djspiewak

Quick Tips for Fast Code on the JVM

I was talking to a coworker recently about general techniques that almost always form the core of any effort to write very fast, down-to-the-metal hot path code on the JVM, and they pointed out that there really isn't a particularly good place to go for this information. It occurred to me that, really, I had more or less picked up all of it by word of mouth and experience, and there just aren't any good reference sources on the topic. So… here's my word of mouth.

This is by no means a comprehensive gist. It's also important to understand that the techniques that I outline in here are not 100% absolute either. Performance on the JVM is an incredibly complicated subject, and while there are rules that almost always hold true, the "almost" remains very salient. Also, for many or even most applications, there will be other techniques that I'm not mentioning which will have a greater impact. JMH, Java Flight Recorder, and a good profiler are your very best friend! Mea

djenriquez

#!/bin/bash
# On Amazon Linux
mkdir -p /tmp/rsyslog-fix && cd /tmp/rsyslog-fix

git clone https://github.com/edenhill/librdkafka.git

# Install Libredkafka
cd librdkafka && . configure
make
sudo make install

rondy

FWIW: I (@rondy) am not the creator of the content shared here, which is an excerpt from Edmond Lau's book. I simply copied and pasted it from another location and saved it as a personal note, before it gained popularity on news.ycombinator.com. Unfortunately, I cannot recall the exact origin of the original source, nor was I able to find the author's name, so I am can't provide the appropriate credits.

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Effective Engineer - Notes

• By Edmond Lau
• Highly Recommended 👍
• http://www.theeffectiveengineer.com/

What's an Effective Engineer?

topnotcher

package syslog;

import java.net.DatagramSocket;
import java.net.DatagramPacket;

import org.apache.commons.logging.Log;
import org.apache.commons.logging.LogFactory;


/**

debasishg

General Background and Overview

1. 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.
2. Models and Issues in Data Stream Systems
3. 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
4. Approximate Frequency Counts over Data Streams by Gurmeet Singh Manku & Rajeev Motwani : One of the early papers on the subject.
5. [Methods for Finding Frequent Items in Data Streams](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.187.9800&rep=rep1&t

raphw

import org.junit.Before;
import org.junit.Test;
import sun.misc.Unsafe;
import sun.reflect.ReflectionFactory;

import java.lang.reflect.Constructor;
import java.lang.reflect.Field;
import java.lang.reflect.Modifier;

import static org.junit.Assert.assertEquals;

longestword.py

def LongestWord(sen): 
  l = []
  for w in sen.split(' '):
    l.append(''.join(ch for ch in w if ch.isalnum()))
  return max(l,key=len)

stefanv

#!/usr/bin/python
# coding=utf-8

# Python version of Zach Holman's "spark"
# https://github.com/holman/spark
# by Stefan van der Walt <stefan@sun.ac.za>

"""
USAGE: