kenmsj

Built-in

diskutil erasevolume HFS+ 'RAM Disk' `hdiutil attach -nobrowse -nomount ram://XXXXX`

where XXXXX is the size of the RAM disk in terms of memory blocks.

Notes:

kenmsj

My Elasticsearch cheatsheet with example usage via rest api (still a work-in-progress)

Shortlinks:

• Cluster Health
  • Index Level
  • Shard Level
• Nodes Overview
• Indices Overview
• Cluster Maintenance

kenmsj

# Full configuration of mongod and mongos file, based on MongoDB 3.6.
# Includes links for official docs, for each section of config.
# 
# Are not mentioned in this document:
# - Deprecated options as of 3.6;
# - Options only available to mmapv1;
# - Windows Service Options;
#
# This document is divided as follows:
# - Options for both mongod and mongos;

kenmsj

##################### ElasticSearch Configuration Example #####################

# This file contains an overview of various configuration settings,
# targeted at operations staff. Application developers should
# consult the guide at <http://elasticsearch.org/guide>.
#
# The installation procedure is covered at
# <http://elasticsearch.org/guide/en/elasticsearch/reference/current/setup.html>.
#
# ElasticSearch comes with reasonable defaults for most settings,

kenmsj

Common Options

-#, --progress-bar Make curl display a simple progress bar instead of the more informational standard meter.

-b, --cookie <name=data> Supply cookie with request. If no =, then specifies the cookie file to use (see -c).

-c, --cookie-jar <file name> File to save response cookies to.

kenmsj

Installing Postgres via Brew

Pre-Reqs

Brew Package Manager

In your command-line run the following commands:

1. brew doctor
2. brew update

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public static void postNewComment(Context context,final UserAccount userAccount,final String comment,final int blogId,final int postId){
    mPostCommentResponse.requestStarted();
    RequestQueue queue = Volley.newRequestQueue(context);
    StringRequest sr = new StringRequest(Request.Method.POST,"http://api.someservice.com/post/comment", new Response.Listener<String>() {
        @Override
        public void onResponse(String response) {
            mPostCommentResponse.requestCompleted();
        }
    }, new Response.ErrorListener() {
        @Override

kenmsj

import org.apache.spark.mllib.linalg.distributed.RowMatrix
import org.apache.spark.mllib.linalg._
import org.apache.spark.{SparkConf, SparkContext}

// To use the latest sparse SVD implementation, please build your spark-assembly after this
// change: https://github.com/apache/spark/pull/1378

// Input tsv with 3 fields: rowIndex(Long), columnIndex(Long), weight(Double), indices start with 0
// Assume the number of rows is larger than the number of columns, and the number of columns is
// smaller than Int.MaxValue

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#!/bin/bash

# From http://tech.serbinn.net/2010/shell-script-to-create-ramdisk-on-mac-os-x/
#

ARGS=2
E_BADARGS=99

if [ $# -ne $ARGS ] # correct number of arguments to the script;
then

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/*
This example uses Scala.  Please see the MLlib documentation for a Java example.

Try running this code in the Spark shell.  It may produce different topics each time (since LDA includes some randomization), but it should give topics similar to those listed above.

This example is paired with a blog post on LDA in Spark: http://databricks.com/blog
Spark: http://spark.apache.org/
*/

import scala.collection.mutable