VACUUM 参数优化

上面已经介绍过了以下设置表级 AUTOVACUUM 相关参数和 autovacuum_max_workers：

ALTER TABLE pgbench_accounts SET (autovacuum_vacuum_scale_factor = 0.1, autovacuum_vacuum_threshold = 2000);
ALTER TABLE pgbench_accounts SET (autovacuum_analyze_scale_factor = 0.05, autovacuum_analyze_threshold = 2000);

下面就以下常用 VACUUM 参数详细介绍如何进行调优。

maintenance_work_mem 参数

#maintenance_work_mem = 64MB            # min 1MB
#autovacuum_work_mem = -1               # min 1MB, or -1 to use maintenance_work_mem

vacuum_cost_delay 参数

#vacuum_cost_delay = 0
#autovacuum_vacuum_cost_delay = 20ms

vacuum_cost_limit 参数

#vacuum_cost_limit = 200
#autovacuum_vacuum_cost_limit = -1

参数优化测试用例

使用 pgbench 生成 5000 万测试数据。

pgbench -i -s 500 alvindb

设置表级 AUTOVACUUM 相关参数：

ALTER TABLE pgbench_accounts SET (autovacuum_vacuum_scale_factor = 0.1, autovacuum_vacuum_threshold = 2000);
ALTER TABLE pgbench_accounts SET (autovacuum_analyze_scale_factor = 0.05, autovacuum_analyze_threshold = 2000);

删除 2000 万数据：

DELETE FROM pgbench_accounts WHERE aid>=1500001 AND aid <=3500000;
DELETE FROM pgbench_accounts WHERE aid>=15000001 AND aid <=17000000;
DELETE FROM pgbench_accounts WHERE aid>=25000001 AND aid <=28000000;
DELETE FROM pgbench_accounts WHERE aid>=35000001 AND aid <=38000000;
DELETE FROM pgbench_accounts WHERE aid>=40000001; 

另外，将 autovacuum_naptime 设置为 3min 以给删除数据留够时间。

alvindb=> SHOW autovacuum_naptime;
 autovacuum_naptime 
--------------------
 3min
(1 row)

AUTOVACUUM 测试

首先采用默认参数：

SHOW maintenance_work_mem;
 maintenance_work_mem 
----------------------
 64MB
(1 row)
SHOW autovacuum_work_mem;
 autovacuum_work_mem 
---------------------
 -1
(1 row)
SHOW autovacuum_vacuum_cost_delay;
 autovacuum_vacuum_cost_delay 
------------------------------
 20ms
(1 row)
SHOW autovacuum_vacuum_cost_limit;
 autovacuum_vacuum_cost_limit 
------------------------------
 -1
(1 row)
SHOW vacuum_cost_limit;
 vacuum_cost_limit 
-------------------
 200
(1 row)

执行测试用例的同时，通过如下 SQL 查询

SELECT * FROM pg_stat_activity WHERE backend_type ~ "autovacuum worker" AND pid <> pg_backend_pid();watch 1

可以看到， AUTOVACUUM 已触发，并且 autovacuum worker 已启动，先是 query 为空，而后 query 为 VACUUM ANALYZE public.pgbench_accounts。

2021年11月07日 星期日 23时17分09秒 (every 1s)

-[ RECORD 1 ]----+------------------------------
datid            | 37509
datname          | alvindb
pid              | 16660
usesysid         | 
usename          | 
application_name | 
client_addr      | 
client_hostname  | 
client_port      | 
backend_start    | 2021-11-07 23:17:09.427627+08
xact_start       | 2021-11-07 23:17:09.426378+08
query_start      | 
state_change     | 
wait_event_type  | 
wait_event       | 
state            | 
backend_xid      | 
backend_xmin     | 13180269
query            | 
backend_type     | autovacuum worker

2021年11月07日 星期日 23时17分10秒 (every 1s)

-[ RECORD 1 ]----+---------------------------------------------------
datid            | 37509
datname          | alvindb
pid              | 16660
usesysid         | 
usename          | 
application_name | 
client_addr      | 
client_hostname  | 
client_port      | 
backend_start    | 2021-11-07 23:17:09.427627+08
xact_start       | 2021-11-07 23:17:09.459083+08
query_start      | 2021-11-07 23:17:09.459083+08
state_change     | 2021-11-07 23:17:09.459084+08
wait_event_type  | 
wait_event       | 
state            | active
backend_xid      | 
backend_xmin     | 13180269
query            | autovacuum: VACUUM ANALYZE public.pgbench_accounts
backend_type     | autovacuum worker

完成后，根据 last_autoanalyze 和 last_autovacuum 得知是先做了 VACUUM，然后做 ANALYZE:

schemaname                      | public
relname                         | pgbench_accounts
autovacuum_vacuum_scale_factor  | 0.1
autovacuum_vacuum_threshold     | 2000
autovacuum_analyze_scale_factor | 0.05
autovacuum_analyze_threshold    | 2000
n_live_tup                      | 30000000
reltuples                       | 30000000
autovacuum_analyze_trigger      | 1502001
n_mod_since_analyze             | 0
rows_to_mod_before_analyze      | 1502001
last_autoanalyze                | 2021-11-07 23:22:41.640812+08
autovacuum_vacuum_trigger       | 3002001
n_dead_tup                      | 142848
rows_to_delete_before_vacuum    | 2859153
last_autovacuum                 | 2021-11-07 23:22:14.06792+08

查看 PostgreSQL 日志，得知 VACUUM 用时 304.60 s，ANALYZE 用时 27.56 s。

[    2021-11-07 23:22:14.067 CST 16660 6187edf5.4114 1 3/238595 13180270]LOG:  automatic vacuum of table "alvindb.public.pgbench_accounts": in
dex scans: 2
        pages: 35699 removed, 165914 remain, 576 skipped due to pins, 0 skipped frozen
        tuples: 6190880 removed, 30142834 remain, 0 are dead but not yet removable, oldest xmin: 13180269
        buffer usage: 419596 hits, 40795 misses, 100492 dirtied
        avg read rate: 4.185 MB/s, avg write rate: 10.310 MB/s
        system usage: CPU: user: 14.02 s, system: 1.41 s, elapsed: 304.60 s

[    2021-11-07 23:22:41.640 CST 16660 6187edf5.4114 2 3/238596 13180271]LOG:  automatic analyze of table "alvindb.public.pgbench_accounts" system usage: CPU: user: 3.25 s, system: 7.58 s, elapsed: 27.56 s

通过调整参数，逐步并反复测试，结果如下：

根据如上测试，可以验证，适当增大 autovacuum_work_mem 和 autovacuum_vacuum_cost_limit、减少 autovacuum_vacuum_cost_delay 可提高 AUTOVACUUM 性能。

手动 VACUUM ANALYZE 测试

下面测试手动 VACUUM ANALYZE。测试方法基本与 AUTOVACUUM 一样。

这里主要简单测试下 maintenance_work_mem，其他不再重复测试。

关闭 autovacuum 并将 vacuum_cost_delay 设置为 0，并手动执行如下 SQL:

VACUUM ANALYZE pgbench_accounts;

用时统计如下：

根据如上测试，可以验证，适当增大 maintenance_work_mem 和 vacuum_cost_limit、减少 vacuum_cost_delay 可提高 AUTOVACUUM 性能。

与 AUTOVACUUM 不同的是，手动 VACUUM 可以通过如下方式设置参数。这样，在实际工作中，就可以灵活调整参数而不需要改配置文件了。

SET vacuum_cost_delay = 10;
VACUUM ANALYZE pgbench_accounts;

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