Results for process-stats

Framework to manage asynchronous execution of multiple compute units communicating using messages

PDF to pixel buffer — pure Rust, zero Poppler. For the CLI tool: cargo install rasterrocket-cli

CLI — renders PDF pages to pixel files. Drop-in pdftoppm replacement. Pure Rust, zero Poppler.

Pixel types and colour math for the rasterrocket PDF renderer

PPM/PGM/PBM/PNG output for the rasterrocket PDF renderer

FreeType glyph cache and outline rendering for the rasterrocket PDF renderer

Native PDF content-stream interpreter — poppler-free render path for rasterrocket

Lazy, zero-copy PDF file parser for the rasterrocket render pipeline

Software rasterizer — path fill, compositing, and AVX-512/AVX2/NEON SIMD for the rasterrocket PDF renderer

Get memory usage and CPU time on Linux and Windows

Monitor process memory and CPU over time

Get process starting time from /proc/PID/stat.

CMDA plugin to process logdata and save stats to a database

raindrops is a real-time stats toolkit to show statistics for Rack HTTP servers. It is designed for preforking servers such as unicorn, but should support any Rack HTTP server on platforms supporting POSIX shared memory. It may also be used as a generic scoreboard for sharing atomic counters across multiple processes.

A library, which allows easily reading various process stats from the proc filesystem

Journalist is a gem that intends to make it really easy to get visibility into any running Ruby processes with metrics like GC stats, performance and allocations.

raindrops is a real-time stats toolkit to show statistics for Rack HTTP servers. It is designed for preforking servers such as unicorn, but should support any Rack HTTP server on platforms supporting POSIX shared memory. It may also be used as a generic scoreboard for sharing atomic counters across multiple processes.

raindrops is a real-time stats toolkit to show statistics for Rack HTTP servers. It is designed for preforking servers such as unicorn, but should support any Rack HTTP server on platforms supporting POSIX shared memory. It may also be used as a generic scoreboard for sharing atomic counters across multiple processes.

== FEATURES: * Input your data as an array of hashes * Input a report layout, built using a Ruby DSL * Outputs ASCII pivot tables suitable for fast reports * Pretty fast: takes less than a second to process 1,000 records of data by a report with 100 rows and 10 columns. == SYNOPSIS: require 'rubygems' require 'crosstab' data = [{:gender => "M", :age => 1}, {:gender => "F", :age => 2}, {:gender => "M", :age => 3}] my_crosstab = crosstab data do table do title "Q.A Age:" group "18 - 54" do row "18 - 34", :age => 1 row "35 - 54", :age => 2 end row "55 or older", :age => 3 end banner do column "Total" group "Gender" do column "Male", :gender => "M" column "Female", :gender => "F" end end end puts my_crosstab.to_s # => ... Table 1 Q.A Age: Gender ---------------- Total Male Female (A) (B) (C) ------- ------- ------- (BASE) 3 2 1 18 - 54 2 1 1 ----------------------------- 67% 50% 100% 18 - 34 1 1 -- 33% 50% 35 - 54 1 -- 1 33% 100% 55 or older 1 1 -- 33% 50% == JUST THE BEGINNING: * I hope to add in later releases: * New export formats: html, pdf, csv, excel. * More stats than just frequency and percentage: mean, median, std. deviation, std. error, and significance testing * Optional row and table suppression for low frequencies * Optional table rows populating from the data * Optional table ranking -- automatically reorder rows based in descending order based on frequencies observed == REQUIREMENTS: * None

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