Machine-Learning Preprocessing Functions in Confluent Cloud for Apache Flink

The following built-in functions are available for ML preprocessing in Confluent Cloud for Apache Flink®. These functions help transform features into representations more suitable for downstream processors.

ML_BUCKETIZE	ML_CHARACTER_TEXT_SPLITTER	ML_FILE_FORMAT_TEXT_SPLITTER
ML_LABEL_ENCODER	ML_MAX_ABS_SCALER	ML_MIN_MAX_SCALER
ML_NGRAMS	ML_NORMALIZER	ML_ONE_HOT_ENCODER
ML_RECURSIVE_TEXT_SPLITTER	ML_ROBUST_SCALER	ML_STANDARD_SCALER

ML_BUCKETIZE

Bucketizes numerical values into discrete bins based on split points.

Syntax

ML_BUCKETIZE(value, splitBucketPoints [, bucketNames])

Description

The ML_BUCKETIZE function divides numerical values into discrete buckets based on specified split points. Each bucket represents a range of values, and the function returns the bucket index or name for each input value.

Arguments

• value: Numerical expression to be bucketized. If the input value is NaN or NULL, it is bucketized to the NULL bucket.
• splitBucketPoints: Array of numerical values that define the bucket boundaries, or split points.
  • If the splitBucketPoints array is empty, an exception is thrown.
  • Any split points that are NaN or NULL are removed from the splitBucketPoints array.
  • splitBucketPoints must be in ascending order, or an exception is thrown.
  • Duplicates are removed from splitBucketPoints.
• bucketNames: (Optional) Array of names of the buckets defined in splitBucketPoints.
  • If the bucketNames array is not provided, buckets are named bin_NULL, bin_1, bin_2 … bin_n, with n being the total number of buckets in splitBucketPoints.
  • If the bucketNames array is provided, names must be in the same order as in the splitBucketPoints array.
  • Names for all of the buckets must be provided, including the NULL bucket, or an exception is thrown.
  • If the bucketNames array is provided, the first name is the name for the NULL bucket.

Example

-- returns 'bin_2'
SELECT ML_BUCKETIZE(2, ARRAY[1, 4, 7]);

-- returns 'b2'
SELECT ML_BUCKETIZE(2, ARRAY[1, 4, 7], ARRAY['b_null','b1','b2','b3','b4']);

ML_CHARACTER_TEXT_SPLITTER

Splits text into chunks based on character count and separators.

Syntax

ML_CHARACTER_TEXT_SPLITTER(text, chunkSize, chunkOverlap, separator,
isSeparatorRegex [, trimWhitespace] [, keepSeparator] [, separatorPosition])

Description

The ML_CHARACTER_TEXT_SPLITTER function splits text into chunks based on character count and specified separators. This is useful for processing large text documents into smaller, manageable pieces.

If any argument other than text is NULL, an exception is thrown.

The returned array of chunks has the same order as the input.

The function tries to keep every chunk within the chunkSize limit, but if a chunk is more than the limit, it is returned as is.

Arguments

• text: The input text to be split. If the input text is NULL, it is returned as is.
• chunkSize: The size of each chunk. If chunkSize < 0 or chunkOverlap > chunkSize, an exception is thrown.
• chunkOverlap: The number of overlapping characters between chunks. If chunkOverlap < 0, an exception is thrown.
• separator: The separator used for splitting.
• isSeparatorRegex: Whether the separator is a regex pattern.
• trimWhitespace: (Optional) Whether to trim whitespace from chunks. The default is TRUE.
• keepSeparator: (Optional) Whether to keep the separator in the chunks. The default is FALSE.
• separatorPosition: (Optional) The position of the separator. Valid values are START or END. The default is START. START means place the separator at the start of the following chunk, and END means place the separator at the end of the previous chunk.

Example

-- returns ['This is the text I would like to ch', 'o chunk up. It is the example text ', 'ext for this exercise']
SELECT ML_CHARACTER_TEXT_SPLITTER('This is the text I would like to chunk up. It is the example text for this exercise', 35, 4, '', TRUE, FALSE, TRUE, 'END');

ML_FILE_FORMAT_TEXT_SPLITTER

Splits text into chunks based on specific file format patterns.

Syntax

ML_FILE_FORMAT_TEXT_SPLITTER(text, chunkSize, chunkOverlap, formatName,
[trimWhitespace] [, keepSeparator] [, separatorPosition])

Description

The ML_FILE_FORMAT_TEXT_SPLITTER function splits text into chunks based on specific file format patterns. It uses format-specific separators to split code intelligently or structure text.

The returned array of chunks has the same order as the input.

The function starts splitting the chunks with the first separator in the separators list. If a chunk is bigger than chunkSize, the function splits the chunk recursively using the next separator in the separators list for the given file format. If separators are exhausted, and the remaining text is bigger than chunkSize, the function returns the smallest chunk possible, even though it is bigger than chunkSize.

Arguments

• text: The input text to be split. If the input text is NULL, it is returned as is.

• chunkSize: The size of each chunk. If chunkSize < 0 or chunkOverlap > chunkSize, an exception is thrown.

• chunkOverlap: The number of overlapping characters between chunks. If chunkOverlap < 0, an exception is thrown.

• formatName: ENUM of the format names. Valid values are:

  Valid values for formatName

  • C
  • CPP
  • CSHARP
  • ELIXIR
  • GO
  • HTML
  • JAVA
  • JAVASCRIPT
  • JSON
  • KOTLIN
  • LATEX
  • MARKDOWN
  • PHP
  • PYTHON
  • RUBY
  • RUST
  • SCALA
  • SQL
  • SWIFT
  • TYPESCRIPT
  • XML

• trimWhitespace: (Optional) Whether to trim whitespace from chunks. The default is TRUE.
• keepSeparator: (Optional) Whether to keep the separator in the chunks. The default is FALSE.
• separatorPosition: (Optional) The position of the separator. Valid values are START or END. The default is START. START means place the separator at the start of the following chunk, and END means place the separator at the end of the previous chunk.

Example

-- returns ['def hello_world():\n print("Hello, World!")', '# Call the function\nhello_world()']
SELECT ML_FILE_FORMAT_TEXT_SPLITTER('def hello_world():\n print("Hello, World!")\n\n# Call the function\nhello_world()\n', 50, 0, 'PYTHON');

ML_LABEL_ENCODER

Encodes categorical variables into numerical labels.

Syntax

ML_LABEL_ENCODER(input, categories [, includeZeroLabel])

Description

The ML_LABEL_ENCODER function encodes categorical variables into numerical labels. Each unique category is assigned a unique integer label.

Arguments

• input: Input value to encode.
  • If the input value is NULL, NaN, or Infinity, it is considered in the unknown category, which is given the 0 label.
  • If the input value is not one of the categories, it is labeled as -1 or 0 depending on includeZeroLabel: -1 if includeZeroLabel is TRUE and 0 if includeZeroLabel is FALSE.
• categories: Arrays of category values to encode input value to. Category values must be the same type as the input value.
  • If the categories array is empty, all inputs are considered to be in the unknown category, which is given the 0 label.
  • The categories array can’t be NULL, or an exception is thrown.
  • The categories array can’t have NULL or duplicate values, or an exception is thrown.
  • The categories array must be sorted in ascending lexicographical order, or an exception is thrown.
• includeZeroLabel: (Optional) The start index for valid categories is 0. The default is FALSE.
  • If includeZeroLabel is TRUE, the valid categories index starts at 0, and unknown values are labeled as -1.
  • If includeZeroLabel is FALSE, the valid categories index starts at 1, and unknown values are labeled as 0.

Example

-- returns 1
SELECT ML_LABEL_ENCODER('abc', ARRAY['abc', 'def', 'efg', 'hikj']);

-- returns 0
SELECT ML_LABEL_ENCODER('abc', ARRAY['abc', 'def', 'efg', 'hikj'], TRUE );

ML_MAX_ABS_SCALER

Scales numerical values by their maximum absolute value.

Syntax

ML_MAX_ABS_SCALER(value, absoluteMax)

Description

The ML_MAX_ABS_SCALER function scales numerical values by dividing them by the maximum absolute value. This preserves zero entries in sparse data.

Arguments

• value: Numerical expression to be scaled. If the input value is NULL, NaN, or Infinity, it is returned as is.
• absoluteMax: Absolute Maximum value of the feature data seen in the dataset.
  • If absoluteMax is NULL or NaN, an exception is thrown.
  • If absoluteMax is Infinity, 0 is returned.
  • If absoluteMax is 0, the scaled value is returned as is.

Example

-- returns 0.2
SELECT ML_MAX_ABS_SCALER(1, 5);

ML_MIN_MAX_SCALER

Scales numerical values to a specified range using min-max normalization.

Syntax

ML_MIN_MAX_SCALER(value, min, max)

Description

The ML_MIN_MAX_SCALER function scales numerical values to a specified range using min-max normalization. The function transforms values to the range [0, 1] by default, or to a custom range if min and max are specified.

Arguments

• value: Numerical expression to be scaled. If the input value is NULL, NaN, or Infinity, it is returned as is.
  • If value > max, it is set to 1.0.
  • If value < min, it is set to 0.0.
  • If max == min, the range is set to 1.0 to avoid division by zero.
• min: Minimum value of the feature data seen in the dataset. If min is NULL, NaN, or Infinity, an exception is thrown.
• max: Maximum value of the feature data seen in the dataset.
  • If max is NULL, NaN, or Infinity, an exception is thrown.
  • If max < min, an exception is thrown.

Example

-- returns 0.25
SELECT ML_MIN_MAX_SCALER(2, 1, 5);

ML_NGRAMS

Generates n-grams from an array of strings.

Syntax

ML_NGRAMS(input [, nValue] [, separator])

Description

The ML_NGRAMS function generates n-grams from an array of strings. N-grams are contiguous sequences of n items from a given sample of text.

The ordering of the returned output is the same as the input array.

Arguments

• input: Array of CHAR or VARCHAR to return n-gram for.
  • If the input array has NULL, it is ignored while forming N-GRAMS.
  • If the input array is NULL or empty, an empty N-GRAMS array is returned.
  • Empty strings in the input array are treated as is.
  • Strings with only whitespace are treated as empty strings.
• nValue: (Optional) N value of n-gram function. The default is 2.
  • If nValue < 1, an exception is thrown.
  • If nValue > input.size(), an empty N-GRAMS array is returned.
• separator: (Optional) Characters to join n-gram values with. The default is whitespace.

Example

-- returns ['ab', 'cd', 'de', 'pwe']
SELECT ML_NGRAMS(ARRAY['ab', 'cd', 'de', 'pwe'], 1, '#');

-- returns ['ab#cd', 'cd#de']
SELECT ML_NGRAMS(ARRAY['ab','cd','de', NULL], 2, '#');

ML_NORMALIZER

Normalizes numerical values using p-norm normalization.

Syntax

ML_NORMALIZER(value, normValue)

Description

The ML_NORMALIZER function normalizes numerical values using p-norm normalization. This scales each sample to have unit norm.

Arguments

• value: Numerical expression to be scaled. If the input value is NULL, NaN, or Infinity, it is returned as is.
• normValue: Calculated norm value of the feature data using p-norm.
  • If normValue is NULL or NaN, an exception is thrown.
  • If normValue is Infinity, 0 is returned.
  • If normValue is 0, which is only possible when all the values are 0, the input value is returned as is.

Example

-- returns 0.6
SELECT ML_NORMALIZER(3.0, 5.0);

ML_ONE_HOT_ENCODER

Encodes categorical variables into a binary vector representation.

Syntax

ML_ONE_HOT_ENCODER(input, categories [, dropLast] [, handleUnknown])

Description

The ML_ONE_HOT_ENCODER function encodes categorical variables into a binary vector representation. Each category is represented by a binary vector where only one element is 1 and the rest are 0.

Arguments

• input: Input value to encode. If the input value is NULL, it is considered to be in the unknown category.
• categories: Array of category values to encode input value to. The input argument must be of same type as the categories array.
  • If the categories array is empty, an exception is thrown.
  • The categories array can’t be NULL, or an exception is thrown.
  • The categories array can’t have NULL or duplicate values, or an exception is thrown.
• dropLast: (Optional) Whether to drop the last category. The default is TRUE. By default, the last category is dropped, to prevent perfectly collinear features.
• handleUnknown: (Optional) ERROR, IGNORE, KEEP options to indicate how to handle unknown values. The default is IGNORE.
  • If handleUnknown is ERROR, an exception is thrown when the input is an unknown value.
  • If handleUnknown is IGNORE, unknown values are ignored and values of all the columns are 0.
  • If handleUnknown is KEEP, the unknown category column has value 1.
  • If handleUnknown is KEEP, the last column is for the unknown category.

Example

-- returns [1, 0, 0, 0]
SELECT ML_ONE_HOT_ENCODER('abc', ARRAY['abc', 'def', 'efg', 'hikj']);

-- returns [0, 0, 0, 0, 1]
SELECT ML_ONE_HOT_ENCODER('abcd', ARRAY['abc', 'def', 'efg', 'hik'], TRUE, 'KEEP' );

ML_RECURSIVE_TEXT_SPLITTER

Splits text into chunks using multiple separators recursively.

Syntax

ML_RECURSIVE_TEXT_SPLITTER(text, chunkSize, chunkOverlap [, separators]
[, isSeparatorRegex] [, trimWhitespace] [, keepSeparator]
[, separatorPosition])

Description

The ML_RECURSIVE_TEXT_SPLITTER function splits text into chunks using multiple separators recursively. It starts with the first separator and recursively applies subsequent separators if chunks are still too large.

If any argument other than text is NULL, an exception is thrown.

The returned array of chunks has the same order as the input.

Arguments

• text: The input text to be split. If the input text is NULL, it is returned as is.
• chunkSize: The size of each chunk. If chunkSize < 0 or chunkOverlap > chunkSize, an exception is thrown.
• chunkOverlap: The number of overlapping characters between chunks. If chunkOverlap < 0, an exception is thrown.
• separators: (Optional) The list of separators used for splitting. The default is ["\n\n", "\n", " ", ""]
• isSeparatorRegex: (Optional) Whether the separator is a regex pattern. The default is FALSE
• trimWhitespace: (Optional) Whether to trim whitespace from chunks. The default is TRUE
• keepSeparator: (Optional) Whether to keep the separator in the chunks. The default is FALSE
• separatorPosition: (Optional) The position of the separator. Valid values are START or END. The default is START. START means place the separator at the start of the following chunk, and END means place the separator at the end of the previous chunk.

Example

-- returns ['Hello', '. world', '!']
SELECT ML_RECURSIVE_TEXT_SPLITTER('Hello. world!', 0, 0, ARRAY['[!]','[.]'], TRUE, TRUE, TRUE, 'START');

ML_ROBUST_SCALER

Scales numerical values using statistics that are robust to outliers.

Syntax

ML_ROBUST_SCALER(value, median, firstQuartile, thirdQuartile [,
withCentering, withScaling)

Description

The ML_ROBUST_SCALER function scales numerical values using statistics that are robust to outliers. It removes the median and scales the data according to the quantile range.

Arguments

• value: Numerical expression to be scaled. If the input value is NULL, NaN, or Infinity, it is returned as is.
• median: Median of the feature data seen in the training dataset. If median is NULL, NaN, or Infinity, an exception is thrown.
• firstQuartile: First Quartile of feature data seen in the dataset. If firstQuartile is NULL, NaN, or Infinity, an exception is thrown.
• thirdQuartile: Third Quartile of feature data seen in the dataset.
  • If thirdQuartile is NULL, NaN, or Infinity, an exception is thrown.
  • If thirdQuartile - firstQuartile = 0, the range is set to 1.0 to avoid division by zero.
• withCentering: (Optional) Boolean value indicating to center the numerical value using median before scaling. The default is TRUE. If withCentering is FALSE, the median value is ignored.
• withScaling: (Optional) Boolean value indicating to scale the numerical value using IQR after centering. The default is TRUE. If withScaling is FALSE, the firstQuartile and thirdQuartile values are ignored.

Example

-- returns 0.3333333333333333
SELECT ML_ROBUST_SCALER(2, 1, 0, 3, TRUE, TRUE);

ML_STANDARD_SCALER

Standardizes numerical values by removing the mean and scaling to unit variance.

Syntax

ML_STANDARD_SCALER(value, mean, standardDeviation [, withCentering]
[, withScaling])

Description

The ML_STANDARD_SCALER function standardizes numerical values by removing the mean and scaling to unit variance. This is useful for features that follow a normal distribution.

Arguments

• value: Numerical expression to be scaled. If the input value is NULL, NaN or Infinity, it is returned as is.
• mean: Mean of the feature data seen in the dataset. If mean is NULL, NaN or Infinity, an exception is thrown.
• standardDeviation: Standard Deviation of the feature data seen in the dataset. If standardDeviation is NULL or NaN, an exception is thrown.
  • If standardDeviation is Infinity, 0 is returned.
  • If standardDeviation is 0, the value does not need to be scaled, so it is returned as is.
• withCentering: (Optional) Boolean value indicating to center the numerical value using mean before scaling. The default is TRUE. If withCentering is FALSE, the mean value is ignored.
• withScaling: (Optional) Boolean value indicating to scale the numerical value using std after centering. The default is TRUE. If withScaling is FALSE, the standardDeviation value is ignored.

Example

-- returns 0.2
SELECT ML_STANDARD_SCALER(2, 1, 5, TRUE, TRUE);

Other built-in functions

• Aggregate Functions
• Collection Functions
• Comparison Functions
• Conditional Functions
• Datetime Functions
• Hash Functions
• JSON Functions
• ML Preprocessing Functions
• Model Inference Functions
• Numeric Functions
• String Functions
• Table API Functions

Related content

• AI Model Inference Functions
• Build AI with Flink SQL
• Flink SQL Queries

Note

This website includes content developed at the Apache Software Foundation under the terms of the Apache License v2.