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The Big Data Ecosystem at LinkedIn Jay Kreps
Me Background in data not infrastructure LinkedIn’s SNA team Original co-author of some LinkedIn open source projects (Voldemort, Azkaban, Kafka)
This Talk We are in a renaissance of data infrastructure. How do all these pieces fit together?
Why the current obsession with “Big Data”?
The goal of modern data infrastructure is to make many small computers act like one big one.
The Old Picture
The New Picture
Polyglot persistence?
Infrastructure Icebergs 90k lines of tooling and monitoring, 30k lines of logic Dedicated engineers, operations Training First three nines come from operations
This is (still) a very immature space. Which systems should we have?
Infrastructure is sculpted by applications and constraints Projects are defined by trade-offs
Constraints Hardware Jeff Dean: Numbers everyone should know David Patterson: Latency lags bandwidth $$$ Other Path dependence Complexity Resources
Applications
Common categories of non-CRUD Recommendations & Matching Graphs Search Data Normalization News feed Analysis & Monitoring
Social Graph
Search
Recommendations: People
Recommendations: Jobs
Recommendations: Newsfeed
Data Normalization
Analytics
Infrastructure Search Lucene Bobo (facets), Zoie (real-time indexing), Sensei (distribution) Social Graph Storage Oracle Voldemort Espresso Streams Databus Kafka Offline Hadoop & friends (Pig, Hive, Azkaban, etc)
Three Major Paradigms Request/Response Search Social Graph Storage Streams Kafka Batch Hadoop
Most features are multi-paradigm
Request/Response Search Social Graph Storage Voldemort Espresso
Request/Response Patterns Broker, scatter-gather Storage systems: only  Partitioning strategy Latency oriented
Batch: Hadoop Uses Ad hoc Production batch Ecosystem Hive, Pig Azkaban (workflow) Avro data Data in: Kafka Data out: Voldemort, Kafka
Why do batch if you have real-time? Batch advantages Safety Easy Throughput Simplicity Economics Tricky bit: engineering the data cycle
Why do streaming? You have to glue all these systems together Throughput as good as batch Latency much better Metaphor more natural for low latency than Hadoop
What makes successful infrastructure systems? Operability and Operations Monitoring Simplicity Documentation Broad adoption Lazy users Open source
Open Source Data > Infrastructure Open source creates better code—even with few outside contributors Commercial infrastructure not interesting
Open Source Projects We made Voldemort: Key/Value storage Sensei, Bobo, Zoie: Elastic, faceted, real-time search with Lucene Kafka: Persistent, distributed data streams Norbert: Cluster aware RPC, load balancing, and group membership And others… We stole Hadoop, Pig, Hive Lucene Netty, Jetty Zookeeper Avro Apache Traffic Server
The End jay.kreps@gmail.com http://www.linkedin.com/in/jaykreps http://twitter.com/jaykreps http://sna-projects.com

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The Big Data Ecosystem at LinkedIn

Editor's Notes

  1. Good news for users, bad news for distributed systems nerdsFilesystems take a decade to mature. Don’t expect this will be easier.