Amazon DynamoDB
Amazon DynamoDB, a key-value and document databank, delivers single-digit millisecond performance on any scale. It is a fully managed, multiregional, multimaster, durable database that offers built-in security, backup, restore, and in-memory cache for internet-scale apps. DynamoDB can process more than 10 trillion requests per hour and can handle peak requests of more than 20,000,000 requests per second.
Many of the fastest-growing businesses in the world, such as Lyft, Redfin, and Airbnb, as well as enterprises like Samsung, Toyota and Capital One, rely on DynamoDB's scale and performance to support mission-critical workloads.
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Google Cloud Memorystore
Redis and Memcached are now more reliable, available, and scalable. Memorystore automates complex tasks such as patching, monitoring, failover, and high availability for open-source Redis and Memcached so you can spend more of your time programming. Start small and scale up your instance. Memorystore for Memcached supports clusters up to 5 TB, supporting millions of QPS with very low latency. Redis Memorystore instances are replicated across two zones, providing a 99.9% availability guarantee. Instances are monitored constantly and with automatic failover--applications experience minimal disruption. You can choose from two of the most popular open-source caching engines to build your application. Memorystore is protocol compatible and supports Redis and Memcached. Choose the engine that best suits your needs and budget.
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Lucid KV
Lucid is still in development. However, we want to create a fast, secure, and distributed key-value storage accessible through an HTTP API. We also want to offer persistence, encryption WebSocket streaming and replication, and many other features. Private keys storage, IoT (to collect statistics data and save it), Distributed cache, service discovery distributed configuration, blob storage, replication, etc.
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SwayDB
High performance and resource efficiency with embedded persistent and in-memory key value storage engine. It is designed to manage bytes on-disk efficiently and in-memory efficiently by recognising reoccurring pattern in serialised bytes. The core implementation can be restricted to any data model (SQL or NoSQL) or storage type (Disk, RAM). Although the core has many configurations that can easily be tuned for specific use-cases we plan to implement automatic runtime tuning once we are able collect and analyse runtime machine statistics and read-write patterns. You can manage data by creating familiar data structures such as Map, Set, Queue and SetMap. These data structures can be easily converted to native Java or Scala collections. Conditional updates/data modification can be done with any Java, Scala, or any native JVM Code - No query language.
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