Bin Shi created PHOENIX-4912: -------------------------------- Summary: Make Table Sampling algorithm to accommodate to the imbalance row distribution across guide posts Key: PHOENIX-4912 URL: https://issues.apache.org/jira/browse/PHOENIX-4912 Project: Phoenix Issue Type: Improvement Affects Versions: 5.0.0 Reporter: Bin Shi Assignee: Bin Shi The current implementation of table sampling is based on the assumption "Every two consecutive guide posts contains the equal number of rows" which isn't accurate in practice, and once we collect multiple versions of cells and the deleted rows, the thing will become worse. In details, the current implementation of table sampling is (see BaseResultIterators.getParallelScan() which calls sampleScans(...) at the end of function) as described below: # Iterate all parallel scans generated; # For each scan, if getHashHode(start row key of the scan) MOD 100 < tableSamplingRate (See TableSamplerPredicate.java) then pick this scan; otherwise discard this scan. The problem can be formalized as: We have a group of scans and each scan is defined as . Now we want to randomly pick X groups so that the sum of count of rows in the selected groups is close to Y, where Y = the total count of rows of all scans T * table sampling rate R. To resolve the above problem, one of algorithms that we can consider are described below: ArrayList TableSampling(ArrayList scans, T, R) {       ArrayList pickedScans = new ArrayList();     Y = T * R;     for (scan in scans) {         if (Y <= 0) break;         if (getHashCode(Ki) MOD 100 < R) {             // then pick this scan, and adjust T, R, Y accordingly             pickedScans.Add(scan);             T -= Ci;             Y -= Ci;             if (T != 0 && Y > 0) {                   R = Y / T;             }         }     }     return pickedScans; } -- This message was sent by Atlassian JIRA (v7.6.3#76005)