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Deploying Kafka at Dropbox
Alternately: how to handle 10,000,000 QPS in one cluster (but don't)
The Plan
• Welcome
• Use Case
• Initial Design
• Iterations of Woe
• Current Setup
• Future Plans
Your Speakers
• Mark Smith <zorkian@dropbox.com>

formerly of Google, Bump, StumbleUpon, etc

likes small airplanes and not getting paged

• Sean Fellows <fellows@dropbox.com>

formerly of Google

likes corgis and distributed systems
The Plan
• Welcome
• Use Case
• Initial Design
• Iterations of Woe
• Current Setup
• Future Plans
Dropbox
• Over 500 million signups
• Exabyte scale storage system
• Multiple hardware locations + AWS
Log Events
• Wide distribution (1,000 categories)
• Several do >1M QPS each + long tail
• About 200TB/day (raw)
• Payloads range from empty to 15MB JSON blobs
Current System
• Existing system based on Scribe + HDFS
• Aggregate to single destination for analytics
• Powers Hive and standard map-reduce type analytics

Want: real-time stream processing!
The Plan
• Welcome
• Use Case
• Initial Design
• Iterations of Woe
• Current Setup
• Future Plans
Initial Design
• One big cluster
• 20 brokers: 96GB RAM, 16x2TB disk, JBOD config
• ZK ensemble run separately (5 members)
• Kafka 0.8.2 from Github
• LinkedIn configuration recommendations
The Plan
• Welcome
• Use Case
• Initial Design
• Iterations of Woe
• Current Setup
• Future Plans
Unexpected Catastrophes
• Disks failure or reaching 100%
• Repair is manual, won't expire unless caught up
• Crash looping, controller load
• Simultaneous restarts
• Even graceful, recovery is sometimes very bad (even 0.9!)
• Rebalancing is dangerous
• Saturates disks, partitions fall out of ISRs, offline, etc
System Errors
• Controller issues
• Sometimes goes AWOL with e.g. big rebalances
• Can have multiple controllers (during serial operations)
• Cascading OOMs
• Too many connections
Lack of Tooling
• Usually left to the reader
• Few best practices
• But we love Kafka Manager
• More to come later!
Newer Clients
• State of Go/Python clients
• Bad behavior at scale
• Laserbeam, retries, backoff
• Too many connections == OOM
• Good clients take time
Bad Configs
• Many, many tunables -- lots of rope
• Unclean leader election
• Preferred leader automation
• Disk threads (thanks Gwen!)
• Little modern documentation on running at scale
• Todd Palino helped us out early, tho, so thank you!
The Plan
• Welcome
• Use Case
• Initial Design
• Iterations of Woe
• Current Setup
• Future Plans
Hardware
• Hardware RAID 10
• ~25TB usable/box (spinning rust)
• During broker replacement
• 200ms p99 commit latency down to 10ms!
• Failure tolerance, full disk protection
• Canary cluster
Monitoring
• MPS vs QPS (metadata reqs!)
• Bad Stuff graph
• Disk utilization/latency
• Heap usage
• Number of controllers
Tooling
• Rolling restarter (health checks!)
• Rate limited partition rebalancer (MPS)
• Config verifier/enforcer
• Coordinated consumption (pre-0.9)
• Auditing framework
Customer Culture
• Topics : organization :: partitions : scale
• Do not hash to partitions
• No ordering requirements
• Namespaces and ownership are required
Success! x
• Kafka goes fast (18M+ MPS on 20 brokers)
• Multiple parallel consumption
• Low latency (at high produce rates)
• 0.9 is leaps ahead of 0.8.2 (upgrade!)
• Supportable by a small team (at our scale)
The Plan
• Welcome
• Use Case
• Initial Design
• Iterations of Woe
• Current Setup
• Future Plans
The Future
• Big is fun but has problems
• Open source our tooling
• Moving towards replication
• Automatic up-partitioning and rebalancing
• Expanding auditing to clients
• Low volume latencies
Deploying Kafka at Dropbox
• Mark Smith <zorkian@dropbox.com>
• Sean Fellows <fellows@dropbox.com>
We would love to talk with other people who are running Kafka at similar
scales. Email us!
And... questions! (If we have time.)

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Deploying Kafka at Dropbox, Mark Smith, Sean Fellows

  • 1. Deploying Kafka at Dropbox Alternately: how to handle 10,000,000 QPS in one cluster (but don't)
  • 2. The Plan • Welcome • Use Case • Initial Design • Iterations of Woe • Current Setup • Future Plans
  • 3. Your Speakers • Mark Smith <zorkian@dropbox.com>
 formerly of Google, Bump, StumbleUpon, etc
 likes small airplanes and not getting paged
 • Sean Fellows <fellows@dropbox.com>
 formerly of Google
 likes corgis and distributed systems
  • 4. The Plan • Welcome • Use Case • Initial Design • Iterations of Woe • Current Setup • Future Plans
  • 5. Dropbox • Over 500 million signups • Exabyte scale storage system • Multiple hardware locations + AWS
  • 6. Log Events • Wide distribution (1,000 categories) • Several do >1M QPS each + long tail • About 200TB/day (raw) • Payloads range from empty to 15MB JSON blobs
  • 7. Current System • Existing system based on Scribe + HDFS • Aggregate to single destination for analytics • Powers Hive and standard map-reduce type analytics
 Want: real-time stream processing!
  • 8. The Plan • Welcome • Use Case • Initial Design • Iterations of Woe • Current Setup • Future Plans
  • 9. Initial Design • One big cluster • 20 brokers: 96GB RAM, 16x2TB disk, JBOD config • ZK ensemble run separately (5 members) • Kafka 0.8.2 from Github • LinkedIn configuration recommendations
  • 10. The Plan • Welcome • Use Case • Initial Design • Iterations of Woe • Current Setup • Future Plans
  • 11. Unexpected Catastrophes • Disks failure or reaching 100% • Repair is manual, won't expire unless caught up • Crash looping, controller load • Simultaneous restarts • Even graceful, recovery is sometimes very bad (even 0.9!) • Rebalancing is dangerous • Saturates disks, partitions fall out of ISRs, offline, etc
  • 12. System Errors • Controller issues • Sometimes goes AWOL with e.g. big rebalances • Can have multiple controllers (during serial operations) • Cascading OOMs • Too many connections
  • 13. Lack of Tooling • Usually left to the reader • Few best practices • But we love Kafka Manager • More to come later!
  • 14. Newer Clients • State of Go/Python clients • Bad behavior at scale • Laserbeam, retries, backoff • Too many connections == OOM • Good clients take time
  • 15. Bad Configs • Many, many tunables -- lots of rope • Unclean leader election • Preferred leader automation • Disk threads (thanks Gwen!) • Little modern documentation on running at scale • Todd Palino helped us out early, tho, so thank you!
  • 16. The Plan • Welcome • Use Case • Initial Design • Iterations of Woe • Current Setup • Future Plans
  • 17. Hardware • Hardware RAID 10 • ~25TB usable/box (spinning rust) • During broker replacement • 200ms p99 commit latency down to 10ms! • Failure tolerance, full disk protection • Canary cluster
  • 18. Monitoring • MPS vs QPS (metadata reqs!) • Bad Stuff graph • Disk utilization/latency • Heap usage • Number of controllers
  • 19.
  • 20. Tooling • Rolling restarter (health checks!) • Rate limited partition rebalancer (MPS) • Config verifier/enforcer • Coordinated consumption (pre-0.9) • Auditing framework
  • 21.
  • 22. Customer Culture • Topics : organization :: partitions : scale • Do not hash to partitions • No ordering requirements • Namespaces and ownership are required
  • 23. Success! x • Kafka goes fast (18M+ MPS on 20 brokers) • Multiple parallel consumption • Low latency (at high produce rates) • 0.9 is leaps ahead of 0.8.2 (upgrade!) • Supportable by a small team (at our scale)
  • 24. The Plan • Welcome • Use Case • Initial Design • Iterations of Woe • Current Setup • Future Plans
  • 25. The Future • Big is fun but has problems • Open source our tooling • Moving towards replication • Automatic up-partitioning and rebalancing • Expanding auditing to clients • Low volume latencies
  • 26. Deploying Kafka at Dropbox • Mark Smith <zorkian@dropbox.com> • Sean Fellows <fellows@dropbox.com> We would love to talk with other people who are running Kafka at similar scales. Email us! And... questions! (If we have time.)
  翻译: