Evolution of a Real-Time Web Analytics Platform

Talk about data stores in use at GoSquared at the AllYourBase conference.

Transcript

1. The Evolution of a Real-Time Analytics Platform Geoff Wagstaff @TheDeveloper
2. The Now dashboard
3. The Trends dashboard
4. Building Real-Time Analytics Behind the “Now” dashboard
5. Back in 2009 1 server LAMP stack Conventional hosting
6. LiveStats v1
8. Meltdown!
9. Problem? First taste of scale WRITES
10. Reads are easy to scale Primary Writes Replica 1 Replica 2 Replica 3 Reads Reads Reads
11. Writes? Not so much. Primary MANY WRITES! Replica 1 Replica 2 Replica 3 Reads Reads Reads :(
12. Scale Horizontally
13. Node Node Node Requests Requests Requests NginX -> PHP-FPM <--> Memcache
14. Problems
15. Stupidly high data transfer: several TB per day DB -> app -> DB round trips High latency on DB ops Race conditions
16. Redis to the rescue! “Advanced in-memory key-value store”
17. Rich Data types
18. Rich Data types Keys Hashes Lists Sets Sorted Sets GET SET HGET HSET HMSET LPUSH LPOP BLPOP SADD SREM SRANGE ZADD ZREM ZRANGE ZINTERSTORE
19. Distributed locks Service Service Service Fast counters Fan-out Pub/Sub broadcast Message queues redis-1 redis-2 Solved concurrency problems
20. ACID
21. A C I D tomic onsistent solated urable MySQL MongoDB Other ACID DBs:
22. Fast
23. Fast Redis 2.6.16 on 2.4GHz i7 MBP
24. Single-process, one per core Run on m1.medium - 1 core, 3.5GB memory Redis cluster is coming! Now on Elasticache Redis deployment
25. Behind the “Trends” dashboard Building Historical Analytics
26. Trends v1
27. Sharded MySQL from outset Aging Unreliable Trends v1
28. The Trends dashboard
29. MongoDB vs Cassandra
30. MongoDB Document store: no schema, flexible Compelling replication & sharding features Fast in-place field updates similar to Redis
31. Attempt #1: Store & aggregate Document for each list item, timestamp and site Aggregation framework: match, group, sort Collection per list type Flexible Made app simpler Huge number of documents Slow aggregate queries: ~1s+ ✔ ✔ X X
32. Attempt #2 Document per list, timestamp and site Collection per list type Faster lookups (no aggregation) Fewer documents Smaller _id Document size limit Unordered High data transfer ✔ ✔ ✔ X X X
33. MongoStat
34. Downsides High random I/O Document size & relocation Fragmentation Database lock
35. K.O. MongoDB
36. Cassandra Distributed hash ring: masterless Linear scalability Built for scale + write throughput
37. CQL
38. CQL SELECT sql AS cql FROM mysql WHERE query_language = “good” Not as scary as Column Families + Thrift SQL Schemas + Querying
39. CQL CREATE TABLE d_aggregate_day ( sid int, ts int, s text, v counter PRIMARY KEY (sid, ts, s)) partition key cluster key Distributed counters!
40. B ASE
41. B A S E asically vailable oft-state ventually consistent
42. Eventual consistency isn’t a problem More efficient with the disk Low maintenance Cheap
43. Redis + Cassandra = win Redis as a speed layer + aggregator for lists Cassandra as timeseries counter storage Collector Redis Cassandra Periodic flushes to Cassandra
44. Exploit DBs strengths Build an indestructible service Use the best tools for the job
45. Thanks! Geoff Wagstaff @TheDeveloper engineering.gosquared.com