Tutorial: Transforming input data

This tutorial will demonstrate how to use transform specs to filter and transform input data during ingestion.

For this tutorial, we’ll assume you’ve already downloaded Apache Druid as described in the single-machine quickstart and have it running on your local machine.

It will also be helpful to have finished Tutorial: Loading a file and Tutorial: Querying data.

Sample data

We’ve included sample data for this tutorial at quickstart/tutorial/transform-data.json, reproduced here for convenience:

  1. {"timestamp":"2018-01-01T07:01:35Z","animal":"octopus",  "location":1, "number":100}
  2. {"timestamp":"2018-01-01T05:01:35Z","animal":"mongoose", "location":2,"number":200}
  3. {"timestamp":"2018-01-01T06:01:35Z","animal":"snake", "location":3, "number":300}
  4. {"timestamp":"2018-01-01T01:01:35Z","animal":"lion", "location":4, "number":300}

Load data with transform specs

We will ingest the sample data using the following spec, which demonstrates the use of transform specs:

  1. {
  2.   "type" : "index_parallel",
  3.   "spec" : {
  4.     "dataSchema" : {
  5.       "dataSource" : "transform-tutorial",
  6.       "timestampSpec": {
  7.         "column": "timestamp",
  8.         "format": "iso"
  9.       },
  10.       "dimensionsSpec" : {
  11.         "dimensions" : [
  12.           "animal",
  13.           { "name": "location", "type": "long" }
  14.         ]
  15.       },
  16.       "metricsSpec" : [
  17.         { "type" : "count", "name" : "count" },
  18.         { "type" : "longSum", "name" : "number", "fieldName" : "number" },
  19.         { "type" : "longSum", "name" : "triple-number", "fieldName" : "triple-number" }
  20.       ],
  21.       "granularitySpec" : {
  22.         "type" : "uniform",
  23.         "segmentGranularity" : "week",
  24.         "queryGranularity" : "minute",
  25.         "intervals" : ["2018-01-01/2018-01-03"],
  26.         "rollup" : true
  27.       },
  28.       "transformSpec": {
  29.         "transforms": [
  30.           {
  31.             "type": "expression",
  32.             "name": "animal",
  33.             "expression": "concat('super-', animal)"
  34.           },
  35.           {
  36.             "type": "expression",
  37.             "name": "triple-number",
  38.             "expression": "number * 3"
  39.           }
  40.         ],
  41.         "filter": {
  42.           "type":"or",
  43.           "fields": [
  44.             { "type": "selector", "dimension": "animal", "value": "super-mongoose" },
  45.             { "type": "selector", "dimension": "triple-number", "value": "300" },
  46.             { "type": "selector", "dimension": "location", "value": "3" }
  47.           ]
  48.         }
  49.       }
  50.     },
  51.     "ioConfig" : {
  52.       "type" : "index_parallel",
  53.       "inputSource" : {
  54.         "type" : "local",
  55.         "baseDir" : "quickstart/tutorial",
  56.         "filter" : "transform-data.json"
  57.       },
  58.       "inputFormat" : {
  59.         "type" :"json"
  60.       },
  61.       "appendToExisting" : false
  62.     },
  63.     "tuningConfig" : {
  64.       "type" : "index_parallel",
  65.       "maxRowsPerSegment" : 5000000,
  66.       "maxRowsInMemory" : 25000
  67.     }
  68.   }
  69. }

In the transform spec, we have two expression transforms:

  • super-animal: prepends “super-“ to the values in the animal column. This will override the animal column with the transformed version, since the transform’s name is animal.
  • triple-number: multiplies the number column by 3. This will create a new triple-number column. Note that we are ingesting both the original and the transformed column.

Additionally, we have an OR filter with three clauses:

  • super-animal values that match “super-mongoose”
  • triple-number values that match 300
  • location values that match 3

This filter selects the first 3 rows, and it will exclude the final “lion” row in the input data. Note that the filter is applied after the transformation.

Let’s submit this task now, which has been included at quickstart/tutorial/transform-index.json:

  1. bin/post-index-task --file quickstart/tutorial/transform-index.json --url http://localhost:8081

Query the transformed data

Let’s run bin/dsql and issue a select * from "transform-tutorial"; query to see what was ingested:

  1. dsql> select * from "transform-tutorial";
  2. ┌──────────────────────────┬────────────────┬───────┬──────────┬────────┬───────────────┐
  3. │ __time                   │ animal         │ count │ location │ number │ triple-number │
  4. ├──────────────────────────┼────────────────┼───────┼──────────┼────────┼───────────────┤
  5. │ 2018-01-01T05:01:00.000Z │ super-mongoose │     1 │        2 │    200 │           600 │
  6. │ 2018-01-01T06:01:00.000Z │ super-snake    │     1 │        3 │    300 │           900 │
  7. │ 2018-01-01T07:01:00.000Z │ super-octopus  │     1 │        1 │    100 │           300 │
  8. └──────────────────────────┴────────────────┴───────┴──────────┴────────┴───────────────┘
  9. Retrieved 3 rows in 0.03s.

The “lion” row has been discarded, the animal column has been transformed, and we have both the original and transformed number column.