- ·上一篇文章:面向对象思想的一点哲学探讨
- ·下一篇文章:Boost库对unicode字符集的支持方式探究
基于Mongodb分布式存储物理文件
在之前的文章中介绍了如何对关系型数据数据通过auto-sharding进行分布式数据存储,今天介绍如何对物理文件(小文件,基本小于100K)进行分布式存储。
接着看一下要配置的测试环境(与前一篇中类似):
模拟2个shard服务和一个config服务, 均运行在10.0.4.85机器上,只是端口不同:
Shard1:27020
Shard2:27021
Config:27022
Mongos启动时默认使用的27017端口
在C,D,E磁盘下分别建立如下文件夹:
mongodb\bin
mongodb\db
然后用CMDming令行依次打开相应文件夹下的mongd文件:
c:\mongodb\bin\mongod --dbpath c:\mongodb\db\ --port 27020
d:\mongodb\bin\mongod --dbpath d:\mongodb\db\ --port 27021
e:\mongodb\bin\mongod --configsvr --dbpath e:\mongodb\db\ --port 27022 (注:config配置服务器)
启动mongos时,默认开启了27017端口
e:\mongodb\bin\mongos --configdb 10.0.4.85:27022
然后打开mongo:
E:\mongodb\bin>mongo 回车 (有时加端口会造成下面的addshardming令出问题)
> use admin
switched to db admin
> db.runCommand( { addshard : "10.0.4.85:27020", allowLocal : 1, maxSize:2 , minKey:1, maxKey:10} )
--添加sharding,maxsize单位是M,此处设置比较小的数值只为演示sharding效果
{ "shardAdded" : "shard0000", "ok" : 1 }
> db.runCommand( { addshard : "10.0.4.85:27021", allowLocal : 1, minKey:1000} )
{ "shardAdded" : "shard0001", "ok" : 1 }
注:如果要移除sharding,可用下面写法
db.runCommand( { removeshard : "localhost:10000" } );
> db.runCommand({listshards:1}); --查看shard节点列表
> config = connect("10.0.4.85:27022")
> config = config.getSisterDB("config")
> dnt_mongodb=db.getSisterDB("dnt_mongodb");
dnt_mongodb
> db.runCommand({enablesharding:"dnt_mongodb"})
{ "ok" : 1 }
> db.printShardingStatus()
sharding version: { "_id" : 1, "version" : 3 }
shards:
{
"_id" : "shard0000",
"host" : "10.0.4.85:27020",
"maxSize" : NumberLong( 2 )
}
{ "_id" : "shard0001", "host" : "10.0.4.85:27021" }
databases:
{ "_id" : "admin", "partitioned" : false, "primary" : "config" }
{ "_id" : "dnt_mongodb", "partitioned" : true, "primary" : "shard0001" }
{ "collectionsharded" : "dnt_mongodb.attach_gfstream.chunks", "ok" : 1 }
注:运行上面ming令之前需要设置files_id为唯一索引[unique index]。
创建完sharding和设置相应信息后,我们加载一下测试数据,我用下面代码来读取要本地文件,然后批量向mongodb中添加(通过循环修改文件名来添加相同大小的文件)。
/// 上传文件到mongodb
/// </summary>
/// <param name="uploadDir">要上传文件所在路径</param>
/// <param name="fileName">要上传的文件名</param>
/// <returns></returns>
public bool UploadFile(string uploadDir, string fileName)
{
for (int i = 1; i < 10000; i++)
{
try
{
Mongo mongo = mongoDB;
mongo.Connect();
IMongoDatabase DB = mongo["dnt_mongodb"];
using (FileStream fileStream = new FileStream(uploadDir + fileName, FileMode.Open))
{
int nFileLen = (int)fileStream.Length;
byte[] myData = new Byte[nFileLen];
fileStream.Read(myData, 0, nFileLen);
GridFile fs = new GridFile(DB, "attach_gfstream");
using (GridFileStream gfs = fs.Create(fileName + i))
{
gfs.Write(myData, 0, nFileLen);
}
}
mongo.Disconnect();
}
catch { }
}
return true;
}
在批量添加约10000次(约10000个文件)之后,mongodb开始把sharding出来的chunk从shard0000分布到shard0001上,我们可以用下面指令来进行验证:
> db.printShardingStatus()
sharding version: { "_id" : 1, "version" : 3 }
shards:
{
"_id" : "shard0000",
"host" : "10.0.4.85:27020",
"maxSize" : NumberLong( 2 )
}
{ "_id" : "shard0001", "host" : "10.0.4.85:27021" }
databases:
{ "_id" : "admin", "partitioned" : false, "primary" : "config" }
{ "_id" : "dnt_mongodb", "partitioned" : true, "primary" : "shard0000" }
dnt_mongodb.attach_gfstream.chunks chunks:
{ "files_id" : { $minKey : 1 } } -->> { "files_id" : ObjectId("4c85fd02145a9b1534010d89") } on : shard0001 { "t" : 2000, "i" : 0 }
{ "files_id" : ObjectId("4c85fd02145a9b1534010d89") } -->> { "files_id" : ObjectId("4c85fdec145a9b0b340005a7") } on : shard0000 { "t" :3000, "i" : 1 }
{ "files_id" : ObjectId("4c85fdec145a9b0b340005a7") } -->> { "files_id" : ObjectId("4c85fe08145a9b0b34000aaf") } on : shard0001 { "t" :3000, "i" : 4 }
{ "files_id" : ObjectId("4c85fe08145a9b0b34000aaf") } -->> { "files_id" : ObjectId("4c85fe27145a9b0b34000fb7") } on : shard0001 { "t" :4000, "i" : 1 }
{ "files_id" : ObjectId("4c85fe27145a9b0b34000fb7") } -->> { "files_id" : ObjectId("4c85fe43145a9b0b340014bf") } on : shard0000 { "t" :4000, "i" : 7 }
{ "files_id" : ObjectId("4c85fe43145a9b0b340014bf") } -->> { "files_id" : ObjectId("4c85fe61145a9b0b340019c7") } on : shard0000 { "t" :4000, "i" : 8 }
{ "files_id" : ObjectId("4c85fe61145a9b0b340019c7") } -->> { "files_id" : ObjectId("4c85fe7b145a9b0b34001ecf") } on : shard0000 { "t" :5000, "i" : 1 }
{ "files_id" : ObjectId("4c85fe7b145a9b0b34001ecf") } -->> { "files_id" : ObjectId("4c85fe9a145a9b0b340023d7") } on : shard0001 { "t" :5000, "i" : 4 }
{ "files_id" : ObjectId("4c85fe9a145a9b0b340023d7") } -->> { "files_id" : ObjectId("4c85feb7145a9b0b340028df") } on : shard0001 { "t" :6000, "i" : 1 }
{ "files_id" : ObjectId("4c85feb7145a9b0b340028df") } -->> { "files_id" : ObjectId("4c85feea145a9b0b340032ef") } on : shard0000 { "t" :6000, "i" : 4 }
{ "files_id" : ObjectId("4c85feea145a9b0b340032ef") } -->> { "files_id" : ObjectId("4c85ff25145a9b0b34003cff") } on : shard0000 { "t" :7000, "i" : 1 }
{ "files_id" : ObjectId("4c85ff25145a9b0b34003cff") } -->> { "files_id" : ObjectId("4c85ff57145a9b0b3400470f") } on : shard0001 { "t" :7000, "i" : 4 }
{ "files_id" : ObjectId("4c85ff57145a9b0b3400470f") } -->> { "files_id" : ObjectId("4c85ff87145a9b0b3400511f") } on : shard0001 { "t" :8000, "i" : 1 }
{ "files_id" : ObjectId("4c85ff87145a9b0b3400511f") } -->> { "files_id" : ObjectId("4c85ffcd145a9b0b34005b2f") } on : shard0000 { "t" :8000, "i" : 16 }
{ "files_id" : ObjectId("4c85ffcd145a9b0b34005b2f") } -->> { "files_id" : ObjectId("4c85fff7145a9b0b3400653f") } on : shard0000 { "t" :8000, "i" : 17 }
{ "files_id" : ObjectId("4c85fff7145a9b0b3400653f") } -->> { "files_id" : ObjectId("4c860021145a9b0b34006f4f") } on : shard0000 { "t" :8000, "i" : 18 }
{ "files_id" : ObjectId("4c860021145a9b0b34006f4f") } -->> { "files_id" : ObjectId("4c86004f145a9b0b3400795f") } on : shard0000 { "t" :8000, "i" : 19 }
{ "files_id" : ObjectId("4c86004f145a9b0b3400795f") } -->> { "files_id" : ObjectId("4c860080145a9b0b3400836f") } on : shard0000 { "t" :9000, "i" : 1 }
{ "files_id" : ObjectId("4c860080145a9b0b3400836f") } -->> { "files_id" : ObjectId("4c8600b5145a9b0b34008d7f") } on : shard0001 { "t" :9000, "i" : 7 }
{ "files_id" : ObjectId("4c8600b5145a9b0b34008d7f") } -->> { "files_id" : ObjectId("4c860115145a9b0b3400a183") } on : shard0001 { "t" :9000, "i" : 8 }
{ "files_id" : ObjectId("4c860115145a9b0b3400a183") } -->> { "files_id" : ObjectId("4c860198145a9b0b3400b587") } on : shard0001 { "t" :10000, "i" : 1 }
{ "files_id" : ObjectId("4c860198145a9b0b3400b587") } -->> { "files_id" : ObjectId("4c8601fc145a9b0b3400c98b") } on : shard0000 { "t" :10000, "i" : 11 }
{ "files_id" : ObjectId("4c8601fc145a9b0b3400c98b") } -->> { "files_id" : ObjectId("4c86025b145a9b0b3400dd8f") } on : shard0000 { "t" :10000, "i" : 12 }
{ "files_id" : ObjectId("4c86025b145a9b0b3400dd8f") } -->> { "files_id" : ObjectId("4c8602ca145a9b0b3400f193") } on : shard0000 { "t" :10000, "i" : 13 }
{ "files_id" : ObjectId("4c8602ca145a9b0b3400f193") } -->> { "files_id" : ObjectId("4c860330145a9b0b34010597") } on : shard0000 { "t" :10000, "i" : 14 }
{ "files_id" : ObjectId("4c860330145a9b0b34010597") } -->> { "files_id" : { $maxKey : 1 } } on : shard0000 { "t" : 10000, "i" : 15 }
基于Mongodb分布式存储物理文件