Postgres performance comparing GCP and AWS

From: Maurici Meneghetti <maurici(dot)meneghetti(at)bixtecnologia(dot)com(dot)br>
To: pgsql-performance(at)lists(dot)postgresql(dot)org
Cc: Igor Gois <igor(at)bixtecnologia(dot)com(dot)br>
Subject: Postgres performance comparing GCP and AWS
Date: 2021-02-23 18:12:19
Message-ID: CAMzrzcaeYtkJN+tQWpKhpdaAeD-EjxJ3Pfcpfy-QsOKnrdy17w@mail.gmail.com
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Hi everyone,

I have 2 postgres instances created from the same dump (backup), one on a
GCP VM and the other on AWS RDS. The first instance takes 18 minutes and
the second one takes less than 20s to run this simples query:
SELECT "Id", "DateTime", "SignalRegisterId", "Raw" FROM
"SignalRecordsBlobs" WHERE "SignalSettingId" = 103 AND "DateTime" BETWEEN
'2019-11-28T14:00:12.540200000' AND '2020-07-23T21:12:32.249000000';
I’ve run this query a few times to make sure both should be reading data
from cache.
I expect my postgres on GPC to be at least similar to the one managed by
AWS RDS so that I can work on improvements parallelly and compare.

*DETAILS:Query explain for Postgres on GCP VM:*Bitmap Heap Scan on
SignalRecordsBlobs SignalRecordsBlobs (cost=18.80..2480.65 rows=799
width=70) (actual time=216.766..776.032 rows=5122 loops=1)
Filter: (("DateTime" >= \'2019-11-28 14:00:12.5402\'::timestamp without
time zone) AND ("DateTime" <= \'2020-07-23 21:12:32.249\'::timestamp
without time zone))
Heap Blocks: exact=5223
Buffers: shared hit=423 read=4821
-> Bitmap Index Scan on IDX_SignalRecordsBlobs_SignalSettingId
(cost=0.00..18.61 rows=824 width=0) (actual time=109.000..109.001
rows=5228 loops=1)
Index Cond: ("SignalSettingId" = 103)
Buffers: shared hit=3 read=18
Planning time: 456.315 ms
Execution time: 776.976 ms

*Query explain for Postgres on AWS RDS:*Bitmap Heap Scan on
SignalRecordsBlobs SignalRecordsBlobs (cost=190.02..13204.28 rows=6213
width=69) (actual time=2.215..14.505 rows=5122 loops=1)
Filter: (("DateTime" >= \'2019-11-28 14:00:12.5402\'::timestamp without
time zone) AND ("DateTime" <= \'2020-07-23 21:12:32.249\'::timestamp
without time zone))
Heap Blocks: exact=5209
Buffers: shared hit=3290 read=1948
-> Bitmap Index Scan on IDX_SignalRecordsBlobs_SignalSettingId
(cost=0.00..188.46 rows=6405 width=0) (actual time=1.159..1.159 rows=5228
loops=1)
Index Cond: ("SignalSettingId" = 103)
Buffers: shared hit=3 read=26
Planning time: 0.407 ms
Execution time: 14.87 ms

*PostgreSQL version number running:• VM on GCP*: PostgreSQL 11.10 (Debian
11.10-0+deb10u1) on x86_64-pc-linux-gnu, compiled by gcc (Debian 8.3.0-6)
8.3.0, 64-bit
*• Managed by RDS on AWS:* PostgreSQL 11.10 on x86_64-pc-linux-gnu,
compiled by gcc (GCC) 4.8.5 20150623 (Red Hat 4.8.5-11), 64-bit

*How PostgreSQL was installed:• VM on GCP*: Already installed when created
VM running Debian on Google Console.
*• Managed by RDS on AWS:* RDS managed the installation.

*Changes made to the settings in the postgresql.conf file:*Here are some
postgres parameters that might be useful:
*Instance on VM on GCP (2 vCPUs, 2 GB memory, 800 GB disk):*
• effective_cache_size: 1496MB
• maintenance_work_mem: 255462kB (close to 249MB)
• max_wal_size: 1GB
• min_wal_size: 512MB
• shared_buffers: 510920kB (close to 499MB)
• max_locks_per_transaction 1000
• wal_buffers: 15320kB (close to 15MB)
• work_mem: 2554kB
• effective_io_concurrency: 200
• dynamic_shared_memory_type: posix
On this instance we installed a postgres extension called timescaledb to
gain performance on other tables. Some of these parameters were set using
recommendations from that extension.

*Instance managed by RDS (2 vCPUs, 2 GiB RAM, 250GB disk, 750 de IOPS):*
• effective_cache_size: 1887792kB (close to 1844MB)
• maintenance_work_mem: 64MB
• max_wal_size: 2GB
• min_wal_size: 192MB
• shared_buffers: 943896kB (close to 922MB)
• max_locks_per_transaction 64

*Operating system and version by runing "uname -a":• VM on GCP:* Linux
{{{my instance name}}} 4.19.0-14-cloud-amd64 #1 SMP Debian 4.19.171-2
(2021-01-30) x86_64 GNU/Linux
*• Managed by AWS RDS:* Aparently Red Hay as shown using SELECT version();

*Program used to connect to PostgreSQL:* Python psycopg2.connect() to
create the connection and pandas read_sql_query() to query using that
connection.

Thanks in advance

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