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NetflixOSS Open House Lightning talks
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NetflixOSS Open HouseLightning talks
Jordan ZimmermanNetflix Platform Team
jzimmerman@netflix.com@randgalt
Jordan Zimmerman@randgalt
jzimmerman@netflix.com
NetflixOSS projects I work on
Correct ZooKeeper use is hard!
● Curator is a set of Java libraries that make using ZooKeeper much easier and enforce best practices.
● Curator is focused on the recipes: locks, leaders, etc. Most people interested in ZooKeeper don't need to be concerned with the details of connection management, etc. What they want is a simple way to use the recipes.
● Connection management, retries, recipes, best practices.● Recipes!
Leader Latch, Leader Election, Shared Reentrant Lock, Shared Lock, Shared Reentrant Read Write Lock, Shared Semaphore, Multi Shared Lock, Distributed Queue, Distributed Id Queue, Distributed Priority Queue, Distributed Delay Queue, Simple Distributed Queue, Barrier, Double Barrier, Shared counter, Distributed Atomic Long, Path Cache, Node Cache
ZooKeeper Ops is very hard!
● Instance Monitoring● Log Cleanup● Backup/Restore● Cluster-wide Configuration● Rolling Ensemble Changes● Automatic Instance Management● Visualizer● ZooKeeper Data Mutation● Curator Integration● Rich REST API
A library of extensions and utilities that enhance Google Guice to provide:
● Classpath scanning and automatic binding● Lifecycle management● Configuration to field mapping● Field validation● Parallelized object warmup● Lazy singleton● Fine grained, more concurrent singleton● Generic binding annotations
● At Netflix● Billions of log lines per day● BI reporting, Monitoring, Debugging● Log4j for several years
BLITZ4j
● What we faced?● Traffic increased – per instance logging increased● Contentions in logging● Impact in application response time● Deadlocks during reconfiguration
● What is wrong with Log4j?● Strict locking model● Synchronization semantics everywhere● Impacting application response for logging a few lines?
● What is Blitz4j?● Seamlessly replace all contention points● Decouple logging and application● Asynchronous appender – optimized, configurable● Dynamic configurability
● How can your application benefit?● Does your application use log4j and logs heavily?● Immediate performance boost ● Include blitz4j in classpath and couple of lines of configuration
● REST service ● Primarily useful in the AWS cloud● Find other middle-tier services in the cloud● Service -farm of instances providing functionality● identified by well known name
EUREKA
● Why discover other services?● Services find other services to interact - midde tier● In AWS cloud, this communication can be tricky● Instances come and go, ASG scales up/down● Interacting services should be aware of this● ELB can track this – but exposes internet traffic● DNS can discover services – but cannot maintain automatically● Eureka tracks this information and relays to all communicating services
● What does Eureka provide?● Finding instances for middle-tier communication and loadbalancing● Other management activities - take instances in/out of live traffic● Share application-specific metadata between services● Built-in Resilience to network partitions, zone failures,eureka peer outages
VMore about Eureka and Blitz4j - Visit us at the booth
Ribbon
Inter-process communication library with software load balancers and client● Cornerstone of Netflix Internal Web Services (NIWS), a Service Oriented
Architecture● Multiple built-in load balancing schemes
○ round robin, response time weighted, circuit breaker enabled● Cloud ready
○ Integration with Eureka to provide dynamic server pools in AWS○ AWS specific features - zone affinity, zone avoidance, connection
priming● Highly configurable JSR 311 based REST Client● Coming soon
○ Annotation based provider for easy serialization/deserialization and cache support
○ SLA measurement for clients○ Response cache for REST client○ Asynchronous/batch operation for REST client
ArchaiusArchaius is a dynamic configuration library
● Enable configuration change at runtime without restarting● Framework to poll configuration source or listen to external property
related events● Supports configuration sources from generic URLs, JDBC, Amazon
DynamoDB, ZooKeeper and jCloud● Central place to organize properties into "buckets" by their nature
and priorities● Coming soon
○ Service that manage properties with multiple dimensions○ Property Management UI○ Property validation and change notification
Astyanax /əˈstaɪ.ənæks/ ● Cassandra Java Client Library - Higher level fluent API
● Connection Pool
○ Bag, Round Robin, Token Aware, Rack aware
○ Connection Priming
○ Failover + Retry
○ Latency optimization
● Optimized for running in the cloud
○ Node discovery
○ Monitoring
○ Metrics
● Recipes - Common Cassandra Data Models
○ Entity Mapping
○ Message Queue (w/ Quartz like scheduling)
○ All rows reader with checkpoints
○ Distributed lock
○ Large object store
Priam - Jason Brown (@jasobrown)
- Coprocess for running cassandra in ec2- backup & recovery- configuration- token management (*)
Cassandra virtual nodes
- new feature in c* 1.2- multiple, non-contiguous shards per instance
- simplifies operations for growing / shrinking c * cluster
CassJMeter
- plugin for Apache JMeter- load testing cassandra- swappable clients
- astyanax- hector- fat client
Edda
● REST Webservice● Crawls Amazon's AWS APIs
○ Stores results as versioned JSON docs● Dynamic Querying
$ curl "http://edda/api/v2/view/instances;publicIpAddress=1.2.3.4"["i-012345678b"]
● View History (resources that no longer exist)
$ curl "http://edda/api/v2/view/instances;publicIpAddress=1.2.3.4;_since=0"["i-0123456789","i-012345678a","i-012345678b"]
● View Changes (diff over time of a resource)
Founder of the Netflix Simian Army
The Netflix Simian Army
• Chaos Monkey• Chaos Gorilla• Latency Monkey• Janitor Monkey• Conformity Monkey
• Circus Monkey• Doctor Monkey• Howler Monkey• Security Monkey• Chaos Kong• Efficiency Monkey
99.99%30 = 99.7% uptime
TurbineMetrics Stream Aggregator
Puneet Oberai
API Platform
● Low latency streaming infrastructure
● Pluggable Cloud Instance Discovery● Config based (Use Archaius)● File system● Eureka Plugin
● Data agnostic
Rx JavaFunctional Reactive Programming
What do these ops have in common?
● Query for all videos that a Netflix member has rated >= 4
● Drag and Drop event
They're both queries.
ObserverIterator
● next()● hasNext()● throw ex
● update(item)● ???● ???
Two Design Patterns
ObserverIterator
● next()● hasNext()● throw ex
● onNext(item)● onCompleted()● onError(ex)
Two Design Patterns
Example: Videos with Rating >= 4.0
Iterable<Video> videosWithHighRating =
netflixMember.getVideoLists().
map({
Iterable<Video> videoList ->
return videoList.
filter({ Video video -> video.getRating() >= 4.0 });
}).
merge();
Example: Drag EventObservable<MouseEvent> mouseDowns = // convert a legacy Observable
Observable<MouseEvent> mouseMoves = // ...
Observable<MouseEvent> mouseUps = // ...
Observable<MouseEvent> mouseDrags =
mouseDowns.
map({
MouseEvent mouseDownEvent ->
return mouseMoves.
takeUntil(mouseUps);
}).
merge();
Spin Locks
Semaphores
Dead Locks
Race Conditions
ThreadsObservable1. map
2. filter
3. merge
4. reduce
5. zip
@AsgardOSS
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