Hive Interview Questions

Hive is an information warehouse programming which is utilized for encourages questioning and overseeing vast data sets residing in dispersed storage. Hive language nearly looks like SQL language called HiveQL. Hive also permits conventional map to reduce projects to customize mappers and reducers when it is awkward or wasteful to execute the logic in HiveQL (User Defined Functions UDFS)

Hive is helpful when influencing information to warehouse applications when you are dealing with static information rather than dynamic information.

• When the application is on high latency (high reaction time)
• When a big data set collection is kept up
• When we are utilizing queries instead of scripting

Various methods of the hive are as follows:

• Local mode
• Map reduce mode

The Hive can work in the above modes relying upon the size of data nodes in Hadoop.

Key segments of Hive Architecture incorporate

• Meta-score
• User Interface
• Driver
• Execute Engine
• Compiler

Managed and External tables are the two different kinds of tables in hive used to enhance how information is loaded, managed and controlled

Two types of tables, which are used are:

• Managed Table -: Managed table is also known as an internal table. This is the default table in Hive. When we make a table in Hive without specifying it as external, naturally we will get a Managed table. If we make a table as a managed table, the table will be made in a specific area in HDFS.
• External table: External table is made up for external use as when the information is utilized outside Hive. At whatever point we need to erase the table’s meta information and we need to keep the table’s information as it seems to be, we utilize External table. The external table just erases the pattern of the table.

Meta-store in Hive stores the meta information utilizing RDBMS and an open source ORM (Object Relational Model) layer called Data Nucleus which changes over the object portrayal into a relational schema.

Hive meta-store comprises of two major units:

• A service that gives meta-store access to other Apache Hive administrations.
• Disk storage for the Hive metadata, which is separate from HDFS stockpiling.

Ans7. Hive is made out of

• Clients
• Services
• Storage and Computing

Hcatalog can be utilized to share information structures with external systems. Hcatalog gives access to hive meta-store to clients of other devices on Hadoop with the goal that they can read and compose information to hive’s data warehouse.

In Hive the analysis of the inner structure of the segments, columns, and complex items are finished utilizing Object Inspector functionality. Question Inspector functionality makes availability to the inner fields, which are present inside the objects.

Hive Server2 is a server interface. Various functions, which are followed by Hive Server2 are as follows:

• Works against Hive by enabling remote customers to execute questions.
• The outcomes of inquiries specified are retrieved

Propelled highlights:

• Multi-customer concurrency
• Authentication

The segments of a Hive question processor are as follows:

• Logical Plan Generation
• Physical Plan Generation
• UDF’s and UDAF’s
• Execution Engine
• Operators
• Semantic Analyzer
• Optimizer
• Type Checking
• Parser

Here are the partitions in Hive:

• Partitions are a method for isolating tables into different parts based on partition keys.
• Partition is utilized when the table has at least one Partition keys.
• Partition act as necessary key components that decide how the information is stored in the table.

Serialization and de-serialization designs are prominently known as SerDes. Hive enables the system to read or write information in a specific format. These formats parse the organized or unstructured data bytes put away in HDFS by the definition of Hive tables.

Views are Similar to tables In Hive; They are produced based on various requirements:

• Any results can be spared asset data as a view in Hive
• Similar to views utilized as a part of SQL in use.
• All kind of DML tasks can be performed on a view.

• Local Meta-store:In local meta-store design, the meta-store service keeps running in the same JVM in which the Hive service is running and associates with a database running in a different JVM, either on a similar machine or a remote machine.
• Remote Meta-store: In the remote meta-store design, the meta-store service keeps running alone separating JVM and not in the Hive benefit JVM. Different procedures communicate with the meta-store server utilizing Thrift Network APIs. You can have at least one meta-store servers for this situation to give greater accessibility.

In Hive, you can pick an internal table

• If the preparing data accessible in the local file system.
• If we need Hive to deal with the entire lifecycle of data including the cancellation

You can pick an External table

• If processing information accessible in HDFS
• Useful when the documents are being utilized outside of Hive

Both hive and HBase can be utilized in different technologies that depend on Hadoop. Hive happens to be an infrastructure warehouse of information, which is utilized on Hadoop while HBase is NoSQL. The key esteem stores which keep running on Hadoop themselves. The hive will also enable the individuals who know about SQL run a few of jobs in MapReduce when Hbase will also bolster 4 of the activities, for example, put, get, scan and erase. The HBase happens to be useful for questioning for information yet Hive then again is useful for questioning information is analytical and is gathered over a while.

In SMB join in Hive, every mapper peruses a bucket from the first table and the relating bucket from the second table, and after that, a merge sort join is performed. Sort Merge Bucket (SMB) joins in the hive is for the most utilized as there are no restrictions on file or segment or table join. SMB join can best be utilized when the tables are huge. In SMB join the sections are bucketed and arranged to utilize the join segments. All tables ought to have a similar number of buckets in SMB join.

Hadoop developers consider the exhibit as their inputs and convert them into a different table row. To change over data types into wanted table formats Hive is basically utilizing detonate.

Hive variable is made in the Hive condition that can be referenced by Hive contents. It is utilized to pass a few values to the hive inquiries when the queries begin executing.