ewhauser

# Use csshX to open terminals on the nodes found in a search
# knife exec csshx.rb 'role:the_role'

abort("usage: knife exec csshx SEARCH_TERM") unless ARGV[2]

nodes = search(:node, "#{ARGV[2].to_s}").collect { |n| n.fqdn }
exec('csshX ' + nodes.join(' ')) unless nodes.length < 1

exit 0

ewhauser

package com.exacttarget.bloomin.jedis;

import com.google.common.base.Preconditions;
import com.twitter.common.net.pool.Connection;
import redis.clients.jedis.Jedis;

public class JedisConnection implements Connection<Jedis, Jedis> {

  private final Jedis jedis;

ewhauser

package org.apache.flume.kafka;

import com.cloudera.flume.core.Event;
import com.cloudera.flume.core.EventImpl;
import com.cloudera.util.Clock;
import kafka.api.FetchRequest;
import kafka.javaapi.consumer.SimpleConsumer;
import kafka.message.Message;
import kafka.server.KafkaConfig;
import kafka.server.KafkaServer;

ewhauser

#!/usr/bin/env ruby

require 'rubygems'
require 'getoptlong'
require 'socket'
require 'json'
require 'timeout'
require 'open-uri'

host = "localhost"

ewhauser

These notes are from Facebook's Analytics @ Scale conference.  I didn't take notes from the presentation or discussions with developers, so feel free to correct any inconsistencies:

- Presto is an ANSI SQL engine built on top of HDFS
- Similar functionality to Cloudera's Impala
- Facebook started developing this project prior to the Impala annoucement, some different design choices
- Implemented in Java
- Queries execute around 10x faster than Hive, aggregation based queries can be 100x times faster
- Byte code generation is used for efficient predicate processing
- Efficient fixed memory data structures are used (very low GC overhead)
- Presto daemons do not have to run on data nodes

ewhauser

Keybase proof

I hereby claim:

• I am ewhauser on github.
• I am ewhauser (https://keybase.io/ewhauser) on keybase.
• I have a public key ASDgt9UmpXcLznvoNPS0LsfUbzCUQPcbaceAg8QC2CAZlgo

To claim this, I am signing this object:

ewhauser

#!/usr/bin/env python3
"""
Requirements:
pip install requests python-dateutil
"""
import dateutil.parser as dp
import csv
import sys

from optparse import OptionParser

ewhauser

SELECT repo.name, JSON_EXTRACT(payload, '$.issue.number') as issue_id, COUNT(*) as count, JSON_EXTRACT(payload, '$.issue.title') as title
FROM `githubarchive.month.*`
WHERE type = 'IssueCommentEvent'
and repo.name = 'angular/angular'
and (_TABLE_SUFFIX like '2019%' or _TABLE_SUFFIX like '2018%' or _TABLE_SUFFIX like '2017%')
GROUP BY repo.name, issue_id, title
ORDER BY count desc

ewhauser

SELECT
  FORMAT_DATE('%F', d) as id,
  d AS full_date,
  EXTRACT(YEAR FROM d) AS year,
  EXTRACT(WEEK FROM d) AS year_week,
  EXTRACT(DAY FROM d) AS year_day,
  EXTRACT(YEAR FROM d) AS fiscal_year,
  FORMAT_DATE('%Q', d) as fiscal_qtr,
  EXTRACT(MONTH FROM d) AS month,
  FORMAT_DATE('%B', d) as month_name,

ewhauser

name: "Terraform"
on:
  push:
    branches:
      - main
    paths:
      - terraform/**
  pull_request:
    branches:
      - main