Fast Failover Using MySQL and ZooKeeper

Presented at Percona Live, April 4, 2014

Fast failover is not just an option for building reliable systems in the cloud - it is a requirement. Unfortunately, it seems like every storage solution out there (whether it's SQL or NoSQL) has its own proprietary monitoring and failover mechanism. In this presentation, we explore Zookeeper as a mechanism for fast, application-driven failover for both MySQL and Redis in the cloud. By using Zookeeper and client-level integration, we can avoid the "magic" of network-level failover (such as elastic IPs) as well as the latency and complexity of proxy-level solutions. We demonstrate that Zookeeper is a practical cross-platform strategy from any language, with examples of failover in Ruby, Java, and Node.js applications.

Transcript

1. Fast Failover Using MySQL and ZooKeeper Sunny Gleason Distributed Systems Engineer, SunnyCloud April 4, 2014
2. Who am I? • Sunny Gleason – Distributed Systems Engineer – SunnyCloud, Boston MA • Prior Web Services Work – Amazon – Ning • Focus: Scalable, Reliable Storage Systems for Structured & Unstructured Data 2
3. What’s this all about? • As Cloud systems evolve, availability expectations are getting higher and higher • Fast Failover is a requirement • But, the most common techniques applied for failover can be brittle and overly blunt • Developers seldom know or care about failover mechanics • There has to be a better way! 3
4. What do we mean by availability? 4 Availability Goal “Nines” Annual Downtime 99% (2 nines) 5256 min 87.6 hr 99.9% (3 nines) 525 min 8.7 hr 99.99% (4 nines) 52 min 99.999% (5 nines) 5 min 99.9999% (6 nines) 30 sec
5. Escalation, Activation & Availability • If failure detection, escalation and failover take > 15-30 min total, tough to get 4 nines • Goal: minimize time for failure detect & failover (and mitigate perils of auto-failover) • This presentation just focuses on reducing failover time and complexity 5
6. Applying ZooKeeper to Fast Failover • Every datastore has its own mechanism for service discovery, monitoring, failover • We can use ZooKeeper as a fault-tolerant datastore for service discovery • Use WebSocket or HTTP long-polling instead of making all the apps become ZooKeeper clients • Near-instantaneous results, while avoiding the “magic” of network-level failover 6 Precedent: http://engineering.pinterest.com/post/77933733851/zookeeper-resilience-at-pinterest
7. Common MySQL Deployment Scenario • Running Master-Master with Replication configured in both directions • Writes directed to a single active master • Active master database is identified to application by a DNS name
 (myapp-db-master.xyz.com) • Name resolves to a virtual IP (VIP) • VIP is “flipped” manually or automatically 7
8. Virtual IPs & DNS Updates • A Virtual IP (VIP) is an additional IP bound to a network interface • Linux & other operating systems allow VIPs to be bound / unbound dynamically • VIP works b/c MAC address -> IP not 1:1 • AWS EC2 has similar concept of “Elastic IP” — like a VIP, but slower to propagate and and harder to debug • DNS entries have TTLs which can be set low 8 More Info: http://scale-out-blog.blogspot.com/2011/01/virtual-ip-addresses-and-their.html
9. Starting Point: Replicated Master-Master 9 MySQL Master A MySQL Master B DNS + VIP Serves RW Queries Serves R Queries (or not) FAILOVER! MySQL Master A MySQL Master B DNS + VIP STONITH! Recover... RW Queries Master-Master repl MySQL Master C Serves R Queries (once online, or not) Break replication Reconfigure replication
10. Virtual IPs and/or DNS-based Discovery • Heavyweight : affects all nodes • Complicated : VIP requires coordination, DNS propagation tricky to verify • Slow : ARP cache and DNS have TTL • Error Prone : hardware/OS have ARP quirks, applications don’t honor DNS TTL (Java) • Unidirectional : no feedback channel for service clients 10
11. Virtual IPs and/or DNS-based Discovery 11 source: http://www.engravingawardsgifts.com/sledgehammers.html
12. What if we use a Layer 4 / Layer 7 proxy? • Single point of failure in front of the DBs • Or, multiple points of failure that need to be coordinated • How is the proxy cluster discovered? Round-Robin DNS? VIPs? • Proxy solves fast failover, but not high availability • Can we do better? 12
13. Properties of a Better Solution • Applications embed a standard callback for configuration changes • Fast : propagate & verify in millis, not minutes • Explicit : not dependent on network “magic”, creates verifiable events at coordinator and client-side • Fine-grained : offers precise control of failover • Bidirectional : provides feedback channel from client back to the coordinator, channel for backpressure • Straightforward : easy for mere mortals to debug & understand, useful on everyday basis 13
14. ZooKeeper & WebSocket for Fast Failover 14
15. ZooKeeper Architecture 15 source: http://nofluffjuststuff.com/blog/scott_leberknight/2013/07/ distributed_coordination_with_zookeeper_part_4_architecture_from_30_000_feet
16. ZooKeeper Properties • Symmetric distributed “small data” store with automatic, fast leader election • A minority of cluster cannot make progress, staleness is bounded/configurable • Clients see continuously advancing view of data over time • More info: • ZK Book: http://shop.oreilly.com/product/0636920028901.do • Blog: http://www.sleberknight.com/blog/sleberkn/entry/ distributed_coordination_with_zookeeper_part3 • Fault-Tolerance: http://aphyr.com/posts/291-call-me-maybe-zookeeper 16
17. ZooKeeper Data Model 17 source: http://zookeeper.apache.org/doc/trunk/zookeeperOver.html
18. ZooKeeper Data Model • Client connections to ZK are session-based • Data is a hierarchy of nodes • All nodes are children of the root node • Nodes have single parent, 0 or more children • Nodes may hold up to 1MB of data • Nodes have metadata: version, ctime, mtime • Nodes may be ephemeral, deleted upon session close • Clients can watch nodes for instant change notification 18
19. ZooKeeper Disclaimers • ZooKeeper is not a database • ZooKeeper is a highly specialized store with specific properties • Protect it from bloat! • Keep data small • Restrict number of direct clients • Write apps so they still function if the cluster becomes unavailable (i.e. during major version upgrades) • Study the ZooKeeper guide closely and certify ZK in production before using it for mission-critical use cases 19
20. ZkWs: ZooKeeper & WebSocket 20 ZK Node A (follower) ZK Node B (leader) ZK Node C (follower) WS Service A WS Service B WS Service C Client Client Client Client Zone 1 Zone 2 Zone 3 ELB / RR DNS Walk
21. ZkWs: WebSocket & ZooKeeper • Use WebSocket to provide a simple, HTTP-based protocol on top of ZooKeeper read-only watches • Clients connect to any WS server, watches 1+ ZK paths • WS Service aggregates ZK clients, simplifies client connection pool development • Configuration updates propagate within milliseconds • Client reconnects automatically if connection lost • In progress: client caches latest good settings to reduce impact of config service (allows cluster to be taken down for upgrades) 21
22. ZkWs Service & Clients • Initial implementation of ZkWs server is ~35 lines of CoffeeScript • Work is in progress to make it 5k lines of Java • WS-enabled Client Implementations: • Java update client with DataSource wrapper & atomic/transparent activation • Ruby update client • Node.JS update client 22
23. ZkWs Service # (this is coffeescript) zkc = -> zk = {} zk.client = zookeeper.createClient zkCfg zk.client.once 'connected', -> console.log 'Connected to the ZK server.' zk.client.connect() zk.watch = (path, socket) -> finish = (err, data) -> socket.emit 'update', 
 {path:path,value:data.toString()} notify = -> zk.client.getData path, notify, finish getAndSend = -> zk.client.getData path, notify, finish getAndSend() zk ! client = zkc() ! io.sockets.on 'connection', (socket) -> console.log 'client connected' socket.on 'watch', (data) -> console.log 'client subscribe', arguments client.watch(data.path, socket) socket.on 'disconnect', (socket) -> console.log 'client disconnected', arguments 23
24. ZkWs Java DynamicDataSource @Override public synchronized void updated(String path, Map<String, Object> properties) {
 if (!zkPath.equals(path)) { return; } log.info("configuration updating [{}]", path); try { DataSource newInstance = createDataSource(properties); doSleep(properties); DataSource original = this.instance.getAndSet(newInstance); doClose(original); ... 24
25. ZkWs Ruby Update Client require 'rubygems' require 'zkws-client' ! handler = lambda {|data| update_db_connection(data) } ! client = ZkWs::Client.new(‘http://ws1.zkws.io:8080/', handler) client.watch ‘/dbconfigPath' ! ... 25
26. ZkWs Node.JS Update Client # (this is coffeescript) ! callback = (path) -> (data) -> updateDbConn(data) ! udc = new UpdateClient(‘ws1.zkws.io’, {port:8080}) 
 udc.watch({path:’/dbConfig'}, callback("/dbConfig")) 26
27. ZkWs In Practice • “Early Prototype” stage • All open source under non-imposing licenses • https://github.com/sunnycode/zkws-server-js • https://github.com/sunnycode/zkws-client-java • https://github.com/sunnycode/zkws-client-ruby • https://github.com/sunnycode/zkws-client-js • If you’re interested in this stuff, please reach out • Or, if you’d like me to run ZkWs as a service for you 27
28. Next Steps • Apply to other data stores & services: Redis, MongoDB, Web Service discovery • Local data caching to allow cluster upgrades • Jitter in activation - prevent thundering herd, allow rolling updates • Bidirectional communication - failover acks and errors, service backpressure • Keep bullet-proofing clients & server • Global Replication Models using PubNub 28
29. Going Global with PubNub • Easy to set up WebSocket in AWS Availability Zones in one Region, supporting a small number of servers • What if we wanted to efficiently manage configuration across multiple geo regions? • ZooKeeper latencies scale poorly in a widely-distributed network • PubNub provides infrastructure for fast global real-time communication 29
30. PubNub Global Network 30 PubNub Data Center 1 PubNub Data Center 2 PubNub Data Center 3 PubNub Data Center ... n PubNub Network Data Center ! ! ! ! Node Node Node Node Node Node Node Node US-East Data Center ! ! ! ! Node Node Node Node Node Node Node Node Europe
31. PubNub Benefits • Easy to use Publish/Subscribe API with SocketIO support (works with ZkWs) • Global Presence • Every AWS Availability Zone, ~1ms latency • Rackspace, Softlayer & Azure • Full worldwide propagation within ~250ms • Proactive message replication & storage - when service connects, messages are already there • Initial target application: global service discovery and failover 31
32. ZkWs Conclusion • Current failover Mechanisms can be: • complicated • brittle • slow • datastore-specific • With ZkWs, failover can be • fast • precise • simpler & more debuggable for DBAs & Devs • fun! 32
33. Questions? 33 Thank You!