rosedb is a fast, stable and embedded key-value (k-v) database based on bitcask. Its on-disk files are organized as LSM trees, optimizing for write throughput. rosedb supports a variety of of data structures such as string, list, hash, set and sorted set. rosedb is written in pure Go. It is production-ready.
redis cli embedded database key-value data-structures throughput ttl kv-store rosedb support-expirationKeyv provides a consistent interface for key-value storage across multiple backends via storage adapters. It supports TTL based expiry, making it suitable as a cache or a persistent key-value store. It works with any storage that implements the Map API. It handles all JSON types plus Buffer. It has wide range of efficient, well tested storage adapters.
key-value cache key value store ttl key-value-storemoize is a consistently blazing fast memoization library for JavaScript. It handles multiple parameters (including default values) without any additional configuration, and offers a large number of options to satisfy any number of potential use-cases. All parameter types are supported, including circular objects, functions, etc. There are also a number of shortcut methods to memoize for unique use-cases.
memoization performance cache expire lru memoize optimize promise ttlMemoize functions - An optimization used to speed up consecutive function calls by caching the result of calls with identical input
memoize function mem memoization cache caching optimize performance ttl expire promiseAugment LevelUP to handle a new 'ttl' option on put() and batch() that specifies the number of milliseconds an entry should remain in the data store. After the TTL, the entry will be automatically cleared for you.Requires LevelUP, Level or level-hyper to be installed separately.
leveldb levelup level ttlRequires LevelUP (or Level) and sublevel to be installed separately.Also builds on LevelTTL to provide TTL-based cache timeouts, you'll need to initialise it too. You can also set a custom check-frequency when you initialise LevelTTL.
level leveldb levelup cache ttlUseful for speeding up consecutive function calls by caching the result of calls with identical input.See the mem docs.
promise memoize mem memoization function cache caching optimize performance ttl expire async await promises time out cancel bluebirdnemo-rocksdb is compatible with rocksdb, and I added TTL feature on it, supporting to put a record with any specified TTL. The performance is close to rocksdb, so you can use it without worring about performance penalty. It uses rocksdb as a submodule, so it's easy to upgrade rocksdb to the latest version if needed, now it's using rocksdb(v5.0.1). Besides, it is going to be used in Nemo as a submodule.you can create iterator by NewIterator() to iterate db, it will ignore the expired records automaticly.
rocksdb ttlEncrypt/decrypt JavaScript objects as base64 strings with optional TTL support.Create an instance of encrypter, secret could be a string or an array of strings.
base64 encrypt decrypt object array json string ttlSmall and efficient cache provider for Node.JS with In-memory, File, Redis and MongoDB engines. Create cacheman instance. It accepts an name(optional) and options(optional). options can contain ttl to set the default "Time To Live" in seconds, delimiter to change the delimiter used for array keys (default: ':'), Promise can set a Promise library to use for promises, engine that could be "memory", "in file", "redis" or "mongo", and the corresponding engine options that can be passed like port, host, etc.
cache file redis memory mongodb caching mongo store ttl middleware bucketMongoDB standalone caching library for Node.JS and also cache engine for cacheman. Create cacheman-mongo instance. options are mongo valid options including port, host, database and collection.
cache mongodb caching mongo store ttl cachemanRedis standalone caching library for Node.JS and also cache engine for cacheman. Create cacheman-redis instance. options are redis valid options including port and host.
cache redis caching store ttl cachemanCaching mongoose queries easier with cacheman that supports in-memory, and Redis engines. Then later any find query will be cached for 30 seconds.
plugin mongoose query cache data mongo mongodb redis store memory catbox ttlRequires LevelUP (or Level) and sublevel to be installed separately. Also builds on LevelTTL to provide TTL-based cache timeouts, you'll need to initialise it too. You can also set a custom check-frequency when you initialise LevelTTL.
level leveldb levelup cache ttlgo-ttlmap is a Go package that provides an in-memory key-value cache with notification events for storing TTL-based expirable items. For advanced usage, make sure to check the available documentation here.
map key-value ttl in-memoryAWS CloudFormation supports now TTL, so this plugin for Serverless is useless. More information about how to use it in the AWS documentation. It's not possible to use DynamoDB's TTL feature with CloudFormation yet. Use this plugin to configure TTL for your DynamoDB with Serverless.
serverless serverless-framework serverless-plugin dynamodb aws cloudformation plugin ttlUse memcached as the query cacher for mongoose. Inspired by Jonathan Brumley's mongoose-cachebox, but compatible with mongoose 3.8 and uses memcached instead of redis. The API is very similar. Credits to https://github.com/Faleij for all the features related to stream query. Then later any find or findOne query will be cached for 60 seconds.
plugin mongoose query cache data mongo mongodb memcached memory ttlA Mongoose caching module that works exactly how you would expect it to, with the latest version of Mongoose. You can also pass a custom key into the .cache() method, which you can then use later to clear the cached content.
cache mongo mongodb mongoose redis store ttlThe ultimate Javascript cache module. Unobtrusive and failsafe caching of anything. Caches functions instead of data. When you cache a function using hamster(fn), a function is returned. You can use this function in exactly the same way as the original. No other part of your code should ever have to care about that the function is cached.
cache memory function ttl lru
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