Download to read offline
![Avoiding
“Level
Confusion”
[Catataxis]
• Business
Level
Objects
(BLO?)
– Customers,
Movies,
etc
– Conceptual:
Exist
only
between
the
ears
• Abstract
Types
– AbstracMons
that
try
to
model
aspects
of
the
business
level
objects
– O?en
captured
by
Java
interfaces
• ImplementaMons
– Specific
coded
implementaMons
of
the
abstract
types
– Java
class,
or
a
collecMon
of
rows
in
a
database…](https://image.slidesharecdn.com/archtutoriallondon-2of3-120313165913-phpapp02/75/Cloud-Architecture-Tutorial-Platform-Component-Architecture-2of3-43-2048.jpg)
Uploaded byAdrian Cockcroft
Cloud Architecture Tutorial - Platform Component Architecture (2of3)
This document provides an overview of a presentation on cloud architecture and anti-architecture patterns. The presentation discusses moving a company's primary data store from a centralized SQL database to a distributed Cassandra database in the cloud. An initial prototype backup solution was overengineered, becoming complex and taking too long to implement fully. This highlighted the importance of defining anti-architecture constraints upfront to guide development in a simpler direction. The presentation concludes with a discussion of differences between the company's existing datacenter architecture and goals for a cloud architecture, focusing on replacing centralized components with distributed and decoupled alternatives.
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Cloud Architecture Tutorial - Platform Component Architecture (2of3)
- 1. Cloud Architecture Tutorial Pla$orm Component Architecture Part 2 of 3 Qcon London March 5th, 2012 Adrian Cockcro? @adrianco #ne$lixcloud hCp://www.linkedin.com/in/adriancockcro?
- 2. Don’t Do That! A Discussion of AnM-‐Architecture (wriCen as an Ignite talk)
- 3. Architecture PaCerns to guide detailed design and construcMon
- 4. AnM-‐Architecture Constraints that limit detailed design and construcMon
- 5.
- 6. How could that happen?
- 7. Anatomy of a Failure
- 8. What I Wanted • Moving to Cassandra as primary data store • We need backups! • We are running on AWS… I want Cassandra backups to S3 Start with full backup, incremental later Restore to a different Cassandra cluster
- 9. AddiMonal Goals I would like it next week -‐ Keep it simple No single point of failure Get once a day full backup working first
- 10. Prototype • Created S3 bucket • Carefully figured out a good S3 path hierarchy • Wrote a simple backup script • Added it to cron • …. • Profit! (total Mme half a day)
- 11. Now comes the hard part! Restore is trickier, Cassandra is wriCen in Java, programmer from another team takes over… Here’s the S3 bucket, backups are being collected already, please figure out how to restore it. Done by next week perhaps?
- 12. Days Pass… • Programmer is re-‐wriMng backup in python • Installs Python 2.7 on CentOS, breaks yum • Backup remotely invoked from a central point • Cassandra patched to do incremental backups
- 13. Weeks Pass… • Python based full backup & restore works! • But only to the Cassandra cluster it came from • Incremental backup works! • Restore not done yet…
- 14. Cassandra in ProducMon We do have backups running now, right? We’ll get right on it… I want the producKon backup restored in test. Oh, didn’t implement that feature yet…
- 15. Whoops! ProducMon data trashed while sefng up backup Luckily – it was recoverable from elsewhere
- 16. Months Pass • Python prototype re-‐wriCen in Java (Priam) • Integrated with other management funcMons • Decentralized backups again (yay!) • Reliable backups • Restore to test • Not simple • Took too long…
- 17. AnM-‐Architecture • Define the things you don’t want • Constrain the outcome • Check that the constraints are being met • … • Profit!
- 18.
- 19. AnM-‐Architecture Define the space the thing will inhabit (All pictures in this secMon were found on google images)
- 20. Cloud Architecture PaCerns Where do we start?
- 21. Goals • Faster – Lower latency than the equivalent datacenter web pages and API calls – Measured as mean and 99th percenMle – For both first hit (e.g. home page) and in-‐session hits for the same user • Scalable – Avoid needing any more datacenter capacity as subscriber count increases – No central verMcally scaled databases – Leverage AWS elasMc capacity effecMvely • Available – SubstanMally higher robustness and availability than datacenter services – Leverage mulMple AWS availability zones – No scheduled down Mme, no central database schema to change • ProducMve – OpMmize agility of a large development team with automaMon and tools – Leave behind complex tangled datacenter code base (~8 year old architecture) – Enforce clean layered interfaces and re-‐usable components
- 22. Datacenter AnM-‐PaCerns What do we currently do in the datacenter that prevents us from meeMng our goals?
- 23. Architecture • So?ware Architecture – The abstracMons and interfaces that developers build against • Systems Architecture – The service instances that define availability, scalability • Compose-‐ability – so?ware architecture that is independent of the systems architecture – decoupled flexible building block components
- 24. Rewrite from Scratch Not everything is cloud specific Pay down technical debt Robust paCerns
- 25. Ne$lix Datacenter vs. Cloud Arch Central SQL Database Distributed Key/Value NoSQL SMcky In-‐Memory Session Shared Memcached Session ChaCy Protocols Latency Tolerant Protocols Tangled Service Interfaces Layered Service Interfaces Instrumented Code Instrumented Service PaCerns Fat Complex Objects Lightweight Serializable Objects Components as Jar Files Components as Services
- 26. The Central SQL Database • Datacenter has a central database – Everything in one place is convenient unMl it fails – Customers, movies, history, configuraMon • Schema changes require downMme AnK-‐paMern impacts scalability, availability
- 27. The Distributed Key-‐Value Store • Cloud has many key-‐value data stores – More complex to keep track of, do backups etc. – Each store is much simpler to administer DBA – Joins take place in java code – No schema to change, no scheduled downMme • Mean Latency for Simple Key Lookup Queries – Memcached is dominated by network latency <1ms – Cassandra around one millisecond – Oracle for simple queries is a few milliseconds – DynamoDB around 5ms – SimpleDB replicaMon and REST overheads >10ms
- 28. The SMcky Session • Datacenter SMcky Load Balancing – Efficient caching for low latency – Tricky session handling code • Encourages concentrated funcMonality – one service that does everything – Middle Mer load balancer had issues in pracMce AnK-‐paMern impacts producKvity, availability
- 29. Shared Session State • ElasMc Load Balancer – We don’t use the cookie based rouMng opMon – External “session caching” with memcached • More flexible fine grain services – Any instance can serve any request – Works beCer with auto-‐scaled instance counts
- 30. ChaCy Opaque and BriCle Protocols • Datacenter service protocols – Assumed low latency for many simple requests • Based on serializing exisMng java objects – Inefficient formats – IncompaMble when definiMons change AnK-‐paMern causes producKvity, latency and availability issues
- 31. Robust and Flexible Protocols • Cloud service protocols – JSR311/Jersey is used for REST/HTTP service calls – Custom client code includes service discovery – Support complex data types in a single request • Apache Avro – Evolved from Protocol Buffers and Thri? – Includes JSON header defining key/value protocol – Avro serializaMon is half the size and several Mmes faster than Java serializaMon, more work to code
- 32. Persisted Protocols • Persist Avro in Memcached – Save space/latency (zigzag encoding, half the size) – New keys are ignored – Missing keys are handled cleanly • Avro protocol definiMons – Less briCle across versions – Can be wriCen in JSON or generated from POJOs – It’s hard, needs beCer tooling
- 33. Tangled Service Interfaces • Datacenter implementaMon is exposed – Oracle SQL queries mixed into business logic • Tangled code – Deep dependencies, false sharing • Data providers with sideways dependencies – Everything depends on everything else AnK-‐paMern affects producKvity, availability
- 34. Untangled Service Interfaces • New Cloud Code With Strict Layering – Compile against interface jar – Can use spring runMme binding to enforce – Fine grain services as components • Service interface is the service – ImplementaMon is completely hidden – Can be implemented locally or remotely – ImplementaMon can evolve independently
- 35. Untangled Service Interfaces Two layers: • SAL -‐ Service Access Library – Basic serializaMon and error handling – REST or POJO’s defined by data provider • ESL -‐ Extended Service Library – Caching, conveniences, can combine several SALs – Exposes faceted type system (described later) – Interface defined by data consumer in many cases
- 36. Service InteracMon PaCern Sample Swimlane Diagram
- 37. Service Architecture PaCerns • Internal Interfaces Between Services – Common paCerns as templates – Highly instrumented, observable, analyMcs – Service Level Agreements – SLAs • Library templates for generic features – Instrumented Ne$lix Base Servlet template – Instrumented generic client interface template – Instrumented S3, SimpleDB, Memcached clients
- 38. CLIENT Request Start Timestamp, Client Inbound Request End outbound deserialize end Timestamp serialize start Mmestamp Mmestamp Inbound Client deserialize outbound start serialize end Mmestamp Mmestamp Client network receive Mmestamp Service Request Client Network send Mmestamp Instruments Every Service network send Mmestamp Step in the call Service Network receive Mmestamp Service Service outbound inbound serialize end serialize start Mmestamp Mmestamp Service Service outbound inbound serialize start SERVICE execute serialize end request start Mmestamp Mmestamp Mmestamp, execute request end Mmestamp
- 39. Boundary Interfaces • Isolate teams from external dependencies – Fake SAL built by cloud team – Real SAL provided by data provider team later – ESL built by cloud team using faceted objects • Fake data sources allow development to start – e.g. Fake IdenMty SAL for a test set of customers – Development solidifies dependencies early – Helps external team provide the right interface
- 40. One Object That Does Everything • Datacenter uses a few big complex objects – Movie and Customer objects are the foundaMon – Good choice for a small team and one instance – ProblemaMc for large teams and many instances • False sharing causes tangled dependencies – UnproducMve re-‐integraMon work AnK-‐paMern impacKng producKvity and availability
- 41. An Interface For Each Component • Cloud uses faceted Video and Visitor – Basic types hold only the idenMfier – Facets scope the interface you actually need – Each component can define its own facets • No false-‐sharing and dependency chains – Type manager converts between facets as needed – video.asA(PresentaMonVideo) for www – video.asA(MerchableVideo) for middle Mer
- 42. Basic Types Epistemology and Design By Stan Lanning
- 43. Avoiding “Level Confusion” [Catataxis] • Business Level Objects (BLO?) – Customers, Movies, etc – Conceptual: Exist only between the ears • Abstract Types – AbstracMons that try to model aspects of the business level objects – O?en captured by Java interfaces • ImplementaMons – Specific coded implementaMons of the abstract types – Java class, or a collecMon of rows in a database…
- 44. Facets • No single Abstract Type captures everything about a BLO – Different teams see different “facets” • Customer: Account status; Billing history; Viewing history; A/B test assignments • Movie: Availability; Popularity; Synopsis; Cast – Loosely coupled, Mghtly aligned(!) • All facets for a BLO should inherit from one “basic” type that has minimal behavior
- 45. Basic Types • Module external interfaces deal in basic types; internal calls are free to use more complex facets • Generic machinery to switch between facets Business Level Object Java Basic Type Movie (TV show…) Video Customer Visitor Category VTag Country ISOCountry
- 46. Type Manager • Holds the “factory” objects that manage instances of facets – Typically one factory per facet – Factories free to implement any instance management policy they want • Factories register with the Type Manager – callers never interact directly with the factories – Mock managers?
- 47. Switching Facets • Each Basic Type B implements a method that uses the Type Manager to find facet implementaMons of the same BLO <T extends B> T asA(Class<T> c)! • Example: Visitor visitor = xxx; ABClient abClient = visitor.asA(ABClient.class); assert(visitor.equals(abClient));! • Look Ma, no cast! – Facets are equal, but not necessarily ==.
- 48. IDs! (huh) What are they good for? • IDs exist because implementaKons need to externalize objects and maintain their idenKty – Persist in a DB, or talk to a remote service – Different implementaMons of a type of BLO model the same object iff they have the same ID – Basic Types use IDs to manage facets, determine equality, etc
- 49. ConverMng IDs ßàObjects Long id = xx;! MyVisitor visitor =! TypeManager.findObject(Visitor.class, id)! .asA(MyVisitor.class);! assert(id.equals(visitor.getId());! // Or more efficiently…! MyVisitor visitor2 =! TypeManager.findObject(Visitor.class, id,! MyVisitor.class);! // There are also efficient bulk conversion methods! Collection<Long> ids = xxx;! List<MyVisitor> visitors =! TypeManager.findObjects(Visitor.class, ids,! MyVisitor.class);! !
- 50. Stan’s Soap Box • Don’t pass around IDs when you mean to refer to the BLO; that is Level Confusion • Using Basic Types helps the compiler help you; compile Mme problems are beCer than run Mme problems • More readable by people, but beware that asA operaMons may be a lot of work • (Is this a way to approximate mulMple-‐ inheritance in Java?)
- 51. So?ware Architecture PaCerns • Object Models – Basic and derived types, facets, serializable – Pass by reference within a service – Pass by value between services • ComputaMon and I/O Models – Service ExecuMon using Best Effort / Futures – Common thread pool management – Circuit breakers to manage and contain failures
- 52. Model Driven Architecture • TradiMonal Datacenter PracMces – Lots of unique hand-‐tweaked systems – Hard to enforce paCerns – Some use of Puppet to automate changes • Model Driven Cloud Architecture – Perforce/Ivy/Jenkins based builds for everything – Every producMon instance is a pre-‐baked AMI – Every applicaMon is managed by an Autoscaler Every change is a new AMI
- 53. Ne$lix Cloud Pla$orm Ne$lix ApplicaMons Ne$lix Cloud Pla$orm / PaaS AWS Specific Partner Ne$lix Legacy Code Interfaces Datacenter AWS Services Partner Services Services
- 54. Ne$lix PaaS Principles • Maximum FuncMonality – Developer producMvity and agility • Leverage as much of AWS as possible – AWS is making huge investments in features/scale • Interfaces that isolate Apps from AWS – Avoid lock-‐in to specific AWS API details • Portability is a long term goal – Gets easier as other vendors catch up with AWS
- 55. Ne$lix Global PaaS • Architecture Features and Overview • Portals and Explorers • Pla$orm Services • Pla$orm APIs • Pla$orm Frameworks • Persistence • Scalability Benchmark
- 56. Global PaaS? Toys are nice, but this is the real thing… • Supports all AWS Availability Zones and Regions • Supports mulMple AWS accounts {test, prod, etc.} • Cross Region/Acct Data ReplicaMon and Archiving • InternaMonalized, Localized and GeoIP rouMng • Security is fine grain, dynamic AWS keys • Autoscaling to thousands of instances • Monitoring for millions of metrics • ProducMve for 100s of developers on one product • 23M+ users USA, Canada, LaMn America, UK, Eire
- 57. Basic PaaS EnMMes • AWS Based EnMMes – Instances and Machine Images, ElasMc IP Addresses – Security Groups, Load Balancers, Autoscale Groups – Availability Zones and Geographic Regions • Ne$lix PaaS EnMMes – ApplicaMons (registered services) – Clusters (versioned Autoscale Groups for an App) – ProperMes (dynamic hierarchical configuraMon)
- 58. Core PaaS Services • AWS Based Services – S3 storage, to 5TB files, parallel mulMpart writes – SQS – Simple Queue Service. Messaging layer. • Ne$lix Based Services – EVCache – memcached based ephemeral cache – Cassandra – distributed data store • External Services – GeoIP Lookup interfaced to a vendor – Keystore HSM in Ne$lix Datacenter
- 59. Instance Architecture Linux Base AMI (CentOS or Ubuntu) OpMonal Apache frontend, Java (JDK 6 or 7) memcached, non-‐java apps Tomcat AppDynamics appagent Monitoring Log rotaMon ApplicaMon servlet, base Healthcheck, status to S3 GC and thread server, pla$orm, interface servlets, JMX interface, AppDynamics dump logging jars for dependent services Servo autoscale machineagent Epic
- 60. Security Architecture • Instance Level Security baked into base AMI – Login: ssh only allowed via portal (not between instances) – Each app type runs as its own userid app{test|prod} • AWS Security, IdenMty and Access Management – Each app has its own security group (firewall ports) – Fine grain user roles and resource ACLs • Key Management – AWS Keys dynamically provisioned, easy updates – High grade app specific key management support
- 61. Core Pla$orm Frameworks and APIs
- 62. Portals and Explorers • Ne$lix ApplicaMon Console (NAC) – Primary AWS provisioning/config interface • AWS Usage Analyzer – Breaks down costs by applicaMon and resource • Cassandra Explorer – Browse clusters, keyspaces, column families • Base Server Explorer – Browse service endpoints configuraMon, perf
- 65. AWS Usage for test, carefully omifng any $ numbers…
- 66.
- 67.
- 68. Pla$orm Services • Discovery – service registry for “ApplicaMons” • IntrospecMon – Entrypoints • Cryptex – Dynamic security key management • Geo – Geographic IP lookup • Pla$ormservice – Dynamic property configuraMon • LocalizaMon – manage and lookup local translaMons • Evcache – ephemeral volaMle cache • Cassandra – Cross zone/region distributed data store • Zookeeper – Distributed CoordinaMon (Curator) • Various proxies – access to old datacenter stuff
- 69. IntrospecMon -‐ Entrypoints • REST API for tools, apps, explorers, monkeys… – E.g. GET /REST/v1/instance/$INSTANCE_ID • AWS Resources – Autoscaling Groups, EIP Groups, Instances • Ne$lix PaaS Resources – Discovery ApplicaMons, Clusters of ASGs, History
- 70. Entrypoints Queries MongoDB used for low traffic complex queries against complex objects DescripAon Range expression Find all acMve instances. all() Find all instances associated with a group %(cloudmonkey) name. Find all instances associated with a /^cloudmonkey$/discovery() discovery group. Find all auto scale groups with no instances. asg(),-‐has(INSTANCES;asg()) How many instances are not in an auto count(all(),-‐info(eval(INSTANCES;asg()))) scale group? What groups include an instance? *(i-‐4e108521) What auto scale groups and elasMc load filter(TYPE;asg,elb;*(i-‐4e108521)) balancers include an instance? What instance has a given public ip? filter(PUBLIC_IP;174.129.188.{0..255};all())
- 71. Metrics Framework • System and ApplicaMon – CollecMon, AggregaMon, Querying and ReporMng – Non-‐blocking logging, avoids log4j lock contenMon – Honu-‐Streaming -‐> S3 -‐> EMR -‐> Hive • Performance, Robustness, Monitoring, Analysis – Tracers, Counters – explicit code instrumentaMon log – Real Time Tracers/Counters – SLA – service level response Mme percenMles – Servo annotated JMX extract to Cloudwatch • Latency Monkey Infrastructure – Inject random delays into service responses
- 72. ConfiguraAon Management • Ne$lixConfiguraMon – ValidaMon Framework – Sitewide ProperMes Explorer • Pla$ormService • Mapping Service • ZooKeeper (Curator) • InstanceIdenMty
- 73. Interprocess CommunicaAon • Discovery Service registry for “applicaMons” – “here I am” call every 30s, drop a?er 3 missed – “where is everyone” call – Redundant, distributed, moving to Zookeeper • NIWS – Ne$lix Internal Web Service client – So?ware Middle Tier Load Balancer – Failure retry moves to next instance – Many opMons for encoding, etc.
- 74. Security Key Management • AKMS – Dynamic Key Management interface – Update AWS keys at runMme, no restart – All keys stored securely, none on disk or in AMI • Cryptex -‐ Flexible key store – Low grade keys processed in client – Medium grade keys processed by Cryptex service – High grade keys processed by hardware (Ingrian)
- 75. AWS Persistence Services • SimpleDB – Got us started, migrated to Cassandra now – NFSDB -‐ Instrumented wrapper library – Domain and Item sharding (workarounds) • S3 – Upgraded/Instrumented JetS3t based interface – Supports mulMpart upload and 5TB files – Global S3 endpoint management
- 76. Aside: Adrian’s Rant on CAP Theorem Choose Consistency or Availability when ParAAoned • Instances and Networks will fail • Network failure = ParMMon “P” is a given • Distributed Systems: two choices – CP or AP • “Vendor claims CA” – Usually they mean available when instances fail • Master-‐Slave = Consistent when ParMMoned – You can’t write unless you can see the master • No-‐Master = Available when ParMMoned – Writes proceed, conflicts will be patched up later
- 77. What Ne$lix Needed from NoSQL
- 78. Basic Requirements • Supports running on Amazon EC2 • Supports Amazon Availability Zones • Low latency, low latency variance • High and scalable read and write throughput • Large and scalable capacity, no external sharding • “AP” Eventually Consistent • Data integrity checks and repairs • Online Snapshot Backup, Restore/Rollback
- 79. Scenario – Immediate Read a?er Write Q1: Is rouMng and replicaMon zone aware? TV Device New New Favorite Round Robin Favorites Load Balancer List API API (zone A) (Zone B) Append New New Favorites Favorite List Favorites Favorites (zone A) (Zone B) ReplicaMon
- 80. Network ParMMon Q2: What happens next? TV Device New New Favorite Round Robin Favorites Load Balancer List API API (zone A) (Zone B) Append New New Favorites Favorite List Favorites Favorites (zone A) (Zone B) No ReplicaMon
- 81. Network ParMMon Q3: Supports Append vs. Read/Modify/Write? TV Device New New Favorite Round Robin Favorites Load Balancer List RMW API API (zone A) (Zone B) Old New New Favorites Favorites Favorites List List List Favorites Favorites (zone A) (Zone B) ReplicaMon
- 82. Silent Data CorrupMon Q4: How is it detected and corrected? TV Device New New Favorite Round Robin Favorites Load Balancer List API API (zone A) (Zone B) Append New New Favorites Favorite List Favorites Favorites (zone A) (Zone B) ReplicaMon corrupted on disk or via network
- 83. NePlix PlaPorm Persistence • Ephemeral VolaMle Cache – evcache – Discovery-‐aware memcached based backend – Client abstracMons for zone aware replicaMon – OpMon to write to all zones, fast read from local • Cassandra – Highly available and scalable (more later…) • MongoDB – Complex object/query model for small scale use • MySQL – Hard to scale, legacy and small relaMonal models
- 84. Why Cassandra? • We value Availability over Consistency – AP – Cassandra is a Java distributed systems toolkit • We have a building full of Java engineers – Riak is in Erlang – a blessing and a curse… • We want FOSS + Support – Voldemort doesn’t have a support model • Writes are intrinsically harder than reads – Hbase is CP opMmized for reads & single namenode issues • Cassandra works, running ~55 clusters – Step by step into full producMon over the last year
- 85. Priam – Cassandra AutomaMon Available at hCp://github.com/ne$lix • Ne$lix Pla$orm Tomcat Code • Zero touch auto-‐configuraMon • State management for Cassandra JVM • Token allocaMon and assignment • Broken node auto-‐replacement • Full and incremental backup to S3 • Restore sequencing from S3 • Grow/Shrink Cassandra “ring”
- 86. Astyanax Available at hCp://github.com/ne$lix • Cassandra java client • API abstracMon on top of Thri? protocol • “Fixed” ConnecMon Pool abstracMon (vs. Hector) – Round robin with Failover – Retry-‐able operaMons not Med to a connecMon – Ne$lix PaaS Discovery service integraMon – Host reconnect (fixed interval or exponenMal backoff) – Token aware to save a network hop – lower latency – Latency aware to avoid compacMng/repairing nodes – lower variance • Batch mutaMon: set, put, delete, increment • Simplified use of serializers via method overloading (vs. Hector) • ConnecMonPoolMonitor interface for counters and tracers • Composite Column Names replacing deprecated SuperColumns
- 87. IniMalizing Astyanax //Configuration either set in code or nfastyanax.properties platform.ListOfComponentsToInit=LOGGING,APPINFO,DISCOVERY netflix.environment=test default.astyanax.readConsistency=CL_QUORUM default.astyanax.writeConsistency=CL_QUORUM MyCluster.MyKeyspace.astyanax.servers=127.0.0.1 // Must initialize platform for discovery to work NFLibraryManager.initLibrary(PlatformManager.class, props, false, true); NFLibraryManager.initLibrary(NFAstyanaxManager.class, props, true, false); // Open a keyspace instance Keyspace keyspace = KeyspaceFactory.openKeyspace(”MyCluster”,”MyKeyspace");
- 88. 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") .setIsPaginaMng() .withColumnRange(new RangeBuilder().setMaxSize(pageize).build()); while (!(columns = query.execute().getResult()).isEmpty()) { for (Column<String> c : columns) { } } } catch (ConnecMonExcepMon e) { }
- 89. Data MigraMon to Cassandra
- 90. Distributed Key-‐Value Stores • Cloud has many key-‐value data stores – More complex to keep track of, do backups etc. – Each store is much simpler to administer DBA – Joins take place in java code • No schema to change, no scheduled downMme • Latency for typical queries – Memcached is dominated by network latency <1ms – Cassandra takes a few milliseconds – SimpleDB replicaMon and REST auth overheads >10ms
- 91. MulA-‐Regional Data ReplicaAon • IR Framework – Datacenter Item Replicator – Built in 2009, first step to the cloud – Oracle to SimpleDB or Cassandra via poll and push – Return updates to Oracle via SQS message queue • SimpleDB or S3 to Cassandra – Data migraMon tool for global Ne$lix • Global SimpleDB and S3 ReplicaMon – Cross region async updates USA to Europe
- 92. TransiAonal Steps • BidirecMonal ReplicaMon – Oracle to SimpleDB – Queued reverse path using SQS – Backups remain in Datacenter via Oracle • New Cloud-‐Only Data Sources – Cassandra based – No replicaMon to Datacenter – Backups performed in the cloud
- 93. API AWS EC2 Front End Load Balancer Discovery Service API Proxy API etc. Load Balancer Component API SQS Services Oracl e Oracle Oracle Cassandra memcached ReplicaMon memcached EC2 Internal Disks NePlix S3 Data Center SimpleDB
- 94. Cufng the Umbilical • TransiMon Oracle Data Sources to Cassandra – Offload Datacenter Oracle hardware – Free up capacity for growth of remaining services • TransiMon SimpleDB+Memcached to Cassandra – Primary data sources that need backup – Keep simplest small use cases for now • New challenges – Backup, restore, archive, business conMnuity – Business Intelligence integraMon
- 95. API AWS EC2 Front End Load Balancer Discovery Service API Proxy Load Balancer Component API Services memcached Cassandra EC2 Internal Disks Backup S3 SimpleDB
- 96. High Availability • Cassandra stores 3 local copies, 1 per zone – Synchronous access, durable, highly available – Read/Write One fastest, least consistent -‐ ~1ms – Read/Write Quorum 2 of 3, consistent -‐ ~3ms • AWS Availability Zones – Separate buildings – Separate power etc. – Fairly close together
- 97. “TradiMonal” Cassandra Write Data Flows Single Region, MulMple Availability Zone, Not Token Aware Cassandra • Disks • Zone A 2 2 4 2 1. Client Writes to any Cassandra 3 3 Cassandra If a node goes offline, Cassandra Node • Disks 5 • Disks 5 hinted handoff 2. Coordinator Node • Zone C 1 • Zone A completes the write replicates to nodes when the node comes and Zones Non Token back up. 3. Nodes return ack to Aware coordinator Clients Requests can choose to 4. Coordinator returns 3 wait for one node, a Cassandra Cassandra ack to client • Disks • Disks 5 quorum, or all nodes to 5. Data wriCen to • Zone C • Zone B ack the write internal commit log disk (no more than Cassandra SSTable disk writes and • Disks 10 seconds later) • Zone B compacMons occur asynchronously
- 98. Astyanax -‐ Cassandra Write Data Flows Single Region, MulMple Availability Zone, Token Aware Cassandra • Disks • Zone A 1. Client Writes to Cassandra 2 2 Cassandra If a node goes offline, nodes and Zones • Disks 3 • Disks 3 hinted handoff 2. Nodes return ack to • Zone C 1 • Zone A completes the write client 3. Data wriCen to Token when the node comes back up. internal commit log Aware disks (no more than Clients 2 Requests can choose to 10 seconds later) Cassandra Cassandra wait for one node, a • Disks • Disks 3 quorum, or all nodes to • Zone C • Zone B ack the write Cassandra SSTable disk writes and • Disks • Zone B compacMons occur asynchronously
- 99. Data Flows for MulM-‐Region Writes Token Aware, Consistency Level = Local Quorum 1. Client writes to local replicas If a node or region goes offline, hinted handoff 2. Local write acks returned to completes the write when the node comes back up. Client which conMnues when Nightly global compare and repair jobs ensure 2 of 3 local nodes are everything stays consistent. commiCed 3. Local coordinator writes to remote coordinator. Cassandra 100+ms latency 4. When data arrives, remote Cassandra • Disks • Disks • Zone A • Zone A coordinator node acks and Cassandra 2 2 Cassandra Cassandra 4 Cassandra 6 6 3 5 Disks 6 copies to other remote zones 6 • Disks • Disks • Zone C • Zone A • • Zone C 4 Disks A • • Zone 1 4 5. Remote nodes ack to local US EU coordinator Clients Clients Cassandra 2 Cassandra Cassandra 5 Cassandra 6. Data flushed to internal • Disks • Zone C • Disks 6 • Zone B • Disks • Zone C • Disks 6 • Zone B commit log disks (no more Cassandra Cassandra than 10 seconds later) • Disks • Disks • Zone B • Zone B
- 100. Remote Copies • Cassandra duplicates across AWS regions – Asynchronous write, replicates at desMnaMon – Doesn’t directly affect local read/write latency • Global Coverage – Business agility – Follow AWS… ? • Local Access ? ? – BeCer latency 3 A 3 – Fault IsolaMon
- 101. Cassandra Backup • Full Backup Cassandra Cassandra Cassandra – Time based snapshot – SSTable compress -‐> S3 Cassandra Cassandra • Incremental S3 Backup Cassandra Cassandra – SSTable write triggers compressed copy to S3 Cassandra Cassandra • Archive Cassandra Cassandra – Copy cross region A
- 102. Cassandra Restore • Full Restore Cassandra Cassandra Cassandra – Replace previous data • New Ring from Backup Cassandra Cassandra – New name old data S3 Backup Cassandra Cassandra • Scripted – Create new instances Cassandra Cassandra – Parallel load -‐ fast Cassandra Cassandra
- 103. Cassandra Online AnalyMcs • Brisk = Hadoop + Cass Cassandra – “Cassandra Enterprise” Brisk Cassandra – Use split Brisk ring Brisk Cassandra – Size each separately S3 • Direct Access Cassandra Backup Cassandra – Keyspaces – Hive/Pig/Map-‐Reduce Cassandra Cassandra – Hdfs as a keyspace Cassandra Cassandra – Distributed namenode
- 104. ETL for Cassandra • Data is de-‐normalized over many clusters! • Too many to restore from backups for ETL • SoluMon – 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
- 105. Cassandra Archive A Appropriate level of paranoia needed… • Archive could be un-‐readable – Restore S3 backups weekly from prod to test, and daily ETL • Archive could be stolen – PGP Encrypt archive • AWS East Region could have a problem – Copy data to AWS West • ProducMon AWS Account could have an issue – Separate Archive account with no-‐delete S3 ACL • AWS S3 could have a global problem – Create an extra copy on a different cloud vendor….
- 106. Extending to MulM-‐Region In producMon for UK/Eire support 1. Create cluster in EU Take a Boeing 737 on a domesMc flight, upgrade it to a 747 by adding more engines, fuel and bigger wings 2. Backup US cluster to S3 and fly it to Europe without landing it on the way… 3. Restore backup in EU 4. Local repair EU cluster 5. Global repair/join Cassandra 100+ms latency Cassandra 1 • Disks • Disks • Zone A • Zone A Cassandra Cassandra Cassandra Cassandra • Disks • Disks • Disks • Disks • Zone C • Zone A • Zone C • Zone A US 5 EU Clients Clients Cassandra Cassandra Cassandra Cassandra • Disks • Disks • Disks • Disks • Zone C • Zone B • Zone C • Zone B Cassandra Cassandra • Disks • Disks • Zone B 3 • Zone B 4 2 S3
- 107. Tools and AutomaMon • Developer and Build Tools – Jira, Perforce, Eclipse, Jenkins, Ivy, ArMfactory – Builds, creates .war file, .rpm, bakes AMI and launches • Custom Ne$lix ApplicaMon Console – AWS Features at Enterprise Scale (hide the AWS security keys!) – Auto Scaler Group is unit of deployment to producMon • Open Source + Support – Apache, Tomcat, Cassandra, Hadoop – Datastax support for Cassandra, AWS support for Hadoop via EMR • Monitoring Tools – Alert processing gateway into Pagerduty – AppDynamics – Developer focus for cloud hCp://appdynamics.com
- 108. NoSQL Developer MigraMon • Jason Brown @jasobrown – Cassandra from the Trenches – slideshare.net/ne$lix • Mark Atwood, "Guide to NoSQL, redux” – YouTube hCp://youtu.be/zAbFRiyT3LU
- 109. Open Sourcing the Ne$lix PaaS
- 110. Open Source Strategy • Release PaaS Components git-‐by-‐git – Source at github.com/ne$lix – Intros and techniques at techblog.ne$lix.com – Blog post or new code every week or so • MoMvaMons – Give back to Apache licensed OSS community – MoMvate, retain, hire top engineers – Create a community that adds features and fixes
- 111. Current OSS Projects and Posts Github / Techblog Priam Exhibitor Servo Apache Project Techblog Post Astyanax Curator Autoscaling scripts CassJMeter Zookeeper Honu Cassandra EVCache Circuit Breaker Aegisthus
- 112. Takeaway NePlix has built and deployed a scalable global PlaPorm as a Service. Key components of the NePlix PaaS are being released as Open Source projects so you can build your own custom PaaS. hCp://github.com/Ne$lix hCp://techblog.ne$lix.com hCp://slideshare.net/Ne$lix hCp://www.linkedin.com/in/adriancockcro? @adrianco #ne$lixcloud End of Part 2 of 3