iomonad iomonad

Device Tree Overlays on Archlinux

Dependencies

base-devel

You require a working archlinuxarm operarting system and the development tools:

$ pacman -S base-devel

Kafka 0.11.0.0 (Confluent 3.3.0) added support to manipulate offsets for a consumer group via cli kafka-consumer-groups command.

List the topics to which the group is subscribed

kafka-consumer-groups --bootstrap-server <kafkahost:port> --group <group_id> --describe

Note the values under "CURRENT-OFFSET" and "LOG-END-OFFSET". "CURRENT-OFFSET" is the offset where this consumer group is currently at in each of the partitions.

Reset the consumer offset for a topic (preview)

Clojure Destructuring Tutorial and Cheat Sheet

(Related blog post)

Simply put, destructuring in Clojure is a way extract values from a datastructure and bind them to symbols, without having to explicitly traverse the datstructure. It allows for elegant and concise Clojure code.

Vectors and Sequences

Moved to git repository: https://github.com/denji/nginx-tuning

NGINX Tuning For Best Performance

For this configuration you can use web server you like, i decided, because i work mostly with it to use nginx.

Generally, properly configured nginx can handle up to 400K to 500K requests per second (clustered), most what i saw is 50K to 80K (non-clustered) requests per second and 30% CPU load, course, this was 2 x Intel Xeon with HyperThreading enabled, but it can work without problem on slower machines.

You must understand that this config is used in testing environment and not in production so you will need to find a way to implement most of those features best possible for your servers.

Common

export OPT=/opt
export BUILDS=/some/where/mini_linux
mkdir -p $BUILDS

Linux kernel

General Background and Overview

Probabilistic Data Structures for Web Analytics and Data Mining : A great overview of the space of probabilistic data structures and how they are used in approximation algorithm implementation.
Models and Issues in Data Stream Systems
Philippe Flajolet’s contribution to streaming algorithms : A presentation by Jérémie Lumbroso that visits some of the hostorical perspectives and how it all began with Flajolet
Approximate Frequency Counts over Data Streams by Gurmeet Singh Manku & Rajeev Motwani : One of the early papers on the subject.
[Methods for Finding Frequent Items in Data Streams](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.187.9800&rep

	;; run with
	;; clj -Sdeps '{:deps {gdocs {:git/url "https://gist.github.com/souenzzo/df540002607b15378f8014237e499fdd" :sha "fee00617c75fc24c74931aa4200f74666c5b66b6"}}}' -m gdocs

	{:paths ["."]
	:deps {org.clojure/clojure {:mvn/version "1.10.0"}
	com.google.api-client/google-api-client {:mvn/version "1.28.0"}
	com.google.oauth-client/google-oauth-client-jetty {:mvn/version "1.28.0"}
	com.google.apis/google-api-services-sheets {:mvn/version "v4-rev566-1.25.0"}}}

	; A REPL-based, annotated Seesaw tutorial
	; Please visit https://github.com/daveray/seesaw for more info
	;
	; This is a very basic intro to Seesaw, a Clojure UI toolkit. It covers
	; Seesaw's basic features and philosophy, but only scratches the surface
	; of what's available. It only assumes knowledge of Clojure. No Swing or
	; Java experience is needed.
	;
	; This material was first presented in a talk at @CraftsmanGuild in
	; Ann Arbor, MI.

	defmodule Expng do

	defstruct [:width, :height, :bit_depth, :color_type, :compression, :filter, :interlace, :chunks]

	def png_parse(<<
	0x89, 0x50, 0x4E, 0x47, 0x0D, 0x0A, 0x1A, 0x0A,
	_length :: size(32),
	"IHDR",
	width :: size(32),
	height :: size(32),