Examples with ColumnStatisticsData org.apache.hadoop.hive.metastore.api.ColumnStatisticsData used on opensource projects

Example 96 with ColumnStatisticsData

use of org.apache.hadoop.hive.metastore.api.ColumnStatisticsData in project hive by apache.

the class DateColumnStatsAggregator method extrapolate.

@Override
public void extrapolate(ColumnStatisticsData extrapolateData, int numParts, int numPartsWithStats, Map<String, Double> adjustedIndexMap, Map<String, ColumnStatisticsData> adjustedStatsMap, double densityAvg) {
    int rightBorderInd = numParts;
    DateColumnStatsDataInspector extrapolateDateData = new DateColumnStatsDataInspector();
    Map<String, DateColumnStatsData> extractedAdjustedStatsMap = new HashMap<>();
    for (Map.Entry<String, ColumnStatisticsData> entry : adjustedStatsMap.entrySet()) {
        extractedAdjustedStatsMap.put(entry.getKey(), entry.getValue().getDateStats());
    }
    List<Map.Entry<String, DateColumnStatsData>> list = new LinkedList<>(extractedAdjustedStatsMap.entrySet());
    // get the lowValue
    Collections.sort(list, new Comparator<Map.Entry<String, DateColumnStatsData>>() {

        @Override
        public int compare(Map.Entry<String, DateColumnStatsData> o1, Map.Entry<String, DateColumnStatsData> o2) {
            return o1.getValue().getLowValue().compareTo(o2.getValue().getLowValue());
        }
    });
    double minInd = adjustedIndexMap.get(list.get(0).getKey());
    double maxInd = adjustedIndexMap.get(list.get(list.size() - 1).getKey());
    long lowValue = 0;
    long min = list.get(0).getValue().getLowValue().getDaysSinceEpoch();
    long max = list.get(list.size() - 1).getValue().getLowValue().getDaysSinceEpoch();
    if (minInd == maxInd) {
        lowValue = min;
    } else if (minInd < maxInd) {
        // left border is the min
        lowValue = (long) (max - (max - min) * maxInd / (maxInd - minInd));
    } else {
        // right border is the min
        lowValue = (long) (max - (max - min) * (rightBorderInd - maxInd) / (minInd - maxInd));
    }
    // get the highValue
    Collections.sort(list, new Comparator<Map.Entry<String, DateColumnStatsData>>() {

        @Override
        public int compare(Map.Entry<String, DateColumnStatsData> o1, Map.Entry<String, DateColumnStatsData> o2) {
            return o1.getValue().getHighValue().compareTo(o2.getValue().getHighValue());
        }
    });
    minInd = adjustedIndexMap.get(list.get(0).getKey());
    maxInd = adjustedIndexMap.get(list.get(list.size() - 1).getKey());
    long highValue = 0;
    min = list.get(0).getValue().getHighValue().getDaysSinceEpoch();
    max = list.get(list.size() - 1).getValue().getHighValue().getDaysSinceEpoch();
    if (minInd == maxInd) {
        highValue = min;
    } else if (minInd < maxInd) {
        // right border is the max
        highValue = (long) (min + (max - min) * (rightBorderInd - minInd) / (maxInd - minInd));
    } else {
        // left border is the max
        highValue = (long) (min + (max - min) * minInd / (minInd - maxInd));
    }
    // get the #nulls
    long numNulls = 0;
    for (Map.Entry<String, DateColumnStatsData> entry : extractedAdjustedStatsMap.entrySet()) {
        numNulls += entry.getValue().getNumNulls();
    }
    // we scale up sumNulls based on the number of partitions
    numNulls = numNulls * numParts / numPartsWithStats;
    // get the ndv
    long ndv = 0;
    Collections.sort(list, new Comparator<Map.Entry<String, DateColumnStatsData>>() {

        @Override
        public int compare(Map.Entry<String, DateColumnStatsData> o1, Map.Entry<String, DateColumnStatsData> o2) {
            return Long.compare(o1.getValue().getNumDVs(), o2.getValue().getNumDVs());
        }
    });
    long lowerBound = list.get(list.size() - 1).getValue().getNumDVs();
    long higherBound = 0;
    for (Map.Entry<String, DateColumnStatsData> entry : list) {
        higherBound += entry.getValue().getNumDVs();
    }
    if (useDensityFunctionForNDVEstimation && densityAvg != 0.0) {
        ndv = (long) ((highValue - lowValue) / densityAvg);
        if (ndv < lowerBound) {
            ndv = lowerBound;
        } else if (ndv > higherBound) {
            ndv = higherBound;
        }
    } else {
        minInd = adjustedIndexMap.get(list.get(0).getKey());
        maxInd = adjustedIndexMap.get(list.get(list.size() - 1).getKey());
        min = list.get(0).getValue().getNumDVs();
        max = list.get(list.size() - 1).getValue().getNumDVs();
        if (minInd == maxInd) {
            ndv = min;
        } else if (minInd < maxInd) {
            // right border is the max
            ndv = (long) (min + (max - min) * (rightBorderInd - minInd) / (maxInd - minInd));
        } else {
            // left border is the max
            ndv = (long) (min + (max - min) * minInd / (minInd - maxInd));
        }
    }
    extrapolateDateData.setLowValue(new Date(lowValue));
    extrapolateDateData.setHighValue(new Date(highValue));
    extrapolateDateData.setNumNulls(numNulls);
    extrapolateDateData.setNumDVs(ndv);
    extrapolateData.setDateStats(extrapolateDateData);
}

Example 97 with ColumnStatisticsData

use of org.apache.hadoop.hive.metastore.api.ColumnStatisticsData in project hive by apache.

the class DecimalColumnStatsAggregator method aggregate.

@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);
        }
        DecimalColumnStatsDataInspector decimalColumnStatsData = (DecimalColumnStatsDataInspector) cso.getStatsData().getDecimalStats();
        if (decimalColumnStatsData.getNdvEstimator() == null) {
            ndvEstimator = null;
            break;
        } else {
            // check if all of the bit vectors can merge
            NumDistinctValueEstimator estimator = decimalColumnStatsData.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) {
        DecimalColumnStatsDataInspector aggregateData = null;
        long lowerBound = 0;
        long higherBound = 0;
        double densityAvgSum = 0.0;
        for (ColStatsObjWithSourceInfo csp : colStatsWithSourceInfo) {
            ColumnStatisticsObj cso = csp.getColStatsObj();
            DecimalColumnStatsDataInspector newData = (DecimalColumnStatsDataInspector) cso.getStatsData().getDecimalStats();
            lowerBound = Math.max(lowerBound, newData.getNumDVs());
            higherBound += newData.getNumDVs();
            densityAvgSum += (MetaStoreUtils.decimalToDouble(newData.getHighValue()) - MetaStoreUtils.decimalToDouble(newData.getLowValue())) / newData.getNumDVs();
            if (ndvEstimator != null) {
                ndvEstimator.mergeEstimators(newData.getNdvEstimator());
            }
            if (aggregateData == null) {
                aggregateData = newData.deepCopy();
            } else {
                if (MetaStoreUtils.decimalToDouble(aggregateData.getLowValue()) < MetaStoreUtils.decimalToDouble(newData.getLowValue())) {
                    aggregateData.setLowValue(aggregateData.getLowValue());
                } else {
                    aggregateData.setLowValue(newData.getLowValue());
                }
                if (MetaStoreUtils.decimalToDouble(aggregateData.getHighValue()) > MetaStoreUtils.decimalToDouble(newData.getHighValue())) {
                    aggregateData.setHighValue(aggregateData.getHighValue());
                } else {
                    aggregateData.setHighValue(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) ((MetaStoreUtils.decimalToDouble(aggregateData.getHighValue()) - MetaStoreUtils.decimalToDouble(aggregateData.getLowValue())) / densityAvg);
                if (estimation < lowerBound) {
                    estimation = lowerBound;
                } else if (estimation > higherBound) {
                    estimation = higherBound;
                }
            } else {
                estimation = (long) (lowerBound + (higherBound - lowerBound) * ndvTuner);
            }
            aggregateData.setNumDVs(estimation);
        }
        columnStatisticsData.setDecimalStats(aggregateData);
    } else {
        // we need extrapolation
        LOG.debug("start extrapolation for " + colName);
        Map<String, Integer> indexMap = new HashMap<>();
        for (int index = 0; index < partNames.size(); index++) {
            indexMap.put(partNames.get(index), index);
        }
        Map<String, Double> adjustedIndexMap = new HashMap<>();
        Map<String, ColumnStatisticsData> adjustedStatsMap = new HashMap<>();
        // while we scan the css, we also get the densityAvg, lowerbound and
        // higerbound when useDensityFunctionForNDVEstimation is true.
        double densityAvgSum = 0.0;
        if (ndvEstimator == null) {
            // the traditional extrapolation methods.
            for (ColStatsObjWithSourceInfo csp : colStatsWithSourceInfo) {
                ColumnStatisticsObj cso = csp.getColStatsObj();
                String partName = csp.getPartName();
                DecimalColumnStatsData newData = cso.getStatsData().getDecimalStats();
                if (useDensityFunctionForNDVEstimation) {
                    densityAvgSum += (MetaStoreUtils.decimalToDouble(newData.getHighValue()) - MetaStoreUtils.decimalToDouble(newData.getLowValue())) / newData.getNumDVs();
                }
                adjustedIndexMap.put(partName, (double) indexMap.get(partName));
                adjustedStatsMap.put(partName, cso.getStatsData());
            }
        } else {
            // we first merge all the adjacent bitvectors that we could merge and
            // derive new partition names and index.
            StringBuilder pseudoPartName = new StringBuilder();
            double pseudoIndexSum = 0;
            int length = 0;
            int curIndex = -1;
            DecimalColumnStatsDataInspector aggregateData = null;
            for (ColStatsObjWithSourceInfo csp : colStatsWithSourceInfo) {
                ColumnStatisticsObj cso = csp.getColStatsObj();
                String partName = csp.getPartName();
                DecimalColumnStatsDataInspector newData = (DecimalColumnStatsDataInspector) cso.getStatsData().getDecimalStats();
                // already checked it before.
                if (indexMap.get(partName) != curIndex) {
                    // There is bitvector, but it is not adjacent to the previous ones.
                    if (length > 0) {
                        // we have to set ndv
                        adjustedIndexMap.put(pseudoPartName.toString(), pseudoIndexSum / length);
                        aggregateData.setNumDVs(ndvEstimator.estimateNumDistinctValues());
                        ColumnStatisticsData csd = new ColumnStatisticsData();
                        csd.setDecimalStats(aggregateData);
                        adjustedStatsMap.put(pseudoPartName.toString(), csd);
                        if (useDensityFunctionForNDVEstimation) {
                            densityAvgSum += (MetaStoreUtils.decimalToDouble(aggregateData.getHighValue()) - MetaStoreUtils.decimalToDouble(aggregateData.getLowValue())) / aggregateData.getNumDVs();
                        }
                        // reset everything
                        pseudoPartName = new StringBuilder();
                        pseudoIndexSum = 0;
                        length = 0;
                        ndvEstimator = NumDistinctValueEstimatorFactory.getEmptyNumDistinctValueEstimator(ndvEstimator);
                    }
                    aggregateData = null;
                }
                curIndex = indexMap.get(partName);
                pseudoPartName.append(partName);
                pseudoIndexSum += curIndex;
                length++;
                curIndex++;
                if (aggregateData == null) {
                    aggregateData = newData.deepCopy();
                } else {
                    if (MetaStoreUtils.decimalToDouble(aggregateData.getLowValue()) < MetaStoreUtils.decimalToDouble(newData.getLowValue())) {
                        aggregateData.setLowValue(aggregateData.getLowValue());
                    } else {
                        aggregateData.setLowValue(newData.getLowValue());
                    }
                    if (MetaStoreUtils.decimalToDouble(aggregateData.getHighValue()) > MetaStoreUtils.decimalToDouble(newData.getHighValue())) {
                        aggregateData.setHighValue(aggregateData.getHighValue());
                    } else {
                        aggregateData.setHighValue(newData.getHighValue());
                    }
                    aggregateData.setNumNulls(aggregateData.getNumNulls() + newData.getNumNulls());
                }
                ndvEstimator.mergeEstimators(newData.getNdvEstimator());
            }
            if (length > 0) {
                // we have to set ndv
                adjustedIndexMap.put(pseudoPartName.toString(), pseudoIndexSum / length);
                aggregateData.setNumDVs(ndvEstimator.estimateNumDistinctValues());
                ColumnStatisticsData csd = new ColumnStatisticsData();
                csd.setDecimalStats(aggregateData);
                adjustedStatsMap.put(pseudoPartName.toString(), csd);
                if (useDensityFunctionForNDVEstimation) {
                    densityAvgSum += (MetaStoreUtils.decimalToDouble(aggregateData.getHighValue()) - MetaStoreUtils.decimalToDouble(aggregateData.getLowValue())) / aggregateData.getNumDVs();
                }
            }
        }
        extrapolate(columnStatisticsData, partNames.size(), colStatsWithSourceInfo.size(), adjustedIndexMap, adjustedStatsMap, densityAvgSum / adjustedStatsMap.size());
    }
    LOG.debug("Ndv estimatation for {} is {} # of partitions requested: {} # of partitions found: {}", colName, columnStatisticsData.getDecimalStats().getNumDVs(), partNames.size(), colStatsWithSourceInfo.size());
    statsObj.setStatsData(columnStatisticsData);
    return statsObj;
}

Example 98 with ColumnStatisticsData

use of org.apache.hadoop.hive.metastore.api.ColumnStatisticsData in project hive by apache.

the class DoubleColumnStatsAggregator method aggregate.

@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);
        }
        DoubleColumnStatsDataInspector doubleColumnStatsData = (DoubleColumnStatsDataInspector) cso.getStatsData().getDoubleStats();
        if (doubleColumnStatsData.getNdvEstimator() == null) {
            ndvEstimator = null;
            break;
        } else {
            // check if all of the bit vectors can merge
            NumDistinctValueEstimator estimator = doubleColumnStatsData.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) {
        DoubleColumnStatsDataInspector aggregateData = null;
        long lowerBound = 0;
        long higherBound = 0;
        double densityAvgSum = 0.0;
        for (ColStatsObjWithSourceInfo csp : colStatsWithSourceInfo) {
            ColumnStatisticsObj cso = csp.getColStatsObj();
            DoubleColumnStatsDataInspector newData = (DoubleColumnStatsDataInspector) cso.getStatsData().getDoubleStats();
            lowerBound = Math.max(lowerBound, newData.getNumDVs());
            higherBound += newData.getNumDVs();
            densityAvgSum += (newData.getHighValue() - newData.getLowValue()) / newData.getNumDVs();
            if (ndvEstimator != null) {
                ndvEstimator.mergeEstimators(newData.getNdvEstimator());
            }
            if (aggregateData == null) {
                aggregateData = newData.deepCopy();
            } else {
                aggregateData.setLowValue(Math.min(aggregateData.getLowValue(), newData.getLowValue()));
                aggregateData.setHighValue(Math.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) ((aggregateData.getHighValue() - aggregateData.getLowValue()) / densityAvg);
                if (estimation < lowerBound) {
                    estimation = lowerBound;
                } else if (estimation > higherBound) {
                    estimation = higherBound;
                }
            } else {
                estimation = (long) (lowerBound + (higherBound - lowerBound) * ndvTuner);
            }
            aggregateData.setNumDVs(estimation);
        }
        columnStatisticsData.setDoubleStats(aggregateData);
    } else {
        // we need extrapolation
        LOG.debug("start extrapolation for " + colName);
        Map<String, Integer> indexMap = new HashMap<>();
        for (int index = 0; index < partNames.size(); index++) {
            indexMap.put(partNames.get(index), index);
        }
        Map<String, Double> adjustedIndexMap = new HashMap<>();
        Map<String, ColumnStatisticsData> adjustedStatsMap = new HashMap<>();
        // while we scan the css, we also get the densityAvg, lowerbound and
        // higerbound when useDensityFunctionForNDVEstimation is true.
        double densityAvgSum = 0.0;
        if (ndvEstimator == null) {
            // the traditional extrapolation methods.
            for (ColStatsObjWithSourceInfo csp : colStatsWithSourceInfo) {
                ColumnStatisticsObj cso = csp.getColStatsObj();
                String partName = csp.getPartName();
                DoubleColumnStatsData newData = cso.getStatsData().getDoubleStats();
                if (useDensityFunctionForNDVEstimation) {
                    densityAvgSum += (newData.getHighValue() - newData.getLowValue()) / newData.getNumDVs();
                }
                adjustedIndexMap.put(partName, (double) indexMap.get(partName));
                adjustedStatsMap.put(partName, cso.getStatsData());
            }
        } else {
            // we first merge all the adjacent bitvectors that we could merge and
            // derive new partition names and index.
            StringBuilder pseudoPartName = new StringBuilder();
            double pseudoIndexSum = 0;
            int length = 0;
            int curIndex = -1;
            DoubleColumnStatsData aggregateData = null;
            for (ColStatsObjWithSourceInfo csp : colStatsWithSourceInfo) {
                ColumnStatisticsObj cso = csp.getColStatsObj();
                String partName = csp.getPartName();
                DoubleColumnStatsDataInspector newData = (DoubleColumnStatsDataInspector) cso.getStatsData().getDoubleStats();
                // already checked it before.
                if (indexMap.get(partName) != curIndex) {
                    // There is bitvector, but it is not adjacent to the previous ones.
                    if (length > 0) {
                        // we have to set ndv
                        adjustedIndexMap.put(pseudoPartName.toString(), pseudoIndexSum / length);
                        aggregateData.setNumDVs(ndvEstimator.estimateNumDistinctValues());
                        ColumnStatisticsData csd = new ColumnStatisticsData();
                        csd.setDoubleStats(aggregateData);
                        adjustedStatsMap.put(pseudoPartName.toString(), csd);
                        if (useDensityFunctionForNDVEstimation) {
                            densityAvgSum += (aggregateData.getHighValue() - aggregateData.getLowValue()) / aggregateData.getNumDVs();
                        }
                        // reset everything
                        pseudoPartName = new StringBuilder();
                        pseudoIndexSum = 0;
                        length = 0;
                        ndvEstimator = NumDistinctValueEstimatorFactory.getEmptyNumDistinctValueEstimator(ndvEstimator);
                    }
                    aggregateData = null;
                }
                curIndex = indexMap.get(partName);
                pseudoPartName.append(partName);
                pseudoIndexSum += curIndex;
                length++;
                curIndex++;
                if (aggregateData == null) {
                    aggregateData = newData.deepCopy();
                } else {
                    aggregateData.setLowValue(Math.min(aggregateData.getLowValue(), newData.getLowValue()));
                    aggregateData.setHighValue(Math.max(aggregateData.getHighValue(), newData.getHighValue()));
                    aggregateData.setNumNulls(aggregateData.getNumNulls() + newData.getNumNulls());
                }
                ndvEstimator.mergeEstimators(newData.getNdvEstimator());
            }
            if (length > 0) {
                // we have to set ndv
                adjustedIndexMap.put(pseudoPartName.toString(), pseudoIndexSum / length);
                aggregateData.setNumDVs(ndvEstimator.estimateNumDistinctValues());
                ColumnStatisticsData csd = new ColumnStatisticsData();
                csd.setDoubleStats(aggregateData);
                adjustedStatsMap.put(pseudoPartName.toString(), csd);
                if (useDensityFunctionForNDVEstimation) {
                    densityAvgSum += (aggregateData.getHighValue() - aggregateData.getLowValue()) / aggregateData.getNumDVs();
                }
            }
        }
        extrapolate(columnStatisticsData, partNames.size(), colStatsWithSourceInfo.size(), adjustedIndexMap, adjustedStatsMap, densityAvgSum / adjustedStatsMap.size());
    }
    LOG.debug("Ndv estimatation for {} is {}. # of partitions requested: {}. # of partitions found: {}", colName, columnStatisticsData.getDoubleStats().getNumDVs(), partNames.size(), colStatsWithSourceInfo.size());
    statsObj.setStatsData(columnStatisticsData);
    return statsObj;
}

Example 99 with ColumnStatisticsData

use of org.apache.hadoop.hive.metastore.api.ColumnStatisticsData in project hive by apache.

the class DoubleColumnStatsAggregator method extrapolate.

@Override
public void extrapolate(ColumnStatisticsData extrapolateData, int numParts, int numPartsWithStats, Map<String, Double> adjustedIndexMap, Map<String, ColumnStatisticsData> adjustedStatsMap, double densityAvg) {
    int rightBorderInd = numParts;
    DoubleColumnStatsDataInspector extrapolateDoubleData = new DoubleColumnStatsDataInspector();
    Map<String, DoubleColumnStatsData> extractedAdjustedStatsMap = new HashMap<>();
    for (Map.Entry<String, ColumnStatisticsData> entry : adjustedStatsMap.entrySet()) {
        extractedAdjustedStatsMap.put(entry.getKey(), entry.getValue().getDoubleStats());
    }
    List<Map.Entry<String, DoubleColumnStatsData>> list = new LinkedList<>(extractedAdjustedStatsMap.entrySet());
    // get the lowValue
    Collections.sort(list, new Comparator<Map.Entry<String, DoubleColumnStatsData>>() {

        @Override
        public int compare(Map.Entry<String, DoubleColumnStatsData> o1, Map.Entry<String, DoubleColumnStatsData> o2) {
            return Double.compare(o1.getValue().getLowValue(), o2.getValue().getLowValue());
        }
    });
    double minInd = adjustedIndexMap.get(list.get(0).getKey());
    double maxInd = adjustedIndexMap.get(list.get(list.size() - 1).getKey());
    double lowValue = 0;
    double min = list.get(0).getValue().getLowValue();
    double max = list.get(list.size() - 1).getValue().getLowValue();
    if (minInd == maxInd) {
        lowValue = min;
    } else if (minInd < maxInd) {
        // left border is the min
        lowValue = (max - (max - min) * maxInd / (maxInd - minInd));
    } else {
        // right border is the min
        lowValue = (max - (max - min) * (rightBorderInd - maxInd) / (minInd - maxInd));
    }
    // get the highValue
    Collections.sort(list, new Comparator<Map.Entry<String, DoubleColumnStatsData>>() {

        @Override
        public int compare(Map.Entry<String, DoubleColumnStatsData> o1, Map.Entry<String, DoubleColumnStatsData> o2) {
            return Double.compare(o1.getValue().getHighValue(), o2.getValue().getHighValue());
        }
    });
    minInd = adjustedIndexMap.get(list.get(0).getKey());
    maxInd = adjustedIndexMap.get(list.get(list.size() - 1).getKey());
    double highValue = 0;
    min = list.get(0).getValue().getHighValue();
    max = list.get(list.size() - 1).getValue().getHighValue();
    if (minInd == maxInd) {
        highValue = min;
    } else if (minInd < maxInd) {
        // right border is the max
        highValue = (min + (max - min) * (rightBorderInd - minInd) / (maxInd - minInd));
    } else {
        // left border is the max
        highValue = (min + (max - min) * minInd / (minInd - maxInd));
    }
    // get the #nulls
    long numNulls = 0;
    for (Map.Entry<String, DoubleColumnStatsData> entry : extractedAdjustedStatsMap.entrySet()) {
        numNulls += entry.getValue().getNumNulls();
    }
    // we scale up sumNulls based on the number of partitions
    numNulls = numNulls * numParts / numPartsWithStats;
    // get the ndv
    long ndv = 0;
    long ndvMin = 0;
    long ndvMax = 0;
    Collections.sort(list, new Comparator<Map.Entry<String, DoubleColumnStatsData>>() {

        @Override
        public int compare(Map.Entry<String, DoubleColumnStatsData> o1, Map.Entry<String, DoubleColumnStatsData> o2) {
            return Long.compare(o1.getValue().getNumDVs(), o2.getValue().getNumDVs());
        }
    });
    long lowerBound = list.get(list.size() - 1).getValue().getNumDVs();
    long higherBound = 0;
    for (Map.Entry<String, DoubleColumnStatsData> entry : list) {
        higherBound += entry.getValue().getNumDVs();
    }
    if (useDensityFunctionForNDVEstimation && densityAvg != 0.0) {
        ndv = (long) ((highValue - lowValue) / densityAvg);
        if (ndv < lowerBound) {
            ndv = lowerBound;
        } else if (ndv > higherBound) {
            ndv = higherBound;
        }
    } else {
        minInd = adjustedIndexMap.get(list.get(0).getKey());
        maxInd = adjustedIndexMap.get(list.get(list.size() - 1).getKey());
        ndvMin = list.get(0).getValue().getNumDVs();
        ndvMax = list.get(list.size() - 1).getValue().getNumDVs();
        if (minInd == maxInd) {
            ndv = ndvMin;
        } else if (minInd < maxInd) {
            // right border is the max
            ndv = (long) (ndvMin + (ndvMax - ndvMin) * (rightBorderInd - minInd) / (maxInd - minInd));
        } else {
            // left border is the max
            ndv = (long) (ndvMin + (ndvMax - ndvMin) * minInd / (minInd - maxInd));
        }
    }
    extrapolateDoubleData.setLowValue(lowValue);
    extrapolateDoubleData.setHighValue(highValue);
    extrapolateDoubleData.setNumNulls(numNulls);
    extrapolateDoubleData.setNumDVs(ndv);
    extrapolateData.setDoubleStats(extrapolateDoubleData);
}

Example 100 with ColumnStatisticsData

use of org.apache.hadoop.hive.metastore.api.ColumnStatisticsData in project hive by apache.

the class LongColumnStatsAggregator method aggregate.

@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);
        }
        LongColumnStatsDataInspector longColumnStatsData = (LongColumnStatsDataInspector) cso.getStatsData().getLongStats();
        if (longColumnStatsData.getNdvEstimator() == null) {
            ndvEstimator = null;
            break;
        } else {
            // check if all of the bit vectors can merge
            NumDistinctValueEstimator estimator = longColumnStatsData.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) {
        LongColumnStatsDataInspector aggregateData = null;
        long lowerBound = 0;
        long higherBound = 0;
        double densityAvgSum = 0.0;
        for (ColStatsObjWithSourceInfo csp : colStatsWithSourceInfo) {
            ColumnStatisticsObj cso = csp.getColStatsObj();
            LongColumnStatsDataInspector newData = (LongColumnStatsDataInspector) cso.getStatsData().getLongStats();
            lowerBound = Math.max(lowerBound, newData.getNumDVs());
            higherBound += newData.getNumDVs();
            densityAvgSum += (newData.getHighValue() - newData.getLowValue()) / newData.getNumDVs();
            if (ndvEstimator != null) {
                ndvEstimator.mergeEstimators(newData.getNdvEstimator());
            }
            if (aggregateData == null) {
                aggregateData = newData.deepCopy();
            } else {
                aggregateData.setLowValue(Math.min(aggregateData.getLowValue(), newData.getLowValue()));
                aggregateData.setHighValue(Math.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) ((aggregateData.getHighValue() - aggregateData.getLowValue()) / densityAvg);
                if (estimation < lowerBound) {
                    estimation = lowerBound;
                } else if (estimation > higherBound) {
                    estimation = higherBound;
                }
            } else {
                estimation = (long) (lowerBound + (higherBound - lowerBound) * ndvTuner);
            }
            aggregateData.setNumDVs(estimation);
        }
        columnStatisticsData.setLongStats(aggregateData);
    } else {
        // we need extrapolation
        LOG.debug("start extrapolation for " + colName);
        Map<String, Integer> indexMap = new HashMap<>();
        for (int index = 0; index < partNames.size(); index++) {
            indexMap.put(partNames.get(index), index);
        }
        Map<String, Double> adjustedIndexMap = new HashMap<>();
        Map<String, ColumnStatisticsData> adjustedStatsMap = new HashMap<>();
        // while we scan the css, we also get the densityAvg, lowerbound and
        // higerbound when useDensityFunctionForNDVEstimation is true.
        double densityAvgSum = 0.0;
        if (ndvEstimator == null) {
            // the traditional extrapolation methods.
            for (ColStatsObjWithSourceInfo csp : colStatsWithSourceInfo) {
                ColumnStatisticsObj cso = csp.getColStatsObj();
                String partName = csp.getPartName();
                LongColumnStatsData newData = cso.getStatsData().getLongStats();
                if (useDensityFunctionForNDVEstimation) {
                    densityAvgSum += (newData.getHighValue() - newData.getLowValue()) / newData.getNumDVs();
                }
                adjustedIndexMap.put(partName, (double) indexMap.get(partName));
                adjustedStatsMap.put(partName, cso.getStatsData());
            }
        } else {
            // we first merge all the adjacent bitvectors that we could merge and
            // derive new partition names and index.
            StringBuilder pseudoPartName = new StringBuilder();
            double pseudoIndexSum = 0;
            int length = 0;
            int curIndex = -1;
            LongColumnStatsDataInspector aggregateData = null;
            for (ColStatsObjWithSourceInfo csp : colStatsWithSourceInfo) {
                ColumnStatisticsObj cso = csp.getColStatsObj();
                String partName = csp.getPartName();
                LongColumnStatsDataInspector newData = (LongColumnStatsDataInspector) cso.getStatsData().getLongStats();
                // already checked it before.
                if (indexMap.get(partName) != curIndex) {
                    // There is bitvector, but it is not adjacent to the previous ones.
                    if (length > 0) {
                        // we have to set ndv
                        adjustedIndexMap.put(pseudoPartName.toString(), pseudoIndexSum / length);
                        aggregateData.setNumDVs(ndvEstimator.estimateNumDistinctValues());
                        ColumnStatisticsData csd = new ColumnStatisticsData();
                        csd.setLongStats(aggregateData);
                        adjustedStatsMap.put(pseudoPartName.toString(), csd);
                        if (useDensityFunctionForNDVEstimation) {
                            densityAvgSum += (aggregateData.getHighValue() - aggregateData.getLowValue()) / aggregateData.getNumDVs();
                        }
                        // reset everything
                        pseudoPartName = new StringBuilder();
                        pseudoIndexSum = 0;
                        length = 0;
                        ndvEstimator = NumDistinctValueEstimatorFactory.getEmptyNumDistinctValueEstimator(ndvEstimator);
                    }
                    aggregateData = null;
                }
                curIndex = indexMap.get(partName);
                pseudoPartName.append(partName);
                pseudoIndexSum += curIndex;
                length++;
                curIndex++;
                if (aggregateData == null) {
                    aggregateData = newData.deepCopy();
                } else {
                    aggregateData.setLowValue(Math.min(aggregateData.getLowValue(), newData.getLowValue()));
                    aggregateData.setHighValue(Math.max(aggregateData.getHighValue(), newData.getHighValue()));
                    aggregateData.setNumNulls(aggregateData.getNumNulls() + newData.getNumNulls());
                }
                ndvEstimator.mergeEstimators(newData.getNdvEstimator());
            }
            if (length > 0) {
                // we have to set ndv
                adjustedIndexMap.put(pseudoPartName.toString(), pseudoIndexSum / length);
                aggregateData.setNumDVs(ndvEstimator.estimateNumDistinctValues());
                ColumnStatisticsData csd = new ColumnStatisticsData();
                csd.setLongStats(aggregateData);
                adjustedStatsMap.put(pseudoPartName.toString(), csd);
                if (useDensityFunctionForNDVEstimation) {
                    densityAvgSum += (aggregateData.getHighValue() - aggregateData.getLowValue()) / aggregateData.getNumDVs();
                }
            }
        }
        extrapolate(columnStatisticsData, partNames.size(), colStatsWithSourceInfo.size(), adjustedIndexMap, adjustedStatsMap, densityAvgSum / adjustedStatsMap.size());
    }
    LOG.debug("Ndv estimatation for {} is {} # of partitions requested: {} # of partitions found: {}", colName, columnStatisticsData.getLongStats().getNumDVs(), partNames.size(), colStatsWithSourceInfo.size());
    statsObj.setStatsData(columnStatisticsData);
    return statsObj;
}