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Cherry pick Rows out in EXPLAIN ANALYZE #670

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@robozmey robozmey commented Oct 15, 2024

Cherry pick yezzey-gp/ygp@1d41230

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Add "Rows out" print in cdbexplain_showExecStats

set gp_enable_explain_rows_out=on;

drop table if exists tt; create table tt (a int, b int) distributed randomly;

explain (analyze,verbose) insert into tt select * from generate_series(1,1000)a,generate_series(1,1000)b;

 Insert  (cost=0.00..495560.34 rows=333334 width=8) (actual time=3.829..1148.458 rows=333741 loops=1)
   Output: generate_series_1.generate_series, generate_series.generate_series, "outer".ColRef_0002, generate_series_1.generate_series
   Executor Memory: 1kB  Segments: 3  Max: 1kB (segment 0)
   Rows out: 333333.33 rows avg x 3 workers, 333741 rows max (seg1), 333054 rows min (seg2).
   ->  Redistribute Motion 3:3  (slice1; segments: 3)  (cost=0.00..479935.34 rows=333334 width=12) (actual time=3.707..761.241 rows=333741 loops=1)
         Output: generate_series_1.generate_series, generate_series.generate_series, "outer".ColRef_0002
         Rows out: 333333.33 rows avg x 3 workers, 333741 rows max (seg1), 333054 rows min (seg2).
         ->  Result  (cost=0.00..479922.82 rows=333334 width=12) (actual time=0.226..882.580 rows=1000000 loops=1)
               Output: generate_series_1.generate_series, generate_series.generate_series, 1
               Rows out: 333333.33 rows avg x 3 workers, 1000000 rows max (seg1), 0 rows min (seg0).
               ->  Result  (cost=0.00..479918.82 rows=333334 width=8) (actual time=0.225..631.202 rows=1000000 loops=1)
                     Output: generate_series_1.generate_series, generate_series.generate_series
                     One-Time Filter: (gp_execution_segment() = 1)
                     Rows out: 333333.33 rows avg x 3 workers, 1000000 rows max (seg1), 0 rows min (seg0).
                     ->  Nested Loop  (cost=0.00..479898.05 rows=333334 width=8) (actual time=0.220..386.554 rows=1000000 loops=1)
                           Output: generate_series_1.generate_series, generate_series.generate_series
                           Join Filter: true
                           Rows out: 333333.33 rows avg x 3 workers, 1000000 rows max (seg1), 0 rows min (seg0).
                           ->  Function Scan on pg_catalog.generate_series generate_series_1  (cost=0.00..0.00 rows=334 width=4) (actual time=0.102..0.333 rows=1000 loops=1)
                                 Output: generate_series_1.generate_series
                                 Function Call: generate_series(1, 1000)
                                 work_mem: 40kB  Segments: 1  Max: 40kB (segment 1)
                                 Rows out: 333.33 rows avg x 3 workers, 1000 rows max (seg1), 0 rows min (seg0).
                           ->  Function Scan on pg_catalog.generate_series  (cost=0.00..0.00 rows=334 width=4) (actual time=0.000..0.092 rows=999 loops=1001)
                                 Output: generate_series.generate_series
                                 Function Call: generate_series(1, 1000)
                                 work_mem: 40kB  Segments: 1  Max: 40kB (segment 1)
                                 Rows out: 333333.67 rows avg x 3 workers, 1000001 rows max (seg1), 0 rows min (seg0).
 Planning time: 5.981 ms
   (slice0)    Executor memory: 87K bytes avg x 3 workers, 87K bytes max (seg0).
   (slice1)    Executor memory: 97K bytes avg x 3 workers, 172K bytes max (seg1).  Work_mem: 40K bytes max.
 Memory used:  128000kB
 Optimizer: Pivotal Optimizer (GPORCA)
 Execution time: 1180.349 ms
(34 rows)
explain (analyze,verbose,format text) select * from tt where a > b;

                                                          QUERY PLAN                                                           
-------------------------------------------------------------------------------------------------------------------------------
 Gather Motion 3:1  (slice1; segments: 3)  (cost=0.00..431.00 rows=1 width=8) (actual time=2.534..223.679 rows=499500 loops=1)
   Output: a, b
   ->  Seq Scan on public.tt  (cost=0.00..431.00 rows=1 width=8) (actual time=0.145..127.350 rows=166742 loops=1)
         Output: a, b
         Filter: (tt.a > tt.b)
         Rows out: 166500.00 rows avg x 3 workers, 166742 rows max (seg0), 166340 rows min (seg2).
 Planning time: 2.578 ms
   (slice0)    Executor memory: 183K bytes.
   (slice1)    Executor memory: 43K bytes avg x 3 workers, 43K bytes max (seg0).
 Memory used:  128000kB
 Optimizer: Pivotal Optimizer (GPORCA)
 Execution time: 245.018 ms
(12 rows)

Why are the changes needed?

"Rows out" is useful for auto explain

Does this PR introduce any user-facing change?

If yes, please clarify the previous behavior and the change this PR proposes.

How was this patch tested?

This feature tests in regression gp_explain test

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* Add "Rows out" print in cdbexplain_showExecStats

set gp_enable_explain_rows_out=on;

explain (analyze,verbose,format text) select * from tt where a > b;

                                                          QUERY PLAN
-------------------------------------------------------------------------------------------------------------------------------
 Gather Motion 3:1  (slice1; segments: 3)  (cost=0.00..431.00 rows=1 width=8) (actual time=2.534..223.679 rows=499500 loops=1)
   Output: a, b
   ->  Seq Scan on public.tt  (cost=0.00..431.00 rows=1 width=8) (actual time=0.145..127.350 rows=166742 loops=1)
         Output: a, b
         Filter: (tt.a > tt.b)
         Rows out: 166500.00 rows avg x 3 workers, 166742 rows max (seg0), 166340 rows min (seg2).
...
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Hiiii, @robozmey welcome!🎊 Thanks for taking the effort to make our project better! 🙌 Keep making such awesome contributions!

@robozmey robozmey changed the title Rows out в EXPLAIN ANALYZE Rows out in EXPLAIN ANALYZE Oct 15, 2024
@robozmey robozmey changed the title Rows out in EXPLAIN ANALYZE Cherry pick Rows out in EXPLAIN ANALYZE Oct 15, 2024
@@ -277,6 +277,7 @@ int gp_hashagg_groups_per_bucket = 5;
int gp_motion_slice_noop = 0;

/* Cloudberry Database Experimental Feature GUCs */
bool gp_enable_explain_rows_out = false;
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It is better to send a param to explain command than using a guc to control if print out the "rows out"

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Rows out feature created for auto_explain. If we add "rows out" as parameter of explain, then we'll need change auto_explain source, and auto_explain wont be compatible with vanilla

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 auto_explain wont be compatible with vanilla

Hmm... cannot understand. Could you give more details? Is auto_explain aslo included in vanilla?

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auto_explain is included in vanilla https://github.com/greenplum-db/gpdb-archive/tree/main/contrib/auto_explain

Also GUC gp_enable_explain_rows_out is made by analogy with GUC gp_enable_explain_allstat

src/include/cdb/cdbvars.h Show resolved Hide resolved
src/backend/commands/explain_gp.c Show resolved Hide resolved
fanfuxiaoran
fanfuxiaoran previously approved these changes Nov 4, 2024
my-ship-it
my-ship-it previously approved these changes Nov 5, 2024
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Do we have a Parallel Plan test(ex: cbdb_parallel.sql) to ensure the output is as expected?

ntuples_imin);
}
else {
// ExplainOpenGroup("Rows Out", NULL, false, es);
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Please remove unused codes

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Removed

@robozmey robozmey dismissed stale reviews from my-ship-it and fanfuxiaoran via d6aa761 November 20, 2024 02:11
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Do we have a Parallel Plan test(ex: cbdb_parallel.sql) to ensure the output is as expected?

I'm not sure what we need Parallel Plan test, but I expanded gp_explain.sql test

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avamingli commented Nov 26, 2024

Do we have a Parallel Plan test(ex: cbdb_parallel.sql) to ensure the output is as expected?

I'm not sure what we need Parallel Plan test, but I expanded gp_explain.sql test

In GPDB, there is one process executed the Slice on each segment. And explain use the statistic of that gang of QEs to compute the results.
In CBDB, we have Parallel feature. For a parallel plan, there may be multiple QEs execute the same Slice on each segment.
We call them parallel workers.

See CBDB style README https://github.com/apache/cloudberry/blob/11333c0b4d3a4962b0a6610ceb5b6d7a12e45ec4/src/backend/optimizer/README.cbdb.parallel for more details.
And most cases could be found in https://github.com/apache/cloudberry/blob/11333c0b4d3a4962b0a6610ceb5b6d7a12e45ec4/src/test/regress/sql/cbdb_parallel.sql

This is a significant difference between CBDB and GPDB.

In parallel plan cases, what do the statistic results should be? Do current codes compute results right or it's already as expected?
Maybe the current codes don't need to do anything else more, but we should be clear about that.
In any cases, there should be test cases to verify the explain output.

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In parallel plan cases, what do the statistic results should be? Do current codes compute results right or it's already as expected?
Maybe the current codes don't need to do anything else more, but we should be clear about that.

Yes, agree! Looks we don't a lot things for the parallel workers. Cloudberry uses motion to gather the slice result both from workers on different segments and from the parallel worker on same segment . So the EXPLAIN ANALYZE command can still work properly to collect results from all workers.

But we should confirm the EXPLAIN ANALYZE output format for the plan contains parallel workers. what should it be like?

 Rows out: 333333.33 rows avg x 3 workers, 1000000 rows max (seg1), 0 rows min (seg0).

Do we need to add the parallel worker info into this line? like

 Rows out: 333333.33 rows avg x 3 workers from xx segments?, 1000000 rows max (seg1 worker?), 0 rows min (seg0 worker?).

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@fanfuxiaoran Thanks for your analysis and verification.

Do we need to add the parallel worker info into this line? like

 Rows out: 333333.33 rows avg x 3 workers from xx segments?, 1000000 rows max (seg1 worker?), 0 rows min (seg0 wo

Sounds good, with a worker info is more reasonable of parallel plan.

@my-ship-it my-ship-it added the cherry-pick cherry-pick upstream commts label Dec 20, 2024
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