Mongodb分布式集群副本集+分片
目录
简介
1. 副本集
开启复制集后，主节点会在 local 库下生成一个集合叫 oplog.rs，这是一个有限集合，也就是大小是固定的。其中记录的是整个mongod实例一段时间内数据库的所有变更（插入/更新/删除）操作，当空间用完时新记录自动覆盖最老的记录
MongoDB复制集(副本集):由一组实列(进程)组成；包含一个Primary节点和多个Secondary节点，用户的所有写操作写入Primary ，Secondary通过oplog来同步primary的数据;可以通过心跳检测机制,一旦primary出现故障,则就会通过仲裁节点从secondary选取一个新的主节点
Primary:主节点，由选择产生,负责客户端的写操作,产生oplog日志文件
Secondary:从节点；负责客户端的读操作;
Arbiter:仲裁节点;只参与选举的投票;不会成为Primary和secondary,任意节点宕机，复制集将不能提供服务了（无法选出Primary），这时可以给复制集添加一个Arbiter节点，即使有节点宕机，仍能选出Primary**
1.1 MongoDB选举的原理
MongoDB的节点分为三种类型，分别为标准节点（host）、被动节点（passive）和仲裁节点（arbiter）
只有标准节点才有可能被选举为活跃节点（主节点），拥有选举权。被动节点有完整副本，不可能成为活跃节点，具有选举权。仲裁节点不复制数据，不可能成为活跃节点，只有选举权。说白了就是只有标准节点才有可能被选举为主节点，即使在一个复制集中说有的标准节点都宕机，被动节点和仲裁节点也不会成为主节点
标准节点与被动节点的区别：priority值高者是标准节点，低者则为被动节点
选举规则是票数高的获胜，priority是优先权01000的值，相当于额外增加01000的票数。选举结果：票数高者获胜；若票数相同，数据新者获胜。
1.2 复制过程
-
客户端的数据进来；
-
数据操作写入到日志缓冲；
-
数据写入到数据缓冲；
-
把日志缓冲中的操作日志放到OPLOG中来；
-
返回操作结果到客户端（异步）；
-
后台线程进行OPLOG复制到从节点，这个频率是非常高的，比日志刷盘频率还要高，从节点会一直监听主节点，OPLOG一有变化就会进行复制操作；
-
后台线程进行日志缓冲中的数据刷盘，非常频繁（默认100）毫秒，也可自行设置（30-60）；
后台线程进行数据缓冲中的数据刷盘，默认是60秒；
2. 分片技术
复制集主要用来实现自动故障转移从而达到高可用的目的，然而，随着业务规模的增长和时间的推移，业务数据量会越来越大，当前业务数据可能只有几百GB不到，一台DB服务器足以搞定所有的工作，而一旦业务数据量扩充大几个TB几百个TB时，就会产生一台服务器无法存储的情况，此时，需要将数据按照一定的规则分配到不同的服务器进行存储、查询等，即为分片集群。分片集群要做到的事情就是数据分布式存储
存储方式：数据集被拆分成数据块（chunk），每个数据块包含多个doc，数据块分布式存储在分片集群中。
2.1 角色
Config server：MongoDB负责追踪数据块在shard上的分布信息，每个分片存储哪些数据块，叫做分片的元数据，保存在config server上的数据库 config中，一般使用3台config
server，所有config server中的config数据库必须完全相同（建议将config server部署在不同的服务器，以保证稳定性）；
Shard server：将数据进行分片，拆分成数据块（chunk），每个trunk块的大小默认为64M，数据块真正存放的单位；
Mongos server：数据库集群请求的入口，所有的请求都通过mongos进行协调，查看分片的元数据，查找chunk存放位置，mongos自己就是一个请求分发中心，在生产环境通常有多mongos作为请求的入口，防止其中一个挂掉所有的mongodb请求都没有办法操作。
总结：应用请求mongos来操作mongodb的增删改查，配置服务器存储数据库元信息，并且和mongos做同步，数据最终存入在shard（分片）上，为了防止数据丢失，同步在副本集中存储了一份，仲裁节点在数据存储到分片的时候决定存储到哪个节点。
2.2 分片的片键
概述：片键是文档的一个属性字段或是一个复合索引字段，一旦建立后则不可改变，片键是拆分数据的关键的依据，如若在数据极为庞大的场景下，片键决定了数据在分片的过程中数据的存储位置，直接会影响集群的性能；
注：创建片键时，需要有一个支撑片键运行的索引；
2.3 片键分类
1.递增片键：使用时间戳，日期，自增的主键，ObjectId，_id等，此类片键的写入操作集中在一个分片服务器上，写入不具有分散性，这会导致单台服务器压力较大，但分割比较容易，这台服务器可能会成为性能瓶颈；
2.哈希片键：也称之为散列索引，使用一个哈希索引字段作为片键，优点是使数据在各节点分布比较均匀，数据写入可随机分发到每个分片服务器上，把写入的压力分散到了各个服务器上。但是读也是随机的，可能会命中更多的分片，但是缺点是无法实现范围区分；
3.组合片键： 数据库中没有比较合适的键值供片键选择，或者是打算使用的片键基数太小（即变化少如星期只有7天可变化），可以选另一个字段使用组合片键，甚至可以添加冗余字段来组合；
4.标签片键：数据存储在指定的分片服务器上，可以为分片添加tag标签，然后指定相应的tag，比如让10...(T)出现在shard0000上，11...(Q)出现在shard0001或shard0002上，就可以使用tag让均衡器指定分发；
环境介绍
分布式mongodb集群副本集+分片
CentOS Linux release 7.9.2009
Mongodb:4.0.21
| IP | 路由服务端口 | 配置服务端口 | 分片1端口 | 分片2端口 | 分片3端 |
|---|---|---|---|---|---|
| 172.16.245.102 | 27017 | 27018 | 27001 | 27002 | 27003 |
| 172.16.245.103 | 27017 | 27018 | 27001 | 27002 | 27003 |
| 172.16.245.104 | 27017 | 27018 | 27001 | 27002 | 27003 |
1.获取软件包
wget https://fastdl.mongodb.org/linux/mongodb-linux-x86_64-4.0.21.tgz
2.创建路由、配置、分片等的相关目录与文件
三台服务器相同操作
mkdir -p /data/mongodb/conf
mkdir -p /data/mongodb/data/config
mkdir -p /data/mongodb/data/shard1
mkdir -p /data/mongodb/data/shard2
mkdir -p /data/mongodb/data/shard3
mkdir -p /data/mongodb/log/config.log
mkdir -p /data/mongodb/log/mongos.log
mkdir -p /data/mongodb/log/shard1.log
mkdir -p /data/mongodb/log/shard2.log
mkdir -p /data/mongodb/log/shard3.log
touch /data/mongodb/log/config.log/config.log
touch /data/mongodb/log/mongos.log/mongos.log
touch /data/mongodb/log/shard1.log/shard1.log
touch /data/mongodb/log/shard2.log/shard2.log
touch /data/mongodb/log/shard3.log/shard3.log
3. 配置服务器部署mongodb
3台服务器执行相同操作
[root@node5 conf]# vim /data/mongodb/conf/config.conf
[root@node5 conf]# cat /data/mongodb/conf/config.conf
dbpath=/data/mongodb/data/config
logpath=/data/mongodb/log/config.log/config.log
port=27018 #端口号
logappend=true
fork=true
maxConns=5000
replSet=configs #副本集名称
configsvr=true
bind_ip=0.0.0.0
4. 配置复本集
分别启动三台服务器的配置服务
[root@node5 conf]# /data/mongodb/bin/mongod -f /data/mongodb/conf/config.conf
连接mongo,只需在任意一台机器执行即可
[root@node5 conf]# /data/mongodb/bin/mongo --host 172.16.245.102 --port 27018
进入数据库以后切换数据库
use admin
初始化副本集
rs.initiate({_id:"configs",members:[{_id:0,host:"172.16.245.102:27018"},{_id:1,host:"172.16.245.103:27018"}, {_id:2,host:"172.16.245.104:27018"}]})
其中_id:"configs"的configs是上面config.conf配置文件里的复制集名称，把三台服务器的(指定相应的IP)配置服务组成复制集
查看状态
configs:PRIMARY> rs.status()
{
"set" : "configs", #副本集名称
"date" : ISODate("2020-12-22T06:39:04.184Z"),
"myState" : 1,
"term" : NumberLong(1),
"syncingTo" : "",
"syncSourceHost" : "",
"syncSourceId" : -1,
"configsvr" : true,
"heartbeatIntervalMillis" : NumberLong(2000),
"optimes" : {
"lastCommittedOpTime" : {
"ts" : Timestamp(1608619142, 1),
"t" : NumberLong(1)
},
"readConcernMajorityOpTime" : {
"ts" : Timestamp(1608619142, 1),
"t" : NumberLong(1)
},
"appliedOpTime" : {
"ts" : Timestamp(1608619142, 1),
"t" : NumberLong(1)
},
"durableOpTime" : {
"ts" : Timestamp(1608619142, 1),
"t" : NumberLong(1)
}
},
"lastStableCheckpointTimestamp" : Timestamp(1608619122, 1),
"electionCandidateMetrics" : {
"lastElectionReason" : "electionTimeout",
"lastElectionDate" : ISODate("2020-12-22T05:31:42.975Z"),
"electionTerm" : NumberLong(1),
"lastCommittedOpTimeAtElection" : {
"ts" : Timestamp(0, 0),
"t" : NumberLong(-1)
},
"lastSeenOpTimeAtElection" : {
"ts" : Timestamp(1608615092, 1),
"t" : NumberLong(-1)
},
"numVotesNeeded" : 2,
"priorityAtElection" : 1,
"electionTimeoutMillis" : NumberLong(10000),
"numCatchUpOps" : NumberLong(0),
"newTermStartDate" : ISODate("2020-12-22T05:31:42.986Z"),
"wMajorityWriteAvailabilityDate" : ISODate("2020-12-22T05:31:44.134Z")
},
"members" : [
{
"_id" : 0,
"name" : "172.16.245.102:27018", #副本1
"health" : 1,
"state" : 1,
"stateStr" : "PRIMARY",
"uptime" : 4383,
"optime" : {
"ts" : Timestamp(1608619142, 1),
"t" : NumberLong(1)
},
"optimeDate" : ISODate("2020-12-22T06:39:02Z"),
"syncingTo" : "",
"syncSourceHost" : "",
"syncSourceId" : -1,
"infoMessage" : "",
"electionTime" : Timestamp(1608615102, 1),
"electionDate" : ISODate("2020-12-22T05:31:42Z"),
"configVersion" : 1,
"self" : true,
"lastHeartbeatMessage" : ""
},
{
"_id" : 1,
"name" : "172.16.245.103:27018", #副本2
"health" : 1,
"state" : 2,
"stateStr" : "SECONDARY",
"uptime" : 4052,
"optime" : {
"ts" : Timestamp(1608619142, 1),
"t" : NumberLong(1)
},
"optimeDurable" : {
"ts" : Timestamp(1608619142, 1),
"t" : NumberLong(1)
},
"optimeDate" : ISODate("2020-12-22T06:39:02Z"),
"optimeDurableDate" : ISODate("2020-12-22T06:39:02Z"),
"lastHeartbeat" : ISODate("2020-12-22T06:39:02.935Z"),
"lastHeartbeatRecv" : ISODate("2020-12-22T06:39:03.044Z"),
"pingMs" : NumberLong(85),
"lastHeartbeatMessage" : "",
"syncingTo" : "172.16.245.102:27018",
"syncSourceHost" : "172.16.245.102:27018",
"syncSourceId" : 0,
"infoMessage" : "",
"configVersion" : 1
},
{
"_id" : 2,
"name" : "172.16.245.104:27018", #副本3
"health" : 1,
"state" : 2,
"stateStr" : "SECONDARY",
"uptime" : 4052,
"optime" : {
"ts" : Timestamp(1608619142, 1),
"t" : NumberLong(1)
},
"optimeDurable" : {
"ts" : Timestamp(1608619142, 1),
"t" : NumberLong(1)
},
"optimeDate" : ISODate("2020-12-22T06:39:02Z"),
"optimeDurableDate" : ISODate("2020-12-22T06:39:02Z"),
"lastHeartbeat" : ISODate("2020-12-22T06:39:03.368Z"),
"lastHeartbeatRecv" : ISODate("2020-12-22T06:39:03.046Z"),
"pingMs" : NumberLong(85),
"lastHeartbeatMessage" : "",
"syncingTo" : "172.16.245.102:27018",
"syncSourceHost" : "172.16.245.102:27018",
"syncSourceId" : 0,
"infoMessage" : "",
"configVersion" : 1
}
],
"ok" : 1,
"operationTime" : Timestamp(1608619142, 1),
"$gleStats" : {
"lastOpTime" : Timestamp(0, 0),
"electionId" : ObjectId("7fffffff0000000000000001")
},
"lastCommittedOpTime" : Timestamp(1608619142, 1),
"$clusterTime" : {
"clusterTime" : Timestamp(1608619142, 1),
"signature" : {
"hash" : BinData(0,"AAAAAAAAAAAAAAAAAAAAAAAAAAA="),
"keyId" : NumberLong(0)
}
}
}
configs:PRIMARY>
等几十秒左右，执行上面的命令查看状态，三台机器的配置服务就已形成复制集，其中1台为PRIMARY，其他2台为SECONDARY
5. 分片服务部署
3台服务器执行相同操作
在/data/mongodb/conf目录创建shard1.conf、shard2.conf、shard3.conf，内容如下
[root@node3 conf]# ls
config.conf mongos.conf shard1.conf shard2.conf shard3.conf
[root@node3 conf]# cat shard1.conf
dbpath=/data/mongodb/data/shard1
logpath=/data/mongodb/log/shard1.log/shard1.log
port=27001
logappend=true
fork=true
maxConns=5000
storageEngine=mmapv1
shardsvr=true
replSet=shard1
bind_ip=0.0.0.0
[root@node3 conf]# cat shard2.conf
dbpath=/data/mongodb/data/shard2
logpath=/data/mongodb/log/shard2.log/shard2.log
port=27002
logappend=true
fork=true
maxConns=5000
storageEngine=mmapv1
shardsvr=true
replSet=shard2
bind_ip=0.0.0.0
[root@node3 conf]# cat shard3.conf
dbpath=/data/mongodb/data/shard3
logpath=/data/mongodb/log/shard3.log/shard3.log
port=27003
logappend=true
fork=true
maxConns=5000
storageEngine=mmapv1
shardsvr=true
replSet=shard3
bind_ip=0.0.0.0
端口分别是27001、27002、27003，分别对应shard1.conf、shard2.conf、shard3.conf
在3台机器的相同端口形成一个分片的复制集，由于3台机器都需要这3个文件，所以根据这9个配置文件分别启动分片服务
三台机器都需要启动分片服务，节点1启动shard1 节点2启动shard1 节点2启动shard1 ....
[root@node3 conf]# /data/mongodb/bin/mongond -f /data/mongodb/conf/shard1.conf
[root@node3 conf]# /data/mongodb/bin/mongond -f /data/mongodb/conf/shard2.conf
[root@node3 conf]# /data/mongodb/bin/mongond -f /data/mongodb/conf/shard3.conf
6. 将分片配置为复制集
连接mongo，只需在任意一台机器执行即可
mongo --host 172.16.245.103 --port 27001
这里以shard1为例，其他两个分片则再需对应连接到27002、27003的端口进行操作即可
进入数据库admin
use admin
初始化三个分片副本集集群
rs.initiate({_id:"shard1",members:[{_id:0,host:"172.16.245.102:27001"},{_id:1,host:"172.16.245.103:27001"},{_id:2,host:"172.16.245.104:27001"}]})
rs.initiate({_id:"shard2",members:[{_id:0,host:"172.16.245.102:27002"},{_id:1,host:"172.16.245.103:27002"},{_id:2,host:"172.16.245.104:27002"}]})
rs.initiate({_id:"shard3",members:[{_id:0,host:"172.16.245.102:27003"},{_id:1,host:"172.16.245.103:27003"},{_id:2,host:"172.16.245.104:27003"}]})
7. 路由服务部署
3台服务器执行相同操作
在/data/mongodb/conf目录创建mongos.conf，内容如下
[root@node4 conf]# cat mongos.conf
logpath=/data/mongodb/log/mongos.log/mongos.log
logappend = true
port = 27017
fork = true
configdb = configs/172.16.245.102:27018,172.16.245.103:27018,172.16.245.104:27018
maxConns=20000
bind_ip=0.0.0.0
启动mongos
分别在三台服务器启动：
[root@node4 conf]# /data/mongodb/bin/mongos -f /data/mongodb/conf/mongos.conf
8. 启动分片功能
连接mongo
mongo --host 172.16.245.102 --port 27017
mongos>use admin
添加分片，只需在一台机器执行即可
mongos>sh.addShard("shard1/172.16.245.102:27001,172.16.245.103:27001,172.16.245.104:27001")
mongos>sh.addShard("shard2/172.16.245.102:27002,172.16.245.103:27002,172.16.245.104:27002")
mongos>sh.addShard("shard3/172.16.245.102:27003,172.16.245.103:27003,172.16.245.104:27003")
mongos> sh.status()
--- Sharding Status ---
sharding version: {
"_id" : 1,
"minCompatibleVersion" : 5,
"currentVersion" : 6,
"clusterId" : ObjectId("5fe184bf29ea91799b557a8b")
}
shards:
{ "_id" : "shard1", "host" : "shard1/172.16.245.102:27001,172.16.245.103:27001,172.16.245.104:27001", "state" : 1 }
{ "_id" : "shard2", "host" : "shard2/172.16.245.102:27002,172.16.245.103:27002,172.16.245.104:27002", "state" : 1 }
{ "_id" : "shard3", "host" : "shard3/172.16.245.102:27003,172.16.245.103:27003,172.16.245.104:27003", "state" : 1 }
active mongoses:
"4.0.21" : 3
autosplit:
Currently enabled: yes
balancer:
Currently enabled: yes
Currently running: no
Failed balancer rounds in last 5 attempts: 0
Migration Results for the last 24 hours:
No recent migrations
databases:
{ "_id" : "calon", "primary" : "shard1", "partitioned" : true, "version" : { "uuid" : UUID("2a4780da-8f33-4214-88f8-c9b1a3140299"), "lastMod" : 1 } }
{ "_id" : "config", "primary" : "config", "partitioned" : true }
config.system.sessions
shard key: { "_id" : 1 }
unique: false
balancing: true
chunks:
shard1 1
{ "_id" : { "$minKey" : 1 } } -->> { "_id" : { "$maxKey" : 1 } } on : shard1 Timestamp(1, 0)
{ "_id" : "test", "primary" : "shard2", "partitioned" : false, "version" : { "uuid" : UUID("d59549a4-3e68-4a7d-baf8-67a4d8372b76"), "lastMod" : 1 } }
{ "_id" : "ycsb", "primary" : "shard3", "partitioned" : true, "version" : { "uuid" : UUID("6d491868-245e-4c86-a5f5-f8fcd308b45e"), "lastMod" : 1 } }
ycsb.usertable
shard key: { "_id" : "hashed" }
unique: false
balancing: true
chunks:
shard1 2
shard2 2
shard3 2
{ "_id" : { "$minKey" : 1 } } -->> { "_id" : NumberLong("-6148914691236517204") } on : shard1 Timestamp(1, 0)
{ "_id" : NumberLong("-6148914691236517204") } -->> { "_id" : NumberLong("-3074457345618258602") } on : shard1 Timestamp(1, 1)
{ "_id" : NumberLong("-3074457345618258602") } -->> { "_id" : NumberLong(0) } on : shard2 Timestamp(1, 2)
{ "_id" : NumberLong(0) } -->> { "_id" : NumberLong("3074457345618258602") } on : shard2 Timestamp(1, 3)
{ "_id" : NumberLong("3074457345618258602") } -->> { "_id" : NumberLong("6148914691236517204") } on : shard3 Timestamp(1, 4)
{ "_id" : NumberLong("6148914691236517204") } -->> { "_id" : { "$maxKey" : 1 } } on : shard3 Timestamp(1, 5)
9.实现分片功能
设置分片chunk大小
mongos>use config
mongos>db.setting.save({"_id":"chunksize","value":1}) #设置块大小为1M是方便实验，不然需要插入海量数据
10. 启用数据库分片并进行测试
mongos> use shardbtest;
switched to db shardbtest
mongos>
mongos>
mongos> sh.enableSharding("shardbtest");
{
"ok" : 1,
"operationTime" : Timestamp(1608620190, 4),
"$clusterTime" : {
"clusterTime" : Timestamp(1608620190, 4),
"signature" : {
"hash" : BinData(0,"AAAAAAAAAAAAAAAAAAAAAAAAAAA="),
"keyId" : NumberLong(0)
}
}
}
mongos> sh.shardCollection("shardbtest.usertable",{"_id":"hashed"}); #为 shardbtest裤中的usertable表进行分片基于id的哈希分片
{
"collectionsharded" : "shardbtest.usertable",
"collectionUUID" : UUID("2b5a8bcf-6e31-4dac-831f-5fa414253655"),
"ok" : 1,
"operationTime" : Timestamp(1608620216, 36),
"$clusterTime" : {
"clusterTime" : Timestamp(1608620216, 36),
"signature" : {
"hash" : BinData(0,"AAAAAAAAAAAAAAAAAAAAAAAAAAA="),
"keyId" : NumberLong(0)
}
}
}
mongos> for(i=1;i<=3000;i++){db.usertable.insert({"id":i})} #模拟插入3000条的数据
WriteResult({ "nInserted" : 1 })
11. 查看分片验证
mongos> db.usertable.stats();
{
"sharded" : true,
"paddingFactorNote" : "paddingFactor is unused and unmaintained in 3.0. It remains hard coded to 1.0 for compatibility only.",
"userFlags" : 1,
"capped" : false,
"ns" : "shardbtest.usertable",
"count" : 3000, #总3000
"numExtents" : 9,
"size" : 144096,
"storageSize" : 516096,
"totalIndexSize" : 269808,
"indexSizes" : {
"_id_" : 122640,
"_id_hashed" : 147168
},
"avgObjSize" : 48,
"maxSize" : NumberLong(0),
"nindexes" : 2,
"nchunks" : 6,
"shards" : {
"shard3" : {
"ns" : "shardbtest.usertable",
"size" : 48656,
"count" : 1013, #shard3写入1013
"avgObjSize" : 48,
"numExtents" : 3,
"storageSize" : 172032,
"lastExtentSize" : 131072,
"paddingFactor" : 1,
"paddingFactorNote" : "paddingFactor is unused and unmaintained in 3.0. It remains hard coded to 1.0 for compatibility only.",
"userFlags" : 1,
"capped" : false,
"nindexes" : 2,
"totalIndexSize" : 89936,
"indexSizes" : {
"_id_" : 40880,
"_id_hashed" : 49056
},
"ok" : 1,
"operationTime" : Timestamp(1608620309, 1),
"$gleStats" : {
"lastOpTime" : {
"ts" : Timestamp(1608620272, 38),
"t" : NumberLong(1)
},
"electionId" : ObjectId("7fffffff0000000000000001")
},
"lastCommittedOpTime" : Timestamp(1608620309, 1),
"$configServerState" : {
"opTime" : {
"ts" : Timestamp(1608620307, 1),
"t" : NumberLong(1)
}
},
"$clusterTime" : {
"clusterTime" : Timestamp(1608620309, 1),
"signature" : {
"hash" : BinData(0,"AAAAAAAAAAAAAAAAAAAAAAAAAAA="),
"keyId" : NumberLong(0)
}
}
},
"shard2" : {
"ns" : "shardbtest.usertable",
"size" : 49232,
"count" : 1025, #shard2写入1025
"avgObjSize" : 48,
"numExtents" : 3,
"storageSize" : 172032,
"lastExtentSize" : 131072,
"paddingFactor" : 1,
"paddingFactorNote" : "paddingFactor is unused and unmaintained in 3.0. It remains hard coded to 1.0 for compatibility only.",
"userFlags" : 1,
"capped" : false,
"nindexes" : 2,
"totalIndexSize" : 89936,
"indexSizes" : {
"_id_" : 40880,
"_id_hashed" : 49056
},
"ok" : 1,
"operationTime" : Timestamp(1608620306, 1),
"$gleStats" : {
"lastOpTime" : {
"ts" : Timestamp(1608620272, 32),
"t" : NumberLong(1)
},
"electionId" : ObjectId("7fffffff0000000000000001")
},
"lastCommittedOpTime" : Timestamp(1608620306, 1),
"$configServerState" : {
"opTime" : {
"ts" : Timestamp(1608620307, 1),
"t" : NumberLong(1)
}
},
"$clusterTime" : {
"clusterTime" : Timestamp(1608620309, 1),
"signature" : {
"hash" : BinData(0,"AAAAAAAAAAAAAAAAAAAAAAAAAAA="),
"keyId" : NumberLong(0)
}
}
},
"shard1" : {
"ns" : "shardbtest.usertable",
"size" : 46208,
"count" : 962, #shard1写入962
"avgObjSize" : 48,
"numExtents" : 3,
"storageSize" : 172032,
"lastExtentSize" : 131072,
"paddingFactor" : 1,
"paddingFactorNote" : "paddingFactor is unused and unmaintained in 3.0. It remains hard coded to 1.0 for compatibility only.",
"userFlags" : 1,
"capped" : false,
"nindexes" : 2,
"totalIndexSize" : 89936,
"indexSizes" : {
"_id_" : 40880,
"_id_hashed" : 49056
},
"ok" : 1,
"operationTime" : Timestamp(1608620308, 1),
"$gleStats" : {
"lastOpTime" : {
"ts" : Timestamp(1608620292, 10),
"t" : NumberLong(1)
},
"electionId" : ObjectId("7fffffff0000000000000001")
},
"lastCommittedOpTime" : Timestamp(1608620308, 1),
"$configServerState" : {
"opTime" : {
"ts" : Timestamp(1608620307, 1),
"t" : NumberLong(1)
}
},
"$clusterTime" : {
"clusterTime" : Timestamp(1608620309, 1),
"signature" : {
"hash" : BinData(0,"AAAAAAAAAAAAAAAAAAAAAAAAAAA="),
"keyId" : NumberLong(0)
}
}
}
},
"ok" : 1,
"operationTime" : Timestamp(1608620309, 1),
"$clusterTime" : {
"clusterTime" : Timestamp(1608620309, 1),
"signature" : {
"hash" : BinData(0,"AAAAAAAAAAAAAAAAAAAAAAAAAAA="),
"keyId" : NumberLong(0)
}
}
}
11. 副本节点是否已同步数据
mongos> show dbs
admin 0.000GB
calon 0.078GB
config 0.235GB
shardbtest 0.234GB
test 0.078GB
ycsb 0.234GB
mongos> use shardbtest
switched to db shardbtest
mongos> db.usertable.stats();
{
"sharded" : true,
"paddingFactorNote" : "paddingFactor is unused and unmaintained in 3.0. It remains hard coded to 1.0 for compatibility only.",
"userFlags" : 1,
"capped" : false,
"ns" : "shardbtest.usertable",
"count" : 3000,
"numExtents" : 9,
"size" : 144096,
"storageSize" : 516096,
"totalIndexSize" : 269808,
"indexSizes" : {
"_id_" : 122640,
"_id_hashed" : 147168
},
"avgObjSize" : 48,
"maxSize" : NumberLong(0),
"nindexes" : 2,
"nchunks" : 6,
"shards" : {
"shard2" : {
"ns" : "shardbtest.usertable",
"size" : 49232,
"count" : 1025,
"avgObjSize" : 48,
"numExtents" : 3,
"storageSize" : 172032,
"lastExtentSize" : 131072,
"paddingFactor" : 1,
"paddingFactorNote" : "paddingFactor is unused and unmaintained in 3.0. It remains hard coded to 1.0 for compatibility only.",
"userFlags" : 1,
"capped" : false,
"nindexes" : 2,
"totalIndexSize" : 89936,
"indexSizes" : {
"_id_" : 40880,
"_id_hashed" : 49056
},
"ok" : 1,
"operationTime" : Timestamp(1608620886, 6),
"$gleStats" : {
"lastOpTime" : Timestamp(0, 0),
"electionId" : ObjectId("7fffffff0000000000000001")
},
"lastCommittedOpTime" : Timestamp(1608620886, 6),
"$configServerState" : {
"opTime" : {
"ts" : Timestamp(1608620888, 1),
"t" : NumberLong(1)
}
},
"$clusterTime" : {
"clusterTime" : Timestamp(1608620888, 1),
"signature" : {
"hash" : BinData(0,"AAAAAAAAAAAAAAAAAAAAAAAAAAA="),
"keyId" : NumberLong(0)
}
}
},
"shard3" : {
"ns" : "shardbtest.usertable",
"size" : 48656,
"count" : 1013,
"avgObjSize" : 48,
"numExtents" : 3,
"storageSize" : 172032,
"lastExtentSize" : 131072,
"paddingFactor" : 1,
"paddingFactorNote" : "paddingFactor is unused and unmaintained in 3.0. It remains hard coded to 1.0 for compatibility only.",
"userFlags" : 1,
"capped" : false,
"nindexes" : 2,
"totalIndexSize" : 89936,
"indexSizes" : {
"_id_" : 40880,
"_id_hashed" : 49056
},
"ok" : 1,
"operationTime" : Timestamp(1608620889, 1),
"$gleStats" : {
"lastOpTime" : Timestamp(0, 0),
"electionId" : ObjectId("7fffffff0000000000000001")
},
"lastCommittedOpTime" : Timestamp(1608620889, 1),
"$configServerState" : {
"opTime" : {
"ts" : Timestamp(1608620888, 1),
"t" : NumberLong(1)
}
},
"$clusterTime" : {
"clusterTime" : Timestamp(1608620889, 1),
"signature" : {
"hash" : BinData(0,"AAAAAAAAAAAAAAAAAAAAAAAAAAA="),
"keyId" : NumberLong(0)
}
}
},
"shard1" : {
"ns" : "shardbtest.usertable",
"size" : 46208,
"count" : 962,
"avgObjSize" : 48,
"numExtents" : 3,
"storageSize" : 172032,
"lastExtentSize" : 131072,
"paddingFactor" : 1,
"paddingFactorNote" : "paddingFactor is unused and unmaintained in 3.0. It remains hard coded to 1.0 for compatibility only.",
"userFlags" : 1,
"capped" : false,
"nindexes" : 2,
"totalIndexSize" : 89936,
"indexSizes" : {
"_id_" : 40880,
"_id_hashed" : 49056
},
"ok" : 1,
"operationTime" : Timestamp(1608620888, 1),
"$gleStats" : {
"lastOpTime" : Timestamp(0, 0),
"electionId" : ObjectId("7fffffff0000000000000001")
},
"lastCommittedOpTime" : Timestamp(1608620888, 1),
"$configServerState" : {
"opTime" : {
"ts" : Timestamp(1608620888, 1),
"t" : NumberLong(1)
}
},
"$clusterTime" : {
"clusterTime" : Timestamp(1608620888, 1),
"signature" : {
"hash" : BinData(0,"AAAAAAAAAAAAAAAAAAAAAAAAAAA="),
"keyId" : NumberLong(0)
}
}
}
},
"ok" : 1,
"operationTime" : Timestamp(1608620889, 1),
"$clusterTime" : {
"clusterTime" : Timestamp(1608620889, 1),
"signature" : {
"hash" : BinData(0,"AAAAAAAAAAAAAAAAAAAAAAAAAAA="),
"keyId" : NumberLong(0)
}
}
}
mongos>
以上就实现了mongodb复制集的高可用以及分片
