Apache Arrow columnar in-memory format
Node.js atomic and non-atomic counters, rate limiting tools, protection from DoS and brute-force attacks at scale
Cross-platform process cpu % and memory usage of a PID
A list of CSS features and their positions in the process of becoming implemented web standards
Read data from stdin.
Tiny but powerful full-text search engine for browser and Node
A set of efficient utilities that extend the use of JSON (streaming, estimate size, NDJSON/JSONL, etc.)
minimal implementation of a PassThrough stream
KTX 2.0 (.ktx2) parser and serializer.
In-memory state adapter for chat (development/testing)
This is lightweight memory stream module for node.js.
fs read and write streams based on minipass
Process manager UI for Electron applications
fs read and write streams based on minipass
Read a project manifest (called package.json in most cases)
Static AST checker for accessibility rules on JSX elements.
Binary value packing and unpacking
A faster & low-memory replacement for geoip-lite, a node library that maps IPs to geographical information
Apache Arrow columnar in-memory format
node.js library for reading and extraction of ZIP archives
Get running processes
Redis Server for testing. The server will allow you to connect your favorite client library to the Redis Server and run parallel integration tests isolated from each other.
MongoDB Server for testing (auto-download latest version). The server will allow you to connect your favourite ODM or client library to the MongoDB Server and run parallel integration tests isolated from each other.
MongoDB Server for testing (core package, without autodownload). The server will allow you to connect your favourite ODM or client library to the MongoDB Server and run parallel integration tests isolated from each other.
Read memory from another process.
A rust library that can read/write the memory of other processes.
Sampling profiler for Python programs
Read memory from another process.
Read memory from another process.
Read memory from another process.
DSL for temporally files read/write in the object oriented way (system tmp). Manage tmp files in the super easy way! This dsl let you have simply way to commands and create variables on file system by default in the actual systems (cross platform) tmp folder. Sometimes it can be useful for multi processing (forked processes), but the main goal is not made for shared memory management! The goal is to provide dsl for easy tmp files making on the filesystem in the object oriented way (real objects and not simply strings). By default i's always IO work and not memory, everything you save with this will be IO command and not memory
Ruby bindings for archive_r, a libarchive-based library for processing many archive formats. It streams entry data directly from the source to recursively read nested archives without extracting to temporary files or loading large in-memory buffers.
enumerator_io allows you to wrap an enumerator in an IO-compatible interface, enabling chunked reads and efficient memory usage. Ideal for streaming large files or processing data in real-time without buffering everything into memory.
Geoptima is a suite of applications for measuring and locating mobile/cellular subscriber experience on GPS enabled smartphones. It is produced by AmanziTel AB in Helsingborg, Sweden, and supports many phone manufacturers, with free downloads from the various app stores, markets or marketplaces. This Ruby library is capable of reading the JSON format files produced by these phones and reformating them as CSV, GPX and PNG for further analysis in Excel. This is a simple and independent way of analysing the data, when compared to the full-featured analysis applications and servers available from AmanziTel. If you want to analyse a limited amount of data in excel, or with Ruby, then this GEM might be for you. If you want to analyse large amounts of data, from many subscribers, or over long periods of time then rather consider the NetView and Customer IQ applications from AmanziTel at www.amanzitel.com. Current features available in the library and the show_geoptima command: * Import one or many JSON files * Organize data by device id (IMEI) into datasets * Split by event type * Time ordering and time correlation (associate data from one event to another): ** Add GPS locations to other events (time window and interpolation algorithms) ** Add signal strenth, battery level, etc. to other events * Export event tables to CSV format for further processing in excel * Make and export GPS traces in GPX and PNG format for simple map reports The amount of data possible to process is limited by memory, since all data is imported in ruby data structures for procssing. If you need to process larger amounts of data, you will need a database-driven approach, like that provided by AmanziTel's NetView and Customer IQ solutions. This Ruby gem is actually used by parts of the data pre-processing chain of 'Customer IQ', but it not used by the main database and statistics engine that generates the reports.
Ply is a ruby gem for reading Stanford PLY-format 3D model files. The PLY file format is a flexible format for storing semi-structured binary data, and is often used to stored polygonalized 3D models generated with range scanning hardware. You can find some examples of the format at the {Stanford 3D Scanning Repository}[http://graphics.stanford.edu/data/3Dscanrep/]. Ply provides a simple API for quick access to the data in a PLY file (including examining the structure of a particular file's content), and an almost-as-simple event-driven API which can be used to process extremely large ply files in a streaming fashion, without needing to keep the full dataset represented in the file in memory. Ply handles all three types of PLY files (ascii, binary-big-endian and binary-little-endian). If you don't have any Stanford PLY files on hand, you probably don't need this gem, but if you're curious, the PLY file format is described at Wikipedia[http://en.wikipedia.org/wiki/PLY_(file_format)].
# 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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