PostgreSQL VACUUM 之深入浅出 (二)
AUTOVACUUM
AUTOVACUUM 简介
PostgreSQL 提供了 AUTOVACUUM 的机制。
autovacuum 不仅会自动进行 VACUUM，也会自动进行 ANALYZE，以分析统计信息用于执行计划。
在 postgresql.conf 中，autovacuum 参数已默认打开。
autovacuum = on
autovacuum 打开后，会有一个 autovacuum launcher 进程
$ ps -ef|grep postgres|grep autovacuum|grep -v grep
postgres 28398 28392 0 Nov13 ? 00:00:19 postgres: autovacuum launcher
在 pg_stat_activity 也可以看到 backend_type 为 autovacuum launcher 的连接:
psql -d alvindb -U postgres
alvindb=# \x
Expanded display is on.
alvindb=# SELECT * FROM pg_stat_activity WHERE backend_type = 'autovacuum launcher';
-[ RECORD 1 ]----+------------------------------
datid |
datname |
pid | 28398
usesysid |
usename |
application_name |
client_addr |
client_hostname |
client_port |
backend_start | 2021-11-13 23:18:00.406618+08
xact_start |
query_start |
state_change |
wait_event_type | Activity
wait_event | AutoVacuumMain
state |
backend_xid |
backend_xmin |
query |
backend_type | autovacuum launcher
那么 AUTOVACUUM 多久运行一次？
autovacuum launcher 会每隔 autovacuum_naptime ，创建 autovacuum worker，检查是否需要做 autovacuum。
psql -d alvindb -U postgres
alvindb=# SELECT * FROM pg_stat_activity WHERE backend_type = 'autovacuum worker';
-[ RECORD 1 ]----+------------------------------
datid | 13220
datname | postgres
pid | 32457
usesysid |
usename |
application_name |
client_addr |
client_hostname |
client_port |
backend_start | 2021-11-06 23:32:53.880281+08
xact_start |
query_start |
state_change |
wait_event_type |
wait_event |
state |
backend_xid |
backend_xmin |
query |
backend_type | autovacuum worker
autovacuum_naptime 默认为 1min：
#autovacuum_naptime = 1min # time between autovacuum runs
autovacuum 又是根据什么标准决定是否进行 VACUUM 和 ANALYZE 呢?
当 autovacuum worker 检查到，
dead tuples 大于 vacuum threshold 时，会自动进行 VACUUM。
vacuum threshold 公式如下：
vacuum threshold = vacuum base threshold + vacuum scale factor * number of tuples
增删改的行数据大于 analyze threshold 时，会自动进行 ANALYZE。
analyze threshold 公式如下：
analyze threshold = analyze base threshold + analyze scale factor * number of tuples
对应 postgresql.conf 中相关参数如下：
#autovacuum_vacuum_threshold = 50 # min number of row updates before vacuum
#autovacuum_analyze_threshold = 50 # min number of row updates before analyze
#autovacuum_vacuum_scale_factor = 0.2 # fraction of table size before vacuum
#autovacuum_analyze_scale_factor = 0.1 # fraction of table size before analyze
dead tuples 为 pg_stat_user_tables.n_dead_tup(Estimated number of dead rows)
alvindb=> SELECT * FROM pg_stat_user_tables WHERE schemaname = 'alvin' AND relname = 'tb_test_vacuum';
-[ RECORD 1 ]-------+---------------
relid | 37409
schemaname | alvin
relname | tb_test_vacuum
seq_scan | 2
seq_tup_read | 0
idx_scan | 0
idx_tup_fetch | 0
n_tup_ins | 0
n_tup_upd | 0
n_tup_del | 0
n_tup_hot_upd | 0
n_live_tup | 0
n_dead_tup | 0
n_mod_since_analyze | 0
last_vacuum |
last_autovacuum |
last_analyze |
last_autoanalyze |
vacuum_count | 0
autovacuum_count | 0
analyze_count | 0
autoanalyze_count | 0
那么 number of tuples 是哪个列的值？是 pg_stat_user_tables.n_live_tup(Estimate number of live rows)？还是实际的 count 值?
其实是 pg_class.reltuples (Estimate number of live rows in the table used by the planner)。
alvindb=> SELECT u.schemaname,u.relname,c.reltuples,u.n_live_tup,u.n_mod_since_analyze,u.n_dead_tup,u.last_autoanalyze,u.last_autovacuum
FROM
pg_stat_user_tables u, pg_class c, pg_namespace n
WHERE n.oid = c.relnamespace
AND c.relname = u.relname
AND n.nspname = u.schemaname
AND u.schemaname = 'alvin'
AND u.relname = 'tb_test_vacuum'
-[ RECORD 1 ]-------+---------------
schemaname | alvin
relname | tb_test_vacuum
reltuples | 0
n_live_tup | 0
n_mod_since_analyze | 0
n_dead_tup | 0
last_autoanalyze |
last_autovacuum |
所以 AUTO VACUUM 具体公式如下：
pg_stat_user_tables.n_dead_tup > autovacuum_vacuum_threshold + autovacuum_vacuum_scale_factor * pg_class.reltuples
同理，AUTO ANALYZE 具体公式如下：
pg_stat_user_tables.n_mod_since_analyze > autovacuum_analyze_threshold + autovacuum_analyze_scale_factor * pg_class.reltuples
精准触发 AUTOVACUUM
下面实测一下 autovacuum。为了测试方便，autovacuum_naptime 临时修改为 5s，这样触发了临界条件，只需要等 5s 就能看到效果，而不是等 1min。
修改参数如下:
autovacuum_naptime = 5s
autovacuum_vacuum_threshold = 100 # min number of row updates before vacuum
autovacuum_analyze_threshold = 100 # min number of row updates before analyze
autovacuum_vacuum_scale_factor = 0.2 # fraction of table size before vacuum
autovacuum_analyze_scale_factor = 0.1 # fraction of table size before analyze
接下来通过一步一步测试，精准触发 autovacuum。
为了方便测试，通过如下 AUTOVACUUM 计算 SQL 计算需要删除或修改的数据行数。
alvindb=> WITH v AS (
SELECT * FROM
(SELECT setting AS autovacuum_vacuum_scale_factor FROM pg_settings WHERE name = 'autovacuum_vacuum_scale_factor') vsf,
(SELECT setting AS autovacuum_vacuum_threshold FROM pg_settings WHERE name = 'autovacuum_vacuum_threshold') vth,
(SELECT setting AS autovacuum_analyze_scale_factor FROM pg_settings WHERE name = 'autovacuum_analyze_scale_factor') asf,
(SELECT setting AS autovacuum_analyze_threshold FROM pg_settings WHERE name = 'autovacuum_analyze_threshold') ath
),
t AS (
SELECT
c.reltuples,u.*
FROM
pg_stat_user_tables u, pg_class c, pg_namespace n
WHERE n.oid = c.relnamespace
AND c.relname = u.relname
AND n.nspname = u.schemaname
AND u.schemaname = 'alvin'
AND u.relname = 'tb_test_vacuum'
)
SELECT
schemaname,
relname,
autovacuum_vacuum_scale_factor,
autovacuum_vacuum_threshold,
autovacuum_analyze_scale_factor,
autovacuum_analyze_threshold,
n_live_tup,
reltuples,
autovacuum_analyze_trigger,
n_mod_since_analyze,
autovacuum_analyze_trigger - n_mod_since_analyze AS rows_to_mod_before_auto_analyze,
last_autoanalyze,
autovacuum_vacuum_trigger,
n_dead_tup,
autovacuum_vacuum_trigger - n_dead_tup AS rows_to_delete_before_auto_vacuum,
last_autovacuum
FROM (
SELECT
schemaname,
relname,
autovacuum_vacuum_scale_factor,
autovacuum_vacuum_threshold,
autovacuum_analyze_scale_factor,
autovacuum_analyze_threshold,
floor(autovacuum_analyze_scale_factor::numeric * reltuples) + 1 + autovacuum_analyze_threshold::int AS autovacuum_analyze_trigger,
floor(autovacuum_vacuum_scale_factor::numeric * reltuples) + 1 + autovacuum_vacuum_threshold::int AS autovacuum_vacuum_trigger,
reltuples,
n_live_tup,
n_dead_tup,
n_mod_since_analyze,
last_autoanalyze,
last_autovacuum
FROM
v,
t) a;
-[ RECORD 1 ]---------------------+---------------
schemaname | alvin
relname | tb_test_vacuum
autovacuum_vacuum_scale_factor | 0.2
autovacuum_vacuum_threshold | 100
autovacuum_analyze_scale_factor | 0.1
autovacuum_analyze_threshold | 100
n_live_tup | 0
reltuples | 0
autovacuum_analyze_trigger | 101
n_mod_since_analyze | 0
rows_to_mod_before_auto_analyze | 101
last_autoanalyze |
autovacuum_vacuum_trigger | 101
n_dead_tup | 0
rows_to_delete_before_auto_vacuum | 101
last_autovacuum |
根据计算公式，
pg_stat_user_tables.n_mod_since_analyze > 100 + 0.1 * 0
即当修改的行数大于 100，即为 101 时，将触发 AUTO ANALYZE。
先插入 100 行数据，
alvindb=> SELECT clock_timestamp();
clock_timestamp
-------------------------------
2021-11-06 20:45:57.669183+08
(1 row)
alvindb=> INSERT INTO tb_test_vacuum(test_num) SELECT gid FROM generate_series(1,100,1) gid;
INSERT 0 100
此时，通过如下计算可以看到，再更新 1 行，将触发 AUTO ANALYZE。
schemaname | alvin
relname | tb_test_vacuum
autovacuum_vacuum_scale_factor | 0.2
autovacuum_vacuum_threshold | 100
autovacuum_analyze_scale_factor | 0.1
autovacuum_analyze_threshold | 100
n_live_tup | 100
reltuples | 0
autovacuum_analyze_trigger | 101
n_mod_since_analyze | 100
rows_to_mod_before_auto_analyze | 1
last_autoanalyze |
autovacuum_vacuum_trigger | 101
n_dead_tup | 0
rows_to_delete_before_auto_vacuum | 101
last_autovacuum |
此时，统计信息为空:
alvindb=> SELECT * FROM pg_stats WHERE schemaname = 'alvin' AND tablename = 'tb_test_vacuum';
(0 rows)
现在插入最后一条数据，
alvindb=> SELECT clock_timestamp();
clock_timestamp
-------------------------------
2021-11-06 20:46:31.034422+08
(1 row)
alvindb=> INSERT INTO tb_test_vacuum(test_num) SELECT gid FROM generate_series(101,101,1) gid;
INSERT 0 1
执行 AUTOVACUUM 计算 SQL， 可以看到，已触发 AUTO ANALYZE:
schemaname | alvin
relname | tb_test_vacuum
autovacuum_vacuum_scale_factor | 0.2
autovacuum_vacuum_threshold | 100
autovacuum_analyze_scale_factor | 0.1
autovacuum_analyze_threshold | 100
n_live_tup | 101
reltuples | 101
autovacuum_analyze_trigger | 111
n_mod_since_analyze | 0
rows_to_mod_before_auto_analyze | 111
last_autoanalyze | 2021-11-06 20:46:39.88796+08
autovacuum_vacuum_trigger | 121
n_dead_tup | 0
rows_to_delete_before_auto_vacuum | 121
last_autovacuum |
可以看到表 tb_test_vacuum 统计信息已更新：
alvindb=> SELECT * FROM pg_stats WHERE schemaname = 'alvin' AND tablename = 'tb_test_vacuum';
查看 PostgreSQL 日志，可以看到
[ 2021-11-06 20:46:39.887 CST 6816 6186792f.1aa0 1 3/173948 13179359]LOG: automatic analyze of table "alvindb.alvin.tb_test_vacuum" system usage: CPU: user: 0.00 s, system: 0.00 s, elapsed: 0.00 s
PostgreSQL 日志中是否记录 AUTOVACUUM 由参数 log_autovacuum_min_duration 控制，默认关闭。
#log_autovacuum_min_duration = -1 # -1 disables, 0 logs all actions and
# their durations, > 0 logs only
# actions running at least this number
# of milliseconds.
可将该参数改为 0，即记录所有的 AUTOVACUUM 操作。
log_autovacuum_min_duration = 0
从 AUTOVACUUM 计算 SQL 的执行结果得知，再修改 111 行将触发 AUTO ANALYZE。
rows_to_mod_before_auto_analyze | 111
rows_to_delete_before_auto_vacuum | 121
先修改 110 行，并 sleep 6s。
alvindb=> SELECT clock_timestamp();
clock_timestamp
------------------------------
2021-11-06 20:47:30.75553+08
(1 row)
alvindb=> INSERT INTO tb_test_vacuum(test_num) SELECT gid FROM generate_series(102,111,1) gid;
INSERT 0 10
alvindb=> UPDATE tb_test_vacuum SET test_num = test_num WHERE test_num <= 100;
UPDATE 100
alvindb=> SELECT pg_sleep(6);
pg_sleep
----------
(1 row)
alvindb=> SELECT clock_timestamp();
clock_timestamp
-------------------------------
2021-11-06 20:47:43.465651+08
(1 row)
从 AUTOVACUUM 计算 SQL 的执行结果得知，修改后 110 行并 sleep 6s (前面已将 autovacuum_naptime 设置成了 5s)后，AUTO ANALYZE 并未触发。
schemaname | alvin
relname | tb_test_vacuum
autovacuum_vacuum_scale_factor | 0.2
autovacuum_vacuum_threshold | 100
autovacuum_analyze_scale_factor | 0.1
autovacuum_analyze_threshold | 100
n_live_tup | 111
reltuples | 101
autovacuum_analyze_trigger | 111
n_mod_since_analyze | 110
rows_to_mod_before_auto_analyze | 1
last_autoanalyze | 2021-11-06 20:46:39.88796+08
autovacuum_vacuum_trigger | 121
n_dead_tup | 100
rows_to_delete_before_auto_vacuum | 21
last_autovacuum |
再修改 1 行预计将触发 AUTO ANALYZE。此时删除一行：
alvindb=> SELECT clock_timestamp();
clock_timestamp
-------------------------------
2021-11-06 20:47:55.746411+08
(1 row)
alvindb=> DELETE FROM tb_test_vacuum WHERE test_id = 111;
DELETE 1
alvindb=> SELECT pg_sleep(6);
pg_sleep
----------
(1 row)
alvindb=> SELECT clock_timestamp();
clock_timestamp
-------------------------------
2021-11-06 20:48:01.796389+08
(1 row)
从 AUTOVACUUM 计算 SQL 的查询结果中的 last_autoanalyze 得知，已精准触发 AUTO ANALYZE。
并且从 rows_to_delete_before_auto_vacuum 得知，预计删除 22 行后，将触发 AUTO VACUUM。
schemaname | alvin
relname | tb_test_vacuum
autovacuum_vacuum_scale_factor | 0.2
autovacuum_vacuum_threshold | 100
autovacuum_analyze_scale_factor | 0.1
autovacuum_analyze_threshold | 100
n_live_tup | 110
reltuples | 110
autovacuum_analyze_trigger | 112
n_mod_since_analyze | 0
rows_to_mod_before_auto_analyze | 112
last_autoanalyze | 2021-11-06 20:48:04.928899+08
autovacuum_vacuum_trigger | 123
n_dead_tup | 101
rows_to_delete_before_auto_vacuum | 22
last_autovacuum |
先删除 (UPDATE = DELETE + INSERT) 21 行：
alvindb=> SELECT clock_timestamp();
clock_timestamp
-------------------------------
2021-11-06 20:48:32.313706+08
(1 row)
alvindb=> UPDATE tb_test_vacuum SET test_num = test_num WHERE test_num <= 21;
UPDATE 21
alvindb=> SELECT pg_sleep(6);
pg_sleep
----------
(1 row)
alvindb=> SELECT clock_timestamp();
clock_timestamp
-------------------------------
2021-11-06 20:48:38.454997+08
(1 row)
从 AUTOVACUUM 计算 SQL 的查询结果中的 last_autovacuum 得知，还未触发 AUTO VACUUM。
并且从 rows_to_delete_before_auto_vacuum 得知，预计删除 1 行后，将触发 AUTO VACUUM。
schemaname | alvin
relname | tb_test_vacuum
autovacuum_vacuum_scale_factor | 0.2
autovacuum_vacuum_threshold | 100
autovacuum_analyze_scale_factor | 0.1
autovacuum_analyze_threshold | 100
n_live_tup | 110
reltuples | 110
autovacuum_analyze_trigger | 112
n_mod_since_analyze | 21
rows_to_mod_before_auto_analyze | 91
last_autoanalyze | 2021-11-06 20:48:04.928899+08
autovacuum_vacuum_trigger | 123
n_dead_tup | 122
rows_to_delete_before_auto_vacuum | 1
last_autovacuum |
此时删除一行
alvindb=> SELECT clock_timestamp();
clock_timestamp
-------------------------------
2021-11-06 20:48:39.174009+08
(1 row)
alvindb=> DELETE FROM tb_test_vacuum WHERE test_id = 110;
DELETE 1
alvindb=> SELECT pg_sleep(6);
pg_sleep
----------
(1 row)
alvindb=> SELECT clock_timestamp();
clock_timestamp
-------------------------------
2021-11-06 20:48:45.213537+08
(1 row)
从 AUTOVACUUM 计算 SQL 的查询结果中的 last_autovacuum 得知，已精准触发 AUTO VACUUM！
schemaname | alvin
relname | tb_test_vacuum
autovacuum_vacuum_scale_factor | 0.2
autovacuum_vacuum_threshold | 100
autovacuum_analyze_scale_factor | 0.1
autovacuum_analyze_threshold | 100
n_live_tup | 109
reltuples | 109
autovacuum_analyze_trigger | 111
n_mod_since_analyze | 22
rows_to_mod_before_auto_analyze | 89
last_autoanalyze | 2021-11-06 20:48:04.928899+08
autovacuum_vacuum_trigger | 122
n_dead_tup | 0
rows_to_delete_before_auto_vacuum | 122
last_autovacuum | 2021-11-06 20:48:49.914345+08
查看 PostgreSQL 日志，可以看到
[ 2021-11-06 20:48:49.914 CST 7207 618679b1.1c27 1 3/174162 0]LOG: automatic vacuum of table "alvindb.alvin.tb_test_vacuum": index scans: 1
pages: 0 removed, 1 remain, 0 skipped due to pins, 0 skipped frozen
tuples: 123 removed, 109 remain, 0 are dead but not yet removable, oldest xmin: 13179371
buffer usage: 59 hits, 4 misses, 4 dirtied
avg read rate: 121.832 MB/s, avg write rate: 121.832 MB/s
system usage: CPU: user: 0.00 s, system: 0.00 s, elapsed: 0.00 s
buffer usage: 59 hits, 4 misses, 4 dirtied
avg read rate: 121.832 MB/s, avg write rate: 121.832 MB/s
system usage: CPU: user: 0.00 s, system: 0.00 s, elapsed: 0.00 s
那么问题来了，autovacuum_vacuum_scale_factor 为 0.2 对于所有的表都合适吗？1 亿数据量的表有 2000 万 dead tuples 以上才会触发 AUTO VACUUM，这意味着表越大越不容易触发 AUTO VACUUM。怎么可以解决这个问题呢？
精准触发表级 AUTOVACUUM
可以根据需要，在表上设置合理的 autovacuum_vacuum_scale_factor。对于大表，可以设置小点的 autovacuum_vacuum_scale_factor，如 0.1。
下面带你一步一步设置并精确触发表级的 AUTO ANALYZE 和 AUTO VACUUM。
这次将采用大一点的数据量进行测试。考虑到手动创建表，插入数据等比较麻烦，接下来测试利用 PostgreSQL 自带的工具 pgbench。
使用 pgbench 创建 10 万行数据的测试表：
$ pgbench -i alvindb
dropping old tables...
creating tables...
generating data...
100000 of 100000 tuples (100%) done (elapsed 0.38 s, remaining 0.00 s)
vacuuming...
creating primary keys...
done.
修改表级参数：
alvindb=> ALTER TABLE pgbench_accounts SET (autovacuum_vacuum_scale_factor = 0.1, autovacuum_vacuum_threshold = 2000);
ALTER TABLE
alvindb=> ALTER TABLE pgbench_accounts SET (autovacuum_analyze_scale_factor = 0.05, autovacuum_analyze_threshold = 2000);
ALTER TABLE
按照之前 AUTOVACUUM 计算 SQL ，可知要修改 11001 行才会触发 AUTO ANALYZE， 要有约 21001 个 dead tuples 才会触发 AUTO VACUUM。
schemaname | public
relname | pgbench_accounts
autovacuum_vacuum_scale_factor | 0.2
autovacuum_vacuum_threshold | 1000
autovacuum_analyze_scale_factor | 0.1
autovacuum_analyze_threshold | 1000
n_live_tup | 100000
reltuples | 100000
autovacuum_analyze_trigger | 11001
n_mod_since_analyze | 0
rows_to_mod_before_auto_analyze | 11001
last_autoanalyze |
autovacuum_vacuum_trigger | 21001
n_dead_tup | 0
rows_to_delete_before_auto_vacuum | 21001
last_autovacuum |
现在设置了表级的参数以后，从如下 表级 AUTOVACUUM 计算 SQL ，可知修改 7001 行就可以触发 AUTO ANALYZE， 有约 12001 个 dead tuples 就可以触发 AUTO VACUUM。更重要的是，表级的 AUTOVACUUM 参数不会对其他表产生影响，只对已设置的表有效，也可以对不同大小的表设置不同的参数，还可以随时调整！
表级 AUTOVACUUM 计算 SQL
alvindb=> WITH v AS (
SELECT (SELECT split_part(x, '=', 2) FROM unnest(c.reloptions) q (x) WHERE x ~ '^autovacuum_vacuum_scale_factor=' ) as autovacuum_vacuum_
scale_factor,
(SELECT split_part(x, '=', 2) FROM unnest(c.reloptions) q (x) WHERE x ~ '^autovacuum_vacuum_threshold=' ) as autovacuum_vacuum_thresh
old,
(SELECT split_part(x, '=', 2) FROM unnest(c.reloptions) q (x) WHERE x ~ '^autovacuum_analyze_scale_factor=' ) as autovacuum_analyze_s
cale_factor,
(SELECT split_part(x, '=', 2) FROM unnest(c.reloptions) q (x) WHERE x ~ '^autovacuum_analyze_threshold=' ) as autovacuum_analyze_thre
shold
FROM pg_class c
LEFT JOIN pg_namespace n ON n.oid = c.relnamespace
WHERE n.nspname IN ('public')
AND c.relname = 'pgbench_accounts'
),
t AS (
SELECT
c.reltuples,u.*
FROM
pg_stat_user_tables u, pg_class c, pg_namespace n
WHERE n.oid = c.relnamespace
AND c.relname = u.relname
AND n.nspname = u.schemaname
AND u.schemaname = 'public'
AND u.relname = 'pgbench_accounts'
)
SELECT
schemaname,
relname,
autovacuum_vacuum_scale_factor,
autovacuum_vacuum_threshold,
autovacuum_analyze_scale_factor,
autovacuum_analyze_threshold,
n_live_tup,
reltuples,
autovacuum_analyze_trigger,
n_mod_since_analyze,
autovacuum_analyze_trigger - n_mod_since_analyze AS rows_to_mod_before_analyze,
last_autoanalyze,
autovacuum_vacuum_trigger,
n_dead_tup,
autovacuum_vacuum_trigger - n_dead_tup AS rows_to_delete_before_vacuum,
last_autovacuum
FROM (
SELECT
schemaname,
relname,
autovacuum_vacuum_scale_factor,
autovacuum_vacuum_threshold,
autovacuum_analyze_scale_factor,
autovacuum_analyze_threshold,
floor(autovacuum_analyze_scale_factor::numeric * reltuples) + 1 + autovacuum_analyze_threshold::int AS autovacuum_analyze_trigger,
floor(autovacuum_vacuum_scale_factor::numeric * reltuples) + 1 + autovacuum_vacuum_threshold::int AS autovacuum_vacuum_trigger,
reltuples,
n_live_tup,
n_dead_tup,
n_mod_since_analyze,
last_autoanalyze,
last_autovacuum
FROM
v,
t) a;
-[ RECORD 1 ]-------------------+-----------------
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 | 100000
reltuples | 100000
autovacuum_analyze_trigger | 7001
n_mod_since_analyze | 0
rows_to_mod_before_analyze | 7001
last_autoanalyze |
autovacuum_vacuum_trigger | 12001
n_dead_tup | 0
rows_to_delete_before_vacuum | 12001
last_autovacuum |
现在已预测到要修改的行数，接下来一步一步来触发一下表级的 AUTO ANALYZE 和 AUTO VACUUM。
先删除 7000 行数据：
alvindb=> SELECT clock_timestamp();
clock_timestamp
-------------------------------
2021-11-06 23:33:03.252622+08
(1 row)
alvindb=> DELETE FROM pgbench_accounts WHERE aid<=7000;
DELETE 7000
alvindb=> SELECT pg_sleep(6);
pg_sleep
----------
(1 row)
alvindb=> SELECT clock_timestamp();
clock_timestamp
-------------------------------
2021-11-06 23:33:09.363536+08
(1 row)
根据表级 AUTOVACUUM 计算 SQL 执行结果的 rows_to_mod_before_analyze 得知，再修改 1 行将触发 AUTO 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 | 93000
reltuples | 100000
autovacuum_analyze_trigger | 7001
n_mod_since_analyze | 7000
rows_to_mod_before_analyze | 1
last_autoanalyze |
autovacuum_vacuum_trigger | 12001
n_dead_tup | 7000
rows_to_delete_before_vacuum | 5001
last_autovacuum |
再修改 1 行：
alvindb=> SELECT clock_timestamp();
clock_timestamp
-------------------------------
2021-11-06 23:33:30.649717+08
(1 row)
alvindb=> UPDATE pgbench_accounts SET bid = bid WHERE aid=7001;
UPDATE 1
alvindb=> SELECT pg_sleep(6);
pg_sleep
----------
(1 row)
alvindb=> SELECT clock_timestamp();
clock_timestamp
-------------------------------
2021-11-06 23:33:36.705928+08
(1 row)
根据表级 AUTOVACUUM 计算 SQL 执行结果的 last_autoanalyze 得知，已精准触发 AUTO 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 | 93000
reltuples | 93000
autovacuum_analyze_trigger | 6651
n_mod_since_analyze | 0
rows_to_mod_before_analyze | 6651
last_autoanalyze | 2021-11-06 23:33:40.87317+08
autovacuum_vacuum_trigger | 11301
n_dead_tup | 7001
rows_to_delete_before_vacuum | 4300
last_autovacuum |
从 PostgreSQL 日志中也可以看到 AUTO ANALYZE 被触发了：
[ 2021-11-06 23:33:40.873 CST 32646 6186a054.7f86 1 6/1393 13179750]LOG: automatic analyze of table "alvindb.public.pgbench_accounts" syst
em usage: CPU: user: 0.04 s, system: 0.03 s, elapsed: 0.11 s
并且，根据 rows_to_delete_before_vacuum 得知，再删除 4300 行就可以触发 AUTO VACUUM。
接下来先删除 4299 行，以测试临界值：
alvindb=> SELECT clock_timestamp();
clock_timestamp
-------------------------------
2021-11-06 23:33:43.867176+08
(1 row)
alvindb=> UPDATE pgbench_accounts SET bid = bid WHERE aid>=95702;
UPDATE 4299
alvindb=> SELECT pg_sleep(6);
pg_sleep
----------
(1 row)
alvindb=> SELECT clock_timestamp();
clock_timestamp
-------------------------------
2021-11-06 23:33:50.016447+08
(1 row)
autovacuum_naptime 为 5s，此时并未触发 AUTO VACUUM。
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 | 93000
reltuples | 93000
autovacuum_analyze_trigger | 6651
n_mod_since_analyze | 4299
rows_to_mod_before_analyze | 2352
last_autoanalyze | 2021-11-06 23:33:40.87317+08
autovacuum_vacuum_trigger | 11301
n_dead_tup | 11300
rows_to_delete_before_vacuum | 1
last_autovacuum |
再删除 (UPDATE = DELETE + INSERT) 1 行 :
alvindb=> SELECT clock_timestamp();
clock_timestamp
-------------------------------
2021-11-06 23:33:53.326483+08
(1 row)
alvindb=> UPDATE pgbench_accounts SET bid = bid WHERE aid=7002;
UPDATE 1
alvindb=> SELECT pg_sleep(6);
pg_sleep
----------
(1 row)
alvindb=> SELECT clock_timestamp();
clock_timestamp
-------------------------------
2021-11-06 23:33:59.439375+08
(1 row)
从如下结果中的 last_autovacuum 得知，此时已精确触发 AUTO VACUUM！
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 | 93000
reltuples | 93000
autovacuum_analyze_trigger | 6651
n_mod_since_analyze | 4300
rows_to_mod_before_analyze | 2351
last_autoanalyze | 2021-11-06 23:33:40.87317+08
autovacuum_vacuum_trigger | 11301
n_dead_tup | 0
rows_to_delete_before_vacuum | 11301
last_autovacuum | 2021-11-06 23:34:00.956936+08
从 PostgreSQL 日志中也可以看到 AUTO VACUUM 被触发了：
[ 2021-11-06 23:34:00.956 CST 32710 6186a068.7fc6 1 6/1455 0]LOG: automatic vacuum of table "alvindb.public.pgbench_accounts": index scans
: 1
pages: 0 removed, 421 remain, 0 skipped due to pins, 0 skipped frozen
tuples: 2 removed, 93000 remain, 0 are dead but not yet removable, oldest xmin: 13179755
buffer usage: 967 hits, 60 misses, 7 dirtied
avg read rate: 10.067 MB/s, avg write rate: 1.174 MB/s
system usage: CPU: user: 0.01 s, system: 0.00 s, elapsed: 0.18 s
公众号
关注 DBA Daily 公众号，第一时间收到文章的更新。
通过一线 DBA 的日常工作，学习实用数据库技术干货！
公众号优质文章推荐
[PG Upgrade Series] Extract Epoch Trap
[PG Upgrade Series] Toast Dump Error
GitLab supports only PostgreSQL now
PostgreSQL VACUUM 之深入浅出 (二)的更多相关文章
- PostgreSQL VACUUM 之深入浅出 (一)
前言 VACUUM 是 PostgreSQL MVCC (Multiversion concurrency control) 实现的核心机制之一,是 PostgreSQL 正常运行的重要保证.本文将通 ...
- PostgreSQL VACUUM 之深入浅出 (三)
VACUUM 相关参数 对 VACUUM 有了一定的了解之后,下面系统介绍下 VACUUM 相关参数. VACUUM 相关参数主要分为三大类. 第一类 与资源相关参数 #--------------- ...
- 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 ...
- java代理的深入浅出(二)-CGLIB
java代理的深入浅出(二)-CGLIB 1.基本原理 CGLIB的原理就是生成一个要代理类的子类,子类重写要代理的类的所有不是final的方法.在子类中拦截所有父类方法的调用,拦截下来交给设置的Me ...
- postgresql vacuum操作
postgresql vacuum操作 PostgreSQL数据库管理工作中,定期vacuum是一个重要的工作.vacuum的效果: 1.1释放,再利用 更新/删除的行所占据的磁盘空间. 1.2更新P ...
- 路径分析—PostgreSQL+GeoServer+Openlayers（二）
路径分析-QGIS+PostgreSQL+PostGIS+pgRouting(一) 路径分析-PostgreSQL+GeoServer+Openlayers(二) 前言 上一篇文章中实现数据库层面的路 ...
- postgresql逻辑结构--表（二）
一.创建表 语法: create table table_name( col01_name data_type, col02_name data_type, col03_name data_type, ...
- Postgresql VACUUM COPY等
1.VACUUM VACUUM回收dead tuples占用的存储空间. 在一般的PostgreSQL操作中,被update操作删除或废弃的元组不会从物理表中删除; 它们一直存在,直到执行VACUUM ...
- 02-Unity深入浅出(二)
一. Unity声明周期 Unity容器为我们提供了6种生命周期,便于我们根据项目需求来选择使用. (1). 瞬时.默认省略即为瞬时,无论单线程还是多线程,每次都重新创建对象.new Transien ...
随机推荐
- postman设置token等关联参数
登陆时登录成功后服务器会返回一个token,这个token作为第二步骤的入参:第二个步骤请求成功后服务器会返回一个新token,然后这个token作为第三步骤的入参!如此一来的话,要用postman做 ...
- github与gitlab创建新仓库
github创建新仓库 然后根据下一页的命令提示进行即可 gitlab创建新仓库 git init git remote add origin git@***.***.**.**:user/proje ...
- [Altium Designer 学习]怎样添加3D模型
对于为给PCB添加3D模型,很多人觉得这是个绣花针的活,中看不中用.在我看来这也未必,特别是常用的3D模型能在网上下载的今天,只需要几个简单的操作,就能使你的PCB更加赏心悦目.除此之外,3D模型还有 ...
- java string 转化为json_java String 转Json报错
缺少jar包依赖: java.lang.NoClassDefFoundError: org/apache/commons/beanutils/DynaBean 缺少commons-beanutils- ...
- 【刷题-LeetCode】216. Combination Sum III
Combination Sum III Find all possible combinations of k numbers that add up to a number n, given tha ...
- IoC容器-Bean管理注解方式（完全注解开发）
完全注解开发 (1)创建配置类,替代xml配置文件 (2)编写测试类 在实际中一般用springboot做
- 3D建模服务提供更高效、专业的能力，“筑”力开发者
3D建模服务(3D Modeling Kit)是HMS Core在图形图像领域又一技术开放.3D建模产品的定位就是要做快速.简洁.低成本的3D制作能力,并陆续开放给有3D模型.动画游戏制作等能力诉求的 ...
- DBeaver下载安装与连接MySQL数据库
一.Dbeaver下载 官网下载地址:Download | DBeaver Community 点击"Windows 64 bit (installer)"即可进行下载. 二.Db ...
- 「数据结构」Link-Cut Tree(LCT)
#1.0 简述 #1.1 动态树问题 维护一个森林,支持删除某条边,加入某条边,并保证加边.删边之后仍然是森林.我们需要维护这个森林的一些信息. 一般的操作有两点连通性,两点路径权值和等等. #1.2 ...
- 分享一个基于 ABP(.NET 5.0) + vue-element-admin 管理后台
1.前言 分享一个基于ABP(.NET 5.0) + vue-element-admin项目.希望可以降低新手对于ABP框架的学习成本,感兴趣的同学可以下载项目启动运行一下.对于想选型采用ABP框架的 ...