[
https://issues.apache.org/jira/browse/PHOENIX-4912?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Bin Shi updated PHOENIX-4912:
-----------------------------
Description:
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 <the start row key denoted as Ki, the count of rows denoted as Ci>.
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
denoted as T * table sampling rate denoted as R (0 <= R <= 100) / 100.00.
To resolve the above problem, one of algorithms that we can consider are
described below. The core idea is to adjust T, R, Y after each pick, so the new
problem is a child problem of the original problem.
{code:java}
ArrayList<Scan> TableSampling(ArrayList<Scan> scans, T, R) {
ArrayList<Scan> pickedScans = new ArrayList<Scan>();
Y = T * R / 100.00;
for (scan<Ki, Ci> 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 = 100.00 * Y / T;
}
}
}
return pickedScans;
}
{code}
was:
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 <the start row key denoted as Ki, the count of rows denoted as Ci>.
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
denoted as T * table sampling rate denoted as R (0 <= R <= 100).
To resolve the above problem, one of algorithms that we can consider are
described below. The core idea is to adjust T, R, Y after each pick, so the new
problem is a child problem of the original problem.
{code:java}
ArrayList<Scan> TableSampling(ArrayList<Scan> scans, T, R) {
ArrayList<Scan> pickedScans = new ArrayList<Scan>();
Y = T * R / 100.00;
for (scan<Ki, Ci> 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 = 100.00 * Y / T;
}
}
}
return pickedScans;
}
{code}
> 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, 4.15.0
> Reporter: Bin Shi
> Assignee: Bin Shi
> Priority: Major
>
> 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 <the start row key denoted as Ki, the count of rows denoted as
> Ci>. 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 denoted as T * table sampling rate denoted as R (0 <= R <= 100) /
> 100.00.
> To resolve the above problem, one of algorithms that we can consider are
> described below. The core idea is to adjust T, R, Y after each pick, so the
> new problem is a child problem of the original problem.
> {code:java}
> ArrayList<Scan> TableSampling(ArrayList<Scan> scans, T, R) {
> ArrayList<Scan> pickedScans = new ArrayList<Scan>();
> Y = T * R / 100.00;
> for (scan<Ki, Ci> 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 = 100.00 * Y / T;
> }
> }
> }
> return pickedScans;
> }
> {code}
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