EcZachly

-- first query all the users 
WITH offsets AS (SELECT a.*,
                        EXTRACT(hour FROM ptn.utc_offset) AS utc_offset
                 FROM bootcamp.attendees a
                          JOIN pg_timezone_names ptn ON a.timezone = ptn.name
                 WHERE a.bootcamp_version = 3
                   AND a.timezone IS NOT NULL
                   AND a.content_delivery = 'Live'::text
                  ),
-- then aggregate the users by track and offset, we want matching timezones to fill up first

dcastro

import shapeless._
import shapeless.ops.record.Keys
import shapeless.ops.hlist.Selector
import shapeless.tag._

/**
  * Gets the name of a case class's field, in a type-safe way.
  * If the class does not have a field with the given name, the program won't compile.
  *
  * Kinda similar to C#'s `nameof` operator, but not bolted onto the language

Daenyth

Operation	Input	Result	Notes
map	F[A] , A => B	F[B]	Functor
apply	F[A] , F[A => B]	F[B]	Applicative
(fa, fb, ...).mapN	(F[A], F[B], ...) , (A, B, ...) => C	F[C]	Applicative
(fa, fb, ...).tupled	(F[A], F[B], ...)	F[(A, B, ...)]	Applicative
flatMap	F[A] , A => F[B]	F[B]	Monad
traverse	F[A] , A => G[B]	G[F[A]]	Traversable; fa.traverse(f) == fa.map(f).sequence; "foreach with effects"
sequence	F[G[A]]	G[F[A]]	Same as fga.traverse(identity)
attempt	F[A]	F[Either[E, A]]	Given ApplicativeError[F, E]

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

Integralist

See my working application (and additional notes) here:

https://github.com/integralist/simple-rpm

Other information that led to the above repository, can be found below

References

• http://www.rpm.org/max-rpm/s1-rpm-anywhere-different-build-area.html
• http://www.tldp.org/HOWTO/RPM-HOWTO/build.html

akkomar

import scala.concurrent.ExecutionContext.Implicits.global
import scala.concurrent.duration._
import scala.concurrent.{Await, Future}

val futures: List[Future[List[Int]]] =
  List.fill(10)(Future {
    Thread.sleep(100)
    List(1, 2, 3)
  })

etorreborre

  /**
   * Let's say I can read things from a "Store"
   */
   trait Store[F] {
     def read(path: Path): F[String]
   }
   
  /**
   * Now I want to read more specific things, like a Configuration
   *

mitchwongho

# Assuming an Ubuntu Docker image
$ docker run -it <image> /bin/bash

jboner

Latency Comparison Numbers (~2012)
----------------------------------
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