Skip to main content

Lakeshack

A small rustic shack on the shores of a big lake A small rustic shack on the shores of a big lake

A simplified data lakehouse, more of a data lakeshack, optimized for retrieving filtered records from Parquet files. Similar to the various lakehouse solutions (Iceberg, Hudi, Delta Lake), Lakeshack gathers up the min/max values for specified columns from each Parquet file and stores them into a database (Metastore). When you want to query for a set of records, it first checks the Metastore to get the list of Parquet files that might have the desired records, and then only queries those Parquet files. The files may be stored locally or in S3. You may query using either native pyarrow or leverage S3 Select.

To acheive optimal performance, a partitioning & clustering strategy (which specifies how the records are written to the Parquet files) should align with the main query pattern expected to be used on the data. See the documentation for more information on this.

Installation

Lakeshack may be install using pip:

pip install lakeshack

Documentation

Documentation can be found at https://mhendrey.github.io/lakeshack

Metadata

Release files for lakeshack 0.2.3

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for lakeshack 0.2.3
File Size Uploaded Provenance Actions
lakeshack-0.2.3.tar.gz 29.1 kB Without provenance Details for lakeshack-0.2.3.tar.gz

Built distribution (wheel)

Table of built distributions (wheels) for lakeshack 0.2.3
File Interpreter ABI Platform Actions
lakeshack-0.2.3-py3-none-any.whl Python 3 none any Details for lakeshack-0.2.3-py3-none-any.whl

Total release size: 62.0 kB

Release files / lakeshack-0.2.3.tar.gz

Download URL lakeshack-0.2.3.tar.gz
Size 29.1 kB
Tags Source
SHA-256 checksum
How to use checksums
2adfc4838e5e691534e8a73e072c4c84f83d72f30a282a70a931463dc6abb0ef
BLAKE2b-256 checksum
How to use checksums
75352b9b341df5282f8d821a60f3db1ea0ed8fe64cef83e2af3c1816dead13cf
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/4.0.2 CPython/3.10.9

Release files / lakeshack-0.2.3-py3-none-any.whl

Download URL lakeshack-0.2.3-py3-none-any.whl
Size 32.9 kB
Tags Python 3
SHA-256 checksum
How to use checksums
a373af67761f41f6a18f549913b8939267d294e9ff9ba8a62af58d453a6ecd76
BLAKE2b-256 checksum
How to use checksums
bf7d657b45890d8a57cacf244f458cdc37e1407851298fcf6929591baf3c9d2c
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/4.0.2 CPython/3.10.9

Security for lakeshack 0.2.3

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

No provenance found

None of the files in this release were uploaded with provenance metadata.

A note on package security

PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.

To learn more about protecting yourself from malicious actors, visit our package security documentation.

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page