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Big Data in Real-Time at Twitter
- 1. Big Data in Real-Time at Twitter by Nick Kallen (@nk)
- 3. What is Real-Time Data? • On-line queries for a single web request • Off-line computations with very low latency • Latency and throughput are equally important • Not talking about Hadoop and other high-latency, Big Data tools
- 4. The four data problems • Tweets • Timelines • Social graphs • Search indices
- 6. What is a Tweet? • 140 character message, plus some metadata • Query patterns: • by id • by author • (also @replies, but not discussed here) • Row Storage
- 7. Find by primary key: 4376167936
- 8. Find all by user_id: 749863
- 9. Original Implementation id user_id text created_at 20 12 just setting up my twttr 2006-03-21 20:50:14 29 12 inviting coworkers 2006-03-21 21:02:56 34 16 Oh shit, I just twittered a little. 2006-03-21 21:08:09 • Relational • Single table, vertically scaled • Master-Slave replication and Memcached for read throughput.
- 10. Original Implementation Master-Slave Replication Memcached for reads
- 11. Problems w/ solution • Disk space: did not want to support disk arrays larger than 800GB • At 2,954,291,678 tweets, disk was over 90% utilized.
- 12. PARTITION
- 13. Possible implementations Partition by primary key Partition 1 Partition 2 id user_id id user_id 20 ... 21 ... 22 ... 23 ... 24 ... 25 ...
- 14. Possible implementations Partition by primary key Partition 1 Partition 2 id user_id id user_id 20 ... 21 ... 22 ... 23 ... 24 ... 25 ... Finding recent tweets by user_id queries N partitions
- 15. Possible implementations Partition by user id Partition 1 Partition 2 id user_id id user_id ... 1 21 2 ... 1 23 2 ... 3 25 2
- 16. Possible implementations Partition by user id Partition 1 Partition 2 id user_id id user_id ... 1 21 2 ... 1 23 2 ... 3 25 2 Finding a tweet by id queries N partitions
- 17. Current Implementation Partition by time id user_id 24 ... Partition 2 23 ... id user_id Partition 1 22 ... 21 ...
- 18. Current Implementation Queries try each Partition by time partition in order id until enough data user_id 24 ... is accumulated Partition 2 23 ... id user_id Partition 1 22 ... 21 ...
- 19. LOCALITY
- 20. Low Latency PK Lookup Memcached 1ms MySQL <10ms* * Depends on the number of partitions searched
- 21. Principles • Partition and index • Exploit locality (in this case, temporal locality) • New tweets are requested most frequently, so usually only 1 partition is checked
- 22. Problems w/ solution • Write throughput • Have encountered deadlocks in MySQL at crazy tweet velocity • Creating a new temporal shard is a manual process and takes too long; it involves setting up a parallel replication hierarchy. Our DBA hates us
- 23. Future solution Partition k1 Partition k2 id user_id id user_id 20 ... 21 ... 22 ... 23 ... Partition u1 Partition u2 user_id ids user_id ids 12 20, 21, ... 13 48, 27, ... 14 25, 32, ... 15 23, 51, ... • Cassandra (non-relational) • Primary Key partitioning • Manual secondary index on user_id • Memcached for 90+% of reads
- 24. The four data problems • Tweets • Timelines • Social graphs • Search indices
- 26. What is a Timeline? • Sequence of tweet ids • Query pattern: get by user_id • Operations: • append • merge • truncate • High-velocity bounded vector • Space-based (in-place mutation)
- 27. Tweets from 3 different people
- 28. Original Implementation SELECT * FROM tweets WHERE user_id IN (SELECT source_id FROM followers WHERE destination_id = ?) ORDER BY created_at DESC LIMIT 20
- 29. Original Implementation SELECT * FROM tweets WHERE user_id IN (SELECT source_id FROM followers WHERE destination_id = ?) ORDER BY created_at DESC LIMIT 20 Crazy slow if you have lots of friends or indices can’t be kept in RAM
- 31. Current Implementation • Sequences stored in Memcached • Fanout off-line, but has a low latency SLA • Truncate at random intervals to ensure bounded length • On cache miss, merge user timelines
- 32. Throughput Statistics date average tps peak tps fanout ratio deliveries 10/7/2008 30 120 175:1 21,000 4/15/2010 700 2,000 600:1 1,200,000
- 34. MEMORY HIERARCHY
- 35. Possible implementations • Fanout to disk • Ridonculous number of IOPS required, even with fancy buffering techniques • Cost of rebuilding data from other durable stores not too expensive • Fanout to memory • Good if cardinality of corpus * bytes/datum not too many GB
- 36. Low Latency get append fanout 1ms 1ms <1s* * Depends on the number of followers of the tweeter
- 37. Principles • Off-line vs. Online computation • The answer to some problems can be pre-computed if the amount of work is bounded and the query pattern is very limited • Keep the memory hierarchy in mind • The efficiency of a system includes the cost of generating data from another source (such as a backup) times the probability of needing to
- 38. The four data problems • Tweets • Timelines • Social graphs • Search indices
- 40. What is a Social Graph? • List of who follows whom, who blocks whom, etc. • Operations: • Enumerate by time • Intersection, Union, Difference • Inclusion • Cardinality • Mass-deletes for spam • Medium-velocity unbounded vectors • Complex, predetermined queries
- 44. Inclusion Temporal enumeration Cardinality
- 46. Intersection: Deliver to people who follow both @aplusk and @foursquare
- 47. Original Implementation source_id destination_id 20 12 29 12 34 16 • Single table, vertically scaled • Master-Slave replication
- 48. Index Original Implementation source_id destination_id 20 12 29 12 34 16 • Single table, vertically scaled • Master-Slave replication
- 49. Index Index Original Implementation source_id destination_id 20 12 29 12 34 16 • Single table, vertically scaled • Master-Slave replication
- 50. Problems w/ solution • Write throughput • Indices couldn’t be kept in RAM
- 51. Current solution Forward Backward source_id destination_id updated_at x destination_id source_id updated_at x 20 12 20:50:14 x 12 20 20:50:14 x 20 13 20:51:32 12 32 20:51:32 20 16 12 16 • Partitioned by user id • Edges stored in “forward” and “backward” directions • Indexed by time • Indexed by element (for set algebra) • Denormalized cardinality
- 52. Current solution Forward Backward source_id destination_id updated_at x destination_id source_id updated_at x 20 12 20:50:14 x 12 20 20:50:14 x 20 13 20:51:32 12 32 20:51:32 20 16 12 16 • Partitioned by user id • Edges stored in “forward” and “backward” directions Partitioned by user • Indexed by time • Indexed by element (for set algebra) • Denormalized cardinality
- 53. Edges stored in both directions Current solution Forward Backward source_id destination_id updated_at x destination_id source_id updated_at x 20 12 20:50:14 x 12 20 20:50:14 x 20 13 20:51:32 12 32 20:51:32 20 16 12 16 • Partitioned by user id • Edges stored in “forward” and “backward” directions Partitioned by user • Indexed by time • Indexed by element (for set algebra) • Denormalized cardinality
- 54. Challenges • Data consistency in the presence of failures • Write operations are idempotent: retry until success • Last-Write Wins for edges • (with an ordering relation on State for time conflicts) • Other commutative strategies for mass-writes
- 55. Low Latency write cardinality iteration write ack inclusion materialize 1ms 100edges/ms* 1ms 16ms 1ms * 2ms lower bound
- 56. Principles • It is not possible to pre-compute set algebra queries • Simple distributed coordination techniques work • Partition, replicate, index. Many efficiency and scalability problems are solved the same way
- 57. The four data problems • Tweets • Timelines • Social graphs • Search indices
- 59. What is a Search Index? • “Find me all tweets with these words in it...” • Posting list • Boolean and/or queries • Complex, ad hoc queries • Relevance is recency* * Note: there is a non-real-time component to search, but it is not discussed here
- 60. Intersection of three posting lists
- 61. Original Implementation term_id doc_id 20 12 20 86 34 16 • Single table, vertically scaled • Master-Slave replication for read throughput
- 62. Problems w/ solution • Index could not be kept in memory
- 63. Current Implementation term_id doc_id 24 ... Partition 2 23 ... term_id doc_id Partition 1 22 ... 21 ... • Partitioned by time • Uses MySQL • Uses delayed key-write
- 64. Problems w/ solution • Write throughput • Queries for rare terms need to search many partitions • Space efficiency/recall • MySQL requires lots of memory
- 66. Future solution • Document partitioning • Time partitioning too • Merge layer • May use Lucene instead of MySQL
- 67. Principles • Partition so that work can be parallelized • Temporal locality is not always enough
- 68. The four data problems • Tweets • Timelines • Social graphs • Search indices
- 69. Summary Statistics writes/ reads/second cardinality bytes/item durability second Tweets 100k 850 12b 300b durable Timelines 80k 1.2m a lot 3.2k non Graphs 100k 20k 13b 110 durable Search 13k 21k† 315m‡ 1k durable † tps * 25 postings ‡ 75 partitions * 4.2m tweets
- 70. Principles • All engineering solutions are transient • Nothing’s perfect but some solutions are good enough for a while • Scalability solutions aren’t magic. They involve partitioning, indexing, and replication • All data for real-time queries MUST be in memory. Disk is for writes only. • Some problems can be solved with pre-computation, but a lot can’t • Exploit locality where possible
- 71. Appendix