HBASE-29039 Optimize read performance for accumulated delete markers on the same row or cell by EungsopYoo · Pull Request #6557 · apache/hbase

EungsopYoo · 2024-12-23T06:02:52Z

No description provided.

…row or cell

Apache9 · 2024-12-29T13:24:29Z

I checked the code, we do have logic to seek to next row or column when we hit a delte family cell.

hbase/hbase-server/src/main/java/org/apache/hadoop/hbase/regionserver/querymatcher/ScanQueryMatcher.java

Line 207 in 28c4353

switch (deleteResult) {

But the problem is that, seems we will return earlier before we actually call this method here

hbase/hbase-server/src/main/java/org/apache/hadoop/hbase/regionserver/querymatcher/NormalUserScanQueryMatcher.java

Line 76 in 28c4353

returnCode = checkDeleted(deletes, cell);

The above code block

 if (PrivateCellUtil.isDelete(typeByte)) {
boolean includeDeleteMarker =
seePastDeleteMarkers ? tr.withinTimeRange(timestamp) : tr.withinOrAfterTimeRange(timestamp);
if (includeDeleteMarker) {
this.deletes.add(cell);
}
return MatchCode.SKIP;
}

Seems incorrect, we will always return MatchCode.SKIP if we get a delete maker...

I think why we do not find this before is that, usually there will be only one delete maker, so when we check the next cell, we will fall through and call the checkDeleted method so we will seek to next row or column.

Here the scenario is that we have bunch of delete makrer, then here we will see them all instead of seek to next row or column, since we will always go into the code block above and return MatchCode.SKIP.

I think we should try to optimize the logic of the above code block.

Thanks.

virajjasani · 2024-12-30T22:39:58Z

FYI @kadirozde

EungsopYoo · 2025-01-17T05:35:16Z

 if (PrivateCellUtil.isDelete(typeByte)) {
boolean includeDeleteMarker =
seePastDeleteMarkers ? tr.withinTimeRange(timestamp) : tr.withinOrAfterTimeRange(timestamp);
if (includeDeleteMarker) {
this.deletes.add(cell);
}
return MatchCode.SKIP;
}
Seems incorrect, we will always return MatchCode.SKIP if we get a delete maker...
I think why we do not find this before is that, usually there will be only one delete maker, so when we check the next cell, we will fall through and call the checkDeleted method so we will seek to next row or column.
Here the scenario is that we have bunch of delete makrer, then here we will see them all instead of seek to next row or column, since we will always go into the code block above and return MatchCode.SKIP.
I think we should try to optimize the logic of the above code block.

f6739e2
I removed the early return MatchCode.SKIP statement. But some test cases related to NewVersionBehavior were failed after that.

74ecf71
So I restored return MatchCode.SKIP with some exceptional conditions.

Apache-HBase · 2025-01-17T05:47:53Z

🎊 +1 overall

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Apache-HBase · 2025-01-17T09:01:35Z

💔 -1 overall

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virajjasani · 2025-01-18T01:12:04Z

+ private boolean canOptimizeReadDeleteMarkers() {
+ // for simplicity, optimization works only for these cases
+ return !seePastDeleteMarkers && scanMaxVersions == 1 && !visibilityLabelEnabled
+ && getFilter() == null && !(deletes instanceof NewVersionBehaviorTracker);
+ }


@EungsopYoo have you also considered Dual File Compaction #5545 ?
Could you also run some perf test comparing Dual File Compaction with this optimization? This might be really helpful.

@virajjasani
I have reviewed Dual File Compaction you mentioned. This PR and Dual File Compaction have something in common, especially handling delete markers. But I think there are some differences.
This PR focuses on the accumulated delete markers of the same row or cell, but that handles delete marker of different rows or columns. And this PR can optimize read from both of MemStore and StoreFiles, but that can optimize read from StoreFiles only.
So I think they are complementary and can be used all together.

Thanks @EungsopYoo, this is what I was also expecting.
On the Jira https://issues.apache.org/jira/browse/HBASE-25972, Kadir has also provided how full scan is improvement is observed using PE (second comment on the Jira). Could you also run the same steps to see how much improvement you observe using this PR?

@virajjasani
PE does not have the test case Put, Delete and Get on the same row. Should I add the new test case and run it, maybe?

It's not necessary because the steps mentioned in the Jira will take care of adding many delete markers so you can follow the exact same steps. Thank you!

To me, this improvement is only meaningful when the scanned data is in memstore assuming that that the skip list will be used for jumping from one column to the next (I have not looked at the code in detail recently so I assume it is the case). However, when HBase scans data from HFile, do we have data structures in place to jump from one column to next one? I think we do not have one. No only we linearly scan the cells within a row, we also linearly scan all rows within a HBase block, do not we? So I did not understand why skipping to the next column would be a significant optimization in general.

Thanks @EungsopYoo, how about you keep KEEP_DELETED_CELL false above and see how perf numbers look like with and without your patch?
I have run the same tests again except KEEP_DELETED_CELL is set false.
master
scan with dual file compaction enabled 4047ms
scan with dual file compaction disabled 4277ms
scan with delete markers and dual file compaction disabled 2.5031 seconds
scan with delete markers and dual file compaction enabled 0.0198 seconds
this PR
scan with dual file compaction enabled 4134ms
scan with dual file compaction disabled 3383ms
scan with delete markers and dual file compaction disabled 3.4726 seconds
scan with delete markers and dual file compaction enabled 0.0245 seconds
It looks like there is some performance degradation on the result 3. I will dig into it.

To me, this improvement is only meaningful when the scanned data is in memstore assuming that that the skip list will be used for jumping from one column to the next (I have not looked at the code in detail recently so I assume it is the case). However, when HBase scans data from HFile, do we have data structures in place to jump from one column to next one? I think we do not have one. No only we linearly scan the cells within a row, we also linearly scan all rows within a HBase block, do not we? So I did not understand why skipping to the next column would be a significant optimization in general.
https://issues.apache.org/jira/browse/HBASE-29039
The performance tests on the Jira description are just the cases of reading from MemStore only. So I have run new performance tests, reading from StoreFiles only with or without dual file compaction.
create 'test', 'c' java_import org.apache.hadoop.hbase.client.Delete java_import org.apache.hadoop.hbase.TableName java_import java.lang.System con = @hbase.instance_variable_get(:@connection) table = con.getTable(TableName.valueOf('test')) 1000.times do |i| # batch 10000 deletes with different timestamps every 10 seconds now = System.currentTimeMillis() dels = 10000.times.map do |i| del = Delete.new(Bytes.toBytes('row')) del.addFamily(Bytes.toBytes('c'), now + i) end table.delete(dels) sleep(10) flush 'test' # Trigger manually minor compaction because of compaction is not triggered automatically. But I don't know why yet compact 'test' if i % 10 == 0 puts "i - #{i}" # Read after flush from StoreFiles get 'test', 'row' end
master - without dual file compaction
i - 0 COLUMN CELL 0 row(s) Took 0.0184 seconds ... i - 10 COLUMN CELL 0 row(s) Took 0.0206 seconds ... i - 20 COLUMN CELL 0 row(s) Took 0.0398 seconds ... i - 30 COLUMN CELL 0 row(s) Took 0.0462 seconds ... i - 40 COLUMN CELL 0 row(s) Took 0.0517 seconds ... i - 50 COLUMN CELL 0 row(s) Took 0.0730 seconds ... i - 60 COLUMN CELL 0 row(s) Took 0.0903 seconds
master - with dual file compaction
i - 0 COLUMN CELL 0 row(s) Took 0.0050 seconds ... i - 10 COLUMN CELL 0 row(s) Took 0.0175 seconds ... i - 20 COLUMN CELL 0 row(s) Took 0.0265 seconds ... i - 30 COLUMN CELL 0 row(s) Took 0.0310 seconds ... i - 40 COLUMN CELL 0 row(s) Took 0.0205 seconds ... i - 50 COLUMN CELL 0 row(s) Took 0.0532 seconds ... i - 60 COLUMN CELL 0 row(s) Took 0.0360 seconds
this PR - without dual file compaction
i - 0 COLUMN CELL 0 row(s) Took 0.0061 seconds ... i - 10 COLUMN CELL 0 row(s) Took 0.0102 seconds ... i - 20 COLUMN CELL 0 row(s) Took 0.0052 seconds ... i - 30 COLUMN CELL 0 row(s) Took 0.0073 seconds ... i - 40 COLUMN CELL 0 row(s) Took 0.0077 seconds ... i - 50 COLUMN CELL 0 row(s) Took 0.0115 seconds ... i - 60 COLUMN CELL 0 row(s) Took 0.0101 seconds
this PR - with dual file compaction
i - 0 COLUMN CELL 0 row(s) Took 0.0046 seconds ... i - 10 COLUMN CELL 0 row(s) Took 0.0103 seconds ... i - 20 COLUMN CELL 0 row(s) Took 0.0074 seconds ... i - 30 COLUMN CELL 0 row(s) Took 0.0067 seconds ... i - 40 COLUMN CELL 0 row(s) Took 0.0077 seconds ... i - 50 COLUMN CELL 0 row(s) Took 0.0091 seconds i - 60 COLUMN CELL 0 row(s) Took 0.0106 seconds
The results show that the optimization of this PR works on reading from StoreFiles too, even without dual file compaction. What do you think about this results?

Thanks @EungsopYoo, how about you keep KEEP_DELETED_CELL false above and see how perf numbers look like with and without your patch?
I have run the same tests again except KEEP_DELETED_CELL is set false.
master
scan with dual file compaction enabled 4047ms
scan with dual file compaction disabled 4277ms
scan with delete markers and dual file compaction disabled 2.5031 seconds
scan with delete markers and dual file compaction enabled 0.0198 seconds
this PR
scan with dual file compaction enabled 4134ms
scan with dual file compaction disabled 3383ms
scan with delete markers and dual file compaction disabled 3.4726 seconds
scan with delete markers and dual file compaction enabled 0.0245 seconds
It looks like there is some performance degradation on the result 3. I will dig into it.
https://github.com/EungsopYoo/hbase/blob/63901155caf5c226b02564128669234c08251e8d/hbase-server/src/main/java/org/apache/hadoop/hbase/regionserver/querymatcher/NormalUserScanQueryMatcher.java#L81-L99
The slight performance degradation is due to removing of early return of MatchCode.SKIP in the normal cases. Because of the removal of early return, checkDeleted() method is executed more than before, and then some burden of computation is added. I found the result by removing added code blocks one by one.

hbase/hbase-server/src/main/java/org/apache/hadoop/hbase/regionserver/StoreScanner.java
Line 644 in 6390115
ScanQueryMatcher.MatchCodeqcode = matcher.match(cell, prevCell);

hbase/hbase-server/src/main/java/org/apache/hadoop/hbase/regionserver/StoreScanner.java
Lines 758 to 768 in 6390115
caseSEEK_NEXT_COL:
seekOrSkipToNextColumn(cell);
NextStatestateAfterSeekNextColumn = needToReturn(outResult);
if (stateAfterSeekNextColumn != null) {
returnscannerContext.setScannerState(stateAfterSeekNextColumn).hasMoreValues();
}
break;
caseSKIP:
this.heap.next();
break;

With some more digging, I found the actual cause of the degradation is return value of matcher.match(). It is very lightweight to process the return value of SKIP. But it is much more heavier to process the return value of SEEK_NEXT_COL.

EungsopYoo · 2025-08-05T06:19:44Z

@virajjasani @Apache9 @kadirozde
The review seems to have stalled. Is there anything I can do to help move it forward?

HBASE-30036 (apache#7993) consolidates redundant delete markers on flush, preventing them from growing unbounded in HFiles. However, markers still accumulate in the memstore before flush, degrading read performance. HBASE-29039 addresses this from the read path side. Both are needed for full coverage. There is an open PR (apache#6557), but the review process has been stalled. This is an alternative approach with fewer code changes, hopefully making it easier to reach consensus. When a DeleteColumn or DeleteFamily marker is encountered during a normal user scan, the matcher currently returns SKIP, forcing the scanner to advance one cell at a time. This causes read latency to degrade linearly with the number of accumulated delete markers for the same row or column. Since these are range deletes that mask all remaining versions of the column, seek past the entire column immediately via columns.getNextRowOrNextColumn(). This is safe because cells arrive in timestamp descending order, so any puts newer than the delete have already been processed. For DeleteFamily, also fix getKeyForNextColumn in ScanQueryMatcher to bypass the empty-qualifier guard (HBASE-18471) when the cell is a DeleteFamily marker. Without this, the seek barely advances past the current cell instead of jumping to the first real qualified column. The optimization is skipped when: - seePastDeleteMarkers is true (KEEP_DELETED_CELLS) - newVersionBehavior is enabled (sequence IDs determine visibility) - the delete marker is not tracked (visibility labels)

charlesconnell · 2026-04-04T12:47:09Z

@EungsopYoo Could you please fill in the PR description, describing the logic you are introducing? That will help me understand this change.

charlesconnell · 2026-04-04T13:35:30Z

Thinking more about this, I believe there are correctness problems. I don't think it is safe to use SKIP_NEXT_COL for a plain Type.Delete because you can't know what other cells you might be skipping, and some might need to be included in the scan result. Here are some test cases you add to TestStoreScannerDeleteMarkerOptimization that pass on master, and fail on this branch, showing examples of this:


/**
* Type-specific Delete at T=2 kills only Put@T=2. Put@T=1 is uncovered and
* must be returned as the latest visible version.
*/
@Test
public void testDeleteWithOlderUncoveredPut() throws IOException {
kvs.add(create("r", CF, "q", 2, Type.Delete, ""));
kvs.add(create("r", CF, "q", 2, Type.Put, "v2"));
kvs.add(create("r", CF, "q", 1, Type.Put, "v1"));
Pair<List<ExtendedCell>, Long> result = scanAll();
// Put@1 is not covered by Delete@2 (type-specific) — it must be returned
assertEquals("uncovered Put@1 should be returned", 1, result.getFirst().size());
assertEquals(kvs.get(2), result.getFirst().get(0));
}
/**
* Same as above but with multiple columns. Delete@2 on "q" should only
* affect Put@2 on "q". Put@1 on "q" should still be visible, along with
* the put on "q1".
*/
@Test
public void testDeleteWithOlderUncoveredPutMultipleColumns() throws IOException {
kvs.add(create("r", CF, "q", 2, Type.Delete, ""));
kvs.add(create("r", CF, "q", 2, Type.Put, "v2"));
kvs.add(create("r", CF, "q", 1, Type.Put, "v1"));
kvs.add(create("r", CF, "q1", 1, Type.Put, "v1"));
Pair<List<ExtendedCell>, Long> result = scanAll();
// Both Put@1 on "q" and Put@1 on "q1" should be returned
assertEquals("should return 2 cells", 2, result.getFirst().size());
assertEquals(kvs.get(2), result.getFirst().get(0));
assertEquals(kvs.get(3), result.getFirst().get(1));
}
/**
* DeleteFamilyVersion at T=2 kills all columns at T=2. But Put@1 on
* the same column is uncovered and must be returned.
*/
@Test
public void testDeleteFamilyVersionWithOlderUncoveredPut() throws IOException {
kvs.add(create("r", CF, "", 2, Type.DeleteFamilyVersion, ""));
kvs.add(create("r", CF, "q", 2, Type.Put, "v2"));
kvs.add(create("r", CF, "q", 1, Type.Put, "v1"));
Pair<List<ExtendedCell>, Long> result = scanAll();
// Put@1 is not covered by DeleteFamilyVersion@2 — it must be returned
assertEquals("uncovered Put@1 should be returned", 1, result.getFirst().size());
assertEquals(kvs.get(2), result.getFirst().get(0));
}

EungsopYoo · 2026-04-06T02:50:00Z

Closing in favor of #8001, which addresses the correctness issue you pointed out by limiting the optimization to DeleteColumn and DeleteFamily only.

EungsopYoo changed the title ~~Optimize read performance for accumulated delete markers on the same row or cell~~HBASE-29039 Optimize read performance for accumulated delete markers on the same row or cellDec 23, 2024

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terence-yoo added 3 commits December 26, 2024 11:25

optimize read performance for accumulated delete markers on the same …
eb68bdc
…row or cell

handle visibilityLabelEnabled
fbf042e

handle scan with filter
80044c2

EungsopYoo force-pushed the HBASE-29039 branch from 8075b0d to 80044c2Compare December 26, 2024 02:26

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terence-yoo added 2 commits January 16, 2025 10:00

remove unnecessary codes
202ea7d

remove incorrect early return of MatchCode.SKIP
f6739e2

EungsopYoo force-pushed the HBASE-29039 branch from fc55b25 to f6739e2Compare January 16, 2025 03:00

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handle failed test cases
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make visibilityLabelEnabled a instance variable
6390115

EungsopYoo force-pushed the HBASE-29039 branch from 91636d1 to 6390115Compare January 17, 2025 05:05

virajjasani reviewed Jan 18, 2025
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junegunn mentioned this pull request Mar 29, 2026
HBASE-29039 Seek past delete markers instead of skipping one at a time #8001
Merged

EungsopYoo closed this Apr 6, 2026

EungsopYoo deleted the HBASE-29039 branch April 8, 2026 13:50

	caseSEEK_NEXT_COL:
	seekOrSkipToNextColumn(cell);
	NextStatestateAfterSeekNextColumn = needToReturn(outResult);
	if (stateAfterSeekNextColumn != null) {
	returnscannerContext.setScannerState(stateAfterSeekNextColumn).hasMoreValues();
	}
	break;

	caseSKIP:
	this.heap.next();
	break;

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