User-defined functions in legacy SQL

This document details how to use JavaScript user-defined functions in legacy SQL query syntax. The preferred query syntax for BigQuery is GoogleSQL. For information on user-defined functions in GoogleSQL, see GoogleSQL user-defined functions.

BigQuery legacy SQL supports user-defined functions (UDFs) written in JavaScript. A UDF is similar to the "Map" function in a MapReduce: it takes a single row as input and produces zero or more rows as output. The output can potentially have a different schema than the input.

For information on user-defined functions in GoogleSQL, see User-defined functions in GoogleSQL.

UDF example

// UDF definition
function urlDecode(row, emit) {
  emit({title: decodeHelper(row.title),
        requests: row.num_requests});
}

// Helper function with error handling
function decodeHelper(s) {
  try {
    return decodeURI(s);
  } catch (ex) {
    return s;
  }
}

// UDF registration
bigquery.defineFunction(
  'urlDecode',  // Name used to call the function from SQL

  ['title', 'num_requests'],  // Input column names

  // JSON representation of the output schema
  [{name: 'title', type: 'string'},
   {name: 'requests', type: 'integer'}],

  urlDecode  // The function reference
);

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UDF structure

function name(row, emit) {
  emit(<output data>);
}

BigQuery UDFs operate on individual rows of a table or subselect query results. The UDF has two formal parameters:

  • row: an input row.
  • emit: a hook used by BigQuery to collect output data. The emit function takes one parameter: a JavaScript object that represents a single row of output data. The emit function can be called more than once, such as in a loop, to output multiple rows of data.

The following code example shows a basic UDF.

function urlDecode(row, emit) {
  emit({title: decodeURI(row.title),
        requests: row.num_requests});
}

Registering the UDF

You must register a name for your function so that it can be invoked from BigQuery SQL. The registered name doesn't have to match the name you used for your function in JavaScript.

bigquery.defineFunction(
  '<UDF name>',  // Name used to call the function from SQL

  ['<col1>', '<col2>'],  // Input column names

  // JSON representation of the output schema
  [<output schema>],

  // UDF definition or reference
  <UDF definition or reference>
);

Input columns

The input column names must match the names (or aliases, if applicable) of the columns in the input table or subquery.

For input columns that are records, you must specify—in the input column list—the leaf fields that you want to access from the record.

For example, if you have a record that stores a person's name and age:

person RECORD REPEATED
  name STRING OPTIONAL
  age INTEGER OPTIONAL

The input specifier for the name and age would be:

['person.name', 'person.age']

Use of ['person'] without the name or age would generate an error.

The resulting output will match the schema; you'll have an array of JavaScript objects, where each object has a "name" and an "age" property. For example:

[ {name: 'alice', age: 23}, {name: 'bob', age: 64}, ... ]

Output schema

You must provide BigQuery with the schema or structure of the records your UDF produces, represented as JSON. The schema can contain any supported BigQuery data types, including nested records. The supported type specifiers are:

  • boolean
  • float
  • integer
  • record
  • string
  • timestamp

The following code example shows the syntax for records in the output schema. Each output field requires a name and type attribute. Nested fields must also contain a fields attribute.

[{name: 'foo_bar', type: 'record', fields:
  [{name: 'a', type: 'string'},
   {name: 'b', type: 'integer'},
   {name: 'c', type: 'boolean'}]
}]

Each field can contain an optional mode attribute, which supports the following values:

  • nullable : this is the default and may be omitted.
  • required : if specified, the given field must be set to a value and cannot be undefined.
  • repeated : if specified, the given field must be an array.

Rows passed to the emit() function must match the data types of the output schema. Fields represented in the output schema that are omitted in the emit function will output as nulls.

UDF definition or reference

If you prefer, you can define the UDF inline in bigquery.defineFunction. For example:

bigquery.defineFunction(
  'urlDecode',  // Name used to call the function from SQL

  ['title', 'num_requests'],  // Input column names

  // JSON representation of the output schema
  [{name: 'title', type: 'string'},
   {name: 'requests', type: 'integer'}],

  // The UDF
  function(row, emit) {
    emit({title: decodeURI(row.title),
          requests: row.num_requests});
  }
);

Otherwise, you can define the UDF separately, and pass a reference to the function in bigquery.defineFunction. For example:

// The UDF
function urlDecode(row, emit) {
  emit({title: decodeURI(row.title),
        requests: row.num_requests});
}

// UDF registration
bigquery.defineFunction(
  'urlDecode',  // Name used to call the function from SQL

  ['title', 'num_requests'],  // Input column names

  // JSON representation of the output schema
  [{name: 'title', type: 'string'},
   {name: 'requests', type: 'integer'}],

  urlDecode  // The function reference
);

Error handling

If an exception or error is thrown during the processing of a UDF, the entire query will fail. You can use a try-catch block to handle errors. For example:

// The UDF
function urlDecode(row, emit) {
  emit({title: decodeHelper(row.title),
        requests: row.num_requests});
}

// Helper function with error handling
function decodeHelper(s) {
  try {
    return decodeURI(s);
  } catch (ex) {
    return s;
  }
}

// UDF registration
bigquery.defineFunction(
  'urlDecode',  // Name used to call the function from SQL

  ['title', 'num_requests'],  // Input column names

  // JSON representation of the output schema
  [{name: 'title', type: 'string'},
   {name: 'requests', type: 'integer'}],

  urlDecode  // The function reference
);

Running a query with a UDF

You can use UDFs in legacy SQL with the bq command-line tool or the BigQuery API. The Google Cloud console doesn't support UDFs in legacy SQL.

Using the bq command-line tool

To run a query containing one or more UDFs, specify the --udf_resource flag in the bq command-line tool from the Google Cloud CLI. The value of the flag can be either a Cloud Storage (gs://...) URI or the path to a local file. To specify multiple UDF resource files, repeat this flag.

Use the following syntax to run a query with a UDF:

bq query --udf_resource=<file_path_or_URI> <sql_query>

The following example runs a query that uses a UDF stored in a local file and a SQL query that is also stored in a local file.

Creating the UDF

You can store the UDF in Cloud Storage or as a local text file. For example, to store the following urlDecode UDF, create a file named urldecode.js and paste the following JavaScript code into the file before saving the file.

// UDF definition
function urlDecode(row, emit) {
  emit({title: decodeHelper(row.title),
        requests: row.num_requests});
}

// Helper function with error handling
function decodeHelper(s) {
  try {
    return decodeURI(s);
  } catch (ex) {
    return s;
  }
}

// UDF registration
bigquery.defineFunction(
  'urlDecode',  // Name used to call the function from SQL

  ['title', 'num_requests'],  // Input column names

  // JSON representation of the output schema
  [{name: 'title', type: 'string'},
   {name: 'requests', type: 'integer'}],

  urlDecode  // The function reference
);

Creating the query

You can also store the query in a file to keep your command line from becoming too verbose. For example, you can create a local file named query.sql and paste the following BigQuery statement into the file.

#legacySQL
SELECT requests, title
FROM
  urlDecode(
    SELECT
      title, sum(requests) AS num_requests
    FROM
      [fh-bigquery:wikipedia.pagecounts_201504]
    WHERE language = 'fr'
    GROUP EACH BY title
  )
WHERE title LIKE '%ç%'
ORDER BY requests DESC
LIMIT 100

After saving the file you can reference the file on the command line.

Running the query

After defining the UDF and the query in separate files, you can reference them in the command line. For example, the following command runs the query that you saved as the file named query.sql and references the UDF that you created.

$ bq query --udf_resource=urldecode.js "$(cat query.sql)"

Using the BigQuery API

configuration.query

Queries that use UDFs must contain userDefinedFunctionResources elements that provide the code, or locations to code resources, to be used in the query. The supplied code must include registration function invocations for any UDFs referenced by the query.

Code resources

Your query configuration may include JavaScript code blobs, as well as references to JavaScript source files stored in Cloud Storage.

Inline JavaScript code blobs are populated in the inlineCode section of a userDefinedFunctionResource element. However, code that will be reused or referenced across multiple queries should be persisted in Cloud Storage and referenced as an external resource.

To reference a JavaScript source file from Cloud Storage, set the resourceURI section of the userDefinedFunctionResource element to the file's gs:// URI.

The query configuration may contain multiple userDefinedFunctionResource elements. Each element may contain either an inlineCode or a resourceUri section.

Example

The following JSON example illustrates a query request that references two UDF resources: one blob of inline code, and one file lib.js to be read from Cloud Storage. In this example, myFunc and the registration invocation for myFunc are provided by lib.js.

{
  "configuration": {
    "query": {
      "userDefinedFunctionResources": [
        {
          "inlineCode": "var someCode = 'here';"
        },
        {
          "resourceUri": "gs://some-bucket/js/lib.js"
        }
      ],
      "query": "select a from myFunc(T);"
    }
  }
}

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Best practices

Developing your UDF

You can use our UDF test tool to test and debug your UDF without running up your BigQuery bill.

Pre-filter your input

If your input can be easily filtered down before being passed to a UDF, your query will likely be faster and cheaper.

In the running a query example, a subquery is passed as the input to urlDecode, instead of a full table. The [fh-bigquery:wikipedia.pagecounts_201504] table has approximately 5.6 billion rows, and if we ran the UDF on the entire table, the JavaScript framework would need to process over 21 times more rows than it would with the filtered subquery.

Avoid persistent mutable state

Do not store or access mutable state across UDF calls. The following code example describes this scenario:

// myCode.js
var numRows = 0;

function dontDoThis(r, emit) {
  emit({rowCount: ++numRows});
}

// The query.
SELECT max(rowCount) FROM dontDoThis(t);

The above example will not behave as expected, because BigQuery shards your query across many nodes. Each node has a standalone JavaScript processing environment that accumulates separate values for numRows.

Use memory efficiently

The JavaScript processing environment has limited memory available per query. UDF queries that accumulate too much local state may fail due to memory exhaustion.

Expand select queries

You must explicitly list the columns being selected from a UDF. SELECT * FROM <UDF name>(...) isn't supported.

To examine the structure of the input row data, you can use JSON.stringify() to emit a string output column:

bigquery.defineFunction(
  'examineInputFormat',
  ['some', 'input', 'columns'],
  [{name: 'input', type: 'string'}],
  function(r, emit) {
    emit({input: JSON.stringify(r)});
  }
);

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Limits

  • The amount of data that your UDF outputs when processing a single row should be approximately 5 MB or less.
  • Each user is limited to running approximately 6 UDF queries in a specific project at the same time. If you receive an error that you're over the concurrent query limit, wait a few minutes and try again.
  • A UDF can timeout and prevent your query from completing. Timeouts can be as short as 5 minutes, but can vary depending on several factors, including how much user CPU time your function consumes and how large your inputs and outputs to the JS function are.
  • A query job can have a maximum of 50 UDF resources (inline code blobs or external files).
  • Each inline code blob is limited to a maximum size of 32 KB. To use larger code resources, store your code in Cloud Storage and reference it as an external resource.
  • Each external code resource is limited to a maximum size of 1 MB.
  • The cumulative size of all external code resources is limited to a maximum of 5 MB.

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Limitations

  • The DOM objects Window, Document and Node, and functions that require them, are unsupported.
  • JavaScript functions that rely on native code are unsupported.
  • Bitwise operations in JavaScript handle only the most significant 32 bits.
  • Because of their non-deterministic nature, queries that invoke user-defined functions cannot use cached results.

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