Get Started

Get Started

These guides demonstrate how to get started quickly with Hazelcast IMDG and Hazelcast Jet.

Hazelcast IMDG

Learn how to store and retrieve data from a distributed key-value store using Hazelcast IMDG. In this guide you’ll learn how to:

  • Create a cluster of 3 members.
  • Start Hazelcast Management Center
  • Add data to the cluster using a sample client in the language of your choice
  • Add and remove some cluster members to demonstrate data balancing capabilities of Hazelcast

Hazelcast Jet

Learn how to build a distributed data processing pipeline in Java using Hazelcast Jet. In this guide you’ll learn how to:

  • Install Hazelcast Jet and form a cluster on your computer
  • Build a simple pipeline that receives a stream of data, does some calculations and outputs some results
  • Submit the pipeline as a job to the cluster and observe the results
  • Scale the cluster up and down while the job is still running

Hazelcast IMDG Python Client

Hazelcast IMDG enables you to use an unparalleled range of massively scalable data structures with your Python applications. You can:

  • Use the Hazelcast Near Cache feature to store frequently read data in your Python process. This provides faster read speeds than traditional caches such as Redis or Memcached.
  • Access all of the Hazelcast data structures plus distributed queues, topics, and more with the Hazelcast IMDG Python Client.
  • Support JSON or serialized objects.
Quick Start + Download

Quick Start + Download

Quick Start

You can install hazelcast python client via:
$ pip install hazelcast-python-client
Or
$ python setup.py install

Version Downloads Documentation
Hazelcast IMDG Python Client 3.12.3 (latest)
03/19/2020
Hazelcast IMDG Python Client 3.12.2
03/02/2020
Hazelcast IMDG Python Client 3.12.1
07/15/2019
Hazelcast IMDG Python Client 3.12
04/30/2019

For all downloads please see the Download Archives

Previous Releases
Roadmap

Roadmap

Python Client 4.0 (Mid November 2020)

  • Hazelcast IMDG 4.0 Changes: Client protocol enhancements, architectural improvements, Serialization improvements.
  • CP Subsystem Support: New Concurrency APIs including FencedLock, AtomicLong, Semaphore, CountDownLatch, and AtomicReference.
  • Backup Acknowledgment: Significantly improved client performance by eliminating the sync backup wait from the client and send the backup ACK to the client.
  • Ownerless Client: Simpler design to track member leaves and joins to the cluster.

Python Client Backlog

  • Hazelcast for Django: Use Hazelcast instead of the default cache in Django.
  • Sessions in Django: Session backend for Django that stores sessions in a Hazelcast.
  • Hazelcast Dashboard for Django: Hazelcast monitoring in django admin.
  • Hazelcast Celery Brocker: An integration with Celery, a distributed task queue.
  • Integrations with Apache Airflow: Hazelcast hooks and sensors for Apache Airflow, an open-source workflow management platform.
  • Integrations with Flask: Standard caching and session backend support for Flask, a lightweight WSGI web application framework.
Client Features

Features Implemented for this Client

DATA STRUCTURES

Python Client 3.12.3

Map

Queue

Set

List

MultiMap

ReplicatedMap

RingBuffer

Topic

Reliable Topic

JCache

N/A

Cardinality Estimator

CONCURRENCY PRIMITIVES

Python Client 3.12.3

Lock

Condition

Semaphore

AtomicLong

AtomicReference

ID Generator

CountDownLatch

CRDT PN Counter

Flake ID Generator

DISTRIBUTED PROCESSING

Python Client 3.12.3

Distributed Executor Service

Event Listeners

Sub-Listener Interfaces for Map Listener

Entry Processor

TRANSACTIONS

Python Client 3.12.3

TxnMap

TxnMultiMap

TxnQueue

TxnList

TxnSet

QUERY

Python Client 3.12.3

Query (Predicates)

Paging predicates

Partition predicate

Built-in Predicates

Continuous Query Caching

N/A

Listener with Predicate

Projections

Fast Aggregations

NEAR CACHE

Python Client 3.12.3

Near Cache Support

HD Memory

N/A

Preload Cache from Last Used

Eventual Consistency Control

CONFIGURATION

Python Client 3.12.3

Declarative Configuration (XML/JSON/YAML)

Programmatic Configuration

Client Configuration Import

Fail Fast on Invalid Configuration

SECURITY

Python Client 3.12.3

SSL Support

XA Transactions

Mutual Authentication

Authorization

Custom Authentication Modules

MANAGEMENT CENTER

Python Client 3.12.3

Management Center Integration / Awareness

Client Near Cache Stats

Client Runtime Stats

Client Operating Systems Stats

CLOUD

Python Client 3.12.3

Hazelcast Cloud

Kubernetes

AWS

Azure

Google Cloud Platform

Pivotal Cloud Foundry

Docker

Apache jclouds

Consul

etcd

Eureka

Heroku

Zookeeper

INFRASTRUCTURE

Python Client 3.12.3

Smart Client

Unisocket Client

Lifecycle Service

HeartBeat

Backup Acknowledgment to Client

Diagnostics

SERIALIZATION

Python Client 3.12.3

DataSerializable

N/A

IdentifiedDataSerializable

Portable Serialization

Portable Serialization

Global Serializers

CLIENT CONNECTIVITY

Python Client 3.12.3

Connection Strategy

Connection Retry

Blue/Green Deployments and Disaster Recovery

Support for Clients & Languages

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