Data Validation with Pyspark SQL ⭐️ (New)#

new in 0.16.0

Apache Spark is an open-source unified analytics engine for large-scale data processing. Spark provides an interface for programming clusters with implicit data parallelism and fault tolerance.

Pyspark is the Python API for Apache Spark, an open source, distributed computing framework and set of libraries for real-time, large-scale data processing.

You can use pandera to validate pyspark.sql.DataFrame objects directly. First, install pandera with the pyspark extra:

pip install pandera[pyspark]

What’s different?#

Compared to the way pandera deals with pandas dataframes, there are some small changes to support the nuances of pyspark SQL and the expectations that users have when working with pyspark SQL dataframes:

  1. The output of schema.validate will produce a dataframe in pyspark SQL even in case of errors during validation. Instead of raising the error, the errors are collected and can be accessed via the dataframe.pandera.errors attribute as shown in this example.


    This design decision is based on the expectation that most use cases for pyspark SQL dataframes means entails a production ETL setting. In these settings, pandera prioritizes completing the production load and saving the data quality issues for downstream rectification.

  2. Unlike the pandera pandas schemas, the default behaviour of the pyspark SQL version for errors is lazy=True, i.e. all the errors would be collected instead of raising at first error instance.

  3. There is no support for lambda based vectorized checks since in spark lambda checks needs UDFs, which is inefficient. However pyspark sql does support custom checks via the register_check_method() decorator.

  4. The custom check has to return a scalar boolean value instead of a series.

  5. In defining the type annotation, there is limited support for default python data types such as int, str, etc. When using the pandera.pyspark API, using pyspark.sql.types based datatypes such as StringType, IntegerType, etc. is highly recommended.

Basic Usage#

In this section, lets look at an end to end example of how pandera would work in a native pyspark implementation.

import pandera.pyspark as pa
import pyspark.sql.types as T

from decimal import Decimal
from pyspark.sql import SparkSession
from pyspark.sql import DataFrame
from pandera.pyspark import DataFrameModel

spark = SparkSession.builder.getOrCreate()

class PanderaSchema(DataFrameModel):
    id: T.IntegerType() = pa.Field(gt=5)
    product_name: T.StringType() = pa.Field(str_startswith="B")
    price: T.DecimalType(20, 5) = pa.Field()
    description: T.ArrayType(T.StringType()) = pa.Field()
    meta: T.MapType(T.StringType(), T.StringType()) = pa.Field()

data = [
    (5, "Bread", Decimal(44.4), ["description of product"], {"product_category": "dairy"}),
    (15, "Butter", Decimal(99.0), ["more details here"], {"product_category": "bakery"}),

spark_schema = T.StructType(
        T.StructField("id", T.IntegerType(), False),
        T.StructField("product", T.StringType(), False),
        T.StructField("price", T.DecimalType(20, 5), False),
        T.StructField("description", T.ArrayType(T.StringType(), False), False),
            "meta", T.MapType(T.StringType(), T.StringType(), False), False
df = spark.createDataFrame(data, spark_schema)
| id|product|   price|         description|                meta|
|  5|  Bread|44.40000|[description of p...|{product_category...|
| 15| Butter|99.00000| [more details here]|{product_category...|

In example above, the PanderaSchema class inherits from the DataFrameModel base class. It has type annotations for 5 fields with 2 of the fields having checks enforced e.g. gt=5 and str_startswith="B".

Just to simulate some schema and data validations, we also defined native spark’s schema spark_schema and enforced it on our dataframe df.

Next, you can use the validate() function to validate pyspark sql dataframes at runtime.

df_out = PanderaSchema.validate(check_obj=df)

After running validate(), the returned object df_out will be a pyspark dataframe extended to hold validation results exposed via a pandera attribute.

Pandera Pyspark Error Report#

new in 0.16.0

You can print the validation results as follows:

import json

df_out_errors = df_out.pandera.errors
print(json.dumps(dict(df_out_errors), indent=4))
    "SCHEMA": {
                "schema": "PanderaSchema",
                "column": "PanderaSchema",
                "check": "column_in_dataframe",
                "error": "column 'product_name' not in dataframe\nRow(id=5, product='Bread', price=Decimal('44.40000'), description=['description of product'], meta={'product_category': 'dairy'})"
        "WRONG_DATATYPE": [
                "schema": "PanderaSchema",
                "column": "description",
                "check": "dtype('ArrayType(StringType(), True)')",
                "error": "expected column 'description' to have type ArrayType(StringType(), True), got ArrayType(StringType(), False)"
                "schema": "PanderaSchema",
                "column": "meta",
                "check": "dtype('MapType(StringType(), StringType(), True)')",
                "error": "expected column 'meta' to have type MapType(StringType(), StringType(), True), got MapType(StringType(), StringType(), False)"
    "DATA": {
        "DATAFRAME_CHECK": [
                "schema": "PanderaSchema",
                "column": "id",
                "check": "greater_than(5)",
                "error": "column 'id' with type IntegerType() failed validation greater_than(5)"

As seen above, the error report is aggregated on 2 levels in a python dict object:

  1. The type of validation: SCHEMA or DATA

  2. The category of errors such as DATAFRAME_CHECK or WRONG_DATATYPE, etc.

This error report is easily consumed by downstream applications such as timeseries visualization of errors over time.


It’s critical to extract errors report from df_out.pandera.errors as any further pyspark operations may reset the attribute.

Granular Control of Pandera’s Execution#

new in 0.16.0

By default, error reports are generated for both schema and data level validation. Adding support for pysqark SQL also comes with more granular control over the execution of Pandera’s validation flow.

This is achieved by introducing configurable settings using environment variables that allow you to control execution at three different levels:

  1. SCHEMA_ONLY: perform schema validations only. It checks that data conforms to the schema definition, but does not perform any data-level validations on dataframe.

  2. DATA_ONLY: perform data-level validations only. It validates that data conforms to the defined checks, but does not validate the schema.

  3. SCHEMA_AND_DATA: (default) perform both schema and data level validations. It runs most exhaustive validation and could be compute intensive.

You can override default behaviour by setting an environment variable from terminal before running the pandera process as:


This will be picked up by pandera to only enforce SCHEMA level validations.

Switching Validation On and Off#

new in 0.16.0

It’s very common in production to enable or disable certain services to save computing resources. We thought about it and thus introduced a switch to enable or disable pandera in production.

You can override default behaviour by setting an environment variable from terminal before running the pandera process as follow:


This will be picked up by pandera to disable all validations in the application.

By default, validations are enabled and depth is set to SCHEMA_AND_DATA which can be changed to SCHEMA_ONLY or DATA_ONLY as required by the use case.

Registering Custom Checks#

pandera already offers an interface to register custom checks functions so that they’re available in the Check namespace. See the extensions document for more information.

Unlike the pandera pandas API, pyspark sql does not support lambda function inside check. It is because to implement lambda functions would mean introducing spark UDF which is expensive operation due to serialization, hence it is better to create native pyspark function.

Note: The output of the function should be a boolean value True for passed and False for failure. Unlike the Pandas version which expect it to be a series of boolean values.

from pandera.extensions import register_check_method
import pyspark.sql.types as T

def new_pyspark_check(pyspark_obj, *, max_value) -> bool:
    """Ensure values of the data are strictly below a maximum value.
    :param max_value: Upper bound not to be exceeded. Must be
        a type comparable to the dtype of the column datatype of pyspark

    cond = col(pyspark_obj.column_name) <= max_value
    return pyspark_obj.dataframe.filter(~cond).limit(1).count() == 0

class Schema(DataFrameModel):

        product: T.StringType()
        code: T.IntegerType() = pa.Field(
                "max_value": 30

Adding Metadata at the Dataframe and Field level#

new in 0.16.0

In real world use cases, we often need to embed additional information on objects. Pandera that allows users to store additional metadata at Field and Schema / Model levels. This feature is designed to provide greater context and information about the data, which can be leveraged by other applications.

For example, by storing details about a specific column, such as data type, format, or units, developers can ensure that downstream applications are able to interpret and use the data correctly. Similarly, by storing information about which columns of a schema are needed for a specific use case, developers can optimize data processing pipelines, reduce storage costs, and improve query performance.

import pyspark.sql.types as T

class PanderaSchema(DataFrameModel):
    """Pandera Schema Class"""

    product_id: T.IntegerType() = pa.Field()
    product_class: T.StringType() = pa.Field(
            "search_filter": "product_pricing",
    product_name: T.StringType() = pa.Field()
    price: T.DecimalType(20, 5) = pa.Field()

    class Config:
        """Config of pandera class"""

        name = "product_info"
        strict = True
        coerce = True
        metadata = {"category": "product-details"}

As seen in above example, product_class field has additional embedded information such as search_filter. This metadata can be leveraged to search and filter multiple schemas for certain keywords.

This is clearly a very basic example, but the possibilities are endless with having metadata at Field and `DataFrame` levels.

We also provided a helper function to extract metadata from a schema as follows:



This feature is available for pyspark.sql and pandas both.