mrfyda

package model

import model.traits.BaseTable
import model.traits.SecureTable
import play.api.libs.Crypto
import scala.slick.driver.PostgresDriver.simple._


case class LoveLetter(id: Long, fromId: Long, toId: Long, content: SecureString)

mather

/* util パッケージで宣言する場合 */
package object util {
  import java.sql.Timestamp
  import com.github.nscala_time.time.Imports.DateTime
  import scala.slick.lifted.MappedTypeMapper
  
  /* 結果値 Timestamp をアプリケーションでは DateTime に変換して使う */
  implicit def dateTimeMapper = MappedTypeMapper.base[DateTime,Timestamp](
    /* f: DateTime -> Timestamp */
    dt => new Timestamp(dt.getMillis), 

new_task.py

#!/usr/bin/env python
import sys

import pika

connection = pika.BlockingConnection(pika.ConnectionParameters(
               'localhost'))
channel = connection.channel()

channel.queue_declare(queue='hello', durable=True)

matope

#NoSQLデータモデリング技法

原文：NoSQL Data Modeling Techniques « Highly Scalable Blog

I translated this article for study. contact matope[dot]ono[gmail] if any problem.

NoSQLデータベースはスケーラビリティ、パフォーマンス、一貫性といった様々な非機能要件から比較される。NoSQLのこの側面は実践と理論の両面からよく研究されている。ある種の非機能特性はNoSQLを利用する主な動機であり、NoSQLシステムによく適用されるCAP定理がそうであるように分散システムの基本的原則だからだ。一方で、NoSQLデータモデリングはあまり研究されておらず、リレーショナルデータベースに見られるようなシステマティックな理論に欠けている。本稿で、私はデータモデリングの視点からのNoSQLシステムファミリーの短い比較といくつかの共通するモデリングテクニックの要約を解説したい。

本稿をレビューして文法を清書してくれたDaniel Kirkdorfferに感謝したいと思う

kencoba

abstract class Product

abstract class PizzaBuilder {
  var dough: String
  var sauce: String
  var topping: String

  def withDough(dough: String): PizzaBuilder
  def withSauce(sauce: String): PizzaBuilder
  def withTopping(topping: String): PizzaBuilder

chitchcock

Stevey's Google Platforms Rant

I was at Amazon for about six and a half years, and now I've been at Google for that long. One thing that struck me immediately about the two companies -- an impression that has been reinforced almost daily -- is that Amazon does everything wrong, and Google does everything right. Sure, it's a sweeping generalization, but a surprisingly accurate one. It's pretty crazy. There are probably a hundred or even two hundred different ways you can compare the two companies, and Google is superior in all but three of them, if I recall correctly. I actually did a spreadsheet at one point but Legal wouldn't let me show it to anyone, even though recruiting loved it.

I mean, just to give you a very brief taste: Amazon's recruiting process is fundamentally flawed by having teams hire for themselves, so their hiring bar is incredibly inconsistent across teams, despite various efforts they've made to level it out. And their operations are a mess; they don't real

fabiant7t

import logging
import math
import mimetypes
from multiprocessing import Pool
import os

from boto.s3.connection import S3Connection
from filechunkio import FileChunkIO