A pyspark lib to validate data quality
ronald-smith-angel, updated
🕥
2022-11-11 07:51:10
Owl Data Sanitizer: A light Spark data validation framework
This is a small framework for data quality validation. This first version works reading spark dataframes from local datasources like local system, s3 or hive and delivers hive tables with quality reports.
Let's follow this example:
Input data from a hive table:
+----------+--------------+--------+---------+------------------+---------+
|GENERAL_ID| NAME| CODE|ADDR_DESC|ULTIMATE_PARENT_ID|PARENT_ID|
+----------+--------------+--------+---------+------------------+---------+
| 1|Dummy 1 Entity|12000123| null| null| null|
| 2| null| null| null| 2| 2|
| 3| null|12000123| null| 3| 3|
| 4| 1| 1| null| 4| 4|
| 5| 1|12000123| null| 5| 5|
| 6| null| 3| null| 6| 6|
| null| null|12000123| null| 11| 7|
| 7| 2| null| null| 8| 8|
+----------+--------------+--------+---------+------------------+---------+
following this validation config with 4 sections:
source_tableincluding the table metadata.correctness_validationsincluding correctness validations per column. the rule must be a valid spark SQL expression.parent_children_constraintsincluding children parent constrains. This means that any parent id should be valid id.compare_related_tables_listincluding comparison with other tables or the same table in other environments.
{
"source_table": {
"name": "test.data_test",
"id_column": "GENERAL_ID",
"unique_column_group_values_per_table": ["GENERAL_ID", "ULTIMATE_PARENT_ID"],
"fuzzy_deduplication_distance": 0,
"output_correctness_table": "test.data_test_correctness",
"output_completeness_table": "test.data_test_completeness",
"output_comparison_table": "test.data_test_comparison"
},
"correctness_validations": [
{
"column": "CODE",
"rule": "CODE is not null and CODE != '' and CODE != 'null'"
},
{
"column": "NAME",
"rule": "NAME is not null and NAME != '' and NAME != 'null'"
},
{
"column": "GENERAL_ID",
"rule": "GENERAL_ID is not null and GENERAL_ID != '' and GENERAL_ID != 'null' and CHAR_LENGTH(GENERAL_ID) < 4"
}
],
"completeness_validations": [
{
"column": "OVER_ALL_COUNT",
"rule": "OVER_ALL_COUNT <= 7"
}
],
"parent_children_constraints": [
{
"column": "GENERAL_ID",
"parent": "ULTIMATE_PARENT_ID"
},
{
"column": "GENERAL_ID",
"parent": "PARENT_ID"
}
],
"compare_related_tables_list": ["test.diff_df", "test.diff_df_2"]
}
Therefore, these results are delivered in two output hive tables:
a). Correctness Report.
- You will see and output col per validation col showing either 1 when there is error or 0 when is clean.
- Sum of error per columns.
+----------+-------------+-------------+-------------------+--------------------------------------+-----------------------------+-------------+--------------------------+-----------------+-----------------+-----------------------+------------------------------------------+---------------------------------+-----------------+
|GENERAL_ID|IS_ERROR_CODE|IS_ERROR_NAME|IS_ERROR_GENERAL_ID|IS_ERROR_GENERAL_ID_ULTIMATE_PARENT_ID|IS_ERROR_GENERAL_ID_PARENT_ID|IS_ERROR__ROW|dt |IS_ERROR_CODE_SUM|IS_ERROR_NAME_SUM|IS_ERROR_GENERAL_ID_SUM|IS_ERROR_GENERAL_ID_ULTIMATE_PARENT_ID_SUM|IS_ERROR_GENERAL_ID_PARENT_ID_SUM|IS_ERROR__ROW_SUM|
+----------+-------------+-------------+-------------------+--------------------------------------+-----------------------------+-------------+--------------------------+-----------------+-----------------+-----------------------+------------------------------------------+---------------------------------+-----------------+
|null |0 |1 |1 |1 |0 |1 |2020-04-17 09:39:04.783505|2 |4 |1 |2 |1 |5 |
|3 |0 |1 |0 |0 |0 |1 |2020-04-17 09:39:04.783505|2 |4 |1 |2 |1 |5 |
|7 |1 |0 |0 |1 |1 |1 |2020-04-17 09:39:04.783505|2 |4 |1 |2 |1 |5 |
|5 |0 |0 |0 |0 |0 |0 |2020-04-17 09:39:04.783505|2 |4 |1 |2 |1 |5 |
|6 |0 |1 |0 |0 |0 |1 |2020-04-17 09:39:04.783505|2 |4 |1 |2 |1 |5 |
|4 |0 |0 |0 |0 |0 |0 |2020-04-17 09:39:04.783505|2 |4 |1 |2 |1 |5 |
|2 |1 |1 |0 |0 |0 |1 |2020-04-17 09:39:04.783505|2 |4 |1 |2 |1 |5 |
|1 |0 |0 |0 |0 |0 |0 |2020-04-17 09:39:04.783505|2 |4 |1 |2 |1 |5 |
+----------+-------------+-------------+-------------------+--------------------------------------+-----------------------------+-------------+--------------------------+-----------------+-----------------+-----------------------+------------------------------------------+---------------------------------+-----------------+
b) Completeness Report.
- The overall count of the dataframe.
- Column checking if the overall count is complete, example: IS_ERROR_OVER_ALL_COUNT.
+--------------+-----------------------+--------------------------+
|OVER_ALL_COUNT|IS_ERROR_OVER_ALL_COUNT|dt |
+--------------+-----------------------+--------------------------+
|8 |1 |2020-04-17 09:39:04.783505|
+--------------+-----------------------+--------------------------+
c). Comparison of schema and values with related dataframes.
NOTE: the result includes for now only the ids that are different and a further join with the source data to see differences is needed.
+--------------+----------------------------------+-----------------+------------------+-----------------+--------------------------+
|df |missing_cols_right |missing_cols_left|missing_vals_right|missing_vals_left|dt |
+--------------+----------------------------------+-----------------+------------------+-----------------+--------------------------+
|test.diff_df_2|GENERAL_ID:string,ADDR_DESC:string|GENERAL_ID:int | | |2020-04-17 09:39:07.572483|
|test.diff_df | | |6,7 | |2020-04-17 09:39:07.572483|
+--------------+----------------------------------+-----------------+------------------+-----------------+--------------------------+
Installation
Install owl sanitizer from PyPI:
pip install owl-sanitizer-data-quality
Then you can call the library.
``` from spark_validation.dataframe_validation.dataframe_validator import CreateHiveValidationDF from spark_validation.common.config import Config
spark_session = SparkSession.builder.enableHiveSupport().getOrCreate() with open(PATH_TO_CONFIG_FILE) as f: config = Config.parse(f) CreateHiveValidationDF.validate(spark_session, config) ```
To use in your spark submit command or airflow dag.
- Add
py_files:[https://pypi.org/project/owl-sanitizer-data-quality/latest/]. application:owl-sanitizer-data-quality/latest/src/spark_validation/dataframe_validation/hive_validator.pyapplication_package:https://pypi.org/project/owl-sanitizer-data-quality/latest/owl-sanitizer-data-quality-latest.tar.gzapplication_params:URL_TO_YOUR_REMOTE_CONFIG_FILE
Contact
Please ask questions about technical issues here on GitHub.
Issues
Bump pyspark from 2.4.5 to 3.2.2
opened on 2022-11-11 07:51:09 by dependabot[bot]Bumps pyspark from 2.4.5 to 3.2.2.
Commits
78a5825Preparing Spark release v3.2.2-rc1ba978b3[SPARK-39099][BUILD] Add dependencies to Dockerfile for building Spark releases001d8b0[SPARK-37554][BUILD] Add PyArrow, pandas and plotly to release Docker image d...9dd4c07[SPARK-37730][PYTHON][FOLLOWUP] Split comments to comply pycodestyle checkbc54a3f[SPARK-37730][PYTHON] Replace use of MPLPlot._add_legend_handle with MPLPlot....c5983c1[SPARK-38018][SQL][3.2] Fix ColumnVectorUtils.populate to handle CalendarInte...32aff86[SPARK-39447][SQL][3.2] Avoid AssertionError in AdaptiveSparkPlanExec.doExecu...be891ad[SPARK-39551][SQL][3.2] Add AQE invalid plan check1c0bd4c[SPARK-39656][SQL][3.2] Fix wrong namespace in DescribeNamespaceExec3d084fe[SPARK-39677][SQL][DOCS][3.2] Fix args formatting of the regexp and like func...- Additional commits viewable in compare view
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Bump pyspark from 2.4.5 to 3.2.2 in /lib
opened on 2022-11-11 07:50:26 by dependabot[bot]Bumps pyspark from 2.4.5 to 3.2.2.
Commits
78a5825Preparing Spark release v3.2.2-rc1ba978b3[SPARK-39099][BUILD] Add dependencies to Dockerfile for building Spark releases001d8b0[SPARK-37554][BUILD] Add PyArrow, pandas and plotly to release Docker image d...9dd4c07[SPARK-37730][PYTHON][FOLLOWUP] Split comments to comply pycodestyle checkbc54a3f[SPARK-37730][PYTHON] Replace use of MPLPlot._add_legend_handle with MPLPlot....c5983c1[SPARK-38018][SQL][3.2] Fix ColumnVectorUtils.populate to handle CalendarInte...32aff86[SPARK-39447][SQL][3.2] Avoid AssertionError in AdaptiveSparkPlanExec.doExecu...be891ad[SPARK-39551][SQL][3.2] Add AQE invalid plan check1c0bd4c[SPARK-39656][SQL][3.2] Fix wrong namespace in DescribeNamespaceExec3d084fe[SPARK-39677][SQL][DOCS][3.2] Fix args formatting of the regexp and like func...- Additional commits viewable in compare view
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Bump numpy from 1.18.3 to 1.22.0 in /lib
opened on 2022-06-22 01:47:14 by dependabot[bot]Bumps numpy from 1.18.3 to 1.22.0.
Release notes
Sourced from numpy's releases.
v1.22.0
NumPy 1.22.0 Release Notes
NumPy 1.22.0 is a big release featuring the work of 153 contributors spread over 609 pull requests. There have been many improvements, highlights are:
- Annotations of the main namespace are essentially complete. Upstream is a moving target, so there will likely be further improvements, but the major work is done. This is probably the most user visible enhancement in this release.
- A preliminary version of the proposed Array-API is provided. This is a step in creating a standard collection of functions that can be used across application such as CuPy and JAX.
- NumPy now has a DLPack backend. DLPack provides a common interchange format for array (tensor) data.
- New methods for
quantile,percentile, and related functions. The new methods provide a complete set of the methods commonly found in the literature.- A new configurable allocator for use by downstream projects.
These are in addition to the ongoing work to provide SIMD support for commonly used functions, improvements to F2PY, and better documentation.
The Python versions supported in this release are 3.8-3.10, Python 3.7 has been dropped. Note that 32 bit wheels are only provided for Python 3.8 and 3.9 on Windows, all other wheels are 64 bits on account of Ubuntu, Fedora, and other Linux distributions dropping 32 bit support. All 64 bit wheels are also linked with 64 bit integer OpenBLAS, which should fix the occasional problems encountered by folks using truly huge arrays.
Expired deprecations
Deprecated numeric style dtype strings have been removed
Using the strings
"Bytes0","Datetime64","Str0","Uint32", and"Uint64"as a dtype will now raise aTypeError.(gh-19539)
Expired deprecations for
loads,ndfromtxt, andmafromtxtin npyio
numpy.loadswas deprecated in v1.15, with the recommendation that users usepickle.loadsinstead.ndfromtxtandmafromtxtwere both deprecated in v1.17 - users should usenumpy.genfromtxtinstead with the appropriate value for theusemaskparameter.(gh-19615)
... (truncated)
Commits
4adc87dMerge pull request #20685 from charris/prepare-for-1.22.0-releasefd66547REL: Prepare for the NumPy 1.22.0 release.125304bwipc283859Merge pull request #20682 from charris/backport-204165399c03Merge pull request #20681 from charris/backport-20954f9c45f8Merge pull request #20680 from charris/backport-20663794b36fUpdate armccompiler.pyd93b14eUpdate test_public_api.py7662c07Update init.py311ab52Update armccompiler.py- Additional commits viewable in compare view
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Bump numpy from 1.18.3 to 1.22.0
opened on 2022-06-22 01:46:22 by dependabot[bot]Bumps numpy from 1.18.3 to 1.22.0.
Release notes
Sourced from numpy's releases.
v1.22.0
NumPy 1.22.0 Release Notes
NumPy 1.22.0 is a big release featuring the work of 153 contributors spread over 609 pull requests. There have been many improvements, highlights are:
- Annotations of the main namespace are essentially complete. Upstream is a moving target, so there will likely be further improvements, but the major work is done. This is probably the most user visible enhancement in this release.
- A preliminary version of the proposed Array-API is provided. This is a step in creating a standard collection of functions that can be used across application such as CuPy and JAX.
- NumPy now has a DLPack backend. DLPack provides a common interchange format for array (tensor) data.
- New methods for
quantile,percentile, and related functions. The new methods provide a complete set of the methods commonly found in the literature.- A new configurable allocator for use by downstream projects.
These are in addition to the ongoing work to provide SIMD support for commonly used functions, improvements to F2PY, and better documentation.
The Python versions supported in this release are 3.8-3.10, Python 3.7 has been dropped. Note that 32 bit wheels are only provided for Python 3.8 and 3.9 on Windows, all other wheels are 64 bits on account of Ubuntu, Fedora, and other Linux distributions dropping 32 bit support. All 64 bit wheels are also linked with 64 bit integer OpenBLAS, which should fix the occasional problems encountered by folks using truly huge arrays.
Expired deprecations
Deprecated numeric style dtype strings have been removed
Using the strings
"Bytes0","Datetime64","Str0","Uint32", and"Uint64"as a dtype will now raise aTypeError.(gh-19539)
Expired deprecations for
loads,ndfromtxt, andmafromtxtin npyio
numpy.loadswas deprecated in v1.15, with the recommendation that users usepickle.loadsinstead.ndfromtxtandmafromtxtwere both deprecated in v1.17 - users should usenumpy.genfromtxtinstead with the appropriate value for theusemaskparameter.(gh-19615)
... (truncated)
Commits
4adc87dMerge pull request #20685 from charris/prepare-for-1.22.0-releasefd66547REL: Prepare for the NumPy 1.22.0 release.125304bwipc283859Merge pull request #20682 from charris/backport-204165399c03Merge pull request #20681 from charris/backport-20954f9c45f8Merge pull request #20680 from charris/backport-20663794b36fUpdate armccompiler.pyd93b14eUpdate test_public_api.py7662c07Update init.py311ab52Update armccompiler.py- Additional commits viewable in compare view
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Bump ipython from 7.13.0 to 7.16.3 in /lib
opened on 2022-01-21 20:22:07 by dependabot[bot]Bumps ipython from 7.13.0 to 7.16.3.
Commits
d43c7c7release 7.16.35fa1e40Merge pull request from GHSA-pq7m-3gw7-gq5x8df8971back to dev9f477b7release 7.16.2138f266bring back release helper from master branch5aa3634Merge pull request #13341 from meeseeksmachine/auto-backport-of-pr-13335-on-7...bcae8e0Backport PR #13335: What's new 7.16.28fcdcd3Pin Jedi to <0.17.2.2486838release 7.16.120bdc6ffix conda build- Additional commits viewable in compare view
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Bump ipython from 7.13.0 to 7.16.3
opened on 2022-01-21 20:20:36 by dependabot[bot]Bumps ipython from 7.13.0 to 7.16.3.
Commits
d43c7c7release 7.16.35fa1e40Merge pull request from GHSA-pq7m-3gw7-gq5x8df8971back to dev9f477b7release 7.16.2138f266bring back release helper from master branch5aa3634Merge pull request #13341 from meeseeksmachine/auto-backport-of-pr-13335-on-7...bcae8e0Backport PR #13335: What's new 7.16.28fcdcd3Pin Jedi to <0.17.2.2486838release 7.16.120bdc6ffix conda build- Additional commits viewable in compare view
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