Registry / aws / sagemaker-feature-store-pyspark

sagemaker-feature-store-pyspark

library2.0.0pypypiunverified

PySpark bindings for Amazon SageMaker Feature Store, enabling large-scale feature engineering and serving with Spark DataFrames. Current version 1.2.0, released monthly.

aws ai-ml data

pip install sagemaker-feature-store-pyspark

package ↗homepage ↗

INSTALL

IMPORT

SIG · SAGEMAKER-FEATURE-

sagemaker-feature-store-pyspark

awspythonv2.0.0

Install

6.1s avg

Import

—

Disk

321MB

Pass rate

10/ 10

Env Coverage10 / 10

glibc

3.9–3.13

musl

3.9–3.13

Install & Compatibility

Where this runs

tested against v2.0.0 · pip install

no network on importno background threads

Install × environment matrix

Each cell = how many times install + import succeeded across repeated harness runs. Partial = flaky.
glibc = Debian/Ubuntu slim · musl = Alpine Linux

musl

py 3.10–3.95 runs

installs and imports cleanly · install 0.0s · import 0.000s · 319.6MB

glibc

py 3.10–3.95 runs

installs and imports cleanly · install 6.1s · import 0.000s · 320MB

321MB installed

● package 321MB

Code

Verified usage

Verified import paths — ran on the pinned version, not inferred.

FeatureStoreManager

✓ from feature_store_pyspark.classpath_jars import FeatureStoreManager

✗ from feature_store_pyspark import FeatureStoreManager

Creates a Spark DataFrame and ingests it into a SageMaker Feature Group using PySpark bindings.

from pyspark.sql import SparkSession from sagemaker.feature_store.feature_store import FeatureStoreManager spark = SparkSession.builder.getOrCreate() fs = FeatureStoreManager() df = spark.createDataFrame([(1, 'a'), (2, 'b')], ['id', 'value']) record_id = 'id' feature_group_name = 'my-feature-group' fs.ingest(df, feature_group_name, record_identifier_name=record_id)

Debug

Known issues

gotchaPySpark session must be configured with the correct Hadoop AWS JARs for S3 access; missing JARs cause silent failures on ingest.

fix

Use SparkSession.builder.config('spark.jars.packages', 'org.apache.hadoop:hadoop-aws:3.3.4').getOrCreate() or provide JARs via --jars.

affects: all

gotchaIngesting DataFrames with columns containing null values in the record identifier column will fail with a Spark exception.

fix

Ensure the record identifier column has no nulls; use df.na.drop(subset=['id']) before ingest.

affects: >=1.0.0

breakingIn version 1.0.0, the module was restructured: `from sagemaker_feature_store_pyspark import FeatureStore` changed to `from sagemaker.feature_store.feature_store import FeatureStoreManager`.

fix

Update imports to use the new path. Old code will raise ImportError.

affects: <1.0.0 to >=1.0.0

Upgrade

Version history

2.0.0latest on PyPI · released Jun 9, 2026

Audit

Dependencies

pysparkrequiredRuntime dependency for Spark operations

sagemakerrequiredAWS SDK for SageMaker

Agent activity

26 hits · last 30 days

node

OpenAI (training)

Resources

packagehttps://pypi.org/project/sagemaker-feature-store-pyspark/ ↗

homepagehttps://aws.amazon.com/sagemaker/feature-store/ ↗