Hazelcast Python Client
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.
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.
pip install hazelcast-python-client
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.
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.
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
Integration with Hazelcast Viridian
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
You can use the following channels for your questions and development/usage issues:
We encourage any type of contribution in the form of issue reports or pull requests.
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
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.
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.
In order to test Hazelcast Python client locally, you will need the following:
Java 8 or newer
Following commands starts the tests:
Test script automatically downloads hazelcast-remote-controller and Hazelcast. The script uses Maven to download those.
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