Johannes Rudolph jrudolph

Get the JDK source (per the OpenJDK instructions):

hg clone http://hg.openjdk.java.net/portola/portola
cd portola
bash ./get_source.sh

You need an existing Alpine with an already-built JDK. I have a Docker image of Alpine with glibc-based Zulu JDK:

# stop active raid
mdadm --stop /dev/md[01]

# destroy partition table on hdds
dd if=/dev/zero of=/dev/sda bs=1M count=512
dd if=/dev/zero of=/dev/sdb bs=1M count=512

# create new partition table
sgdisk -og /dev/sda

Namespace Management (DRAFT)

Purpose

This document describes the guidelines for managing two important namespaces: the package scala._, and the Maven group org.scala-lang.

These questions become more important as we modularize the distribution, and as we publish new modules (such as ScalaJS, scala-async, scala-pickling.)

Docker stats collection for collectd

This script can be used to feed collectd with cpu and memory usage statistics for running docker containers using the collectd exec plugin.

This script will report the used and cached memory as well as the user and system cpu usage by inspecting the appropriate cgroup stat file for each running container.

Usage

This script is intented to be executed by collectd on a host with running docker containers. To use, simply configure the exec plugin in collectd to execute the collectd-docker.sh script. You may need to adjust the script to match your particulars, such as the mount location for cgroup.

Specialization with method handles

Two canonical examples for specialization:

Function1
Tuple2

`Function1` specialization

Specializing Function1 is a lot easier because functions do not have specialized fields.

Latency numbers every programmer should know

L1 cache reference ......................... 0.5 ns
Branch mispredict ............................ 5 ns
L2 cache reference ........................... 7 ns
Mutex lock/unlock ........................... 25 ns
Main memory reference ...................... 100 ns             
Compress 1K bytes with Zippy ............. 3,000 ns  =   3 µs
Send 2K bytes over 1 Gbps network ....... 20,000 ns  =  20 µs
SSD random read ........................ 150,000 ns  = 150 µs

Read 1 MB sequentially from memory ..... 250,000 ns = 250 µs

	#cloud-config

	coreos:
	etcd:
	# generate a new token for each unique cluster from https://discovery.etcd.io/new
	discovery: https://discovery.etcd.io/<token>
	# multi-region deployments, multi-cloud deployments, and droplets without
	# private networking need to use $public_ipv4
	addr: $private_ipv4:4001
	peer-addr: $private_ipv4:7001

	import com.yourkit.probes.*;
	import com.yourkit.api.*;

	@MethodPattern("scala.tools.nsc.Global$Run:advancePhase()")
	public class MemoryProbe {

	public static void onEnter(@This scala.tools.nsc.Global.Run run) {
	scala.reflect.internal.Phase patmatPhase = run.phaseNamed("patmat");
	scala.reflect.internal.Phase postErasurePhase = run.phaseNamed("posterasure");
	scala.reflect.internal.Phase icodePhase = run.phaseNamed("icode");

	import java.util.concurrent.atomic.AtomicReference
	import java.util.concurrent.CountDownLatch
	import scala.concurrent.Future
	import scala.concurrent.ExecutionContext
	import ExecutionContext.Implicits.global

	object TxMapTest {
	/*
	* Example Usage
	* We want to show two threads working with the same data source having both of their effects succeed

	package s

	object Test {
	// Observe that x.companion is statically typed such that foo is callable
	def f1() = {
	val x = new Foo

	println(x) // Foo instance
	println(x.companion) // Foo companion
	println(x.companion.foo) // I'm foo!