Glider

Glider
中间层
开发优化实践

@孤独的登山人

Agenda

 Glider架构
 基本策略
 优化历程
 优化总结
 思维&规范

Glider 位置

数
据主站备库 RAC 主站日志
源
DataX / TimeTunnel

计
算 Hadoop集群实时流数据
层

存
储 MyFOX Prom
层查
询数据中间层 / glider
层

产数据魔方淘宝指数开放API
品

基本策略
Http : keep-alive

Node V0.5.3 +

var agent = new http.Agent({
maxSockets : 100
});

var options = {
agent : agent
…
}

http.get(options. Function(res){
…
});

基本策略
Config : auto-reload

var self = this;
…
fs.watchFile(fpath ,function(curr, prev){
if(self.load_timer) return;

if(curr.ctime.toString() != prev.ctime.toString()){
self.load_timer = setTimeout(function(){
self.loadCfg(fpath);
},LOAD_DELAY);
}
});

基本策略
Socket Pool – Generic Pool

var poolModule = require('generic-pool');

var pool = poolModule.Pool({
name : 'mysql',
create : function(callback){
var Client = require('mysql').Client;
var c = new Client();
…
},
destroy : function(client){
client.end();
},
max : Cfg.maxSockets,
idleTimeoutMillis : Cfg.timeout
});

基本策略
Cluster
 Multi-Worker-Listen

Master.send(listenfd)

Worker.listen(…)

Master.close(listenfd);

 Master-Listen (we use this for fully control)

Master.listen()
Master.send(sockfd)

Worker.receive(sockfd)
CreateReqRes(sockfd)

优化历程
First Time
Siege
Keep-alive
Singel core ‘hello,world’ : 8000qps
(5 cpu core) 4 workers

260 QPS !!!
WHY ??

优化历程
Thinking…
Calculate Each-step Overhead
 Http Requset(skip other steps:)

 Unit/Benchmark Test

var TEST_NUMBER = 100*1000;
var start = new Date();
for(var i=0; i< TEST_NUMBER; i++){
step1();
}
var end = new Date();
console.log(‘time : ’ + (end – start));

优化历程
Step-overhead …

I. URL -> SQL conversion : 0.8ms

II. Main control : 0.5ms

III.Table LeftJoin (2000 X 2000) : 13ms

Equation:
1S / (13 + 0.8 + 0.5)ms * 4 == 280

优化历程
Optimization

URL -> SQL conversion

• Pre-parsing / Template

• keywords replacement

var obj = {
…
id : ‘#id’,
thedate : ‘#date’
}

Result:
0.8ms  0.05 ms

优化历程
Optimization
Table LeftJoin (2000 X 2000)

Language Skills

 避免重复计算
 Array / Object 遍历效率斟酌
 Array.push / array[i] 比较
 引入临时量，避免对象多次查找
 String ‘+’ 与 array.join(‘’) 比较
 使用V8 提供的函数
…

https://github.com/windyrobin/iFrame/blob/master/pp.md

Result:
13ms  5.6 ms

优化历程
Optimization
Table LeftJoin (2000 X 2000)

Change Data Structure

{
[ columns : ["id", "pid", "first"],
{id : 1, tid : 2, first : ‘hello’}, data :[
{id : 2, tid : 3, first: ‘tom’}, *1, 2, ‘hello’+,
{id : 3, tid : 3, first: ‘jim’}, *2, 3, ‘tom’+,
… *3, 3, ‘jim’+,
] …
]
}
Result:
Speed : 5.6 ms  2.6 ms (For computing)
Store size  50% (For memcache)
Transfer size  50% (For JSON response)

优化历程
Now (no cache)

Step Main Control Inner Join Content Deliver QPS
Size
2000 x 2000 0.5ms 1.7ms 1ms(22KB) 1200

500 x 500 0.5ms 0.5ms 0.25ms(5KB) 3100

100 x 100 0.5ms 0.1ms 0.1ms(1KB) 5100

Table 2000 X 2000 : LeftJoin : 2.6ms , InnerJoin : 1.7ms

Content Deliver == JSON  String  Buffer  Socket Write …

Thinking…

NodeJS

For Frontend Engineer ?!!!

Node Platform = JS + System P+ Network P

Stardard

 JS has many pitfalls
 Async mode is tricky
 For fluent team cooperation
 We are designing a server on our own
 The server is a long-time running programe
 We need helps from community
…

Stardard is a MUST !!!

Stardard

We need YOUR contribution!
 减少外部依赖，谨慎选择开源模块
 面向接口编程，不管内部实现如何，接口定义要简洁、清晰、固定
 对于复杂的逻辑运算，不仅要有Unit Test，还要有相应的Benchmark Test
 对于外部的网络请求，一定也要有相应的benchmark test，摸清其 qps，
response time, CPU 耗费
 对于异步回调函数，一定要检查其是否错误，即第一个回调参数，并做相应处理
 谨慎使用 fs 同步操作函数组---除非你清楚知道其不会造成严重后果
…

https://github.com/windyrobin/iFrame/blob/master/style.md
https://github.com/windyrobin/iFrame/blob/master/pp.md
https://github.com/windyrobin/iFrame/blob/master/mng.md

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