标签
PostgreSQL , 流式统计 , insert on conflict , count , avg , min , max , sum
背景
流式统计count, avg, min, max, sum等是一个比较有意思的场景，可用于实时大屏，实时绘制统计图表。
比如菜鸟、淘宝、阿里游戏、以及其他业务系统的FEED日志，按各个维度实时统计输出结果。(实时FEED统计，实时各维度在线人数等)
PostgreSQL insert on conflict语法以及rule, trigger的功能，可以实现对数据的实时统计，ECS 56核的性能指标：
单实例单表单行流式处理性能可以达到39万行/s，批量写入流式处理达到336万行/s的性能。
例子
实时统计每个SID的value的min, max, sum，以及记录数。
1、创建测试表，包含统计字段，统计字段的内容由PostgreSQL自动生成。
create table tbl (
sid int primary key,
v1 int,
crt_time timestamp,
cnt int8 default 1, -- 统计值, 默认为1, 等于1时表示第一条记录
sum_v float8 default 0, -- 统计值, 默认为0
min_v float8 default float8 'Infinity', -- 统计值, 默认设置为这个类型的最大值
max_v float8 default float8 '-Infinity' -- 统计值, 默认设置为这个类型的最小值
);
2、创建明细表，用于检查PostgreSQL流计算的结果是否正确
create table tbl_log (
sid int,
v1 int,
crt_time timestamp
);
3、流计算算法由insert on conflict SQL完成，如下
insert into tbl (sid, v1, crt_time) values (:sid, :v1, now())
on conflict (sid) do update set
v1=excluded.v1,
crt_time=excluded.crt_time,
cnt=tbl.cnt+1,
sum_v=case tbl.cnt when 1 then tbl.v1+excluded.v1 else tbl.sum_v+excluded.v1 end,
min_v=least(tbl.min_v, excluded.v1),
max_v=greatest(tbl.max_v, excluded.v1)
;
4、大批量写入测试
vi test.sql
\set sid random(1,1000000)
\set v1 random(1,100000000)
insert into tbl (sid, v1, crt_time) values (:sid, :v1, now()) on conflict (sid) do update set v1=excluded.v1, crt_time=excluded.crt_time, cnt=tbl.cnt+1, sum_v=case tbl.cnt when 1 then tbl.v1+excluded.v1 else tbl.sum_v+excluded.v1 end, min_v=least(tbl.min_v, excluded.v1), max_v=greatest(tbl.max_v, excluded.v1);
insert into tbl_log values (:sid, :v1, now());
pgbench -M prepared -n -r -P 1 -f ./test.sql -c 32 -j 32 -T 120
5、验证算法正确性
postgres=# \timing
Timing is on.
postgres=# select sid, count(*), sum(v1), min(v1), max(v1) from tbl_log group by sid order by sid limit 10;
sid | count | sum | min | max
-----+-------+-----------+----------+----------
1 | 14 | 740544728 | 11165285 | 90619042
2 | 10 | 414224202 | 2813223 | 83077953
3 | 11 | 501992396 | 13861878 | 79000001
4 | 17 | 902219309 | 23429 | 99312338
5 | 6 | 374351692 | 25582424 | 96340616
6 | 15 | 649447876 | 12987896 | 80478126
7 | 8 | 386687697 | 19697861 | 95097076
8 | 12 | 657650588 | 11339236 | 97211546
9 | 10 | 594843053 | 9192864 | 97362345
10 | 9 | 383123573 | 3877866 | 76604940
(10 rows)
Time: 1817.395 ms (00:01.817)
postgres=# select * from tbl order by sid limit 10;
sid | v1 | crt_time | cnt | sum_v | min_v | max_v
-----+----------+----------------------------+-----+-----------+----------+----------
1 | 26479786 | 2017-11-23 20:27:43.134594 | 14 | 740544728 | 11165285 | 90619042
2 | 25755108 | 2017-11-23 20:27:43.442651 | 10 | 414224202 | 2813223 | 83077953
3 | 51068648 | 2017-11-23 20:27:48.118906 | 11 | 501992396 | 13861878 | 79000001
4 | 81160224 | 2017-11-23 20:27:37.183186 | 17 | 902219309 | 23429 | 99312338
5 | 70208701 | 2017-11-23 20:27:35.399063 | 6 | 374351692 | 40289886 | 96340616
6 | 77536576 | 2017-11-23 20:27:46.04372 | 15 | 649447876 | 12987896 | 80478126
7 | 31153753 | 2017-11-23 20:27:46.54858 | 8 | 386687697 | 19697861 | 95097076
8 | 11339236 | 2017-11-23 20:27:40.947561 | 12 | 657650588 | 11339236 | 97211546
9 | 46103803 | 2017-11-23 20:27:38.450889 | 10 | 594843053 | 9192864 | 92049544
10 | 55630877 | 2017-11-23 20:27:28.944168 | 9 | 383123573 | 3877866 | 76604940
(10 rows)
Time: 0.512 ms
实时统计后，我们的查询响应时间从1817毫秒降到了0.5毫秒，将近4万倍的性能提升。
单表性能压测
vi test.sql
\set sid random(1,1000000)
\set v1 random(1,100000000)
insert into tbl (sid, v1, crt_time) values (:sid, :v1, now()) on conflict (sid) do update set v1=excluded.v1, crt_time=excluded.crt_time, cnt=tbl.cnt+1, sum_v=case tbl.cnt when 1 then tbl.v1+excluded.v1 else tbl.sum_v+excluded.v1 end, min_v=least(tbl.min_v, excluded.v1), max_v=greatest(tbl.max_v, excluded.v1);
pgbench -M prepared -n -r -P 1 -f ./test.sql -c 32 -j 32 -T 300
transaction type: ./test.sql
scaling factor: 1
query mode: prepared
number of clients: 32
number of threads: 32
duration: 300 s
number of transactions actually processed: 57838943
latency average = 0.166 ms
latency stddev = 0.057 ms
tps = 192791.786864 (including connections establishing)
tps = 192805.650917 (excluding connections establishing)
script statistics:
- statement latencies in milliseconds:
0.001 \set sid random(1,1000000)
0.000 \set v1 random(1,100000000)
0.164 insert into tbl (sid, v1, crt_time) values (:sid, :v1, now()) on conflict (sid) do update set v1=excluded.v1, crt_time=excluded.crt_time, cnt=tbl.cnt+1, sum_v=case tbl.cnt when 1 then tbl.v1+excluded.v1 else tbl.sum_v+excluded.v1 end, min_v=least(tbl.min_v, excluded.v1), max_v=greatest(tbl.max_v, excluded.v1);
top - 20:57:35 up 16 days, 3:44, 2 users, load average: 8.67, 2.08, 1.68
Tasks: 497 total, 28 running, 469 sleeping, 0 stopped, 0 zombie
%Cpu(s): 34.8 us, 13.7 sy, 0.0 ni, 51.3 id, 0.1 wa, 0.0 hi, 0.0 si, 0.0 st
KiB Mem : 23094336+total, 79333744 free, 1588292 used, 15002134+buff/cache
KiB Swap: 0 total, 0 free, 0 used. 22219502+avail Mem
单实例流式统计性能
我们这个测试CASE，单个维度，多表，根据CPU剩余，估算得到的性能应该是:
38.5万行/s
单表，批量写入性能压测
vi test.sql
\set sid random(1,1000000)
\set v1 random(1,100000000)
insert into tbl (sid, v1, crt_time) select :sid+id, :v1+id, now() from generate_series(1,100) t(id) on conflict (sid) do update set v1=excluded.v1, crt_time=excluded.crt_time, cnt=tbl.cnt+1, sum_v=case tbl.cnt when 1 then tbl.v1+excluded.v1 else tbl.sum_v+excluded.v1 end, min_v=least(tbl.min_v, excluded.v1), max_v=greatest(tbl.max_v, excluded.v1);
pgbench -M prepared -n -r -P 1 -f ./test.sql -c 28 -j 28 -T 300
scaling factor: 1
query mode: prepared
number of clients: 28
number of threads: 28
duration: 120 s
number of transactions actually processed: 1411743
latency average = 2.380 ms
latency stddev = 0.815 ms
tps = 11764.276597 (including connections establishing)
tps = 11765.715797 (excluding connections establishing)
script statistics:
- statement latencies in milliseconds:
0.001 \set sid random(1,1000000)
0.000 \set v1 random(1,100000000)
2.378 insert into tbl (sid, v1, crt_time) select :sid+id, :v1+id, now() from generate_series(1,100) t(id) on conflict (sid) do update set v1=excluded.v1, crt_time=excluded.crt_time, cnt=tbl.cnt+1, sum_v=case tbl.cnt when 1 then tbl.v1+excluded.v1 else tbl.sum_v+excluded.v1 end, min_v=least(tbl.min_v, excluded.v1), max_v=greatest(tbl.max_v, excluded.v1);
34 processes: 22 running, 11 sleeping, 1 uninterruptable
CPU states: 28.5% user, 0.0% nice, 6.3% system, 65.0% idle, 0.1% iowait
Memory: 173G used, 47G free, 247M buffers, 168G cached
DB activity: 9613 tps, 0 rollbs/s, 0 buffer r/s, 100 hit%, 961050 row r/s, 960824 row w/s
DB I/O: 0 reads/s, 0 KB/s, 0 writes/s, 0 KB/s
DB disk: 1455.4 GB total, 441.9 GB free (69% used)
Swap:
117.65万行/s。
我们这个测试CASE，单个维度，单表，批量写入，根据CPU剩余（单ECS使用多实例或者使用UNLOGGED TABLE），估算得到的性能应该是:
336万行/s
流式统计方案
因为我们统计的维度可能不止一个，例如明细表中的多个字段，可能都会被统计到。
例如
create table tbl(c1 int, c2 int, c3 int, c4 int, c5 int);
select c1, count(*) from tbl group by c1;
select c2,c3, sum(c5) , count(*) from tbl group by c2,c3;
..... 更多维度
这种情况下，我们需要如何实现流式统计呢？
除了使用pipelinedb，实际上PostgreSQL使用insert on conflict和trigger或rule，也能实现一样的功能。
流程设计
1、定义明细表
2、定义每个维度的目标统计表
3、定义维度表的insert on conflict SQL
4、定义明细表trigger或rule，顺序调用insert on conflict 写入多个维度表
例子
1、定义明细表
create table tbl(c1 int not null, c2 int not null, c3 int not null, c4 int not null, c5 int not null);
2、定义每个维度的目标统计表
create table cv1_tbl (c1 int primary key, cnt int8 default 1);
create table cv2_tbl (c2 int, c3 int, c5 int, sum_v float8 default 0, cnt int8 default 1, primary key (c2,c3)) ;
..... 其他维度
3、定义维度表的insert on conflict SQL
insert into cv1_tbl (c1) values (NEW.c1) on conflict (c1) do update set cnt=cv1_tbl.cnt+1;
insert into cv2_tbl (c2,c3,c5) values (NEW.c2, NEW.c3, NEW.c5) on conflict (c2,c3) do update set cnt=cv2_tbl.cnt+1, sum_v=case cv2_tbl.cnt when 1 then cv2_tbl.c5+excluded.c5 else cv2_tbl.sum_v+excluded.c5 end;
4、定义明细表trigger或rule，顺序调用insert on conflict 写入多个维度表
create rule r1 as on insert to tbl do instead insert into cv1_tbl (c1) values (NEW.c1) on conflict (c1) do update set cnt=cv1_tbl.cnt+1;
create rule r2 as on insert to tbl do instead insert into cv2_tbl (c2,c3,c5) values (NEW.c2, NEW.c3, NEW.c5) on conflict (c2,c3) do update set cnt=cv2_tbl.cnt+1, sum_v=case cv2_tbl.cnt when 1 then cv2_tbl.c5+excluded.c5 else cv2_tbl.sum_v+excluded.c5 end;
5、测试
vi test.sql
\set c1 random(1,1000000)
\set c2 random(1,1000000)
\set c3 random(1,1000000)
\set c4 random(1,1000000)
\set c5 random(1,1000000)
insert into tbl values (:c1, :c2, :c3, :c4, :c5);
pgbench -M prepared -n -r -P 1 -f ./test.sql -c 32 -j 32 -T 120
6、测试结果
transaction type: ./test.sql
scaling factor: 1
query mode: prepared
number of clients: 32
number of threads: 32
duration: 120 s
number of transactions actually processed: 18618957
latency average = 0.206 ms
latency stddev = 0.212 ms
tps = 155154.880841 (including connections establishing)
tps = 155174.283641 (excluding connections establishing)
script statistics:
- statement latencies in milliseconds:
0.001 \set c1 random(1,1000000)
0.000 \set c2 random(1,1000000)
0.000 \set c3 random(1,1000000)
0.000 \set c4 random(1,1000000)
0.001 \set c5 random(1,1000000)
0.203 insert into tbl values (:c1, :c2, :c3, :c4, :c5);
7、验证结果
postgres=# select * from cv2_tbl order by cnt desc limit 10;
c2 | c3 | c5 | sum_v | cnt
--------+--------+--------+---------+-----
500568 | 119352 | 173877 | 436710 | 2
873168 | 20848 | 730385 | 1688835 | 2
90752 | 526912 | 622354 | 734505 | 2
273533 | 886999 | 766661 | 1085038 | 2
895573 | 466493 | 648095 | 1191965 | 2
338402 | 436092 | 940920 | 1372244 | 2
915723 | 866856 | 255638 | 947606 | 2
586692 | 543596 | 32905 | 996466 | 2
839232 | 928197 | 402745 | 1249665 | 2
401808 | 997216 | 493644 | 1423618 | 2
(10 rows)
postgres=# select * from cv1_tbl order by cnt desc limit 10;
c1 | cnt
--------+-----
952009 | 44
373778 | 43
483788 | 42
25749 | 42
93605 | 41
386201 | 41
596955 | 40
526220 | 40
91289 | 40
429061 | 40
(10 rows)
壳子表没有写入，把rule改成do also的话，就会写入本地表。是不是很爽呢？
postgres=# select * from tbl;
c1 | c2 | c3 | c4 | c5
----+----+----+----+----
(0 rows)
实例内并行设计
通过定义明细分区表
例子
1、定义明细分区表
2、定义每个维度的目标统计表
3、定义维度表的insert on conflict SQL
4、定义明细分区表trigger或rule，顺序调用insert on conflict 写入多个维度表
实例外并行设计
定义上层哈希分布写
例子
上层应用，或者中间件，实现多个PostgreSQL实例，分布写入。
结合Hyperloglog实现实时UV估算
结合hll数据类型，可以实现实时的UV统计。
create extension hll;
create table tbl (grpid int, userid int, dt date, cnt int8 default 1, hll_userid hll default hll_empty(), primary key (grpid, dt));
insert into tbl (grpid, userid, dt) values () on conflict (grpid, dt) do update set
cnt=tbl.cnt+1,
hll_userid=
case tbl.cnt
when 1 then hll_add(hll_add(tbl.hll_userid, hll_hash_integer(tbl.userid)), hll_hash_integer(excluded.userid))
else hll_add(tbl.hll_userid, hll_hash_integer(excluded.userid))
end ;
压测，18万TPS。
vi test.sql
\set grpid random(1,1000000)
\set userid random(1,1000000000)
insert into tbl (grpid, userid, dt) values (:grpid,:userid,'2017-11-24') on conflict (grpid, dt) do update set cnt=tbl.cnt+1, hll_userid=case tbl.cnt when 1 then hll_add(hll_add(tbl.hll_userid, hll_hash_integer(tbl.userid)), hll_hash_integer(excluded.userid)) else hll_add(tbl.hll_userid, hll_hash_integer(excluded.userid)) end ;
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: 21713334
latency average = 0.155 ms
latency stddev = 0.071 ms
tps = 180938.313421 (including connections establishing)
tps = 180959.906404 (excluding connections establishing)
script statistics:
- statement latencies in milliseconds:
0.001 \set grpid random(1,1000000)
0.000 \set userid random(1,1000000000)
0.153 insert into tbl (grpid, userid, dt) values (:grpid,:userid,'2017-11-24') on conflict (grpid, dt) do update set cnt=tbl.cnt+1, hll_userid= case tbl.cnt when 1 then hll_add(hll_add(tbl.hll_userid, hll_hash_integer(tbl.userid)), hll_hash_integer(excluded.userid)) else hll_add(tbl.hll_userid, hll_hash_integer(excluded.userid)) end ;
通过hyperloglog类型查询UV估值如下，靠谱：
postgres=# select * from tbl limit 10;
grpid | userid | dt | cnt | hll_userid
--------+-----------+------------+-----+-------------------------------------------------------------------------------------------------------------------
71741 | 197976232 | 2017-11-24 | 5 | \x128b7fd534b8dfe5a72bbedd5b6c577ce9fb9fef7835561513628850f173084507f0bd7ed996166036a970
801374 | 373207765 | 2017-11-24 | 3 | \x128b7f1dd66eba7e70d9c550284e6d9870994f5f5b52f71f224d6e
565216 | 502576520 | 2017-11-24 | 7 | \x128b7f9c4eb2a37de228d8b959a3eb6875033eb9e5dae4c7a7a873037cc095c3f7b01506556992f5aeee9c2a29d4eeb4db71f92ce501619432a864
35036 | 868953081 | 2017-11-24 | 10 | \x128b7fa2249c2c7ca51016c477335c6c4e539dd369dd2ea9ab587ce6e3c3c88019dfc33361f5e97ab2db9e3475e0afefc5dc84547c9cc650d2c3ae61b7772ff8a3b36b63bfef7de0eff9f779d598d341edae11
950403 | 122708335 | 2017-11-24 | 9 | \x128b7fbb52bc26a18960fec0e5ef0b5d38015dc59f0bad2126d34ce0f19952682a1359257a39cb05a02cf0437f98ce664da1094e8173f33cc1df79547c86939e25bc096179d0a0cfe98b5c
173872 | 321068334 | 2017-11-24 | 7 | \x128b7fab5e34d66f513600c19356d876f80d37f13d28f4efc2d6ae0974487c0aa3f5e509affd49827908d35b7c4b009f57ff6376be2b1ea27b1204
786334 | 501502479 | 2017-11-24 | 5 | \x128b7f8b5e2d419433c147df779ac0ab34b25a060ecbdd5a896ee229a5ad32a00a060d516c141199609d3f
960665 | 855235921 | 2017-11-24 | 7 | \x128b7f95b32567416b5750ecb0c44a76480566f1d98aa6632a3ceeffe5dd8b8de96ffc2447dd5d74e20e993b38a6b242f2c78c678b60d542d68949
61741 | 945239318 | 2017-11-24 | 6 | \x128b7f885766f21f40b6b5b3783e764d90fd28c10af4a996cb5dcec8ea749905d0c5cb1de8b191b4f9e6775d597c247710ab71
postgres=# select grpid,userid,cnt,hll_cardinality(hll_userid) from tbl limit 10;
grpid | userid | cnt | hll_cardinality
--------+-----------+-----+-----------------
775333 | 642518584 | 13 | 13
17670 | 542792727 | 11 | 11
30079 | 311255630 | 14 | 14
61741 | 945239318 | 10 | 10
808051 | 422418318 | 14 | 14
620850 | 461130760 | 12 | 12
256591 | 415325936 | 15 | 15
801374 | 373207765 | 9 | 9
314023 | 553568037 | 12 | 12
HLL插件的知识参考如下:
https://github.com/aggregateknowledge/postgresql-hll
《Greenplum 最佳实践 - 估值插件hll的使用(以及hll分式聚合函数优化)》
《PostgreSQL hll (HyperLogLog) extension for "State of The Art Cardinality Estimation Algorithm" - 3》
《PostgreSQL hll (HyperLogLog) extension for "State of The Art Cardinality Estimation Algorithm" - 2》
《PostgreSQL hll (HyperLogLog) extension for "State of The Art Cardinality Estimation Algorithm" - 1》
结合UDF功能，简化流计算SQL复杂度
结合PostgreSQL的UDF功能，可以大大的简化SQL的复杂度
例子
create or replace function func1(int, int, date) returns void as $$
declare
begin
insert into tbl (grpid, userid, dt) values ($1,$2,$3) on conflict (grpid, dt)
do update set
cnt=tbl.cnt+1,
hll_userid=
case tbl.cnt
when 1
then hll_add(hll_add(tbl.hll_userid, hll_hash_integer(tbl.userid)), hll_hash_integer(excluded.userid))
else
hll_add(tbl.hll_userid, hll_hash_integer(excluded.userid))
end ;
end;
$$ language plpgsql strict;
使用函数接口写入，不用拼写长长的SQL。
LOG+实时计算设计
如果要记录明细，同时要实时统计。可以使用rule进行设计。
create or replace rule R1 AS on INSERT TO log_table do also XXXXXXXXXXXXXXX;
如果是增量写，可以通过where来过滤不需要的写入（位点）。
create or replace rule R1 AS on INSERT TO log_table WHERE (位点条件,如 id>10000000) do also XXXXXXXXXXXXXXX;
这种方法，数据既写log_table，同时又会执行流式统计XXXXX COMMAND。
同类需求和案例
《PostgreSQL 异步消息实践 - Feed系统实时监测与响应(如 电商主动服务) - 分钟级到毫秒级的实现》
《HTAP数据库 PostgreSQL 场景与性能测试之 27 - (OLTP) 物联网 - FEED日志, 流式处理 与 阅后即焚 (CTE)》
《HTAP数据库 PostgreSQL 场景与性能测试之 22 - (OLTP) merge insert|upsert|insert on conflict|合并写入》
《HTAP数据库 PostgreSQL 场景与性能测试之 32 - (OLTP) 高吞吐数据进出(堆存、行扫、无需索引) - 阅后即焚(JSON + 函数流式计算)》
《HTAP数据库 PostgreSQL 场景与性能测试之 31 - (OLTP) 高吞吐数据进出(堆存、行扫、无需索引) - 阅后即焚(读写大吞吐并测)》
《打造云端流计算、在线业务、数据分析的业务数据闭环 - 阿里云RDS、HybridDB for PostgreSQL最佳实践》
