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

Example 6 with ColumnStatisticsData

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

the class ColumnStatsAggregatorFactory method newColumnStaticsObj.

public static ColumnStatisticsObj newColumnStaticsObj(String colName, String colType, _Fields type) {
    ColumnStatisticsObj cso = new ColumnStatisticsObj();
    ColumnStatisticsData csd = new ColumnStatisticsData();
    cso.setColName(colName);
    cso.setColType(colType);
    switch(type) {
        case BOOLEAN_STATS:
            csd.setBooleanStats(new BooleanColumnStatsData());
            break;
        case LONG_STATS:
            csd.setLongStats(new LongColumnStatsData());
            break;
        case DOUBLE_STATS:
            csd.setDoubleStats(new DoubleColumnStatsData());
            break;
        case STRING_STATS:
            csd.setStringStats(new StringColumnStatsData());
            break;
        case BINARY_STATS:
            csd.setBinaryStats(new BinaryColumnStatsData());
            break;
        case DECIMAL_STATS:
            csd.setDecimalStats(new DecimalColumnStatsData());
            break;
        default:
            throw new RuntimeException("Woh, bad.  Unknown stats type!");
    }
    cso.setStatsData(csd);
    return cso;
}

Example 7 with ColumnStatisticsData

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

the class DecimalColumnStatsAggregator 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;
    DecimalColumnStatsData extrapolateDecimalData = new DecimalColumnStatsData();
    Map<String, DecimalColumnStatsData> extractedAdjustedStatsMap = new HashMap<>();
    for (Map.Entry<String, ColumnStatisticsData> entry : adjustedStatsMap.entrySet()) {
        extractedAdjustedStatsMap.put(entry.getKey(), entry.getValue().getDecimalStats());
    }
    List<Map.Entry<String, DecimalColumnStatsData>> list = new LinkedList<Map.Entry<String, DecimalColumnStatsData>>(extractedAdjustedStatsMap.entrySet());
    // get the lowValue
    Collections.sort(list, new Comparator<Map.Entry<String, DecimalColumnStatsData>>() {

        public int compare(Map.Entry<String, DecimalColumnStatsData> o1, Map.Entry<String, DecimalColumnStatsData> 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());
    double lowValue = 0;
    double min = HBaseUtils.getDoubleValue(list.get(0).getValue().getLowValue());
    double max = HBaseUtils.getDoubleValue(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, DecimalColumnStatsData>>() {

        public int compare(Map.Entry<String, DecimalColumnStatsData> o1, Map.Entry<String, DecimalColumnStatsData> 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());
    double highValue = 0;
    min = HBaseUtils.getDoubleValue(list.get(0).getValue().getHighValue());
    max = HBaseUtils.getDoubleValue(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, DecimalColumnStatsData> 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, DecimalColumnStatsData>>() {

        public int compare(Map.Entry<String, DecimalColumnStatsData> o1, Map.Entry<String, DecimalColumnStatsData> o2) {
            return o1.getValue().getNumDVs() < o2.getValue().getNumDVs() ? -1 : 1;
        }
    });
    long lowerBound = list.get(list.size() - 1).getValue().getNumDVs();
    long higherBound = 0;
    for (Map.Entry<String, DecimalColumnStatsData> 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));
        }
    }
    extrapolateDecimalData.setLowValue(StatObjectConverter.createThriftDecimal(String.valueOf(lowValue)));
    extrapolateDecimalData.setHighValue(StatObjectConverter.createThriftDecimal(String.valueOf(highValue)));
    extrapolateDecimalData.setNumNulls(numNulls);
    extrapolateDecimalData.setNumDVs(ndv);
    extrapolateData.setDecimalStats(extrapolateDecimalData);
}

Example 8 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(String colName, List<String> partNames, List<ColumnStatistics> css) throws MetaException {
    ColumnStatisticsObj statsObj = null;
    // check if all the ColumnStatisticsObjs contain stats and all the ndv are
    // bitvectors
    boolean doAllPartitionContainStats = partNames.size() == css.size();
    boolean isNDVBitVectorSet = true;
    String colType = null;
    for (ColumnStatistics cs : css) {
        if (cs.getStatsObjSize() != 1) {
            throw new MetaException("The number of columns should be exactly one in aggrStats, but found " + cs.getStatsObjSize());
        }
        ColumnStatisticsObj cso = cs.getStatsObjIterator().next();
        if (statsObj == null) {
            colType = cso.getColType();
            statsObj = ColumnStatsAggregatorFactory.newColumnStaticsObj(colName, colType, cso.getStatsData().getSetField());
        }
        if (numBitVectors <= 0 || !cso.getStatsData().getLongStats().isSetBitVectors() || cso.getStatsData().getLongStats().getBitVectors().length() == 0) {
            isNDVBitVectorSet = false;
            break;
        }
    }
    ColumnStatisticsData columnStatisticsData = new ColumnStatisticsData();
    if (doAllPartitionContainStats || css.size() < 2) {
        LongColumnStatsData aggregateData = null;
        long lowerBound = 0;
        long higherBound = 0;
        double densityAvgSum = 0.0;
        NumDistinctValueEstimator ndvEstimator = null;
        if (isNDVBitVectorSet) {
            ndvEstimator = new NumDistinctValueEstimator(numBitVectors);
        }
        for (ColumnStatistics cs : css) {
            ColumnStatisticsObj cso = cs.getStatsObjIterator().next();
            LongColumnStatsData newData = cso.getStatsData().getLongStats();
            if (useDensityFunctionForNDVEstimation) {
                lowerBound = Math.max(lowerBound, newData.getNumDVs());
                higherBound += newData.getNumDVs();
                densityAvgSum += (newData.getHighValue() - newData.getLowValue()) / newData.getNumDVs();
            }
            if (isNDVBitVectorSet) {
                ndvEstimator.mergeEstimators(new NumDistinctValueEstimator(newData.getBitVectors(), ndvEstimator.getnumBitVectors()));
            }
            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 (isNDVBitVectorSet) {
            // 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 {
            if (useDensityFunctionForNDVEstimation) {
                // We have estimation, lowerbound and higherbound. We use estimation
                // if it is between lowerbound and higherbound.
                double densityAvg = densityAvgSum / partNames.size();
                long estimation = (long) ((aggregateData.getHighValue() - aggregateData.getLowValue()) / densityAvg);
                if (estimation < lowerBound) {
                    aggregateData.setNumDVs(lowerBound);
                } else if (estimation > higherBound) {
                    aggregateData.setNumDVs(higherBound);
                } else {
                    aggregateData.setNumDVs(estimation);
                }
            } else {
            // Without useDensityFunctionForNDVEstimation, we just use the
            // default one, which is the max of all the partitions and it is
            // already done.
            }
        }
        columnStatisticsData.setLongStats(aggregateData);
    } else {
        // we need extrapolation
        Map<String, Integer> indexMap = new HashMap<String, Integer>();
        for (int index = 0; index < partNames.size(); index++) {
            indexMap.put(partNames.get(index), index);
        }
        Map<String, Double> adjustedIndexMap = new HashMap<String, Double>();
        Map<String, ColumnStatisticsData> adjustedStatsMap = new HashMap<String, ColumnStatisticsData>();
        // while we scan the css, we also get the densityAvg, lowerbound and
        // higerbound when useDensityFunctionForNDVEstimation is true.
        double densityAvgSum = 0.0;
        if (!isNDVBitVectorSet) {
            // the traditional extrapolation methods.
            for (ColumnStatistics cs : css) {
                String partName = cs.getStatsDesc().getPartName();
                ColumnStatisticsObj cso = cs.getStatsObjIterator().next();
                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.
            NumDistinctValueEstimator ndvEstimator = new NumDistinctValueEstimator(numBitVectors);
            StringBuilder pseudoPartName = new StringBuilder();
            double pseudoIndexSum = 0;
            int length = 0;
            int curIndex = -1;
            LongColumnStatsData aggregateData = null;
            for (ColumnStatistics cs : css) {
                String partName = cs.getStatsDesc().getPartName();
                ColumnStatisticsObj cso = cs.getStatsObjIterator().next();
                LongColumnStatsData newData = 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;
                    }
                    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(new NumDistinctValueEstimator(newData.getBitVectors(), ndvEstimator.getnumBitVectors()));
            }
            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(), css.size(), adjustedIndexMap, adjustedStatsMap, densityAvgSum / adjustedStatsMap.size());
    }
    statsObj.setStatsData(columnStatisticsData);
    return statsObj;
}

Example 9 with ColumnStatisticsData

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

the class ColumnStatsMergerFactory method newColumnStaticsObj.

public static ColumnStatisticsObj newColumnStaticsObj(String colName, String colType, _Fields type) {
    ColumnStatisticsObj cso = new ColumnStatisticsObj();
    ColumnStatisticsData csd = new ColumnStatisticsData();
    cso.setColName(colName);
    cso.setColType(colType);
    switch(type) {
        case BOOLEAN_STATS:
            csd.setBooleanStats(new BooleanColumnStatsData());
            break;
        case LONG_STATS:
            csd.setLongStats(new LongColumnStatsData());
            break;
        case DOUBLE_STATS:
            csd.setDoubleStats(new DoubleColumnStatsData());
            break;
        case STRING_STATS:
            csd.setStringStats(new StringColumnStatsData());
            break;
        case BINARY_STATS:
            csd.setBinaryStats(new BinaryColumnStatsData());
            break;
        case DECIMAL_STATS:
            csd.setDecimalStats(new DecimalColumnStatsData());
            break;
        default:
            throw new RuntimeException("Woh, bad.  Unknown stats type!");
    }
    cso.setStatsData(csd);
    return cso;
}

Example 10 with ColumnStatisticsData

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

the class ColumnStatsTask method unpackPrimitiveObject.

private void unpackPrimitiveObject(ObjectInspector oi, Object o, String fieldName, ColumnStatisticsObj statsObj) {
    if (o == null) {
        return;
    }
    // First infer the type of object
    if (fieldName.equals("columntype")) {
        PrimitiveObjectInspector poi = (PrimitiveObjectInspector) oi;
        String s = ((StringObjectInspector) poi).getPrimitiveJavaObject(o);
        ColumnStatisticsData statsData = new ColumnStatisticsData();
        if (s.equalsIgnoreCase("long")) {
            LongColumnStatsData longStats = new LongColumnStatsData();
            statsData.setLongStats(longStats);
            statsObj.setStatsData(statsData);
        } else if (s.equalsIgnoreCase("double")) {
            DoubleColumnStatsData doubleStats = new DoubleColumnStatsData();
            statsData.setDoubleStats(doubleStats);
            statsObj.setStatsData(statsData);
        } else if (s.equalsIgnoreCase("string")) {
            StringColumnStatsData stringStats = new StringColumnStatsData();
            statsData.setStringStats(stringStats);
            statsObj.setStatsData(statsData);
        } else if (s.equalsIgnoreCase("boolean")) {
            BooleanColumnStatsData booleanStats = new BooleanColumnStatsData();
            statsData.setBooleanStats(booleanStats);
            statsObj.setStatsData(statsData);
        } else if (s.equalsIgnoreCase("binary")) {
            BinaryColumnStatsData binaryStats = new BinaryColumnStatsData();
            statsData.setBinaryStats(binaryStats);
            statsObj.setStatsData(statsData);
        } else if (s.equalsIgnoreCase("decimal")) {
            DecimalColumnStatsData decimalStats = new DecimalColumnStatsData();
            statsData.setDecimalStats(decimalStats);
            statsObj.setStatsData(statsData);
        } else if (s.equalsIgnoreCase("date")) {
            DateColumnStatsData dateStats = new DateColumnStatsData();
            statsData.setDateStats(dateStats);
            statsObj.setStatsData(statsData);
        }
    } else {
        // invoke the right unpack method depending on data type of the column
        if (statsObj.getStatsData().isSetBooleanStats()) {
            unpackBooleanStats(oi, o, fieldName, statsObj);
        } else if (statsObj.getStatsData().isSetLongStats()) {
            unpackLongStats(oi, o, fieldName, statsObj);
        } else if (statsObj.getStatsData().isSetDoubleStats()) {
            unpackDoubleStats(oi, o, fieldName, statsObj);
        } else if (statsObj.getStatsData().isSetStringStats()) {
            unpackStringStats(oi, o, fieldName, statsObj);
        } else if (statsObj.getStatsData().isSetBinaryStats()) {
            unpackBinaryStats(oi, o, fieldName, statsObj);
        } else if (statsObj.getStatsData().isSetDecimalStats()) {
            unpackDecimalStats(oi, o, fieldName, statsObj);
        } else if (statsObj.getStatsData().isSetDateStats()) {
            unpackDateStats(oi, o, fieldName, statsObj);
        }
    }
}