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How to Retrieve Password from JCEKS file in Spark
In the data ingestion stage into Hadoop from RBDMS sources, it often requires password to hit source tables in RDBMS databases. Passing hard password directly is highly unsafe and bad practice in real time applications. So, password can be encrypted by creating JCEKS file. JCEKS is basically a keystore file saved in the Java Cryptography Extension KeyStore (JCEKS) format; used as an alternative keystore to the Java Keystore (JKS) format for the Java platform; stores encoded keys. When working on Spark application which deals with RDBMS sources JCEKS need to be decrypted to query the source tables. Below is the handy function to retrieve password from JCEKS file- Using PySpark Using Scala
Joins in Spark SQL- Shuffle Hash, Sort Merge, BroadCast
Apache Spark SQL component comes with catalyst optimizer which smartly optimizes the jobs by re-arranging the order of transformations and by implementing some special joins according to datasets. Spark performs these joins internally or you can force it to perform them. It’s worthwhile to know this topic, so that it comes to rescue when optimizing the jobs according to your use case. Shuffle Hash Join Shuffle hash join shuffles the data based on join keys and then perform the join. The shuffled hash join ensures that data on each partition will contain the same keys by partitioning the second dataset with the same default partitioner as the first, so that the keys with the same hash value from both datasets are in the same partition. It follows the classic map-reduce pattern: First ...
Two Sum
Given an array of integers, return indices of the two numbers such that they add up to a specific target. You may assume that each input would have exactly one solution, and you may not use the same element twice. Given nums = [1, 4, 8, 13], target = 12, Because nums[1] + nums[2] = 4 + 8 = 12, return [1, 2]. Solution:
Data Structures – Linked List
What is Linked List? A linked list is a linear data structure where each element is a separate object. Each element (we will call it a node) of a list is comprising of two items – the data and a reference to the next node. The last node has a reference to null. The entry point into a linked list is called the head of the list. It should be noted that head is not a separate node, but the reference to the first node. If the list is empty then the head is a null reference. A linked list is a dynamic data structure. The number of nodes in a list is not fixed and can grow and shrink on demand. Any application which has to deal with an unknown number of objects will need to use a linked list. Types of Linked list: Singly linked list. Doubly linked list. Circular linked list. where last nod...
Data Structures – Array
What is an Array? An array is collection of items stored at contiguous memory locations. The idea is to store multiple items of same type(Homogeneous) together. This makes it easier to calculate the position of each element by simply adding an offset to a base value, i.e., the memory location of the first element of the array (generally denoted by the name of the array). What is Contiguous Memory? Contiguous memory allocation is a classical memory allocation model that assigns a process consecutive memory blocks (that is, memory blocks having consecutive addresses). Contiguous memory allocation is one of the oldest memory allocation schemes. When a process needs to execute, memory is requested by the process. The size of the process is compared with the amount of contiguous main memory ava...
Introduction to Data Structures
Definition: Simple definition of Data structure is organizing the data in memory. It is a systematic way to organize data in order to use it efficiently. There are different ways to organize data in structure. One example is Array. Array is collection of elements i.e., collection of memory locations, it is the memory locations that we store the values. In the array, structure of data is sequential it occupies contiguous memory locations types of data structures. Type Description Linear In Linear data structures, the data items are arranged in a linear sequence. Example: Array Non-Linear In Non-Linear data structures, the data items are not in sequence. Example: Tree, Graph Homogeneous In homogeneous data structures, all the elements are of same type. Example: Array Non-Homogeneous In Non-H...
Understanding Tail recursion in Scala
Tail recursion is little tricky concept in Scala and takes time to master it completely. Before we get into Tail recursion, lets try to look into recursion. A Recursive function is the function which calls itself. If some action is repetitive, we can call the same piece of code again. Recursion could be applied to problems where you use regular loops to solve it. Factorial program with regular loops – [code lang=”scala”] def factorial(n: Int): Int = { var fact = 1 for(i <- 1 to n) { fact = fact * i; } return fact } [/code] The same can be re-written with recursion like below – [code lang=”scala”] def factorialWithRecursion(n: Int): Int = { if (n == 0) return 1 else return n * factorialWithRecursion(n-1) } [/code] In the recursive approach, we return e...
Memory Management in Spark and its tuning
Spark has two kinds of memory- 1.Execution Memory which is used to store temporary data of shuffles, joins, sorts, and aggregations 2. Storage Memory which is used to cache RDDs and data frames Executor has some amount of total memory, which is divided into two parts, the execution block and the storage block.This is governed by two configuration options. 1. spark.executor.memory > It is the total amount of memory which is available to executors. It is 1 gigabyte by default 2. spark.memory.fraction > Fraction of the total memory available for execution and storage. In early version of Spark, these two kinds of memory were fixed. And if your job was to fill all the execution space, Spark had to spill data to disk, reducing performance of the application. On the other hand, if your...
How to flatten JSON in Spark Dataframe
How to flatten whole JSON containing ArrayType and StructType in it? In order to flatten a JSON completely we don’t have any predefined function in Spark. We can write our own function that will flatten out JSON completely. We will write a function that will accept DataFrame. For each field in the DataFrame we will get the DataType. If the field is of ArrayType we will create new column with exploding the ArrayColumn using Spark explode_outer function. If the field is of StructType we will create new column with parentfield_childfield for each field in the StructType Field. This is a recursive function. Once the function doesn’t find any ArrayType or StructType. It will return the flattened DataFrame. Otherwise, It will it iterate through the schema to completely flatten out the JSON...
How to create Spark Dataframe on HBase table[Code Snippets]
There is no direct library to create Dataframe on HBase table like how we read Hive table with Spark sql. This post gives the way to create dataframe on top of Hbase table. You need to add hbase-client dependency to achieve this. Below is the link to get the dependency. https://mvnrepository.com/artifact/org.apache.hbase/hbase-client/2.1.0 Lets say the hbase table is ’emp’ with rowKey as ’empID’ and columns are ‘name’ and ‘city’ under the column-family named – ‘metadata’. Case class -EmpRow is used in order to give the structure to the dataframe. newAPIHadoopRDD is the API available in Spark to create RDD on hbase, configurations need to passed as shown below. Dataframe will be created when you parse this RDD on case class. ...
How to Add Serial Number to Spark Dataframe
You may required to add Serial number to Spark Dataframe sometimes. It can be done with the spark function called monotonically_increasing_id(). It generates a new column with unique 64-bit monotonic index for each row. But it isn’t significant, as the sequence changes based on the partition. In short, random numbers will be assigned which are out of sequence. If the goal is add serial number to the dataframe, you can use zipWithIndex method available on RDD. below is how you can achieve the same on dataframe. [code lang=”python”] from pyspark.sql.types import LongType, StructField, StructType def dfZipWithIndex (df, offset=1, colName="rowId"): ”’ Enumerates dataframe rows is native order, like rdd.ZipWithIndex(), but on a dataframe and preserves a ...
XGBoost for Regression[Case Study]
Using Gradient Boosting for Regression Problems Introduction : The goal of the blogpost is to equip beginners with basics of gradient boosting regressor algorithm and quickly help them to build their first model. We will mainly focus on the modeling side of it . The data cleaning and preprocessing parts would be covered in detail in an upcoming post. Gradient Boosting for regression builds an additive model in a forward stage-wise fashion; it allows for the optimization of arbitrary differentiable loss functions. In each stage a regression tree is fit on the negative gradient of the given loss function. The idea of boosting came out of the idea of whether a weak learner can be modified to become better.A weak hypothesis or weak learner is defined as one whose performance is at least slig...
