Snowpark Checkpoints library: Collectors¶

The snowpark-checkpoints-collectors package offers a function for extracting information from the PySpark DataFrames. You can then use that data to validate against the converted Snowpark DataFrames to ensure behavioral equivalence.

To insert a new checkpoint collection point, use the following function:

Function signature:
```
def collect_dataframe_checkpoint(df: SparkDataFrame,
  checkpoint_name: str,
  sample: Optional[float],
  mode: Optional[CheckpointMode],
  output_path: Optional[str]) -> None:
```
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Function parameters:
- df: The PySpark DataFrame
- checkpoint_name: The name of the checkpoint
  
  Starts with a letter (A-Z, a-z) or an underscore (_) and contains only letters, underscores, and numbers (0-9).
- sample: (Optional) The sample size
  
  The default value is 1.0 (entire PySpark DataFrame) in a range from 0 to 1.0.
- mode: (Optional) The execution mode
  
  The options are SCHEMA (default) and DATAFRAME.
- output_path: (Optional) The path to the file where the checkpoint is saved
  
  The default value is the current working directory.

The collection process generates a JSON output file, called checkpoint_collection_result.json, that contains the following information about the result for each collection point:

A timestamp for when the collection point started
The relative path of the file where the collection point is
The line of code of the file where the collection point is
The name of the collection point checkpoint
The result of the collection point (fail or pass)

Schema inference collected data mode (Schema)¶

This is the default mode, which leverages Pandera schema inference to obtain the metadata and checks that will be evaluated for the specified DataFrame. This mode also collects custom data from columns of the DataFrame based on the PySpark type.

The column data and checks are collected based on the PySpark type of the column (see the following tables). For any column, no matter its type, the custom data collected includes the name of the column, the type of the column, nullable, the count of rows, the count of not null rows, and the count of null rows.

Custom data is collected based on the PySpark type of the column¶
Column type	Custom data collected
Numeric (`byte`, `short`, `integer`, `long`, `float` and `double`)	Minimum value; maximum value; mean value; decimal precision (in case of integer type, the value is zero); standard deviation
Date	Minimum value; maximum value; date format (%Y-%m-%d)
DayTimeIntervalType and YearMonthIntervalType	Minimum value; maximum value
Timestamp	Minimum value; maximum value; date format (%Y-%m-%dH:%M:%S)
Timestamp ntz	Minimum value; maximum value; date format (%Y-%m-%dT%H:%M:%S%z)
String	Minimum length value; maximum length value
Char	PySpark handles any literal as a string type; therefore, char is not a valid type.
Varchar	PySpark handles any literal as a string type; therefore, Varchar is not a valid type.
Decimal	Minimum value; maximum value; mean value; decimal precision
Array	Type of the value; if allowed, null as an element; proportion of null values; maximum array size; minimum array size; mean size of arrays; whether all arrays have the same size
Binary	Maximum size; minimum size; mean size; whether all elements have the same size
Map	Type of the key; type of the value; if allowed, null as a value; proportion of null values; maximum map size; minimum map size; mean map size; whether all maps have the same size
Null	NullType represents None because the type data cannot be determined; therefore, it is not possible to get information from this type.
Struct	The metadata of the struct for each structField: `name`, `type`, `nullable`, `rows count`, `rows not null count` and `rows null count`. It is an array.

It also defines a set of predefined validation checks for each data type detailed in the following table:

Checks are collected based on the type of the column¶
Type	Pandera checks	Additional checks
Boolean	Each value is True or False.	The count of True and False values
Numeric (`byte`, `short`, `integer`, `long`, `float` and `double`)	Each value is in the range of min value and max value.	Decimal precision; mean value; standard deviation
Date	N/A	Minimum and maximum values
Timestamp	Each value is in the range of min value and max value.	The format of the value
Timestamp ntz	Each value is in the range of min value and max value.	The format of the value
String	Each value length is in the range of min and max length.	None
Char	PySpark handles any literal as a string type; therefore, `char` is not a valid type.
Varchar	PySpark handles any literal as a string type; therefore, `Varchar` is not a valid type.
Decimal	N/A	N/A
Array	N/A	None
Binary	N/A	None
Map	N/A	None
Null	N/A	N/A
Struct	N/A	None

This mode allows the user to define a sample of a DataFrame to collect, but it is optional. By default, the collection works with the entire DataFrame. The size of the sample must represent the population statistically.

Pandera can only infer the schema of a pandas DataFrame, which implies that the PySpark DataFrame must be converted into a pandas DataFrame, which can affect the columns’ type resolutions. In particular, pandera infers the following PySpark types as object types: string, array, map, null, struct, and binary.

The output of this mode is a JSON file for each collected DataFrame, and the name of the file is the same as the checkpoint. This file contains information related to the schema and has two sections:

The Pandera schema section contains the data inferred by pandera such as name, type (pandas), whether the column allows null values, and other information for each column, and checks whether the column is based on the PySpark type. It is a DataFrameSchema object of pandera.
The custom data section is an array of the custom data collected by each column based on the PySpark type.

Note

The collection package might have memory issues when processing large PySpark DataFrames. To work with a subset of the data instead of the entire PySpark DataFrame, you can set the sample parameter in the collection function to a value between 0.0 and 1.0.

DataFrame collected data mode (DataFrame)¶

This mode collects the data of the PySpark DataFrame. In this case, the mechanism saves all data of the given DataFrame in parquet format. Using the default user Snowflake connection, it tries to upload the parquet files into the Snowflake temporal stage and create a table based on the information in the stage. The name of the file and the table are the same as the checkpoint.

The output of this mode is a parquet file result of the DataFrame saved and a table with the DataFrame data in the default Snowflake configuration connection.