Ingestion Transformations

Raw source data often needs to undergo some transformations before it is pushed to Pinot.

Transformations include extracting records from nested objects, applying simple transform functions on certain columns, filtering out unwanted columns, as well as more advanced operations like joining between datasets.

A preprocessing job is usually needed to perform these operations. In streaming data sources, you might write a Samza job and create an intermediate topic to store the transformed data.

For simple transformations, this can result in inconsistencies in the batch/stream data source and increase maintenance and operator overhead.

To make things easier, Pinot supports transformations that can be applied via the table config.

If a new column is added to your table or schema configuration during ingestion, incorrect data may appear in the consuming segment(s). To ensure accurate values are reloaded, see how to add a new column during ingestion.

Transformation functions

Pinot supports the following functions:

• Groovy functions

• Built-in functions

A transformation function cannot mix Groovy and built-in functions; only use one type of function at a time.

Groovy functions

Groovy functions can be defined using the syntax:

Groovy({groovy script}, argument1, argument2...argumentN)

Any valid Groovy expression can be used.

⚠️ Enabling Groovy

Allowing executable Groovy in ingestion transformation can be a security vulnerability. To enable Groovy for ingestion, set the following controller configuration:

controller.disable.ingestion.groovy=false

If not set, Groovy for ingestion transformation is disabled by default.

Built-in Pinot functions

All the functions defined in this directory annotated with @ScalarFunction (for example, toEpochSeconds) are supported ingestion transformation functions.

Below are some commonly used built-in Pinot functions for ingestion transformations.

DateTime functions

These functions enable time transformations.

toEpochXXX

Converts from epoch milliseconds to a higher granularity.

Function name	Description
toEpochSeconds	Converts epoch millis to epoch seconds. Usage:"toEpochSeconds(millis)"
toEpochMinutes	Converts epoch millis to epoch minutes Usage: "toEpochMinutes(millis)"
toEpochHours	Converts epoch millis to epoch hours Usage: "toEpochHours(millis)"
toEpochDays	Converts epoch millis to epoch days Usage: "toEpochDays(millis)"

toEpochXXXRounded

Converts from epoch milliseconds to another granularity, rounding to the nearest rounding bucket. For example, 1588469352000 (2020-05-01 42:29:12) is 26474489 minutesSinceEpoch. `toEpochMinutesRounded(1588469352000) = 26474480 (2020-05-01 42:20:00)

Function Name	Description
toEpochSecondsRounded	Converts epoch millis to epoch seconds, rounding to nearest rounding bucket"toEpochSecondsRounded(millis, 30)"
toEpochMinutesRounded	Converts epoch millis to epoch seconds, rounding to nearest rounding bucket"toEpochMinutesRounded(millis, 10)"
toEpochHoursRounded	Converts epoch millis to epoch seconds, rounding to nearest rounding bucket"toEpochHoursRounded(millis, 6)"
toEpochDaysRounded	Converts epoch millis to epoch seconds, rounding to nearest rounding bucket"toEpochDaysRounded(millis, 7)"

fromEpochXXX

Converts from an epoch granularity to milliseconds.

Function Name	Description
fromEpochSeconds	Converts from epoch seconds to milliseconds "fromEpochSeconds(secondsSinceEpoch)"
fromEpochMinutes	Converts from epoch minutes to milliseconds "fromEpochMinutes(minutesSinceEpoch)"
fromEpochHours	Converts from epoch hours to milliseconds "fromEpochHours(hoursSinceEpoch)"
fromEpochDays	Converts from epoch days to milliseconds "fromEpochDays(daysSinceEpoch)"

Simple date format

Converts simple date format strings to milliseconds and vice versa, per the provided pattern string.

Function name	Description
ToDateTime	Converts from milliseconds to a formatted date time string, as per the provided pattern "toDateTime(millis, 'yyyy-MM-dd')"
FromDateTime	Converts a formatted date time string to milliseconds, as per the provided pattern "fromDateTime(dateTimeStr, 'EEE MMM dd HH:mm:ss ZZZ yyyy')"

Note

Letters that are not part of Simple Date Time legend (https://docs.oracle.com/javase/8/docs/api/java/text/SimpleDateFormat.html) need to be escaped. For example:

"transformFunction": "fromDateTime(dateTimeStr, 'yyyy-MM-dd''T''HH:mm:ss')"

JSON functions

Function name	Description
json_format	Converts a JSON/AVRO complex object to a string. This json map can then be queried using jsonExtractScalar function. "json_format(jsonMapField)"

Types of transformation

Filtering

Records can be filtered as they are ingested. A filter function can be specified in the filterConfigs in the ingestionConfigs of the table config.

"tableConfig": {
    "tableName": ...,
    "tableType": ...,
    "ingestionConfig": {
        "filterConfig": {
            "filterFunction": "<expression>"
        }
    }
}

If the expression evaluates to true, the record will be filtered out. The expressions can use any of the transform functions described in the previous section.

Consider a table that has a column timestamp. If you want to filter out records that are older than timestamp 1589007600000, you could apply the following function:

"ingestionConfig": {
    "filterConfig": {
        "filterFunction": "Groovy({timestamp < 1589007600000}, timestamp)"
    }
}

Consider a table that has a string column campaign and a multi-value column double column prices. If you want to filter out records where campaign = 'X' or 'Y' and sum of all elements in prices is less than 100, you could apply the following function:

"ingestionConfig": {
    "filterConfig": {
        "filterFunction": "Groovy({(campaign == \"X\" || campaign == \"Y\") && prices.sum() < 100}, prices, campaign)"
    }
}

Filter config also supports SQL-like expression of built-in scalar functions for filtering records (starting v 0.11.0+). Example:

"ingestionConfig": {
    "filterConfig": {
        "filterFunction": "strcmp(campaign, 'X') = 0 OR strcmp(campaign, 'Y') = 0 OR timestamp < 1589007600000"
    }
}

Column transformation

Transform functions can be defined on columns in the ingestion config of the table config.

{ "tableConfig": {
    "tableName": ...,
    "tableType": ...,
    "ingestionConfig": {
        "transformConfigs": [{
          "columnName": "fieldName",
          "transformFunction": "<expression>"
        }]
    },
    ...
}

For example, imagine that our source data contains the prices and timestamp fields. We want to extract the maximum price and store that in the maxPrices field and convert the timestamp into the number of hours since the epoch and store it in the hoursSinceEpoch field. You can do this by applying the following transformation:

pinot-table-offline.json

{
"tableName": "myTable",
...
"ingestionConfig": {
    "transformConfigs": [{
      "columnName": "maxPrice",
      "transformFunction": "Groovy({prices.max()}, prices)" // groovy function
    },
    {
      "columnName": "hoursSinceEpoch",
      "transformFunction": "toEpochHours(timestamp)" // built-in function
    }]
  }
}

Below are some examples of commonly used functions.

String concatenation

Concat firstName and lastName to get fullName

"ingestionConfig": {
    "transformConfigs": [{
      "columnName": "fullName",
      "transformFunction": "Groovy({firstName+' '+lastName}, firstName, lastName)"
    }]
}

Find an element in an array

Find max value in array bids

"ingestionConfig": {
    "transformConfigs": [{
      "columnName": "maxBid",
      "transformFunction": "Groovy({bids.max{ it.toBigDecimal() }}, bids)"
    }]
}

Time transformation

Convert timestamp from MILLISECONDS to HOURS

"ingestionConfig": {
    "transformConfigs": [{
      "columnName": "hoursSinceEpoch",
      "transformFunction": "Groovy({timestamp/(1000*60*60)}, timestamp)"
    }]
}

Column name change

Change name of the column from user_id to userId

"ingestionConfig": {
    "transformConfigs": [{
      "columnName": "userId",
      "transformFunction": "Groovy({user_id}, user_id)"
    }]
}

Extract value from a column containing space

Pinot doesn't support columns that have spaces, so if a source data column has a space, we'll need to store that value in a column with a supported name. To extract the value from first Name into the column firstName, run the following:

"ingestionConfig": {
    "transformConfigs": [{
      "columnName": "firstName",
      "transformFunction": "\"first Name \""
    }]
}

Ternary operation

If eventType is IMPRESSION set impression to 1. Similar for CLICK.

"ingestionConfig": {
    "transformConfigs": [{
      "columnName": "impressions",
      "transformFunction": "Groovy({eventType == 'IMPRESSION' ? 1: 0}, eventType)"
    },
    {
      "columnName": "clicks",
      "transformFunction": "Groovy({eventType == 'CLICK' ? 1: 0}, eventType)"
    }]
}

AVRO Map

Store an AVRO Map in Pinot as two multi-value columns. Sort the keys, to maintain the mapping. 1) The keys of the map as map_keys 2) The values of the map as map_values

"ingestionConfig": {
    "transformConfigs": [{
      "columnName": "map2_keys",
      "transformFunction": "Groovy({map2.sort()*.key}, map2)"
    },
    {
      "columnName": "map2_values",
      "transformFunction": "Groovy({map2.sort()*.value}, map2)"
    }]
}

Chaining transformations

Transformations can be chained. This means that you can use a field created by a transformation in another transformation function.

For example, we might have the following JSON document in the data field of our source data:

{
  "userId": "12345678__foo__othertext"
}

We can apply one transformation to extract the userId and then another one to pull out the numerical part of the identifier:

"ingestionConfig": {
    "transformConfigs": [
      {
        "columnName": "userOid",
        "transformFunction": "jsonPathString(data, '$.userId')"
      },
      {
        "columnName": "userId",
        "transformFunction": "Groovy({Long.valueOf(userOid.substring(0, 8))}, userOid)"
      }
   ]
}

Flattening

There are 2 kinds of flattening:

One record into many

This is not natively supported as of yet. You can write a custom Decoder/RecordReader if you want to use this. Once the Decoder generates the multiple GenericRows from the provided input record, a List<GenericRow> should be set into the destination GenericRow, with the key $MULTIPLE_RECORDS_KEY$. The segment generation drivers will treat this as a special case and handle the multiple records case.

Extract attributes from complex objects

Feature TBD

Add a new column during ingestion

If a new column is added to table or schema configuration during ingestion, incorrect data may appear in the consuming segment(s).

To ensure accurate values are reloaded, do the following:

1. Pause consumption (and wait for pause status success): $ curl -X POST {controllerHost}/tables/{tableName}/pauseConsumption

2. Apply new table or schema configurations.

3. Reload segments using the Pinot Controller API or Pinot Admin Console.

4. Resume consumption: $ curl -X POST {controllerHost}/tables/{tableName}/resumeConsumption