{"categories":[{"label":"Python Modules","url":"https://skillfed.io/packages/category/software-development-libraries-python-modules/21"}],"enrichment":{"capability":"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.","skillfed_tags":["distributed-computing","in-memory-data-grid","async-client"],"use_cases":["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."],"what_it_does":"Hazelcast Python Client is a connector library that lets your Python code interact with a Hazelcast cluster\u2014a 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.\n\nThe 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.","worth_installing":"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."},"id":"hazelcast-python-client","links":{"html":"https://skillfed.io/packages/hazelcast-python-client","md":"https://skillfed.io/packages/hazelcast-python-client.md","pypi":"https://pypi.org/project/hazelcast-python-client/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-06-25","license_spdx":null,"license_treatment":"permissive","name":"hazelcast-python-client","python_support":"unspecified","summary":"Hazelcast Python Client"},"popularity":{"monthly_downloads":93755,"position":13364,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"5.7.0"}