标签
PostgreSQL , 实时轨迹 , IoT , 车联网 , GIS , 离散IO , 顺序IO , IO放大 , 聚合 , PGLZ压缩解压缩 , IN OUT函数消耗
背景
车联网场景，一个行程由若干个点组成，业务需要按行程查询，按。
但是问题来了：
一个行程包含多个点，那么一次查询意味着需要查询并返回多个点（多条记录）。
由于有多个设备（例如汽车），大概率可能同时活跃，同时上传点，因此以堆表存储的话，一个行程中的多条记录，实际上在数据库的表里面，存储在多个BLOCK里面。
如上分析，性能问题：IO放大。
如何优化？
1、行程记录，按行聚集存储。
类似cluster操作。
2、行程记录，合并到单条，聚集存储。
类似将一个行程多条记录聚合。
例子
下面分别测试几种优化方法带来的性能优化效果。
1、cluster
2、array 聚合带压缩
3、array 聚合不带压缩
4、jsonb 聚合带压缩
5、jsonb 聚合不带压缩
6、text 聚合带压缩
7、text 聚合不带压缩
1 原始状态
create unlogged table t_sensor(id serial8 primary key, sid int8, att text, crt_time timestamp);
create index idx_t_sensor_1 on t_sensor (sid, crt_time);
vi test.sql
\set sid random(1,10000)
insert into t_sensor(sid,att,crt_time) values (:sid, md5(random()::text), now());
pgbench -M prepared -n -r -P 1 -f ./test.sql -c 50 -j 50 -t 2000000
查看一个行程，需要访问11227个数据块。
postgres=# explain (analyze,verbose,timing,costs,buffers) select * from t_sensor where sid=1 order by crt_time;
QUERY PLAN
--------------------------------------------------------------------------------------------------------------------------------------------
Index Scan using idx_t_sensor_1 on public.t_sensor (cost=0.49..253.36 rows=11121 width=57) (actual time=0.020..47.591 rows=11189 loops=1)
Output: id, sid, att, crt_time
Index Cond: (t_sensor.sid = 1)
Buffers: shared hit=3406 read=7821
Planning Time: 0.092 ms
Execution Time: 48.303 ms
(6 rows)
postgres=# select 119*11227*8/1024.0;
?column?
--------------------
10437.601562500000
(1 row)
压测性能
vi test.sql
\set sid random(1,10000)
select * from t_sensor where sid=:sid order by crt_time ;
pgbench -M prepared -n -r -P 1 -f ./test.sql -c 56 -j 56 -T 120
transaction type: ./test.sql
scaling factor: 1
query mode: prepared
number of clients: 56
number of threads: 56
duration: 120 s
number of transactions actually processed: 14307
latency average = 470.175 ms
latency stddev = 43.500 ms
tps = 119.037842 (including connections establishing)
tps = 119.098221 (excluding connections establishing)
statement latencies in milliseconds:
0.003 \set sid random(1,10000)
470.178 select * from t_sensor where sid=:sid order by crt_time ;
2 cluster
按行程，时间索引聚集。一次查询返回一条聚合后的记录。
postgres=# cluster t_sensor USING idx_t_sensor_1 ;
查询一个行程，扫描174个数据块。IO 骤降。
postgres=# explain (analyze,verbose,timing,costs,buffers) select * from t_sensor where sid=1 order by crt_time;
QUERY PLAN
-------------------------------------------------------------------------------------------------------------------------------------------
Index Scan using idx_t_sensor_1 on public.t_sensor (cost=0.49..253.39 rows=11149 width=57) (actual time=0.018..2.212 rows=11189 loops=1)
Output: id, sid, att, crt_time
Index Cond: (t_sensor.sid = 1)
Buffers: shared hit=174
Planning Time: 0.094 ms
Execution Time: 2.816 ms
(6 rows)
压测性能，见末尾。
3 array 带压缩
create unlogged table t_sensor_agg(sid int8 primary key, agg t_sensor[]);
insert into t_sensor_agg select sid,array_agg(t_sensor order by crt_time) from t_sensor group by sid;
瓶颈：array类型的INPUT OUTPUT接口，以及压缩解压缩接口
Samples: 3M of event 'cpu-clock', Event count (approx.): 559749813667
Overhead Shared Object Symbol
24.59% postgres [.] array_out
20.70% postgres [.] record_out
6.74% postgres [.] pglz_decompress
3.78% libc-2.17.so [.] __memcpy_ssse3_back
2.72% postgres [.] pg_ltostr_zeropad
2.34% [kernel] [k] run_timer_softirq
2.23% postgres [.] pg_lltoa
2.13% postgres [.] 0x000000000035c350
1.76% postgres [.] heap_deform_tuple
1.49% libc-2.17.so [.] __strlen_sse2_pminub
1.36% postgres [.] appendStringInfoChar
1.36% [kernel] [k] copy_user_enhanced_fast_string
1.29% postgres [.] 0x000000000035c36c
1.28% postgres [.] 0x000000000035c362
1.17% postgres [.] FunctionCall1Coll
0.92% postgres [.] hash_search_with_hash_value
0.86% [kernel] [k] _raw_spin_unlock_irqrestore
0.84% postgres [.] j2date
0.82% postgres [.] 0x000000000035c357
0.76% postgres [.] palloc
0.76% postgres [.] lookup_type_cache
0.67% postgres [.] 0x000000000035c360
0.66% postgres [.] timestamp2tm
0.64% [kernel] [k] rcu_process_callbacks
0.64% [kernel] [k] __do_softirq
vi test.sql
\set sid random(1,10000)
select * from t_sensor_agg4 where sid=:sid ;
4 array 不带压缩
create unlogged table t_sensor_agg1(sid int8 primary key, agg t_sensor[]);
alter table t_sensor_agg1 alter column agg set storage external;
insert into t_sensor_agg1 select sid,array_agg(t_sensor order by crt_time) from t_sensor group by sid;
瓶颈：array类型的INPUT OUTPUT接口
Samples: 1M of event 'cpu-clock', Event count (approx.): 310127790569
Overhead Shared Object Symbol
26.06% postgres [.] array_out
21.44% postgres [.] record_out
4.20% libc-2.17.so [.] __memcpy_ssse3_back
2.86% [kernel] [k] run_timer_softirq
2.75% postgres [.] pg_ltostr_zeropad
2.65% postgres [.] heap_deform_tuple
2.28% postgres [.] pg_lltoa
2.14% postgres [.] 0x000000000035c350
1.87% [kernel] [k] copy_user_enhanced_fast_string
1.52% libc-2.17.so [.] __strlen_sse2_pminub
1.47% postgres [.] appendStringInfoChar
1.32% postgres [.] 0x000000000035c36c
1.30% postgres [.] 0x000000000035c362
1.20% postgres [.] FunctionCall1Coll
1.11% postgres [.] hash_search_with_hash_value
0.87% postgres [.] j2date
0.85% postgres [.] 0x000000000035c357
0.81% [kernel] [k] _raw_spin_unlock_irqrestore
0.76% postgres [.] lookup_type_cache
0.75% postgres [.] 0x000000000046d33b
0.74% postgres [.] palloc
0.72% [kernel] [k] rcu_process_callbacks
0.68% postgres [.] timestamp2tm
0.68% postgres [.] pfree
5 jsonb 带压缩
create unlogged table t_sensor_agg2(sid int8 primary key, agg jsonb);
insert into t_sensor_agg2 select sid,jsonb_agg(t_sensor order by crt_time) from t_sensor group by sid;
6 jsonb 不带压缩
create unlogged table t_sensor_agg3(sid int8 primary key, agg jsonb);
alter table t_sensor_agg3 alter column agg set storage external;
insert into t_sensor_agg3 select sid,jsonb_agg(t_sensor order by crt_time) from t_sensor group by sid;
7 text 带压缩
create unlogged table t_sensor_agg4(sid int8 primary key, agg text);
insert into t_sensor_agg4 select sid,string_agg(t_sensor::text, '|' order by crt_time) from t_sensor group by sid;
8 text 不带压缩
create unlogged table t_sensor_agg5(sid int8 primary key, agg text);
alter table t_sensor_agg5 alter column agg set storage external;
insert into t_sensor_agg5 select sid,string_agg(t_sensor::text, '|' order by crt_time) from t_sensor group by sid;
9 index only scan 类似聚集表效果
所有内容作为INDEX的KEY，类似聚集表的效果(相邻内容在同一个INDEX PAGE里面)。查询时走INDEX ONLY SCAN扫描方法，扫描的BLOCK最少。
注意：btree 索引内容不能超过1/3 PAGE (因为BTREE是双向链表，一个PAGE至少要有一条有效记录，所以有这个限制。)。
写入数据
create table t_sensor (id serial8 primary key, sid int8, att text, crt_time timestamp);
create index idx_t_sensor_1 on t_sensor (sid, crt_time, att, id);
vi test.sql
\set sid random(1,10000)
insert into t_sensor(sid,att,crt_time) values (:sid, md5(random()::text), now());
pgbench -M prepared -n -r -P 1 -f ./test.sql -c 50 -j 50 -t 2000000
transaction type: ./test.sql
scaling factor: 1
query mode: prepared
number of clients: 50
number of threads: 50
number of transactions per client: 2000000
number of transactions actually processed: 100000000/100000000
latency average = 0.193 ms
latency stddev = 0.461 ms
tps = 257995.418591 (including connections establishing)
tps = 258024.212148 (excluding connections establishing)
statement latencies in milliseconds:
0.001 \set sid random(1,10000)
0.192 insert into t_sensor(sid,att,crt_time) values (:sid, md5(random()::text), now());
生成VM文件（autovacuum触发时会自动生成，但是为了立马看效果，手工执行一下。）
vacuum analyze t_sensor;
INDEX ONLY SCAN, IO减少效果如下：
postgres=# explain (analyze,verbose,timing,costs,buffers) select * from t_sensor where sid=2 order by crt_time;
QUERY PLAN
---------------------------------------------------------------------------------------------------------------------------------------------
Index Only Scan using idx_t_sensor_1 on public.t_sensor (cost=0.60..70.41 rows=9960 width=57) (actual time=0.019..2.109 rows=9978 loops=1)
Output: id, sid, att, crt_time
Index Cond: (t_sensor.sid = 2)
Heap Fetches: 0
Buffers: shared hit=235
Planning Time: 0.090 ms
Execution Time: 2.652 ms
(7 rows)
查询性能：
vi test.sql
\set sid random(1,10000)
select * from t_sensor where sid=:sid order by crt_time;
pgbench -M prepared -n -r -P 1 -f ./test.sql -c 28 -j 28 -T 120
transaction type: ./test.sql
scaling factor: 1
query mode: prepared
number of clients: 28
number of threads: 28
duration: 120 s
number of transactions actually processed: 283638
latency average = 11.844 ms
latency stddev = 1.931 ms
tps = 2363.410561 (including connections establishing)
tps = 2363.913145 (excluding connections establishing)
statement latencies in milliseconds:
0.002 \set sid random(1,10000)
11.842 select * from t_sensor where sid=:sid order by crt_time;
小结
目标数据分散在多个BLOCK中，引起IO放大的问题，通过聚集存储，或者通过聚合存储，可以很好的解决这个问题。
聚合后，另一个瓶颈则是聚合后的类型（array,jsonb,text）的IN OUT接口。
| / | 表存储 | 行程查询 qps |
|---|---|---|
| 原始(IO 放大) | 8880 MB | 119 |
| 顺序(无IO 放大) | 8880 MB | 2057 |
| index only scan(类似聚集表)(无IO 放大) | 8880 MB | 2363 |
| 聚合array(压缩) | 4523 MB | 2362 |
| 聚合array(不压缩) | 8714 MB | 2515 |
| 聚合json(压缩) | 5052 MB | 3102 |
| 聚合json(不压缩) | 13 GB | 3184 |
| 聚合text(压缩) | 4969 MB | 6057 |
| 聚合text(不压缩) | 7692 MB | 5997 |
从上面的测试，可以看到IN OUT函数接口的开销，text<jsonb<array(composite array)。
实际的优化例子，可参考末尾的几篇文章。例如：
1、按时间分区，旧的分区使用cluster，按行程整理数据，使用AB表切换，解决IO放大的问题。
2、异步聚合，将点数据准实时按行程ID，聚合到聚合后的行程表。
3、使用INDEX ONLY SCAN, 达到聚集表效果。对业务无任何侵入性。(例如按天分区，加全量(业务需要查询的字段)索引。)，相当于两倍存储空间(一份在堆表，一份在索引中)。
参考
《PostgreSQL IoT，车联网 - 实时轨迹、行程实践》
