Strata lightening-talk

1. Data Insights in Netﬂix Danny Yuan (@g9yuayon) Jae Bae Friday, March 1, 13 1

2. Who Am I? Friday, March 1, 13 2

3. Who Am I? Member of Netﬂix’s Platform Engineering team, working on very large scale data infrastructure (@g9yuayon) Friday, March 1, 13 2

4. Who Am I? Member of Netﬂix’s Platform Engineering team, working on very large scale data infrastructure (@g9yuayon) Built and operated Netﬂix’s cloud crypto service Friday, March 1, 13 2

5. Who Am I? Member of Netﬂix’s Platform Engineering team, working on very large scale data infrastructure (@g9yuayon) Built and operated Netﬂix’s cloud crypto service Worked with Jae Bae on querying multi-dimensional data in real time Friday, March 1, 13 2

6. Friday, March 1, 13 3 Developers usually think about monitoring metrics when “real-time” data is mentioned. We have powerful monitoring systems that track millions of metrics per second. But I’m not going to talk about it today. Monitoring metric is crucial data. That itself would warrant another multi-hour talk by our monitoring team. :-)

7. No Monitoring Metrics Today Friday, March 1, 13 3 Developers usually think about monitoring metrics when “real-time” data is mentioned. We have powerful monitoring systems that track millions of metrics per second. But I’m not going to talk about it today. Monitoring metric is crucial data. That itself would warrant another multi-hour talk by our monitoring team. :-)

8. photo credit: http://www.ﬂickr.com/photos/decade_null/142235888/sizes/o/in/photostream/ Friday, March 1, 13 4 Instead, I’m going to talk about logs. Why is it interesting at all?

9. 1,500,000 Friday, March 1, 13 5 During peak hours, our data pipeline collects over 1.5 million log events per second

10. 70,000,000,000 Friday, March 1, 13 6 Or 70 billions a day on average.

11. Server Farm Log Filter Sink Plugin Hadoop Server Farm Kafka Log Filter Sink Plugin Druid Log Collectors Server Farm Log Filter Sink Plugin ElasticSearch photo credit: http://www.flickr.com/photos/decade_null/142235888/sizes/m/in/photostream/ Friday, March 1, 13 7 We have this tens of thousands of machines, all of which send log data over a robust data pipeline to highly reliable data collectors. The collectors then filter the data, transform the data, and dispatch the data to to different destinations for further processing. Photo credit: http://www.flickr.com/photos/decade_null/142235888/sizes/m/in/ photostream/

12. Highly Reliable Data Pipeline Server Farm Log Filter Sink Plugin Hadoop Server Farm Kafka Log Filter Sink Plugin Druid Log Collectors Server Farm Log Filter Sink Plugin ElasticSearch photo credit: http://www.flickr.com/photos/decade_null/142235888/sizes/m/in/photostream/ Friday, March 1, 13 7 We have this tens of thousands of machines, all of which send log data over a robust data pipeline to highly reliable data collectors. The collectors then filter the data, transform the data, and dispatch the data to to different destinations for further processing. Photo credit: http://www.flickr.com/photos/decade_null/142235888/sizes/m/in/ photostream/

13. A Humble Beginning Friday, March 1, 13 8 We didn’t build everything in one night. Actually, we had a humble start. I did a lot of log scraping like these. I also used R to analyze logs. But these are speciﬁc tasks, and at some point

17. Friday, March 1, 13 9 Something happened. Our traffic turned into a hockey stick, and the number of applications exploded. So, log traffic also exploded. Simple log scraping wouldn’t cut it any more.

18. Friday, March 1, 13 9 Something happened. Our traffic turned into a hockey stick, and the number of applications exploded. So, log traffic also exploded. Simple log scraping wouldn’t cut it any more.

19. Application Application Application Application Application Application Application Application Application Application Friday, March 1, 13 9 Something happened. Our traffic turned into a hockey stick, and the number of applications exploded. So, log traffic also exploded. Simple log scraping wouldn’t cut it any more.

20. So We Evolved Friday, March 1, 13 10 So we evolved. One thing we built was a hadoop grep. This tool searches TBs of data. It is much more useful that the one provided by Apache Hadoop Distribution, because it supports many more Grep options like context, sorting by columns, and etc. And DSE’s Hadoop-as-a- service greatly helps each team.

21. So We Evolved Friday, March 1, 13 10 So we evolved. One thing we built was a hadoop grep. This tool searches TBs of data. It is much more useful that the one provided by Apache Hadoop Distribution, because it supports many more Grep options like context, sorting by columns, and etc. And DSE’s Hadoop-as-a- service greatly helps each team.

22. So We Evolved hgrep -C 10 -k 5,2,3 'users.*[1-9]{3}' *catalina.out s3//bucket Friday, March 1, 13 10 So we evolved. One thing we built was a hadoop grep. This tool searches TBs of data. It is much more useful that the one provided by Apache Hadoop Distribution, because it supports many more Grep options like context, sorting by columns, and etc. And DSE’s Hadoop-as-a- service greatly helps each team.

23. So We Evolved hgrep -C 10 -k 5,2,3 'users.*[1-9]{3}' *catalina.out s3//bucket Friday, March 1, 13 10 So we evolved. One thing we built was a hadoop grep. This tool searches TBs of data. It is much more useful that the one provided by Apache Hadoop Distribution, because it supports many more Grep options like context, sorting by columns, and etc. And DSE’s Hadoop-as-a- service greatly helps each team.

24. Friday, March 1, 13 11 A search tool that searches live instances’ logs is also developed.

30. Field Name Field Value Client “API” Server “Cryptex” StatusCode 200 ResponseTime 73 Friday, March 1, 13 12 Hive becomes indispensable.

31. Friday, March 1, 13 13 DSE Sting is a bless.

34. Friday, March 1, 13 14 So we built yet another tool to scratch it with the help of Druid.

35. Still, We Have a Real-Time Itch Friday, March 1, 13 14 So we built yet another tool to scratch it with the help of Druid.

36. Friday, March 1, 13 15 Error summary in the past 10 seconds. You get to slice and dice through arbitrary combination of different dimension across multiple time series. Trends over search query of “90210” by Canadians How many people started streaming any episode of House of Cards in the past hour, grouped

39. Friday, March 1, 13 16 A query of all the users who started streaming House of Cards in the past three hours, and results came back in seconds.

42. Interested? Friday, March 1, 13 17

43. See You Tomorrow Friday, March 1, 13 18 If you’re interested in how we did the real-time interactive queries with the help of Druid, do come to our talk. See you tomorrow

Strata lightening-talk

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