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How LinkedIn is leveraging kafka for log aggregation and
monitoring
Akbar KM, Muzammil Rehman
SRE Linkedin
Agenda
● What is Pub Sub?
● Kafka basics
● Kafka for monitoring
● inGraph’s
● ELK at Linkedin
What is pub sub ?
Producer Consumer
Producer
Consumer
Topic
1
Topic
2
Topic
3
subscribe
publish(topic, msg)
Publish subscribe
system
msg
msg
Kafka
Consumer Groups
inGraph
● inGraphs is a visualization frontend for a linkedin’s metrics
● Agent is a library used by all java application
● Data is transported over kafka
● Collector consume and write to rrd
● Features
○ Rest based
○ Self service
○ Little over 500,000 metrics collected per minute
○ Each RRD is roughly 815k. Each RRD is written to two collectors
to maintain data integrity
○ We currently have 1.4TB of SSD in production colo
ELK at linkedin
● ELK (Elasticsearch, Logstash,
Kibana)
○ E -> Near Real-Time Search &
Analysis
○ L -> Plumbing and Glue for all
your data
○ K -> Line graphs, pie charts,
dashboard
● Logstash kafka plugin consumes
logs and end up written into
elasticsearch
Why Kafka
1. Near real time delivery
2. Supports multiple consumers groups aka queuing
3. No overhead on clients that comes up logstash agent
4. Kafka scales horizontally
5. Supports REST out of the box
6. In house support
Reference
http://engineering.linkedin.com/52/autometrics-
self-service-metrics-collection
ELK http://www.elastic.co/guide/

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Rootconf

  • 1. How LinkedIn is leveraging kafka for log aggregation and monitoring Akbar KM, Muzammil Rehman SRE Linkedin
  • 2. Agenda ● What is Pub Sub? ● Kafka basics ● Kafka for monitoring ● inGraph’s ● ELK at Linkedin
  • 3. What is pub sub ? Producer Consumer Producer Consumer Topic 1 Topic 2 Topic 3 subscribe publish(topic, msg) Publish subscribe system msg msg
  • 6. inGraph ● inGraphs is a visualization frontend for a linkedin’s metrics ● Agent is a library used by all java application ● Data is transported over kafka ● Collector consume and write to rrd ● Features ○ Rest based ○ Self service ○ Little over 500,000 metrics collected per minute ○ Each RRD is roughly 815k. Each RRD is written to two collectors to maintain data integrity ○ We currently have 1.4TB of SSD in production colo
  • 7. ELK at linkedin ● ELK (Elasticsearch, Logstash, Kibana) ○ E -> Near Real-Time Search & Analysis ○ L -> Plumbing and Glue for all your data ○ K -> Line graphs, pie charts, dashboard ● Logstash kafka plugin consumes logs and end up written into elasticsearch
  • 8. Why Kafka 1. Near real time delivery 2. Supports multiple consumers groups aka queuing 3. No overhead on clients that comes up logstash agent 4. Kafka scales horizontally 5. Supports REST out of the box 6. In house support