Search Results for "distributed shared memory"
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Showing 14 open source projects for "distributed shared memory"
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Stop Storing Third-Party Tokens in Your DatabaseRolling your own OAuth token storage can be a security liability. Token Vault securely stores access and refresh tokens from federated providers and handles exchange and renewal automatically. Connected accounts, refresh exchange, and privileged worker flows included.
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MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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JetCache
JetCache is a Java cache framework
...It provides more powerful annotations than those in Spring Cache. The annotations in JetCache support native TTL, two-level caching, and automatically refresh in distributed environments, also you can manipulate Cache instances by your code. Currently, there are four implementations: RedisCache, TairCache(not open source on github), CaffeineCache (in memory) and a simple LinkedHashMapCache (in memory). -
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RIBs
Uber's cross-platform mobile architecture framework
...Plus, most RIB logic is decoupled from child RIB logic. This makes RIB classes easy to test and reason about independently. Tooling for developer productivity. RIBs come with IDE tooling around code generation, memory leak detection, static analysis and runtime integrations. -
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Apache Sedona
Cluster computing framework for processing large-scale geospatial data
Apache Sedona™ is a cluster computing system for processing large-scale spatial data. Sedona extends existing cluster computing systems, such as Apache Spark and Apache Flink, with a set of out-of-the-box distributed Spatial Datasets and Spatial SQL that efficiently load, process, and analyze large-scale spatial data across machines. According to our benchmark and third-party research papers, Sedona runs 2X - 10X faster than other Spark-based geospatial data systems on computation-intensive query workloads. According to our benchmark and third-party research papers, Sedona has 50% less peak memory consumption than other Spark-based geospatial data systems for large-scale in-memory query processing. ... -
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Netty-socketio
Socket.IO server implemented on Java
...CentOS, 1 CPU, 4GB RAM runned on VM, CPU 10%, Memory 15%, 6000 xhr-long polling sessions or 15000 websockets sessions, 4000 messages per second. -
Go From AI Idea to AI App FastAccess Gemini 3 and 200+ models. Build chatbots, agents, or custom models with built-in monitoring and scaling.
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Micronaut
Micronaut Application Framework
Micronaut is a modern, JVM-based, full-stack Java framework designed for building modular, easily testable JVM applications with support for Java, Kotlin, and the Groovy language. Micronaut is developed by the creators of the Grails framework and takes inspiration from lessons learned over the years building real-world applications from monoliths to microservices using Spring, Spring Boot, and Grails. Micronaut aims to provide all the tools necessary to build JVM applications. With Micronaut... -
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Apache Flink
Stream processing framework with powerful stream
Apache Flink is a distributed engine for stateful computations over data streams and batches, designed for low-latency processing at scale. Its core runtime executes dataflow graphs with fine-grained backpressure and checkpointing, allowing applications to recover consistently from failures. Flink’s event-time model and watermarks enable accurate out-of-order processing, windowing, and complex time semantics that typical real-time systems struggle with. Developers program against high-level... -
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Light-4J
A fast, lightweight and more productive microservices framework
Light means lightweight, lightning-fast and shedding light on how to program with modern Java SE for cloud-native deployment. I had been working on the Java EE platforms since early 2000 and suffered performance and productivity issues. In 2014, I realized that the IT industry was moving from Monolithic to Microservices and from on-premise data centers to the public clouds. To reduce the production cost for my applications, I need to find a lightweight platform that has a small memory... -
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picocli
Framework for building GraalVM-enabled command line apps
...Picocli-based applications can have command line TAB completion showing available options, option parameters, and subcommands, for any level of nested subcommands. Picocli-based applications can be ahead-of-time compiled to a GraalVM native image, with extremely fast startup time and lower memory requirements, which can be distributed as a single executable file. Picocli generates beautiful documentation for your application (HTML, PDF and Unix man pages). Another distinguishing feature of picocli is how it aims to let users run picocli-based applications without requiring picocli as an external dependency. -
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JavaCá&Lá
A Java Distributed Shared Memory Middleware
How can we easily develop applications for multicore or multicomputer architectures? We frequently use middlewares for that problem, but they are often complex and less productive. This way, at DECOM-UFOP, we have a middleware, named JavaCa&La or just JCL, to build portable and scalable solutions over many high performance computer architectures. Our middleware APIs is simple, intuitive and designed for young and senior programmers who don’t have high performance programming skills. Using... -
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As of now, you can get Ehcache only from ehcache.org or Maven central. A simple, fast, thread safe, standards based cache for Java, and provides memory and disk stores and distributed operation for clusters. ehcache is widely used in such open source projects as Hibernate and Spring. Terracotta offers enterprise edition
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Tuple Spaces
Tuple space with time outs and transactions.
Java implementation of a Tuplespace. Moved to https://github.com/mike-k-houghton/tuplespace A Tuple is an ordered list of items. A Tuple Space is a form of associative memory where entries, tuples, are stored in the space and are retrieved using search criteria that are based on the intrinsic properties of the tuples. The two key operations are put get And that is it! There are refinements on these operations including, for example, timeouts where the tuple will only exist... -
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Library that offers users an easy to use interface to synchronize shared data access following the software transactional memory principle.
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Photon provides very fast access to data containers (queues, maps, etc.) in shared memory - it can retrieve millions of data records per second. It also uses some RDB concepts like transactions and crash recovery. See web site for details.
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Jalin aims to bring Linux's IPC facilities within reach of Java programs. Java programs using Jalin will be able to communicate using a variety of techniques from shared memory segments to message queues.
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