PyDeequ

PyDeequ is a Python API for Deequ, a library built on top of Apache Spark for defining "unit tests for data", which measure data quality in large datasets. PyDeequ is written to support usage of Deequ in Python.

There are 4 main components of Deequ, and they are:

• Metrics Computation:
  • Profiles leverages Analyzers to analyze each column of a dataset.
  • Analyzers serve here as a foundational module that computes metrics for data profiling and validation at scale.
• Constraint Suggestion:
  • Specify rules for various groups of Analyzers to be run over a dataset to return back a collection of constraints suggested to run in a Verification Suite.
• Constraint Verification:
  • Perform data validation on a dataset with respect to various constraints set by you.
• Metrics Repository
  • Allows for persistence and tracking of Deequ runs over time.

🎉 Announcements 🎉

We've release a blogpost on integrating PyDeequ onto AWS leveraging services such as AWS Glue, Athena, and SageMaker! Check it out: Monitor data quality in your data lake using PyDeequ and AWS Glue.

Quickstart

The following will quickstart you with some basic usage. For more in-depth examples, take a look in the tutorials/ directory for executable Jupyter notebooks of each module. For documentation on supported interfaces, view the documentation.

Installation

You can install PyDeequ via pip.

pip install pydeequ

Set up a PySpark session

from pyspark.sql import SparkSession, Row
import pydeequ

spark = (SparkSession
    .builder
    .config("spark.jars.packages", pydeequ.deequ_maven_coord)
    .config("spark.jars.excludes", pydeequ.f2j_maven_coord)
    .getOrCreate())

df = spark.sparkContext.parallelize([
            Row(a="foo", b=1, c=5),
            Row(a="bar", b=2, c=6),
            Row(a="baz", b=3, c=None)]).toDF()

Analyzers

from pydeequ.analyzers import *

analysisResult = AnalysisRunner(spark) \
                    .onData(df) \
                    .addAnalyzer(Size()) \
                    .addAnalyzer(Completeness("b")) \
                    .run()
                    
analysisResult_df = AnalyzerContext.successMetricsAsDataFrame(spark, analysisResult)
analysisResult_df.show()

Profile

from pydeequ.profiles import *

result = ColumnProfilerRunner(spark) \
    .onData(df) \
    .run()

for col, profile in result.profiles.items():
    print(profile)

Constraint Suggestions

from pydeequ.suggestions import *

suggestionResult = ConstraintSuggestionRunner(spark) \
             .onData(df) \
             .addConstraintRule(DEFAULT()) \
             .run()

# Constraint Suggestions in JSON format
print(suggestionResult) 

Constraint Verification

from pydeequ.checks import *
from pydeequ.verification import *

check = Check(spark, CheckLevel.Warning, "Review Check")

checkResult = VerificationSuite(spark) \
    .onData(df) \
    .addCheck(
        check.hasSize(lambda x: x >= 3) \
        .hasMin("b", lambda x: x == 0) \
        .isComplete("c")  \
        .isUnique("a")  \
        .isContainedIn("a", ["foo", "bar", "baz"]) \
        .isNonNegative("b")) \
    .run()
    
checkResult_df = VerificationResult.checkResultsAsDataFrame(spark, checkResult)
checkResult_df.show()

Repository

Save to a Metrics Repository by adding the useRepository() and saveOrAppendResult() calls to your Analysis Runner.

from pydeequ.repository import *
from pydeequ.analyzers import *

metrics_file = FileSystemMetricsRepository.helper_metrics_file(spark, 'metrics.json')
repository = FileSystemMetricsRepository(spark, metrics_file)
key_tags = {'tag': 'pydeequ hello world'}
resultKey = ResultKey(spark, ResultKey.current_milli_time(), key_tags)

analysisResult = AnalysisRunner(spark) \
    .onData(df) \
    .addAnalyzer(ApproxCountDistinct('b')) \
    .useRepository(repository) \
    .saveOrAppendResult(resultKey) \
    .run()

To load previous runs, use the repository object to load previous results back in.

result_metrep_df = repository.load() \
    .before(ResultKey.current_milli_time()) \ 
    .forAnalyzers([ApproxCountDistinct('b')]) \
    .getSuccessMetricsAsDataFrame()

Contributing

Please refer to the contributing doc for how to contribute to PyDeequ.

License

This library is licensed under the Apache 2.0 License.