Linkedin Databus

Why?

关系型数据库仍然作为主要的primary data store的方案
Relational Databases have been around for a long time and have become a trusted storage medium for all of a company's data.
传统的数据仓库的ETL和OLAP方案
Data is pulled off this primary data store, transformed, and then stored in a secondary data store, such as a data warehouse.
The industry typically uses ETL to run nightly jobs to give executives a view of the previous day's, week's, month's, year's business performance.

OLTP (Online Transaction Processing) vs. OLAP (Online Analytic Processing) :
This differentiates between their uses -- OLTP for primary data serving, OLAP for analytic processing of a modified copy of the primary data.

BUT, 近来产生大量near-real-time data needs
At LinkedIn, it also feeds real-time search indexes, real-time network graph indexes, cache coherency, Database Read Replicas, etc... These are examples of LinkedIn's near-real-time data needs.

对于这样的需求, ETL和OLAP无法满足实时性
我们讨论的是, 怎么把数据从Primay data store以near-real-time搬到另一个地方处理的问题?

 

How?

Linkedin Databus, 可以让变更事件的延长达到微秒级,每台服务器每秒可以处理数千次数据吞吐变更事件,同时还支持无限回溯能力和丰富的变更订阅功能

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如何获取变更?

处理这种需求有两种常用方式:

应用驱动双向写:这种模式下,应用层同时向数据库和另一个消息系统发起写操作。这种实现看起来简单,因为可以控制向数据库写的应用代码。但是,它会引入一致性问题,因为没有复杂的协调协议(比如两阶段提交协议或者paxos算法),所以当出现问题时,很难保证数据库和消息系统完全处于相同的锁定状态。两个系统需要精确完成同样的写操作,并以同样的顺序完成序列化。如果写操作是有条件的或是有部分更新的语义,那么事情就会变得更麻烦。

数据库日志挖掘:将数据库作为唯一真实数据来源,并将变更从事务或提交日志中提取出来。这可以解决一致性问题,但是很难实现,因为 Oracle和MySQL这样的数据库有私有的交易日志格式和复制冗余解决方案,难以保证版本升级之后的可用性。由于要解决的是处理应用代码发起的数据变更,然后写入到另一个数据库中,冗余系统就得是用户层面的,而且要与来源无关。对于快速变化的技术公司,这种与数据来源的独立性非常重要,可以避免应用栈的技术锁定,或是绑死在二进制格式上。

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如果要求不是很严格, 采用第一种方法也是可以接受的, 在存DB成功后, 再写pub-sub system

 

如何微秒级的传递变更?

Relays, 中继

中继就是Memory buffer, 仍然是空间换时间的策略, 如果需要速度足够快, 就需要Relay足够多, 离client足够近, 因为client从Relay memory buffer中取数据的速度是无法优化的. 如何组织Relay集群, 有如下两种方式,

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A Databus Relay will pull the recently committed transactions from the source Database (e.g. Oracle, MySQL, etc...) (Step 1).
The Relay will deserialize this data into a compact form (Avro etc...) and store the result in a circular in-memory buffer.

Clients (subscribers) listening for events will pull recent online changes as they appear in the Relay (Step 2).

A Bootstrap component is also listening to on-line changes as they appear in the Relay.(Step 3)

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首先, 为了保证效率需要把变更数据转化为比较高效的格式(如Avro), 并且放到circular in-memory buffer
然后, Client(subscribers)侦听并从Relay的memory buffer中把更新数据Pull过去, 不能使用Push模式, 因为不同的分析效率可能有很大区别.
在Relay, 数据是放在memory buffer中的, memory是有限的, 所以采用circular方式
问题是, 每个client的要求是不一样的, 你无法知道什么时候数据真正失效, 所以必须有方法来保存历史数据, 那就是Bootstrap

 

用户有两种情况会用到Bootstrap,

1. Slow client, 需要的数据在relay中已经被覆盖, 所以需要去Bootstrap里面取

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2. New client, 需要取所有的历史数据, Bootstrap之所以得名

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Databus' Bootstrap

One of the most innovative features of Databus is its Bootstrap component.
Data Change Capture systems have existed for a long time (e.g. Oracle Streams). However, all of these systems put load on the primary data store when a consumer falls behind.

Bootstrapping a brand new consumer is another problem. It typically involves a very manual process -- i.e. restore the previous night's snapshot on a temporary Oracle instance, transform the data and transfer it to the consumer, then apply changes since the snapshot, etc...

Databus's Bootstrap component handles both of the above use-cases in a seamless, automated fashion.

Databus最具创新的是Bootstrap, 因为虽然Data Change Capture一直存在, 但是如同第一版Databus, 有个比较严重的问题是
Relay只能buffer最新的数据, 对于老数据, Relay会作为proxy从primary data store直接取数据, 然后返回给client
所以对于slow client, 这样会大大增加primary data store的负担.

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同时对于new client, 如果需要获取全部数据, 是很麻烦的, very manual process
而Bootstrap可以完全seamless的解决上面所有的问题, 确实算是创新

 

How Does Databus' Bootstrap Component Work?

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Bootstrap把更新不断的读到Log storage里面, 然后再批量的导入Snapshot Storage中
这样设计出于效率考虑, 对于Snapshot可以使用Raw Files实现, 而Log storage需要不断更新, 需要使用类似DB取实现.

The Databus Bootstrap component is made up of 2 types of storage,
Log Storage serves Consolidated Deltas
Snapshot Storage serves Consistent Snapshots

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1. As shown earlier, the Bootstrap component listens for online changes as they occur in the Relay. A LogWriter appends these changes to Log Storage.

2. A Log Applier applies recent operations in Log Storage to Snapshot Storage

3. If a new subscriber connects to Databus, the subscriber will bootstrap from the Bootstrap Server running inside the Bootstrap component

4. The client will first get a Consistent Snapshot from Snapshot Storage

5. The client will then get outstanding Consolidated Deltas from Log Storage

6. Once the client has caught up to within the Relay's in-memory buffer window, the client will switch to reading from the Relay

 

和Kafka有什么区别

Where as DataBus is used for Database change capture and replication, Kafka is used for application-level data streams

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在linkedin自己的架构中, 他们的关系是这样的
就现在状态而言, databus更侧重于DB的change capture, 并且完全基于memory应该latency更优秀些
对于其他场景, Kafka更通用一些...

 

Github

https://github.com/linkedin/databus

LinkedIn: Creating a Low Latency Change Data Capture System with Databus

http://highscalability.com/blog/2012/3/19/linkedin-creating-a-low-latency-change-data-capture-system-w.html

Databus: LinkedIn's Change Data Capture Pipeline SOCC 2012

http://www.slideshare.net/ShirshankaDas/databus-socc-2012

LinkedIn Data Infrastructure (QCon London 2012)

http://www.slideshare.net/r39132/linkedin-data-infrastructure-qcon-london-2012

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