Big Data Day LA 2016/ Big Data Track - Real Time Analytics with Druid - Guillaume Torch, Big Data Engineer - GumGum

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GumGum uses Druid to ingest more than 30 billion events every day, which can be queried almost as soon as they happen with a very low response time. This is a tell-all talk about GumGum's love story with Druid, how Druid works and how GumGum leverages Druid's capabilities.

Technologie

Real Time Analytics at Scale with
Guillaume Torche
Saturday 9th July
Big Data LA 2016

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What does Real Time mean?
Response time
Data freshness
Response Time

About me
Guillaume Torche
Born and raised in France
First experience in Big Data at Aubay in Paris
Started working for GumGum 2 years ago
About one year and a half of experience with Druid

Invented In Image advertising in 2008
http://gumgum.com/gallery
Processing 2.6B image impressions / month
10B impressions / month
10B events / day
2000 premium publishers
132 employees - 37.5% YOY growth

Interactive data exploration
Fast results
Access fresh data

Before we met Big Data
MySQL for Real Time reporting
Data aggregation with Storm
Batch updates of MySQL metrics

Brief of Druid
Built by another Ad tech company
Used by top tech companies

Key features
Arbitrary Slice-N-Dice data
Fast aggregations on time series data
Highly available
Real Time
Distributed

Queries
Broker
Realtime
Historical
Stream
Deep storage

Queries
Broker
Realtime
Historical
Stream
Deep storage
Coordinator

Queries
Broker
Realtime
Historical
Stream
Deep storage
Coordinator
Batch
Data
Ingestion
Ingestion

Column compression - Dictionary encoding
Timestamp Product Vertical Ad impressions Clicks Revenue
2015-01-01T00:01:35Z In-Image Gaming 15 3 10
2015-01-04T00:03:48Z In-Screen Gaming 5 1 3
2015-01-03T00:08:05Z Native Fashion 2 0 0.5
2015-01-02T00:02:35Z In-Image Fashion 25 15 50
● Create internal ids:
○ Gaming => 0, Fashion => 1
● Store column:
○ Vertical => [ 0 0 1 1 ]

Inverted Index
Row Vertical
0 Gaming
1 Gaming
2 Fashion
3 Fashion
Gaming
[1, 1, 0, 0]
Fashion
[0, 0, 1, 1]
Gaming
OR
Fashion
[1, 1, 1, 1]

How does Druid achieve low
latency ingestion?

Write optimized
Read optimized
Flush
S1
S2
S3
S4 S5Final
Segment
Historical

https://www.linkedin.com/in/guillaume-torche-4a209790
http://imply.io/
http://druid.io
http://gumgum.com/careers

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Big Data Day LA 2016/ Big Data Track - Real Time Analytics with Druid - Guillaume Torch, Big Data Engineer - GumGum

1. Real Time Analytics at Scale with Guillaume Torche Saturday 9th July Big Data LA 2016

2. What is analytics at scale ?

3. {"id":"165a0b33-fd2d-4f45-83e4-a373cc4e6333","t":1435633205023,"cl":"js","ua":"Mozilla/5.0 (iPhone; CPU iPhone OS 8_3 like Mac OS X) AppleWebKit/600.1.4 (KHTML, like Gecko) GSA/6.0.51363 Mobile/12F70 Safari/600.1.4","ip":"70.109.46.30","cc":"US","rg":"CA","ct":"Santa Maria","pc":"93455","mc":855,"isp":"Verizon Internet Services","bf":"6c52d0492fa92bd912d3a53a3c864e5a47008779","vst":"62784988-5d30-40a2-a334- 43e456499ac6"},{"v":"1.1","e":"view","si":632,"t":"56c89c04","ab":25465,"pv":"6cff666d-92c5- 42ac-9f94-39a40f9f297d","pu":"http://www.nytimes.com/2016/01/08/business/international/a-new- economic-era-for-china-goes-off-the- rails.html?hp&action=click&pgtype=Homepage&clickSource=story-heading&module=first-column- region&region=top-news&WT.nav=top- news&_r=0","af":false,"rpm":0.65999997,"pc":0.25,"nc":0,"dsp":27,"dai":"pqdmnbu","dci":"rpz224 3","do":"*.nytimes.com","vi":3,"pi":1134,"pri":3,"uti":16,"adi":300,"ei":3,"spi":172,"atmi":20 8,"ct":"rtb","cli":3,"cid":1256}

4. What does Real Time mean?

5. What does Real Time mean? Response time Data freshness Response Time

6. Data Freshness

7. About me Guillaume Torche Born and raised in France First experience in Big Data at Aubay in Paris Started working for GumGum 2 years ago About one year and a half of experience with Druid

8. Invented In Image advertising in 2008 http://gumgum.com/gallery Processing 2.6B image impressions / month 10B impressions / month 10B events / day 2000 premium publishers 132 employees - 37.5% YOY growth

9. Interactive data exploration Fast results Access fresh data

10. Before we met Big Data MySQL for Real Time reporting Data aggregation with Storm Batch updates of MySQL metrics

11.

12. Brief of Druid Built by another Ad tech company Used by top tech companies

13. Key features Arbitrary Slice-N-Dice data Fast aggregations on time series data Highly available Real Time Distributed

14. Overall Architecture

15. Queries Broker Realtime Historical Stream Deep storage

16. Data Balance & Replication

17. Queries Broker Realtime Historical Stream Deep storage Coordinator

18. Data Ingestion

19. Queries Broker Realtime Historical Stream Deep storage Coordinator Batch Data Ingestion Ingestion

20. Why is Druid fast?

21. Column compression - Dictionary encoding Timestamp Product Vertical Ad impressions Clicks Revenue 2015-01-01T00:01:35Z In-Image Gaming 15 3 10 2015-01-04T00:03:48Z In-Screen Gaming 5 1 3 2015-01-03T00:08:05Z Native Fashion 2 0 0.5 2015-01-02T00:02:35Z In-Image Fashion 25 15 50 ● Create internal ids: ○ Gaming => 0, Fashion => 1 ● Store column: ○ Vertical => [ 0 0 1 1 ]

22. Inverted Index Row Vertical 0 Gaming 1 Gaming 2 Fashion 3 Fashion Gaming [1, 1, 0, 0] Fashion [0, 0, 1, 1] Gaming OR Fashion [1, 1, 1, 1]

23. How does Druid achieve low latency ingestion?

24. Realtime Stream

25. Write optimized Read optimized Flush S1 S2 S3 S4 S5Final Segment Historical

26.

27.

28. https://www.linkedin.com/in/guillaume-torche-4a209790 http://imply.io/ http://druid.io http://gumgum.com/careers

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http://pivot.druid.va.ggops.com:9090/
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Big Data Day LA 2016/ Big Data Track - Real Time Analytics with Druid - Guillaume Torch, Big Data Engineer - GumGum

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