Apache Hive Interview Questions and Answers
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Best Apache Hive Interview Questions and Answers
Are you one among the talented aspirants who wish to pursue their career in a futuristic technology like Apache Hive? Are you about to give your apache Hive interview? If your answer is yes for these questions then you have come to the right place. With an aim to help the aspirants like you we have collected all the top Hive Interview questions from the Hadoop Hive industry experts.
This blog consists of frequently asked Hive interview questions suitable for beginners and experienced candidates. By the end of this Top Apache Hive interview questions blog, you will gain all the confidence and knowledge required to crack your Hive Interview in the very first attempt. If you have attended any Hive interview earlier and have not found similar questions in this blog post? Please do comment on them in the comment section. We will try to include them in this post. So that it may help fellow job seekers like you.
Apache Hive is a data warehouse system which is built on the top of the Hadoop platform. This Apache hive simplifies the work of data analysis whether it may be structured or semi-structured. As the importance of data and the value that it contains have grabbed the attention of the organizations across the world and Hive is one the topmost platforms for analyzing data sets and gaining insights out of it. There are a good number of job opportunities for skilled Apache Hive professionals. Let’s get into the Apache Hive Interview questions and answers blog.
Following are the Top Hive Interview questions which we have gathered and grouped here based on the industry experts’ opinion.
Top Apache Hive Interview Questions and Answers
Apache Hive is an advanced warehouse project built on top of Hadoop. This platform is specialized in data analysis and also allows data query options. Hive works similar to SQL and provides an interface through which you can query the data stored in files and database systems. And Apache hive is one of the widely used data analysis and querying tools by the top corporations worldwide.
Apache Hive has got the capability to support all type of client applications that are written in:
- PHP
- Java
- C++
- Ruby
- Python
Metastore is considered as a repository that is designed to store the metadata information by taking the help of the systems like RDBMS and open source ORM (Object Relational Model).
In Apache Hive the default option for storing the metadata is the HDFS directory. One can choose the other directories over the HDFS directory through hive.metastore.warehouse.dir in the configuration.
Local Metastore:
In the configuration of Local metastore, the given metastore service, as well as Hive service, will run on the same Java Virtual Machine (JVM) and connect with databases running in separate JVM that may be in the same machine or on a remote machine.
Remote Metastore:
In the Remote metastore, the Metastore service and Apache Hive service will run on separate JVMs. All other processes use Thrift Network API’s to connect with metastore servers. Here in Remote metastore, you can have more than one metastore server for high availability.
The following are the key differences between external and managed tables:
- In Managed tables, the entire metadata and table data gets deleted in the event of dropping off a managed table.
- The external table is quite different from it and the Hive only deletes the metadata information related to a table and leaves the table data resided in HDFS.
The default database for metastore by Apache Hive is a Derby database instance and it is backed by a local disk for the metastore. This process is also termed as an embedded metastore configuration.
| HBase | Hive |
| It is Built on the top of the HDFS | Hive is built on top of the Apache Hadoop |
| Operations in HBase usually runs in real-time on its own database | All the Hive queries are treated as MapReduce jobs internally |
| It accepts random access to the database | Hive also provides random access to data |
| It is not capable to provide high latency for the huge volumes of data | It supports high latency even for the huge volumes of data. |
Managed tables are also called Hive Owned tables. The managed tables are the place where the whole lifecycle of a table data is stored, controlled, and managed by Hive.
We can change the default location of a managed table by using the clause – LOCATION ‘<hdfs_path>’.
In Apache Hive we use SORT BY instead of ORDER BY whenever we work with large sets of data. The reason behind using SORT BY is that it comes with multiple reducers. These reduce the time taken for execution. Whereas ORDER BY consists of the only single reduces which takes a longer time than usual to execute the process.
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Hive arranges the tables into partitions to create a similar type of data together. Every table in Hive consists of one or more partition key’s which helps in recognizing a specific partition. The Partition is also called as the sub-directory in the table directory.
Partitioning helps the users in arranging the data in a required manner in the Hive table. This would result in allowing the system to scan the relevant data instead of scanning the entire data.
For example: Let us assume that we have the transaction log data of a business website related to the years like 2018, 2019, 2020, etc. So here you can use the partition key to know the data of a specific year let’s say the year 2019, this will reduce the data scanning by eliminating 2018 and 2020.
In Dynamic partitioning the values of partition columns are revealed during the runtime, i,e, the values are known when you load data into Hive tables.
The following are the common way to use dynamic partitioning:
- To transfer the data from a current non-partitioned table which helps in decreasing latency and improves sampling.
- To know the values of partitions earlier
The ObjectInspector is a feature that allows us to analyze individual columns and internal structure of a row object in Hive. This also provides a seamless way to access complex objects that can be stored in varied formats in the memory.
- A standard Java object
- An instance of the Java class
- A lazily initialized object
The ObjectInspector lets the users know the structure of an object and also helps in accessing the internal fields of an object.
In general Hadoop, Developers treat an array as the input and transform it into a unique table row. Hive uses Explode to convert complex data types into easy to understand table formats.
Following are the three main data types supported by Hive:
- Arrays
- Structs
- Maps
Buckets allow Hive to divide Hive table data into various directories or files. Buckets in the Hive enhances the querying process.
Below mentioned is the list of Hive Query processors:
- Metadata Layer (ql/metadata)
- Parse and Semantic Analysis (ql/parse)
- Map/Reduce Execution Engine (ql/exec)
- Sessions (ql/session)
- Type Interfaces (ql/typeinfo)
- Tools (ql/tools)
- Hive Function Framework (ql/udf)
- Plan Components (ql/plan)
- Optimizer (ql/optimizer)
Hive variables are generally referenced by Hive scripting languages. A variable is capable of passing some value to a query before it gets executed.
There are few popular Hive Query optimization methods and one among them is Hive Index. The Hive Index allows faster access to a column or group of columns in the Hive database. This reduces the need for the database system to read all the tables to find the selected data.
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The Hcatolog is generally used when we share the data structures with external systems. The Hcatalogue enables the accessibility to Hive metastore which enables other Hadoop tools users to read and write data to Hive’s warehouse.
We have four main types of joins in Hive:
- JOIN: This is the same as outer join in SQL
- FULL OUTER JOIN: This Join fulfills the Join condition by combining the left and right joins.
- LEFT OUTER JOIN: Using this Join one can return all the rows from the left table even if there are no matches in the given right side table.
- RIGHT OUTER JOIN: In this join total rows from the right side table either there are no matches in the left side table.
The following are the commonly used Hive services:
- Hive Web Interface (hwi)
- Command Line Interface (CLI)
- Printing the contents of an RC file using the tool reflects.
- HiveServer (live server)
- Metastore
- Jar
In Apache Hive a query processor converts the SQL to a graph of MapReduce jobs. It follows a time frame execution process so that each job can be executed in the order of dependencies. The following are the different components of query processors.
- Parser
- Type Checking
- Semantic Analyser
- Logical Plan Generation
- Physical Plan Generation
- Optimizer
- Operators
- Execution Engine
- UDF’s and UDAF’s.
Yes, we can overwrite the Hadoop MapReduce configuration by making the modification to Hive configuration setting files.
The Default database of Hive Metastore is Derby.
Following are the times when you can use Hive:
- To develop data warehouse applications
- While dealing with static data
- While using queries rather than scripting
- For managing a large dataset.
Based on the data nodes size in Hadoop, Hive can be available in two in two different modes:
- Local mode
- MapReduce mode
Below mentioned are the key components of Hive Architecture:
- User Interface
- Metastore
- Compiler
- Execute Engine
- Driver
A hive consists of 3 major parts which are:
- Hive Services
- Hive Clients
- Hive computing and storage
HiveServer2 is a Server interface and executes the following functions:
- Allows remote clients to perform queries in Hive
- Retrieves the results of targeted queries.
In Hive, Views are the same as tables and are produced based on the needs.
The views feature is available in
- Hive Views are used the same as views used in SQL
- One can store any result set data as a view
- It supports all type of DML operations
We hope that this Apache Hive interview questions and answers blog helped you in gaining knowledge of all the important Hive questions. Mastering these Hive frequently asked interview questions will help you in gaining the confidence levels and lets you crack the interview at the very first attempt. We also add the latest apache hive interview questions to this blog, so stay updated. Happy learning!
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