Video and slides synchronized, mp3 and slide download available at URL http://bit.ly/1LarzvW. Paul Dix discusses experiences building InfluxDB, an open source distributed time series database, in Go. He talks about what drove the decision to use Go, what's been really great about developing in the language, and a few of the pains that they’ve had along the way. He also digs into what performance characteristics they’ve seen in Go 1.4 vs. Go 1.5, which has a new garbage collector. Filmed at qconsf.com. Paul Dix is the CEO of InfluxDB. He is the editor for Addison Wesley's "Data and Analytics", the author of "Service Oriented Design with Ruby and Rails" and the founder of the NYC Machine Learning Meetup.

1. Experiences Building InfluxDB
in Go
Paul Dix
paul@influxdb.com
@pauldix

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4. Who am I?
informs my experience working with a language like Go

5. co-founder, CEO, programmer

6. Author

7. Languages worked with Professionally in order
• VBScript
• Delphi
• C# and VB.NET
• Ruby & Javascript
• Java
• C (only a little)
• Ruby & Javascript
• Scala
• Go

8. Mix of static and dynamic, but
most time in dynamic languages

9. Open source time series database written in Go

10. What’s a time series?

11. Stock trades and quotes

12. Metrics

13. Analytics

14. Events

15. Sensor data

16. Two kinds of time series
data…

17. Regular time series
t0 t1 t2 t3 t4 t6 t7
Samples at regular intervals

18. Irregular time series
t0 t1 t2 t3 t4 t6 t7
Events whenever they come in

19. Why’d we pick Go?

20. Some project requirements
• Self contained binary install

21. Some project requirements
• Self contained binary install
• Performance

22. previous experience with it

23. faster development than
working with C/C++

24. growing community

25. simplicity of the language
significant advantage for picking up new programmers and contributors

26. InfluxDB Project Stats
• First Commit - September 26th, 2013
• 176 Contributors
• 68,000 LOC

27. Team background
• Java
• Scala
• Python
• Ruby
• C++

28. What has been great

29. Static typing
can’t believe I’m saying this

30. Community

31. Performance

32. What surprised us

33. GC hasn’t been a problem

34. Contributors with no previous
Go experience
simplicity of the language wins again

35. Haven’t really missed generics

36. except when I do…

37. // Sort methods
func (a Values) Len() int { return len(a) }
func (a Values) Swap(i, j int) { a[i], a[j] = a[j], a[i] }
func (a Values) Less(i, j int) bool {
return a[i].Time().UnixNano() < a[j].Time().UnixNano()
}

38. Duplicate code for each data type
select percentile(90, value) from cpu
where time > now() - 1d
group by time(10m)

39. // Iterator represents an iterator over a series.
type Iterator interface {
SeekTo(seek int64) (key int64, value interface{})
Next() (key int64, value interface{})
Ascending() bool
} we have to cast this later

40. Costs on performance
f := val.(float64)
// do some math

41. // FloatIterator represents an iterator over a series.
type FloatIterator interface {
SeekTo(seek int64) (key int64, value float64)
Next() (key int64, value float64)
Ascending() bool
}

42. Implement for every function
func MapMean(input *MapInput) interface{} {
if len(input.Items) == 0 {
return nil
}
out := &meanMapOutput{}
for _, item := range input.Items {
out.Count++
switch v := item.Value.(type) {
case float64:
out.Total += v
case int64:
out.Total += float64(v)
out.ResultType = Int64Type
}
}
return out
}

43. Could use interfaces, but there’s a
performance penalty there too…

44. What has been bad

45. Dependency management

46. Experiences migrating to 1.5

47. Compile times longer
on a project this size it’s noticeable

48. Performance more about our code

49. Go 1.5 issue forced us to revert
https://github.com/golang/go/issues/12233

50. Unit and integration tests won’t
save you.
if you operate at scale, only full blown scale tests will tell you anything and even
then you may not find it

51. Q&A
paul@influxdb.com
@pauldix

52. Watch the video with slide synchronization on
InfoQ.com!
http://www.infoq.com/presentations/influxdb-
go