hazelcast-python-client

What it is and what it does

Hazelcast Python Client is a connector library that lets your Python code interact with a Hazelcast cluster—a distributed, partitioned in-memory data platform. It provides access to cluster-hosted data structures (maps, sets, queues, topics, locks, counters) and cluster-wide operations through a Future-based asynchronous API, so your code can issue requests without blocking and attach callbacks to handle responses.

The client is designed for applications that need to store and retrieve data across multiple machines, benefit from automatic partitioning and replication, or coordinate work using distributed primitives. It supports both local development (via Docker) and production deployments to Hazelcast Cloud or self-managed clusters. Configuration is minimal when client and cluster are on the same machine, but the library lets you specify cluster addresses, authentication, and lifecycle listeners for more complex setups.

Use it for:

• Build a distributed cache layer for a web service by storing session data or computed results in a Hazelcast map shared across multiple application servers.
• Implement a work queue or task distribution system using Hazelcast queues to coordinate jobs across multiple Python workers.
• Use distributed locks and semaphores from the CP Subsystem to coordinate access to shared resources in a multi-process or multi-machine environment.
• Store and query large datasets that don't fit in a single machine's memory by leveraging Hazelcast's partitioned map and near-cache for local subset caching.
• Generate cluster-wide unique IDs using FlakeIdGenerator for distributed systems that need coordinated ID allocation.

Worth the install?

AI-flagged interpretation of the facts on this page — verify before relying

Hazelcast Python Client connects your Python application to a Hazelcast cluster to access distributed in-memory data structures (maps, sets, queues, locks) and perform cluster-wide operations using a Future-based asynchronous API.

Yes, if you have a Hazelcast cluster running or plan to deploy one. The client is actively maintained, has no code dependencies, and provides a clean async API for distributed data access. Install it only if your architecture already includes or will include a Hazelcast cluster; it is not useful as a standalone library. No known vulnerabilities.

Install

hazelcast-python-client on PyPI

pip install hazelcast-python-client

uv add hazelcast-python-client

poetry add hazelcast-python-client

Installing hazelcast-python-client

Before you install

Low friction to install; the package itself has no runtime dependencies. Active maintenance with a release 50 days ago and continuous commits. Requires a separate Hazelcast cluster to be running—either locally via Docker or on remote machines—which is the primary operational prerequisite, not a code dependency.

License in practice

Apache 2.0 permissive license allows commercial use, modification, and distribution with minimal restrictions; suitable for most projects.

Quickstart

pip install hazelcast-python-client

import hazelcast

client = hazelcast.HazelcastClient()
distributed_map = client.get_map("my-map")
distributed_map.set("key", "value").result()
value = distributed_map.get("key").result()
client.shutdown()

A running Hazelcast cluster is required; the client cannot operate standalone. For local development, start a cluster via Docker: docker run -p 5701:5701 hazelcast/hazelcast:latest

Verify before relying

• Exact Python version support range (classifiers list 3.11–3.14 but requires_python is unspecified)
• Whether VectorCollection (Beta) feature is production-ready or experimental
• Performance characteristics and throughput limits for typical workloads

Package facts

Evidence: hazelcast_python_client-5.7.0-py3-none-any.whl

Keywords: hazelcast, hazelcast client, In-Memory Data Grid, Distributed Computing

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