Interactive analytics at scale with druid
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The document presents a detailed analysis of advertising costs and revenues based on different devices and publishers, using time-series data to aggregate revenue and sales for a specific client over a set date range. Additionally, it provides metrics such as total clicks and costs associated with advertising campaigns. Insights are derived from customer data concerning performance across various platforms, including mobile and desktop devices.
![{
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"]
}](https://image.slidesharecdn.com/interactiveanalyticsatscalewithdruid-150319125248-conversion-gate01/85/Interactive-analytics-at-scale-with-druid-45-320.jpg)
![{
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"]
}](https://image.slidesharecdn.com/interactiveanalyticsatscalewithdruid-150319125248-conversion-gate01/85/Interactive-analytics-at-scale-with-druid-46-320.jpg)
![{
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"]
}](https://image.slidesharecdn.com/interactiveanalyticsatscalewithdruid-150319125248-conversion-gate01/85/Interactive-analytics-at-scale-with-druid-47-320.jpg)
![{
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"]
}](https://image.slidesharecdn.com/interactiveanalyticsatscalewithdruid-150319125248-conversion-gate01/85/Interactive-analytics-at-scale-with-druid-48-320.jpg)
![{
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"]
}](https://image.slidesharecdn.com/interactiveanalyticsatscalewithdruid-150319125248-conversion-gate01/85/Interactive-analytics-at-scale-with-druid-49-320.jpg)
![{
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"]
}](https://image.slidesharecdn.com/interactiveanalyticsatscalewithdruid-150319125248-conversion-gate01/85/Interactive-analytics-at-scale-with-druid-50-320.jpg)
![{
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"]
}](https://image.slidesharecdn.com/interactiveanalyticsatscalewithdruid-150319125248-conversion-gate01/85/Interactive-analytics-at-scale-with-druid-51-320.jpg)
![{
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"]
}](https://image.slidesharecdn.com/interactiveanalyticsatscalewithdruid-150319125248-conversion-gate01/85/Interactive-analytics-at-scale-with-druid-53-320.jpg)
Interactive analytics at scale with druid
- 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€
- 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€
- 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"] }
- 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"] }
- 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
- 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
- 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