Running Ne)lix on Cassandra in the Cloud

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1 røde x stadig vises, skal du muligvis slette billedet og indsætte det igen. Running Ne)lix on in the Cloud Adrian Netflix Inc.!

2 in DK

3 Blah Blah Blah (I m skipping all the cloud intro etc. did that yesterday Ne%lix runs in the cloud, if you hadn t figured that out already you aren t paying acendon and should go read slideshare.net/ne%lix)

4 Ne%lix Deployed on AWS Content Logs Play WWW API CS Content Management S3 Terabytes DRM Sign- Up Metadata InternaDonal CS lookup EC2 Encoding EMR CDN roudng Search Device Config DiagnosDcs & AcDons S3 Petabytes Hive & Pig Bookmarks Movie Choosing TV Movie Choosing Customer Call Log CDNs ISPs Terabits Customers Business Intelligence Logging RaDngs Social Facebook CS AnalyDcs

5 on AWS A highly available and durable deployment pacern

6 Service PaCern Service REST Clients Cluster Managed by Priam Between 6 and 72 nodes Data Access REST Service Astyanax Client Datacenter Update Flow Appdynamics Service Flow VisualizaDon

7 ProducDon Deployment Over 50 Clusters Over 500 nodes Over 30TB of daily backups Biggest cluster 72 nodes 1 cluster over 250Kwrites/s

8 High Availability stores 3 local copies, 1 per zone Synchronous access, durable, highly available Read/Write One fastest, use for fire and forget Read/Write Quorum 2 of 3, use for read- ader- write AWS Availability Zones Separate buildings Separate power etc. Fairly close together

9 Triple Replicated Persistence maintenance drops individual replicas Load Balancers Zone A and Evcache Replicas Zone B and Evcache Replicas Zone C and Evcache Replicas

10 Instance Architecture Linux Base AMI (CentOS) Priam Manager Token Management, Backups, Autoscaling Tomcat/Java7 Monitoring Log rotadon AppDynamics machineagent Etc. Java7 AppDynamics appagent monitoring GC and thread dump logging 1.09

11 Priam AutomaDon Available at hcp://github.com/ne%lix Ne%lix Pla%orm Tomcat Code Zero touch auto- configuradon State management for JVM Token allocadon and assignment Broken node auto- replacement Full and incremental backup to S3 Restore sequencing from S3 Grow/Shrink ring

12 Astyanax Available at hcp://github.com/ne%lix Features Complete abstracdon of connecdon pool from RPC protocol Fluent Style API OperaDon retry with backoff Token aware Recipes Distributed row lock (without zookeeper) MulD- DC row lock Uniqueness constraint MulD- row uniqueness constraint Chunked and muld- threaded large file storage

13 Astyanax Query Example Paginate through all columns in a row ColumnList<String> columns; int pageize = 10; try { RowQuery<String, String> query = keyspace.preparequery(cf_standard1).getkey("a").setispaginadng().withcolumnrange(new RangeBuilder().setMaxSize(pageize).build()); while (!(columns = query.execute().getresult()).isempty()) { for (Column<String> c : columns) { } } } catch (ConnecDonExcepDon e) { }

14 TradiDonal Write Data Flows Single Region, MulDple Availability Zone, Not Token Aware 1. Client Writes to any Node 2. Coordinator Node replicates to nodes and Zones 3. Nodes return ack to coordinator 4. Coordinator returns ack to client 5. Data wricen to internal commit log disk (no more than 10 seconds later) 5 Zone C Zone B 3 Zone A Non Token Aware Clients Zone A Zone B Zone C If a node goes offline, hinted handoff completes the write when the node comes back up. Requests can choose to wait for one node, a quorum, or all nodes to ack the write SSTable disk writes and compacdons occur asynchronously

15 Astyanax - Write Data Flows Single Region, MulDple Availability Zone, Token Aware Zone A 1. Client Writes to local coordinator 2. Coodinator writes to other zones 3. Nodes return ack 4. Data wricen to internal commit log disks (no more than 10 seconds later) 4 Zone C Zone B 3 1 Token Aware Clients Zone A Zone B 2 4 Zone C If a node goes offline, hinted handoff completes the write when the node comes back up. Requests can choose to wait for one node, a quorum, or all nodes to ack the write SSTable disk writes and compacdons occur asynchronously

16 Data Flows for MulD- Region Writes Token Aware, Consistency Level = Local Quorum 1. Client writes to local replicas 2. Local write acks returned to Client which condnues when 2 of 3 local nodes are commiced 3. Local coordinator writes to remote coordinator. 4. When data arrives, remote coordinator node acks and copies to other remote zones 5. Remote nodes ack to local coordinator 6. Data flushed to internal commit log disks (no more than 10 seconds later) 6 Zone C Zone B 2 If a node or region goes offline, hinted handoff completes the write when the node comes back up. Nightly global compare and repair jobs ensure everything stays consistent. Zone A 1 US Clients Zone A 2 Zone B Zone C ms latency 5 Zone C Zone B Zone A Zone B 4 EU Clients 6 Zone A 5 Zone C

17 Extending to MulD- Region Added producdon UK/Ireland support with no downdme Minimize impact on original cluster using bulk backup move 1. Create cluster in EU 2. Backup US cluster to S3 3. Restore backup in EU 4. Local repair EU cluster 5. Global repair/join Take a Boeing 737 on a domesdc flight, upgrade it to a 747 by adding more engines, fuel and bigger wings and fly it to Europe without landing it on the way Zone A 100+ms latency Zone A 1 Zone C US Clients Zone B 5 Zone C EU Clients Zone B Zone B Zone C Zone B Zone C 2 Zone A S3 3 Zone A 4

18 Backup Full Backup Time based snapshot SSTable compress - > S3 Incremental SSTable write triggers compressed copy to S3 Archive Copy cross region A S3 Backup

19 Explorer Open source on github soon

20 ETL for Data is de- normalized over many clusters! Too many to restore from backups for ETL SoluDon read backup files using Hadoop Aegisthus hcp://techblog.ne%lix.com/2012/02/aegisthus- bulk- data- pipeline- out- of.html High throughput raw SSTable processing Re- normalizes many clusters to a consistent view Extract, Transform, then Load into Teradata

21 Asgard hcp://techblog.ne%lix.com/2012/06/asgard- web- based- cloud- management- and.html Replacement for AWS Console at Scale Groovy/Grails/JVM based Supports all AWS regions on a global basis Specific to AWS feature set Hides the AWS credendals Use AWS IAM to issue restricted keys for Asgard Each Asgard instance manages one account One install each for test, prod, audit accounts

22 Open Source Projects Legend Github / Techblog Apache ContribuDons Techblog Post Coming Soon Priam as a Service Astyanax client for Java CassJMeter test suite Exhibitor Zookeeper as a Service Curator Zookeeper PaCerns EVCache Memcached as a Service Servo and Autoscaling Scripts Honu Log4j streaming to Hadoop Circuit Breaker Robust service pacern MulD- region EC2 datastore support Eureka / Discovery Service Directory Asgard - AutoScaleGroup based AWS console Aegisthus Hadoop ETL for Archaius Dynamics ProperDes Service Chaos Monkey Robustness verificadon Explorers EntryPoints Latency Monkey Governator - Library lifecycle and dependency injecdon Server- side latency/error injecdon Janitor Monkey Odin Workflow orchestradon REST Client + mid- Der LB Bakeries and AMI Async logging ConfiguraDon REST endpoints Build dynaslaves

23 Benchmarks and Scalability

24 Scalability from 48 to 288 nodes on AWS hcp://techblog.ne%lix.com/2011/11/benchmarking- cassandra- scalability- on.html Client Writes/s by node count Replica?on Factor = Used 288 of m1.xlarge 4 CPU, 15 GB RAM, 8 ECU 0.86 Benchmark config only existed for about 1hr

25 Some people skate to the puck, I skate to where the puck is going to be Wayne Gretzky

26 on AWS The Past Instance: m2.4xlarge Storage: 2 drives, 1.7TB CPU: 8 Cores, 26 ECU RAM: 68GB Network: 1Gbit IOPS: ~500 Throughput: ~100Mbyte/s Cost: $1.80/hr The Future Instance: hi1.4xlarge Storage: 2 SSD volumes, 2TB CPU: 8 HT cores, 35 ECU RAM: 64GB Network: 10Gbit IOPS: ~100,000 Throughput: ~1Gbyte/s Cost: $3.10/hr

27 Disk vs. SSD Benchmark Same Throughput, Lower Latency, Half Cost

28 Things we don t do

29 Things we do do. Run benchmarks. Live on stage. (but this Dme it s a recording).

30 YOLO

31 Live Demo Workload Jenkins automadon Jmeter load driver Asgard provisioning Priam instance management Traffic Reading/wriDng whole 100 column rows Randomly selected from 25M row keys Run for 10minutes, then double ring size

32 Screenshots from Live Demo Summit 2012 last July Live on stage demo

33 Asgard cass_perf apps, with no instances running

34 Jenkins Jenkins perf_test jobs

35 Jmeter Setup Build parameters

36 Jmeter Setup Build parameters

37 Jmeter Setup Build parameters

38 Asgard IiniDal set of cass instances up and running

39 Kiklos (open source soon) Clusters growing from 12 to 24 in- service, bootstrapping, garbage- collecdng, cass- down hcp://explorers.us- east- 1.dyntest.ne%lix.net: 7001/jr/cassandradashboard

40 Kiklos Clusters growing from 12 to 24 in- service, bootstrapping, garbage- collecdng, cass- down

41 Disclaimers We didn t have Dme to tune the demo These are the plots from the live demo run Run s need to be longer to get to steady state Data size only reached around 5GB per node Plenty of I wonder why it did that remains It s a fair comparison, but not the best absolute performance possible for this workload and configuradon When you remove the IO bocleneck, the next few boclenecks appear

42 AcDvity during the talk 10:30-11:30 Custom AppDynamics dashboard showing CPU and IOPS per node

43 Jmeter Plots Plots are the output of the Jenkins build Each instance has its own set of plots Each availability zone has its own summary plots One of the three zone summary plots is compared for each metric Plot collecdon is currently duplicated as we are transidoning from Epic to Atlas

44 Jenkins Collected results and graphs ader job has completed

45 The past m2.4xlarge Instances per zone The future hi1.4xlarge

46 The past m2.4xlarge TransacDons per zone, same as total client transacdons The future hi1.4xlarge

47 The past m2.4xlarge The future hi1.4xlarge

48 The past m2.4xlarge Thousands of Microseconds The future hi1.4xlarge

49 The past m2.4xlarge Microseconds The future hi1.4xlarge

50 The past m2.4xlarge The future hi1.4xlarge

51 The past m2.4xlarge The future hi1.4xlarge

52 Next Steps Migrate ProducDon to SSD Several clusters done ~100 SSD nodes running Autoscale using Priam 1.2 Vnodes make this easier Shrink cluster every night

53 Skynet A Ne%lix Hackday project that might just terminate the world (hack currently only implemented in Powerpoint luckly)

54 The Plot (kinda) Skynet is a sendent computer that defends itself when people try to turn it off Connor is the guy who eventually turns it off Terminator is the robot sent to kill Connor

55 The Hacktors Cass_skynet is a cluster that detects that it is being acacked and responds Connor_monkey kills cass_skynet nodes Terminator_monkey kills connor_monkey nodes

56 The HackDon Cass_skynet stores a history of its world and acdon scripts that trigger from what it sees AcDon response to losing a node Auto- replace node and grow cluster size by one AcDon response to losing more nodes Replicate cluster into a new zone or region AcDon response to seeing a Connor_monkey Startup a Terminator_monkey in that zone

57 ImplementaDon (plan) Priam Autoreplace missing nodes Grow cass_skynet cluster Increase replicadon to new zone (new code) Increase replicadon to new region (new code) Keyspaces AcDons scripts to be run Memory record event log of everything seen Cron job once a minute Extract acdons from and execute Chaos Monkey configuradon Terminator_monkey: pick a zone, kill any connor_monkey Connor_monkey: kill any cass_skynet or terminator_monkey

58 SimulaDon

59 Takeaway Ne-lix has built and deployed a scalable global pla-orm based on and AWS. Key components of the Ne-lix PaaS are being released as Open Source projects so you can build your own custom PaaS. SSD s in the cloud are awesome. hcp://github.com/ne%lix hcp://techblog.ne%lix.com hcp://slideshare.net/ne%lix #ne%lixcloud #cassandra12

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