数据库内核月报－ 2016/04 - PgSQL · 性能优化 · 如何潇洒的处理每天上百TB的数据增量 - 《数据库内核月报》

TEST 结果
软件环境
- 数据库配置
查询性能(索引能力)

这类场景常见于运营商网关数据和金融行业数据，产生量大，并且要求快速插入大数据库中持久化保存。另外, 用户如果需要流式实时处理，可以参考基于PostgreSQL的流式处理方案，一天处理1万亿的实时流式处理是如何实现的？PostgreSQL “物联网”应用 - 1 实时流式数据处理案例(万亿每天)

TEST CASE

平均每条记录长度360字节（比较常见的长度）；
时间字段创建索引；
每轮测试插入12TB数据，插入完12T后清除数据继续插入。循环；
测试满24小时停止测试；
统计24小时插入的记录数；

24小时一共完成12轮测试，平均每轮测试耗时7071秒。 506万行/s，1.78 GB/s，全天插入4372亿，154TB数据。

测试的硬件环境


2. xfs
3. PostgreSQL 9.5

数据库配置

./configure --prefix=/home/digoal/pgsql9.5.1 --with-blocksize=32 --with-segsize=128 --with-wal-blocksize=32 --with-wal-segsize=64
make && make install

PostgreSQL支持hugepage的方法

参数

listen_addresses = '0.0.0.0'            # what IP address(es) to listen on;
fsync=on
port = 1921                             # (change requires restart)
max_connections = 600                   # (change requires restart)
superuser_reserved_connections = 13     # (change requires restart)
unix_socket_directories = '.'   # comma-separated list of directories
unix_socket_permissions = 0700          # begin with 0 to use octal notation
tcp_keepalives_idle = 60                # TCP_KEEPIDLE, in seconds;
tcp_keepalives_interval = 10            # TCP_KEEPINTVL, in seconds;
tcp_keepalives_count = 10               # TCP_KEEPCNT;
shared_buffers = 256GB                   # min 128kB
huge_pages = on                 # on, off, or try
work_mem = 512MB                                # min 64kB
maintenance_work_mem = 1GB              # min 1MB
autovacuum_work_mem = 1GB               # min 1MB, or -1 to use maintenance_work_mem
dynamic_shared_memory_type = posix      # the default is the first option
bgwriter_delay = 10ms                   # 10-10000ms between rounds
bgwriter_lru_maxpages = 1000            # 0-1000 max buffers written/round
bgwriter_lru_multiplier = 2.0
synchronous_commit = off                # synchronization level;
full_page_writes = on                  # recover from partial page writes
wal_buffers = 2047MB                    # min 32kB, -1 sets based on shared_buffers
wal_writer_delay = 10ms         # 1-10000 milliseconds
checkpoint_timeout = 55min              # range 30s-1h
max_wal_size = 512GB
checkpoint_completion_target = 0.9      # checkpoint target duration, 0.0 - 1.0
effective_cache_size = 40GB
log_destination = 'csvlog'              # Valid values are combinations of
logging_collector = on          # Enable capturing of stderr and csvlog
log_directory = 'pg_log'                # directory where log files are written,
log_filename = 'postgresql-%Y-%m-%d_%H%M%S.log' # log file name pattern,
log_file_mode = 0600                    # creation mode for log files,
log_truncate_on_rotation = on           # If on, an existing log file with the
log_checkpoints = off
log_connections = off
log_disconnections = off
log_error_verbosity = verbose           # terse, default, or verbose messages
log_timezone = 'PRC'
log_autovacuum_min_duration = 0 # -1 disables, 0 logs all actions and
datestyle = 'iso, mdy'
timezone = 'PRC'
lc_messages = 'C'                       # locale for system error message
lc_monetary = 'C'                       # locale for monetary formatting
lc_numeric = 'C'                        # locale for number formatting
lc_time = 'C'                           # locale for time formatting
default_text_search_config = 'pg_catalog.english'
autovacuum=off

创建测试表 : 每32K的block存储89条记录, 每条记录360字节。

postgres=# select string_agg(i,'') from (select md5(random()::text) i from generate_series(1,10) t(i)) t(i);
                               string_agg
----------------------------------------------------------------------
 53d3ec7adbeacc912a45bdd8557b435be848e4b1050dc0f5e46b75703d4745833541b5dabc177db460b6b1493961fc72c478daaaac74bcc89aec4f946a496028d9cff1cc4144f738e01ea36436455c216aa697d87fe1f87ceb49134a687dc69cba34c9951d0c9ce9ca82bba229d56874af40498dca5f
d8dfb9c877546db76c35a3362d6bdba6472d3919289b6eaeeab58feb4f6e79592fc1dd8253fd4c588a29
(1 row)
postgres=# create unlogged table test(crt_time timestamp, info text default '53d3ec7adbeacc912a45bdd8557b435be848e4b1050dc0f5e46b75703d4745833541b5dabc177db460b6b1493961fc72c478daaaac74bcc89aec4f946a496028d9cff1cc4144f738e01ea36436455c216aa697d87fe1f87ceb49134a687dc69cba34c9951d0c9ce9ca82bba229d56874af40498dca5f
d8dfb9c877546db76c35a3362d6bdba6472d3919289b6eaeeab58feb4f6e79592fc1dd8253fd4c588a29');
postgres=# alter table test alter column info set storage plain;
postgres=# select ctid from test limit 1000;

创建多个分表，用于减少 block extend 冲突。

PostgreSQL 黑科技 BRIN 索引方法

这里使用的是brin范围索引，PostgreSQL 针对物联网流式数据的黑科技。

postgres=# \di
                 List of relations
 Schema |    Name     | Type  |  Owner   |  Table
--------+-------------+-------+----------+---------
 public | idx_test1   | index | postgres | test1
 public | idx_test100 | index | postgres | test100
 public | idx_test101 | index | postgres | test101
 public | idx_test102 | index | postgres | test102
 public | idx_test103 | index | postgres | test103
 public | idx_test104 | index | postgres | test104
 public | idx_test105 | index | postgres | test105
 public | idx_test106 | index | postgres | test106
......
......
 public | idx_test90  | index | postgres | test90
 public | idx_test91  | index | postgres | test91
 public | idx_test92  | index | postgres | test92
 public | idx_test93  | index | postgres | test93
 public | idx_test94  | index | postgres | test94
 public | idx_test95  | index | postgres | test95
 public | idx_test96  | index | postgres | test96
 public | idx_test97  | index | postgres | test97
 public | idx_test98  | index | postgres | test98
 public | idx_test99  | index | postgres | test99
(128 rows)

生成测试脚本, 一个连接一次插入178条记录，占用2个32KB的block :

vi test.sql
insert into test(crt_time) values (now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now()),(now());
for ((i=1;i<=128;i++)) do sed "s/test/test$i/" test.sql > ./test$i.sql; done

开始测试前清除数据：

do language plpgsql $$
declare
i int;
sql text;
begin
  for i in 1..128 loop
    sql := 'truncate test'||i;
    execute sql;
  end loop;
end;
$$;

测试方法：每轮测试插入12TB数据。通过以下方式控制：

使用128个并行连接，每个连接执行1572864个事务；
一共执行201326592个事务（每个事务插入178条记录）；
一共插入35836133376条记录（358.36 亿记录）（共计12TB 数据，索引空间另算）。

进行下一轮测试前，输出日志，并TRUNCATE所有的数据，然后重复以上测试。直到测试满24小时，输出统计数据。

vi test.sh
#!/bin/bash
if [ $# -ne 5 ]; then
  echo "please use: $0 ip port dbname user pwd"
  exit 1
fi
IP=$1
PORT=$2
DBNAME=$3
USER=$4
PASSWORD=$5
export PGPASSWORD=$PASSWORD
DEP_CMD="psql"
which $DEP_CMD
if [ $? -ne 0 ]; then
  echo -e "dep commands: $DEP_CMD not exist."
  exit 1
fi
truncate() {
psql -h $IP -p $PORT -U $USER $DBNAME <<EOF
do language plpgsql \$\$
declare
i int;
sql text;
begin
  for i in 1..128 loop
    sql := 'truncate test'||i;
  end loop;
end;
\$\$;
\q
EOF
}
# truncate data first
truncate
START=`date +%s`
echo "`date +%F%T` $START"
for ((x=1;x>0;x++))
do
# ------------------------------------------------------
echo "Round $x test start: `date +%F%T` `date +%s`"
for ((i=1;i<=128;i++))
do
  pgbench -M prepared -n -r -f ./test$i.sql -h $IP -p $PORT -U $USER $DBNAME -c 1 -j 1 -t 1572864 >>./$i.log 2>&1 &
done
wait
echo "Round $x test end: `date +%F%T` `date +%s`"
# ------------------------------------------------------
if [ $((`date +%s`-$START)) -gt 86400 ]; then
  echo "end `date +%F%T` `date +%s`"
  echo "duration second: $((`date +%s`-$START))"
  exit 0
fi
echo "Round $x test end, start truncate `date +%F%T` `date +%s`"
truncate
echo "Round $x test end, end truncate `date +%F%T` `date +%s`"
done

测试

postgres=# select min(crt_time),max(crt_time) from test1;
            min             |            max
----------------------------+----------------------------
 2016-04-08 00:32:26.842728 | 2016-04-08 02:29:41.583367
(1 row)
postgres=# explain select count(*) from test1 where crt_time between '2016-04-08 00:32:00' and '2016-04-08 00:33:00';
                                                                            QUERY PLAN
-------------------------------------------------------------------------------------------------------------------------------------------------------------------
 Aggregate  (cost=1183919.81..1183919.82 rows=1 width=0)
   ->  Bitmap Heap Scan on test1  (cost=14351.45..1180420.19 rows=1399849 width=0)
         Recheck Cond: ((crt_time >= '2016-04-08 00:32:00'::timestamp without time zone) AND (crt_time <= '2016-04-08 00:33:00'::timestamp without time zone))
         ->  Bitmap Index Scan on idx_test1  (cost=0.00..14001.49 rows=1399849 width=0)
               Index Cond: ((crt_time >= '2016-04-08 00:32:00'::timestamp without time zone) AND (crt_time <= '2016-04-08 00:33:00'::timestamp without time zone))
(5 rows)
Time: 0.382 ms
postgres=# select count(*) from test1 where crt_time between '2016-04-08 00:32:00' and '2016-04-08 00:33:00';
  count
---------
 2857968
(1 row)
Time: 554.474 ms

小结

这个CASE主要的应用场景是实时的大数据入库，例如物联网的应用场景，大量的传感器会产生庞大的数据。又比如传统的运营商网关，也会有非常庞大的流量数据或业务数据需要实时的入库。索引方面，用到了PostgreSQL黑科技BRIN。
除了实时入库，用户如果需要流式实时处理，可以参考基于PostgreSQL的流式处理方案，一天处理1万亿的实时流式处理是如何实现的？

瓶颈, 还是在IO上面 , 有几个表现，TOP大量进程处于D(front io)状态。

 w: S  --  Process Status
    The status of the task which can be one of:
       ’D’ = uninterruptible sleep
       ’R’ = running
       ’S’ = sleeping
       ’T’ = traced or stopped
       ’Z’ = zombie

所有块设备的使用率均达100% 。清理数据时：

 Device:         rrqm/s   wrqm/s     r/s     w/s   rsec/s   wsec/s avgrq-sz avgqu-sz   await  svctm  %util
 dfa               0.00     0.00 5807.39 167576.65 1464080.93 1340613.23    16.18   535.69    3.02   0.01 116.77
 dfb               0.00     0.00 5975.10 185132.68 1506714.40 1481061.48    15.63   459.46    2.32   0.01 110.62
 dfc               0.00     0.00 5715.56 182584.05 1440771.98 1460672.37    15.41   568.02    2.93   0.01 112.37

插入数据时：

 Device:         rrqm/s   wrqm/s     r/s     w/s   rsec/s   wsec/s avgrq-sz avgqu-sz   await  svctm  %util
 dfa               0.00     0.00    0.00 235936.00     0.00 1887488.00     8.00  2676.34   11.17   0.00  99.10
 dfc               0.00     0.00    0.00 239830.00     0.00 1918632.00     8.00    10.66    0.04   0.00 101.30

有几个工具你可能用得上，perf、systemtap和goprof。如果要较全面的分析，建议把 PostgreSQL –enable-profiling 打开用于诊断。