Common Commands of Hive

What is Hive?

Before learning about the various Hive commands in Hadoop, let us get familiar with the concept of Hive.

Hive is a Hadoop-based data warehouse and SQL-like query language solution. It provides a helpful translator between you and your data, converting complex searches into simpler terms. Hive organizes data into tables and databases like a librarian organizes books for quick retrieval. This format allows for effective storage and allows users to access and analyze data without having to delve into technical details.

Consider a massive sales database, for example. Without Hive, you must navigate through data layers using complex commands. However, Hive allows you to simply SELECT and GROUP BY products, summarising sales by category using standard SQL queries. This feature enables non-technical people to gain insights quickly.

Creating Databases and Tables

This section delves into the various Hive commands in Hadoop used for creating databases and tables in Hive.

Creating Databases

To create a database in Hive, the CREATE DATABASE command is used. It follows a simple syntax:

Output:

If the database sales_data already exists, the output would be:

Here's a breakdown of the components:

• CREATE DATABASE: The command to initiate the database creation process.
• IF NOT EXISTS: An optional clause that prevents an error if the database already exists.
• database_name: Replace this with your desired name for the database.

Example:

Creating Tables

Hive makes it easy to define tables to organize your data. The basic syntax for creating a table is as follows:

Output:

If the table sales_records already exists, the output would be:

Here's a quick explanation of the command:

• CREATE TABLE: The command to start table creation.
• IF NOT EXISTS: An optional clause that prevents errors if the table exists.
• table_name: Choose a name for your table.
• column1, column2, ...: List the columns in your table and their corresponding data types.

Example:

In this example, a table called sales_records is constructed with the columns order_id, product_name, quantity, and total_sales, as per the specified data types.

Hive's ability to construct databases and tables establishes the groundwork for efficiently organizing and managing your data. These structures give an organized framework for dealing with massive amounts of data, allowing for powerful data analysis and reporting.

Loading Data into Tables

In this section, we will delve into common Hive commands in Hadoop used for loading data into Hive tables.

1. LOAD DATA INPATH:
   This command imports data from the Hadoop Distributed File System (HDFS) into a Hive table. Specifying the source directory in HDFS and the target Hive table is necessary. For example:

2. LOAD DATA LOCAL INPATH:
   This command, like the last one, loads data into a Hive table. However, it supports loading data from a local file system path on the Hive operating machine. For example:

3. INSERT INTO TABLE:
   This command inserts data from one Hive table into another. It is useful for transforming and populating tables. Here's how it works:

4. INSERT OVERWRITE TABLE:
   You can use this command when you need to replace the data in a Hive table with new data. It removes existing data from the table and inserts the new data. An example usage is as follows:

Querying Data with HiveQL

Let's delve into the Hive commands in Hadoop that help streamline data querying tasks.

1. SELECT:
   The SELECT statement retrieves specific columns from a table. For example, to extract the names and ages of employees from the employee_info table, the query would look like this:

2. WHERE:
   This command filters data based on specified conditions. If you want to find employees who are above 30 years old, you can run the below query:

3. GROUP BY:
   The GROUP BY clause groups rows sharing common values and then applies aggregate functions to them. To find the average salary of employees by department, you need to run the below query:

4. JOIN:
   This command combines data from two or more tables based on a common column. For instance, if you have an employee_info table and a department_info table, you can join them to associate employees with their respective departments:

5. ORDER BY:
   To sort query results in ascending or descending order, use ORDER BY with the ASC or DESC clause. For instance, run the following query to sort employee names alphabetically:

6. LIMIT:
   The LIMIT clause restricts the number of rows a query returns. To retrieve only the top 10 highest-paid employees, run the following query:

Managing Views and Indexes

In this section, we'll explore some Hive commands in Hadoop used to manage views and indexes in Hive.

Managing Views

Hive views give users a logical abstraction over the underlying data, making it easier to create complicated searches and improving query efficiency. A query is defined and given a name when a view is created, and it may then be used as a temporary table in subsequent queries.

To create a view, use the following syntax:

Consider the case when you have a table called sales_data with a lot of data. To display products with revenue over a predetermined level, you can create a view called high_revenue_products as follows:

Managing Indexes

Indexes are essential for improving query performance since they quicken data retrieval. Hive supports creating indexes on particular table columns, enabling faster data access when querying those fields.

To create an index, you can use the following syntax:

Consider a table called customer_orders that contains millions of records. You may greatly speed up the queries that use order_date as a filter by creating an index on the order_date column:

Data Export and Import

In this section, we will look into various Hive commands in Hadoop that enable seamless data export and import.

Importing Data

1. LOAD DATA INPATH:
   This command is used to load data from HDFS into a Hive table. The data is read directly from the specified HDFS location and loaded into the table.

   Example:

2. LOAD DATA LOCAL INPATH:
   It is similar to the previous command but it loads data from the local file system of the machine running the Hive query.

   Example:

3. INSERT INTO TABLE:
   This command is used to insert data from one Hive table into another. It can be used to insert data from a staging table into a final table, for example.

   Example:

4. CREATE TABLE AS:
   This command allows you to create a new table based on the result of a SELECT query. It is often used to transform and load data into a new table.

   Example:

5. CREATE EXTERNAL TABLE:
   When you want to reference data in an external location without moving it to HDFS, you can create an external table. Data remains in its original location, but Hive manages its metadata.

   Example:

Exporting Data

1. INSERT OVERWRITE DIRECTORY:
   This command can be useful to export data as text files to a particular HDFS directory. If data already exists in the destination directory, it is overwritten.

   Example:

Understanding these common commands will help you considerably streamline your Hive data migration process. These commands give you the flexibility and control you require in your big data activities, whether it is exporting data for analysis or importing external data for further research.