-
Type:
Bug
-
Resolution: Duplicate
-
Priority:
Major - P3
-
None
-
Affects Version/s: 3.0.5
-
Component/s: Querying
-
None
-
ALL
-
It costs more than 20 mins to query 1 hour's data, which is about 10,000 doucuments, and the server's 100GB memory can be used up by these queries.
Details:
I setup a server with wiredtiger engine, config like:
wiredTiger:
engineConfig:
cacheSizeGB: 100
statisticsLogDelaySecs: 0
journalCompressor: snappy
directoryForIndexes: true
collectionConfig:
blockCompressor: snappy
indexConfig:
prefixCompression: true
I have a collection named 'orders'. The documents look like:
{
order_id:485548,
order_status:
[
{
update_time:ISODate("2015-10-28T02:45:00Z"),
status:1
},
{
update_time:ISODate("2015-10-28T03:18:00Z"),
status:2
}
]
}
I built an index:
"key" : {"order_status.update_time" : -1, "order_status.status" : -1}
then I run this query:
db.order.find({ "order_status" : { "$elemMatch" : { "update_time" : { "$gte" : ISODate("2015-10-28T02:00:00Z"), "$lt" : ISODate("2015-10-28T03:00:00Z") }, "status" : 2 } } }).explain();
I get:
"indexBounds" :
{
"order_status.update_time" :
[
"[new Date(1446001200000), true)"
],
"order_status.status" :
[
"[2.0, 2.0]"
]
}
I guess this means scan all data before '2015-10-28T03:00:00Z', which is more than 40GB data.
after I change the query to
db.order.find({ "order_status" : { "$elemMatch" : { "update_time" : { "$gte" : ISODate("2015-10-28T02:00:00Z") }, "status" : 2 } } }).explain();
I get:
"indexBounds" :
{
"order_status.update_time" :
[
"[new Date(9223372036854775807), new Date(1445997600000)]"
],
"order_status.status" :
[
"[2.0, 2.0]"
]
}
This looks better and runs much faster (finished within a second), but how can I get my original query work?
- duplicates
-
SERVER-20616 Plan ranker sampling from the beginning of a query's execution can result in poor plan selection
-
- Backlog
-
- related to
-
-
- Closed
-