Interactive analytics at scale with druid

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How do you run analytics queries on a dataset that grows by billions of entries a day? What if you need to be able to drill into it, filter it or aggregate it by any available dimension? On demand, without precomputing, and with sub second latency? Oh and this isn't for an internal dashboard. The interface is customer-facing and is going to be accessed by thousands of clients. In this talk, I'll tell you how Criteo, one of the biggest ad tech firms in the world, uses Druid to build its new analytics platform. Druid is an open-source, real-time data store designed to power interactive applications at scale. I’ll walk you through its architecture, explain how it scales and how data is stored on disk and in memory to serve queries faster than you can blink.

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SELECT sum(revenue) AS “Revenue",
sum(sales) AS “Sales"
FROM customer-insights
WHERE client_id = 2255 AND date BETWEEN "2014-08-01" AND "2014-08-08"
GROUP BY day(date)

${ queryType: "Timeseries", dataSource: "customer-insights", granularity: "day", aggregations: [ { type: "longSum", fieldName: "revenue", name: "Revenue" }, { type: "longSum ", fieldName: "sales", name: "Sales" } ], filter: { type: "selector", dimension: "client_id", value: 2255 }, intervals: ["2014-08-01T00:00/2014-08-08T00"] }$

${ queryType: "groupBy", dataSource: "customer-insights", granularity: "all", dimensions: ["device"], aggregations: [ { type: "longSum", fieldName: "clicks", name: "Clicks" }, { type: "longSum", fieldName: "cost", name: "Cost" } ], filter: { type: "selector", dimension: "client_id", value: 2255 }, intervals: ["2014-08-01T00:00/2014-08-08T00"] }$

Iphone Google 0.35€08:12:00
Android Yahoo 0.2€08:12:00
Iphone Google 0.1€08:12:37
Android Yahoo 0.2€08:12:38
Iphone Google 0.15€08:12:39
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joe@mail.com
bob@mail.com
tony@mail.com

Iphone Google Computer 0.1€08:12:37
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Google
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Microsoft
Google
Yahoo
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Google
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Google
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Google 1,0,0,0,1
Yahoo 0,1,0,1,0
Microsoft 0,0,1,0,0

Filter Time period Aggregated rows Time
Biggest advertiser 6 months 1.5M 225ms
Android devices 1 month 42M 750ms
Desktop 1 month 110M 910ms
RTB networks 7 months 753M 2.4s

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The presentation explores the development and application of artificial intelligence (AI) from its inception to its current status in the modern world. The term "artificial intelligence" was first coined by John McCarthy in 1956 to describe efforts to develop computer programs capable of performing tasks that typically require human intelligence. This concept was first introduced at a conference held at Dartmouth College, where programs demonstrated capabilities such as playing chess, proving theorems, and interpreting texts. In the early stages, Alan Turing contributed to the field by defining intelligence as the ability of a being to respond to certain questions intelligently, proposing what is now known as the Turing Test to evaluate the presence of intelligent behavior in machines. As the decades progressed, AI evolved significantly. The 1980s focused on machine learning, teaching computers to learn from data, leading to the development of models that could improve their performance based on their experiences. The 1990s and 2000s saw further advances in algorithms and computational power, which allowed for more sophisticated data analysis techniques, including data mining. By the 2010s, the proliferation of big data and the refinement of deep learning techniques enabled AI to become mainstream. Notable milestones included the success of Google's AlphaGo and advancements in autonomous vehicles by companies like Tesla and Waymo. A major theme of the presentation is the application of generative AI, which has been used for tasks such as natural language text generation, translation, and question answering. Generative AI uses large datasets to train models that can then produce new, coherent pieces of text or other media. The presentation also discusses the ethical implications and the need for regulation in AI, highlighting issues such as privacy, bias, and the potential for misuse. These concerns have prompted calls for comprehensive regulations to ensure the safe and equitable use of AI technologies. Artificial intelligence has also played a significant role in healthcare, particularly highlighted during the COVID-19 pandemic, where it was used in drug discovery, vaccine development, and analyzing the spread of the virus. The capabilities of AI in healthcare are vast, ranging from medical diagnostics to personalized medicine, demonstrating the technology's potential to revolutionize fields beyond just technical or consumer applications. In conclusion, AI continues to be a rapidly evolving field with significant implications for various aspects of society. The development from theoretical concepts to real-world applications illustrates both the potential benefits and the challenges that come with integrating advanced technologies into everyday life. The ongoing discussion about AI ethics and regulation underscores the importance of managing these technologies responsibly to maximize their their benefits while minimizing potential harms.

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How to Troubleshoot Apps for the Modern Connected Worker

Artificial Intelligence: Facts and Myths

Interactive analytics at scale with druid

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23. By advertiser… By country… … …

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30. Device Publisher Cost Iphone Google 0.1€ Android Yahoo 0.2€ Dimension Cost Iphone 0.1€ Android 0.2€ Google 0.1€ Yahoo 0.1€ Iphone, Google 0.1€ Android, Yahoo 0,2€

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32. Device Publisher Product Cost Iphone Google Computer 0.1€ Android Yahoo Cloth 0.2€ Dimension Cost Iphone 0.1€ Android 0.2€ Google 0.1€ Yahoo 0.1€ Computer 0.1€ Cloth 0.2€ Iphone, Google 0.1€ Iphone, Computer 0.1€ Google, Computer 0.1€ Android, Yahoo 0.2€ Android, Cloth 0.2€ Yahoo, Cloth 0.2€ Iphone, Google, Computer 0.1€ Android, Yahoo, Cloth 0.2€

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44. SELECT sum(revenue) AS “Revenue", sum(sales) AS “Sales" FROM customer-insights WHERE client_id = 2255 AND date BETWEEN "2014-08-01" AND "2014-08-08" GROUP BY day(date)

45. { queryType: "Timeseries", dataSource: "customer-insights", granularity: "day", aggregations: [ { type: "longSum", fieldName: "revenue", name: "Revenue" }, { type: "longSum ", fieldName: "sales", name: "Sales" } ], filter: { type: "selector", dimension: "client_id", value: 2255 }, intervals: ["2014-08-01T00:00/2014-08-08T00"] }

46. { queryType: "Timeseries", dataSource: "customer-insights", granularity: "day", aggregations: [ { type: "longSum", fieldName: "revenue", name: "Revenue" }, { type: "longSum ", fieldName: "sales", name: "Sales" } ], filter: { type: "selector", dimension: "client_id", value: 2255 }, intervals: ["2014-08-01T00:00/2014-08-08T00"] }

47. { queryType: "Timeseries", dataSource: "customer-insights", granularity: "day", aggregations: [ { type: "longSum", fieldName: "revenue", name: "Revenue" }, { type: "longSum ", fieldName: "sales", name: "Sales" } ], filter: { type: "selector", dimension: "client_id", value: 2255 }, intervals: ["2014-08-01T00:00/2014-08-08T00"] }

48. { queryType: "Timeseries", dataSource: "customer-insights", granularity: "day", aggregations: [ { type: "longSum", fieldName: "revenue", name: "Revenue" }, { type: "longSum ", fieldName: "sales", name: "Sales" } ], filter: { type: "selector", dimension: "client_id", value: 2255 }, intervals: ["2014-08-01T00:00/2014-08-08T00"] }

49. { queryType: "Timeseries", dataSource: "customer-insights", granularity: "day", aggregations: [ { type: "longSum", fieldName: "revenue", name: "Revenue" }, { type: "longSum ", fieldName: "sales", name: "Sales" } ], filter: { type: "selector", dimension: "client_id", value: 2255 }, intervals: ["2014-08-01T00:00/2014-08-08T00"] }

50. { queryType: "Timeseries", dataSource: "customer-insights", granularity: "day", aggregations: [ { type: "longSum", fieldName: "revenue", name: "Revenue" }, { type: "longSum ", fieldName: "sales", name: "Sales" } ], filter: { type: "selector", dimension: "client_id", value: 2255 }, intervals: ["2014-08-01T00:00/2014-08-08T00"] }

51. { queryType: "Timeseries", dataSource: "customer-insights", granularity: "day", aggregations: [ { type: "longSum", fieldName: "revenue", name: "Revenue" }, { type: "longSum ", fieldName: "sales", name: "Sales" } ], filter: { type: "selector", dimension: "client_id", value: 2255 }, intervals: ["2014-08-01T00:00/2014-08-08T00"] }

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53. { queryType: "groupBy", dataSource: "customer-insights", granularity: "all", dimensions: ["device"], aggregations: [ { type: "longSum", fieldName: "clicks", name: "Clicks" }, { type: "longSum", fieldName: "cost", name: "Cost" } ], filter: { type: "selector", dimension: "client_id", value: 2255 }, intervals: ["2014-08-01T00:00/2014-08-08T00"] }

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58. Iphone Google 0.35€08:12:00 Android Yahoo 0.2€08:12:00 Iphone Google 0.1€08:12:37 Android Yahoo 0.2€08:12:38 Iphone Google 0.15€08:12:39 Iphone Google 0.1€08:12:40

59. Iphone Google 0.1€08:12:37 Android Yahoo 0.2€08:12:38 Iphone Google 0.15€08:13:02 Iphone Google 0.1€08:13:08 bob@mail.com joe@mail.com bob@mail.com tony@mail.com

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64. Iphone Google Computer 0.1€08:12:37 Android Yahoo Cloth 0.2€08:12:38 Iphone Google Computer 0.1€08:12:37 Android Yahoo Cloth 0.2€08:12:38

65. Iphone Google Computer 0.1€08:12:37 Android Yahoo Cloth 0.2€08:12:38

66. Google Yahoo Microsoft Google Yahoo 1 2 3 1 2

67. Google Yahoo Microsoft Google Yahoo Google 1,0,0,0,1 Yahoo 0,1,0,1,0 Microsoft 0,0,1,0,0

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72. Filter Time period Aggregated rows Time Biggest advertiser 6 months 1.5M 225ms Android devices 1 month 42M 750ms Desktop 1 month 110M 910ms RTB networks 7 months 753M 2.4s

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