Friday, 2 December 2016

How to generate large amount of sample data with simple techniques for Big Data Projects

Kalyan Big Data Projects

How to generate large amount of sample data with simple techniques for Big Data Projects

Follow the below commands to generate large amount of sample data.


Create 'kalyan_bigdata_projects' folder in user home (i.e /home/orienit)


Command: mkdir /home/orienit/kalyan_bigdata_projects





Copy 'kalyan-bigdata-examples.jar' jar file into '/home/orienit/kalyan_bigdata_projects' folder




We are going to learn below Use Cases


Use Case1: Generating Sample Server Logs with simple command

Use Case2: Generating Sample Users in JSON format with simple command
Use Case3: Generating Sample Users in CSV format with simple command
Use Case4: Generating Sample Users in TSV format with simple command
Use Case5: Generating Sample Users in DELIMITED format with simple command
Use Case6: Generating Sample Product Log in JSON format with simple command
Use Case7: Generating Sample Product Log in CSV format with simple command
Use Case8: Generating Sample Product Log in TSV format with simple command
Use Case9: Generating Sample Product Log in DELIMITED format with simple command


Use Case1: Generating Sample Server Logs with simple command


java -cp /home/orienit/kalyan_bigdata_projects/kalyan-bigdata-examples.jar \

com.orienit.kalyan.examples.GenerateServerLog \
-f /tmp/serverlog.txt \
-n 100 \
-s 10 \
-d 2016/01/01 \
-w 5





Read SERVER LOG data




Use Case: Generating Sample Users with simple command


java -cp /home/orienit/kalyan_bigdata_projects/kalyan-bigdata-examples.jar \

com.orienit.kalyan.examples.GenerateUsers




We can pass different arguments for above command


-d => field delimiter like (tab, comma, semicolon, etc )

-f => output file path
-n => number of users, maximum number is 10000
-s => starting number of user id, bydefault is 1
-w => waiting time in milli sec, bydefault is 100 millisec


Use Case2: Generating Sample Users in JSON format with simple command


java -cp /home/orienit/kalyan_bigdata_projects/kalyan-bigdata-examples.jar \

com.orienit.kalyan.examples.GenerateUsers \
-f /tmp/users.json \
-n 10 \
-s 1




Read JSON Data






Use Case3: Generating Sample Users in CSV format with simple command


java -cp /home/orienit/kalyan_bigdata_projects/kalyan-bigdata-examples.jar \

com.orienit.kalyan.examples.GenerateUsers \
-f /tmp/users.csv \
-d ',' \
-n 10 \
-s 1





Read CSV data





Use Case4: Generating Sample Users in TSV format with simple command


java -cp /home/orienit/kalyan_bigdata_projects/kalyan-bigdata-examples.jar \

com.orienit.kalyan.examples.GenerateUsers \
-f /tmp/users.tsv \
-d '\t' \
-n 10 \
-s 1



Read TSV data






Use Case5: Generating Sample Users in DELIMITED format with simple command


java -cp /home/orienit/kalyan_bigdata_projects/kalyan-bigdata-examples.jar \

com.orienit.kalyan.examples.GenerateUsers \
-f /tmp/users.txt \
-d '#' \
-n 10 \
-s 1




Read Any DELIMITED Data




Use Case: Generating Sample Product Log with simple command


java -cp /home/orienit/kalyan_bigdata_projects/kalyan-bigdata-examples.jar \

com.orienit.kalyan.examples.GenerateProductLog




We can pass different arguments for above command


-d => field delimiter like (tab, comma, semicolon, etc )

-f => output file path
-l => number of logs, maximum number is 100000
-n => number of users, maximum number is 10000
-w => waiting time in milli sec, bydefault is 100 millisec


Use Case6: Generating Sample Product Log in JSON format with simple command


java -cp /home/orienit/kalyan_bigdata_projects/kalyan-bigdata-examples.jar \

com.orienit.kalyan.examples.GenerateProductLog \
-f /tmp/productlog.json \
-n 10 \
-l 20




Read JSON data





Use Case7: Generating Sample Product Log in CSV format with simple command


java -cp /home/orienit/kalyan_bigdata_projects/kalyan-bigdata-examples.jar \

com.orienit.kalyan.examples.GenerateProductLog \
-f /tmp/productlog.csv \
-d ',' \
-n 10 \
-l 20




Read CSV data





Use Case8: Generating Sample Product Log in TSV format with simple command


java -cp /home/orienit/kalyan_bigdata_projects/kalyan-bigdata-examples.jar \

com.orienit.kalyan.examples.GenerateProductLog \
-f /tmp/productlog.tsv \
-d '\t' \
-n 10 \
-l 20





Read TSV data





Use Case9: Generating Sample Product Log in DELIMITED format with simple command


java -cp /home/orienit/kalyan_bigdata_projects/kalyan-bigdata-examples.jar \

com.orienit.kalyan.examples.GenerateProductLog \
-f /tmp/productlog.txt \
-d '#' \
-n 10 \
-l 20





Read Any DELIMITED data






Sunday, 27 November 2016

Spark Interview Questions & Answers - Part 3

1.What is Apache Spark?
Spark is a fast, easy-to-use and flexible data processing framework. It has an advanced execution engine supporting cyclic data  flow and in-memory computing. Spark can run on Hadoop, standalone or in the cloud and is capable of accessing diverse data sources including HDFS, HBase, Cassandra and others.

2.Explain key features of Spark.

  • Allows Integration with Hadoop and files included in HDFS.
  • Spark has an interactive language shell as it has an independent Scala (the language in which Spark is written) interpreter
  • Spark consists of RDD’s (Resilient Distributed Datasets), which can be cached across computing nodes in a cluster.
  • Spark supports multiple analytic tools that are used for interactive query analysis , real-time analysis and graph processing


3.Define RDD.
RDD is the acronym for Resilient Distribution Datasets – a fault-tolerant collection of operational elements that run parallel. The partitioned data in RDD is immutable and distributed. There are primarily two types of RDD:

  • Parallelized Collections : The existing RDD’s running parallel with one another
  • Hadoop datasets: perform function on each file record in HDFS or other storage system


4.What does a Spark Engine do?
Spark Engine is responsible for scheduling, distributing and monitoring the data application across the cluster.

5.Define Partitions?
As the name suggests, partition is a smaller and logical division of data  similar to ‘split’ in MapReduce. Partitioning is the process to derive logical units of data to speed up the processing process. Everything in Spark is a partitioned RDD.

6.What operations RDD support?

  • Transformations
  • Actions


7.What do you understand by Transformations in Spark?
Transformations are functions applied on RDD, resulting into another RDD. It does not execute until an action occurs. map() and filer() are examples of transformations, where the former applies the function passed to it on each element of RDD and results into another RDD. The filter() creates a new RDD by selecting elements form current RDD that pass function argument.

8. Define Actions.
An action helps in bringing back the data from RDD to the local machine. An action’s execution is the result of all previously created transformations. reduce() is an action that implements the function passed again and again until one value if left. take() action takes all the values from RDD to local node.

9.Define functions of SparkCore.
Serving as the base engine, SparkCore performs various important functions like memory management, monitoring jobs, fault-tolerance, job scheduling and interaction with storage systems.

10.What is RDD Lineage?
Spark does not support data replication in the memory and thus, if any data is lost, it is rebuild using RDD lineage. RDD lineage is a process that reconstructs lost data partitions. The best is that RDD always remembers how to build from other datasets.

11.What is Spark Driver?
Spark Driver is the program that runs on the master node of the machine and declares transformations and actions on data RDDs. In simple terms, driver in Spark creates SparkContext, connected to a given Spark Master.

The driver also delivers the RDD graphs to Master, where the standalone cluster manager runs.

12.What is Hive on Spark?
Hive contains significant support for Apache Spark, wherein Hive execution is configured to Spark:
hive> set spark.home=/location/to/sparkHome;
hive> set hive.execution.engine=spark;

Hive on Spark supports Spark on yarn mode by default.

13.Name commonly-used Spark Ecosystems.

  • Spark SQL (Shark)- for developers
  • Spark Streaming for processing live data streams
  • GraphX for generating and computing graphs
  • MLlib (Machine Learning Algorithms)
  • SparkR to promote R Programming in Spark engine.


14.Define Spark Streaming.
Spark supports stream processing – an extension to the Spark API , allowing stream processing of live data streams. The data from different sources like Flume, HDFS is streamed and finally processed to file systems, live dashboards and databases. It is similar to batch processing as the input data is divided into streams like batches.

15.What is GraphX?
Spark uses GraphX for graph processing to build and transform interactive graphs. The GraphX component enables programmers to reason about structured data at scale.

16.What does MLlib do?
MLlib is scalable machine learning library provided by Spark. It aims at making machine learning easy and scalable with common learning algorithms and use cases like clustering, regression filtering, dimensional reduction, and alike.

17.What is Spark SQL?
SQL Spark, better known as Shark is a novel module introduced in Spark to work with structured data and perform structured data processing. Through this module, Spark executes relational SQL queries on the data. The core of the component supports an altogether different RDD called SchemaRDD, composed of rows objects and schema objects defining data type of each column in the row. It is similar to a table in relational database.

18.What is a Parquet file?
Parquet is a columnar format file supported by many other data processing systems. Spark SQL performs both read and write operations with Parquet file and consider it be one of the best big data analytics format so far.

19.What file systems Spark support?
• Hadoop Distributed File System (HDFS)
• Local File system
• S3

20.What is Yarn?
Similar to Hadoop, Yarn is one of the key features in Spark, providing a central and resource management platform to deliver scalable operations across the cluster . Running Spark on Yarn necessitates a binary distribution of Spar as built on Yarn support.

21.List the functions of Spark SQL.
Spark SQL is capable of:
• Loading data from a variety of structured sources
• Querying data using SQL statements, both inside a Spark program and from external tools that connect to Spark SQL through standard database connectors (JDBC/ODBC). For instance, using business intelligence tools like Tableau
• Providing rich integration between SQL and regular Python/Java/Scala code, including the ability to join RDDs and SQL tables, expose custom functions in SQL, and more

22.What are benefits of Spark over MapReduce?

  • Due to the availability of in-memory processing, Spark implements the processing around 10-100x faster than Hadoop MapReduce. MapReduce makes use of persistence storage for any of the data processing tasks.
  • Unlike Hadoop, Spark provides in-built libraries to perform multiple tasks form the same core like batch processing, Steaming, Machine learning, Interactive SQL queries. However, Hadoop only supports batch processing.
  • Hadoop is highly disk-dependent whereas Spark promotes caching and in-memory data storage
  • Spark is capable of performing computations multiple times on the same dataset. This is called iterative computation while there is no iterative computing implemented by Hadoop.


23.Is there any benefit of learning MapReduce, then?
Yes, MapReduce is a paradigm used by many big data tools including Spark as well. It is extremely relevant to use MapReduce when the data grows bigger and bigger. Most tools like Pig and Hive convert their queries into MapReduce phases to optimize them better.

24.What is Spark Executor?
When SparkContext connect to a cluster manager, it acquires an Executor on nodes in the cluster. Executors are Spark processes that run computations and store the data on the worker node. The final tasks by SparkContext are transferred to executors for their execution.

25.Name types of Cluster Managers in Spark.
The Spark framework supports three major types of Cluster Managers:

  • Standalone: a basic manager to set up a cluster
  • Apache Mesos: generalized/commonly-used cluster manager, also runs Hadoop MapReduce and other applications
  • Yarn: responsible for resource management in Hadoop


26.What do you understand by worker node?
Worker node refers to any node that can run the application code in a cluster.

27.What is PageRank?
A unique feature and algorithm in graph, PageRank is the measure of each vertex in the graph. For instance, an edge from u to v represents endorsement of v’s importance by u. In simple terms, if a user at Instagram is followed massively, it will rank high on that platform.

28.Do you need to install Spark on all nodes of Yarn cluster while running Spark on Yarn?
No because Spark runs on top of Yarn.

29.Illustrate some demerits of using Spark.
Since Spark utilizes more storage space compared to Hadoop and MapReduce, there may arise certain problems. Developers need to be careful while running their applications in Spark. Instead of running everything on a single node, the work must be distributed over multiple clusters. 

30.How to create RDD?
Spark provides two methods to create RDD:
• By parallelizing a collection in your Driver program. This makes use of SparkContext’s ‘parallelize’ method
val data = Array(2,4,6,8,10)
val distData = sc.parallelize(data)

• By loading an external dataset from external storage like HDFS, HBase, shared file system

Spark Interview Questions & Answers - Part 2

Q1: Say I have a huge list of numbers in RDD(say myrdd). And I wrote the following code to compute average:
def myAvg(x, y):
 return (x+y)/2.0;
avg = myrdd.reduce(myAvg);

What is wrong with it? And How would you correct it?
Ans: The average function is not commutative and associative;
I would simply sum it and then divide by count.
def sum(x, y):
 return x+y;
total = myrdd.reduce(sum);
avg = total / myrdd.count();
The only problem with the above code is that the total might become very big thus over flow. So, I would rather divide each number by count and then sum in the following way.
cnt = myrdd.count();
def devideByCnd(x):
 return x/cnt;
myrdd1 = myrdd.map(devideByCnd);
avg = myrdd.reduce(sum);

Q2: Say I have a huge list of numbers in a file in HDFS. Each line has one number.And I want to compute the square root of sum of squares of these numbers. How would you do it?
Ans:
# We would first load the file as RDD from HDFS on spark
numsAsText = sc.textFile("hdfs://namenode:9000/user/kayan/mynumbersfile.txt");
# Define the function to compute the squares
def toSqInt(str):
 v = int(str);
 return v*v;
#Run the function on spark rdd as transformation
nums = numsAsText.map(toSqInt);

#Run the summation as reduce action
total = nums.reduce(sum)

#finally compute the square root. For which we need to import math.
import math;
print math.sqrt(total);

Q3: Is the following approach correct? Is the sqrtOfSumOfSq a valid reducer?

numsAsText =sc.textFile("hdfs://namenode:9000/user/kalyan/mynumbersfile.txt");
def toInt(str):
 return int(str);
nums = numsAsText.map(toInt);
def sqrtOfSumOfSq(x, y):
 return math.sqrt(x*x+y*y);
total = nums.reduce(sum)
import math;
print math.sqrt(total);
Ans: Yes. The approach is correct and sqrtOfSumOfSq is a valid reducer.

Q4: Could you compare the pros and cons of the your approach (in Question 2 above) and my approach (in Question 3 above)?
Ans:
You are doing the square and square root as part of reduce action while I am squaring in map() and summing in reduce in my approach.
My approach will be faster because in your case the reducer code is heavy as it is calling math.sqrt() and reducer code is generally executed approximately n-1 times the spark RDD.
The only downside of my approach is that there is a huge chance of integer overflow because I am computing the sum of squares as part of map.

Q5: If you have to compute the total counts of each of the unique words on spark, how would you go about it?
Ans:

#This will load the bigtextfile.txt as RDD in the spark
lines = sc.textFile("hdfs://namenode:9000/user/kalyan/bigtextfile.txt");


#define a function that can break each line into words
def toWords(line):
      return line.split();

# Run the toWords function on each element of RDD on spark as flatMap transformation.
# We are going to flatMap instead of map because our function is returning multiple values.

words = lines.flatMap(toWords);

# Convert each word into (key, value) pair. Her key will be the word itself and value will be 1.
def toTuple(word):
     return (word, 1);

wordsTuple = words.map(toTuple);

# Now we can easily do the reduceByKey() action.
def sum(x, y):
    return x+y;

counts = wordsTuple.reduceByKey(sum)

# Now, print
counts.collect()

Q6: In a very huge text file, you want to just check if a particular keyword exists. How would you do this using Spark?
Ans:
lines = sc.textFile("hdfs://namenode:9000/user/kalyan/bigtextfile.txt");
def isFound(line):
 if line.find(“mykeyword”) > -1:
  return 1;
 return 0;
foundBits = lines.map(isFound);
sum = foundBits.reduce(sum);
if sum > 0:
 print “FOUND”;
else:
 print “NOT FOUND”;

Q7: Can you improve the performance of this code in previous answer?
Ans: Yes. 
The search is not stopping even after the word we are looking for has been found. Our map code would keep executing on all the nodes which is very inefficient.
We could utilize accumulators to report whether the word has been found or not and then stop the job. Something on these line:


import thread, threading
from time import sleep
result = "Not Set"
lock = threading.Lock()
accum = sc.accumulator(0)
def map_func(line):
 #introduce delay to emulate the slowness
 sleep(1); 
 if line.find("Adventures") > -1:
  accum.add(1);
  return 1;
 return 0;
def start_job():
 global result
 try:
  sc.setJobGroup("job_to_cancel", "some description")
  lines = sc.textFile("hdfs://namenode:9000/user/kalyan/wordcount/input/big.txt");
  result = lines.map(map_func);
  result.take(1);
 except Exception as e:
  result = "Cancelled"
 lock.release()
def stop_job():
 while accum.value < 3 :
  sleep(1);
 sc.cancelJobGroup("job_to_cancel")
supress = lock.acquire()
supress = thread.start_new_thread(start_job, tuple())
supress = thread.start_new_thread(stop_job, tuple())
supress = lock.acquire()
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