Putting Apache Kafka to Use!

Transcription

1 Putting Apache Kafka to Use! Building a Real-time Data Platform for Event Streams! JAY KREPS, CONFLUENT!

2 A Couple of Themes!

3 Theme 1: Rise of Events!

4 Theme 2: Immutability Everywhere! Level! Example! Immutable Alternative! Mutable local state! Counter in a for loop! Functional Programming! Mutable process-wide state! ConcurrentHashMap! Functional Programming!! Mutable on disk structures! B-Tree! LSM! Distributed systems! Dynamo-like key-value store! State machine replication! Mutability in databases! RDBMS! Event Sourcing! Company-wide data flow! Double write! Kafka!

5 Theme 3: Datacenter-Level Thinking!

6 Experience at LinkedIn!

7 2009: We want all our data in Hadoop!!

8 What is all our data?!

9 Initial approach: gut it out!

10 Problems! Data coverage! Many source systems! Relational DBs! Log files! Metrics! Messaging systems! Many data formats! Constant change! New schemas! New data sources!

11 Needed: organizational scalability! Θ(N) => Θ(1)!

12 How does everything else work?!?!

13 Relational database changes! Apps and Services OLTP Queries Relational Databases Cache Data Guard CSV Dump Poll For Changes ODS Hadoop App App App Caches & Derived Stores Relational Data Warehouse Transforms Transforms

14 NoSQL! App App App Key-value Store ETL Load Hadoop

15 User events! Apps and Services Apps and Services Apps and Services HTTP Log Aggregation NFS rsync NFS Load Transform & Load Hadoop Relational Data Warehouse Transform

16 Application Logs! Apps and Services Apps and Services Apps and Services Splunk

17 Messaging! App App App App App Broker Broker Processor Processor Processor Processor App App App Broker Processor Processor Processor Processor

18 Metrics and operational data! App App App Monitoring

19 This is a giant mess! Apps and Services Apps and Services Apps and Services OLTP Queries ActiveMQ HTTP HTTP Monitoring ActiveMQ Apps Apps Splunk Relational Databases Cache Poll For Changes App App App Data Guard Apps ODS CSV Dump Apps Hadoop Key-value Store Load Log Aggregation NFS rsync NFS Caches & Derived Stores Relational Data Warehouse Transforms Transform & Load Transforms

20 Impossible ideas! Publish data from Hadoop to a search index! Run a SQL query to find the biggest latency bottleneck! Run a SQL query to find common error patterns! Low latency monitoring of database changes or user activity! Incorporate popularity in real-time display and relevance algorithms! Products that incorporate user activity!

21 An infrastructure solution?!

22 Idea: Stream Data Platform! Search Impala Apps Monitoring Hive RDBMS Stream Data Platform:? HADOOP: Offline Data DWH NoSQL Real-time Analytics Stream Processing Spark Map- Reduce Synchronous Req/Response Near real time 0-100s ms > 100s ms Offline batch > 1 hour

23 First Attempt: Messaging systems!!

24 Problems! Throughput! Batch systems! Persistence! Stream Processing! Ordering guarantees! Partitioning!

25 Second Attempt: Build Kafka!!

26 What does it do?! Producer Producer Producer Producer Producer Kafka Cluster Consumer Consumer Consumer Consumer Consumer

27 Commit Log Abstraction! Reader 1 Reader Writes Old New

28 Logs & Publish-Subscribe Messaging! Source System writes Log reads reads Destination System A Destination System B

29 A Kafka Topic! Partition Partition Writes Partition Old New

30 Replication! Server 1 Server 2 Server 3 A:0 A:0 A:0 A:1 A:1 A:1 B:0 B:0 Controller

31 Scaling Consumers! Kafka Cluster Server 1 Server 2 P0 P3 P1 P2 C1 C2 C3 C4 C5 C6 Consumer Group A Consumer Group B

32 Kafka: A Modern Distributed System for Streams! Scalability of a filesystem! Hundreds of MB/sec/server throughput! Many TB per server! Guarantees of a database! Messages strictly ordered! All data persistent! Distributed by default! Replication! Partitioning model! Producers, Consumers, and Brokers all fault tolerant and horizontally scalable!

33 Stream Data Platform! Search Impala Apps Monitoring Hive RDBMS KAFKA: Stream Data Platform HADOOP: Offline Data DWH NoSQL Real-time Analytics Stream Processing Spark Map- Reduce Synchronous Req/Response Near real time 0-100s ms > 100s ms Offline batch > 1 hour

34 Batch Data => Batch Processing!

35 Stream processing is a! generalization! of batch processing! and request/response processing!

36 Request/Response processing:! One input => One output!

37 Batch processing:! All inputs => All outputs!

38 Stream Processing:! Some inputs => some outputs! (you choose how much some is)!

39 Stream Processing a la carte! Input Kafka Topic Transform Transform Transform Intermediate Kafka Topic Your code cat input grep foo wc -l Transform Transform Transform Output Kafka Topic Hadoop Live Data Store

40 Stream Processing with Frameworks! +! =! Stream Processing!

41 Unix Pipes, Modernized! cat /usr/share/dict/words wc -l

42 On Schemas! Bad Schemas < No Schemas < Good Schemas!

43 Put it all together! Apps Apps Apps Apps Social Graph Search Key-Value Oracle Newsfeed OLAP Storage Log Search Apps Apps Monitoring Security & Fraud Real-time Analytics Kafka Samza Hadoop Teradata

44 At LinkedIn! Everything in the company is a real-time stream! > 800 billion messages written per day! > 2.9 trillion messages read per day! ~ 1 PB of stream data! Tens of thousands of producer processes! Backbone for data stores! Search! Social Graph! Newsfeed! Primary storage (in progress)! Basis for stream processing!

45 Elsewhere!

46 Why this is the future! 1. System diversity is increasing! 2. Data diversity and volume is increasing! 3. The world is getting faster! 4. The technology exists!

47 Mission: Make this a practical reality everywhere! Product! Apache Kafka! Schemas and metadata management! Connectors for common systems! Monitor data flow end-to-end! Stream processing integration!

48 Questions?! stream-data-platform-1! Apache