(流式、lambda、触发器)实时处理大比拼 - 物联网(IoT)\金融,时序处理最佳实践

背景

越来越多的数据要求实时的分析、聚合、展示最新值、展示异常值、实时的搜索。

例如金融数据、物联网传感器的数据、网络游戏的在线数据等等。

关于实时搜索，可以参考这篇最佳实践：

《行为、审计日志实时索引/实时搜索 - 最佳实践》

关于海量数据的"写入、共享、存储、计算"，以及离线分析，则可以参考这篇最佳实践：

《海量数据 "写入、共享、存储、计算" - 最佳实践》

关于实时分析、实时更新、实时聚合、实时展示最新值、异常值，是本文的主要内容。

提起实时分析，不得不说流式计算，用户可以参考本文：

《流计算风云再起 - PostgreSQL携PipelineDB力挺IoT》

pipelinedb是一个SQL接口的流计算数据库，正在进行插件化的改造，未来可以作为PostgreSQL数据库的插件使用。

本文将以传感器数据的实时写入、实时更新最新值、实时统计为例，分析三种不同的方案（流式、lambda式、同步实时）的优缺点。

场景设计

有一百万个传感器，每个传感器定期上报数据，用户需求：

1. 实时的查看传感器的最新值，

2. 实时按时间段查看传感器历史数据的统计值。

3. 实时查看传感器的历史明细数据。

4. 实时按其他维度查看传感器历史数据的统计值。

由于数据量可能非常庞大（100TB级），为了实现这4个需求，要求统计数据需要实时或准实时的被计算出来。

表结构设计

明细数据

create table sensor_data(    pk serial8 primary key, -- 主键    ts timestamp,  -- 时间戳    sid int,  -- 传感器ID    val numeric(10,2)  -- 数据
);

实时聚合设计

1. 每个传感器最后的value

create table sensor_lastdata(    sid int primary key,  -- 传感器ID，主键    last_ts timestamp,  -- 时间戳    last_val numeric(10,2)  -- 值
);

2. 每个传感器每个时段（例如小时）的所有值，总和，记录数，最大值，最小值，平均值，方差。

create table sensor_aggdata(    sid int,  -- 传感器ID    ts_group varchar(10),  -- 时间维度分组，例如小时(yyyymmddhh24)    sum_val numeric,  -- 和    min_val numeric(10,2),  -- 最小值    max_val numeric(10,2),  -- 最大值    avg_val numeric(10,2),  -- 平均值    count_val int,  -- 计数    all_vals numeric(10,2)[],  -- 明细值    unique (sid,ts_group)  -- 唯一约束
);

3. 按地域或其他维度，实时统计传感器上报的数据

略

如何从明细数据取传感器的最新值

取出每个传感器ID的最新值。使用SQL来取，有两种方法，一种是聚合，另一种是窗口函数。

插入一批测试数据

postgres=#  insert into sensor_data(ts,sid,val) select clock_timestamp(), random()*100, random()*10000 from generate_series(1,100000);

方法1，聚合。

按SID分组，将VAL聚合为数组（按PK逆序排序），取数组的第一个VALUE。

参考用法：https://www.postgresql.org/docs/9.6/static/functions-aggregate.html

postgres=#  select sid, (array_agg(ts order by pk desc))[1] as last_ts, (array_agg(val order by pk desc))[1] as last_val from sensor_data group by sid;    sid |          last_ts           | last_val
-----+----------------------------+----------    0 | 2017-05-18 14:09:10.625812 |  6480.54    1 | 2017-05-18 14:09:10.627607 |  9644.29    2 | 2017-05-18 14:09:10.627951 |  3995.04    3 | 2017-05-18 14:09:10.627466 |   840.80    4 | 2017-05-18 14:09:10.627703 |  1500.59    5 | 2017-05-18 14:09:10.627813 |  3109.42    6 | 2017-05-18 14:09:10.62754  |  4131.31    7 | 2017-05-18 14:09:10.627851 |  9333.88
......

方法2，窗口。

postgres=# select sid,ts,val from (select sid,ts,val,row_number() over(partition by sid order by pk desc) as rn from sensor_data) t where rn=1;    sid |             ts             |   val
-----+----------------------------+---------    0 | 2017-05-18 14:09:10.625812 | 6480.54    1 | 2017-05-18 14:09:10.627607 | 9644.29    2 | 2017-05-18 14:09:10.627951 | 3995.04    3 | 2017-05-18 14:09:10.627466 |  840.80    4 | 2017-05-18 14:09:10.627703 | 1500.59    5 | 2017-05-18 14:09:10.627813 | 3109.42    6 | 2017-05-18 14:09:10.62754  | 4131.31    7 | 2017-05-18 14:09:10.627851 | 9333.88
......

这两种方法哪种好一点呢？请看执行计划

postgres=# set work_mem ='16MB';
SET
postgres=# explain (analyze,verbose,timing,costs,buffers) select sid, (array_agg(ts order by pk desc))[1] as last_ts, (array_agg(val order by pk desc))[1] as last_val from sensor_data group by sid;    QUERY PLAN
------------------------------------------------------------------------------------------------------------------------------------    GroupAggregate  (cost=7117.15..7823.57 rows=101 width=44) (actual time=29.628..88.095 rows=101 loops=1)    Output: sid, (array_agg(ts ORDER BY pk DESC))[1], (array_agg(val ORDER BY pk DESC))[1]    Group Key: sensor_data.sid    Buffers: shared hit=736    ->  Sort  (cost=7117.15..7293.38 rows=70490 width=26) (actual time=29.273..36.249 rows=70490 loops=1)    Output: sid, ts, pk, val    Sort Key: sensor_data.sid    Sort Method: quicksort  Memory: 8580kB    Buffers: shared hit=736    ->  Seq Scan on public.sensor_data  (cost=0.00..1440.90 rows=70490 width=26) (actual time=0.243..9.768 rows=70490 loops=1)    Output: sid, ts, pk, val    Buffers: shared hit=736    Planning time: 0.077 ms    Execution time: 88.489 ms
(14 rows)    postgres=# explain (analyze,verbose,timing,costs,buffers) select sid,ts,val from (select sid,ts,val,row_number() over(partition by sid order by pk desc) as rn from sensor_data) t where rn=1;    QUERY PLAN
------------------------------------------------------------------------------------------------------------------------------------------    Subquery Scan on t  (cost=7117.15..9408.08 rows=352 width=18) (actual time=46.074..81.377 rows=101 loops=1)    Output: t.sid, t.ts, t.val    Filter: (t.rn = 1)    Rows Removed by Filter: 70389    Buffers: shared hit=736    ->  WindowAgg  (cost=7117.15..8526.95 rows=70490 width=34) (actual time=46.072..76.115 rows=70490 loops=1)    Output: sensor_data.sid, sensor_data.ts, sensor_data.val, row_number() OVER (?), sensor_data.pk    Buffers: shared hit=736    ->  Sort  (cost=7117.15..7293.38 rows=70490 width=26) (actual time=46.065..51.742 rows=70490 loops=1)    Output: sensor_data.sid, sensor_data.pk, sensor_data.ts, sensor_data.val    Sort Key: sensor_data.sid, sensor_data.pk DESC    Sort Method: quicksort  Memory: 8580kB    Buffers: shared hit=736    ->  Seq Scan on public.sensor_data  (cost=0.00..1440.90 rows=70490 width=26) (actual time=0.245..9.863 rows=70490 loops=1)    Output: sensor_data.sid, sensor_data.pk, sensor_data.ts, sensor_data.val    Buffers: shared hit=736    Planning time: 0.100 ms    Execution time: 82.480 ms
(18 rows)

实时更新、统计 - 设计与压测

1. lambda

lambda方式，传感器数据写入明细表，以任务调度的方式，从明细表取出数据并删除，将取出的数据进行增量统计，合并到统计结果中。

统计维度可能较多，为了并行，剥离数据获取和删除部分的功能。

批量获取并删除明细数据，按pk排序，批量获取若干条。

函数如下：

create or replace function get_sensor_data(i_limit int) returns sensor_data[] as $$
declare    arr_pk int8[];    arr_sensor_data sensor_data[];
begin    select array_agg(t.sensor_data), array_agg((t.sensor_data).pk)    into arr_sensor_data, arr_pk    from (select sensor_data from sensor_data order by pk limit i_limit for update skip locked) t ;    delete from sensor_data WHERE pk = any (arr_pk);    return arr_sensor_data;
end;
$$ language plpgsql strict;

明细数据获取到之后，继续下一步的动作。

存在则更新，不存在则插入，采用PostgreSQL的insert on conflict语法。

1. 实时更新传感器的最新值

insert into sensor_lastdata    select sid, (array_agg(ts order by pk desc))[1] as last_ts, (array_agg(val order by pk desc))[1] as last_val from     unnest(get_sensor_data(1000))     group by sid
on conflict (sid) do update set last_ts=excluded.last_ts,last_val=excluded.last_val;

2. 批量增量统计传感器的值

统计值的合并方法请关注SQL内容，明细数据按SID聚合为数组按PK顺序存放。

insert into sensor_aggdata (sid,ts_group,sum_val,min_val,max_val,avg_val,count_val,all_vals)
select sid,to_char(ts,'yyyymmddhh24'),sum(val),min(val),max(val),avg(val),count(val),array_agg(val order by pk) from unnest(get_sensor_data(1000))     group by sid,to_char(ts,'yyyymmddhh24')    on conflict (sid,ts_group) do update set     sum_val=sensor_aggdata.sum_val+excluded.sum_val,    min_val=least(sensor_aggdata.min_val, excluded.min_val),    max_val=greatest(sensor_aggdata.max_val, excluded.max_val),    avg_val=(sensor_aggdata.sum_val+excluded.sum_val)/(sensor_aggdata.count_val+excluded.count_val),    count_val=sensor_aggdata.count_val+excluded.count_val,    all_vals=array_cat(sensor_aggdata.all_vals, excluded.all_vals);

压测

create table sensor_data(    pk serial8 primary key, -- 主键    ts timestamp,  -- 时间戳    sid int,  -- 传感器ID    val numeric(10,2)  -- 数据
);    create table sensor_lastdata(    sid int primary key,  -- 传感器ID，主键    last_ts timestamp,  -- 时间戳    last_val numeric(10,2)  -- 值
);    create table sensor_aggdata(    sid int,  -- 传感器ID    ts_group varchar(10),  -- 时间维度分组，例如小时(yyyymmddhh24)    sum_val numeric,  -- 和    min_val numeric(10,2),  -- 最小值    max_val numeric(10,2),  -- 最大值    avg_val numeric(10,2),  -- 平均值    count_val int,  -- 计数    all_vals numeric(10,2)[],  -- 明细值    unique (sid,ts_group)  -- 唯一约束
);

压测1，实时写入，并实时更新传感器的最新值

vi ins.sql
\set sid random(1,1000000)
insert into sensor_data(ts,sid,val) values (clock_timestamp(), :sid, random()*1000);

每次合并5万条

vi lambda1.sql
insert into sensor_lastdata select sid, (array_agg(ts order by pk desc))[1] as last_ts, (array_agg(val order by pk desc))[1] as last_val from unnest(get_sensor_data(50000)) group by sid on conflict (sid) do update set last_ts=excluded.last_ts,last_val=excluded.last_val;

写入约10万条/s。

pgbench -M prepared -n -r -P 1 -f ./ins.sql -c 64 -j 64 -T 120    transaction type: ./ins.sql
scaling factor: 1
query mode: prepared
number of clients: 64
number of threads: 64
duration: 120 s
number of transactions actually processed: 12742596
latency average = 0.603 ms
latency stddev = 2.163 ms
tps = 106184.095420 (including connections establishing)
tps = 106188.650794 (excluding connections establishing)
script statistics:    - statement latencies in milliseconds:    0.001  \set sid random(1,1000000)    0.602  insert into sensor_data(ts,sid,val) values (clock_timestamp(), :sid, random()*1000);

增量消费，并更新最新值约5万条/s。

pgbench -M prepared -n -r -P 1 -f ./lambda1.sql -c 1 -j 1 -T 1200    progress: 236.0 s, 1.0 tps, lat 649.196 ms stddev 0.000
progress: 237.0 s, 2.0 tps, lat 868.952 ms stddev 6.024
progress: 238.0 s, 1.0 tps, lat 728.553 ms stddev 0.000
progress: 239.0 s, 258.1 tps, lat 5.335 ms stddev 44.167
progress: 240.0 s, 850.9 tps, lat 0.983 ms stddev 14.506
progress: 241.0 s, 7962.2 tps, lat 0.146 ms stddev 3.672
progress: 242.0 s, 13488.1 tps, lat 0.074 ms stddev 0.006    postgres=# select count(*) from sensor_data;    count
-------    0
(1 row)    postgres=# select * from sensor_lastdata  limit 10;    sid  |          last_ts           | last_val
------+----------------------------+----------    672 | 2017-05-18 16:33:43.569255 |   196.01    178 | 2017-05-18 16:33:31.23651  |   593.16    686 | 2017-05-18 16:33:38.792138 |   762.95    4906 | 2017-05-18 16:33:43.498217 |   150.13    544 | 2017-05-18 16:33:45.338635 |   410.31    165 | 2017-05-18 16:33:28.393902 |   678.75    625 | 2017-05-18 16:33:37.077898 |   229.06    1316 | 2017-05-18 16:33:45.218268 |    27.55    3091 | 2017-05-18 16:33:33.320828 |   697.75    340 | 2017-05-18 16:33:31.567852 |    24.18
(10 rows)

每批统计10万时，性能可以略微提升

progress: 211.0 s, 1.0 tps, lat 1428.401 ms stddev 0.000
progress: 212.0 s, 0.0 tps, lat -nan ms stddev -nan
progress: 213.0 s, 1.0 tps, lat 1375.766 ms stddev 0.000
progress: 214.0 s, 2665.9 tps, lat 0.699 ms stddev 23.234
progress: 215.0 s, 8963.1 tps, lat 0.083 ms stddev 0.008
progress: 216.0 s, 1699.4 tps, lat 0.741 ms stddev 12.434
progress: 217.0 s, 13247.9 tps, lat 0.075 ms stddev 0.006

压测2，实时写入，并批量增量统计传感器的值

每次合并10万条

vi lambda2.sql
insert into sensor_aggdata (sid,ts_group,sum_val,min_val,max_val,avg_val,count_val,all_vals) select sid,to_char(ts,'yyyymmddhh24'),sum(val),min(val),max(val),avg(val),count(val),array_agg(val order by pk) from unnest(get_sensor_data(100000))   group by sid,to_char(ts,'yyyymmddhh24')  on conflict (sid,ts_group) do update set     sum_val=sensor_aggdata.sum_val+excluded.sum_val,    min_val=least(sensor_aggdata.min_val, excluded.min_val),    max_val=greatest(sensor_aggdata.max_val, excluded.max_val),    avg_val=(sensor_aggdata.sum_val+excluded.sum_val)/(sensor_aggdata.count_val+excluded.count_val),    count_val=sensor_aggdata.count_val+excluded.count_val,    all_vals=array_cat(sensor_aggdata.all_vals, excluded.all_vals);

写入约10万条/s。

pgbench -M prepared -n -r -P 1 -f ./ins.sql -c 64 -j 64 -T 120    transaction type: ./ins.sql
scaling factor: 1
query mode: prepared
number of clients: 64
number of threads: 64
duration: 120 s
number of transactions actually processed: 12753950
latency average = 0.602 ms
latency stddev = 2.733 ms
tps = 106272.985233 (including connections establishing)
tps = 106277.604416 (excluding connections establishing)
script statistics:    - statement latencies in milliseconds:    0.001  \set sid random(1,1000000)    0.601  insert into sensor_data(ts,sid,val) values (clock_timestamp(), :sid, random()*1000);

增量消费，并统计约4.4万条/s。

pgbench -M prepared -n -r -P 1 -f ./lambda2.sql -c 1 -j 1 -T 1200    progress: 287.0 s, 1.0 tps, lat 2107.584 ms stddev 0.000
progress: 288.0 s, 0.0 tps, lat -nan ms stddev -nan
progress: 289.0 s, 100.1 tps, lat 29.854 ms stddev 213.634
progress: 290.0 s, 1855.0 tps, lat 0.540 ms stddev 5.677
progress: 291.0 s, 8447.0 tps, lat 0.118 ms stddev 0.005    postgres=# select * from sensor_aggdata limit 10;    sid   |  ts_group  | sum_val  | min_val | max_val | avg_val | count_val |                                                                      all_vals
--------+------------+----------+---------+---------+---------+-----------+--------------------------------------------------------------------------------  6 | 2017051816 |  1842.71 |   42.47 |  577.09 |  307.12 |         6 | {42.47,559.47,577.09,193.62,75.74,394.32}    2 | 2017051816 |  5254.01 |   69.98 |  861.77 |  437.83 |        12 | {628.03,77.15,662.74,69.98,337.83,563.70,750.44,423.81,158.27,861.77,649.27,71.02}    226 | 2017051816 |  2756.42 |  144.00 |  680.45 |  344.55 |         8 | {350.57,144.00,194.23,352.52,680.45,302.66,420.01,311.98}    509 | 2017051816 |  6235.10 |   44.98 |  939.43 |  566.83 |        11 | {939.43,598.33,741.12,535.66,44.98,732.00,694.66,440.00,327.80,312.98,868.14}    20 | 2017051816 |  4684.00 |    7.01 |  878.64 |  425.82 |        11 | {209.70,288.67,76.35,544.31,289.33,7.01,841.21,878.64,418.05,651.01,479.72}    934042 | 2017051816 | 10210.41 |   46.44 |  945.59 |  486.21 |        21 | {235.86,656.24,450.73,945.59,932.06,256.10,46.44,903.74,694.43,713.79,523.25,325.82,333.67,603.01,743.63,137.48,238.60,321.65,466.50,70.49,611.33}   960 | 2017051816 |  3621.60 |   20.59 |  895.01 |  603.60 |         6 | {347.70,876.07,895.01,20.59,871.64,610.59}    81 | 2017051816 |  4209.38 |  459.06 |  949.42 |  701.56 |         6 | {716.38,949.42,706.20,459.06,613.36,764.96}    723065 | 2017051816 |  7176.00 |   12.37 |  983.84 |  512.57 |        14 | {869.29,715.48,323.42,595.29,983.84,700.06,716.37,741.55,137.88,12.37,334.74,951.94,46.85,46.92}    77 | 2017051816 |  5394.54 |   87.43 |  872.90 |  490.41 |        11 | {301.87,777.52,872.90,219.96,87.43,525.80,308.87,509.80,383.90,608.52,797.97}
(10 rows)

2. 流式计算

流式计算，使用PipelineDB，创建stream（即明细表），然后创建实时更新表，以及统计表。

1. 创建传感器明细数据stream。

create sequence seq;  -- 创建PK序列    pipeline=# create stream sensor_data(
pk int8, -- 用于排序，取最新值的PK
ts timestamp, -- 时间戳
sid int, -- 传感器ID
val numeric(10,2)  -- 值
);    CREATE STREAM

2. 创建实时更新传感器最新值的CONTINUOUS VIEW

请使用pipelinedb独有的获取分组最新值的聚合函数

keyed_max ( key, value )  Returns the value associated with the “highest” key.
keyed_min ( key, value )  Returns the value associated with the “lowest” key.

请勿使用(array_agg(ts order by pk desc))[1]，pipelinedb对agg(order by)支持不好

-- pipelinedb目前对agg(order by)支持不佳，测试写入时报错
CREATE CONTINUOUS VIEW sensor_lastdata1 AS     select sid, (array_agg(ts order by pk desc))[1] as last_ts, (array_agg(val order by pk desc))[1] as last_val     from  sensor_data    group by sid;    -- 1. 请使用这个SQL代替上面的SQL
CREATE CONTINUOUS VIEW sensor_lastdata1 AS     select sid, keyed_max(pk, ts) as last_ts, keyed_max(pk, val) as last_val     from  sensor_data    group by sid;    -- pipelinedb目前不支持window function，使用keyed_max, keyed_min代替。
CREATE CONTINUOUS VIEW sensor_lastdata2 AS     select sid,ts as last_ts,val as last_val from sensor_data    where row_number() over(partition by sid order by pk desc)=1;    ERROR:  subqueries in continuous views cannot contain window functions

3. 创建实时统计传感器数值，以及明细聚合的CONTINUOUS VIEW

-- pipelinedb目前对agg(order by)支持不佳，测试写入时报错
CREATE CONTINUOUS VIEW sensor_aggdata1 AS     select     sid,    to_char(ts,'yyyymmddhh24') as ts_group,    sum(val) as sum_val,    min(val) as min_val,    max(val) as max_val,    avg(val) as avg_val,    count(val) as count_val,    array_agg(val order by pk) as all_vals    from sensor_data    group by sid,to_char(ts,'yyyymmddhh24');    -- 2. 请使用这个SQL代替上面的SQL
CREATE CONTINUOUS VIEW sensor_aggdata1 AS     select     sid,    to_char(ts,'yyyymmddhh24') as ts_group,    sum(val) as sum_val,    min(val) as min_val,    max(val) as max_val,    avg(val) as avg_val,    count(val) as count_val,    jsonb_object_agg (pk, val) as all_vals    from sensor_data    group by sid,to_char(ts,'yyyymmddhh24');

4. 激活CONTINUOUS VIEW

pipeline=# activate sensor_lastdata1;
ACTIVATE
pipeline=# activate sensor_aggdata1;
ACTIVATE

压测

vi ins.sql    \set sid random(1,1000000)
insert into sensor_data(pk,ts,sid,val) values (nextval('seq'), clock_timestamp(), :sid, random()*1000);

pipelinedb目前对agg(order by)支持不佳的报错，如果你没有使用替代SQL，会收到如下报错。

/home/digoal/pgsql10/bin/pgbench -M prepared -n -r -P 1 -f ./ins.sql -c 1 -j 1 -T 100    progress: 1.0 s, 12.0 tps, lat 1.302 ms stddev 0.455
WARNING:  a background worker crashed while processing this batch
HINT:  Some of the tuples inserted in this batch might have been lost.
progress: 2.0 s, 16.0 tps, lat 70.528 ms stddev 253.719
WARNING:  a background worker crashed while processing this batch
HINT:  Some of the tuples inserted in this batch might have been lost.
WARNING:  a background worker crashed while processing this batch
HINT:  Some of the tuples inserted in this batch might have been lost.
WARNING:  a background worker crashed while processing this batch
HINT:  Some of the tuples inserted in this batch might have been lost.

使用替代SQL，压测结果:

1. 聚合values，压测结果：

写入速度12.7万/s。

/home/digoal/pgsql10/bin/pgbench -M prepared -n -r -P 1 -f ./ins.sql -c 256 -j 256 -T 100    transaction type: ./ins.sql
scaling factor: 1
query mode: prepared
number of clients: 256
number of threads: 256
duration: 100 s
number of transactions actually processed: 12840629
latency average = 1.994 ms
latency stddev = 14.671 ms
tps = 127857.131372 (including connections establishing)
tps = 127864.890658 (excluding connections establishing)
script statistics:  - statement latencies in milliseconds:  0.001  \set sid random(1,1000000)    1.997  insert into sensor_data(pk,ts,sid,val) values (nextval('seq'), clock_timestamp(), :sid, random()*1000);

pipeline=# select * from sensor_aggdata1 limit 10;
-[ RECORD 1 ]----------------------------------------------------------------------------------------------------------------------------------------------
sid       | 444427
ts_group  | 2017052410
sum_val   | 4902.07
min_val   | 18.69
max_val   | 980.26
avg_val   | 445.6427272727272727
count_val | 11
all_vals  | {"41971591": 731.45, "42075280": 69.63, "42629210": 980.26, "45243895": 18.69, "45524545": 320.88, "46971341": 741.88, "47036195": 357.47, "47895869": 562.16, "49805560": 136.78, "51753795": 344.00, "53039367": 638.87}

2. 不聚合VALUES，压测结果：

写入速度20万/s。

CREATE CONTINUOUS VIEW sensor_aggdata2 AS     select     sid,    to_char(ts,'yyyymmddhh24') as ts_group,    sum(val) as sum_val,    min(val) as min_val,    max(val) as max_val,    avg(val) as avg_val,    count(val) as count_val    -- jsonb_object_agg (pk, val) as all_vals    from sensor_data    group by sid,to_char(ts,'yyyymmddhh24');

/home/digoal/pgsql10/bin/pgbench -M prepared -n -r -P 1 -f ./ins.sql -c 256 -j 256 -T 100    transaction type: ./ins.sql
scaling factor: 1
query mode: prepared
number of clients: 256
number of threads: 256
duration: 100 s
number of transactions actually processed: 20940292
latency average = 1.222 ms
latency stddev = 0.423 ms
tps = 208834.531839 (including connections establishing)
tps = 208854.792937 (excluding connections establishing)
script statistics:    - statement latencies in milliseconds:    0.001  \set sid random(1,1000000)    1.222  insert into sensor_data(pk,ts,sid,val) values (nextval('seq'), clock_timestamp(), :sid, random()*1000);    pipeline=# select * from sensor_aggdata2;    sid  |  ts_group  |   sum_val   | min_val | max_val |       avg_val        | count_val
------+------------+-------------+---------+---------+----------------------+-----------    196 | 2017051815 | 11462397.00 |    0.00 |  999.99 | 503.1780948200175593 |     22780    833 | 2017051815 | 11479990.49 |    0.07 |  999.99 | 498.4365443730461966 |     23032    700 | 2017051815 | 11205820.52 |    0.04 |  999.97 | 497.1967574762623125 |     22538    83 | 2017051815 | 11466423.01 |    0.01 |  999.93 | 501.3959075604530150 |     22869    526 | 2017051815 | 11389541.40 |    0.01 |  999.99 | 503.4496485877204615 |     22623    996 | 2017051815 | 11416373.92 |    0.03 |  999.99 | 502.1938996172964413 |     22733    262 | 2017051815 | 11458700.05 |    0.03 |  999.98 | 499.5509656465254163 |     22938    542 | 2017051815 | 11365373.33 |    0.00 |  999.95 | 499.6427366246098387 |     22747
......

3. 实时

实时的写入明细，同步更新最终状态。

（同步统计不推荐使用，对写入的RT性能影响比较大）

实时更新传感器最终状态表

create table sensor_lastdata(    sid int primary key,    last_ts timestamp,    last_val numeric(10,2)
);

压测1，更新传感器实时状态

vi ins.sql    \set sid random(1,1000000)
insert into sensor_lastdata values (:sid, now(), random()*1000) on conflict (sid) do update set last_ts=excluded.last_ts,last_val=excluded.last_val;

性能，约18万/s。

/home/digoal/pgsql10/bin/pgbench -M prepared -n -r -P 1 -f ./ins.sql -c 128 -j 128 -T 100    transaction type: ./ins.sql
scaling factor: 1
query mode: prepared
number of clients: 128
number of threads: 128
duration: 100 s
number of transactions actually processed: 18659587
latency average = 0.686 ms
latency stddev = 2.566 ms
tps = 186557.140033 (including connections establishing)
tps = 186565.458460 (excluding connections establishing)
script statistics:    - statement latencies in milliseconds:    0.001  \set sid random(1,1000000)    0.684  insert into sensor_lastdata values (:sid, now(), random()*1000) on conflict (sid) do update set last_ts=excluded.last_ts,last_val=excluded.last_val;

三种方案对比

性能对比

1. 写入明细记录的速度

lambda方式：10.6万/s

流计算方式(含val明细聚合)：12.78万/s

流计算方式(不含val明细聚合)：20.8万/s

2. 更新最终状态速度

lambda方式：5.98万/s

实时方式：18.6万/s

流计算方式：20.8万/s

3. 统计速度

lambda方式(含val明细聚合)：4.4万/s

流计算方式(含val明细聚合)：12.78万/s

流计算方式(不含val明细聚合)：20.8万/s

优劣与适用场合对比

1. lambda方式

性能中规中矩，通过UDF + 增量调度，支持所有的统计模式。

目前这个方案有成熟的用户案例(某大数据平台)，支持了每天数TB的数据准实时统计。

同时也期待PG社区开发这样的功能：

delete from table order by pk limit xxx skip locked returning array_agg(ts),array_agg(val) group by sid;

这种QUERY将以最小的开销，从数据中删除并返回一批记录。相比本例，也许能提升一倍性能。

2. 流计算方式

性能最高，使用也便利，推荐使用。

将来pipelinedb插件化之后，使用起来就更加方便了。

3. 实时方式

如果只是用来更新最终状态，建议使用，开发工作量最少，不需要调度。

参考

《PostgreSQL upsert功能(insert on conflict do)的用法》

《PostgreSQL 如何实现upsert与新旧数据自动分离》

《[转载]postgresql 9.5版本之前实现upsert功能》

《流计算风云再起 - PostgreSQL携PipelineDB力挺IoT》

《行为、审计日志实时索引/实时搜索 - 最佳实践》

《海量数据 "写入、共享、存储、计算" - 最佳实践》

http://docs.pipelinedb.com/streams.html