ekzhu / datasketch

MinHash, LSH, LSH Forest, Weighted MinHash, HyperLogLog, HyperLogLog++, LSH Ensemble and HNSW

Home Page:https://ekzhu.github.io/datasketch

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datasketch: Big Data Looks Small

https://static.pepy.tech/badge/datasketch/month

datasketch gives you probabilistic data structures that can process and search very large amount of data super fast, with little loss of accuracy.

This package contains the following data sketches:

Data Sketch Usage
MinHash estimate Jaccard similarity and cardinality
Weighted MinHash estimate weighted Jaccard similarity
HyperLogLog estimate cardinality
HyperLogLog++ estimate cardinality

The following indexes for data sketches are provided to support sub-linear query time:

Index For Data Sketch Supported Query Type
MinHash LSH MinHash, Weighted MinHash Jaccard Threshold
MinHash LSH Forest MinHash, Weighted MinHash Jaccard Top-K
MinHash LSH Ensemble MinHash Containment Threshold
HNSW Any Custom Metric Top-K

datasketch must be used with Python 3.7 or above, NumPy 1.11 or above, and Scipy.

Note that MinHash LSH and MinHash LSH Ensemble also support Redis and Cassandra storage layer (see MinHash LSH at Scale).

Install

To install datasketch using pip:

pip install datasketch

This will also install NumPy as dependency.

To install with Redis dependency:

pip install datasketch[redis]

To install with Cassandra dependency:

pip install datasketch[cassandra]

About

MinHash, LSH, LSH Forest, Weighted MinHash, HyperLogLog, HyperLogLog++, LSH Ensemble and HNSW

https://ekzhu.github.io/datasketch

License:MIT License


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