| Name | snowpark-checkpoints-validators JSON |
| Version |
0.1.0rc1
JSON |
| download |
| home_page | None |
| Summary | Migration tools for Snowpark |
| upload_time | 2025-01-15 19:40:03 |
| maintainer | None |
| docs_url | None |
| author | Snowflake Inc. |
| requires_python | <3.12,>=3.9 |
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analytics
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db
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# Snowpark Checkpoints Validators
---
**NOTE**
This package is on Private Preview.
---
**snowpark-checkpoints-validators** is a package designed to validate Snowpark DataFrames against predefined schemas and checkpoints. This package ensures data integrity and consistency by performing schema and data validation checks at various stages of a Snowpark pipeline.
## Features
- Validate Snowpark DataFrames against predefined Pandera schemas.
- Perform custom checks and skip specific checks as needed.
- Generate validation results and log them for further analysis.
- Support for sampling strategies to validate large datasets efficiently.
- Integration with PySpark for cross-validation between Snowpark and PySpark DataFrames.
## Functionalities
### Validate DataFrame Schema from File
The `validate_dataframe_checkpoint` function validates a Snowpark DataFrame against a checkpoint schema file or dataframe.
```python
from snowflake.snowpark_checkpoints.checkpoint import validate_dataframe_checkpoint
validate_dataframe_checkpoint(
df: SnowparkDataFrame,
checkpoint_name: str,
job_context: Optional[SnowparkJobContext] = None,
mode: Optional[CheckpointMode] = CheckpointMode.SCHEMA,
custom_checks: Optional[dict[Any, Any]] = None,
skip_checks: Optional[dict[Any, Any]] = None,
sample_frac: Optional[float] = 1.0,
sample_number: Optional[int] = None,
sampling_strategy: Optional[SamplingStrategy] = SamplingStrategy.RANDOM_SAMPLE,
output_path: Optional[str] = None,
)
```
- `df`: Snowpark DataFrame to validate.
- `checkpoint_name`: Name of the checkpoint schema file or DataFrame.
- `job_context`: Snowpark job context.
- `mode`: Checkpoint mode (schema or data).
- `custom_checks`: Custom checks to perform.
- `skip_checks`: Checks to skip.
- `sample_frac`: Fraction of the DataFrame to sample.
- `sample_number`: Number of rows to sample.
- `sampling_strategy`: Sampling strategy to use.
- `output_path`: Output path for the checkpoint report.
### Usage Example
```python
from snowflake.snowpark import Session
from snowflake.snowpark import DataFrame as SnowparkDataFrame
from snowflake.snowpark_checkpoints.checkpoint import validate_dataframe_checkpoint
session = Session.builder.getOrCreate()
df = session.read.format("csv").load("data.csv")
validate_dataframe_checkpoint(
df,
"schema_checkpoint",
job_context=session,
mode=CheckpointMode.SCHEMA,
sample_frac=0.1,
sampling_strategy=SamplingStrategy.RANDOM_SAMPLE
)
```
### Check with Spark Decorator
The `check_with_spark` decorator converts any Snowpark DataFrame arguments to a function, samples them, and converts them to PySpark DataFrames. It then executes a provided Spark function and compares the outputs between the two implementations.
```python
from snowflake.snowpark_checkpoints.spark_migration import check_with_spark
@check_with_spark(
job_context: Optional[SnowparkJobContext],
spark_function: Callable,
checkpoint_name: str,
sample_number: Optional[int] = 100,
sampling_strategy: Optional[SamplingStrategy] = SamplingStrategy.RANDOM_SAMPLE,
check_dtypes: Optional[bool] = False,
check_with_precision: Optional[bool] = False,
output_path: Optional[str] = None,
)
def snowpark_fn(df: SnowparkDataFrame):
...
```
- `job_context`: Snowpark job context.
- `spark_function`: PySpark function to execute.
- `checkpoint_name`: Name of the check.
- `sample_number`: Number of rows to sample.
- `sampling_strategy`: Sampling strategy to use.
- `check_dtypes`: Check data types.
- `check_with_precision`: Check with precision.
- `output_path`: Output path for the checkpoint report.
### Usage Example
```python
from snowflake.snowpark import Session
from snowflake.snowpark import DataFrame as SnowparkDataFrame
from snowflake.snowpark_checkpoints.spark_migration import check_with_spark
session = Session.builder.getOrCreate()
df = session.read.format("csv").load("data.csv")
@check_with_spark(
job_context=session,
spark_function=lambda df: df.withColumn("COLUMN1", df["COLUMN1"] + 1),
checkpoint_name="Check_Column1_Increment",
sample_number=100,
sampling_strategy=SamplingStrategy.RANDOM_SAMPLE,
)
def increment_column1(df: SnowparkDataFrame):
return df.with_column("COLUMN1", df["COLUMN1"] + 1)
increment_column1(df)
```
### Pandera Snowpark Decorators
The decorators `@check_input_schema` and `@check_output_schema` allow for sampled schema validation of Snowpark DataFrames in the input arguments or in the return value.
```python
from snowflake.snowpark_checkpoints.checkpoint import check_input_schema, check_output_schema
@check_input_schema(
pandera_schema: DataFrameSchema,
checkpoint_name: str,
sample_frac: Optional[float] = 1.0,
sample_number: Optional[int] = None,
sampling_strategy: Optional[SamplingStrategy] = SamplingStrategy.RANDOM_SAMPLE,
job_context: Optional[SnowparkJobContext],
output_path: Optional[str] = None,
)
def snowpark_fn(df: SnowparkDataFrame):
...
@check_output_schema(
pandera_schema: DataFrameSchema,
checkpoint_name: str,
sample_frac: Optional[float] = 1.0,
sample_number: Optional[int] = None,
sampling_strategy: Optional[SamplingStrategy] = SamplingStrategy.RANDOM_SAMPLE,
job_context: Optional[SnowparkJobContext],
output_path: Optional[str] = None,
)
def snowpark_fn(df: SnowparkDataFrame):
...
```
- `pandera_schema`: Pandera schema to validate.
- `checkpoint_name`: Name of the checkpoint schema file or DataFrame.
- `sample_frac`: Fraction of the DataFrame to sample.
- `sample_number`: Number of rows to sample.
- `sampling_strategy`: Sampling strategy to use.
- `job_context`: Snowpark job context.
- `output_path`: Output path for the checkpoint report.
### Usage Example
The following will result in a Pandera `SchemaError`:
```python
from pandas import DataFrame as PandasDataFrame
from pandera import DataFrameSchema, Column, Check
from snowflake.snowpark import Session
from snowflake.snowpark import DataFrame as SnowparkDataFrame
from snowflake.snowpark_checkpoints.checkpoint import check_output_schema
df = PandasDataFrame({
"COLUMN1": [1, 4, 0, 10, 9],
"COLUMN2": [-1.3, -1.4, -2.9, -10.1, -20.4],
})
out_schema = DataFrameSchema({
"COLUMN1": Column(int8, Check(lambda x: 0 <= x <= 10, element_wise=True)),
"COLUMN2": Column(float, Check(lambda x: x < -1.2)),
})
@check_output_schema(out_schema, "output_schema_checkpoint")
def preprocessor(dataframe: SnowparkDataFrame):
return dataframe.with_column("COLUMN1", lit('Some bad data yo'))
session = Session.builder.getOrCreate()
sp_dataframe = session.create_dataframe(df)
preprocessed_dataframe = preprocessor(sp_dataframe)
```
## License
This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for more details.
Raw data
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"maintainer_email": null,
"keywords": "Snowflake, Snowpark, analytics, cloud, database, db",
"author": "Snowflake Inc.",
"author_email": null,
"download_url": "https://files.pythonhosted.org/packages/b0/95/822b77233e01b3f79c5559b9b692c4006237014b3869858cbffb0aeadefe/snowpark_checkpoints_validators-0.1.0rc1.tar.gz",
"platform": null,
"description": "# Snowpark Checkpoints Validators\n\n---\n**NOTE**\n\nThis package is on Private Preview.\n\n---\n\n**snowpark-checkpoints-validators** is a package designed to validate Snowpark DataFrames against predefined schemas and checkpoints. This package ensures data integrity and consistency by performing schema and data validation checks at various stages of a Snowpark pipeline.\n\n## Features\n\n- Validate Snowpark DataFrames against predefined Pandera schemas.\n- Perform custom checks and skip specific checks as needed.\n- Generate validation results and log them for further analysis.\n- Support for sampling strategies to validate large datasets efficiently.\n- Integration with PySpark for cross-validation between Snowpark and PySpark DataFrames.\n\n## Functionalities\n\n### Validate DataFrame Schema from File\n\nThe `validate_dataframe_checkpoint` function validates a Snowpark DataFrame against a checkpoint schema file or dataframe.\n\n```python\nfrom snowflake.snowpark_checkpoints.checkpoint import validate_dataframe_checkpoint\n\nvalidate_dataframe_checkpoint(\n df: SnowparkDataFrame,\n checkpoint_name: str,\n job_context: Optional[SnowparkJobContext] = None,\n mode: Optional[CheckpointMode] = CheckpointMode.SCHEMA,\n custom_checks: Optional[dict[Any, Any]] = None,\n skip_checks: Optional[dict[Any, Any]] = None,\n sample_frac: Optional[float] = 1.0,\n sample_number: Optional[int] = None,\n sampling_strategy: Optional[SamplingStrategy] = SamplingStrategy.RANDOM_SAMPLE,\n output_path: Optional[str] = None,\n)\n```\n\n- `df`: Snowpark DataFrame to validate.\n- `checkpoint_name`: Name of the checkpoint schema file or DataFrame.\n- `job_context`: Snowpark job context.\n- `mode`: Checkpoint mode (schema or data).\n- `custom_checks`: Custom checks to perform.\n- `skip_checks`: Checks to skip.\n- `sample_frac`: Fraction of the DataFrame to sample.\n- `sample_number`: Number of rows to sample.\n- `sampling_strategy`: Sampling strategy to use.\n- `output_path`: Output path for the checkpoint report.\n\n### Usage Example\n\n```python\nfrom snowflake.snowpark import Session\nfrom snowflake.snowpark import DataFrame as SnowparkDataFrame\nfrom snowflake.snowpark_checkpoints.checkpoint import validate_dataframe_checkpoint\n\nsession = Session.builder.getOrCreate()\ndf = session.read.format(\"csv\").load(\"data.csv\")\n\nvalidate_dataframe_checkpoint(\n df,\n \"schema_checkpoint\",\n job_context=session,\n mode=CheckpointMode.SCHEMA,\n sample_frac=0.1,\n sampling_strategy=SamplingStrategy.RANDOM_SAMPLE\n)\n```\n\n### Check with Spark Decorator\n\nThe `check_with_spark` decorator converts any Snowpark DataFrame arguments to a function, samples them, and converts them to PySpark DataFrames. It then executes a provided Spark function and compares the outputs between the two implementations.\n\n```python\nfrom snowflake.snowpark_checkpoints.spark_migration import check_with_spark\n\n@check_with_spark(\n job_context: Optional[SnowparkJobContext],\n spark_function: Callable,\n checkpoint_name: str,\n sample_number: Optional[int] = 100,\n sampling_strategy: Optional[SamplingStrategy] = SamplingStrategy.RANDOM_SAMPLE,\n check_dtypes: Optional[bool] = False,\n check_with_precision: Optional[bool] = False,\n output_path: Optional[str] = None,\n)\ndef snowpark_fn(df: SnowparkDataFrame):\n ...\n```\n\n- `job_context`: Snowpark job context.\n- `spark_function`: PySpark function to execute.\n- `checkpoint_name`: Name of the check.\n- `sample_number`: Number of rows to sample.\n- `sampling_strategy`: Sampling strategy to use.\n- `check_dtypes`: Check data types.\n- `check_with_precision`: Check with precision.\n- `output_path`: Output path for the checkpoint report.\n\n### Usage Example\n\n```python\nfrom snowflake.snowpark import Session\nfrom snowflake.snowpark import DataFrame as SnowparkDataFrame\nfrom snowflake.snowpark_checkpoints.spark_migration import check_with_spark\n\nsession = Session.builder.getOrCreate()\ndf = session.read.format(\"csv\").load(\"data.csv\")\n\n@check_with_spark(\n job_context=session,\n spark_function=lambda df: df.withColumn(\"COLUMN1\", df[\"COLUMN1\"] + 1),\n checkpoint_name=\"Check_Column1_Increment\",\n sample_number=100,\n sampling_strategy=SamplingStrategy.RANDOM_SAMPLE,\n)\ndef increment_column1(df: SnowparkDataFrame):\n return df.with_column(\"COLUMN1\", df[\"COLUMN1\"] + 1)\n\nincrement_column1(df)\n```\n\n### Pandera Snowpark Decorators\n\nThe decorators `@check_input_schema` and `@check_output_schema` allow for sampled schema validation of Snowpark DataFrames in the input arguments or in the return value.\n\n```python\nfrom snowflake.snowpark_checkpoints.checkpoint import check_input_schema, check_output_schema\n\n@check_input_schema(\n pandera_schema: DataFrameSchema,\n checkpoint_name: str,\n sample_frac: Optional[float] = 1.0,\n sample_number: Optional[int] = None,\n sampling_strategy: Optional[SamplingStrategy] = SamplingStrategy.RANDOM_SAMPLE,\n job_context: Optional[SnowparkJobContext],\n output_path: Optional[str] = None,\n)\ndef snowpark_fn(df: SnowparkDataFrame):\n ...\n\n@check_output_schema(\n pandera_schema: DataFrameSchema,\n checkpoint_name: str,\n sample_frac: Optional[float] = 1.0,\n sample_number: Optional[int] = None,\n sampling_strategy: Optional[SamplingStrategy] = SamplingStrategy.RANDOM_SAMPLE,\n job_context: Optional[SnowparkJobContext],\n output_path: Optional[str] = None,\n)\ndef snowpark_fn(df: SnowparkDataFrame):\n ...\n```\n\n- `pandera_schema`: Pandera schema to validate.\n- `checkpoint_name`: Name of the checkpoint schema file or DataFrame.\n- `sample_frac`: Fraction of the DataFrame to sample.\n- `sample_number`: Number of rows to sample.\n- `sampling_strategy`: Sampling strategy to use.\n- `job_context`: Snowpark job context.\n- `output_path`: Output path for the checkpoint report.\n\n### Usage Example\n\nThe following will result in a Pandera `SchemaError`:\n\n```python\nfrom pandas import DataFrame as PandasDataFrame\nfrom pandera import DataFrameSchema, Column, Check\nfrom snowflake.snowpark import Session\nfrom snowflake.snowpark import DataFrame as SnowparkDataFrame\nfrom snowflake.snowpark_checkpoints.checkpoint import check_output_schema\n\ndf = PandasDataFrame({\n \"COLUMN1\": [1, 4, 0, 10, 9],\n \"COLUMN2\": [-1.3, -1.4, -2.9, -10.1, -20.4],\n})\n\nout_schema = DataFrameSchema({\n \"COLUMN1\": Column(int8, Check(lambda x: 0 <= x <= 10, element_wise=True)),\n \"COLUMN2\": Column(float, Check(lambda x: x < -1.2)),\n})\n\n@check_output_schema(out_schema, \"output_schema_checkpoint\")\ndef preprocessor(dataframe: SnowparkDataFrame):\n return dataframe.with_column(\"COLUMN1\", lit('Some bad data yo'))\n\nsession = Session.builder.getOrCreate()\nsp_dataframe = session.create_dataframe(df)\n\npreprocessed_dataframe = preprocessor(sp_dataframe)\n```\n\n## License\n\nThis project is licensed under the MIT License. See the [LICENSE](LICENSE) file for more details.\n",
"bugtrack_url": null,
"license": " Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. Definitions. \"License\" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document. \"Licensor\" shall mean the copyright owner or entity authorized by the copyright owner that is granting the License. \"Legal Entity\" shall mean the union of the acting entity and all other entities that control, are controlled by, or are under common control with that entity. For the purposes of this definition, \"control\" means (i) the power, direct or indirect, to cause the direction or management of such entity, whether by contract or otherwise, or (ii) ownership of fifty percent (50%) or more of the outstanding shares, or (iii) beneficial ownership of such entity. \"You\" (or \"Your\") shall mean an individual or Legal Entity exercising permissions granted by this License. \"Source\" form shall mean the preferred form for making modifications, including but not limited to software source code, documentation source, and configuration files. \"Object\" form shall mean any form resulting from mechanical transformation or translation of a Source form, including but not limited to compiled object code, generated documentation, and conversions to other media types. \"Work\" shall mean the work of authorship, whether in Source or Object form, made available under the License, as indicated by a copyright notice that is included in or attached to the work (an example is provided in the Appendix below). \"Derivative Works\" shall mean any work, whether in Source or Object form, that is based on (or derived from) the Work and for which the editorial revisions, annotations, elaborations, or other modifications represent, as a whole, an original work of authorship. For the purposes of this License, Derivative Works shall not include works that remain separable from, or merely link (or bind by name) to the interfaces of, the Work and Derivative Works thereof. \"Contribution\" shall mean any work of authorship, including the original version of the Work and any modifications or additions to that Work or Derivative Works thereof, that is intentionally submitted to Licensor for inclusion in the Work by the copyright owner or by an individual or Legal Entity authorized to submit on behalf of the copyright owner. For the purposes of this definition, \"submitted\" means any form of electronic, verbal, or written communication sent to the Licensor or its representatives, including but not limited to communication on electronic mailing lists, source code control systems, and issue tracking systems that are managed by, or on behalf of, the Licensor for the purpose of discussing and improving the Work, but excluding communication that is conspicuously marked or otherwise designated in writing by the copyright owner as \"Not a Contribution.\" \"Contributor\" shall mean Licensor and any individual or Legal Entity on behalf of whom a Contribution has been received by Licensor and subsequently incorporated within the Work. 2. Grant of Copyright License. Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable copyright license to reproduce, prepare Derivative Works of, publicly display, publicly perform, sublicense, and distribute the Work and such Derivative Works in Source or Object form. 3. Grant of Patent License. Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable (except as stated in this section) patent license to make, have made, use, offer to sell, sell, import, and otherwise transfer the Work, where such license applies only to those patent claims licensable by such Contributor that are necessarily infringed by their Contribution(s) alone or by combination of their Contribution(s) with the Work to which such Contribution(s) was submitted. If You institute patent litigation against any entity (including a cross-claim or counterclaim in a lawsuit) alleging that the Work or a Contribution incorporated within the Work constitutes direct or contributory patent infringement, then any patent licenses granted to You under this License for that Work shall terminate as of the date such litigation is filed. 4. Redistribution. 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