1. Gorilla: A Fast, Scalable, In-Memory Time Series Database
Facebook vldb2015
王夏青

2. Abstract Gorilla: Facebook’s in-memory TSDB(time series database)
Key: strike the right balance between efficiency, scalability, and reliability
Optimize for remaining highly available for writes and reads, even in the face of failures
At the expense of possibly dropping small amounts of data on the write path

3. Abstract Introduction Background & Requirements
Comparison with TSDB systems
Gorilla Architecture
New Tools on Gorilla
Experience & Future Work
Conclude

4. Introduction Motivation:
Large-scale internet services: highly-available and responsive for their users
An important requirement: accurately monitor the health and performance of the underlying system and quickly identify and diagnose problems
Scale: thousands of individual systems running on many thousands of machines, often across multiple geo-replicated datacenters

5. Introduction Constraints: Gorilla: Writes dominate State transitions
High availability
Fault tolerance
Gorilla:
New TSDB satisfies these constraints
Functions as a write-through cache of the most recent data entering the monitoring system

6. Introduction Insight:
Users of monitoring systems do not place much emphasis on individual data point s but rather on aggregate analysis
Do not store any user data so traditional ACID guarantees are not a core requirement
Recent data points are of higher value than older points

7. Introduction Challenge: High data insertion rate Total data quantity
Real-time aggregation
Reliability requirements

8. Background & Requirements
Operational Data Store(ODS)
Monitoring system read performance issues

9. Background & Requirements
2 billion unique time series identified by a string key
700 million data points added per minute
Store data for 26 hours
More than 40,000 queries per second at peak
Read succeed in under one millisecond
Support time series with 15 second granularity
Two in-memory, not co-located replicas
Always server reads even when a single server crashes
Ability to quickly scan over all in memory data
Support at least 2x growth per year

10. Comparison with TSDB Systems
Existing solutions:
OpenTSDB
Whisper(Graphite)
InfluxDB

11. Gorilla Architecture

12. Gorilla Architecture Monitoring data:
3-tuple of a string key, a 64 bit time stamp integer and a double precision floating point value
A new time series compression algorithm
Arrange in-memory data structures to allow fast and efficient scans of all data while maintaining constant time lookup of individual time series

13. Gorilla Architecture

14. Gorilla Architecture
Compressing time stamps
Compressing values

15. Gorilla Architecture

16. Gorilla Architecture

17. Gorilla Architecture

18. Gorilla Architecture

19. Gorilla Architecture

20. Gorilla Architecture

21. Gorilla Architecture

22. Gorilla Architecture In-memory data structures: Timeseries Map(TSmap)
Shared-pointers
Read-write spin lock & 1-byte spin lock

23. Gorilla Architecture

24. Gorilla Architecture On disk structures: GlusterFS
A Gorilla host -> multiple shards
A single directory per shard
Each directory:
Key lists
Append-only logs
Complete block files
Checkpoint files

25. Gorilla Architecture
Tolerating single node, temporary failures with zero observable downtime
Localized failures(such as a network cut to an entire region)

26. New Tools on Gorilla
Correlation engine
Charting
Aggregations

27. Experience & Future Work
Fault tolerance:
Network cuts
Disaster readiness
Configuration changes and code pushes
Bug
Single node failures

28. Experience & Future Work
Site wide error rate debugging

29. Experience & Future Work
Lessons learned
Prioritize recent data over historical data
Read latency matters
High availability trumps resource efficiency

30. Experience & Future Work
Add a second, larger data store between in-memory Gorilla and HBase based on flash storage
Rewrite write path to wait longer before writing to HBase

31. Conclusion
Gorilla: a new in-memory times series database deployed at Facebook
Functions as a write through cache for monitoring data
Described a new compression scheme that allows us to efficiently store monitoring data
Reduces production query latency
Enables new monitoring tools
Verified Gorilla’s fault tolerance capabilities

32. Q&A
THANKS