hazelcast-python-client
Hazelcast Python Client
Decision gist · record as of 2026-08-14
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.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- 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
- Low friction to install; the package itself has no runtime dependencies.
License · maintenance · safety
Apache 2.0 (permissive) — Apache 2.0 permissive license allows commercial use, modification, and distribution with minimal restrictions; suitable for most projects.
last release 2026-06-25 (50 days) · last repo commit 2026-07-30 · 116 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 93,755 downloads/mo, #13,364 on PyPI
Alternatives
Verify before relying
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()- 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
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 on it.
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
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.
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
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()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
| License | Apache 2.0 permissive |
| Python support | Not specified |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | None |
| Maintenance | Actively maintained 50 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 93,755 / month, #13,364 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableIntended Audience :: DevelopersLicense :: OSI Approved :: Apache Software LicenseNatural Language :: EnglishOperating System :: OS IndependentProgramming Language :: PythonProgramming Language :: Python :: 3Programming Language :: Python :: 3 :: OnlyProgramming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Programming Language :: Python :: Implementation :: CPythonTopic :: Software Development :: Libraries :: Python Modules |
Evidence: hazelcast_python_client-5.7.0-py3-none-any.whl
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