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PostgreSQL 性能優化方法 - 1
使用一個非常容易理解的案例，講解PostgreSQL的優化手段。
本文成文自2012年初，有一些性能指標數據已經過時了，但是優化方法沒有過時，請關注優化的方法。
現在的硬件和軟件，一台主機達到的性能數據已經可以替代我當時測試的8台主機性能了。
【軟件環境】
CentOS 5 x64
PostgreSQL 9.1.3 (還是以前測試的，所以用的老版本，你可以用最新的版本測試比如9.5)
plproxy 2.3
pgbouncer 1.4.2
【測試模型】
設計一個包含INSERT, UPDATE, SELECT語句的業務模型用於本優化案例.
業務邏輯 :
【圖1】
【測試表】
create table user_info
(userid int,
engname text,
cnname text,
occupation text,
birthday date,
signname text,
email text,
qq numeric,
crt_time timestamp without time zone,
mod_time timestamp without time zone
);
create table user_session
(userid int,
logintime timestamp(0) without time zone,
login_count bigint default 0,
logouttime timestamp(0) without time zone,
online_interval interval default interval '0'
);
create table user_login_rec
(userid int,
login_time timestamp without time zone,
ip inet
);
create table user_logout_rec
(userid int,
logout_time timestamp without time zone,
ip inet
);
【初始化數據】
insert into user_info (userid,engname,cnname,occupation,birthday,signname,email,qq,crt_time,mod_time)
select generate_series(1,20000000),
'digoal.zhou',
'德哥',
'DBA',
'1970-01-01'
,E'公益是一輩子的事, I\'m Digoal.Zhou, Just do it!',
'digoal@126.com',
276732431,
clock_timestamp(),
NULL;
insert into user_session (userid) select generate_series(1,20000000);
set work_mem='2048MB';
set maintenance_work_mem='2048MB';
alter table user_info add constraint pk_user_info primary key (userid);
alter table user_session add constraint pk_user_session primary key (userid);
【業務函數】
-- 模擬用戶登錄的函數
create or replace function f_user_login
(i_userid int,
OUT o_userid int,
OUT o_engname text,
OUT o_cnname text,
OUT o_occupation text,
OUT o_birthday date,
OUT o_signname text,
OUT o_email text,
OUT o_qq numeric
)
as $BODY$
declare
begin
select userid,engname,cnname,occupation,birthday,signname,email,qq
into o_userid,o_engname,o_cnname,o_occupation,o_birthday,o_signname,o_email,o_qq
from user_info where userid=i_userid;
insert into user_login_rec (userid,login_time,ip) values (i_userid,now(),inet_client_addr());
update user_session set logintime=now(),login_count=login_count+1 where userid=i_userid;
return;
end;
$BODY$
language plpgsql;
-- 模擬用戶退出的函數
create or replace function f_user_logout
(i_userid int,
OUT o_result int
)
as $BODY$
declare
begin
insert into user_logout_rec (userid,logout_time,ip) values (i_userid,now(),inet_client_addr());
update user_session set logouttime=now(),online_interval=online_interval+(now()-logintime) where userid=i_userid;
o_result := 0;
return;
exception
when others then
o_result := 1;
return;
end;
$BODY$
language plpgsql;
【搭建測試模型】
1. 搭建環境, 安裝PostgreSQL9.1.3數據庫軟件並初始化數據庫(略).
2. 調整數據庫postgresql.conf參數. 打開日誌, SQL統計, 跟蹤, 以及性能參數, 便於優化過程中取證.
監聽IPv4的所有IP.
listen_addresses = '0.0.0.0'
最大允許1000個連接.
max_connections = 1000
為超級用戶保留3個可用連接.
superuser_reserved_connections = 13
默認的unix socket文件放在/tmp, 修改為$PGDATA, 以確保安全.
unix_socket_directory = '.'
默認的訪問權限是0777, 修改為0700更安全.
unix_socket_permissions = 0700
Linux下麵默認是2小時. tcp的keepalives包發送間隔以及重試次數. 如果你的網絡環境中有設備自動斷開空閑會話，那麼建議你設置心跳時間小於網絡設備的斷鏈接閾值。
tcp_keepalives_idle = 60
tcp_keepalives_interval = 10
tcp_keepalives_count = 6
大的shared_buffers需要大的checkpoint_segments,同時需要申請更多的System V共享內存資源.
這個值不需要設的太大, 因為PostgreSQL還依賴操作係統的cache來提高讀性能, 另外, 寫操作頻繁的數據庫這個設太大反而會增加checkpoint壓力(除非你使用了SSD或者IOPS能力很好的存儲).
建議設置為內存大小的20%
shared_buffers = 1GB
注意現在的PG版本已經支持大頁了，如果需要的共享內存很多，建議使用大頁。
需要在係統操作中開啟大頁支持。 cat /proc/meminfo |grep Hugepagesize 得到 2048 kB , 如果需要8G shared buffer， 輸入 echo "vm.nr_hugepages=4096" (也可以略大)
這個值越大, VACUUM, CREATE INDEX的操作越快, 當然大到一定程度瓶頸就不在內存了, 可能是CPU例如創建索引.
這個值是一個操作的內存使用上限, 而不是一次性分配出去的. 並且需要注意如果開啟了autovacuum, 最大可能有autovacuum_max_workers*maintenance_work_mem的內存被係統消耗掉.
maintenance_work_mem = 512MB
建議防止暴力破解, 密碼複雜度檢測, 開啟pg_stat_statements, 開啟auto_explain, 參考 https://blog.163.com/digoal@126/blog/static/16387704020149852941586
shared_preload_libraries = 'auth_delay,passwordcheck,pg_stat_statements,auto_explain'
一般設置為比係統限製的略少,ulimit -a : stack size (kbytes, -s) 10240
max_stack_depth = 8MB
手動執行vacuum操作時, 默認是沒有停頓執行到底的, 為了防止VACUUM操作消耗太多數據庫服務器硬件資源, 這個值是指vacuum在消耗多少資源後停頓多少時間,以便其他的操作可以使用更多的硬件資源.
vacuum_cost_delay = 10ms
#vacuum_cost_page_hit = 1 # 0-10000 credits
#vacuum_cost_page_miss = 10 # 0-10000 credits
#vacuum_cost_page_dirty = 20 # 0-10000 credits
vacuum_cost_limit = 10000 # 1-10000 credits
默認bgwriter進程執行一次後會停頓200ms再被喚醒執行下一次操作, 當數據庫的寫操作很頻繁的時候, 200ms可能太長, 導致其他進程需要花費過多的時間來進行bgwriter的操作.
bgwriter_delay = 10ms
如果需要做數據庫WAL日誌備份的話至少需要設置成archive級別, 如果需要做hot_standby那麼需要設置成hot_standby, 由於這個值修改需要重啟數據庫, 所以先設置成hot_standby比較好. 當然hot_standby意味著WAL記錄得更詳細, 如果沒有打算做hot_standby設置得越低性能越好.
wal_level = hot_standby
PostgreSQL 9.4已經支持邏輯複製了。如果是9.4，建議設置為logical。
# wal_level = logical # 如果將來打算使用logical複製, 最後先配置好, 不需要停機再改.
wal buffers默認是-1 根據shared_buffers的設置自動調整shared_buffers*3% .最大限製是XLOG的segment_size.
wal_buffers = 16MB
多少個xlog file產生後開始checkpoint操作, 這個值越大, 允許shared_buffer中的被頻繁訪問的髒數據存儲得更久. 一定程度上可以提高數據庫性能. 但是太大的話會導致在數據庫發生checkpoint的時候需要處理更多的髒數據帶來長時間的IO開銷. 太小的話會導致產生更多的WAL文件(因為full page writes=on,CHECKPOINT後的第一次塊的改變要寫全塊, checkpoint越頻繁, 越多的數據更新要寫全塊導致產生更多WAL).
建議設置為shared_buffers除以單個XLOG文件的大小。
checkpoint_segments = 64
這個和checkpoint_segments的效果是一樣的, 隻是觸發的條件是時間條件.
checkpoint_timeout = 5min
歸檔參數的修改也需要重啟數據庫, 所以就先打開吧.
archive_mode = on
這個是歸檔調用的命令, 我這裏用date代替, 所以歸檔的時候調用的是輸出時間而不是拷貝wal文件.
archive_command = '/bin/date'
如果要做hot standby這個必須大於0, 並且修改之後要重啟數據庫所以先設置為32.
max_wal_senders = 32
這是個standby 數據庫參數, 為了方便角色切換, 我一般是所有的數據庫都把他設置為on 的.
hot_standby = on
這個參數是說數據庫中隨機的PAGE訪問的開銷占seq_page_cost的多少倍 , seq_page_cost默認是1. 其他的開銷都是seq_page_cost的倍數. 這些都用於基於成本的執行計劃選擇.
random_page_cost = 2.0
和上一個參數一樣, 用於基於成本的執行計劃選擇. 不是說會用多少cache, 它隻是個度量值. 表示係統有多少內存可以作為操作係統的cache. 越大的話, 數據庫越傾向使用index這種適合random訪問的執行計劃.
建議設置為內存的80%
effective_cache_size = 10GB
下麵是日誌輸出的配置.
log_destination = 'csvlog'
logging_collector = on
log_truncate_on_rotation = on
log_rotation_age = 1d
log_rotation_size = 10MB
這個參數調整的是記錄執行時間超過1秒的SQL到日誌中, 一般用於跟蹤哪些SQL執行時間長.
log_min_duration_statement = 1000ms
記錄每一次checkpoint到日誌中.
log_checkpoints = on
記錄連接和斷開連接
log_connections = on
log_disconnections = on
log_error_verbosity = verbose # 在日誌中輸出代碼位置
記錄鎖等待超過1秒的操作, 一般用於排查業務邏輯上的問題.
log_lock_waits = on
deadlock_timeout = 1s
記錄DDL語句, 一般用於跟蹤數據庫中的危險操作.
log_statement = 'ddl'
這個原本是1024表示跟蹤的SQL在1024的地方截斷, 超過1024將無法顯示全SQL. 修改為2048會消耗更多的內存(基本可以忽略), 不過可以顯示更長的SQL.
track_activity_query_size = 2048
默認autovacuum就是打開的, log_autovacuum_min_duration = 0記錄所有的autovacuum操作.
autovacuum = on
log_autovacuum_min_duration = 0
這個模塊用於記錄數據庫中的最近的1000條SQL以及這些SQL的統計信息, 如執行了多少次, 總共耗時是多少. 一般用於發現業務上最頻繁調用的SQL是什麼, 有針對性的進行SQL優化.
shared_preload_libraries = 'pg_stat_statements'
現在的版本不需要設置custom_variable_classes
custom_variable_classes = 'pg_stat_statements'
auth_delay.milliseconds = 5000 # 認證失敗, 延遲多少毫秒反饋
auto_explain.log_min_duration = 5000 # 記錄超過多少毫秒的SQL當時的執行計劃
auto_explain.log_analyze = true
auto_explain.log_verbose = true
auto_explain.log_buffers = true
auto_explain.log_nested_statements = true
pg_stat_statements.track_utility=off
pg_stat_statements.max = 1000
pg_stat_statements.track = all
其他參數值默認.
這些參數的詳細解釋如有疑問請參考PostgreSQL官方文檔.
3. 新建數據庫用戶digoal, 庫digoal. 並使用前麵的測試模型新建表以及函數, 初始化數據.
下麵的測試過程中隻測登陸部分, 未測試退出部分, 因為登陸過程已經包含了INSERT, UPDATE, SELECT. 基本上可以反映整個調優過程了.
【調優階段1】
使用pgbench進行壓力測試, 發現瓶頸並合理優化.
1. pgbench用到的登陸腳本
cat login.sql
\setrandom userid 1 20000000
select userid,engname,cnname,occupation,birthday,signname,email,qq from user_info where userid=:userid;
insert into user_login_rec (userid,login_time,ip) values (:userid,now(),inet_client_addr());
update user_session set logintime=now(),login_count=login_count+1 where userid=:userid;
2. pgbench用到的退出腳本
cat logout.sql
\setrandom userid 1 20000000
insert into user_logout_rec (userid,logout_time,ip) values (:userid,now(),inet_client_addr());
update user_session set logouttime=now(),online_interval=online_interval+(now()-logintime) where userid=:userid;
3. 壓力測試
pgbench -M simple -r -c 8 -f /home/postgres/test/login.sql -j 8 -n -T 180 -h 172.16.3.33 -p 1921 -U digoal digoal
4. 壓力測試結果
transaction type: Custom query
scaling factor: 1
query mode: simple
number of clients: 8
number of threads: 8
duration: 180 s
number of transactions actually processed: 62675
tps = 348.084647 (including connections establishing)
tps = 348.100337 (excluding connections establishing)
statement latencies in milliseconds:
0.004577 \setrandom userid 1 20000000
12.963789 select userid,engname,cnname,occupation,birthday,signname,email,qq from user_info where userid=:userid;
5.540750 insert into user_login_rec (userid,login_time,ip) values (:userid,now(),inet_client_addr());
4.457834 update user_session set logintime=now(),login_count=login_count+1 where userid=:userid;
5. 瓶頸分析與優化
壓力測試中查看數據庫服務器的iostat -x
avg-cpu: %user %nice %system %iowait %steal %idle
0.69 0.00 0.25 24.11 0.00 74.95
Device: rrqm/s wrqm/s r/s w/s rsec/s wsec/s avgrq-sz avgqu-sz await svctm %util
cciss/c0d0 0.00 6.00 0.00 1.50 0.00 60.00 40.00 0.01 6.67 6.67 1.00
cciss/c0d0p1 0.00 6.00 0.00 1.50 0.00 60.00 40.00 0.01 6.67 6.67 1.00
cciss/c0d0p2 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
cciss/c0d0p3 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
cciss/c0d1 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
cciss/c0d2 0.00 638.50 10.00 217.50 160.00 6444.00 29.03 152.58 707.89 4.40 100.10
cciss/c0d3 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
cciss/c0d4 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
cciss/c0d5 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
dm-0 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
dm-1 0.00 0.00 10.00 866.50 160.00 6932.00 8.09 446.26 510.49 1.14 100.10
dm-2 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
dm-3 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
dm-4 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
操作係統的平均IO請求等待700多毫秒, PostgreSQL數據文件所處的塊設備使用率100%. 存在嚴重的IO性能瓶頸.
使用pgfincore降低讀的物理IO請求.
pgfincore的相關文章可參考如下,
《use posix_fadvise pre-cache frequency data》
https://blog.163.com/digoal@126/blog/static/163877040201062944945126/
《a powerful upgrade from pgfincore 1.0》
https://blog.163.com/digoal@126/blog/static/1638770402011630102117658/
《TOAST table with pgfincore》
https://blog.163.com/digoal@126/blog/static/16387704020120524144140/
pgfincore所起的作用類似EnterpriseDB的InfiniteCache或者熟悉Oracle的朋友可能更易於接受的KEEP BUFFER POOL.
-- 載入os cache
digoal=> select reltoastrelid from pg_class where relname='user_info';
reltoastrelid
---------------
16424
(1 row)
digoal=> select relname from pg_class where oid=16424;
relname
----------------
pg_toast_16421
(1 row)
digoal=> \c digoal postgres
seYou are now connected to database "digoal" as user "postgres".
digoal=# select * from pgfadvise_willneed('pg_toast.pg_toast_16421');
relpath | os_page_size | rel_os_pages | os_pages_free
----------------------------------------------+--------------+--------------+---------------
pg_tblspc/16385/PG_9.1_201105231/16386/16424 | 4096 | 0 | 243865
(1 row)
digoal=# select * from pgfadvise_willneed('digoal.user_info');
relpath | os_page_size | rel_os_pages | os_pages_free
------------------------------------------------+--------------+--------------+---------------
pg_tblspc/16385/PG_9.1_201105231/16386/16421 | 4096 | 262144 | 243834
pg_tblspc/16385/PG_9.1_201105231/16386/16421.1 | 4096 | 262144 | 243834
pg_tblspc/16385/PG_9.1_201105231/16386/16421.2 | 4096 | 244944 | 243834
(3 rows)
digoal=# select * from pgfadvise_willneed('digoal.user_session');
relpath | os_page_size | rel_os_pages | os_pages_free
------------------------------------------------+--------------+--------------+---------------
pg_tblspc/16385/PG_9.1_201105231/16386/16431 | 4096 | 262144 | 243834
pg_tblspc/16385/PG_9.1_201105231/16386/16431.1 | 4096 | 33640 | 243834
(2 rows)
digoal=# select reltoastrelid from pg_class where relname='user_session';
reltoastrelid
---------------
0
(1 row)
digoal=# select * from pgfadvise_willneed('digoal.pk_user_session');
relpath | os_page_size | rel_os_pages | os_pages_free
----------------------------------------------+--------------+--------------+---------------
pg_tblspc/16385/PG_9.1_201105231/16386/16438 | 4096 | 109680 | 243865
(1 row)
digoal=# select * from pgfadvise_willneed('digoal.pk_user_info');
relpath | os_page_size | rel_os_pages | os_pages_free
----------------------------------------------+--------------+--------------+---------------
pg_tblspc/16385/PG_9.1_201105231/16386/16436 | 4096 | 109680 | 235567
(1 row)
【調優階段2】
1. 壓力測試
pgbench -M simple -r -c 8 -f /home/postgres/test/login.sql -j 8 -n -T 180 -h 172.16.3.33 -p 1921 -U digoal digoal
2. 測試結果
transaction type: Custom query
scaling factor: 1
query mode: simple
number of clients: 8
number of threads: 8
duration: 180 s
number of transactions actually processed: 264895
tps = 1471.517096 (including connections establishing)
tps = 1471.585818 (excluding connections establishing)
statement latencies in milliseconds:
0.004226 \setrandom userid 1 20000000
0.459824 select userid,engname,cnname,occupation,birthday,signname,email,qq from user_info where userid=:userid;
2.457797 insert into user_login_rec (userid,login_time,ip) values (:userid,now(),inet_client_addr());
2.501684 update user_session set logintime=now(),login_count=login_count+1 where userid=:userid;
3. 瓶頸分析與優化
SELECT語句的延時已經通過上一個優化階段下降到了0.45毫秒, INSERT和UPDATE語句的平均耗時也從原來的5.5和4.45下降到了2.5.
原因是select的請求在內存中命中了, 因此update和insert不需要和select爭搶物理io請求, 處理效率自然有一定的提高.
但是INSERT和UPDATE的語句延時還有2.5毫秒存在很大的可優化空間.
開啟PostgreSQL的異步提交日誌.
synchronous_commit = off
wal_writer_delay = 10ms
與Oracle的異步日誌差別請參考 :
《PostgreSQL and Oracle's async commit》
https://blog.163.com/digoal@126/blog/static/16387704020121229223072/
【調優階段3】
1. 壓力測試
pgbench -M simple -r -c 8 -f /home/postgres/test/login.sql -j 8 -n -T 180 -h 172.16.3.33 -p 1921 -U digoal digoal
2. 測試結果
transaction type: Custom query
scaling factor: 1
query mode: simple
number of clients: 8
number of threads: 8
duration: 180 s
number of transactions actually processed: 685344
tps = 3751.377919 (including connections establishing)
tps = 3751.568948 (excluding connections establishing)
statement latencies in milliseconds:
0.003474 \setrandom userid 1 20000000
0.418716 select userid,engname,cnname,occupation,birthday,signname,email,qq from user_info where userid=:userid;
0.511601 insert into user_login_rec (userid,login_time,ip) values (:userid,now(),inet_client_addr());
1.188277 update user_session set logintime=now(),login_count=login_count+1 where userid=:userid;
3. 瓶頸分析與優化
客戶端連接使用simple協議, 存在一定的可優化空間.
修改協議為extended, 查看性能提升.
【調優階段4】
1. 壓力測試
pgbench -M extended -r -c 8 -f /home/postgres/test/login.sql -j 8 -n -T 180 -h 172.16.3.33 -p 1921 -U digoal digoal
2. 測試結果
transaction type: Custom query
scaling factor: 1
query mode: extended
number of clients: 8
number of threads: 8
duration: 180 s
number of transactions actually processed: 970981
tps = 5394.015368 (including connections establishing)
tps = 5394.215477 (excluding connections establishing)
statement latencies in milliseconds:
0.003345 \setrandom userid 1 20000000
0.381675 select userid,engname,cnname,occupation,birthday,signname,email,qq from user_info where userid=:userid;
0.296300 insert into user_login_rec (userid,login_time,ip) values (:userid,now(),inet_client_addr());
0.792592 update user_session set logintime=now(),login_count=login_count+1 where userid=:userid;
3. 瓶頸分析與優化
客戶端連接使用extended協議, 存在一定的可優化空間.
修改協議為prepared, 查看性能提升多少.
參見 :
《PostgreSQL prepared statement: SPI_prepare, prepare|execute COMMAND, PL/pgsql STYLE: custom & generic plan cache》
https://blog.163.com/digoal@126/blog/static/1638770402012112452432251/
【調優階段5】
1. 壓力測試
pgbench -M prepared -r -c 8 -f /home/postgres/test/login.sql -j 8 -n -T 180 -h 172.16.3.33 -p 1921 -U digoal digoal
2. 測試結果
transaction type: Custom query
scaling factor: 1
query mode: prepared
number of clients: 8
number of threads: 8
duration: 180 s
number of transactions actually processed: 1044186
tps = 5800.589330 (including connections establishing)
tps = 5800.902491 (excluding connections establishing)
statement latencies in milliseconds:
0.003465 \setrandom userid 1 20000000
0.319665 select userid,engname,cnname,occupation,birthday,signname,email,qq from user_info where userid=:userid;
0.266931 insert into user_login_rec (userid,login_time,ip) values (:userid,now(),inet_client_addr());
0.777822 update user_session set logintime=now(),login_count=login_count+1 where userid=:userid;
3. 瓶頸分析與優化
壓力測試的腳本中使用的是普通的SQL語句, 未使用初始化時用到的登陸函數和退出函數. 使用普通SQL顯然比使用函數多了交互的次數以及每次發送的數據包的大小.
使用函數看看性能能提升多少.
【調優階段6】
1. 模擬用戶登陸腳本
cat login.sql
\setrandom userid 1 20000000
SELECT f_user_login(:userid);
2. 模擬用戶退出腳本
cat logout.sql
\setrandom userid 1 20000000
SELECT f_user_logout(:userid);
3. 壓力測試
pgbench -M prepared -r -c 8 -f /home/postgres/test/login.sql -j 8 -n -T 180 -h 172.16.3.33 -p 1921 -U digoal digoal
4. 測試結果
transaction type: Custom query
scaling factor: 1
query mode: prepared
number of clients: 8
number of threads: 8
duration: 180 s
number of transactions actually processed: 1616746
tps = 8981.596290 (including connections establishing)
tps = 8981.995800 (excluding connections establishing)
statement latencies in milliseconds:
0.004012 \setrandom userid 1 20000000
0.881060 SELECT f_user_login(:userid);
5. 瓶頸分析與優化
到這個時候看起來好像沒什麼好優化的了, 其實不然.
我們知道在整個登陸過程中用到了SELECT, UPDATE, INSERT.
其中UPDATE的表有一個PK索引, 每次更新需要修改數據表的同時還需要更新索引. 所以理論上這個更新操作表越小性能越高.
我們可以通過拆表來提升性能.
提醒，阿裏雲 RDS PG已經提供了分區表功能，而且比社區版本的分區表性能提升100倍，用戶可以選擇RDS PG試一試。
https://yq.aliyun.com/articles/113
如下是社區版本的演示過程 :
拆表 :
create table user_info_0 (like user_info including all);
create table user_info_1 (like user_info including all);
create table user_info_2 (like user_info including all);
create table user_info_3 (like user_info including all);
create table user_info_4 (like user_info including all);
create table user_session_0 (like user_session including all);
create table user_session_1 (like user_session including all);
create table user_session_2 (like user_session including all);
create table user_session_3 (like user_session including all);
create table user_session_4 (like user_session including all);
插入初始化數據 :
insert into user_info_0 (userid,engname,cnname,occupation,birthday,signname,email,qq,crt_time,mod_time)
select generate_series(1,4000000),
'digoal.zhou',
'德哥',
'DBA',
'1970-01-01'
,E'公益是一輩子的事, I\'m Digoal.Zhou, Just do it!',
'digoal@126.com',
276732431,
clock_timestamp(),
NULL;
insert into user_info_1 (userid,engname,cnname,occupation,birthday,signname,email,qq,crt_time,mod_time)
select generate_series(4000001,8000000),
'digoal.zhou',
'德哥',
'DBA',
'1970-01-01'
,E'公益是一輩子的事, I\'m Digoal.Zhou, Just do it!',
'digoal@126.com',
276732431,
clock_timestamp(),
NULL;
insert into user_info_2 (userid,engname,cnname,occupation,birthday,signname,email,qq,crt_time,mod_time)
select generate_series(8000001,12000000),
'digoal.zhou',
'德哥',
'DBA',
'1970-01-01'
,E'公益是一輩子的事, I\'m Digoal.Zhou, Just do it!',
'digoal@126.com',
276732431,
clock_timestamp(),
NULL;
insert into user_info_3 (userid,engname,cnname,occupation,birthday,signname,email,qq,crt_time,mod_time)
select generate_series(12000001,16000000),
'digoal.zhou',
'德哥',
'DBA',
'1970-01-01'
,E'公益是一輩子的事, I\'m Digoal.Zhou, Just do it!',
'digoal@126.com',
276732431,
clock_timestamp(),
NULL;
insert into user_info_4 (userid,engname,cnname,occupation,birthday,signname,email,qq,crt_time,mod_time)
select generate_series(16000001,20000000),
'digoal.zhou',
'德哥',
'DBA',
'1970-01-01'
,E'公益是一輩子的事, I\'m Digoal.Zhou, Just do it!',
'digoal@126.com',
276732431,
clock_timestamp(),
NULL;
insert into user_session_0 (userid) select generate_series(1,4000000);
insert into user_session_1 (userid) select generate_series(4000001,8000000);
insert into user_session_2 (userid) select generate_series(8000001,12000000);
insert into user_session_3 (userid) select generate_series(12000001,16000000);
insert into user_session_4 (userid) select generate_series(16000001,20000000);
創建索引
set work_mem='2048MB';
set maintenance_work_mem='2048MB';
alter table user_info_0 add primary key (userid);
alter table user_info_1 add primary key (userid);
alter table user_info_2 add primary key (userid);
alter table user_info_3 add primary key (userid);
alter table user_info_4 add primary key (userid);
alter table user_session_0 add primary key (userid);
alter table user_session_1 add primary key (userid);
alter table user_session_2 add primary key (userid);
alter table user_session_3 add primary key (userid);
alter table user_session_4 add primary key (userid);
同樣通過pgfincore把他們加載到內存中, 這裏不詳細描述.
新建登陸和退出函數
create or replace function f_user_login_0
(i_userid int,
OUT o_userid int,
OUT o_engname text,
OUT o_cnname text,
OUT o_occupation text,
OUT o_birthday date,
OUT o_signname text,
OUT o_email text,
OUT o_qq numeric
)
as $BODY$
declare
begin
select userid,engname,cnname,occupation,birthday,signname,email,qq
into o_userid,o_engname,o_cnname,o_occupation,o_birthday,o_signname,o_email,o_qq
from user_info_0 where userid=i_userid;
insert into user_login_rec (userid,login_time,ip) values (i_userid,now(),inet_client_addr());
update user_session_0 set logintime=now(),login_count=login_count+1 where userid=i_userid;
return;
end;
$BODY$
language plpgsql;
create or replace function f_user_login_1
(i_userid int,
OUT o_userid int,
OUT o_engname text,
OUT o_cnname text,
OUT o_occupation text,
OUT o_birthday date,
OUT o_signname text,
OUT o_email text,
OUT o_qq numeric
)
as $BODY$
declare
begin
select userid,engname,cnname,occupation,birthday,signname,email,qq
into o_userid,o_engname,o_cnname,o_occupation,o_birthday,o_signname,o_email,o_qq
from user_info_1 where userid=i_userid;
insert into user_login_rec (userid,login_time,ip) values (i_userid,now(),inet_client_addr());
update user_session_1 set logintime=now(),login_count=login_count+1 where userid=i_userid;
return;
end;
$BODY$
language plpgsql;
create or replace function f_user_login_2
(i_userid int,
OUT o_userid int,
OUT o_engname text,
OUT o_cnname text,
OUT o_occupation text,
OUT o_birthday date,
OUT o_signname text,
OUT o_email text,
OUT o_qq numeric
)
as $BODY$
declare
begin
select userid,engname,cnname,occupation,birthday,signname,email,qq
into o_userid,o_engname,o_cnname,o_occupation,o_birthday,o_signname,o_email,o_qq
from user_info_2 where userid=i_userid;
insert into user_login_rec (userid,login_time,ip) values (i_userid,now(),inet_client_addr());
update user_session_2 set logintime=now(),login_count=login_count+1 where userid=i_userid;
return;
end;
$BODY$
language plpgsql;
create or replace function f_user_login_3
(i_userid int,
OUT o_userid int,
OUT o_engname text,
OUT o_cnname text,
OUT o_occupation text,
OUT o_birthday date,
OUT o_signname text,
OUT o_email text,
OUT o_qq numeric
)
as $BODY$
declare
begin
select userid,engname,cnname,occupation,birthday,signname,email,qq
into o_userid,o_engname,o_cnname,o_occupation,o_birthday,o_signname,o_email,o_qq
from user_info_3 where userid=i_userid;
insert into user_login_rec (userid,login_time,ip) values (i_userid,now(),inet_client_addr());
update user_session_3 set logintime=now(),login_count=login_count+1 where userid=i_userid;
return;
end;
$BODY$
language plpgsql;
create or replace function f_user_login_4
(i_userid int,
OUT o_userid int,
OUT o_engname text,
OUT o_cnname text,
OUT o_occupation text,
OUT o_birthday date,
OUT o_signname text,
OUT o_email text,
OUT o_qq numeric
)
as $BODY$
declare
begin
select userid,engname,cnname,occupation,birthday,signname,email,qq
into o_userid,o_engname,o_cnname,o_occupation,o_birthday,o_signname,o_email,o_qq
from user_info_4 where userid=i_userid;
insert into user_login_rec (userid,login_time,ip) values (i_userid,now(),inet_client_addr());
update user_session_4 set logintime=now(),login_count=login_count+1 where userid=i_userid;
return;
end;
$BODY$
language plpgsql;
create or replace function f_user_logout_0
(i_userid int,
OUT o_result int
)
as $BODY$
declare
begin
insert into user_logout_rec (userid,logout_time,ip) values (i_userid,now(),inet_client_addr());
update user_session_0 set logouttime=now(),online_interval=online_interval+(now()-logintime) where userid=i_userid;
o_result := 0;
return;
exception
when others then
o_result := 1;
return;
end;
$BODY$
language plpgsql;
create or replace function f_user_logout_1
(i_userid int,
OUT o_result int
)
as $BODY$
declare
begin
insert into user_logout_rec (userid,logout_time,ip) values (i_userid,now(),inet_client_addr());
update user_session_1 set logouttime=now(),online_interval=online_interval+(now()-logintime) where userid=i_userid;
o_result := 0;
return;
exception
when others then
o_result := 1;
return;
end;
$BODY$
language plpgsql;
create or replace function f_user_logout_2
(i_userid int,
OUT o_result int
)
as $BODY$
declare
begin
insert into user_logout_rec (userid,logout_time,ip) values (i_userid,now(),inet_client_addr());
update user_session_2 set logouttime=now(),online_interval=online_interval+(now()-logintime) where userid=i_userid;
o_result := 0;
return;
exception
when others then
o_result := 1;
return;
end;
$BODY$
language plpgsql;
create or replace function f_user_logout_3
(i_userid int,
OUT o_result int
)
as $BODY$
declare
begin
insert into user_logout_rec (userid,logout_time,ip) values (i_userid,now(),inet_client_addr());
update user_session_3 set logouttime=now(),online_interval=online_interval+(now()-logintime) where userid=i_userid;
o_result := 0;
return;
exception
when others then
o_result := 1;
return;
end;
$BODY$
language plpgsql;
create or replace function f_user_logout_4
(i_userid int,
OUT o_result int
)
as $BODY$
declare
begin
insert into user_logout_rec (userid,logout_time,ip) values (i_userid,now(),inet_client_addr());
update user_session_4 set logouttime=now(),online_interval=online_interval+(now()-logintime) where userid=i_userid;
o_result := 0;
return;
exception
when others then
o_result := 1;
return;
end;
$BODY$
language plpgsql;
【調優階段7】
1. 登陸腳本
cat login*.sql
\setrandom userid 1 4000000
SELECT f_user_login_0(:userid);
\setrandom userid 4000001 8000000
SELECT f_user_login_1(:userid);
\setrandom userid 8000001 12000000
SELECT f_user_login_2(:userid);
\setrandom userid 12000001 16000000
SELECT f_user_login_3(:userid);
\setrandom userid 16000001 20000000
SELECT f_user_login_4(:userid);
2. 退出腳本
cat logout*.sql
\setrandom userid 1 4000000
SELECT f_user_logout_0(:userid);
\setrandom userid 4000001 8000000
SELECT f_user_logout_1(:userid);
\setrandom userid 8000001 12000000
SELECT f_user_logout_2(:userid);
\setrandom userid 12000001 16000000
SELECT f_user_logout_3(:userid);
\setrandom userid 16000001 20000000
SELECT f_user_logout_4(:userid);
3. 壓力測試
pgbench -M prepared -r -c 1 -f /home/postgres/test/login0.sql -j 1 -n -T 180 -h 172.16.3.33 -p 1921 -U digoal digoal >./log.login0 &
pgbench -M prepared -r -c 1 -f /home/postgres/test/login1.sql -j 1 -n -T 180 -h 172.16.3.33 -p 1921 -U digoal digoal >./log.login1 &
pgbench -M prepared -r -c 2 -f /home/postgres/test/login2.sql -j 2 -n -T 180 -h 172.16.3.33 -p 1921 -U digoal digoal >./log.login2 &
pgbench -M prepared -r -c 2 -f /home/postgres/test/login3.sql -j 2 -n -T 180 -h 172.16.3.33 -p 1921 -U digoal digoal >./log.login3 &
pgbench -M prepared -r -c 2 -f /home/postgres/test/login4.sql -j 2 -n -T 180 -h 172.16.3.33 -p 1921 -U digoal digoal >./log.login4 &
4. 測試結果
cat log.log*
transaction type: Custom query
scaling factor: 1
query mode: prepared
number of clients: 1
number of threads: 1
duration: 180 s
number of transactions actually processed: 233348
tps = 1281.818097 (including connections establishing)
tps = 1281.837109 (excluding connections establishing)
statement latencies in milliseconds:
0.003492 \setrandom userid 1 4000000
0.771932 SELECT f_user_login_0(:userid);
transaction type: Custom query
scaling factor: 1
query mode: prepared
number of clients: 1
number of threads: 1
duration: 180 s
number of transactions actually processed: 233466
tps = 1282.514774 (including connections establishing)
tps = 1282.573500 (excluding connections establishing)
statement latencies in milliseconds:
0.003546 \setrandom userid 4000001 8000000
0.771399 SELECT f_user_login_1(:userid);
transaction type: Custom query
scaling factor: 1
query mode: prepared
number of clients: 2
number of threads: 2
duration: 180 s
number of transactions actually processed: 475466
tps = 2612.200783 (including connections establishing)
tps = 2612.281526 (excluding connections establishing)
statement latencies in milliseconds:
0.003605 \setrandom userid 8000001 12000000
0.757312 SELECT f_user_login_2(:userid);
transaction type: Custom query
scaling factor: 1
query mode: prepared
number of clients: 2
number of threads: 2
duration: 180 s
number of transactions actually processed: 468904
tps = 2576.380443 (including connections establishing)
tps = 2576.488485 (excluding connections establishing)
statement latencies in milliseconds:
0.003587 \setrandom userid 12000001 16000000
0.767869 SELECT f_user_login_3(:userid);
transaction type: Custom query
scaling factor: 1
query mode: prepared
number of clients: 2
number of threads: 2
duration: 180 s
number of transactions actually processed: 439381
tps = 2414.347086 (including connections establishing)
tps = 2414.425600 (excluding connections establishing)
statement latencies in milliseconds:
0.004431 \setrandom userid 16000001 20000000
0.817879 SELECT f_user_login_4(:userid);
總計 :
tps = 10167.261183 (including connections establishing)
tps = 10167.261183 (excluding connections establishing)
5. 瓶頸分析與優化
到這裏我們還沒有關注過表空間, 其實這些表拆分後它們還在同一個表空間裏麵. 把它們放在不同的表空間可以擴展它們整體的IO吞吐能力.
postgres=# \db+
List of tablespaces
Name | Owner | Location | Access privileges | Description
------------+----------+---------------------------------------------+---------------------+-------------
digoal | postgres | /pgdata/digoal/1921/data02/pg_tbs/digoal | postgres=C/postgres+|
| | | digoal=C/postgres |
digoal_01 | postgres | /pgdata/digoal/1921/data03/pg_tbs/digoal_01 | postgres=C/postgres+|
| | | digoal=C/postgres |
digoal_02 | postgres | /pgdata/digoal/1921/data04/pg_tbs/digoal_02 | postgres=C/postgres+|
| | | digoal=C/postgres |
digoal_03 | postgres | /pgdata/digoal/1921/data05/pg_tbs/digoal_03 | postgres=C/postgres+|
| | | digoal=C/postgres |
digoal_04 | postgres | /pgdata/digoal/1921/data06/pg_tbs/digoal_04 | postgres=C/postgres+|
| | | digoal=C/postgres |
digoal=> alter table user_info_0 set tablespace digoal_04;
ALTER TABLE
digoal=> alter table user_info_2 set tablespace digoal_01;
ALTER TABLE
digoal=> alter table user_info_3 set tablespace digoal_02;
ALTER TABLE
digoal=> alter table user_info_4 set tablespace digoal_03;
digoal=> alter index user_info_0_pkey set tablespace digoal_04;
ALTER INDEX
digoal=> alter index user_info_2_pkey set tablespace digoal_01;
ALTER INDEX
digoal=> alter index user_info_3_pkey set tablespace digoal_02;
ALTER INDEX
digoal=> alter index user_info_4_pkey set tablespace digoal_03;
digoal=> alter table user_session_0 set tablespace digoal_04;
ALTER TABLE
digoal=> alter table user_session_2 set tablespace digoal_01;
ALTER TABLE
digoal=> alter table user_session_3 set tablespace digoal_02;
ALTER TABLE
digoal=> alter table user_session_4 set tablespace digoal_03;
digoal=> alter index user_session_0_pkey set tablespace digoal_04;
ALTER INDEX
digoal=> alter index user_session_2_pkey set tablespace digoal_01;
ALTER INDEX
digoal=> alter index user_session_3_pkey set tablespace digoal_02;
ALTER INDEX
digoal=> alter index user_session_4_pkey set tablespace digoal_03;
重新把它們加載到內存.
未完待續。
圖一 :
最後更新：2017-04-01 13:44:35