PostgreSQL VACUUM 之深入浅出 (四)
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：
| maintenance_work_mem | autovacuum_vacuum_cost_delay | vacuum_cost_limit | AUTOVACUUM 用时 |
|---|---|---|---|
| 64MB | 20ms | 200 | 304.60 s + 27.56 s |
| 64MB | 2ms | 200 | 39.45 s +8.73 s |
| 64MB | 2ms | 2000 | 18.79 s + 5.50 s |
| 64MB | 2ms | 200 | 42.04 s + 8.00 s |
| 64MB | 20ms | 200 | 329.72 s + 22.82 s |
| 64MB | 0ms | 2000 | 17.72 s + 3.45 s |
| 512MB | 0ms | 2000 | 12.75 s + 3.35 s |
| 64MB | 0ms | 2000 | 15.13 s + 5.45 s |
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：
| maintenance_work_mem | vacuum_cost_delay | vacuum_cost_limit | AUTOVACUUM 用时 |
|---|---|---|---|
| 64MB | 0ms | 2000 | 23.137 s |
| 128MB | 0ms | 2000 | 18.284 s |
| 64MB | 0ms | 2000 | 24.144 s |
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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