Postgres10高性能开发（2）解释计划

Postgres10高性能开发（2）解释计划

解释计划

Explain

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基本使用

利用规划器评估解释计划及其执行成本

explain不执行SQL只进行评估，同时不会评估数据传输的网络开销

explain select * from crm_t_archive_person# Result
Seq Scan on crm_t_archive_person  (cost=0.00..3059.75 rows=53675 width=2739)

• Seq Scan：表示对表顺序扫描
• 0.00：启动成本，SQL段执行开始前已消耗的成本
• 3059.72：总费用，SQL段执行完成总消耗成本，总费用-启动成本=本SQL段自己消耗的成本
• 53675：预计本SQL段输出的行数
• 2739: 预计输出行的平均宽度（单位：byte）

ANALYZE选项

默认关闭

评估并执行SQL记录真实运行结果，不会评估数据传输的网络开销

explain analyze select * from crm_t_archive_person where cl_name_cn like '%AND%'# Result
Seq Scan on crm_t_archive_person  (cost=0.00..1448.71 rows=1 width=827) (actual time=0.813..3.560 rows=2 loops=1)Filter: ((cl_name_cn)::text ~~ '%AND%'::text)Rows Removed by Filter: 5735
Planning time: 0.195 ms
Execution time: 3.597 ms

• actual time=0.026…5.354：0.026为实际启动时间（毫秒），5.354为实际总时间（毫秒）
• rows：实际本SQL段输出的行数
• loops：本SQL段实际执行次数
• Rows Removed by Filter：查询条件实际过滤掉的行数
• Planning time：规划耗时
• Execution time： 执行耗时

BUFFFERS选项

默认关闭

显示说明

• 共享块（shared blocks）命中、读取、弄脏、写入的数量，共享块保存表和索引的数据
• 本地块（local blocks）命中、读取、弄脏、写入的数量，本地块保存临时表和索引数据
• 临时块（temp blocks）读取和写入数量，临时块保存短期生成的排序、Hash、Materialize等数据

块操作说明

• 命中（hits）：从cache中读取，避免了从磁盘直接读取。
• 弄脏（dirtied）：SQL修改了数据使Cache块失效的数量
• 写入（written）：SQL从cache中逐出先前脏块的数量
• 读取（read）：直接从磁盘读取数据

其他选项

选项	默认值	说明
VERBOSE	FALSE	显示每个节点输出的列名等
COST	TRUE	显示每个节点启动成本、总成本、行数、行宽度
TIMING	TRUE	ANALYZE下才能启用，输出每个节点花费的实际启动时间和总时间
SUMMARY	FALSE，在ANALYZE下默认为TRUE	输出摘要信息，如总时间
FORMAT	TEXT	指定输出格式，包括TEXT、XML、JSON、YAML

explain (VERBOSE,BUFFERS FALSE) select * from crm_t_archive_person where cl_name_cn like '%AND%'

单表扫描类型

聚合

HashAggregate

hash聚合根据每个group字段值生成一个hash结果，来实现快速统计

EXPLAIN select count(1) from inv_t_invoice group by cl_sale_unit# Result
HashAggregate  (cost=2323.58..2323.59 rows=1 width=57)Group Key: cl_sale_unit->  Seq Scan on inv_t_invoice  (cost=0.00..2278.72 rows=8972 width=49)

GroupAggregate

根据group字段值排序，然后进行统计

EXPLAIN select count(1) from pms_t_cash_register group by cl_id# Result
Finalize GroupAggregate  (cost=293888.00..435273.53 rows=1581285 width=41)Group Key: cl_id->  Gather Merge  (cost=293888.00..414809.84 rows=930168 width=41)Workers Planned: 1->  Partial GroupAggregate  (cost=292887.99..309165.93 rows=930168 width=41)Group Key: cl_id->  Sort  (cost=292887.99..295213.41 rows=930168 width=33)Sort Key: cl_id->  Parallel Seq Scan on pms_t_cash_register  (cost=0.00..175240.68 rows=930168 width=33)

• 内存消耗GroupAggregate更少，表pms_t_cash_register数据量较大，内存消耗会很多，因此采用了GroupAggregate
• 效率上HashAggregate更快，表inv_t_invoice数据量较小，内存消耗问题不大，因此采用HashAggregate

Seq Scan

全表扫描

EXPLAIN select * from crm_t_archive_person# Result
Seq Scan on crm_t_archive_person  (cost=0.00..3059.75 rows=53675 width=2739)

Index Scan

索引扫描，先通过索引找出符合条件的TID(数据行ID)，在通过RowID去表中查找数据

EXPLAIN select * from crm_t_archive_person where cl_id = ''# Result
Index Only Scan using crm_t_archive_person_pkey on crm_t_archive_person  (cost=0.28..8.30 rows=1 width=36)Index Cond: (cl_id = ''::text)

Bitmap Index Scan

位图索引扫描，先通过索引找出所有符合条件的TID(数据行ID)，并进行排序，然后去表中查找数据

explain select * from crm_t_archive_person where cl_id < '1'# Result
Bitmap Heap Scan on crm_t_archive_person  (cost=47.37..919.51 rows=398 width=827)Recheck Cond: ((cl_id)::text < '1'::text)->  Bitmap Index Scan on crm_t_archive_person_pkey  (cost=0.00..47.27 rows=398 width=0)Index Cond: ((cl_id)::text < '1'::text)

Index Only Scan

通过索引就能查到想要的数据，不需要再访问表

explain select count(1) from crm_t_archive_person where cl_id = ''# Result
Aggregate  (cost=8.30..8.31 rows=1 width=8)->  Index Only Scan using crm_t_archive_person_pkey on crm_t_archive_person  (cost=0.28..8.30 rows=1 width=0)Index Cond: (cl_id = ''::text)

BitmapAnd

多索引结果And

EXPLAIN SELECT * FROM tenk1 WHERE unique1 < 100 AND unique2 > 9000;# Result
Bitmap Heap Scan on tenk1  (cost=25.08..60.21 rows=10 width=244)Recheck Cond: ((unique1 < 100) AND (unique2 > 9000))->  BitmapAnd  (cost=25.08..25.08 rows=10 width=0)->  Bitmap Index Scan on tenk1_unique1  (cost=0.00..5.04 rows=101 width=0)Index Cond: (unique1 < 100)->  Bitmap Index Scan on tenk1_unique2  (cost=0.00..19.78 rows=999 width=0)Index Cond: (unique2 > 9000)

BitmapOr

多索引结果Or

连表索引

Nested Loop

先遍历A，再遍历B
性能为O(A*B)

EXPLAIN SELECT *
FROM tenk1 t1, tenk2 t2
WHERE t1.unique1 < 10 AND t1.unique2 = t2.unique2;# ResultNested Loop  (cost=4.65..118.62 rows=10 width=488)->  Bitmap Heap Scan on tenk1 t1  (cost=4.36..39.47 rows=10 width=244)Recheck Cond: (unique1 < 10)->  Bitmap Index Scan on tenk1_unique1  (cost=0.00..4.36 rows=10 width=0)Index Cond: (unique1 < 10)->  Index Scan using tenk2_unique2 on tenk2 t2  (cost=0.29..7.91 rows=1 width=244)Index Cond: (unique2 = t1.unique2)

Nested Loop中可能会用到Materialize,Materialize表示该查询段只会执行一次，对结果进行物化存储用于后续处理

EXPLAIN SELECT *
FROM tenk1 t1, tenk2 t2
WHERE t1.unique1 < 10 AND t2.unique2 < 10 AND t1.hundred < t2.hundred;# ResultNested Loop  (cost=4.65..49.46 rows=33 width=488)Join Filter: (t1.hundred < t2.hundred)->  Bitmap Heap Scan on tenk1 t1  (cost=4.36..39.47 rows=10 width=244)Recheck Cond: (unique1 < 10)->  Bitmap Index Scan on tenk1_unique1  (cost=0.00..4.36 rows=10 width=0)Index Cond: (unique1 < 10)->  Materialize  (cost=0.29..8.51 rows=10 width=244)->  Index Scan using tenk2_unique2 on tenk2 t2  (cost=0.29..8.46 rows=10 width=244)Index Cond: (unique2 < 10)

Hash Join

联表条件为等于时，有可能将两个结果集(A,B)进行Hash，Hash后能够加快比较速度，性能O(A)+O(B)

EXPLAIN SELECT *
FROM tenk1 t1, tenk2 t2
WHERE t1.unique1 < 100 AND t1.unique2 = t2.unique2;# ResultHash Join  (cost=230.47..713.98 rows=101 width=488)Hash Cond: (t2.unique2 = t1.unique2)->  Seq Scan on tenk2 t2  (cost=0.00..445.00 rows=10000 width=244)->  Hash  (cost=229.20..229.20 rows=101 width=244)->  Bitmap Heap Scan on tenk1 t1  (cost=5.07..229.20 rows=101 width=244)Recheck Cond: (unique1 < 100)->  Bitmap Index Scan on tenk1_unique1  (cost=0.00..5.04 rows=101 width=0)Index Cond: (unique1 < 100)

Merge Join

先对结果集A，B进行排序，再进行联表操作
可用于等于和范围联表条件

EXPLAIN SELECT *
FROM tenk1 t1, onek t2
WHERE t1.unique1 < 100 AND t1.unique2 = t2.unique2;# ResultMerge Join  (cost=198.11..268.19 rows=10 width=488)Merge Cond: (t1.unique2 = t2.unique2)->  Index Scan using tenk1_unique2 on tenk1 t1  (cost=0.29..656.28 rows=101 width=244)Filter: (unique1 < 100)->  Sort  (cost=197.83..200.33 rows=1000 width=244)Sort Key: t2.unique2->  Seq Scan on onek t2  (cost=0.00..148.00 rows=1000 width=244)