Hadoop is becoming a highly popular technology, trending these days, as it helps solve major Big Data problems easily. There is always a demand for Big Data professionals who have experience with Hadoop. But how do you find a job as a Big Data Analyst with Hadoop experience? Well, we have the answers to that.
In this blog, we will discuss Hadoop interview questions and answers that will help you prepare for your next interview. The Hadoop Big Data interview questions are divided categorically for ease. They are divided into HDFS, MapReduce, Hive, Hbase, Sqoop, Flume, ZooKeep, Pig, and Yarn Hadoop interview questions. The Hadoop scenario-based interview questions are also segregated into questions for freshers and Hadoop interview questions for experienced professionals. Let’s get started.
We will start with some generally asked basic Hadoop interview questions.
What is Apache Hadoop?
Hadoop is a collection of open-source software that provides a framework to store and process Big Data using the MapReduce programming model. Hadoop is a part of the Apache project sponsored by the Apache Software Foundation.
Hadoop has helped solve many challenges previously faced with distributed storage and Big Data processing. It has gained popularity among data scientists, and it is the preferred choice of many.
What are the challenges faced with Big Data, and why do we use Hadoop for Big Data?
The three major challenges faced with Big Data are:
Storage: Since Big Data comprises high volume, storing such massive amounts of data is a major issue.
Security: Naturally, enterprises need to ensure that every bit of data is stored safely, and there are no instances of a data leak or data compromise in any other way.
Analytics and insights: It becomes difficult to derive insights, analyze trends, and identify patterns when dealing with such a high volume of data.
Hadoop proves to be one of the best solutions for managing Big Data operations due to the following reasons:
High storage: Hadoop enables the storage of huge raw files easily without schema.
Cost-effective: Hadoop is an economical solution for Big Data distributed storage and processing, as the commodity hardware required to run it is not expensive.
Scalable, reliable, and secure: The data can be easily scaled with Hadoop systems, as any number of new nodes can be added. Additionally, the data can be stored and accessed despite machine failure. Lastly, Hadoop provides a high level of data security.
Can you tell us about the core components of Hadoop?
There are three main components of Hadoop. They are the Hadoop Distributed File System (HDFS), MapReduce, and YARN.
HDFS: It is the storage system of Hadoop. HDFS works on the principle of storing fewer big files than storing a large number of small data files.
MapReduce: It is the data processing layer and processes the data stored in the HDFS in parallel. The processing is done in two phases, Map and Reduce. In Map, the logic code for data processing is specified or mapped. In Reduce, processing tasks like aggregation are specified.
YARN: It is the processing framework and provides resource management, for example, batch processing.
Now, let’s look at some Hadoop Big Data interview questions for students and freshers.
What are the three modes in which Hadoop runs?
Local mode: It is the default run mode in Hadoop. It uses a local file system and is used for input, debugging, and output operations. This mode does not support the use of HDFS.
Pseudo-distributed mode: The difference between local mode and this mode is that each daemon runs as a separate Java process in this mode.
Fully-distributed mode: All daemons are executed in separate nodes and form a multi-node cluster.
What are some limitations of Hadoop?
File size limitations: HDFS can not handle a large number of small files. If you use such files, the NameNode will be overloaded.
Support for only batch-processing: Hadoop does not process streamed data and has support for batch-processing only. This lowers overall performance.
Difficulty in management: It can become difficult to manage complex applications in Hadoop.
Now, let’s move on to some common Hadoop interview questions and answers related to Hadoop HDFS.
Hadoop Interview Questions Related to HDFS
Hadoop HDFS interview questions are important in a Hadoop Administrator interview. Some common HDFS IQ are as follows:
What is meant by a heartbeat in HDFS?
The signal sent between the DataNode and NameNode is called a heartbeat. If there is no response to the signal by the NameNode, it means there is an issue.
HDFS architecture is shown in the diagram above. The NameNode is the master node, whereas the DataNodes are the slave nodes. The NameNode contains information about various DataNodes, such as the location and block size. The DataNodes contain the actual data blocks. They send block reports to the NameNode every ten seconds.
Explain the difference between HDFS and regular FileSystem.
With HDFS, the data is stored on a distributed system while the regular FileSystem stores the data in a single system. Additionally, the data stored on HDFS can be recovered even if the DataNode crashes. But data recovery on the regular FileSystem can become difficult in case of a crash.
What is indexing? How is indexing done in HDFS?
Indexing is used to speed up the process of access to the data. Hadoop has a unique way of indexing. It does not automatically index the data. Instead, it stores the last part of the data that shows where (the address) the next part of the data chunk is stored.
What is meant by a block and block scanner?
In HDFS, a block is the minimum data that is read or written. The default block size is 64MB. A scanner tracks the blocks present on a DataNode. It then verifies them to find errors.
Explain how data is stored in a rack.
As soon as the client is ready to load the file in the cluster, the file contents are divided into racks or data blocks. The client then allocates three DataNodes for each block. Two copies of the data are stored in one rack, while the third copy is stored in another rack.
What is the port number for NameNode?
The port number for NameNode is 50070.
Next, let us look at Hadoop Developer interview questions and questions on MapReduce.
Top Hadoop MapReduce Interview Questions
What is Hadoop MapReduce?
MapReduce is the framework used to process Big Data in parallel, distributed algorithms on a cluster. It works in two steps – mapping and reducing. The mapping procedure performs the data sorting while reducing performs the operations.
What are the Hadoop-specific data types used in MapReduce?
Following are some Hadoop-specific data types that can be used in MapReduce:
What are the different output formats in MapReduce?
The various output formats supported by MapReduce are:
TextOutputFormat: The output is recorded in lines of text.
MapFileOutputFormat: The output is a map file.
SequenceFileAsBinaryOutputFormat: The output is written to a sequence file in binary format.
Explain the three core methods of a reducer.
setup(): It is used to configure various parameters like input data size and heap size.
reduce(): It is the heart of the reducer.
cleanup(): This method is used to clear the temporary files at the end of the reduced task.
Can you tell us about the combiner in MapReduce?
A combiner is a mini reducer that performs the local reducer task. It is used to enhance network bandwidth usage during a task. The combiner, however, has certain limitations. For example, it can only operate on a subset of keys and values and only take input from a single mapper.
Let us now turn to some Hadoop Hive interview questions.
Commonly Asked Hadoop Hive Questions
Explain the difference between HBase and Hive.
The major differences between HBase and Hive are:
HBase is built on HDFS, while Hive is a data warehousing infrastructure.
HBase provides low latency, whereas Hive provides high latency for huge databases.
HBase operations are carried out in real-time, whereas Hive operations are carried out as MapReduce jobs internally.
What do you mean by SerDe in Hive? Explain.
SerDe stands for Serial Deserializer. They are implemented using Java. The interface provides instructions regarding how to process a record. Hive has in-built SerDes. There are also many third-party SerDes that you can use depending on the task.
What is meant by partition in Hive, and why do we do it?
Partition is a sub-directory in the table directory in Hive. Hive organizes tables into partitions for similar data types based on a column or partition key. Partitioning helps reduce query latency, as it scans only the relevant partitioned data.
Where does Hive store the table data by default?
Hive stores the table data by default in /user/hive/warehouse.
If you are running Hive as a server, how can you connect an application?
In such a scenario, an application can be connected in three ways using:
Next, we will discuss Hadoop Hbase interview questions and answers.
Common Hadoop Interview Questions for HBase
What is HBase?
Apache HBase is an open-source database written in Java. It runs on HDFS and provides a fault-tolerant method to store high volumes of sparse data sets.
What are the key components of HBase?
There are five main components of HBase. They are:
Region: It contains the memory data store and the Hfile.
HBase master: It monitors the region server.
Region server: It monitors the region.
Zookeeper: It is responsible for the coordination between the HBase Master component and the client.
Catalog tables: Two main catalog tables store and track all the regions in the HBase system.
What is the difference between HBase and Hive?
HBase is a NoSQL key-value store that runs on top of Hadoop. Hive, on the other hand, is a data warehouse infrastructure built on top of Hadoop. HBase is ideal for real-time querying of Big Data, while Hive is suitable for querying collected data.
What is the difference between HBase and a relational database?
The major differences between the two are as follows:
HBase is a schema-less database, while a relational database is schema-based.
HBase is used to store de-normalized data, whereas relational databases are used to store normalized data.
Explain the three types of tombstone markers for deletion.
Family delete marker: It marks all the columns for deletion from a column family.
Version delete marker: It marks a single version of a column for deletion.
Column delete marker: It marks all versions of a column for deletion.
Frequently Asked Hadoop Interview Questions for Sqoop
Some of the commonly asked Sqoop-related Hadoop interview questions for experienced candidates and freshers are discussed below:
What is the basic introduction to Sqoop along with its features?
Apache Sqoop is one of the primary tools used to transfer data from various RDBMS to HDFS and vice versa. It uses two important tools: Sqoop import and Sqoop export to transfer data from and to RDBMS, such as Hadoop, MySQL, Oracle, and HDFS. Some of the standard Sqoop features are as follows:
Parallel import and export
Multiple RDBMS support
How to import BLOB and CLOB-like big objects in Sqoop?
We can use JDBC-based imports for BLOB and CLOB, as Sqoop does not support direct import function.
Mention some common Sqoop commands
Some common Sqoop commands for handling Hadoop data transfers include:
Eval: It is used to run SQL statements and display the results on the console.
Create-hive-table: It allows importing table definition into the Hive.
Codegen: This command lets you write code that facilitates communication with the database.
Import: It helps import tables and data from various RDBMS to the Hadoop ecosystem.
Export: It helps export tables and data from HDFS to other RDBMS.
Versions: It displays the version of the text and information in the database.
Import-all-tables: This command helps fetch tables.
List-databases: It provides a list of all the databases.
How to control the number of mappers in Sqoop?
The number of mappers can be controlled with the help of the -num-mappers command. The syntax is as follows:
What is the role of the JDBC driver in the Sqoop setup?
The JDBC driver is a connector that allows Sqoop to connect with different RDBMS.
What do you know about Sqoop metastore?
Sqoop metastore is a shared repository where multiple local and remorse users can execute the saved jobs. We can connect to the Sqoop metastore through sqoop-site.xml or with the help of the -meta-connect argument command.
Hadoop Flume Interview Questions
Let us head on to some of the common Hadoop interview questions and answers on the Apache Flume tool. This section covers some Hadoop Big Data interview questions, as Flume can collect a massive amount of structured and unstructured data.
What is Apache Flume used for?
Flume is a service used for gathering and transferring data from a massive amount of log files, especially from social media platforms.
How to use Flume with HBase?
We can use Flume with any one of the HBase sinks:
HBaseSink: It provides secured support for HBase clusters and HBase IPC.
AsyncHBaseSink: It facilitates non-blocking HBase calls.
Explain the common components of Flume
The standard components of Apache Flume include the following:
Sink: It is responsible for transporting data from the source (usually log files) to the destination.
Event: Every single bit of data we transport is called an event.
Agent: Any JVM responsible for running the Flume is an agent.
Source: It is the component through which we can let the data enter the Flume workflow.
Channel: Channel is the duct or the medium through which we transfer data from the sink to the source.
Client: It is used to transmit the event to the source.
What are the three channels in Flume?
There are a total of three available channels in Flume:
Out of all the three, the file channel is the most efficient and reliable. It can transfer data from log files to the source and then the destination with no data loss.
What is an interceptor?
Interceptors are useful in filtering out unwanted log files. We can use them to eliminate events between the source and channel or the channel and sink based on our requirements.
Can you explain the use of sink processors?
We can use sink processors in multiple ways:
To invoke a particular sink from a group.
To provide failovers in case of temporary failure.
It offers load balancing capabilities.
Common Hadoop Interview Questions for ZooKeep Administrator Role
Moving on to the next database system, let us look at some common Hadoop Administrator interview questions for ZooKeeper management.
What is ZooKeeper?
ZooKeeper is a distributed service for maintaining coordination data, such as naming, synchronizing, and configuration information. Due to the distributed environment, ZooKeeper provides numerous benefits like reliability and scalability.
State some key components of ZooKeeper.
The primary components of ZooKeeper architecture are:
Node: These are all the systems installed on the cluster.
ZNode: A different type of node that stores updates and data version information.
Client applications: These are the client-side applications useful in interacting with distributed applications.
Server applications: These are the server-side applications that provide an interface for the client applications to interact with the server.
What are the different types of Znodes available?
There are a total of three Znodes available in ZooKeeper as follows:
Persistence ZNode: These are the ZNodes that remain active even if the creator client disconnects. They are the default type.
Ephemeral ZNode: These are the ZNodes active only until the client who created them is connected.
Sequential ZNode: These are the general ZNodes. They can be persistent or ephemeral.
Can you explain the ZooKeeper Atomic Broadcast (ZAB) protocol?
It is the core protocol of ZooKeeper that updates and maintains all the information securely.
What is the ZooKeeper WebUI?
It is an alternative way to work with ZooKeeper applications through a web user interface.
What are the standard ZooKeeper methods?
The following are the most standard ZooKeeper methods:
create: Creates a new ZNode.
getData: Collects data from the mentioned ZNode.
setData: Sets data in the specified ZNode.
connect: Connects to the ZooKeeper.
delete: Deletes the said ZNode and all the children nodes associated with it.
close: Closes the connection.
Standard Hadoop Pig Interview Questions
In this section, we will be looking at the common Hadoop interview questions related to Hadoop Pig that you might encounter during an interview.
What does the Hadoop Pig framework look like?
There are multiple components in the Hadoop Pig framework; the most standard ones are mentioned below:
Parser: Parse is like a quality analyst of the Pig script. It will check for any errors and miscellaneous scripts and provide an output accordingly.
Optimizer: The optimizer is responsible for carrying out all the optimization tasks, such as projection and push down.
Compiler: It compiles the Pig script and readies it for execution.
Execution engine: All the compiled jobs are finally executed in Hadoop with the execution engine.
Explain the various complex data types in Pig
The complex data types that Pig supports are:
Tuple: A set of values.
Bag: A collection of tuples.
Map: A group of key-value pairs.
What is Flatten?
Flatten is an operator in Pig useful for removing different levels of nesting. Sometimes, the data is stored in a tuple or a bag. We want to remove the nesting levels to even the structured data. For this, we use the Flatten operator.
Explain Grunt Shell.
It is like an interface that allows interaction with the HDFS. It is also sometimes referred to as Pig interactive shell.
What are the differences between Pig and SQL?
The prime differences between the two are as follows:
Pig is a procedural query language, while SQL is declarative.
Pig follows a nested relational data model, while SQL follows a flat one.
Having schema in Pig is optional, but in SQL, it is mandatory.
Pig offers limited query optimization, but SQL offers significant optimization.
Explain a UDF.
UDF stands for User Defined Functions. UDF can be created if some functions are unavailable in built-in operators. You can use other languages like Python and Ruby to create these functions and embed them in a script file.
Top Hadoop Interview Questions on YARN
Lastly, we will go through some Hadoop interview questions and answers on YARN.
What is YARN?
YARN is short for Yet Another Resource Negotiator. It is a cluster management system that handles and manages resource allocation and efficiency.
Explain the resource manager in YARN.
It is the master node responsible for collecting resource and asset information. With the available resources, it runs numerous critical services in Hadoop YARN.
What are the benefits of using YARN in Hadoop?
Following are some of the most crucial benefits of YARN in Hadoop:
Optimizes resource utilization.
Opens up Hadoop to distributed applications other than MapReduce.
Eliminates the MapReduce scalability issue.
Was YARN launched as a replacement for Hadoop MapReduce?
YARN was introduced to enhance the functionalities of MapReduce, but it is not a replacement for the latter. It also supports other distributed applications along with MapReduce.
What are the different scheduling policies you can use in YARN?
The YARN scheduler uses three different scheduling policies to assign resources to various applications.
FIFO scheduler: This scheduler runs on a first-come-first-serve basis. It lines up the application requests based on submission and aligns the resources accordingly.
Capacity scheduler: The capacity scheduler is more logical than the FIFO scheduler. It has a separate queue for smaller requests that require only a handful of resources. As soon as such small requests are submitted, the capacity scheduler starts executing them.
Fair scheduler: The fair scheduler runs on the balancing criteria. It allocates the resources in such a way that all the requests get something, creating a balance. As soon as a new request is submitted and a task starts, the fair scheduler divides the resources and allocates each task’s share accordingly.
We hope that this blog helped you with essential Hadoop interview questions and answers and will further help you prepare for your Hadoop interview. We discussed the most commonly asked Hadoop interview questions and their answers. Be confident and attempt your interview! We are sure you will clear it.
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