1. MySQL key partition and MongoDB TEST
对于业务的激活码需求做了一次关于 mysql,mongodb 的比对.mysql 分为 normal,key partition 数量分别是1亿和10亿数据,mysql 采用直接访问 PK 键,partition key
为 PK,mysql table size 为90G,mongodb table size 为157G。
[liuyang@yhdem ~]$ cat /proc/cpuinfo |grep processor |wc -l
24
[liuyang@yhdem ~]$ cat /etc/issue
Oracle Linux Server release 5.8
Kernel r on an m
mysql evn:
mysql> select version();
+-----------+
| version() |
+-----------+
| 5.5.25a |
+-----------+
1 row in set (0.00 sec)

2. log_bin[OFF] innodb_flush_log_at_trx_commit [2] query_cache_type[OFF]
max_connect_errors[10] max_connections[214] max_user_connections[0]
sync_binlog[0] table_definition_cache[400]
table_open_cache[400] thread_cache_size[8] open_files_limit[30000]
innodb_adaptive_flushing[ON] innodb_adaptive_hash_index[ON]
innodb_buffer_pool_size[30.234375G]
innodb_file_per_table[ON] innodb_flush_log_at_trx_commit[2] innodb_flush_method[]
innodb_io_capacity[200] innodb_lock_wait_timeout[100]
innodb_log_buffer_size[128M]
innodb_log_file_size[200M] innodb_log_files_in_group[2]
innodb_max_dirty_pages_pct[75]
innodb_open_files[1600] innodb_read_io_threads[4] innodb_thread_concurrency[0]
innodb_write_io_threads[4]

3. 以下图片均为 QPS 统计,TPS 测试暂时没有做
no partition table with one billion rows –> small random select by pk

4. xDiskName Busy Read WriteKB|0 |25 |50 |75 100|
xsda 1% 2.0 35.9|> |
xsda1 0% 0.0 0.0|> |
xsda2 0% 0.0 0.0|> |
xsda3 0% 0.0 0.0|> |
xsda4 0% 0.0 0.0|>disk busy not available |
xsda5 0% 0.0 0.0|> |
xsda6 1% 2.0 35.9|> |
xsdb 0% 0.0 55.9|> |
xsdb1 0% 0.0 55.9|> |
xTotals Read-MB/s=0.0 Writes-MB/s=0.2 Transfers/sec=18.0

5. partition table with one billion rows –> small random select by pk

6. xDiskName Busy Read WriteKB|0 |25 |50 |75 100|
xsda 0% 0.0 8.0|> |
xsda1 0% 0.0 0.0|> |
xsda2 0% 0.0 8.0|> |
xsda3 0% 0.0 0.0|> |
xsda4 0% 0.0 0.0|>disk busy not available |
xsda5 0% 0.0 0.0|> |
xsda6 0% 0.0 0.0|> |
xsdb 0% 0.0 201.5| > |
xsdb1 0% 0.0 201.5|W > |
xTotals Read-MB/s=0.0 Writes-MB/s=0.4 Transfers/sec=46.9

7. no partition table with one billion rows –> full random select by pk

8. xDiskName Busy Read WriteMB|0 |25 |50 |75 100|
xsda 0% 0.0 0.0| > |
xsda1 0% 0.0 0.0|> |
xsda2 0% 0.0 0.0|> |
xsda3 0% 0.0 0.0|> |
xsda4 0% 0.0 0.0|>disk busy not available |
xsda5 0% 0.0 0.0|> |
xsda6 0% 0.0 0.0| > |
xsdb 100% 86.8 0.2|RRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRR>
xsdb1 100% 86.8 0.2|RRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRR>
xTotals Read-MB/s=173.6 Writes-MB/s=0.4 Transfers/sec=6448.1

9. partition table with one billion rows –> full random select by pk

10. xDiskName Busy Read WriteMB|0 |25 |50 |75 100|
xsda 0% 0.0 0.0| > |
xsda1 0% 0.0 0.0|> |
xsda2 0% 0.0 0.0| > |
xsda3 0% 0.0 0.0|> |
xsda4 0% 0.0 0.0|>disk busy not available |
xsda5 0% 0.0 0.0|> |
xsda6 0% 0.0 0.0| > |
xsdb 100% 89.6 0.2|RRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRR>
xsdb1 100% 89.6 0.2| >
xTotals Read-MB/s=179.2 Writes-MB/s=0.3 Transfers/sec=6539.3

11. no partition table with 100 million rows –> full random select by pk

12. 下面基于 mongodb 的 TEST.同样为 10 亿的表,157G.
TEST.同样为 亿的表,157G.
[root@db-13 tmp]# mongo
MongoDB shell version: 2.0.8
connecting to: test
> db.foo.totalSize();
157875838416
> db.foo.find().count();
1000000000

13. 第一次 使用128G 满额内存 16thread,10亿 random query:
使用128G 16thread,10亿
[root@db-13 tmp]# mongo test ./mongodb_benchmark_query.js
MongoDB shell version: 2.0.8
connecting to: test
threads: 16 queries/sec: 126151.69666666667
第二次 使用128G 内存 24 thread,10亿中的前1亿数据 random query:
使用128G thread,10亿中的前
亿中的前1
[root@db-13 tmp]# mongo test ./mongodb_benchmark_query.js
MongoDB shell version: 2.0.8
connecting to: test
threads: 24 queries/sec: 166527.42333333334

14. 第三次 使用 mysql 用户启动 mongo 限制 mysql 用户的 mem 为24G 24 thread , 10亿中的前1亿数据 random query :
10亿中的前
亿中的前1
[mysql@db-13 ~]$ ulimit -a
core file size (blocks, -c) 0
data seg size (kbytes, -d) unlimited
scheduling priority (-e) 0
file size (blocks, -f) unlimited
pending signals (-i) 1052672
max locked memory (kbytes, -l) 26055452
max memory size (kbytes, -m) 26055452
open files (-n) 131072
pipe size (512 bytes, -p) 8
POSIX message queues (bytes, -q) 819200
real-time priority (-r) 0
stack size (kbytes, -s) 10240
cpu time (seconds, -t) unlimited
max user processes (-u) unlimited
virtual memory (kbytes, -v) unlimited
file locks (-x) unlimited
[mysql@db-13 tmp]$ mongo test ./mongodb_benchmark_query.js
MongoDB shell version: 2.0.8
connecting to: test
threads: 24 queries/sec: 161358.03333333333

15. 第四次 使用 mysql 用户启动 mongo 限制 mysql 用户的 mem 为24G 24 thread , 10亿 random query ：
10亿
[mysql@db-13 tmp]$ mongo test ./mongodb_benchmark_query.js
MongoDB shell version: 2.0.8
connecting to: test
threads: 24 queries/sec: 2549.2 ----------------------> 这里出现了物理 IO 读写
----- 以下是不用缓存磁盘随机 IO 下的测试结果：
mongodb 初始只用了13M 内存
测试结果：

16. —提供查询脚本
ops = [{op:"findOne", ns:"test.foo", query: {_id : {"#RAND_INT": [ 1 , 100000000 ] } }}]
x=24
{
res = benchRun( {
parallel : x ,
seconds : 60 ,
ops : ops
} );
print("threads: "+ x +"t queries/sec: "+ res.query );
}

17. END :
10亿 normal table 对于1亿 normal table 在内存基于 PK 的访问没有衰减,10亿的 partition table 对于 10亿的 normal table 在内存中衰减了2/3,10亿的 partition
table 对于10亿的 normal table 在 full table out of memory 的情况下 性能有所提升 (另外注意激活码基本只会被访问1次)对于 mongodb 来说,这种业务模式需求
完全可以搞定,在内存充足的情况下 QPS 达到了16W+/s,但是在内存不足的情况下,暴跌至2549,在 mem cache 完全清除的情况下,QPS 为149/s,基于这次业务需求为一
张 very large table (暂估500GB）,因此决定使用 mysql 而不是 mongodb.