为什么PostgreSQL比MongoDB还快之续篇(WiredTiger引擎)
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.iyunv.com/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
[*]-bash-4.1$ mongod --dbpath /data/db2 --storageEngine wiredTiger
[*]2015-04-18T07:51:26.647+0800 I STORAGE 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 MongoDB starting : pid=21287 port=27017 dbpath=/data/db2 64-bit host=hanode1
[*]2015-04-18T07:51:27.563+0800 I CONTROL db version v3.0.2
[*]2015-04-18T07:51:27.563+0800 I CONTROL git version: 6201872043ecbbc0a4cc169b5482dcf385fc464f
[*]2015-04-18T07:51:27.564+0800 I CONTROL OpenSSL version: OpenSSL 1.0.1e-fips 11 Feb 2013
[*]2015-04-18T07:51:27.564+0800 I CONTROL 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 allocator: tcmalloc
[*]2015-04-18T07:51:27.564+0800 I CONTROL options: { storage: { dbPath: "/data/db2", engine: "wiredTiger" } }
[*]2015-04-18T07:51:27.595+0800 I NETWORK waiting for connections on port 27017
2）加载数据
[*]-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
[*]# 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 PRNIVIRTRESSHR S %CPU %MEM TIME+COMMAND
[*]24190 postgres20 0549m 219m 3312 S 114.7 22.1 0:08.94 mongoimport
[*]21287 postgres20 0634m 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
[*]-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
[*]-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 PRNIVIRTRESSHR S %CPU %MEM TIME+COMMAND
[*]27969 postgres20 0752m67m 9304 R 91.4 6.8 0:03.46 mongo
[*]26391 postgres20 0610m 325m11m 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。
[*]-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。
[*]-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
[*]-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。
[*]-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
[*]# 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 PRNIVIRTRESSHR S %CPU %MEM TIME+COMMAND
[*]22470 postgres20 0753m66m 9224 R 88.8 6.7 0:03.94 mongo
[*]21287 postgres20 0634m 344m11m 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
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