无序uuid对数据库的影响

由于最近在做超大表的性能测试，在该过程中发现了无序uuid做主键对表插入性能有一定影响。结合实际情况发现当表的数据量越大，对表插入性能的影响也就越大。

测试环境

PostgreSQL创建插入脚本，测试各种情况的tps。

数据库版本：PostgreSQL 10.4 (ArteryBase 5.0.0, Thunisoft)

操作系统配置：CentOS Linux release 7 ，32GB内存，8 cpu

测试参数：pgbench -M prepared -r -n -j 8 -c 8 -T 60 -f /opt/thunisoft/pgbench_uuid_v4.sql -U sa pgbenchdb

空表，1000w数据，5000w数据，一亿数据的各种主键测试。

测试无序的uuid，有序的uuid，序列，有普通btree，有唯一索引和没有主键的情况

测试

1.创建表

--无序的uuid
pgbenchdb=# create table test_uuid_v4(id char(32) primary key);
CREATE TABLE
--有序的uuid
pgbenchdb=# create table test_time_nextval(id char(32) primary key);
CREATE TABLE
--递增序列
pgbenchdb=# create table test_seq_bigint(id int8 primary key);
CREATE TABLE
--创建序列
 create sequence test_seq start with 1 ;

2.测试脚本

--测试无序uuid脚本
vi pgbench_uuid_v4.sql
insert into test_uuid_v4 (id) values (replace(uuid_generate_v4()::text,'-',''));
--测试有序uuid脚本
vi pgbench_time_nextval.sql
insert into test_time_nextval (id) values (replace(uuid_time_nextval()::text,'-',''));
--测试序列脚本
vi pgbench_seq_bigint.sql
insert into test_seq_bigint (id) values (nextval('test_seq'::regclass));

无序uuid,无数据情况

磁盘使用情况
avg-cpu:  %user   %nice %system %iowait  %steal   %idle
           0.76    0.00    0.38    4.67    0.00   94.19

Device:         rrqm/s   wrqm/s     r/s     w/s    rkB/s    wkB/s avgrq-sz avgqu-sz   await r_await w_await  svctm  %util
sdb               0.00     0.00    0.00    0.00     0.00     0.00     0.00     0.00    0.00    0.00    0.00   0.00   0.00
sda               0.00     0.00    0.00   96.00     0.00  2048.00    42.67     1.02   10.67    0.00   10.67  10.33  99.20
dm-0              0.00     0.00    0.00   96.00     0.00  2048.00    42.67     1.02   10.66    0.00   10.66  10.32  99.10
dm-1              0.00     0.00    0.00    0.00     0.00     0.00     0.00     0.00    0.00    0.00    0.00   0.00   0.00
dm-2              0.00     0.00    0.00    0.00     0.00     0.00     0.00     0.00    0.00    0.00    0.00   0.00   0.00

tps：
[thunisoft@localhost thunisoft]$ pgbench -M prepared -r -n -j 8 -c 8 -T 60 -f /opt/thunisoft/pgbench_uuid_v4.sql -U sa pgbenchdb 
transaction type: /opt/thunisoft/pgbench_uuid_v4.sql
scaling factor: 1
query mode: prepared
number of clients: 8
number of threads: 8
duration: 60 s
number of transactions actually processed: 53494
latency average = 8.974 ms
tps = 891.495404 (including connections establishing)
tps = 891.588967 (excluding connections establishing)
script statistics:
 - statement latencies in milliseconds:
         9.006  insert into test_uuid_v4 (id) values (replace(uuid_generate_v4()::text,'-',''));

无数据情况下，tps

       类别     |  第一次  | 第二次  | 第三次 | 平均值(tps) |%util |await
---------------+---------+---------+---------+---------+-------+-------
 无序uuid		  | 919  	| 907     |  891  |   906     | 99.2% | 10.66   
 有序uuid    	  | 985  	| 882     |  932  |   933     | 98.7% | 4.4
 序列    	      | 1311     | 1277    |  1280 |  1289     | 97.5% | 3.4 

向表里面初始化100w数据

pgbenchdb=# insert into test_uuid_v4 (id) select  replace(uuid_generate_v4()::text,'-','') from generate_series(1,1000000);
INSERT 0 1000000
Time: 43389.817 ms (00:43.390)
pgbenchdb=# insert into test_time_nextval (id) select replace(uuid_time_nextval()::text,'-','') from generate_series(1,1000000);
INSERT 0 1000000
Time: 30585.134 ms (00:30.585)
pgbenchdb=#  insert into test_seq_bigint select generate_series (1,1000000);
INSERT 0 1000000
Time: 9818.639 ms (00:09.819)
无序uuid插入100w需要43s，有序需要30s，序列需要10s。

插入一百万数据后的tps

       类别     |  第一次  | 第二次  | 第三次 | 平均值(tps) |%util |await
---------------+---------+---------+---------+---------+-------+-------
 无序uuid		  | 355  	| 440     |  302  |   365     | 98.8% | 13   
 有序uuid    	  | 948  	| 964     |  870  |   927     | 97.2% | 4.0
 序列    	      | 1159     | 1234    |  1115 |  1169     | 96.6% | 3.5 

插入一千万数据后的tps

       类别     |  第一次  | 第二次  | 第三次 | 平均值(tps) |%util |await
---------------+---------+---------+---------+---------+-------+-------
 无序uuid		  | 260  	| 292     |  227  |   260     | 99.2% | 16.8   
 有序uuid    	  | 817  	| 960     |  883  |   870     | 97.7% | 3.9
 序列       	   | 1305     | 1261    |  1270 |  1278     | 96.8% | 3.0 

插入五千万数据后

向表中插入5kw数据，并且添加主键
pgbenchdb=# insert into test_time_nextval (id) select replace(uuid_time_nextval()::text,'-','') from generate_series(1,50000000);
INSERT 0 50000000
Time: 453985.318 ms (07:33.985)
pgbenchdb=# insert into test_seq_bigint select generate_series (1,50000000);
INSERT 0 50000000
Time: 352206.160 ms (05:52.206)
pgbenchdb=# insert into test_uuid_v4 (id) select  replace(uuid_generate_v4()::text,'-','') from generate_series(1,50000000);
INSERT 0 50000000
Time: 1159689.338 ms (00:19:19.689)

在无主键情况下，插入五千万数据，有序uuid耗时7分钟，序列耗时6分钟，而无序uuid耗时接近20分钟。

pgbenchdb=# alter table test_uuid_v4 add primary key ("id");
ALTER TABLE
Time: 845199.296 ms (14:05.199)
pgbenchdb=# alter table test_time_nextval add primary key ("id");
ALTER TABLE
Time: 932151.103 ms (15:32.151)
pgbenchdb=# alter table test_seq_bigint add primary key ("id");
ALTER TABLE
Time: 148138.871 ms (02:28.139)

pgbenchdb=# select pg_size_pretty(pg_total_relation_size('test_uuid_v4'));
 pg_size_pretty 
----------------
 6072 MB
(1 row)

Time: 0.861 ms
pgbenchdb=#  select pg_size_pretty(pg_total_relation_size('test_time_nextval'));
 pg_size_pretty 
----------------
 6072 MB
(1 row)

Time: 0.942 ms
pgbenchdb=#  select pg_size_pretty(pg_total_relation_size('test_seq_bigint'));
 pg_size_pretty 
----------------
 2800 MB
(1 row)

Time: 0.699 ms

插入5kw后

       类别     |  第一次  | 第二次  | 第三次 | 平均值(tps) |%util |await
---------------+---------+---------+---------+---------+-------+-------
 无序uuid		  | 162  	| 163     |  163  |   163     | 99.6% | 18.4   
 有序uuid    	  | 738  	| 933     |  979  |   883     | 97.7% | 3.9
 序列         	 | 1132     | 1264    |  1265 |  1220     | 96.8% | 3.5 

插入1亿条数据后

       类别     |  第一次  | 第二次  | 第三次 | 平均值(tps) |%util |await
---------------+---------+---------+---------+---------+-------+-------
 无序uuid		  | 121  	| 131     |  143  |   131     | 99.6% | 28.2   
 有序uuid    	  | 819  	| 795     |  888  |   834     | 99.2% | 28.7
 序列      	    | 1193     | 1115    |  1109 |  1139     | 96.8% | 11.3

普通btree索引

上面测了无序uuid，1kw情况下，有主键的tps是260，无主键的tps是1234。尝试测试普通的索引，和唯一索引tps

--创建普通索引
pgbenchdb=# create index i_test_uuid_v4_id on test_uuid_v4(id);
CREATE INDEX
Time: 316367.010 ms (05:16.367)
--创建普通索引后
[thunisoft@localhost thunisoft]$ pgbench -M prepared -r -n -j 8 -c 8 -T 60 -f /opt/thunisoft/pgbench_uuid_v4.sql -U sa pgbenchdb 
transaction type: /opt/thunisoft/pgbench_uuid_v4.sql
scaling factor: 1
query mode: prepared
number of clients: 8
number of threads: 8
duration: 60 s
number of transactions actually processed: 13308
latency average = 36.080 ms
tps = 221.727391 (including connections establishing)
tps = 221.749660 (excluding connections establishing)
script statistics:
 - statement latencies in milliseconds:
        38.512  insert into test_uuid_v4 (id) values (replace(uuid_generate_v4()::text,'-',''));
--创建唯一索引
pgbenchdb=# drop index i_test_uuid_v4_id;
DROP INDEX
Time: 267.451 ms
pgbenchdb=# create unique index i_test_uuid_v4_id on test_uuid_v4(id);
CREATE INDEX
Time: 153372.622 ms (02:33.373)
[thunisoft@localhost thunisoft]$ pgbench -M prepared -r -n -j 8 -c 8 -T 60 -f /opt/thunisoft/pgbench_uuid_v4.sql -U sa pgbenchdb 
^[[3~transaction type: /opt/thunisoft/pgbench_uuid_v4.sql
scaling factor: 1
query mode: prepared
number of clients: 8
number of threads: 8
duration: 60 s
number of transactions actually processed: 13847
latency average = 34.693 ms
tps = 230.593988 (including connections establishing)
tps = 230.620469 (excluding connections establishing)
script statistics:
 - statement latencies in milliseconds:
        36.410  insert into test_uuid_v4 (id) values (replace(uuid_generate_v4()::text,'-',''));
   

无论是普通btree索引和唯一索引，都会影响插入的效率。

删除所有的主键索引

--删除所有主键
alter table test_uuid_v4 drop constraint "test_uuid_v4_pkey";
alter table test_time_nextval drop constraint "test_time_nextval_pkey" ;
alter table test_seq_bigint drop constraint "test_seq_bigint_pkey";

1,--无序uuid：测试pgbench_uuid_v4.sql
[thunisoft@localhost thunisoft]$ pgbench -M prepared -r -n -j 8 -c 8 -T 60 -f /opt/thunisoft/pgbench_uuid_v4.sql -U sa pgbenchdb 
transaction type: /opt/thunisoft/pgbench_uuid_v4.sql
scaling factor: 1
query mode: prepared
number of clients: 8
number of threads: 8
duration: 60 s
number of transactions actually processed: 74109
latency average = 6.479 ms
tps = 1234.842229 (including connections establishing)
tps = 1235.042674 (excluding connections establishing)
script statistics:
 - statement latencies in milliseconds:
         6.112  insert into test_uuid_v4 (id) values (replace(uuid_generate_v4()::text,'-',''));

2、--有序uuid，测试pgbench_time_nextval.sql
[thunisoft@localhost thunisoft]$ pgbench -M prepared -r -n -j 8 -c 8 -T 60 -f /opt/thunisoft/pgbench_time_nextval.sql -U sa pgbenchdb 
transaction type: /opt/thunisoft/pgbench_time_nextval.sql
scaling factor: 1
query mode: prepared
number of clients: 8
number of threads: 8
duration: 60 s
number of transactions actually processed: 74027
latency average = 6.486 ms
tps = 1233.364360 (including connections establishing)
tps = 1233.482292 (excluding connections establishing)
script statistics:
 - statement latencies in milliseconds:
         6.186  insert into test_time_nextval (id) values (replace(uuid_time_nextval()::text,'-',''));
3、--序列，测试pgbench_seq_bigint.sql
[thunisoft@localhost thunisoft]$ pgbench -M prepared -r -n -j 8 -c 8 -T 60 -f /opt/thunisoft/pgbench_seq_bigint.sql -U sa pgbenchdb 
transaction type: /opt/thunisoft/pgbench_seq_bigint.sql
scaling factor: 1
query mode: prepared
number of clients: 8
number of threads: 8
duration: 60 s
number of transactions actually processed: 76312
latency average = 6.290 ms
tps = 1271.832907 (including connections establishing)
tps = 1272.124397 (excluding connections establishing)
script statistics:
 - statement latencies in milliseconds:
         5.916  insert into test_seq_bigint (id) values (nextval('test_seq'::regclass));

删除主键约束后，三种情况下tps非常接近，都达到了1200+。

Btree索引，插入操作的平均tps对比

 类别/平均tps    |  无数据  | 一千万  | 五千万 | 一亿 		|
---------------+---------+---------+---------+---------+
 无序uuid		  | 960  	| 260     |  163  |   131     |
 有序uuid    	  | 933  	| 870     |  883  |   834     |
 序列        	  | 1289     | 1278    |  1220 |  1139     |

根据测试数据可以看出无序的uuid在数据到达1kw后插入数据的tps下降的非常厉害，而有序的uuid和递增序列下降的比较少。到一亿数据的tps有序uuid是无序的6倍，序列是无序uuid的9倍。

创建单独的表空间用来存储索引信息

如果有多快磁盘那么可以将索引和数据分开存储，以此来加快写入的速度。

创建单独的索引空间：

create tablespace indx_test owner sa location ‘/home/tablespace/index_test’;

指定索引存储目录：

create index i_test_uuid_v4_id on test_uuid_v4 using btree(id) tablespace indx_test;

关于有序uuid

测试使用的sequential-uuids插件，生成的有序uuid。

有序uuid的结构为(block ID; random data)，实际上就是把数据拆成两部分，一部分自增，一部分随机。

sequential-uuids

sequential-uuids-git

提供了两种算法：

1.uuid_sequence_nextval(sequence regclass, block_size int default 65536, block_count int default 65536)

前缀为自增序列，如果块ID使用2字节存储，一个索引BLOCK里面可以存储256条记录（假设8K的BLOCK，一条记录包括uuid VALUE（16字节）以及ctid（6字节），所以一个索引页约存储363条记录（8000 /（16 + 6）））

2.uuid_time_nextval(interval_length int default 60, interval_count int default 65536) RETURNS uuid

默认每60秒内的数据的前缀是一样的，前缀递增1，到65535后循环。

使用uuid_time_nextval生成的有序uuid
pgbenchdb=# select id from test_time_nextval;
                id                
----------------------------------
 a18b7dd0ca92b0b5c1844a402f9c6999
 a18b540b8bbe0ddb2b6d0189b2e393c6
 a18b83eb7320b0a90e625185421e065e
 a18bade4ff15e05dab81ecd3f4c2dee4
 a18b79e41c3bc8d2d4ba4b70447e6b29
 a18bdad18d9e0d2fa1d9d675bc7129f0
 a18b13723ec7be9a2f1a3aec5345a88b
 a18bd9d866047aec69a064d30e9493d2
 a18bd76e8c787c7464479502f381e6d7
 a18ba5c0c966f81cfdbeff866618da8d
......

有序uuid前四位有序，后面的随机生成。

结语

1.关于有序的uuid，前4位是有序的，后面都是随机生成的。

2.在该环境中发现，无序uuid随着数据量的不断增大，tps下滑比较厉害。

3.由于btree索引的存在，无序的uuid会导致大量的离散io。导致磁盘使用率高。进而影响插入效率。随着表数据量的增大更加明显。

4.该测试是在普通的磁盘上面测试，并未在ssd上面测试。

5.如果要使用有序uuid，有多种实现方式，还需要考虑分布式情况下生成全局有序uuid。