标签
PostgreSQL , citus , tpc-b , cn mx
背景
《(TPC-H测试 SF=10) PostgreSQL 11 vs 10 vs Deepgreen》 测试结果证明PG已经是一个HTAP数据库，可以同时满足OLTP，OLAP的需求。
citus是PG的一个sharding插件，可以把PG变成一个分布式数据库。目前在苏宁有大量的生产应用跑在citus+pg的环境中。
本文是多机部署与简单的测试。
后面的篇章陆续添加: HA，备份，容灾。最佳实践。性能测试。
期望citus可以具备良好的TP能力，同时AP能力可以像GPDB一样扩展，使得PG如虎添翼。
重点看结果：
OLTP: citus(multi node) vs pg(single)
OLAP: citus(multi node) vs Greenplum(multi node)
环境
(ECS 32核，128G内存，2TB 云盘) * 9
host all all xxx.xxx.xxx.228/32 trust # coordinator node
host all all xxx.xxx.xxx.224/32 trust # worker(data) node
host all all xxx.xxx.xxx.230/32 trust # worker(data) node
host all all xxx.xxx.xxx.231/32 trust # worker(data) node
host all all xxx.xxx.xxx.225/32 trust # worker(data) node
host all all xxx.xxx.xxx.227/32 trust # worker(data) node
host all all xxx.xxx.xxx.232/32 trust # worker(data) node
host all all xxx.xxx.xxx.226/32 trust # worker(data) node
host all all xxx.xxx.xxx.229/32 trust # worker(data) node
CentOS 7.x x64
PostgreSQL 10
Citus 7.5
一、OS 配置
《PostgreSQL 10 + PostGIS + Sharding(pg_pathman) + MySQL(fdw外部表) on ECS 部署指南(适合新用户) - 珍藏级》
1 sysctl
注意某些参数，根据内存大小配置（已说明）
含义详见
cat >>/etc/sysctl.conf <9.2的版本已大幅降低共享内存的使用，单位为字节。
kernel.shmmni = 819200
# 一共能生成多少共享内存段，每个PG数据库集群至少2个共享内存段
net.core.netdev_max_backlog = 10000
net.core.rmem_default = 262144
# The default setting of the socket receive buffer in bytes.
net.core.rmem_max = 4194304
# The maximum receive socket buffer size in bytes
net.core.wmem_default = 262144
# The default setting (in bytes) of the socket send buffer.
net.core.wmem_max = 4194304
# The maximum send socket buffer size in bytes.
net.core.somaxconn = 4096
net.ipv4.tcp_max_syn_backlog = 4096
net.ipv4.tcp_keepalive_intvl = 20
net.ipv4.tcp_keepalive_probes = 3
net.ipv4.tcp_keepalive_time = 60
net.ipv4.tcp_mem = 8388608 12582912 16777216
net.ipv4.tcp_fin_timeout = 5
net.ipv4.tcp_synack_retries = 2
net.ipv4.tcp_syncookies = 1
# 开启SYN Cookies。当出现SYN等待队列溢出时，启用cookie来处理，可防范少量的SYN攻击
net.ipv4.tcp_timestamps = 1
# 减少time_wait
net.ipv4.tcp_tw_recycle = 0
# 如果=1则开启TCP连接中TIME-WAIT套接字的快速回收，但是NAT环境可能导致连接失败，建议服务端关闭它
net.ipv4.tcp_tw_reuse = 1
# 开启重用。允许将TIME-WAIT套接字重新用于新的TCP连接
net.ipv4.tcp_max_tw_buckets = 262144
net.ipv4.tcp_rmem = 8192 87380 16777216
net.ipv4.tcp_wmem = 8192 65536 16777216
net.nf_conntrack_max = 1200000
net.netfilter.nf_conntrack_max = 1200000
vm.dirty_background_bytes = 409600000
# 系统脏页到达这个值，系统后台刷脏页调度进程 pdflush（或其他） 自动将(dirty_expire_centisecs/100）秒前的脏页刷到磁盘
# 默认为10%，大内存机器建议调整为直接指定多少字节
vm.dirty_expire_centisecs = 3000
# 比这个值老的脏页，将被刷到磁盘。3000表示30秒。
vm.dirty_ratio = 95
# 如果系统进程刷脏页太慢，使得系统脏页超过内存 95 % 时，则用户进程如果有写磁盘的操作（如fsync, fdatasync等调用），则需要主动把系统脏页刷出。
# 有效防止用户进程刷脏页，在单机多实例，并且使用CGROUP限制单实例IOPS的情况下非常有效。
vm.dirty_writeback_centisecs = 100
# pdflush（或其他）后台刷脏页进程的唤醒间隔， 100表示1秒。
vm.swappiness = 0
# 不使用交换分区
vm.mmap_min_addr = 65536
vm.overcommit_memory = 0
# 在分配内存时，允许少量over malloc, 如果设置为 1, 则认为总是有足够的内存，内存较少的测试环境可以使用 1 .
vm.overcommit_ratio = 90
# 当overcommit_memory = 2 时，用于参与计算允许指派的内存大小。
vm.swappiness = 0
# 关闭交换分区
vm.zone_reclaim_mode = 0
# 禁用 numa, 或者在vmlinux中禁止.
net.ipv4.ip_local_port_range = 40000 65535
# 本地自动分配的TCP, UDP端口号范围
fs.nr_open=20480000
# 单个进程允许打开的文件句柄上限
# 以下参数请注意
# vm.extra_free_kbytes = 4096000
# vm.min_free_kbytes = 2097152 # vm.min_free_kbytes 建议每32G内存分配1G vm.min_free_kbytes
# 如果是小内存机器，以上两个值不建议设置
# vm.nr_hugepages = 66536
# 建议shared buffer设置超过64GB时 使用大页，页大小 /proc/meminfo Hugepagesize
# vm.lowmem_reserve_ratio = 1 1 1
# 对于内存大于64G时，建议设置，否则建议默认值 256 256 32
EOOF
生效配置
sysctl -p
2 配置OS资源限制
cat >>/etc/security/limits.conf <最好再关注一下/etc/security/limits.d目录中的文件内容，会覆盖/etc/security/limits.conf的配置。
已有进程的ulimit请查看/proc/pid/limits，例如
Limit Soft Limit Hard Limit Units
Max cpu time unlimited unlimited seconds
Max file size unlimited unlimited bytes
Max data size unlimited unlimited bytes
Max stack size 10485760 unlimited bytes
Max core file size 0 unlimited bytes
Max resident set unlimited unlimited bytes
Max processes 11286 11286 processes
Max open files 1024 4096 files
Max locked memory 65536 65536 bytes
Max address space unlimited unlimited bytes
Max file locks unlimited unlimited locks
Max pending signals 11286 11286 signals
Max msgqueue size 819200 819200 bytes
Max nice priority 0 0
Max realtime priority 0 0
Max realtime timeout unlimited unlimited us
如果你要启动其他进程，建议退出SHELL再进一遍，确认ulimit环境配置已生效，再启动。
3 配置OS防火墙
（建议按业务场景设置，这里先清掉）
iptables -F
配置范例：
# 私有网段
-A INPUT -s 192.168.0.0/16 -j ACCEPT
-A INPUT -s 10.0.0.0/8 -j ACCEPT
-A INPUT -s 172.16.0.0/16 -j ACCEPT
4 selinux
如果没有这方面的需求，建议禁用
# vi /etc/sysconfig/selinux
SELINUX=disabled
SELINUXTYPE=targeted
5 关闭透明大页
grub2代，可以使用rc.local。
chmod +x /etc/rc.d/rc.local
cat >> /etc/rc.local < /sys/kernel/mm/transparent_hugepage/enabled
fi
EOOF
当场生效：
echo never > /sys/kernel/mm/transparent_hugepage/enabled
二、soft 配置
yum clean all
1、安装EPEL
http://fedoraproject.org/wiki/EPEL
wget https://dl.fedoraproject.org/pub/epel/epel-release-latest-7.noarch.rpm
rpm -ivh epel-release-latest-7.noarch.rpm
2、安装PostgreSQL yum
https://yum.postgresql.org/repopackages.php
wget https://download.postgresql.org/pub/repos/yum/10/redhat/rhel-7-x86_64/pgdg-centos10-10-2.noarch.rpm
rpm -ivh pgdg-centos10-10-2.noarch.rpm
yum install -y postgresql10*
3、citus
echo "nameserver 8.8.8.8" >>/etc/resolv.conf
curl https://install.citusdata.com/community/rpm.sh | sudo bash
yum install -y citus75_10*
disk
parted -s /dev/vdb mklabel gpt
parted -s /dev/vdb mkpart primary 1MiB 100%
mkfs.ext4 /dev/vdb1 -m 0 -O extent,uninit_bg -E lazy_itable_init=1 -T largefile -L data01
mkdir /data01
echo "LABEL=data01 /data01 ext4 defaults,noatime,nodiratime,nodelalloc,barrier=0,data=writeback 0 0" >> /etc/fstab
mount -a
mkdir /data01/pg10
chown postgres:postgres /data01/pg10
三、env 配置
cat >>/var/lib/pgsql/.bash_profile </var/lib/pgsql/env.sh <四、database 配置
1 init
su - postgres -c "initdb -D \$PGDATA -U postgres --locale=en_US.UTF8 -E UTF8"
2 pg_hba.conf
cat >>/data01/pg10/pg_root1921/pg_hba.conf <
3 postgresql.auto.conf
cat >/data01/pg10/pg_root1921/postgresql.auto.conf <
4 start
su - postgres -c "pg_ctl start"
五、citus 插件配置
1 extension
su - postgres -c "psql -c 'create extension citus;'"
2 add worker (execute only on coordinator node)
su - postgres -c "psql -c \"SELECT * from master_add_node('xxx.xxx.xxx.224', 1921);\""
su - postgres -c "psql -c \"SELECT * from master_add_node('xxx.xxx.xxx.230', 1921);\""
su - postgres -c "psql -c \"SELECT * from master_add_node('xxx.xxx.xxx.231', 1921);\""
su - postgres -c "psql -c \"SELECT * from master_add_node('xxx.xxx.xxx.225', 1921);\""
su - postgres -c "psql -c \"SELECT * from master_add_node('xxx.xxx.xxx.227', 1921);\""
su - postgres -c "psql -c \"SELECT * from master_add_node('xxx.xxx.xxx.232', 1921);\""
su - postgres -c "psql -c \"SELECT * from master_add_node('xxx.xxx.xxx.226', 1921);\""
su - postgres -c "psql -c \"SELECT * from master_add_node('xxx.xxx.xxx.229', 1921);\""
postgres=# SELECT * FROM master_get_active_worker_nodes();
node_name | node_port
----------------+-----------
xxx.xxx.xxx.227 | 1921
xxx.xxx.xxx.229 | 1921
xxx.xxx.xxx.231 | 1921
xxx.xxx.xxx.225 | 1921
xxx.xxx.xxx.224 | 1921
xxx.xxx.xxx.226 | 1921
xxx.xxx.xxx.230 | 1921
xxx.xxx.xxx.232 | 1921
(8 rows)
六、PostgreSQL auto start
chmod +x /etc/rc.d/rc.local
chmod +x /etc/rc.local
cat >>/etc/rc.local <
七、quick usage (only on coordinator node)
1、初始化1亿数据
pgbench -i -s 1000
2、将tpc-b涉及的4张表转换为sharding表（128个shard）
set citus.shard_count =128;
postgres=# select create_distributed_table('pgbench_accounts','aid');
NOTICE: Copying data from local table...
create_distributed_table
--------------------------
(1 row)
select create_distributed_table('pgbench_branches','bid');
select create_distributed_table('pgbench_tellers','tid');
select create_distributed_table('pgbench_history','aid');
postgres=# SELECT pg_size_pretty(citus_total_relation_size('pgbench_accounts'));
pg_size_pretty
----------------
15 GB
(1 row)
八、pgbench OLTP(TPC-B) 压测 - shard 8 VS 128 VS 本地库
1 shard=128
1、只读
pgbench -M prepared -v -r -P 1 -c 128 -j 128 -T 120 -S
transaction type:
scaling factor: 1000
query mode: prepared
number of clients: 128
number of threads: 128
duration: 120 s
number of transactions actually processed: 19658912
latency average = 0.781 ms
latency stddev = 1.471 ms
tps = 163798.762192 (including connections establishing)
tps = 163838.927010 (excluding connections establishing)
script statistics:
- statement latencies in milliseconds:
0.002 \set aid random(1, 100000 * :scale)
0.783 SELECT abalance FROM pgbench_accounts WHERE aid = :aid;
coordinator CPU (0% IDLE)
datanode cpu (91% IDLE)
2、读写
pgbench -M prepared -v -r -P 1 -c 128 -j 128 -T 120
transaction type:
scaling factor: 1000
query mode: prepared
number of clients: 128
number of threads: 128
duration: 120 s
number of transactions actually processed: 1335463
latency average = 11.502 ms
latency stddev = 17.679 ms
tps = 11115.397826 (including connections establishing)
tps = 11118.596481 (excluding connections establishing)
script statistics:
- statement latencies in milliseconds:
0.003 \set aid random(1, 100000 * :scale)
0.001 \set bid random(1, 1 * :scale)
0.001 \set tid random(1, 10 * :scale)
0.001 \set delta random(-5000, 5000)
0.089 BEGIN;
0.985 UPDATE pgbench_accounts SET abalance = abalance + :delta WHERE aid = :aid;
0.522 SELECT abalance FROM pgbench_accounts WHERE aid = :aid;
0.979 UPDATE pgbench_tellers SET tbalance = tbalance + :delta WHERE tid = :tid;
1.468 UPDATE pgbench_branches SET bbalance = bbalance + :delta WHERE bid = :bid;
0.485 INSERT INTO pgbench_history (tid, bid, aid, delta, mtime) VALUES (:tid, :bid, :aid, :delta, CURRENT_TIMESTAMP);
6.970 END;
coordinator CPU (36% IDLE)
datanode cpu (91% IDLE)
2 shard=8
对比shard=8 (刚好每台物理机每个PG实例一个shard)
drop table pgbench_accounts;
drop table pgbench_branches;
drop table pgbench_tellers;
drop table pgbench_history;
pgbench -i -s 1000
set citus.shard_count =8;
select create_distributed_table('pgbench_accounts','aid');
select create_distributed_table('pgbench_branches','bid');
select create_distributed_table('pgbench_tellers','tid');
select create_distributed_table('pgbench_history','aid');
1、只读
pgbench -M prepared -v -r -P 1 -c 128 -j 128 -T 120 -S
transaction type:
scaling factor: 1000
query mode: prepared
number of clients: 128
number of threads: 128
duration: 120 s
number of transactions actually processed: 19587094
latency average = 0.784 ms
latency stddev = 1.376 ms
tps = 163197.269113 (including connections establishing)
tps = 163240.601455 (excluding connections establishing)
script statistics:
- statement latencies in milliseconds:
0.002 \set aid random(1, 100000 * :scale)
0.786 SELECT abalance FROM pgbench_accounts WHERE aid = :aid;
coordinator CPU (0% IDLE)
datanode cpu (92% IDLE)
2、读写
pgbench -M prepared -v -r -P 1 -c 128 -j 128 -T 120
transaction type:
scaling factor: 1000
query mode: prepared
number of clients: 128
number of threads: 128
duration: 120 s
number of transactions actually processed: 1542327
latency average = 9.959 ms
latency stddev = 13.876 ms
tps = 12837.180912 (including connections establishing)
tps = 12840.837336 (excluding connections establishing)
script statistics:
- statement latencies in milliseconds:
0.003 \set aid random(1, 100000 * :scale)
0.001 \set bid random(1, 1 * :scale)
0.001 \set tid random(1, 10 * :scale)
0.001 \set delta random(-5000, 5000)
0.088 BEGIN;
0.982 UPDATE pgbench_accounts SET abalance = abalance + :delta WHERE aid = :aid;
0.525 SELECT abalance FROM pgbench_accounts WHERE aid = :aid;
0.970 UPDATE pgbench_tellers SET tbalance = tbalance + :delta WHERE tid = :tid;
1.266 UPDATE pgbench_branches SET bbalance = bbalance + :delta WHERE bid = :bid;
0.485 INSERT INTO pgbench_history (tid, bid, aid, delta, mtime) VALUES (:tid, :bid, :aid, :delta, CURRENT_TIMESTAMP);
5.639 END;
coordinator CPU (45% IDLE)
datanode cpu (91% IDLE)
3 本地节点(非citus节点, 跨ECS网络测试)
create database testdb;
pgbench -i -s 1000 testdb
1、只读
pgbench -M prepared -v -r -P 1 -c 128 -j 128 -T 120 -h xxx.xxx.xxx.xxx -S testdb
transaction type:
scaling factor: 1000
query mode: prepared
number of clients: 128
number of threads: 128
duration: 120 s
number of transactions actually processed: 58786788
latency average = 0.261 ms
latency stddev = 0.077 ms
tps = 489836.391089 (including connections establishing)
tps = 490056.663695 (excluding connections establishing)
script statistics:
- statement latencies in milliseconds:
0.001 \set aid random(1, 100000 * :scale)
0.261 SELECT abalance FROM pgbench_accounts WHERE aid = :aid;
CPU (0% IDLE)
2、读写
pgbench -M prepared -v -r -P 1 -c 32 -j 32 -T 120 -h xxx.xxx.xxx.xxx testdb
transaction type:
scaling factor: 1000
query mode: prepared
number of clients: 128
number of threads: 128
duration: 120 s
number of transactions actually processed: 4695712
latency average = 3.270 ms
latency stddev = 1.897 ms
tps = 39108.352039 (including connections establishing)
tps = 39120.606464 (excluding connections establishing)
script statistics:
- statement latencies in milliseconds:
0.001 \set aid random(1, 100000 * :scale)
0.001 \set bid random(1, 1 * :scale)
0.001 \set tid random(1, 10 * :scale)
0.000 \set delta random(-5000, 5000)
0.277 BEGIN;
0.378 UPDATE pgbench_accounts SET abalance = abalance + :delta WHERE aid = :aid;
0.302 SELECT abalance FROM pgbench_accounts WHERE aid = :aid;
0.443 UPDATE pgbench_tellers SET tbalance = tbalance + :delta WHERE tid = :tid;
0.711 UPDATE pgbench_branches SET bbalance = bbalance + :delta WHERE bid = :bid;
0.564 INSERT INTO pgbench_history (tid, bid, aid, delta, mtime) VALUES (:tid, :bid, :aid, :delta, CURRENT_TIMESTAMP);
0.592 END;
CPU (0% IDLE)
OLTP(TPC-B)性能对比小结
环境
test case
TPS
coordinator 节点CPU资源消耗
worker节点CPU资源消耗
citus(1+8) shard=8
1亿 tpc-b 只读
16.3万
(0% IDLE)
(92% IDLE)
citus(1+8) shard=128
1亿 tpc-b 只读
16.4万
(0% IDLE)
(91% IDLE)
local PG
1亿 tpc-b 只读
49万
-
(0% IDLE)
citus(1+8) shard=8
1亿 tpc-b 读写
1.28万
(45% IDLE)
(91% IDLE)
citus(1+8) shard=128
1亿 tpc-b 读写
1.11万
(36% IDLE)
(91% IDLE)
local PG
1亿 tpc-b 读写
3.98万
-
(0% IDLE)
九、简单AP测试
主要测试AP能力，以及执行计划。
1、建表，写入25.6亿数据
create table test (id int, c1 int, c2 int, c3 int, info text, crt_time timestamp);
select create_distributed_table('test','id');
insert into test select id, random()*100, random()*100, random()*100, md5(random()::text), clock_timestamp() from generate_series(1,10000000) t(id);
insert into test select * from test;
......
insert into test select * from test;
postgres=# select pg_size_pretty(citus_relation_size('test'));
pg_size_pretty
----------------
222 GB
(1 row)
Time: 44.249 ms
2、执行计划
limit 下推
postgres=# explain select * from test limit 1;
QUERY PLAN
-----------------------------------------------------------------------------------------------------
Limit (cost=0.00..0.00 rows=0 width=0)
-> Custom Scan (Citus Real-Time) (cost=0.00..0.00 rows=0 width=0)
Task Count: 128
Tasks Shown: One of 128
-> Task
Node: host=172.24.211.224 port=1921 dbname=postgres
-> Limit (cost=0.00..0.02 rows=1 width=57)
-> Seq Scan on test_103068 test (cost=0.00..428722.72 rows=20067872 width=57)
(8 rows)
优化器识别分区键，同时where条件下推
postgres=# explain select * from test where id=1;
QUERY PLAN
--------------------------------------------------------------------------------------------------
Custom Scan (Citus Router) (cost=0.00..0.00 rows=0 width=0)
Task Count: 1
Tasks Shown: All
-> Task
Node: host=172.24.211.232 port=1921 dbname=postgres
-> Gather (cost=1000.00..274755.04 rows=226 width=57)
Workers Planned: 5
-> Parallel Seq Scan on test_103075 test (cost=0.00..273732.44 rows=45 width=57)
Filter: (id = 1)
(9 rows)
目标端依旧可以使用并行，25.6亿记录，COUNT大概4.2秒。
postgres=# explain (analyze,verbose,timing,costs,buffers) select count(*) from test ;
QUERY PLAN
---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Aggregate (cost=0.00..0.00 rows=0 width=0) (actual time=4153.954..4153.955 rows=1 loops=1)
Output: COALESCE((pg_catalog.sum(remote_scan.count))::bigint, '0'::bigint)
-> Custom Scan (Citus Real-Time) (cost=0.00..0.00 rows=0 width=0) (actual time=4153.893..4153.911 rows=128 loops=1)
Output: remote_scan.count
Task Count: 128
Tasks Shown: One of 128
-> Task
Node: host=172.24.211.224 port=1921 dbname=postgres
-> Finalize Aggregate (cost=273906.25..273906.26 rows=1 width=8) (actual time=1149.156..1149.156 rows=1 loops=1)
Output: count(*)
Buffers: shared hit=228044
-> Gather (cost=273905.73..273906.24 rows=5 width=8) (actual time=1149.078..1194.615 rows=6 loops=1)
Output: (PARTIAL count(*))
Workers Planned: 5
Workers Launched: 5
Buffers: shared hit=228044
-> Partial Aggregate (cost=272905.73..272905.74 rows=1 width=8) (actual time=1145.740..1145.740 rows=1 loops=6)
Output: PARTIAL count(*)
Buffers: shared hit=228044
Worker 0: actual time=1144.474..1144.474 rows=1 loops=1
Buffers: shared hit=38016
Worker 1: actual time=1144.908..1144.909 rows=1 loops=1
Buffers: shared hit=38102
Worker 2: actual time=1145.096..1145.096 rows=1 loops=1
Buffers: shared hit=37952
Worker 3: actual time=1145.389..1145.389 rows=1 loops=1
Buffers: shared hit=38037
Worker 4: actual time=1145.818..1145.818 rows=1 loops=1
Buffers: shared hit=38199
-> Parallel Seq Scan on public.test_103068 test (cost=0.00..263933.38 rows=3588938 width=0) (actual time=0.016..747.409 rows=3344640 loops=6)
Buffers: shared hit=228044
Worker 0: actual time=0.015..747.436 rows=3345408 loops=1
Buffers: shared hit=38016
Worker 1: actual time=0.015..747.257 rows=3352944 loops=1
Buffers: shared hit=38102
Worker 2: actual time=0.016..748.296 rows=3339776 loops=1
Buffers: shared hit=37952
Worker 3: actual time=0.016..747.316 rows=3347256 loops=1
Buffers: shared hit=38037
Worker 4: actual time=0.017..747.354 rows=3361512 loops=1
Buffers: shared hit=38199
Planning time: 0.327 ms
Execution time: 1194.669 ms
Planning time: 2.564 ms
Execution time: 4154.213 ms
(45 rows)
非分布键的GROUP BY，会分成两个阶段执行，类似Greenplum
《HybridDB PostgreSQL "Sort、Group、distinct 聚合、JOIN" 不惧怕数据倾斜的黑科技和原理 - 多阶段聚合》
多阶段聚合的原理可参考:
《PostgreSQL Oracle 兼容性之 - 自定义并行聚合函数 PARALLEL_ENABLE AGGREGATE》
《Postgres-XC customized aggregate introduction》
《PostgreSQL aggregate function customize》
postgres=# explain select count(*) from test group by c1;
QUERY PLAN
------------------------------------------------------------------------------------------------------------------------------
HashAggregate (cost=0.00..0.00 rows=0 width=0)
Group Key: remote_scan.worker_column_2
-> Custom Scan (Citus Real-Time) (cost=0.00..0.00 rows=0 width=0)
Task Count: 128
Tasks Shown: One of 128
-> Task
Node: host=172.24.211.224 port=1921 dbname=postgres
-> Finalize GroupAggregate (cost=289321.80..289326.60 rows=101 width=12)
Group Key: c1
-> Sort (cost=289321.80..289323.06 rows=505 width=12)
Sort Key: c1
-> Gather (cost=289247.61..289299.12 rows=505 width=12)
Workers Planned: 5
-> Partial HashAggregate (cost=288247.61..288248.62 rows=101 width=12)
Group Key: c1
-> Parallel Seq Scan on test_103068 test (cost=0.00..268179.74 rows=4013574 width=4)
(16 rows)
非分布键的分组聚合，25.6亿数据，耗时约7秒
postgres=# select c1,count(*) from test group by c1 order by count(*) desc limit 10;
c1 | count
----+----------
93 | 25761280
63 | 25748992
86 | 25743104
55 | 25741056
36 | 25730048
33 | 25724928
53 | 25722880
84 | 25701632
81 | 25699072
57 | 25692928
(10 rows)
Time: 6948.230 ms (00:06.948)
postgres=# select count(*) from test;
count
------------
2560000000
(1 row)
Time: 3806.089 ms (00:03.806)
每秒的扫描速度约58.4GB
postgres=# select 222/3.8
postgres-# ;
?column?
---------------------
58.4210526315789474
(1 row)
TPC-H 后面再测试。
主要瓶颈，IO（SSD云盘的最大问题还是读写带宽上，大概只有200多MB/s，本地SSD盘可以达到4GB/s）
OLTP 优化
2阶段，单条SQL涉及9个SHARD.
su - postgres -c "echo \"alter system set citus.multi_shard_commit_protocol='2pc'; select pg_reload_conf();\"|psql -f -"
transaction type: ./test.sql
scaling factor: 1
query mode: prepared
number of clients: 128
number of threads: 128
duration: 120 s
number of transactions actually processed: 1436979
latency average = 10.691 ms
latency stddev = 10.136 ms
tps = 11954.269803 (including connections establishing)
tps = 11957.251498 (excluding connections establishing)
script statistics:
- statement latencies in milliseconds:
0.002 \set id random(1,1000000000)
10.691 insert into test values (:id),(:id+1),(:id+2),(:id+3),(:id+4),(:id+5),(:id+6),(:id+7),(:id+8);
1阶段(但是最后还是要等所有SHARD操作完成后再commit，只不过不是2PC而已，所以可以看到worker上很多idle in transaction的状态)
su - postgres -c "echo \"alter system set citus.multi_shard_commit_protocol='1pc'; select pg_reload_conf();\"|psql -f -"
1阶段，单条SQL涉及9个shard
transaction type: ./test.sql
scaling factor: 1
query mode: prepared
number of clients: 128
number of threads: 128
duration: 120 s
number of transactions actually processed: 2449586
latency average = 6.269 ms
latency stddev = 4.968 ms
tps = 20407.507503 (including connections establishing)
tps = 20412.584813 (excluding connections establishing)
script statistics:
- statement latencies in milliseconds:
0.002 \set id random(1,1000000000)
6.269 insert into test values (:id),(:id+1),(:id+2),(:id+3),(:id+4),(:id+5),(:id+6),(:id+7),(:id+8);
1阶段，涉及2个shard。单个QUERY涉及的SHARD越多，效率越低。
transaction type: ./test.sql
scaling factor: 1
query mode: prepared
number of clients: 128
number of threads: 128
duration: 120 s
number of transactions actually processed: 7894772
latency average = 1.945 ms
latency stddev = 2.312 ms
tps = 65769.534466 (including connections establishing)
tps = 65784.907845 (excluding connections establishing)
script statistics:
- statement latencies in milliseconds:
0.002 \set id random(1,1000000000)
1.946 insert into test values (:id),(:id),(:id),(:id),(:id),(:id),(:id),(:id),(:id+8);
1阶段，涉及1个shard。效率最高。(如果客户端能够拿到分布算法，分布键，键值，并按SHARD将多次写入封装在一条SQL中，可以轻松达到几百万行/s的写入性能。)
transaction type: ./test.sql
scaling factor: 1
query mode: prepared
number of clients: 128
number of threads: 128
duration: 120 s
number of transactions actually processed: 22410273
latency average = 0.685 ms
latency stddev = 8.487 ms
tps = 186717.144028 (including connections establishing)
tps = 186761.579753 (excluding connections establishing)
script statistics:
- statement latencies in milliseconds:
0.002 \set id random(1,1000000000)
0.688 insert into test values (:id),(:id),(:id),(:id),(:id),(:id),(:id),(:id),(:id);
单个QUERY，单个shard，单条记录。
transaction type: ./test.sql
scaling factor: 1
query mode: prepared
number of clients: 128
number of threads: 128
duration: 120 s
number of transactions actually processed: 29441284
latency average = 0.522 ms
latency stddev = 0.762 ms
tps = 245299.791043 (including connections establishing)
tps = 245362.383416 (excluding connections establishing)
script statistics:
- statement latencies in milliseconds:
0.001 \set id random(1,1000000000)
0.522 insert into test values (:id);
OLTP 优化小结
1、尽量不要使用运算符(包括函数)，建议使用常量。
因为所有运算符(包括函数)的计算都在coordinator节点完成。
2、不需要2PC的话尽量不要使用。
su - postgres -c "echo \"alter system set citus.multi_shard_commit_protocol='1pc'; select pg_reload_conf();\"|psql -f -"
3、批量使用单条SQL批量操作（多SQL没有意义）。如果客户端能够拿到分布算法，分布键，键值，并按SHARD将多次写入封装在一条SQL中，可以轻松达到几百万行/s的写入性能。
CN MX : OLTP 读、写能力扩展
1、读能力扩展，增加coordinator节点。通过物理流复制 可以复制若干个COORDINATOR 节点(TP场景worker节点的资源使用率较低，coordinator节点可以在每个worker节点上放一个。)，只读QUERY可以分摊到不同的coordinator节点执行。
2、写能力扩展，增加coordinator节点。可以使用mx功能(隐藏功能，实际上读能力也可以使用这种方法扩展)。
CN节点执行
su - postgres -c "echo \"alter system set citus.replication_model='streaming'; select pg_reload_conf();\"|psql -f -"
添加要同步元数据的WORKER
select * from master_add_node('xxxxx.224',1921);
select * from master_add_node('xxxxx.230',1921);
开启同步到元数据。
select start_metadata_sync_to_node('xxxxx.224',1921);
select start_metadata_sync_to_node('xxxxx.230',1921);
包含元数据的节点，hasmetadata标记位TRUE。
postgres=# select * from pg_dist_node;
nodeid | groupid | nodename | nodeport | noderack | hasmetadata | isactive | noderole | nodecluster
--------+---------+----------------+----------+----------+-------------+----------+----------+-------------
3 | 3 | xxxxx.231 | 1921 | default | f | t | primary | default
4 | 4 | xxxxx.225 | 1921 | default | f | t | primary | default
5 | 5 | xxxxx.227 | 1921 | default | f | t | primary | default
6 | 6 | xxxxx.232 | 1921 | default | f | t | primary | default
7 | 7 | xxxxx.226 | 1921 | default | f | t | primary | default
8 | 8 | xxxxx.229 | 1921 | default | f | t | primary | default
2 | 2 | xxxxx.230 | 1921 | default | t | t | primary | default
1 | 1 | xxxxx.224 | 1921 | default | t | t | primary | default
(8 rows)
仅针对开启同步后，创建的SHARD表，在worker节点才有元数据，之前已经创建的SHARD表，不会自动把元数据同步过去。
对应的worker可以当成cn来使用。
postgres=# explain select * from test5;
QUERY PLAN
-----------------------------------------------------------------------------------
Custom Scan (Citus Real-Time) (cost=0.00..0.00 rows=0 width=0)
Task Count: 128
Tasks Shown: One of 128
-> Task
Node: host=172.24.211.224 port=1921 dbname=postgres
-> Seq Scan on test5_103740 test5 (cost=0.00..22.70 rows=1270 width=36)
(6 rows)
postgres=# explain select * from test6;
QUERY PLAN
-----------------------------------------------------------------------------------
Custom Scan (Citus Real-Time) (cost=0.00..0.00 rows=0 width=0)
Task Count: 128
Tasks Shown: One of 128
-> Task
Node: host=172.24.211.224 port=1921 dbname=postgres
-> Seq Scan on test6_103868 test6 (cost=0.00..22.70 rows=1270 width=36)
(6 rows)
启用CN MX后，1+8 的集群，读写能力如何？
(首先启用MX后，实际上所有节点都可以作为写节点，可用性大幅度提升。另一方面，只要做好每个节点的HA，全局视角的可用性可靠性也可以有保障。)
1 TPC-B (select only)
pgbench -M prepared -n -r -P 1 -c 96 -j 96 -T 120 -S
CN tps: 127378
WORKERs tps: 58473+55709+63161+63510+62721+66301+59628+65486
总 tps: 622367
worker平均剩余cpu 2.5%
2 TPC-B (read write)
CN: pgbench -M prepared -n -r -P 1 -c 96 -j 96 -T 120
WORKER: pgbench -M prepared -n -r -P 1 -c 48 -j 48 -T 120
CN tps: 18207
WORKERs tps: 7467+7368+7287+7391+7316+7442+7661+7440
总 tps: 77579
worker平均剩余cpu 14%
citus 读写能力扩展架构
1、cn流复制，扩展读能力。
2、citus MX特性，指定worker节点携带metadata，扩展读写能力。
3、流复制，异地容灾。使用不同的dns，解决两套集群IP不同的问题。扩展容灾能力。
citus reference
https://docs.citusdata.com/en/v7.5/develop/api.html
配合haproxy,lvs，citus mx功能(前面已提到如何配置)，可以实现对业务完全透明的读写负载均衡。
https://severalnines.com/blog/postgresql-load-balancing-using-haproxy-keepalived
配合jdbc或libpq的mulit-host功能，citus mx功能，可以实现对业务完全透明的读写负载均衡。
《PostgreSQL jdbc multi-host 配置与简单HA、Load Balance实现》
《PostgreSQL libpq multi-host 配置与简单HA实现》
《PostgreSQL 10.0 preview 功能增强 - libpq支持多主机连接(failover,LB)让数据库HA和应用配合更紧密》
