mravi

{
    "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",

mravi

#! /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

//==================================================================
// 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

1. General Background and Overview

• 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

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

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

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

    private String stockName;
    
    private Integer year;
    
    private double[] recordings;