I Heart Log: Real-time Data and Apache Kafka
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This presentation discusses how logs and stream-processing can form a backbone for data flow, ETL, and real-time data processing. It will describe the challenges and lessons learned as LinkedIn built out its real-time data subscription and processing infrastructure. It will also discuss the role of real-time processing and its relationship to offline processing frameworks such as MapReduce.
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I Heart Log: Real-time Data and Apache Kafka
- 1. Real-time Data and Apache Kafka Jay Kreps I ♥ Log
- 2. The Plan 1. Apache Kafka 2. Logs and Distributed Systems 3. Logs and Data Integration 4. Logs and Stream Processing
- 3. Apache Kafka
- 6. Three principles 1. One pipeline to rule them all 2. Stream processing >> messaging 3. Clusters not servers
- 7. Characteristics • 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
- 8. Kafka At LinkedIn • 175 TB of in-flight log data per colo • Low-latency: ~1.5 ms • Replicated to each datacenter • Tens of thousands of data producers • Thousands of consumers • 7 million messages written/sec • 35 million messages read/sec • Hadoop integration
- 9. Open source • Apache Software Foundation • Very healthy usage outside LinkedIn • Broad base of committers • 30 clients in 15 languages • Great ecosystem of supporting tools
- 10. The Plan 1. Apache Kafka 2. Logs and Distributed Systems 3. Logs and Data Integration 4. Logs and Stream Processing
- 11. Kafka is about logs
- 12. What is a log?
- 15. Partitioning
- 16. Logs: pub/sub done right
- 17. Logs And Distributed Systems
- 18. Example: A Fault-tolerant CEO Hash Table
- 22. Primary-backup
- 23. What use is a log?
- 24. The Plan 1. Apache Kafka 2. Logs and Distributed Systems 3. Logs and Data Integration 4. Logs and Stream Processing
- 25. Data Integration
- 26. Types of Data • Database data – Users, products, orders, etc • Events – Clicks, Impressions, Pageviews, etc • Application metrics – CPU usage, requests/sec • Application logs – Service calls, errors
- 27. Systems at LinkedIn • Live Stores – Voldemort – Espresso – Graph – OLAP – Search – InGraphs • Offline – Hadoop – Teradata
- 28. Bad
- 29. Good
- 30. Example: User views job
- 31. The Plan 1. Apache Kafka 2. Logs and Distributed Systems 3. Logs and Data Integration 4. Logs and Stream Processing
- 34. Stream processing is a generalization of batch processing
- 35. Examples • Monitoring • Security • Content processing • Recommendations • Newsfeed • ETL
- 36. Stream Processing = Logs + Jobs
- 37. Systems Can Help
- 39. Example: Top Articles By Company
Notas del editor
- Who are you?What is this talk about? What is a log and what is it good forExciting topic
- Producers, consumers distributed
- First project was an open source clone of Amazon Dynamo (Project Voldemort)Makes explaining things easier
- 1 Pipeline for database data1 Pipeline for metrics1 Pipeline for events1 Pipeline for real-time processingNo pipeline for application logs300ActiveMQ brokers
- 10,000 messages/sec * 100 byte messages = ~1MB/sec
- 200 Kafka-related projects on github1000+ emails/month
- The log is fundamental abstraction Kafka providesYou can use a log as a drop-in replacement for a messaging system, but it can also do a lot more
- What is a log?Traditional uses?Non-traditional uses…
- Time orderedSemi-structured
- List of changesContents of record doesn’t matterIndexed by “time”Not application log (i.e. text file)
- Data model of Kafka: A topicPartitions can be spread over machines, replicated
- The whole system is one big distributed system
- Paxos,Zookeeper (Zab), Raft, etc.Traditional databases, Hbase/Bigtable, Spanner, HDFS namenode, etcLog has two purposes:ReplicationConsistency
- Very important problem
- What if replica is down?Ordering is importantTime is important
- Log is list of changesKey point: can re-create any point-in-timeIn banking: credits and debitsIn software: the version control changelog
- State-machine replication: log the incoming requests (logical logging)
- Log the changed rows (physical logging)
- Outside of distributed systems internals…
- AKA ETLMany systemsEvent dataMost important problem for data-centric companiesIntegration >> ML
- Two exacerbating factors
- One-size fits all
- Database cache coherencyData deployment from HadoopNever get to full connectivity
- Metcalfe’s lawAll data in multi-subscriber, real-time logsThe company is a big distributed system
- Batch is dominant paradigm for data processing, why?First thing you want when you have real-time data streams is real-time transformations
- 1790Collected data by Networks=>stream processing3,929,214 people$44kHorses and wagons are a high latency, batch channel
- Service: One input = one outputBatch job: All inputs = all outputsStream computing: any window = output for that window
- Importance of the log—buffering, multisubscriberOutput goes to a live serving system or another batch processing system (Hadoop, DWH)Examples: RecommendationsEmailMonitoringSecurity
- Storm and SamzaAbout process management – both integrate with KafkaMapReduce and HDFS