Featured Post

SQL Interview Success: Unlocking the Top 5 Frequently Asked Queries

-
Image
 Here are the five top commonly asked SQL queries in the interviews. These you can expect in Data Analyst, or, Data Engineer interviews. Top SQL Queries for Interviews 01. Joins The commonly asked question pertains to providing two tables, determining the number of rows that will return on various join types, and the resultant. Table1 -------- id ---- 1 1 2 3 Table2 -------- id ---- 1 3 1 NULL Output ------- Inner join --------------- 5 rows will return The result will be: =============== 1  1 1   1 1   1 1    1 3    3 02. Substring and Concat Here, we need to write an SQL query to make the upper case of the first letter and the small case of the remaining letter. Table1 ------ ename ===== raJu venKat kRIshna Solution: ========== SELECT CONCAT(UPPER(SUBSTRING(name, 1, 1)), LOWER(SUBSTRING(name, 2))) AS capitalized_name FROM Table1; 03. Case statement SQL Query ========= SELECT Code1, Code2,      CASE         WHEN Code1 = 'A' AND Code2 = 'AA' THEN "A" | "A
Post a Comment

Top Hadoop Architecture Interview Questions

-
The hadoop.apache.org web site defines Hadoop as "a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models." Quite simply, that's the philosophy: to provide a framework that's simple to use, can be scaled easily, and provides fault tolerance and high availability for production usage.

The idea is to use existing low-cost hardware to build a powerful system that can process petabytes of data very efficiently and quickly.

More : Top selected Hadoop Interview Questions

Hadoop achieves this by storing the data locally on its DataNodes and processing it locally as well. All this is managed efficiently by the NameNode, which is the brain of the Hadoop system. All client applications read/write data through NameNode.

Hadoop has two main components: the Hadoop Distributed File System (HDFS) and a framework for processing large amounts of data in parallel using the MapReduce paradigm

HDFS


HDFS is a distributed file system layer that sits on top of the native file system for an operating system. For example, HDFS can be installed on top of ext3, ext4, or XFS file systems for the Ubuntu operating system.

It provides redundant storage for massive amounts of data using cheap, unreliable hardware. At load time, data is distributed across all the nodes. That helps in efficient MapReduce processing. HDFS performs better with a few large files (multi-gigabytes) as compared to a large number of small files, due to the way it is designed.

Files are "write once, read multiple times." Append support is now available for files with the new version, but HDFS is meant for large, streaming reads—not random access. High sustained throughput is favored over low latency.

Files in HDFS are stored as blocks and replicated for redundancy or reliability. By default, blocks are replicated thrice across DataNodes; so three copies of every file are maintained. Also, the block size is much larger than other file systems. For example, NTFS (for Windows) has a maximum block size of 4KB and Linux ext3 has a default of 4KB.

Compare that with the default block size of 64MB that HDFS uses.


Name Node



NameNode (or the "brain") stores metadata and coordinates access to HDFS. Metadata is stored in NameNode's RAM for speedy retrieval and reduces the response time (for NameNode) while providing addresses of data blocks. 

This configuration provides simple, centralized management—and also a single point of failure (SPOF) for HDFS. In previous versions, a Secondary NameNode provided recovery from NameNode failure; but current version provides capability to cluster a Hot Standby (where the standby node takes over all the functions of NameNode without any user intervention) node in Active/Passive configuration to eliminate the SPOF with NameNode and provides NameNode redundancy.

Since the metadata is stored in NameNode's RAM and each entry for a file (with its block locations) takes some space, a large number of small files will result in a lot of entries and take up more RAM than a small number of entries for large files.

Also, files smaller than the block size (smallest block size is 64 MB) will still be mapped to a single block, reserving space they don't need; that's the reason it's preferable to use HDFS for large files instead of small files.

Comments

Post a Comment

Thanks for your message. We will get back you.

Popular posts from this blog

How to Fix datetime Import Error in Python Quickly

-
Image
Here's a quick resolution for import datetime Python error . The reason is your .py python script name and datetime  are the same. I'll show you how this error happens and its resolution.   Here's the Resolution for ImportError I've created a script called 'datetime.py.' to check whether the minute value is 'odd' or not. During the import of my python script, I got the import error cannot import name datetime. My Script: datetime.py My script's intention is to find whether the minute value is odd or not. Python Logic from datetime import datetime odds = [ 1 , 3 , 5 , 7 , 9 , 11 , 13 , 15 , 17 , 19 , 21 , 23 , 25 , 27 , 29 , 31 , 33 , 35 , 37 , 39 , 41 , 43 , 45 , 47 , 49 , 51 , 53 , 55 , 57 , 59 ] right_this_minute = datetime.today().minute if right_this_minute in odds: print ( "This minute seems a little odd." ) else : print ( "Not an odd minute." ) ImportError I have imported my datetime.py from Linux. It gives an error
Read more

Explained Ideal Structure of Python Class

-
Image
When you are designing a class, you need to ensure that the classification of its critical parts is outlined at the beginning. The clearer the initial design, the more performant and scalable the class is. Some of the components in the order in which they should be defined in the class are mentioned as follows. Ideal structure of a class Class variables Constants or default variables are usually defined at the top of the class. For someone who is reading the code, it comes as an easy-to-view consolidated list, and for the interpreter it ensures that all such variables are processed before diving into the main logic of the class, including any other Instance method or constructor. The __init__ method The __init__ method provides information about inputs needed and how to instantiate the class. It is also the constructor of the class, which the very first method called while initializing the class . Special Python methods These methods change the functionality of the class or provide add
Read more

How to Check Kafka Available Brokers

-
Image
Here's the command to check the list of brokers present in a Kafka Cluster. You can say the Broker is the heart of the Kafka cluster. In simple terms, it works as a process. The main function is to receive messages from publishers and gives permission to access messages by consumers. How to Check Available Brokers in Kafka Here is the command: linux$  ./zookeeper-shell.sh zookeeper-IPaddress:2181 | "ls /brokers/ids" Just use the above command to get the number of brokers present in your host (Kafka Cluster). What is Default Broker-id in Kafka  The default broker id is -1 . When you create a new broker, it adds to -1. Then, it gives a new broker id. The broker ids will be generated from  reserved.broker.max.id + 1. Types of broker ids. Use assigned Zookeeper generated broker id. According to Wiki: Kafka runs on a cluster of one or more servers (called brokers), and the partitions of all topics are distributed across the cluster nodes.  Additionally, partitions are replicat
Read more