KeyDB is a high performance fork of Redis with a focus on multithreading, memory efficiency, and high throughput. In addition to multithreading, KeyDB also has features only available in Redis Enterprise such as Active Replication, FLASH storage support, and some not available at all such as direct backup to AWS S3.
 
KeyDB maintains full compatibility with the Redis protocol, modules, and scripts. This includes the atomicity guarantees for scripts and transactions. Because KeyDB keeps in sync with Redis development KeyDB is a superset of Redis functionality, making KeyDB a drop in replacement for existing Redis deployments.
 
In addition to supporting LUA, KeyDB also supports JavaScript using the powerful V8 JIT engine. ModJS lets you write functions in javascript that can in turn be called directly by KeyBD.

KeyDB is a high performance fork of Redis with a focus on multithreading, memory efficiency, and high throughput. In addition to multithreading, KeyDB also has features only available in Redis Enterprise such as Active Replication, FLASH storage support, and some not available at all such as direct backup to AWS S3.

Keydb - A Multithreaded Fork of Redis 

Tarantool is an in-memory database and application server. It is 100% compatible drop-in replacement for Lua 5.1, based on LuaJIT 2.1. It has full support for Lua modules and a rich set of own modules, including cooperative multitasking, non-blocking I/O, access to external databases, etc.
 
The in-memory database supports two data engines: 100% in-memory with optional persistence and a 2-level disk-based B-tree, to use with large data sets. It supports multiple index types: HASH, TREE, RTREE, BITSET. Tarantool is ideal for data-enriched components of scalable Web architecture: queue servers, caches, stateful Web applications.

Tarantool is an in-memory database and application server. It is 100% compatible drop-in replacement for Lua 5.1, based on LuaJIT 2.1. It has full support for Lua modules and a rich set of own modules, including cooperative multitasking, non-blocking I/O, access to external databases, etc.

Tarantool - Get your data in RAM. Get compute close to data. Enjoy the performance

Hazelcast is a clustering and highly scalable data distribution platform for Java. It supports Distributed implementations of java.util.{Queue, Set, List, Map}, java.util.concurrency.locks.Lock, java.util.concurrent.ExecutorService, Distributed Indexing and Query support, Dynamic scaling, partitioning with backups, fail-over, Web-based cluster monitoring tool and lot more. 

Hazelcast - In-Memory Data Grid for Java

Apache Derby is an open source relational database implemented entirely in Java. It has a small footprint that makes it easy to embed in any Java-based application, but it also supports the more familiar client/server mode. It is based on the Java, JDBC, and SQL standards, making code developed more portable to standards-compliant databases.

Apache Derby - Relational Database implemented in Java

BuntDB is a low-level, in-memory, key/value store in pure Go. It persists to disk, is ACID compliant, and uses locking for multiple readers and a single writer. It supports custom indexes and geospatial data. It's ideal for projects that need a dependable database and favor speed over data size.The desire to create BuntDB stems from the need for a new embeddable database for Tile38 and SummitDB.

BuntDB - Embeddable, in-memory key/value database for Go with custom indexing and geospatial support

SummitDB is an in-memory, NoSQL key/value database. It persists to disk, uses the Raft consensus algorithm, is ACID compliant, and built on a transactional and strongly-consistent model. It supports custom indexes, geospatial data, JSON documents, and user-defined JS scripting.Under the hood it utilizes Finn, Redcon, BuntDB, GJSON, and Otto.

SummitDB - In-memory NoSQL database with ACID transactions, Raft consensus, and Redis API

Atlas was developed by Netflix to manage dimensional time series data for near real-time operational insight. Atlas features in-memory data storage, allowing it to gather and report very large numbers of metrics, very quickly. Atlas captures operational intelligence. Whereas business intelligence is data gathered for analyzing trends over time, operational intelligence provides a picture of what is currently happening within a system.

Atlas - In-memory dimensional time series database

Hazelcast Jet is a distributed computing platform built for high-performance stream processing and fast batch processing. It embeds Hazelcast In-Memory Data Grid (IMDG) to provide a lightweight, simple-to-deploy package that includes scalable in-memory storage. Hazelcast Jet performs parallel execution to enable data-intensive applications to operate in near real-time.
 
Hazelcast Jet gives you all the infrastructure you need to build a distributed data processing pipeline within one 10Mb Java JAR: processing, storage and clustering. Hazelcast Jet comes with in-memory operational storage that’s available out-of-the box. This storage is partitioned, distributed and replicated across the Hazelcast Jet cluster for capacity and resiliency. It can be used as an input data buffer, to publish the results of a Hazelcast Jet computation, to connect multiple Hazelcast Jet jobs or as a lookup cache for data enrichment.

Hazelcast Jet is a distributed computing platform built for high-performance stream processing and fast batch processing. It embeds Hazelcast In-Memory Data Grid (IMDG) to provide a lightweight, simple-to-deploy package that includes scalable in-memory storage. Hazelcast Jet performs parallel execution to enable data-intensive applications to operate in near real-time.

Hazelcast Jet - A general purpose distributed data processing engine, built on top of Hazelcast.

H2 database is very fast, open source database engine. 
 It supports : 
 <UL>
	<LI>It supports SQL and JDBC standards</LI>
	<LI>Encrypted database</LI> 
	<LI>Security and Authentication</LI>
	<LI>Multiple index types (b-tree, tree, hash)</LI> 
	<LI>Contains a recovery tool that can dump the contents of the database</LI> 
	<LI>Clustering</LI>
	<LI>Full text search</LI>
	<LI>Temporary Tables</LI> 
	<LI>User-Defined Datatypes</LI>
 </UL>

H2 database is very fast, open source database engine. It supports SQL and JDBC standards. It can run in Embedded and Server mode and it has clustering support.  


H2 Database - Java based Database Engine

Apache Ignite In-Memory Data Fabric is a high-performance, integrated and distributed in-memory platform for computing and transacting on large-scale data sets in real-time, orders of magnitude faster than possible with traditional disk-based or flash technologies. 
 
Apache Ignite has advanced compute and clustering capabilities including Dynamic Sub-Clusters, Clustered Lambda Execution, Clustered ForkJoin Tasks.

Apache Ignite In-Memory Data Fabric is a high-performance, integrated and distributed in-memory platform for computing and transacting on large-scale data sets in real-time, orders of magnitude faster than possible with traditional disk-based or flash technologies. 


Apache Ignite - High performance in-memory data grid

MapDB is an embedded database engine. It provides Maps and other collections backed by disk or memory storage. It offers excellent performance comparable to java collections, but is not limited by GC overhead. It is also a full database engine with storage backends, transactions, cache algorithms, expiration and many other options. MapDB provides concurrent Maps, Sets and Queues backed by disk storage or off-heap memory. 

MapDB - Embedded Database Engine

Apache Geode  is distributed, in-memory database for scale-out applications. All data is stored in-memory for low latency. Performance scales linearly as nodes are added. Data is distributed automatically between nodes to optimize performance. Clusters fail-over to other nodes in case of failures, and rebalance remaining resources. Geode servers can be configured to talk memcached protocol. 

Apache Geode - Distributed, In-memory Database for Scale-Out Applications

STINGER is a package designed to support streaming graph analytics by using in-memory parallel computation to accelerate the computation. STINGER is composed of the core data structure and the STINGER server, algorithms, and an RPC server that can be used to run queries and serve visualizations. 
 
The data structure used is similar to an adjacency list except that the adjacencies of a vertex are stored in a linked list of semi-dense blocks of edges rather than a linked list of individual edges. Also, the vertex array contains more metadata per each vertex than just a pointer to the linked of edge blocks.

STINGER is a package designed to support streaming graph analytics by using in-memory parallel computation to accelerate the computation. STINGER is composed of the core data structure and the STINGER server, algorithms, and an RPC server that can be used to run queries and serve visualizations. 

STINGER - In-memory graph store and dynamic graph analysis platform

Pincaster is an in-memory, persistent data store for geographic data and key/dictionary pairs, with replication and a HTTP/JSON interface.

Pincaster - NoSQL Database with a HTTP/JSON interface

Hazelcast Jet is a distributed computing platform built for high-performance stream processing and fast batch processing. It embeds Hazelcast In Memory Data Grid (IMDG) to provide a lightweight package of a processor and a scalable in-memory storage. It supports distributed java.util.stream API support for Hazelcast data structures such as IMap and IList, Distributed implementations of java.util.{Queue, Set, List, Map} data structures highly optimized to be used for the processing

Hazelcast Jet - Distributed data processing engine, built on top of Hazelcast

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