Serhii Sokoliuk hcoa

Concurrent resource caching for cats

Motivation

cats-effect Resource is extremely handy for managing the lifecycle of stateful resources, for example database or queue connections. It gives a main interface of:

trait Resource[F[_], A] {
  /** - Acquire resource
    * - Run f
 * - guarantee that if acquire ran, release will run, even if `use` is cancelled or `f` fails

Dependency Injection in Functional Programming

There exist several DI frameworks / libraries in the Scala ecosystem. But the more functional code you write the more you'll realize there's no need to use any of them.

A few of the most claimed benefits are the following:

Dependency Injection.
Life cycle management.
Dependency graph rewriting.

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

originally written in Dec 2011, needs updating

Log analysis has always struck me as one of those things that gets too much superficial attention without enough attention to detail. That is, we know that we need to do it, but we don’t talk about how we need to do it. At best, we talk about making sure we collect and archive logs. Analysis plays second fiddle, even though in reality logs without analysis provide almost no value to an organization. And you’ll find greatest value in discovery of the earliest stages of an incident rather than in hindsight to understand what went wrong. Unfortunately, less than 1% of data breach investigations in the 2011 Verizon DBIR started with log analysis and review!

The analysis ideas I present below don’t even begin to represent a comprehensive view. And of course every network is different, so you will need to think about your specific needs. But this may get

	set autoread " reload on external file changes
	set backspace=indent,eol,start " backspace behaviour
	set clipboard=unnamed,unnamedplus " enable clipboard
	set encoding=utf8 " enable utf8 support
	set hidden " hide buffers, don't close
	set mouse=a " enable mouse support
	set nowrap " disable wrapping
	set number " show line numbers
	set term=xterm-256color " terminal type
	set wildmenu wildmode=longest:full,full " wildmode settings

	import java.util.concurrent.atomic.{AtomicLong, AtomicReference}
	import scala.concurrent.duration._

	trait Clock {
	def now: Long
	}

	class SystemClock extends Clock {
	def now: Long = System.nanoTime()
	}

	import java.time.Clock
	import scala.annotation.tailrec
	import scala.collection.mutable

	case class Issue(tokens: Long, timestampMillis: Long)

	class NaiveRateLimiter private (capacity: Long, periodMs: Long, clock: Clock) {
	private var availableCapacity = capacity

	private val issuedTokens = mutable.MutableList[Issue]()

	Latency Comparison Numbers
	--------------------------
	L1 cache reference 0.5 ns
	Branch mispredict 5 ns
	L2 cache reference 7 ns 14x L1 cache
	Mutex lock/unlock 25 ns
	Main memory reference 100 ns 20x L2 cache, 200x L1 cache
	Compress 1K bytes with Zippy 3,000 ns 3 us
	Send 1K bytes over 1 Gbps network 10,000 ns 10 us
	Read 4K randomly from SSD* 150,000 ns 150 us ~1GB/sec SSD

Serhii Sokoliuk hcoa

Concurrent resource caching for cats

Motivation

Dependency Injection in Functional Programming

Contents

Latency numbers every programmer should know