如何高效地遍历 MongoDB 超大集合?

摘要: 给你 1000 万个文档,如何遍历?

本文使用的编程语言是 Node.js,连接 MongoDB 的模块用的是mongoose。但是,本文介绍的方法适用于其他编程语言及其对应的 MongoDB 模块。

错误方法:find()

也许,在遍历 MongoDB 集合时,我们会这样写:

const Promise = require("bluebird");

function findAllMembers() {
return Member.find();
}

async function test() {
const members = await findAllMembers();
let N = 0;
await Promise.mapSeries(members, member => {
N++;
console.log(`name of the ${N}th member: ${member.name}`);
});
console.log(`loop all ${N} members success`);
}

test();

注意,我们使用的是 Bluebird 的mapSeries而非map,members 数组中的元素是一个一个处理的。这样就够了吗?

当 Member 集合中的 document 不多时,比如只有 1000 个时,那确实没有问题。但是当 Member 集合中有 1000 万个 document 时,会发生什么呢?如下:

<--- Last few GCs --->
rt of marking 1770 ms) (average mu = 0.168, current mu = 0.025) finalize [5887:0x43127d0] 33672 ms: Mark-sweep 1398.3 (1425.2) -> 1398.0 (1425.7) MB, 1772.0 / 0.0 ms (+ 0.1 ms in 12 steps since start of marking, biggest step 0.0 ms, walltime since start of marking 1775 ms) (average mu = 0.088, current mu = 0.002) finalize [5887:0x43127d0] 35172 ms: Mark-sweep 1398.5 (1425.7) -> 1398.4 (1428.7) MB, 1496.7 / 0.0 ms (average mu = 0.049, current mu = 0.002) allocation failure scavenge might not succeed


<--- JS stacktrace --->

FATAL ERROR: Ineffective mark-compacts near heap limit Allocation failed - JavaScript heap out of memory
1: 0x8c02c0 node::Abort() [node]
2: 0x8c030c [node]
3: 0xad15de v8::Utils::ReportOOMFailure(v8::internal::Isolate*, char const*, bool) [node]
4: 0xad1814 v8::internal::V8::FatalProcessOutOfMemory(v8::internal::Isolate*, char const*, bool) [node]
5: 0xebe752 [node]
6: 0xebe858 v8::internal::Heap::CheckIneffectiveMarkCompact(unsigned long, double) [node]
7: 0xeca982 v8::internal::Heap::PerformGarbageCollection(v8::internal::GarbageCollector, v8::GCCallbackFlags) [node]
8: 0xecb2b4 v8::internal::Heap::CollectGarbage(v8::internal::AllocationSpace, v8::internal::GarbageCollectionReason, v8::GCCallbackFlags) [node]
9: 0xecba8a v8::internal::Heap::FinalizeIncrementalMarkingIfComplete(v8::internal::GarbageCollectionReason) [node]
10: 0xecf1b7 v8::internal::IncrementalMarkingJob::Task::RunInternal() [node]
11: 0xbc1796 v8::internal::CancelableTask::Run() [node]
12: 0x935018 node::PerIsolatePlatformData::FlushForegroundTasksInternal() [node]
13: 0x9fccff [node]
14: 0xa0dbd8 [node]
15: 0x9fd63b uv_run [node]
16: 0x8ca6c5 node::Start(v8::Isolate*, node::IsolateData*, int, char const* const*, int, char const* const*) [node]
17: 0x8c945f node::Start(int, char**) [node]
18: 0x7f84b6263f45 __libc_start_main [/lib/x86_64-linux-gnu/libc.so.6]
19: 0x885c55 [node]
Aborted (core dumped)

可知,内存不足了。

打印find()返回的 members 数组可知,集合中所有元素都返回了,哪个数组放得下 1000 万个 Object?

正确方法:find().cursor()与 eachAsync()

将整个集合 find()全部返回,这种操作应该避免,正确的方法应该是这样的:

function findAllMembersCursor() {
return Member.find().cursor();
}

async function test() {
const membersCursor = await findAllMembersCursor();
let N = 0;
await membersCursor.eachAsync(member => {
N++;
console.log(`name of the ${N}th member: ${member.name}`);
});
console.log(`loop all ${N} members success`);
}

test();

使用cursor()方法返回 QueryCursor,然后再使用eachAsync()就可以遍历整个集合了,而且不用担心内存不够。

QueryCursor是什么呢?不妨看一下 mongoose 文档:

A QueryCursor is a concurrency primitive for processing query results one document at a time. A QueryCursor fulfills the Node.js streams3 API, in addition to several other mechanisms for loading documents from MongoDB one at a time.

总之,QueryCursor 可以每次从 MongoDB 中取一个 document,这样显然极大地减少了内存使用。

如何测试?

这篇博客介绍的内容很简单,但是也很容易被忽视。如果大家测试一下,印象会更加深刻一些。

测试代码很简单,大家可以查看Fundebug/loop-mongodb-big-collection

我的测试环境是这样的:

  • ubuntu 14.04
  • mongodb 3.2
  • nodejs 10.9.0

1. 使用 Docker 运行 MongoDB

sudo docker run --net=host -d --name mongodb daocloud.io/library/mongo:3.2

2. 使用mgodatagen生成测试数据

使用 mgodatagen,1000 万个 document 可以在 1 分多钟生成!

下载 mgodatagen:https://github.com/feliixx/mgodatagen/releases/download/0.7.3/mgodatagen_linux_x86_64.tar.gz

解压之后,复制到/usr/local/bin 目录即可:

sudo mv mgodatagen /usr/local/bin

mgodatagen 的配置文件mgodatagen-config.json如下:

[
{
"database": "test",
"collection": "members",
"count": 10000000,
"content": {
"name": {
"type": "string",
"minLength": 2,
"maxLength": 8
},
"city": {
"type": "string",
"minLength": 2,
"maxLength": 8
},
"country": {
"type": "string",
"minLength": 2,
"maxLength": 8
},
"company": {
"type": "string",
"minLength": 2,
"maxLength": 8
},
"email": {
"type": "string",
"minLength": 2,
"maxLength": 8
}
}
}
]

执行mgodatagen -f mgodatagen-config.json命令,即可生成 10000 万测试数据。

mgodatagen -f mgodatagen-config.json
Connecting to mongodb://127.0.0.1:27017
MongoDB server version 3.2.13

collection members: done [====================================================================] 100%

+------------+----------+-----------------+----------------+
| COLLECTION | COUNT | AVG OBJECT SIZE | INDEXES |
+------------+----------+-----------------+----------------+
| members | 10000000 | 108 | _id_ 95368 kB |
+------------+----------+-----------------+----------------+

run finished in 1m12.82s

查看 MongoDB,可知新生成的数据有 0.69GB,其实很小,但是使用 find()方法遍历会报错。

show dbs
local 0.000GB
test 0.690GB

3. 执行测试代码

两种不同遍历方法的代码分别位于test1.jstest2.js

参考

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