Distributed Timeseries Database In Go (gophercon India 17)

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Matthew Campbell

How we built a distributed time series database called Vulcan at Digitalocean. Spoken at Gophercon India 2017

Software

Databases written in Go
4 Prometheus
4 CockroachDb
4 InfluxDb
4 Dgraph
4 EtcD
4 Consuld

Use cases for timeseries
4 Stocks
4 Monitoring
4 IOT

About timeseries
4 Timeseries can be lossy
4 Timeseries compress uniquely on data sets
4 Write heavy
4 Key, Time, DataPoint
4 CNX:IND, June 15 12:23, $23.40

Dark days
4 Graphite
4 InfluxDb
4 Mysql storing metrics
4 OpenTSDB (UGGHHHHHH)

Initial architecture
Beta for 3000 customers

Hash sharded Prometheus
3-4 per datacenter

Performance requirements
4 3 Gbits/sec of traffic
4 100k Writes a second
4 50ms Reads
4 100,000 customers to start
4 20 TB of storage

Introducing Vulcan
https://github.com/digitalocean/vulcan

Architecture changes
4 Split to microservices
4 Containerization
4 Message Queues

Timeseries Schema
4 V1 Timeseries Table
4 key (Combined Key)
4 timestamp (Combined Key)
4 datapoint (float64)

4 V2 Timeseries Table
4 key (Combined Key)
4 timestamp range (2hours) (Combined Key)
4 raw data (1kb blob)

4 Index Table
4 Customer (Combined key)
4 keyPrefix (Combined key)
4 time
4 key

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Distributed Timeseries Database In Go (gophercon India 17)

1. Distributed Timeseries Database in Go

2. Go is the future of NoSQL/NewSQL

3. Databases written in Go 4 Prometheus 4 CockroachDb 4 InfluxDb 4 Dgraph 4 EtcD 4 Consuld

4. Talk about architecture

5. What is a timeseries

6. Use cases for timeseries 4 Stocks 4 Monitoring 4 IOT

7. About timeseries 4 Timeseries can be lossy 4 Timeseries compress uniquely on data sets 4 Write heavy 4 Key, Time, DataPoint 4 CNX:IND, June 15 12:23, $23.40

8. Dark days 4 Graphite 4 InfluxDb 4 Mysql storing metrics 4 OpenTSDB (UGGHHHHHH)

9. Prometheus

10.

11. rate(http_request_latency[1m])

12.

13. Initial architecture Beta for 3000 customers

14. Hash sharded Prometheus 3-4 per datacenter

15.

16.

17. Performance requirements 4 3 Gbits/sec of traffic 4 100k Writes a second 4 50ms Reads 4 100,000 customers to start 4 20 TB of storage

18. Introducing Vulcan https://github.com/digitalocean/vulcan

19. Strange PRs

20. A fateful meeting at Soundcloud...

21. Architecture changes 4 Split to microservices 4 Containerization 4 Message Queues

22. Pipelining data

23.

24. Scaling storage

25.

26. Metrics format

27. Timeseries Schema 4 V1 Timeseries Table 4 key (Combined Key) 4 timestamp (Combined Key) 4 datapoint (float64)

28. 4 V2 Chunks (1 KB)

29. 4 V2 Timeseries Table 4 key (Combined Key) 4 timestamp range (2hours) (Combined Key) 4 raw data (1kb blob)

30. 4 Index Table 4 Customer (Combined key) 4 keyPrefix (Combined key) 4 time 4 key

31. In memory query engine

32. Downsampling

33. Final Architecture

34.

35. Questions?

Distributed Timeseries Database In Go (gophercon India 17)

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