Introduction to Sqoop Interview Questions and Answers
Sqoop is an open-source data transfer tool, Sqoop tool transfer the data between Hadoop Ecosystem and Relational Database Servers (RDBMS). It imports the data to the Hadoop file system (HDFS), from Relational Databases such as Oracle, MySQL, etc, also export data from the Hadoop file System to RDMS.
So you have finally found your dream job in Sqoop but are wondering how to crack the Sqoop interview and what could be the probable 2020 Sqoop Interview Questions. Every interview is different and the scope of a job is different too. Keeping this in mind we have designed the most common Sqoop Interview Questions and Answers to help you get success in your interview.
Below are the 15 important 2020 Sqoop Interview Questions and Answers. These questions are divided into two parts are as follows:
Part 1 – Sqoop Interview Questions (Basic)
This first part covers basic Sqoop Interview Questions And Answers.
1. Define Sqoop and why do we use Sqoop?
Answer:
Sqoop is an open-source data transfer tool, which is designed for transferring data between Hadoop Ecosystem and Relational Database Servers (RDBMS). Sqoop is used for importing the data from Relational Databases such as Oracle, MySQL, etc, to the Hadoop file system (HDFS), and also for exporting data from the Hadoop file system to relational databases.
2. What are the different features of Sqoop?
Answer:
Below are the different features supported by the Sqoop –
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- Loading capacity
- Full Loading and Incremental Loading
- Data Compression Techniques
- Importing the SQL queries results
- Data Connectors for all the major databases
- Direct data loading support into Hadoop File Systems
- Security configurations like Kerberos
- Concurrent Import or Export functionalities
Let us move to the next Sqoop Interview Questions.
3. Name the relational databases and Hadoop eco-system sources supported in Sqoop?
Answer:
Sqoop currently supports MySQL, PostgreSQL, Oracle, MSSQL, Teradata and IBM’s Netezza as part of Relation Databases.
Currently supported Hadoop Eco-system destination services are HDFC, Hive, HBase, H Catalog and Accumulo.
Sqoop uses MySQL as the default database.
4. How does Sqoop work?
Answer:
This is the common Sqoop Interview Questions asked in an interview. To perform data transfer, Sqoop uses export and import commands. Map Reduce program will be used in Sqoop internally for storing dataset to HDFS. Commands will be associated with Map tasks to retrieve data from Relational Databases; Reduce task will take the responsibility of placing the retrieved data into the destinations (HDFS/HBase/Hive)
Sqoop also uses various API connectors for connecting with several databases. Sqoop also provides the ability to create custom connectors for meeting specific requirements.
Let’s see the sample commands below for import and export
A command for connecting to MySQL database for importing data from ‘Log’ table
sqoop import --connect jdbc:mysql://localhost/<databasename> --username <USER_NAME> --password <PASSWORD> --table <tablename> --m 1
sqoop import --connect jdbc:mysql://localhost/mytestdb --username root --password admin123 --table log --m 1
A command for exporting data from HDFS to Relational Database
sqoop export --connect jdbc:mysql://localhost/sqoop_export –table <table_name> export-dir /sqoop/emp_last/part-m-00000 --update-key id
sqoop export --connect jdbc:mysql://localhost/sqoop_export --table log_table--export-dir /sqoop/data/foler1/part-m-00000
5. What is Sqoop Metastore? Explain it?
Answer:
The Sqoop Metastore is a tool available in the Sqoop which will be used to configure the Sqoop application to enable the hosting of a shared repository in the form of metadata. This Metastore can be used to execute the jobs and manage a number of users based on user roles and activities. All multiple users can perform multiple tasks or operations concurrently to achieve the tasks efficiently. The Sqoop Metastore will be implemented as an in-memory representation by default. When a job is created within Sqoop, the job definition is stored inside the Metastore and will be listed using Sqoop jobs if needed.
6. What file formats does Sqoop support while importing the data?
Answer:
Sqoop uses two file formats for data import. They are:- Delimited Test File Format and Sequence File Format.
- Delimited Text File Format: Delimited Text Format is the default file format for importing. We can still explicitly specify using the –as- textile argument. Likewise passing the argument will set the delimiter characters between rows and columns.
- Sequence File Format: This file format we can say it’s a binary file format. This type of format file records is stored in custom record specific data types that are exposed as Java Classes.
Let us move to the next Sqoop Interview Questions.
7. Can we control a number of mappers in sqoop? If yes, How?
Answer:
Yes, we can control the number of mappers in Sqoop by specifying the parameter “-num-mappers” in the sqoop command. This parameter can control the number of map tasks, that is nothing but the degree of parallelism will be used by sqoop. The number will be decided based on the requirement.
- Syntax: Use these flags to control the number of mappers: m, -num- mappers
Part 2 – Sqoop Interview Questions (Advanced)
Let us now have a look at the advanced Sqoop Interview Questions.
8. What is Sqoop-merge and explain its use?
Answer:
Sqoop merge is a tool that combines two different datasets that maintain the only version by overwriting the entries in an older version of a dataset with new files to make it the latest version dataset. There happens a process of flattening while merging the two different datasets which preserves the data without any loss and with efficiency and safety. In order to perform this operation merge key command will be used like “–merge-key”
9. What are the differences between Sqoop, flume, and distcp?
Answer:
Both Distcp and Sqoop are used for transferring the data. Sqoop is used for transferring any type of data from one Hadoop cluster to another cluster, whereas Sqoop transfers data between Relational databases and Hadoop ecosystem such as Hive, HDFS, and HBase, etc. But both methods use the same approach to copy the data, which is pull/transfer.
Flume has distributed a tool, follows agent-based architecture, for streaming the logs into the Hadoop ecosystem. Whereas Sqoop is a connector based architecture.
Flume collects and aggregates a huge amount of log data. Flume can collect the data from different types of resources; it doesn’t consider the schema or structured/unstructured data. Flume can pull any type of data. Whereas Sqoop can only import the Relational Database Data, so the schema is mandatory for sqoop to process. Generally, for moving bulk workloads, the flume is the best option.
Let us move to the next Sqoop Interview Questions.
10. What are the data sources supported by Apache Sqoop?
Answer:
The different data sources from various applications supported by the Apache Sqoop are as below:
- Hive
- HBase
- Hadoop Distributed File System (HDFS)
- HCatalog
- Accumulation
11. What are the most used commands/functions in Sqoop?
Answer:
This is the advanced Sqoop Interview Questions asked in an interview. List of basic commands used in Sqoop are as follows:
- Codegen -Codegen is used to generate code to communicate with database records.
- Eval -Sqoop Eval helps in running sample SQL queries against the databases and provides the results on the console.
- Help -Help list the available commands
- Import -Import will import the table into the Hadoop Ecosystem
- Export -Export is used to export HDFS Data to Relational Databases.
- Create-hive-table -This command is useful for importing table definition into Hive
- Import-all-tables -Import-all-tables will import the tables to form Relational Databases to HDFS.
- List-databases -It will list out all the databases present on a server.
- List-tables -It will list out all the tables present in a database.
- Versions -It will display the version information.
- Functions -Parallel import/export, Full load, Incremental Load, Full load, Comparison, Connectors for RDBMS Databases, Kerberos Security Integration, Load data directly into HDFS (Hive/HBase)
12. Explain the Best Practices while importing tables from MySQL or any other databases using Sqoop?
Answer:
While importing the tables from MySQL, we should make sure about a few things like authentication and authorization to the target server and databases. We need to make sure that we have granted necessary privileges on the databases, which are to be accessed and also make sure about the hostname resolution when we connect to the source and destination hostnames. If we don’t have the necessary permission, we will get a connection failure exception while connecting to the database.
13. How do you update the data or rows already exported?
Answer:
To update the rows, that are already exported the destination we can use the parameter “–update-key”. In this, a comma-separated column list is used which uniquely identifies a row and all of these columns are used in the WHERE clause of the generated UPDATE query. SET part of the query will take care of all the other table columns.
Let us move to the next Sqoop Interview Questions.
14. How to configure and install the JDBC driver in Apache Sqoop?
Answer:
The JDB drivers in Apache Sqoop can be configured based on the Hadoop provider such as Cloudera or Hortonworks where it slightly varies in its configuration based on the Hadoop provider. The JDBC in Cloudera can be configured as by creating a library folder like /var/lib/. This can be done for any third party library required to be configured as per the requirement. In this way, any type of database can be configured using its JDBC driver. Apart from the JDBC driver, Apache Sqoop requires a connector to establish a connection between different relational databases. The main components required to establish a connection with the databases is through Driver and Connector of the particular database provider.
15. What is the split-by clause and when do we use it?
Answer:
A split-by parameter is for slicing the data to be imported into multiple parallel tasks. Using this parameter, we can specify the names of the column, these are columns name based on which sqoop will be dividing the data to be imported into multiple chunks and they will be running in a parallel fashion. It is one of the techniques to tune the performance in Sqoop.
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