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.
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()
See the API documentation of
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