Here, column1, column2…are the fields of a table whose values you want to fetch. If you want to fetch all the fields available in the field, then you can use the following syntax −

SELECT * FROM table_name;

Example

Assume we have a table named customers in Impala, with the following data −

ID NAME AGE ADDRESS SALARY

— ——- — ———- ——-

1 Ramesh 32 Ahmedabad 20000

2 Khilan 25 Delhi 15000

3 Hardik 27 Bhopal 40000

4 Chaitali 25 Mumbai 35000

5 kaushik 23 Kota 30000

6 Komal 22 Mp 32000

You can fetch the id, name, and age of all the records of the customers table using select statement as shown below −

[quickstart.cloudera:21000] > select id, name, age from customers;

On executing the above query, Impala fetches id, name, age of all the records from the specified table and displays them as shown below.

Query: select id,name,age from customers

+—-+———-+—–+

| id | name | age |

| 1 | Ramesh | 32 |

| 2 | Khilan | 25 |

| 3 | Hardik | 27 |

| 4 | Chaitali | 25 |

| 5 | kaushik | 23 |

| 6 | Komal | 22 |

+—-+———-+—–+

Fetched 6 row(s) in 0.66s

You can also fetch all the records from the customers table using the select query as shown below.

[quickstart.cloudera:21000] > select name, age from customers;

Query: select * from customers

On executing the above query, Impala fetches and displays all the records from the specified table as shown below.

+—-+———-+—–+———–+——–+

+—-+———-+—–+———–+——–+

| 1 | Ramesh | 32 | Ahmedabad | 20000 |

| 2 | Khilan | 25 | Delhi | 15000 |

| 3 | Hardik | 27 | Bhopal | 40000 |

| 4 | Chaitali | 25 | Mumbai | 35000 |

| 5 | kaushik | 23 | Kota | 30000 |

| 6 | Komal | 22 | MP | 32000 |

+—-+———-+—–+———–+——–+

Fetched 6 row(s) in 0.66s

Fetching the Records using Hue

Open Impala Query editor and type the select Statement in it. And click on the execute button as shown in the following screenshot.

After executing the query, if you scroll down and select the Results tab, you can see the list of the records of the specified table as shown below.

Impala – Describe Statement

The describe statement in Impala is used to give the description of the table. The result of this statement contains the information about a table such as the column names and their data types.

Syntax

Following is the syntax of the Impala describe statement.

Describe table_name;

Example

For example, assume we have a table named customer in Impala, with the following data −

ID NAME AGE ADDRESS SALARY

— ——— —– ———– ———–

1 Ramesh 32 Ahmedabad 20000

2 Khilan 25 Delhi 15000

3 Hardik 27 Bhopal 40000

4 Chaitali 25 Mumbai 35000

5 kaushik 23 Kota 30000

6 Komal 22 Mp 32000

You can get the description of the customer table using the describe statement as shown below −

[quickstart.cloudera:21000] > describe customer;

On executing the above query, Impala fetches the metadata of the specified table and displays it as shown below.

Query: describe customer

+———+——–+———+

| name | type | comment |

+———+——–+———+

| id | int | |

| name | string | |

| age | int | |

| address | string | |

| salary | bigint | |

+———+——–+———+

Fetched 5 row(s) in 0.51s

Describing the Records using Hue

Open Impala Query editor and type the describe statement in it and click on the execute button as shown in the following screenshot.

After executing the query, if you scroll down and select the Results tab, you can see the metadata of the table as shown below.

So, this brings us to the end of blog. This Tecklearn ‘How to fetch the data from one or more tables in a database and fetch description in Impala’ helps you with commonly asked questions if you are looking out for a job in Big Data and Hadoop Domain.

If you wish to learn Impala and build a career in Big Data or Hadoop domain, then check out our interactive, Big Data Hadoop-Architect (All in 1) Combo Training, that comes with 24*7 support to guide you throughout your learning period. Please find the link for course details:

https://www.tecklearn.com/course/bigdata-hadoop-architect-all-in-1-combo-course/

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Hadoop Security

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Introduction to Pig

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Hive Use Cases

Querying with Hive and Impala

Databases and Tables
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Data Types
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Using Hue to Execute Queries
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Data Management

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Data Storage and Performance

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Relational Data Analysis with Hive and Impala

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Working with Impala

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Analyzing Text and Complex Data with Hive

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SerDes
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Importing Relational Data with Apache Sqoop

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Introduction to Impala and Hive

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Modelling and Managing Data with Impala and Hive

Data Storage Overview
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HCatalog
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Data Formats

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Avro Schemas
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Data Partitioning

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Spark Basics

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Spark Caching and Persistence

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Common Patterns in Spark Data Processing

Common Spark Use Cases
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Preview: Spark SQL

Spark SQL and the SQL Context
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Hadoop Testing

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