今年的DTCC大会上，MongoDB中国的唐总带来了《如何在3.0实现7-10倍性能提升》。演讲时顺便倒了点苦水：一些其它数据库喜欢拿MongoDB进行性能PK，但MongoDB之前的开发一直没有怎么关注性能这块，以前也没有发布过官方的性能测试数据，所以结果可想而知。

但是，MongoDB 3.0带来了新的WiredTiger存储引擎,不再像以前(MMAPv1引擎)那样受制于OS内存映射，性能有7-10倍的提升。这里有一份MongoDB的官方性能测试报告，根据这份测试报告，WiredTiger的性能提升主要表现在数据压缩和并行加载上。http://www.mongoing.com/archives/862由于WiredTiger不是默认引擎(根据唐总的说法，以后的版本会考虑作为默认引擎) 。我之前的那篇测试，对比的还是MongoDB的老引擎MMAPv1。http://blog.chinaunix.net/xmlrpc.php?r=blog/article&uid=20726500&id=4960138下面用同样的测试方法看看WiredTiger引擎的表现(嫌测试过程写得太长的话，可以直接跳到后面看测试总结)。

1. MongoDB WiredTiger引擎的测试

1)启用MongoDB的WiredTiger引擎

点击(此处)折叠或打开

-bash-4.1$ mongod --dbpath /data/db2 --storageEngine wiredTiger

2015-04-18T07:51:26.647+0800 I STORAGE [initandlisten] wiredtiger_open config: create,cache_size=1G,session_max=20000,eviction=(threads_max=4),statistics=(fast),log=(enabled=true,archive=true,path=journal,compressor=snappy),checkpoint=(wait=60,log_size=2GB),statistics_log=(wait=0),

2015-04-18T07:51:27.561+0800 I CONTROL [initandlisten] MongoDB starting : pid=21287 port=27017 dbpath=/data/db2 64-bit host=hanode1

2015-04-18T07:51:27.563+0800 I CONTROL [initandlisten] db version v3.0.2

2015-04-18T07:51:27.563+0800 I CONTROL [initandlisten] git version: 6201872043ecbbc0a4cc169b5482dcf385fc464f

2015-04-18T07:51:27.564+0800 I CONTROL [initandlisten] OpenSSL version: OpenSSL 1.0.1e-fips 11 Feb 2013

2015-04-18T07:51:27.564+0800 I CONTROL [initandlisten] build info: Linux ip-10-171-120-213 2.6.32-220.el6.x86_64 #1 SMP Wed Nov 9 08:03:13 EST 2011 x86_64 BOOST_LIB_VERSION=1_49

2015-04-18T07:51:27.564+0800 I CONTROL [initandlisten] allocator: tcmalloc

2015-04-18T07:51:27.564+0800 I CONTROL [initandlisten] options: { storage: { dbPath: "/data/db2", engine: "wiredTiger" } }

2015-04-18T07:51:27.595+0800 I NETWORK [initandlisten] waiting for connections on port 270172)加载数据

点击(此处)折叠或打开

-bash-4.1$ time -p mongoimport --type json --collection json_tables --db benchmark /dev/null 2>/dev/null

real 8.53

user 7.99

sys 4.14

加载期间的系统负载

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[root@hanode1 ~]# top

top - 08:19:09 up 7 days, 11:19, 5 users, load average: 0.48, 0.13, 0.04

Tasks: 151 total, 1 running, 150 sleeping, 0 stopped, 0 zombie

Cpu(s): 24.9%us, 9.3%sy, 0.0%ni, 56.8%id, 6.2%wa, 0.5%hi, 2.4%si, 0.0%st

Mem: 1019320k total, 949576k used, 69744k free, 82976k buffers

Swap: 2064376k total, 62092k used, 2002284k free, 215228k cached

PID USER PR NI VIRT RES SHR S %CPU %MEM TIME+ COMMAND

24190 postgres 20 0 549m 219m 3312 S 114.7 22.1 0:08.94 mongoimport

21287 postgres 20 0 634m 331m 4748 S 34.6 33.3 0:44.48 mongod值得注意的是系统的瓶颈在mongoimport进程(mongoimport的CPU利用率超过了100%)，而不是mongod进程，mongod还有很多的余力。

3)建索引

点击(此处)折叠或打开

-bash-4.1$ echo "db.json_tables.ensureIndex( { \"name\": 1})" |time -p mongo benchmark >/dev/null

real 1.25

user 0.03

sys 0.03

-bash-4.1$ echo "db.json_tables.ensureIndex( { \"type\": 1})" |time -p mongo benchmark >/dev/null

real 0.45

user 0.02

sys 0.02

-bash-4.1$ echo "db.json_tables.ensureIndex( { \"brand\": 1})" |time -p mongo benchmark >/dev/null

real 0.37

user 0.05

sys 0.03

4)查看存储空间大小

点击(此处)折叠或打开

-bash-4.1$ mongo benchmark

MongoDB shell version: 3.0.2

connecting to: benchmark

> db.json_tables.stats()

{

"ns" : "benchmark.json_tables",

"count" : 100001,

"size" : 266284846,

"avgObjSize" : 2662,

"storageSize" : 43167744,

"capped" : false,

"wiredTiger" : {

"metadata" : {

"formatVersion" : 1

},

"creationString" : "allocation_size=4KB,app_metadata=(formatVersion=1),block_allocation=best,block_compressor=snappy,cache_resident=0,checkpoint=(WiredTigerCheckpoint.2=(addr=\"01e208e781e4d41e9d38e208e881e4369503e3e208e981e446480254808080e402928fc0e40291cfc0\",order=2,time=1429314987,size=43118592,write_gen=9063)),checkpoint_lsn=(2,40610048),checksum=uncompressed,collator=,columns=,dictionary=0,format=btree,huffman_key=,huffman_value=,id=5,internal_item_max=0,internal_key_max=0,internal_key_truncate=,internal_page_max=4KB,key_format=q,key_gap=10,leaf_item_max=0,leaf_key_max=0,leaf_page_max=32KB,leaf_value_max=1MB,memory_page_max=10m,os_cache_dirty_max=0,os_cache_max=0,prefix_compression=0,prefix_compression_min=4,split_deepen_min_child=0,split_deepen_per_child=0,split_pct=90,value_format=u,version=(major=1,minor=1)",

"type" : "file",

"uri" : "statistics:table:collection-2--333628209475642491",

"LSM" : {

"bloom filters in the LSM tree" : 0,

"bloom filter false positives" : 0,

"bloom filter hits" : 0,

"bloom filter misses" : 0,

"bloom filter pages evicted from cache" : 0,

"bloom filter pages read into cache" : 0,

"total size of bloom filters" : 0,

"sleep for LSM checkpoint throttle" : 0,

"chunks in the LSM tree" : 0,

"highest merge generation in the LSM tree" : 0,

"queries that could have benefited from a Bloom filter that did not exist" : 0,

"sleep for LSM merge throttle" : 0

},

"block-manager" : {

"file allocation unit size" : 4096,

"blocks allocated" : 9066,

"checkpoint size" : 43118592,

"allocations requiring file extension" : 9046,

"blocks freed" : 27,

"file magic number" : 120897,

"file major version number" : 1,

"minor version number" : 0,

"file bytes available for reuse" : 40960,

"file size in bytes" : 43167744

},

"btree" : {

"btree checkpoint generation" : 8,

"column-store variable-size deleted values" : 0,

"column-store fixed-size leaf pages" : 0,

"column-store internal pages" : 0,

"column-store variable-size leaf pages" : 0,

"pages rewritten by compaction" : 0,

"number of key/value pairs" : 0,

"fixed-record size" : 0,

"maximum tree depth" : 3,

"maximum internal page key size" : 368,

"maximum internal page size" : 4096,

"maximum leaf page key size" : 3276,

"maximum leaf page size" : 32768,

"maximum leaf page value size" : 1048576,

"overflow pages" : 0,

"row-store internal pages" : 0,

"row-store leaf pages" : 0

},

"cache" : {

"bytes read into cache" : 267348602,

"bytes written from cache" : 267938187,

"checkpoint blocked page eviction" : 0,

"unmodified pages evicted" : 0,

"page split during eviction deepened the tree" : 0,

"modified pages evicted" : 26,

"data source pages selected for eviction unable to be evicted" : 3,

"hazard pointer blocked page eviction" : 3,

"internal pages evicted" : 0,

"pages split during eviction" : 26,

"in-memory page splits" : 6,

"overflow values cached in memory" : 0,

"pages read into cache" : 9005,

"overflow pages read into cache" : 0,

"pages written from cache" : 9063

},

"compression" : {

"raw compression call failed, no additional data available" : 0,

"raw compression call failed, additional data available" : 0,

"raw compression call succeeded" : 0,

"compressed pages read" : 9004,

"compressed pages written" : 9020,

"page written failed to compress" : 0,

"page written was too small to compress" : 43

},

"cursor" : {

"create calls" : 14,

"insert calls" : 100001,

"bulk-loaded cursor-insert calls" : 0,

"cursor-insert key and value bytes inserted" : 266602485,

"next calls" : 300006,

"prev calls" : 1,

"remove calls" : 0,

"cursor-remove key bytes removed" : 0,

"reset calls" : 100005,

"search calls" : 0,

"search near calls" : 0,

"update calls" : 0,

"cursor-update value bytes updated" : 0

},

"reconciliation" : {

"dictionary matches" : 0,

"internal page multi-block writes" : 2,

"leaf page multi-block writes" : 28,

"maximum blocks required for a page" : 345,

"internal-page overflow keys" : 0,

"leaf-page overflow keys" : 0,

"overflow values written" : 0,

"pages deleted" : 0,

"page checksum matches" : 204,

"page reconciliation calls" : 32,

"page reconciliation calls for eviction" : 26,

"leaf page key bytes discarded using prefix compression" : 0,

"internal page key bytes discarded using suffix compression" : 9230

},

"session" : {

"object compaction" : 0,

"open cursor count" : 14

},

"transaction" : {

"update conflicts" : 0

}

},

"nindexes" : 4,

"totalIndexSize" : 2826240,

"indexSizes" : {

"_id_" : 864256,

"name_1" : 868352,

"type_1" : 630784,

"brand_1" : 462848

},

"ok" : 1

}WiredTiger引擎的压缩效果确实不错，把253MB的原始数据压缩到了41MB。5)数据查询

匹配9091条记录的查询：点击(此处)折叠或打开-bash-4.1$ echo "db.json_tables.find({ brand: 'ACME'}).count()"|mongo benchmark

MongoDB shell version: 3.0.2

connecting to: benchmark

9091

bye

-bash-4.1$ echo "DBQuery.shellBatchSize = 10000000000;db.json_tables.find({ brand: 'ACME'})"|time -p mongo benchmark >/dev/null

real 3.93

user 3.56

sys 0.11

*)第1次测的时间是12秒，有IO等待的时间，上面是第2次测试的结果，即数据已经被缓存了。看看top的资源占用。点击(此处)折叠或打开Tasks: 158 total, 2 running, 156 sleeping, 0 stopped, 0 zombie

Cpu(s): 22.6%us, 0.4%sy, 0.0%ni, 76.8%id, 0.0%wa, 0.0%hi, 0.2%si, 0.0%st

Mem: 1019320k total, 793584k used, 225736k free, 61424k buffers

Swap: 2064376k total, 64096k used, 2000280k free, 236052k cached

PID USER PR NI VIRT RES SHR S %CPU %MEM TIME+ COMMAND

27969 postgres 20 0 752m 67m 9304 R 91.4 6.8 0:03.46 mongo

26391 postgres 20 0 610m 325m 11m S 1.0 32.7 0:10.95 mongod测试结果和MMAPv1引擎差不多，CPU把时间都耗在客户端的mongo进程上。因为客户端在处理大量输出结果时消耗了太多的资源。为了排除大量数据处理的误导，下面执行一下0匹配的查询。

点击(此处)折叠或打开

-bash-4.1$ echo "DBQuery.shellBatchSize = 10000000000;db.json_tables.find({ brand: 'ACME111'})"|time -p mongo benchmark >/dev/null

real 0.07

user 0.04

sys 0.01

测试结果和MMAPv1引擎也差不多。再试试0匹配的全表扫描。

点击(此处)折叠或打开

-bash-4.1$ echo "db.json_tables.dropIndexes()"|mongo benchmark

MongoDB shell version: 3.0.2

connecting to: benchmark

{

"nIndexesWas" : 4,

"msg" : "non-_id indexes dropped for collection",

"ok" : 1

}

bye

-bash-4.1$echo"DBQuery.shellBatchSize = 10000000000;db.json_tables.find({ brand: 'ACME111'})"|time-p mongo benchmark>/dev/null

real 0.14

user 0.02

sys 0.03

测试结果和MMAPv1引擎比快了有50%左右。

6)数据插入

先清数据

点击(此处)折叠或打开

-bash-4.1$ echo "db.json_tables.drop()"|mongo benchmark

MongoDB shell version: 3.0.2

connecting to: benchmark

true

bye插入数据

点击(此处)折叠或打开

-bash-4.1$ time mongo benchmark --quiet /dev/null

real    1m31.420s

user    0m30.832s

sys    0m50.319s测试结果也和MMAPv1引擎差不多。

并且由于MongoDB的控制台不允许插入大于4k的文档，最后插入的数据没有10万条。

点击(此处)折叠或打开

-bash-4.1$ mongo benchmark

MongoDB shell version: 3.0.2

connecting to: benchmark

> db.json_tables.count()

72728

下面看看插入时的系统负载

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[root@hanode1 ~]# top

top - 08:02:23 up 7 days, 11:02, 5 users, load average: 0.00, 0.02, 0.00

Tasks: 154 total, 2 running, 152 sleeping, 0 stopped, 0 zombie

Cpu(s): 3.3%us, 1.5%sy, 0.0%ni, 76.4%id, 0.0%wa, 0.0%hi, 18.8%si, 0.0%st

Mem: 1019320k total, 888680k used, 130640k free, 83272k buffers

Swap: 2064376k total, 62140k used, 2002236k free, 295144k cached

PID USER PR NI VIRT RES SHR S %CPU %MEM TIME+ COMMAND

22470 postgres 20 0 753m 66m 9224 R 88.8 6.7 0:03.94 mongo

21287 postgres 20 0 634m 344m 11m S 22.3 34.6 0:12.53 mongod这个测试结果也和MMAPv1引擎惊人的相识。那么WiredTiger引擎的插入性能提升在什么地方呢？

其实WiredTiger引擎在插入上主要提升的是并发性能，MMAPv1引擎在并发时是简单粗暴的锁库模式，严重制约并发性能，CPU核心再多性能也上不去。而我的测试是单并发测试，所以看不出MMAPv1引擎的这个致命缺点。

2. 总结

以单并发时的服务端进程CPU消耗作为衡量指标，PG和WiredTiger的对比总结如下：

1)加载

WiredTiger的性能是PG的3倍(注)

2)插入

相差不大，WiredTiger小胜(注)

3)全表扫描(0匹配)

WiredTiger的性能是PG的4倍

4)单点索引扫描(0匹配)

PG的性能是WiredTiger的4倍

5)数据大小

PG的数据大小是WiredTiger的3倍

注意，以上加载和插入的这两个数据以单并发时的服务端进程CPU消耗作为衡量指标的，忽略了MongoDB客户端的高CPU消耗，实际场景中也有可能PG更快。之所以这样对比，是因为：

我们假设好的引擎可以在高并发时可以把负载均衡地分散到所有CPU核心上，对这样的引擎，在CPU成为瓶颈的场景中，单并发时的CPU实际占用时间可以作为一个重要的参考指标。但是，服务端能不能在高并发时实现多CPU核心上的线性或近似线性的性能Scale UP还需看服务端(PG/MongoDB)的素质和具体使用场景。

最后，即使PG在加载和插入的PK上输了也很正常，因为PG是保障了ACID的，WiredTiger却有丢失数据的风险。Journal 默认不会即时刷盘，系统宕机会丢失最多100MB Journal数据