hazelcast-python-client 5.3.0

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

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.

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.

python run_tests.py