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Apache-2.0

简介

该项目架构设计文章:https://my.oschina.net/1Gk2fdm43/blog/5312538

该项目主要为海量日志(秒级GB级)的搜集、传输、存储而设计的全套方案。区别于传统的如ELK系列套件,该项目主要是为了解决超大量级的日志(出入参、链路跟踪日志)从搜集到最后检索查询的中途产生的高昂硬件成本、性能低下等问题。

核心点在于 性能、成本

主要应用方向为: 用户跟踪 ,即根据特定条件,查询用户请求出入参、中途打印的所有日志,即这个链路的日志。

较ELK系列方案(filebeat、mq传输、es存储等常见方案),该框架拥有10倍以上的性能提升,和70%以上的磁盘节省。这意味着,在日志这个功能块上,使用相同的硬件配置,原本只能传输、存储一秒100M的日志,采用该方案,一秒可以处理1GB的日志,且将全链路因日志占用的磁盘空间下降70%。

由于该方案着重于极致的吞吐性能和极低的磁盘占用,而对数据全程进行了压缩。故并不适合于需要es关键字模糊查询的场景使用,而仅支持提前设定好的索引字段查询。数据库使用的是clickhouse集群,配置中心采用的etcd,如果之前没有相关经验的,还涉及了新技术的学习成本,以及需要对部分源码进行改造,故未必适合大部分项目使用。建议关注实现方案和处理超大量级的数据、缓冲、入库等代码逻辑即可,整套思路可适用于多种场景。

该项目适用于日志量极大,且用于节省中转环节如使用kafka等mq进行日志中转的场景。在京东、方舟健客等公司线上使用,由于各公司日志查询时索引字段不同,固必然有一定的开发工作,需要注意。这不是一个开箱即用的项目。

关于性能及机器配置简述,目前线上的worker配置主要为8核32g的docker,32g内存是专门定制的参数,因为worker需要靠纯内存来承接和缓冲大量接收的日志,这样的单机每秒可以承接的日志量为160M-200M,因为是压缩后的,对应原始日志约1个G,约1千万行。

clickhouse机器配置为16核64G内存,单机每秒可以稳定写入180M,再高会丢可用率。对应原始日志约1个G。

查询界面由于目前前端做的不太好,大概放个图大家了解意思就行,就是查用户跟踪的。 可以查询出入参,及请求整个链路的日志情况。 输入图片说明 输入图片说明 输入图片说明

使用说明

使用说明

群已满200人,可加我微信备注JLog,邀请入群 输入图片说明

背景

京东App作为一个巨大量级的请求入口,涉及了诸多系统,为了保证系统的健壮性、和请求溯源,以及出现问题后的问题排查,通常我们保存了用户请求从出入参、系统中途关键节点日志(info、error)、链路日志等,并且会将日志保存一段时间。

日志系统基本划分为几个模块:收集(filebeat、logstash等),传输(kafka、tcp直传等),存储(es,mysql、hive等),查询(kibana、自建)。

以传统日志解决方案为例,当有一个G的日志产生后,日志写本地磁盘(占用磁盘1G),读取后写入mq(mq占用2G,单备份),消费mq写入es(es占用1G),共需占用磁盘4GB,附带着网络带宽占用,和同体量的服务器资源占用(单服务器秒级处理100M)。

则以京东App的体量,当秒级百G日志产生时,所耗费的硬件资源相当庞大,成本已到了难以承受的地步。

该日志框架即为在有所取舍的情况下,支撑海量日志的场景所研发,如前文所讲,该方案在同等硬件配置下,较filebeat+mq+es的模式秒级 日志吞吐量提升10倍,且全链路磁盘占用下降了70%以上

方案简介

详细的可以看开头那篇文章。 这里只放两张图基本能说明原理。 输入图片说明

输入图片说明

输入图片说明

基本流程为通过filter获取web请求出入参、自定义log4j、logback的appender搜集中途打印的日志,通过请求入口时生成的tracerId进行关联,写入本地内存(取代写磁盘),进行压缩(字符串空间占用减少80%以上),通过Udp发往worker端(取代mq),worker接收数据抽取索引字段,并入库clickhouse,除未来查询要用的索引字段外,其他内容全程压缩直至入库。

答疑

一 为什么是UDP而不是TCP?

主要区别,udp是知道对方的ip和端口,把消息发出去后,直接清空本地缓存。而tcp是发出去后,等待对方回复确认收到信息后,才会删除这条消息的缓存。一旦接收端卡顿,则客户端可能堆外内存溢出。并且通信时间翻倍,吞吐量下降。

二 UDP丢消息怎么办?

1 网络拥堵,交换机被打满。则无论udp tcp都发不出去了

2 重启机器,在空档期udp发出去的不会重发

3 worker消费入库不及,主动抛弃

总结:日志允许以上情况下的丢失,无需保证强可靠性

三 1台clickhouse,极限一秒写入180M,对应用户QPS约1万。 那么30台服务器如何支撑大促60万QPS,或更高?

前提:clickhouse性能是明确的,譬如一秒最多写1万用户QPS,则30台最高写30万。那么60万QPS时, 我们只能用别的地方去缓冲。 方案: 定制8核32G机器,单机开一个10万的队列,存放用户请求数据。批量入库,靠内存来吃下 大促陡增的几秒流量。则50台worker机器,即可拥有500万的内存队列,即便高峰是80万、100万,随后 逐步下降至30万、20万并维持。则一个能吃下30万的clickhouse集群足以支撑。 且worker机器可以随时扩容,平时4台支撑日常的1-5万的QPS,大促当天扩容即可,成本极低。

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来自京东App秒级百G级日志搜集、传输、存储解决方案 展开 收起
Java 等 4 种语言
Apache-2.0
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