Top Hive Commands with Examples in HQL

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Mar 22, 2014
Top Hive Commands with Examples in HQL
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In this blog post, let’s discuss top Hive commands with examples.

What is Hive?

Apache Hive is a Data warehouse system which is built to work on Hadoop. It is used to querying and managing large datasets residing in distributed storage. Before becoming a open source project of Apache Hadoop, Hive was originated in Facebook. It provides a mechanism to project structure onto the data in Hadoop and to query that data using a SQL-like language called HiveQL (HQL).

Hive is used because the tables in Hive are similar to tables in a relational database. If you are familiar with SQL, it’s a cakewalk. Many users can simultaneously query the data using Hive-QL.

Wish-to-Learn-Hive

What is HQL?

Hive defines a simple SQL-like query language to querying and managing large datasets called Hive-QL ( HQL ). It’s easy to use if you’re familiar with SQL Language. Hive allows programmers who are familiar with the language to write the custom MapReduce framework to perform more sophisticated analysis.

Uses of Hive:

1. The Apache Hive distributed storage.

2. Hive provides tools to enable easy data extract/transform/load (ETL)

3. It provides the structure on a variety of data formats.

4. By using Hive, we can access files stored in Hadoop Distributed File System (HDFS is used to querying and managing large datasets residing in) or in other data storage systems such as Apache HBase.

Limitations of Hive:

• Hive is not designed for Online transaction processing (OLTP ), it is only used for the Online Analytical Processing.

• Hive supports overwriting or apprehending data, but not updates and deletes.

• In Hive, sub queries are not supported.

Why Hive is used inspite of Pig?

The following are the reasons why Hive is used in spite of Pig’s availability:

  • Hive-QL is a declarative language line SQL, PigLatin is a data flow language.
  • Pig: a data-flow language and environment for exploring very large datasets.
  • Hive: a distributed data warehouse.

Components of Hive:

Metastore :

Hive stores the schema of the Hive tables in a Hive Metastore. Metastore is used to hold all the information about the tables and partitions that are in the warehouse. By default, the metastore is run in the same process as the Hive service and the default Metastore is DerBy Database.

SerDe :

Serializer, Deserializer gives instructions to hive on how to process a record.

Hive Commands :

Data Definition Language (DDL )

DDL statements are used to build and modify the tables and other objects in the database.

Example :

CREATE, DROP, TRUNCATE, ALTER, SHOW, DESCRIBE Statements.

Go to Hive shell by giving the command sudo hive and enter the command ‘create database<data base name>’ to create the new database in the Hive.

Create Hive database using Hive Commands

To list out the databases in Hive warehouse, enter the command ‘show databases’.

List Hive database using Hive Commands

The database creates in a default location of the Hive warehouse. In Cloudera, Hive database store in a /user/hive/warehouse.

List Hive database using Hive Commands

The command to use the database is USE <data base name>

Hive command to use the database

Copy the input data to HDFS from local by using the copy From Local command.

 Input data in Hive

Hive table

When we create a table in hive, it creates in the default location of the hive warehouse. – “/user/hive/warehouse”, after creation of the table we can move the data from HDFS to hive table.

The following command creates a table with in location of “/user/hive/warehouse/retail.db”

Note : retail.db is the database created in the Hive warehouse.

Creating Database in Hive Warehouse.

Describe provides information about the schema of the table.

Describe - Hive Command

Data Manipulation Language (DML )

DML statements are used to retrieve, store, modify, delete, insert and update data in the database.

Example :

LOAD, INSERT Statements.

Syntax :

LOAD data <LOCAL> inpath <file path> into table [tablename]

The Load operation is used to move the data into corresponding Hive table. If the keyword local is specified, then in the load command will give the local file system path. If the keyword local is not specified we have to use the HDFS path of the file.

DML statement - Load Command

DML statement - Load Command

Here are some examples for the LOAD data LOCAL command

DML statement - Load Command

DML statement - Load Command

After loading the data into the Hive table we can apply the Data Manipulation Statements or aggregate functions retrieve the data.

Example to count number of records:

Count aggregate function is used count the total number of the records in a table.

Example to count number of records:

‘create external’ Table :

The create external keyword is used to create a table and provides a location where the table will create, so that Hive does not use a default location for this table. An EXTERNAL table points to any HDFS location for its storage, rather than default storage.

Create External Command in Hive

Create External Command in Hive

Insert Command:

The insert command is used to load the data Hive table. Inserts can be done to a table or a partition.

• INSERT OVERWRITE is used to overwrite the existing data in the table or partition.

• INSERT INTO is used to append the data into existing data in a table. (Note: INSERT INTO syntax is work from the version 0.8)

Insert Command

Insert Command

Example for ‘Partitioned By’ and ‘Clustered By’ Command :

‘Partitioned by‘ is used to divided the table into the Partition and can be divided in to buckets by using the ‘Clustered By‘ command.

Example for 'Partitioned By' and 'Clustered By' Command

Example for 'Partitioned By' and 'Clustered By' Command

When we insert the data Hive throwing errors, the dynamic partition mode is strict and dynamic partition not enabled (by Jeff at dresshead website). So we need to set the following parameters in Hive shell.

set hive.exec.dynamic.partition=true;

To enable dynamic partitions, by default, it’s false

set hive.exec.dynamic.partition.mode=nonstrict;

Dynamic Partitions

Dynamic Partitions

Dynamic Partitions

Partition is done by the category and can be divided in to buckets by using the ‘Clustered By’ command.

Partition

The ‘Drop Table’ statement deletes the data and metadata for a table. In the case of external tables, only the metadata is deleted.

Drop Table statement

Drop Table statement

The ‘Drop Table’ statement deletes the data and metadata for a table. In the case of external tables, only the metadata is deleted.

Load data local inpath ‘aru.txt’ into table tablename and then we check employee1 table by using Select * from table name command

To count the number of records in table by using Select count(*) from txnrecords;

To count the number of records in table by using Select count(*) from txnrecords;

To count the number of records in table by using Select count(*) from txnrecords;

Aggregation :

Select count (DISTINCT category) from tablename;

This command will count the different category of ‘cate’ table. Here there are 3 different categories.

Suppose there is another table cate where f1 is field name of category.

 Count the different category of 'cate' table.

 Count the different category of 'cate' table.

Grouping :

Group command is used to group the result-set by one or more columns.

Select category, sum( amount) from txt records group by category

It calculates the amount of same category.

Group command

The result one table is stored in to another table.

Create table newtablename as select * from oldtablename;

Group command

Join Command :

Here one more table is created in the name ‘mailid’

Join Command

Join Command

Join Operation:

A Join operation is performed to combining fields from two tables by using values common to each.

Join Operation

Left Outer Join:

The result of a left outer join (or simply left join) for tables A and B always contains all records of the “left” table (A), even if the join-condition does not find any matching record in the “right” table (B).

Left Outer Join

Left Outer Join

Right Outer Join:

A right outer join (or right join) closely resembles a left outer join, except with the treatment of the tables reversed. Every row from the “right” table (B) will appear in the joined table at least once.

Right Outer Join

Right Outer Join

Full Join:

The joined table will contain all records from both tables, and fill in NULLs for missing matches on either side.

Full Join

Top Hive Commands with Examples in HQL

Once done with hive we can use quit command to exit from the hive shell.

Exiting from Hive

Hive is just a part of the big puzzle called Big Data and Hadoop. Hadoop is much more than just Hive. Click below to see what other skills you should master in Hadoop.

Learn Big Data Hadoop Today

Got a question for us? Please mention it in the comments section and we will get back to you.

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AwanishAbout Awanish (19 Posts)

Awanish is a Sr. Research Analyst at Edureka. He has rich expertise in Big Data technologies like Hadoop, Spark, Storm, Kafka, Flink. Awanish also holds good knowledge on Devops tools like Git, Jenkins, Docker, Puppet, Nagios. He is passionate about new technologies which provide realistic solutions.


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48 Comments
  • Jeet Mwd

    nice blog with great explanation sir can you provide me any sample project of hive

  • Dhanashri Patil

    Very Nice blog, Can you please take one sample real time project in Hadoop and explain flow and architecture.
    Plz mail to patildhanashri165@gmail.com

  • Vishal

    Very nice Blog…
    Can you please send me some sample real time project on Hadoop to
    dembanivishal@gmail.com

  • peeyush sharma

    Awesome blog… Can you please help me with some one sample realtime project in hadoop.

    • EdurekaSupport

      Hey Peeyush, thanks for checking out our blog. Could you please share your email address. Cheers!

  • purni

    Very helpful blog.. Can you please take one sample realtime project in hadoop and explain flow and architecture.

    • EdurekaSupport

      Hey Purni, thanks for checking out our blog. Please share your contact email address with us and we will get in touch. Cheers!

      • purni
        • EdurekaSupport

          Hi, we have sent some information to you. Cheers!

          • myspace

            Very Nice blog, Can you please take one sample realtime project in hadoop and explain flow and architecture.
            plz mail to mailstomyspace@gmail.com

          • EdurekaSupport

            +myspace, thanks for checking out our blog. We’re glad you liked it.
            We have sent it. Cheers!

  • Rajiv

    Sir, I want to know how insurance company use big data to improve their customer experience using an example. if possible then plz explain flow diagram. like how many layer should be there which file should be used

    • EdurekaSupport

      Hey Rajiv, thanks for checking out our blog. Here are a couple of related blogs that you can refer to for the explanation:
      https://goo.gl/EoTFe8
      https://goo.gl/BxNwye
      Please feel free to write to us if you have any questions. Hope this helps. Cheers!

  • Rajiv

    very helpful blog..nice content..easy to understand ..Sir i want to know that is all data transfered in hadoop is in serialized form

    • EdurekaSupport

      Hey Rajiv, thanks for checking out our blog. With regard to your query, yes, the serialized data will be stored in Hive. To insert data into table, Hive creates an object by using Java. To transfer java objects over network, the data should be serialized. Each field serialized by using Object inspector and finally serialized data stored in Hive table. Hope this helps. Cheers!

      • Rajiv

        thanks sir for doubt clerification

  • Vissu Kangarla

    please tell me the query how to split the information contained in the table in to another table

    • EdurekaSupport

      Hey Vissu, thanks for checking out our blog. You can insert existing data from a table into another table by using select query.
      INSERT INTO table new_table select columnlist FROM secondtable where soma_conditions;
      Hope this helps. Cheers!

  • Gopesh kumar

    i became much acquainted with HIVE with help of this blog.

    • EdurekaSupport

      We are happy we could help you understand Hive concepts. DO keep checking back in for more videos on all your favourite topics.

  • Gopesh kumar

    Nice blog ….i helped me a lot.

    • EdurekaSupport

      We are so happy we could help. Do keep checking back in for more blogs on all your favourite topics!

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