Mongodb 分片 手动维护chunk

htgefwda

去年的笔记
For instance, if a chunk represents a single shard key value, then MongoDB cannot split the chunk even when the chunk exceeds the size at which splits occur.
如果一个chunk只包含一个分片键值，mongodb 就不会split这个chunk,即使这个chunk超过了 chunk需要split时的大小。所以分片键的选择非常重要。
这里举个例子，比如我们使用日期（精确到日） 作为分片键，当某一天的数据非常多时，这个分片键值（比如2015/12/12）的对应的chunk会非常大，
超过64M,但是这个chunk是不可分割的。这会造成数据在各个分片中不平衡，出现性能问题。
所以我们要以 选择性高的字段做为分片键，如果这个字段（比如 日志级别）选择性低，我们可以再添加一个选择性高的字段，两个字段做为分片键。
如果以日期做为分片键，为了避免大的chunk，我们可以把日期精确到 时分秒 然后做分片键。

if your chunk ranges get down to a single key value then no further splits are possible and you get "jumbo" chunks。
以下是 大的chunk的例子：
http://dba.stackexchange.com/que ... unks-will-not-split

一个常见的错误：
Mongos version 3.0.1 Split Chunk Error with Sharding
http://dba.stackexchange.com/que ... -with-sharding?rq=1

手动切割分片：
http://www.cnblogs.com/xuegang/archive/2012/12/27/2836209.html

一、使用splitFind对可分割的chunk 手动分割。
splitFind(namespace, query),query的值必须包括分片键。将一个query指定的chunk,分割为两个基本相等大小的chunk。

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mongos> db.users003.getShardDistribution() Shard shard1 at shard1/192.168.137.111:27017,192.168.137.75:27017 data : 212KiB docs : 3359 chunks : 2 estimated data per chunk : 106KiB estimated docs per chunk : 1679 Shard shard2 at shard2/192.168.137.138:27018,192.168.137.75:27018 data : 211KiB docs : 3337 chunks : 2 estimated data per chunk : 105KiB estimated docs per chunk : 1668 Shard shard3 at shard3/192.168.137.111:27019,192.168.137.138:27019 data : 209KiB docs : 3304 chunks : 2 estimated data per chunk : 104KiB estimated docs per chunk : 1652 Totals data : 633KiB docs : 10000 chunks : 6 Shard shard1 contains 33.58% data, 33.58% docs in cluster, avg obj size on shard : 64B Shard shard2 contains 33.37% data, 33.37% docs in cluster, avg obj size on shard : 64B Shard shard3 contains 33.03% data, 33.04% docs in cluster, avg obj size on shard : 64B mongos> mongos> mongos> AllChunkInfo("test1.users003", true); ChunkID,Shard,ChunkSize,ObjectsInChunk test1.users003-_id_MinKey,shard1,106368,1662 test1.users003-_id_-6148914691236517204,shard1,108608,1697 test1.users003-_id_-3074457345618258602,shard3,107072,1673 test1.users003-_id_0,shard3,104384,1631 test1.users003-_id_3074457345618258602,shard2,110592,1728 test1.users003-_id_6148914691236517204,shard2,102976,1609 ***********Summary Chunk Information*********** Total Chunks: 6 Average Chunk Size (bytes): 106666.66666666667 Empty Chunks: 0 Average Chunk Size (non-empty): 106666.66666666667 mongos> db.users003.count() 10000

执行splitFind之后，chunk被分割为两个基本相同大小的chunk:

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mongos> sh.splitFind("test1.users003",{"name" : "u_100"}) {     "ok" : 0,     "errmsg" : "no shard key found in chunk query { name: \"u_100\" }" } mongos> sh.splitFind("test1.users003",{"_id" : ObjectId("568bdf16e05cf980cec8c455")}) { "ok" : 1 } mongos> mongos> mongos> mongos> mongos> mongos> mongos> AllChunkInfo("test1.users003", true); ChunkID,Shard,ChunkSize,ObjectsInChunk test1.users003-_id_MinKey,shard1,106368,1662 test1.users003-_id_-6148914691236517204,shard1,54272,848 test1.users003-_id_-4665891797978533183,shard1,54336,849 test1.users003-_id_-3074457345618258602,shard3,107072,1673 test1.users003-_id_0,shard3,104384,1631 test1.users003-_id_3074457345618258602,shard2,110592,1728 test1.users003-_id_6148914691236517204,shard2,102976,1609 ***********Summary Chunk Information*********** Total Chunks: 7 Average Chunk Size (bytes): 91428.57142857143 Empty Chunks: 0 Average Chunk Size (non-empty): 91428.57142857143 mongos> db.users003.getShardDistribution() Shard shard1 at shard1/192.168.137.111:27017,192.168.137.75:27017 data : 212KiB docs : 3359 chunks : 3 estimated data per chunk : 70KiB estimated docs per chunk : 1119 Shard shard2 at shard2/192.168.137.138:27018,192.168.137.75:27018 data : 211KiB docs : 3337 chunks : 2 estimated data per chunk : 105KiB estimated docs per chunk : 1668 Shard shard3 at shard3/192.168.137.111:27019,192.168.137.138:27019 data : 209KiB docs : 3304 chunks : 2 estimated data per chunk : 104KiB estimated docs per chunk : 1652 Totals data : 633KiB docs : 10000 chunks : 7 Shard shard1 contains 33.58% data, 33.58% docs in cluster, avg obj size on shard : 64B Shard shard2 contains 33.37% data, 33.37% docs in cluster, avg obj size on shard : 64B Shard shard3 contains 33.03% data, 33.04% docs in cluster, avg obj size on shard : 64B

二、使用splitAt对可分割的chunk 手动分割。
splitAt(namespace, query) 官方解释：
sh.splitAt() splits the original chunk into two chunks. One chunk has a shard key range
that starts with the original lower bound (inclusive) and ends at the specified shard key value (exclusive).
The other chunk has a shard key range that starts with the specified shard key value (inclusive) as the lower bound
and ends at the original upper bound (exclusive).

三、手动迁移chunk
db.runCommand( { moveChunk : "myapp.users" ,
                 find : {username : "smith"} ,
                 to : "mongodb-shard3.example.net" } )
注释：
moveChunk：一个集合的名字要加上数据库的名称：比如test.yql
find：一个查询语句，指定集合中的符合查询的数据或者chunk，系统自动查出from 的shard
to： 指向chunk的目的shard
只要目的shard和源sharad同意指定的chunk由目的shard接管，命令就返回。迁移chunk是一个比较复杂的过程，它包括两个内部通信协议：
1 复制数据，包括在复制过程中的变化的数据
2 确保所有参与迁移的组成部分：目的shard ，源shard ，config server都确定迁移已经完成！

The command will block until the migration is complete.

四、相关脚本

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--显示collection的chunk分布信息 db.collection.getShardDistribution() 显示chunk信息脚本： AllChunkInfo = function(ns, est){     var chunks = db.getSiblingDB("config").chunks.find({"ns" : ns}).sort({min:1}); //this will return all chunks for the ns ordered by min     //some counters for overall stats at the end     var totalChunks = 0;     var totalSize = 0;     var totalEmpty = 0;     print("ChunkID,Shard,ChunkSize,ObjectsInChunk"); // header row     // iterate over all the chunks, print out info for each     chunks.forEach(         function printChunkInfo(chunk) {         var db1 = db.getSiblingDB(chunk.ns.split(".")[0]); // get the database we will be running the command against later         var key = db.getSiblingDB("config").collections.findOne({_id:chunk.ns}).key; // will need this for the dataSize call         // dataSize returns the info we need on the data, but using the estimate option to use counts is less intensive         var dataSizeResult = db1.runCommand({datasize:chunk.ns, keyPattern:key, min:chunk.min, max:chunk.max, estimate:est});         // printjson(dataSizeResult); // uncomment to see how long it takes to run and status                    print(chunk._id+","+chunk.shard+","+dataSizeResult.size+","+dataSizeResult.numObjects);         totalSize += dataSizeResult.size;         totalChunks++;         if (dataSizeResult.size == 0) { totalEmpty++ }; //count empty chunks for summary         }     )     print("***********Summary Chunk Information***********");     print("Total Chunks: "+totalChunks);     print("Average Chunk Size (bytes): "+(totalSize/totalChunks));     print("Empty Chunks: "+totalEmpty);     print("Average Chunk Size (non-empty): "+(totalSize/(totalChunks-totalEmpty))); }   使用示例： mongos> AllChunkInfo("test1.users001", true); ChunkID,Shard,ChunkSize,ObjectsInChunk test1.users001-_id_MinKey,shard3,11347710,171935 test1.users001-_id_-6148914691236517204,shard1,11293458,171113 test1.users001-_id_-3074457345618258602,shard1,11320716,171526 test1.users001-_id_0,shard3,11349096,171956 test1.users001-_id_3074457345618258602,shard2,11340054,171819 test1.users001-_id_6148914691236517204,shard2,11328966,171651 ***********Summary Chunk Information*********** Total Chunks: 6 Average Chunk Size (bytes): 11330000 Empty Chunks: 0 Average Chunk Size (non-empty): 11330000