我有一个这样的集合，包含700MB和100万个文档
{
"_id" : "0455923b34b3",
"identity" : [
{
"currentIdentity" : "90000000",
"identityType" : "ONE",
"identityHistory" : [
{
"identity" : "996969999",
"type" : "ONE",
"from" : "2014-12-14 00:50:06.971",
"to" : "2014-03-14 20:32:33.982"
},
{
"identity" : "9969898899",
"type" : "TWO",
"from" : "2014-12-14 00:50:06.971",
"to" : "2014-03-14 20:32:33.982"
}
]
}
]
}
当Iam尝试像这样查询时
db.customer.aggregate( [
{$match:
{ "identity.identityHistory.from" : {$lt:"2014-09-13 14:18:29.616"} ,
"identity.identityHistory.to" :{$gt:"2014-08-30 09:26:24.842"} } },
{$unwind : "$identity"},
{$unwind:"$identity.identityHistory"},
{$project:{"identity.currentIdentity":1,"identity.identityHistory.identity" : 1 } }
])
我使用性能分析
监控性能1.没有创建索引（colScan），花了526ms
2.在$ match字段上使用复合索引花了29092ms
db.customer.createIndex({"identity.identityHistory.from":1,"identity.identityHistory.to":1})
编辑：
a) RAM:8GB
b) Available RAM:4.3 GB
C) db.stats()
{
"db" : "demomodule",
"collections" : 4,
"objects" : 1200038,
"avgObjSize" : 584.2189480666445,
"dataSize" : 701084938,
"storageSize" : 179236864,
"numExtents" : 0,
"indexes" : 5,
"indexSize" : 31248384,
"ok" : 1
}
d) db.customer.stats()
{
"ns" : "demomodule.customer",
"count" : 999998,
"size" : 686006200,
"avgObjSize" : 686,
"storageSize" : 174927872,
"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.3=(addr=\"01e2868f81e4cad3e2b5e2869081e4c26e4411e2869181e4d9f84752808080e40a6d0fc0e40a6c5fc0\",order=3,time=1446722125,size=174882816,write_gen=21416)),checkpoint_lsn=(85,95479808),checksum=on,collator=,columns=,dictionary=0,format=btree,huffman_key=,huffman_value=,id=63,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=64MB,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-4--2760010649195552578",
"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" : 0,
"checkpoint size" : 174882816,
"allocations requiring file extension" : 0,
"blocks freed" : 0,
"file magic number" : 120897,
"file major version number" : 1,
"minor version number" : 0,
"file bytes available for reuse" : 40960,
"file size in bytes" : 174927872
},
"btree" : {
"btree checkpoint generation" : 104,
"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" : 4,
"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" : 67108864,
"overflow pages" : 0,
"row-store internal pages" : 0,
"row-store leaf pages" : 0
},
"cache" : {
"bytes read into cache" : 695116039,
"bytes written from cache" : 0,
"checkpoint blocked page eviction" : 0,
"unmodified pages evicted" : 0,
"page split during eviction deepened the tree" : 0,
"modified pages evicted" : 0,
"data source pages selected for eviction unable to be evicted" : 0,
"hazard pointer blocked page eviction" : 0,
"internal pages evicted" : 0,
"pages split during eviction" : 0,
"in-memory page splits" : 0,
"overflow values cached in memory" : 0,
"pages read into cache" : 21401,
"overflow pages read into cache" : 0,
"pages written from cache" : 0
},
"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" : 21306,
"compressed pages written" : 0,
"page written failed to compress" : 0,
"page written was too small to compress" : 0
},
"cursor" : {
"create calls" : 11,
"insert calls" : 0,
"bulk-loaded cursor-insert calls" : 0,
"cursor-insert key and value bytes inserted" : 0,
"next calls" : 3018525,
"prev calls" : 1,
"remove calls" : 0,
"cursor-remove key bytes removed" : 0,
"reset calls" : 15650,
"search calls" : 15492,
"search near calls" : 142,
"update calls" : 0,
"cursor-update value bytes updated" : 0
},
"reconciliation" : {
"dictionary matches" : 0,
"internal page multi-block writes" : 0,
"leaf page multi-block writes" : 0,
"maximum blocks required for a page" : 0,
"internal-page overflow keys" : 0,
"leaf-page overflow keys" : 0,
"overflow values written" : 0,
"pages deleted" : 0,
"page checksum matches" : 0,
"page reconciliation calls" : 0,
"page reconciliation calls for eviction" : 0,
"leaf page key bytes discarded using prefix compression" : 0,
"internal page key bytes discarded using suffix compression" : 0
},
"session" : {
"object compaction" : 0,
"open cursor count" : 11
},
"transaction" : {
"update conflicts" : 0
}
},
"nindexes" : 2,
"totalIndexSize" : 29560832,
"indexSizes" : {
"_id_" : 14008320,
"identity.identityHistory.from_1" : 15552512
},
"ok" : 1
}
如何以其他方式构建索引或查询以获得更好的性能？
答案 0 :(得分：-1)
也许尝试这样：
db.customer.aggregate( [
{$unwind : "$identity"},
{$unwind:"$identity.identityHistory"},
{$match: { "identity.identityHistory.from" : {$lt:"2014-09-13 14:18:29.616"} ,
"identity.identityHistory.to" :{$gt:"2014-08-30 09:26:24.842"} } },
{$project:{"identity.currentIdentity":1,"identity.identityHistory.identity" : 1 } }
])
并保留所有三个索引：
db.customer.createIndex（{ “identity.identityHistory.from”：1， “identity.identityHistory.to”：1}）