
                        } else {
                            saveJoinResult = hashSet.contains(currentKey, hashSetResults[hashSetResultCount]);
                        }
                    }
                    switch(saveJoinResult) {
                        case MATCH:
                            allMatchs[allMatchCount++] = batchIndex;
                            // VectorizedBatchUtil.debugDisplayOneRow(batch, batchIndex, CLASS_NAME + " MATCH isSingleValue " + equalKeySeriesIsSingleValue[equalKeySeriesCount] + " currentKey " + currentKey);
                            break;
                        case SPILL:
                            spills[spillCount] = batchIndex;
                            spillHashMapResultIndices[spillCount] = hashSetResultCount;
                            spillCount++;
                            break;
                        case NOMATCH:
                            // VectorizedBatchUtil.debugDisplayOneRow(batch, batchIndex, CLASS_NAME + " NOMATCH" + " currentKey " + currentKey);
                            break;
                    }
                } else {
                    switch(saveJoinResult) {
                        case MATCH:
                            allMatchs[allMatchCount++] = batchIndex;
                            // VectorizedBatchUtil.debugDisplayOneRow(batch, batchIndex, CLASS_NAME + " MATCH duplicate");
                            break;
                        case SPILL:
                            spills[spillCount] = batchIndex;
                            spillHashMapResultIndices[spillCount] = hashSetResultCount;
                            spillCount++;
                            break;
                        case NOMATCH:
                            // VectorizedBatchUtil.debugDisplayOneRow(batch, batchIndex, CLASS_NAME + " NOMATCH duplicate");
                            break;
                    }
                }
            }
            if (haveSaveKey) {
                // Update our counts for the last key.
                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 (isLogDebugEnabled) {
                LOG.debug(CLASS_NAME + " allMatchs " + intArrayToRangesString(allMatchs, allMatchCount) + " spills " + intArrayToRangesString(spills, spillCount) + " spillHashMapResultIndices " + intArrayToRangesString(spillHashMapResultIndices, spillCount) + " hashMapResults " + Arrays.toString(Arrays.copyOfRange(hashSetResults, 0, hashSetResultCount)));
            }
            finishLeftSemi(batch, allMatchCount, spillCount, (VectorMapJoinHashTableResult[]) hashSetResults);
        }
        if (batch.size > 0) {
            // Forward any remaining selected rows.
            forwardBigTableBatch(batch);
        }
    } catch (IOException e) {
        throw new HiveException(e);
    } catch (Exception e) {
        throw new HiveException(e);
    }
}

Example 9 with VectorMapJoinHashTableResult

use of org.apache.hadoop.hive.ql.exec.vector.mapjoin.hashtable.VectorMapJoinHashTableResult in project hive by apache.

the class VectorMapJoinGenerateResultOperator method spillHashMapBatch.

protected void spillHashMapBatch(VectorizedRowBatch batch, VectorMapJoinHashTableResult[] hashTableResults, int[] spills, int[] spillHashTableResultIndices, int spillCount) throws HiveException, IOException {
    if (bigTableVectorSerializeRow == null) {
        setupSpillSerDe(batch);
    }
    for (int i = 0; i < spillCount; i++) {
        int batchIndex = spills[i];
        int hashTableResultIndex = spillHashTableResultIndices[i];
        VectorMapJoinHashTableResult hashTableResult = hashTableResults[hashTableResultIndex];
        spillSerializeRow(batch, batchIndex, hashTableResult);
    }
}