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Description:

hazelcastpythonclient 5.5.0

Hazelcast is an open-source distributed
in-memory data store and computation platform that provides a wide
variety of distributed data structures and concurrency primitives.
Hazelcast Python client is a way to communicate to Hazelcast clusters
and access the cluster data. The client provides a Future-based
asynchronous API suitable for wide ranges of use cases.

Installation

Hazelcast
Hazelcast Python client requires a working Hazelcast cluster to run.
This cluster handles the storage and manipulation of the user data.
A Hazelcast cluster consists of one or more cluster members. These
members generally run on multiple virtual or physical machines and are
connected to each other via the network. Any data put on the cluster is
partitioned to multiple members transparent to the user. It is therefore
very easy to scale the system by adding new members as the data grows.
Hazelcast cluster also offers resilience. Should any hardware or
software problem causes a crash to any member, the data on that member
is recovered from backups and the cluster continues to operate without
any downtime.
The quickest way to start a single member cluster for development
purposes is to use our Docker
images.
docker run -p 5701:5701 hazelcast/hazelcast:5.3.0
You can also use our ZIP or TAR
distributions.
Once you have downloaded, you can start the Hazelcast member using
the bin/hz-start script.

Client
pip install hazelcast-python-client

Overview

Usage
import hazelcast

# Connect to Hazelcast cluster.
client = hazelcast.HazelcastClient()

# Get or create the "distributed-map" on the cluster.
distributed_map = client.get_map("distributed-map")

# Put "key", "value" pair into the "distributed-map" and wait for
# the request to complete.
distributed_map.set("key", "value").result()

# Try to get the value associated with the given key from the cluster
# and attach a callback to be executed once the response for the
# get request is received. Note that, the set request above was
# blocking since it calls ".result()" on the returned Future, whereas
# the get request below is non-blocking.
get_future = distributed_map.get("key")
get_future.add_done_callback(lambda future: print(future.result()))

# Do other operations. The operations below won't wait for
# the get request above to complete.

print("Map size:", distributed_map.size().result())

# Shutdown the client.
client.shutdown()
If you are using Hazelcast and the Python client on the same machine,
the default configuration should work out-of-the-box. However,
you may need to configure the client to connect to cluster nodes that
are running on different machines or to customize client properties.

Configuration
import hazelcast

client = hazelcast.HazelcastClient(
cluster_name="cluster-name",
cluster_members=[
"10.90.0.2:5701",
"10.90.0.3:5701",
],
lifecycle_listeners=[
lambda state: print("Lifecycle event >>>", state),
]
)

print("Connected to cluster")
client.shutdown()
Refer to the documentation
to learn more about supported configuration options.

Features

Distributed, partitioned and queryable in-memory key-value store
implementation, called Map
Eventually consistent cache implementation to store a subset of the
Map data locally in the memory of the client, called Near Cache
Additional data structures and simple messaging constructs such as
Set, MultiMap, Queue, Topic
Cluster-wide unique ID generator, called FlakeIdGenerator
Distributed, CRDT based counter, called PNCounter
Distributed concurrency primitives from CP Subsystem such as
FencedLock, Semaphore, AtomicLong
Similarity search using VectorCollection (Beta)
Integration with Hazelcast Cloud
Support for serverless and traditional web service architectures with
Unisocket and Smart operation modes
Ability to listen to client lifecycle, cluster state, and distributed
data structure events
and many
more

Getting Help
You can use the following channels for your questions and
development/usage issues:

GitHub
repository
Documentation
Slack

Contributing
We encourage any type of contribution in the form of issue reports or
pull requests.

Issue Reports
For issue reports, please share the following information with us to
quickly resolve the problems:

Hazelcast and the client version that you use
General information about the environment and the architecture you
use like Python version, cluster size, number of clients, Java
version, JVM parameters, operating system etc.
Logs and stack traces, if any
Detailed description of the steps to reproduce the issue

Pull Requests
Contributions are submitted, reviewed and accepted using the pull
requests on GitHub. For an enhancement or larger feature, please
create a GitHub issue first to discuss.

Development

Clone the GitHub repository.
Run python setup.py install to install the Python client.

If you are planning to contribute:

Run pip install -r requirements-dev.txt to install development
dependencies.
Use black to reformat the code
by running the black --config black.toml . command.
Use mypy to check type annotations
by running the mypy hazelcast command.
Make sure that tests are passing by following the steps described
in the next section.

Testing
In order to test Hazelcast Python client locally, you will need the
following:

Supported Java virtual machine <https://docs.hazelcast.com/hazelcast/latest/deploy/versioning-compatibility#supported-java-virtual-machines>
Apache Maven <https://maven.apache.org/>

Set the environment variables for credentials:
export HZ_SNAPSHOT_INTERNAL_USERNAME=YOUR_MAVEN_USERNAME
export HZ_SNAPSHOT_INTERNAL_PASSWORD=YOUR_MAVEN_PASSWORD
Following command starts the tests:
python3 run_tests.py
Test script automatically downloads hazelcast-remote-controller and
Hazelcast. The script uses Maven to download those.

License
Apache 2.0 License.

Copyright
Copyright (c) 2008-2023, Hazelcast, Inc. All Rights Reserved.
Visit hazelcast.com for more
information.