Hello hackers,
I've done some performance testing of this feature. Following is my
test case (taken from an earlier thread):
postgres=# CREATE TABLE large_test (num1 bigint, num2 double
precision, num3 double precision);
postgres=# \timing on
postgres=# EXPLAIN (ANALYZE, BUFFERS) INSERT INTO large_test (num1,
num2, num3) SELECT round(random()*10), random(), random()*142 FROM
generate_series(1, 1000000) s(i);
I've kept the publisher and subscriber in two different system.
HEAD:
With 1000000 tuples,
Execution Time: 2576.821 ms, Time: 9.632.158 ms (00:09.632), Spill count: 245
With 10000000 tuples (10 times more),
Execution Time: 30359.509 ms, Time: 95261.024 ms (01:35.261), Spill count: 2442
With the memory accounting patch, following are the performance results:
With 100000 tuples,
logical_decoding_work_mem=64kB, Execution Time: 2414.371 ms, Time:
9648.223 ms (00:09.648), Spill count: 2315
logical_decoding_work_mem=64MB, Execution Time: 2477.830 ms, Time:
9895.161 ms (00:09.895), Spill count 3
With 1000000 tuples (10 times more),
logical_decoding_work_mem=64kB, Execution Time: 38259.227 ms, Time:
105761.978 ms (01:45.762), Spill count: 23149
logical_decoding_work_mem=64MB, Execution Time: 24624.639 ms, Time:
89985.342 ms (01:29.985), Spill count: 23
With logical decoding of in-progress transactions patch and with
streaming on, following are the performance results:
With 100000 tuples,
logical_decoding_work_mem=64kB, Execution Time: 2674.034 ms, Time:
20779.601 ms (00:20.780)
logical_decoding_work_mem=64MB, Execution Time: 2062.404 ms, Time:
9559.953 ms (00:09.560)
With 1000000 tuples (10 times more),
logical_decoding_work_mem=64kB, Execution Time: 26949.588 ms, Time:
196261.892 ms (03:16.262)
logical_decoding_work_mem=64MB, Execution Time: 27084.403 ms, Time:
90079.286 ms (01:30.079)
--
Thanks & Regards,
Kuntal Ghosh
EnterpriseDB: http://www.enterprisedb.com
