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Example 16 with FactScan

use of io.cdap.cdap.data2.dataset2.lib.timeseries.FactScan in project cdap by caskdata.

the class FactTable method findSingleDimensionValue.

/**
 * Searches for first non-null valued dimensions in records that contain given list of dimensions and match given
 * dimension values in given time range. Returned dimension values are those that are not defined in given
 * dimension values.
 * @param allDimensionNames list of all dimension names to be present in the record
 * @param dimensionSlice dimension values to filter by, {@code null} means any non-null value.
 * @param startTs start of the time range, in seconds
 * @param endTs end of the time range, in seconds
 * @return {@link Set} of {@link DimensionValue}s
 */
// todo: pass a limit on number of dimensionValues returned
// todo: kinda not cool API when we expect null values in a map...
public Set<DimensionValue> findSingleDimensionValue(List<String> allDimensionNames, Map<String, String> dimensionSlice, long startTs, long endTs) {
    // Algorithm, briefly:
    // We scan in the records which have given allDimensionNames. We use dimensionSlice as a criteria for scan.
    // If record from the scan has non-null values in the dimensions which are not specified in dimensionSlice,
    // we use first of such dimension as a value to return.
    // When we find value to return, since we only fill a single dimension, we are not interested in drilling down
    // further and instead attempt to fast-forward (jump) to a record that has different value in that dimension.
    // Thus we find all results.
    List<DimensionValue> allDimensions = Lists.newArrayList();
    List<Integer> dimToFillIndexes = Lists.newArrayList();
    for (int i = 0; i < allDimensionNames.size(); i++) {
        String dimensionName = allDimensionNames.get(i);
        if (!dimensionSlice.containsKey(dimensionName)) {
            dimToFillIndexes.add(i);
            allDimensions.add(new DimensionValue(dimensionName, null));
        } else {
            DimensionValue dimensionValue = new DimensionValue(dimensionName, dimensionSlice.get(dimensionName));
            allDimensions.add(dimensionValue);
        }
    }
    // If provided dimensions contain all values filled in, there's nothing to look for
    if (dimToFillIndexes.isEmpty()) {
        return Collections.emptySet();
    }
    Set<DimensionValue> result = Sets.newHashSet();
    int scans = 0;
    int scannedRecords = 0;
    // build a scan
    byte[] startRow = codec.createStartRowKey(allDimensions, null, startTs, false);
    byte[] endRow = codec.createEndRowKey(allDimensions, null, endTs, false);
    endRow = Bytes.stopKeyForPrefix(endRow);
    FuzzyRowFilter fuzzyRowFilter = createFuzzyRowFilter(new FactScan(startTs, endTs, Collections.emptyList(), allDimensions), startRow);
    Scanner scanner = timeSeriesTable.scan(startRow, endRow, fuzzyRowFilter);
    scans++;
    try {
        Row rowResult;
        while ((rowResult = scanner.next()) != null) {
            scannedRecords++;
            // todo: make configurable
            if (scannedRecords > MAX_RECORDS_TO_SCAN_DURING_SEARCH) {
                break;
            }
            byte[] rowKey = rowResult.getRow();
            // filter out columns by time range (scan configuration only filters whole rows)
            if (codec.getTimestamp(rowKey, codec.createColumn(startTs)) < startTs) {
                continue;
            }
            if (codec.getTimestamp(rowKey, codec.createColumn(endTs)) > endTs) {
                // we're done with scanner
                break;
            }
            List<DimensionValue> dimensionValues = codec.getDimensionValues(rowResult.getRow());
            // At this point, we know that the record is in right time range and its dimensions matches given.
            // We try find first non-null valued dimension in the record that was not in given dimensions: we use it to form
            // next drill down suggestion
            int filledIndex = -1;
            for (int index : dimToFillIndexes) {
                // todo: it may be not efficient, if dimensionValues is not array-backed list: i.e. if access by index is
                // not fast
                DimensionValue dimensionValue = dimensionValues.get(index);
                if (dimensionValue.getValue() != null) {
                    result.add(dimensionValue);
                    filledIndex = index;
                    break;
                }
            }
            // todo: fast-forwarding (jumping) should be done on server-side (CDAP-1421)
            if (filledIndex >= 0) {
                scanner.close();
                scanner = null;
                scans++;
                if (scans > MAX_SCANS_DURING_SEARCH) {
                    break;
                }
                startRow = codec.getNextRowKey(rowResult.getRow(), filledIndex);
                scanner = timeSeriesTable.scan(startRow, endRow, fuzzyRowFilter);
            }
        }
    } finally {
        if (scanner != null) {
            scanner.close();
        }
    }
    LOG.trace("search for dimensions completed, scans performed: {}, scanned records: {}", scans, scannedRecords);
    return result;
}
Also used : Scanner(io.cdap.cdap.api.dataset.table.Scanner) DimensionValue(io.cdap.cdap.api.dataset.lib.cube.DimensionValue) Row(io.cdap.cdap.api.dataset.table.Row) FuzzyRowFilter(io.cdap.cdap.data2.dataset2.lib.table.FuzzyRowFilter)

Aggregations

ImmutableList (com.google.common.collect.ImmutableList)6 List (java.util.List)6 Test (org.junit.Test)6 DimensionValue (co.cask.cdap.api.dataset.lib.cube.DimensionValue)5 DimensionValue (io.cdap.cdap.api.dataset.lib.cube.DimensionValue)5 FuzzyRowFilter (co.cask.cdap.data2.dataset2.lib.table.FuzzyRowFilter)3 InMemoryMetricsTable (co.cask.cdap.data2.dataset2.lib.table.inmemory.InMemoryMetricsTable)3 FuzzyRowFilter (io.cdap.cdap.data2.dataset2.lib.table.FuzzyRowFilter)3 InMemoryMetricsTable (io.cdap.cdap.data2.dataset2.lib.table.inmemory.InMemoryMetricsTable)3 ArrayList (java.util.ArrayList)3 TimeValue (co.cask.cdap.api.dataset.lib.cube.TimeValue)2 Row (co.cask.cdap.api.dataset.table.Row)2 Scanner (co.cask.cdap.api.dataset.table.Scanner)2 FactScan (co.cask.cdap.data2.dataset2.lib.timeseries.FactScan)2 FactTable (co.cask.cdap.data2.dataset2.lib.timeseries.FactTable)2 TimeValue (io.cdap.cdap.api.dataset.lib.cube.TimeValue)2 Row (io.cdap.cdap.api.dataset.table.Row)2 Scanner (io.cdap.cdap.api.dataset.table.Scanner)2 FactScan (io.cdap.cdap.data2.dataset2.lib.timeseries.FactScan)2 FactTable (io.cdap.cdap.data2.dataset2.lib.timeseries.FactTable)2