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Building a Real-Time Data Pipeline: Apache Kafka at LinkedIn

Amy W. Tang
Amy W. Tang

The document discusses LinkedIn's implementation of a real-time data pipeline using Apache Kafka, emphasizing the need to leverage large volumes of data for product development. Key strategies include using a central data pipeline, enforcing data cleanliness, optimizing ETL processes, and ensuring evidence-based correctness. It details Kafka's performance at LinkedIn, reporting billions of messages processed daily across numerous services.

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Building a Real-Time Data Pipeline: Apache Kafka at Linkedin Hadoop Summit 2013 Joel Koshy June 2013 LinkedIn Corporation ©2013 All Rights Reserved
HADOOP SUMMIT 2013 Network update stream
LinkedIn Corporation ©2013 All Rights Reserved We have a lot of data. We want to leverage this data to build products. Data pipeline
HADOOP SUMMIT 2013 People you may know
HADOOP SUMMIT 2013 System and application metrics/logging LinkedIn Corporation ©2013 All Rights Reserved 5
How do we integrate this variety of data and make it available to all these systems? LinkedIn Confidential ©2013 All Rights Reserved
HADOOP SUMMIT 2013 Point-to-point pipelines
HADOOP SUMMIT 2013 LinkedIn’s user activity data pipeline (circa 2010)
HADOOP SUMMIT 2013 Point-to-point pipelines
HADOOP SUMMIT 2013 Four key ideas 1. Central data pipeline 2. Push data cleanliness upstream 3. O(1) ETL 4. Evidence-based correctness LinkedIn Corporation ©2013 All Rights Reserved 10
HADOOP SUMMIT 2013 Central data pipeline
First attempt: don’t re-invent the wheel LinkedIn Confidential ©2013 All Rights Reserved
HADOOP SUMMIT 2013
Second attempt: re-invent the wheel! LinkedIn Confidential ©2013 All Rights Reserved
Use a central commit log LinkedIn Confidential ©2013 All Rights Reserved
HADOOP SUMMIT 2013 What is a commit log?
HADOOP SUMMIT 2013 The log as a messaging system LinkedIn Corporation ©2013 All Rights Reserved 17
HADOOP SUMMIT 2013 Apache Kafka LinkedIn Corporation ©2013 All Rights Reserved 18
HADOOP SUMMIT 2013 Usage at LinkedIn  16 brokers in each cluster  28 billion messages/day  Peak rates – Writes: 460,000 messages/second – Reads: 2,300,000 messages/second  ~ 700 topics  40-50 live services consuming user-activity data  Many ad hoc consumers  Every production service is a producer (for metrics)  10k connections/colo LinkedIn Corporation ©2013 All Rights Reserved 19
HADOOP SUMMIT 2013 Usage at LinkedIn LinkedIn Corporation ©2013 All Rights Reserved 20
HADOOP SUMMIT 2013 Four key ideas 1. Central data pipeline 2. Push data cleanliness upstream 3. O(1) ETL 4. Evidence-based correctness LinkedIn Corporation ©2013 All Rights Reserved 21
HADOOP SUMMIT 2013 Standardize on Avro in data pipeline LinkedIn Corporation ©2013 All Rights Reserved 22 { "type": "record", "name": "URIValidationRequestEvent", "namespace": "com.linkedin.event.usv", "fields": [ { "name": "header", "type": { "type": "record", "name": ”TrackingEventHeader", "namespace": "com.linkedin.event", "fields": [ { "name": "memberId", "type": "int", "doc": "The member id of the user initiating the action" }, { "name": ”timeMs", "type": "long", "doc": "The time of the event" }, { "name": ”host", "type": "string", ... ...
HADOOP SUMMIT 2013 Four key ideas 1. Central data pipeline 2. Push data cleanliness upstream 3. O(1) ETL 4. Evidence-based correctness LinkedIn Corporation ©2013 All Rights Reserved 23
HADOOP SUMMIT 2013 Hadoop data load (Camus)  Open sourced: – https://github.com/linkedin/camus  One job loads all events  ~10 minute ETA on average from producer to HDFS  Hive registration done automatically  Schema evolution handled transparently
HADOOP SUMMIT 2013 Four key ideas 1. Central data pipeline 2. Push data cleanliness upstream 3. O(1) ETL 4. Evidence-based correctness LinkedIn Corporation ©2013 All Rights Reserved 25
Does it work? “All published messages must be delivered to all consumers (quickly)” LinkedIn Confidential ©2013 All Rights Reserved
HADOOP SUMMIT 2013 Audit Trail
HADOOP SUMMIT 2013 Kafka replication (0.8)  Intra-cluster replication feature – Facilitates high availability and durability  Beta release available https://dist.apache.org/repos/dist/release/kafka/  Rolled out in production at LinkedIn last week LinkedIn Corporation ©2013 All Rights Reserved 28
HADOOP SUMMIT 2013 Join us at our user-group meeting tonight @ LinkedIn! – Thursday, June 27, 7.30pm to 9.30pm – 2025 Stierlin Ct., Mountain View, CA – http://www.meetup.com/http-kafka-apache-org/events/125887332/ – Presentations (replication overview and use-case studies) from:  RichRelevance  Netflix  Square  LinkedIn LinkedIn Corporation ©2013 All Rights Reserved 29
HADOOP SUMMIT 2013LinkedIn Corporation ©2013 All Rights Reserved 30

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Building a Real-Time Data Pipeline: Apache Kafka at LinkedIn

  • 1. Building a Real-Time Data Pipeline: Apache Kafka at Linkedin Hadoop Summit 2013 Joel Koshy June 2013 LinkedIn Corporation ©2013 All Rights Reserved
  • 3. LinkedIn Corporation ©2013 All Rights Reserved We have a lot of data. We want to leverage this data to build products. Data pipeline
  • 5. HADOOP SUMMIT 2013 System and application metrics/logging LinkedIn Corporation ©2013 All Rights Reserved 5
  • 6. How do we integrate this variety of data and make it available to all these systems? LinkedIn Confidential ©2013 All Rights Reserved
  • 8. HADOOP SUMMIT 2013 LinkedIn’s user activity data pipeline (circa 2010)
  • 10. HADOOP SUMMIT 2013 Four key ideas 1. Central data pipeline 2. Push data cleanliness upstream 3. O(1) ETL 4. Evidence-based correctness LinkedIn Corporation ©2013 All Rights Reserved 10
  • 11. HADOOP SUMMIT 2013 Central data pipeline
  • 12. First attempt: don’t re-invent the wheel LinkedIn Confidential ©2013 All Rights Reserved
  • 14. Second attempt: re-invent the wheel! LinkedIn Confidential ©2013 All Rights Reserved
  • 15. Use a central commit log LinkedIn Confidential ©2013 All Rights Reserved
  • 16. HADOOP SUMMIT 2013 What is a commit log?
  • 17. HADOOP SUMMIT 2013 The log as a messaging system LinkedIn Corporation ©2013 All Rights Reserved 17
  • 18. HADOOP SUMMIT 2013 Apache Kafka LinkedIn Corporation ©2013 All Rights Reserved 18
  • 19. HADOOP SUMMIT 2013 Usage at LinkedIn  16 brokers in each cluster  28 billion messages/day  Peak rates – Writes: 460,000 messages/second – Reads: 2,300,000 messages/second  ~ 700 topics  40-50 live services consuming user-activity data  Many ad hoc consumers  Every production service is a producer (for metrics)  10k connections/colo LinkedIn Corporation ©2013 All Rights Reserved 19
  • 20. HADOOP SUMMIT 2013 Usage at LinkedIn LinkedIn Corporation ©2013 All Rights Reserved 20
  • 21. HADOOP SUMMIT 2013 Four key ideas 1. Central data pipeline 2. Push data cleanliness upstream 3. O(1) ETL 4. Evidence-based correctness LinkedIn Corporation ©2013 All Rights Reserved 21
  • 22. HADOOP SUMMIT 2013 Standardize on Avro in data pipeline LinkedIn Corporation ©2013 All Rights Reserved 22 { "type": "record", "name": "URIValidationRequestEvent", "namespace": "com.linkedin.event.usv", "fields": [ { "name": "header", "type": { "type": "record", "name": ”TrackingEventHeader", "namespace": "com.linkedin.event", "fields": [ { "name": "memberId", "type": "int", "doc": "The member id of the user initiating the action" }, { "name": ”timeMs", "type": "long", "doc": "The time of the event" }, { "name": ”host", "type": "string", ... ...
  • 23. HADOOP SUMMIT 2013 Four key ideas 1. Central data pipeline 2. Push data cleanliness upstream 3. O(1) ETL 4. Evidence-based correctness LinkedIn Corporation ©2013 All Rights Reserved 23
  • 24. HADOOP SUMMIT 2013 Hadoop data load (Camus)  Open sourced: – https://github.com/linkedin/camus  One job loads all events  ~10 minute ETA on average from producer to HDFS  Hive registration done automatically  Schema evolution handled transparently
  • 25. HADOOP SUMMIT 2013 Four key ideas 1. Central data pipeline 2. Push data cleanliness upstream 3. O(1) ETL 4. Evidence-based correctness LinkedIn Corporation ©2013 All Rights Reserved 25
  • 26. Does it work? “All published messages must be delivered to all consumers (quickly)” LinkedIn Confidential ©2013 All Rights Reserved
  • 28. HADOOP SUMMIT 2013 Kafka replication (0.8)  Intra-cluster replication feature – Facilitates high availability and durability  Beta release available https://dist.apache.org/repos/dist/release/kafka/  Rolled out in production at LinkedIn last week LinkedIn Corporation ©2013 All Rights Reserved 28
  • 29. HADOOP SUMMIT 2013 Join us at our user-group meeting tonight @ LinkedIn! – Thursday, June 27, 7.30pm to 9.30pm – 2025 Stierlin Ct., Mountain View, CA – http://www.meetup.com/http-kafka-apache-org/events/125887332/ – Presentations (replication overview and use-case studies) from:  RichRelevance  Netflix  Square  LinkedIn LinkedIn Corporation ©2013 All Rights Reserved 29
  • 30. HADOOP SUMMIT 2013LinkedIn Corporation ©2013 All Rights Reserved 30