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Task output caching is a new kind of cache mechanism in Gradle that aims to save time by, instead of executing a task, reusing results produced by previous executions of the same task with matching inputs. Reusing results can happen between builds happening in the same project, or in two different projects on the same computer, or even between builds running on different computers. Task output caching does not define the service to be used to store and retreive the results. Instead it only specifies a simple protocol that can be implemented to adopt different kinds of existing services as cache backends. We now have sample scenarios you can try out. You can try using the local cache backend the easiest, while using an HTTP cache backend gives you the most versatility.
hface monitors a Hazelcast cluster in real time. It currently supports maps, multimaps and queues. Support for other distributed data structures is coming. it will be collecting the stats from all the nodes and will be sending these stats to hface for aggregation and visual pleasure.
DeviceHive turns any connected device into the part of Internet of Things. It provides the communication layer, control software and multi-platform libraries to bootstrap development of smart energy, home automation, remote sensing, telemetry, remote control and monitoring software and much more. Connect embedded Linux using Python, Node.js or Java libraries and JSON format. Write and read your data via REST, Websockets or MQTT, explore visualization on Grafana charts.
Hazelcast clustering for Kubernetes made easy. It includes a lean Hazelcast container image, based on Alpine Linux, with Kubernetes discovery support. You can test with a local cluster. Check this other repository from yours truly.
Hazelcast (3.10) cluster discovery mechanism for Kubernetes. Kubernetes is an open source orchestration system for Docker containers. It handles scheduling onto nodes in a compute cluster and actively manages workloads to ensure that their state matches the users declared intentions. Using the concepts of "labels" and "pods", it groups the containers which make up an application into logical units for easy management and discovery.
A simple distributed sportsbook example, to demonstrate Hazlecast IMDG and Apache Spark, by showing how an example based on Java 8 collections can be generalized and scaled up to multiple JVMs. Included in this distribution is a white paper deascribing the Betleopard application in detail and how it should be understood.
This is the repository of C++ client implementation for Hazelcast, the open source in-memory data grid. A comparison of features supported by the C++ Client vs the Java client can be found here. You can generate API Documentation via Doxygen from root with the following command.
C# client implementation for Hazelcast, the open source in-memory data grid. A comparison of features supported by various clients can be found here. Hazelcast .Net Client supports .Net Framemork 4.0+ and Net Core 2.0+ .
NOTE: This project is currently in active development. This document explains Node.js client for Hazelcast which uses Hazelcast's Open Client Protocol 1.6. This client works with Hazelcast 3.6 and higher.
The hazelcast-scala API is based on Scala 2.11/2.12 and Hazelcast 3.9, but does not define them as hard dependencies (since it works with both open-source and enterprise Hazelcast, and multiple versions), so make sure to also include the relevant Hazelcast dependencies explicitly. See the Wiki and unit tests for examples of how to use this library.
This is the recommended component to add and configure for deployments with more than one application server (more than one server in a cluster running Moqui). This will add the component to the Moqui runtime/component directory.