Papers We Love Too, June 2015: Haystack
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Slides from Papers We Love, June 2015 on Facebook's Haystack paper. A storage system designed to serve needles with high throughput, and low latency.
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Papers We Love Too, June 2015: Haystack
- 1. Facebook Haystack Finding a needle in Haystack: Facebook's photo storage. An Analysis of Facebook Photo Caching PWL SF - June 30, 2015
- 4. Agenda •The Haystack problem •Design & Architecture •Takeaways
- 7. Needle Storage & Serving For Facebook
- 8. Workload
- 9. •Write Once •Read Often •Delete Rarely
- 10. •Write Once •Read Often •Delete Rarely
- 11. •Write Once •Read Often •Delete Rarely
- 12. Why Haystack as a Paper?
- 13. Why Not?
- 15. 20+ Petabytes
- 16. 120 million new photos a day
- 17. Simple
- 20. History
- 21. Network Attached Storage (NAS) mounted over NFS
- 34. Fetches are expensive • Multiple seeks: • Directory metadata • Inode • File contents • File metadata is 10s of kilobytes • Long-tail uncachable
- 35. Decision to Build • Existing systems unable to be adapted • Hadoop • MySQL • Traditional NAS appliances • Don’t need to solve for the kitchen sink • Log data • Development work
- 36. Haystack Design
- 40. Simple
- 41. Separation of concerns •Haystack Store •Haystack Cache •Haystack Directory
- 42. Read Path
- 43. Write Path
- 44. Store
- 46. How much cache?
- 47. There is no right ratio
- 50. Volume Layer Volume layer above filesystem
- 54. Indexing
- 55. 10 bytes of metadata per photo
- 56. 2-bit overhead
- 57. Read by <Key, Alt Key, Cookie>
- 62. Batch Upload
- 63. Similar to Bitcask & CDB
- 64. Uses XFS
- 66. Fault Tolerance
- 70. Recovery: Rsync* * With QoS
- 72. The Hardware
- 74. Open Vault: KNOX
- 78. Good at reads xor writes not both
- 79. Read Throughput Avg. Read Latency Write Throughput Avg. Write Latency Only Reads 770.6 33.2 - - Only Writes - - 6099.4 4.9 Multiwrite (x16) - - 10843.8 43.9 Reads And Writes 718.1 41.6 232.0 11.9 Latencies Table
- 80. Haystress
- 82. Haystack Store • Responsibilities: • Read needles • Write needles • Append-only • O(1) read cost* *Usually
- 83. Cache
- 85. Organized as DHT
- 86. Not just an LRU
- 89. Request is to write- enabled store
- 90. Haystack Cache •Simple cache •Optimizations, given access patterns
- 91. Directory
- 92. The Rug
- 94. Maps logical volumes to physical machines
- 95. Mapping based on business rules
- 97. Directs writes to relevant logical volume
- 98. Directs reads away from CDN
- 99. Directory • Manages capacity • Manages volume mapping • Manages image mapping • Manages CDN
- 100. Tying it together
- 101. Writes
- 108. Write-path • Involves • Store • Directory • Smart client
- 109. Reads
- 113. Detour: URLs
- 114. Directory uses URLs for directing
- 116. URL Makeup • CDN • Cache Node • Machine ID • Logical Volume ID • Photo ID & Alt ID • Cookie
- 127. Read-path • Involves: • Directory • Cache • CDN
- 128. Insights
- 129. Narrow Scope “That simplicity let us build and deploy a working system in a few months instead of a few years.”
- 130. Sometimes you’re solving the wrong problem
- 131. Smart Clients & Ecosystem Control
- 132. Simple Optimizations
- 134. Thanks & Qs