cluster daemon
A cluster daemon with a rest interface for your app.
clusterd - node js cluster daemon
Generates CRC hashes for strings - for use by node redis clients to determine key slots.
Find the position of grapheme cluster breaks in a string
Credhelper daemon for credential session management
Standalone Node.js SDK for cluster-daemon ↔ Volt server communication — enrollment, control socket, heartbeat and reverse-channel dispatch
Client for prometheus
Fast nd point clustering.
React-leaflet-cluster is a plugin for react-leaflet. A wrapper component of Leaflet.markercluster.
Cluster management for puppeteer
Group points into clusters based on their spatial proximity or properties.
PM2.io Agent Daemon
AWS SDK for JavaScript Ecs Client for Node.js, Browser and React Native
Terminal and Web console for Kubernetes
Takes a set of points and partition them into clusters according to DBSCAN's data clustering algorithm.
A JS client library for the IPFS Cluster HTTP API.
Provides Beautiful Animated Marker Clustering functionality for Leaflet
Matter.js main entrypoint
Layout algorithms for visualizing hierarchical data.
extensible multi-core server manager
cluster workers reload
Sharing Connection among Multi-Process Nodejs
OCI NodeJS client for Cluster Placement Groups Service
Fabric is a small ruby app to perform tasks on servers via SSH. Built around net/ssh and taking heavy inspiration from Capistrano, it currently focuses on managing remote users and their keys on clusters of servers. It does this without the need for a process/daemon/dependency or even ruby being installed on the remote host(s). In the future, it will allow you to create policies for server management and perform sysadmin tasks via a system of recipes.
# Sparrow is a really fast lightweight queue written in Ruby that speaks memcached. # That means you can use Sparrow with any memcached client library (Ruby or otherwise). # # Basic tests shows that Sparrow processes messages at a rate of 850-900 per second. # The load Sparrow can cope with increases exponentially as you add to the cluster. # Sparrow also takes advantage of eventmachine, which uses a non-blocking io, offering great performance. # # Sparrow is a in-memory queue but will persist the data to disk when receiving a term signal. # # Sparrow comes with built in support for daemonization and clustering. # Also included are example libraries and clients. For example: # # require 'memcache' # m = MemCache.new('127.0.0.1:11212') # m['queue_name'] = '1' # Publish to queue # m['queue_name'] #=> 1 Pull next msg from queue # m['queue_name'] #=> nil # m.delete('queue_name) # Delete queue # # # or using the included client: # # class MyQueue < MQ3::Queue # def on_message # logger.info "Received msg with args: #{args.inspect}" # end # end # # MyQueue.servers = [ # MQ3::Protocols::Memcache.new({:host => '127.0.0.1', :port => 11212, :weight => 1}) # ] # MyQueue.publish('test msg') # MyQueue.run # # Messages are deleted as soon as they're read and the order you add messages to the queue probably won't # be the same order when they're removed. # # Additional memcached commands that are supported are: # flush_all # Deletes all queues # version # quit # The memcached commands 'add', and 'replace' just call 'set'. # # Call sparrow with --help for usage options # # The daemonization won't work on Windows. # # Check out the code: # svn checkout http://sparrow.googlecode.com/svn/trunk/ sparrow # # Sparrow was inspired by Twitter's Starling
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