Beam YAML mappings

Beam YAML has the ability to do simple transformations which can be used to get data into the correct shape. The simplest of these is MapToFields which creates records with new fields defined in terms of the input fields.

Field renames

To rename fields one can write

- type: MapToFields
  config:
    fields:
      new_col1: col1
      new_col2: col2

will result in an output where each record has two fields, new_col1 and new_col2, whose values are those of col1 and col2 respectively (which are the names of two fields from the input schema).

One can specify the append parameter which indicates the original fields should be retained similar to the use of * in an SQL select statement. For example

- type: MapToFields
  config:
    append: true
    fields:
      new_col1: col1
      new_col2: col2

will output records that have new_col1 and new_col2 as additional fields. When the append field is specified, one can drop fields as well, e.g.

- type: MapToFields
  config:
    append: true
    drop:
      - col3
    fields:
      new_col1: col1
      new_col2: col2

which includes all original fields except col3 in addition to outputting the two new ones.

Mapping functions

Of course one may want to do transformations beyond just dropping and renaming fields. Beam YAML has the ability to inline simple UDFs. This requires a language specification. For example, we can provide a Python expression referencing the input fields

- type: MapToFields
  config:
    language: python
    fields:
      new_col: "col1.upper()"
      another_col: "col2 + col3"

In addition, one can provide a full Python callable that takes the row as an argument to do more complex mappings (see PythonCallableSource for acceptable formats). Thus, one can write

- type: MapToFields
  config:
    language: python
    fields:
      new_col:
        callable: |
          import re
          def my_mapping(row):
            if re.match("[0-9]+", row.col1) and row.col2 > 0:
              return "good"
            else:
              return "bad"

Once one reaches a certain level of complexity, it may be preferable to package this up as a dependency and simply refer to it by fully qualified name, e.g.

- type: MapToFields
  config:
    language: python
    fields:
      new_col:
        callable: pkg.module.fn

It is also possible to store the function logic in a file and point to the function name, e.g.

- type: MapToFields
  config:
    language: python
    fields:
      new_col:
        path: /path/to/some/udf.py
        name: my_mapping

Currently, in addition to Python, Java, SQL, and JavaScript (experimental) expressions are supported as well

Java

When using Java mappings, the UDF type must be declared, even for simple expressions, e.g.

- type: MapToFields
  config:
    language: java
    fields:
      new_col:
        expression: col1.toUpperCase()

For callable UDFs, Java requires that the function be declared as a class that implements java.util.function.Function, e.g.

- type: MapToFields
  config:
    language: java
    fields:
      new_col:
        callable: |
          import org.apache.beam.sdk.values.Row;
          import java.util.function.Function;
          public class MyFunction implements Function<Row, String> {
            public String apply(Row row) {
              return row.getString("col1").toUpperCase();
            }
          }

SQL

When SQL is used for a MapToFields UDF, it is essentially the SQL SELECT statement.

For example, the query SELECT UPPER(col1) AS new_col, "col2 + col3" AS another_col FROM PCOLLECTION would look like:

- type: MapToFields
  config:
    language: sql
    fields:
      new_col: "UPPER(col1)"
      another_col: "col2 + col3"

keeping in mind that any fields not intended to be included in the output should be added to the drop field.

If one wanted to select a field that collides with a reserved SQL keyword, the field(s) must be surrounded in backticks. For example, say the incoming PCollection has a field “timestamp”, one would have to write:

- type: MapToFields
  config:
    language: sql
    fields:
      new_col: "`timestamp`"

Note: the field mapping tags and fields defined in drop do not need to be escaped. Only the UDF itself needs to be a valid SQL statement.

Generic

If a language is not specified the set of expressions is limited to pre-existing fields and integer, floating point, or string literals. For example

- type: MapToFields
  config:
    fields:
      new_col: col1
      int_literal: 389
      float_litera: 1.90216
      str_literal: '"example"'  # note the double quoting

FlatMap

Sometimes it may be desirable to emit more (or less) than one record for each input record. This can be accomplished by mapping to an iterable type and following the mapping with an Explode operation, e.g.

- type: MapToFields
  config:
    language: python
    fields:
      new_col: "[col1.upper(), col1.lower(), col1.title()]"
      another_col: "col2 + col3"
- type: Explode
  config:
    fields: new_col

will result in three output records for every input record.

If more than one record is to be exploded, one must specify whether the cross product over all fields should be taken. For example

- type: MapToFields
  config:
    language: python
    fields:
      new_col: "[col1.upper(), col1.lower(), col1.title()]"
      another_col: "[col2 - 1, col2, col2 + 1]"
- type: Explode
  config:
    fields: [new_col, another_col]
    cross_product: true

will emit nine records whereas

- type: MapToFields
  config:
    language: python
    fields:
      new_col: "[col1.upper(), col1.lower(), col1.title()]"
      another_col: "[col2 - 1, col2, col2 + 1]"
- type: Explode
  config:
    fields: [new_col, another_col]
    cross_product: false

will only emit three.

The Explode operation can be used on its own if the field in question is already an iterable type.

- type: Explode
  config:
    fields: [col1]

Filtering

Sometimes it can be desirable to only keep records that satisfy a certain criteria. This can be accomplished with a Filter transform, e.g.

- type: Filter
  config:
    keep: "col2 > 0"

For anything more complicated than a simple comparison between existing fields and numeric literals a language parameter must be provided, e.g.

- type: Filter
  config:
    language: python
    keep: "col2 + col3 > 0"

For more complicated filtering functions, one can provide a full Python callable that takes the row as an argument to do more complex mappings (see PythonCallableSource for acceptable formats). Thus, one can write

- type: Filter
  config:
    language: python
    keep:
      callable: |
        import re
        def my_filter(row):
          return re.match("[0-9]+", row.col1) and row.col2 > 0

Once one reaches a certain level of complexity, it may be preferable to package this up as a dependency and simply refer to it by fully qualified name, e.g.

- type: Filter
  config:
    language: python
    keep:
      callable: pkg.module.fn

It is also possible to store the function logic in a file and point to the function name, e.g.

- type: Filter
  config:
    language: python
    keep:
      path: /path/to/some/udf.py
      name: my_filter

This allows the logic of the UDF itself to be more easily developed and tested using standard software engineering practices.

Currently, in addition to Python, Java, SQL, and JavaScript (experimental) expressions are supported as well

Java

When using Java filtering, the UDF type must be declared, even for simple expressions, e.g.

- type: Filter
  config:
    language: java
    keep:
      expression: col2 > 0

For callable UDFs, Java requires that the function be declared as a class that implements java.util.function.Function, e.g.

- type: Filter
  config:
    language: java
    keep:
      callable: |
        import org.apache.beam.sdk.values.Row;
        import java.util.function.Function;
        import java.util.regex.Pattern;
        public class MyFunction implements Function<Row, Boolean> {
          public Boolean apply(Row row) {
            Pattern pattern = Pattern.compile("[0-9]+");
            return pattern.matcher(row.getString("col1")).matches() && row.getInt64("col2") > 0;
          }
        }

SQL

Similar to Mapping Functions, when SQL is used for a MapToFields UDF, it is essentially theSQL WHERE statement.

For example, the query SELECT * FROM PCOLLECTION WHERE col2 > 0 would look like:

- type: Filter
  config:
    language: sql
    keep: "col2 > 0"

If one wanted to filter on a field that collides with a reserved SQL keyword, the field(s) must be surrounded in backticks. For example, say the incoming PCollection has a field “timestamp”, one would have to write:

- type: Filter
  config:
    language: sql
    keep: "`timestamp` > 0"

Partitioning

It can also be useful to send different elements to different places (similar to what is done with side outputs in other SDKs). While this can be done with a set of Filter operations, if every element has a single destination it can be more natural to use a Partition transform instead which sends every element to a unique output. For example, this will send all elements where col1 is equal to "a" to the output Partition.a.

- type: Partition
  input: input
  config:
    by: col1
    outputs: ['a', 'b', 'c']

- type: SomeTransform
  input: Partition.a
  config:
    param: ...

- type: AnotherTransform
  input: Partition.b
  config:
    param: ...

One can also specify the destination as a function, e.g.

- type: Partition
  input: input
  config:
    by: "'even' if col2 % 2 == 0 else 'odd'"
    language: python
    outputs: ['even', 'odd']

One can optionally provide a catch-all output which will capture all elements that are not in the named outputs (which would otherwise be an error):

- type: Partition
  input: input
  config:
    by: col1
    outputs: ['a', 'b', 'c']
    unknown_output: 'other'

Sometimes one wants to split a PCollection into multiple PCollections that aren’t necessarily disjoint. To send elements to multiple (or no) outputs, one could use an iterable column and precede the Partition with an Explode.

- type: Explode
  input: input
  config:
    fields: col1

- type: Partition
  input: Explode
  config:
    by: col1
    outputs: ['a', 'b', 'c']

Types

Beam will try to infer the types involved in the mappings, but sometimes this is not possible. In these cases one can explicitly denote the expected output type, e.g.

- type: MapToFields
  config:
    language: python
    fields:
      new_col:
        expression: "col1.upper()"
        output_type: string

The expected type is given in json schema notation, with the addition that a top-level basic types may be given as a literal string rather than requiring a {type: 'basic_type_name'} nesting.

- type: MapToFields
  config:
    language: python
    fields:
      new_col:
        expression: "col1.upper()"
        output_type: string
      another_col:
        expression: "beam.Row(a=col1, b=[col2])"
        output_type:
          type: 'object'
          properties:
            a:
              type: 'string'
            b:
              type: 'array'
              items:
                type: 'number'

This can be especially useful to resolve errors involving the inability to handle the beam:logical:pythonsdk_any:v1 type.

Dependencies

Often user defined functions need to rely on external dependencies. These can be provided with a dependencies attribute in the transform config. For example

- type: MapToFields
  config:
    language: python
    dependencies:
      - 'scipy>=1.15'
    fields:
      new_col:
        callable: |
          import scipy.special

          def func(t):
            return scipy.special.zeta(complex(1/2, t))

The dependencies are interpreted according to the language, e.g. for Java one provides a list of maven identifiers and/or jars, and for Python one provides a list of pypi package specifiers and/or sdist tarballs. See also the full examples using java dependencies and python dependencies.