mravi’s gists

mravi / StockBean.java

Created December 6, 2014 02:51

Phoenix MR Example

	package org.apache.phoenix.example.bean;
	import java.util.Arrays;
	public final class StockBean {

	private String stockName;

	private Integer year;

	private double[] recordings;

mravi / PhoenixSparkJob.java

Created December 6, 2014 02:55

Phoenix Spark Example

	import java.io.IOException;

	import org.apache.hadoop.conf.Configuration;
	import org.apache.hadoop.hbase.HBaseConfiguration;
	import org.apache.hadoop.hbase.HConstants;
	import org.apache.hadoop.io.NullWritable;
	import org.apache.hadoop.mapreduce.JobContext;
	import org.apache.hadoop.mapreduce.OutputFormat;
	import org.apache.phoenix.mapreduce.PhoenixInputFormat;
	import org.apache.phoenix.mapreduce.PhoenixOutputFormat;

mravi / Access Phoenix Table Using Spark & Zeppelin note book

Last active January 20, 2017 02:01

Phoenix Zeppelin

	1. Checkout source code from https://github.com/apache/incubator-zeppelin
	2. Custom build the code with spark 1.3 and with the respective Hadoop version.
	mvn clean package -Pspark-1.3 -Dhadoop.version=2.6.0 -Phadoop-2.6 -DskipTests
	3. Have the following jars in the spark classpath by placing them in the location $ZEPPELIN_HOME/interpreter/spark
	a. hbase-client.jar
	b. hbase-protocol.jar
	c. hbase-common.jar
	d. phoenix-4.4.x-client-without-hbase.jar

	4. Start Zeppelin

mravi / gist:5a78c56a4e06741f0ad6

Last active September 20, 2015 16:33 — forked from debasishg/gist:8172796

A collection of links for streaming algorithms and data structures

Probabilistic Data Structures for Web Analytics and Data Mining : A great overview of the space of probabilistic data structures and how they are used in approximation algorithm implementation.
Models and Issues in Data Stream Systems
Philippe Flajolet’s contribution to streaming algorithms : A presentation by Jérémie Lumbroso that visits some of the hostorical perspectives and how it all began with Flajolet
Approximate Frequency Counts over Data Streams by Gurmeet Singh Manku & Rajeev Motwani : One of the early papers on the subject.
[Methods for Finding Frequent Items in Data Streams](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.187.9800&rep

mravi / SparkUtils.scala

Created July 26, 2016 13:51 — forked from ibuenros/SparkUtils.scala

Spark productionizing utilities developed by Ooyala, shown in Spark Summit 2014

	//==================================================================
	// SPARK INSTRUMENTATION
	//==================================================================

	import com.codahale.metrics.{MetricRegistry, Meter, Gauge}
	import org.apache.spark.{SparkEnv, Accumulator}
	import org.apache.spark.metrics.source.Source
	import org.joda.time.DateTime
	import scala.collection.mutable

mravi / gist:86e6cab649929dc74d0a9578670cca64

Created January 24, 2017 16:43 — forked from mikeyk/gist:1329319

Testing storage of millions of keys in Redis

	#! /usr/bin/env python

	import redis
	import random
	import pylibmc
	import sys

	r = redis.Redis(host = 'localhost', port = 6389)
	mc = pylibmc.Client(['localhost:11222'])

mravi / ravimagham.json

Created December 29, 2023 23:28

	{
	"public_identifier": "ravimagham",
	"profile_pic_url": "https://s3.us-west-000.backblazeb2.com/proxycurl/person/ravimagham/profile?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=0004d7f56a0400b0000000001%2F20231229%2Fus-west-000%2Fs3%2Faws4_request&X-Amz-Date=20231229T232529Z&X-Amz-Expires=3600&X-Amz-SignedHeaders=host&X-Amz-Signature=f5fdd1dcbc5054bd51f76eff76e920e19e1573942f45315fc69607289d379ef1",
	"background_cover_image_url": null,
	"first_name": "Ravi",
	"last_name": "Magham",
	"full_name": "Ravi Magham",
	"follower_count": 1220,
	"occupation": "Software Engineer at Lyft",
	"headline": "Software engineer",

Ravi Magham mravi