Following is an example of the union clause in Impala. In this example, we arrange the records in both tables in the order of their id’s and limit their number by 3 using two separate queries and joining these queries using the UNION clause.

[quickstart.cloudera:21000] > select * from customers order by id limit 3

union select * from employee order by id limit 3;

On executing, the above query gives the following output.

Query: select * from customers order by id limit 3 union select

* from employee order by id limit 3

+—-+———+—–+———–+——–+

+—-+———+—–+———–+——–+

| 2 | Khilan | 25 | Delhi | 15000 |

| 3 | mahesh | 54 | Chennai | 55000 |

| 1 | subhash | 34 | Delhi | 40000 |

| 2 | ramesh | 44 | Chennai | 50000 |

| 3 | kaushik | 23 | Kota | 30000 |

| 1 | Ramesh | 32 | Ahmedabad | 20000 |

+—-+———+—–+———–+——–+

Fetched 6 row(s) in 3.11s

Impala – With Clause

In case a query is way too complex, we can define aliases to complex parts and include them in the query using the with clause of Impala.

Syntax

Following is the syntax of the with clause in Impala.

with x as (select 1), y as (select 2) (select * from x union y);

Example

Assume we have a table named customers in the database my_db and its contents are as follows −

[quickstart.cloudera:21000] > select * from customers;

Query: select * from customers

+—-+———-+—–+———–+——–+

+—-+———-+—–+———–+——–+

| 1 | Ramesh | 32 | Ahmedabad | 20000 |

| 9 | robert | 23 | banglore | 28000 |

| 2 | Khilan | 25 | Delhi | 15000 |

| 4 | Chaitali | 25 | Mumbai | 35000 |

| 7 | ram | 25 | chennai | 23000 |

| 6 | Komal | 22 | MP | 32000 |

| 8 | ram | 22 | vizag | 31000 |

| 5 | Hardik | 27 | Bhopal | 40000 |

| 3 | kaushik | 23 | Kota | 30000 |

+—-+———-+—–+———–+——–+

Fetched 9 row(s) in 0.59s

In the same way, suppose we have another table named employee and its contents are as follows −

[quickstart.cloudera:21000] > select * from employee;

Query: select * from employee

+—-+———+—–+———+——–+

+—-+———+—–+———+——–+

| 3 | mahesh | 54 | Chennai | 55000 |

| 2 | ramesh | 44 | Chennai | 50000 |

| 4 | Rupesh | 64 | Delhi | 60000 |

| 1 | subhash | 34 | Delhi | 40000 |

+—-+———+—–+———+——–+

Fetched 4 row(s) in 0.59s

Following is an example of the with clause in Impala. In this example, we are displaying the records from both employee and customers whose age is greater than 25 using with clause.

[quickstart.cloudera:21000] >

with t1 as (select * from customers where age>25),

t2 as (select * from employee where age>25)

(select * from t1 union select * from t2);

On executing, the above query gives the following output.

Query: with t1 as (select * from customers where age>25), t2 as (select * from employee where age>25)

(select * from t1 union select * from t2)

+—-+———+—–+———–+——–+

+—-+———+—–+———–+——–+

| 3 | mahesh | 54 | Chennai | 55000 |

| 1 | subhash | 34 | Delhi | 40000 |

| 2 | ramesh | 44 | Chennai | 50000 |

| 5 | Hardik | 27 | Bhopal | 40000 |

| 4 | Rupesh | 64 | Delhi | 60000 |

| 1 | Ramesh | 32 | Ahmedabad | 20000 |

+—-+———+—–+———–+——–+

Fetched 6 row(s) in 1.73s

Impala – Distinct Operator

The distinct operator in Impala is used to get the unique values by removing duplicates.

Syntax

Following is the syntax of the distinct operator.

select distinct columns… from table_name;

Example

Assume that we have a table named customers in Impala and its contents are as follows −

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

Query: select distinct id, name, age, salary from customers

Here you can observe the salary of the customers Ramesh and Chaitali entered twice and using the distinct operator, we can select the unique values as shown below.

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

On executing, the above query gives the following output.

Query: select distinct id, name from customers

+———-+—–+———–+

| name | age | address |

+———-+—–+———–+

| Ramesh | 32 | Ahmedabad |

| Khilan | 25 | Delhi |

| kaushik | 23 | Kota |

| Chaitali | 25 | Mumbai |

| Hardik | 27 | Bhopal |

| Komal | 22 | MP |

+———-+—–+———–+

Fetched 9 row(s) in 1.46s

So, this brings us to the end of blog. This Tecklearn ‘Explanation of Union Clause , With Clause and Distinct Operator 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/

Big Data Hadoop-Architect (All in 1) Combo Training

About the Course

Tecklearn’s Big Data Hadoop-Architect (All in 1) combo includes the following Courses:

BigData Hadoop Analyst
BigData Hadoop Developer
BigData Hadoop Administrator
BigData Hadoop Tester
Big Data Security with Kerberos

Why Should you take BigData Hadoop Combo Training?

Average salary for a Hadoop Administrator ranges from approximately $104,528 to $141,391 per annum – Indeed.com
Average salary for a Spark and Hadoop Developer ranges from approximately $106,366 to $127,619 per annum – Indeed.com
Average salary for a Big Data Hadoop Analyst is $115,819– ZipRecruiter.com

What you will Learn in this Course?

Introduction

The Case for Apache Hadoop
Why Hadoop?
Core Hadoop Components
Fundamental Concepts

HDFS

HDFS Features
Writing and Reading Files
NameNode Memory Considerations
Overview of HDFS Security
Using the Namenode Web UI
Using the Hadoop File Shell

Getting Data into HDFS

Ingesting Data from External Sources with Flume
Ingesting Data from Relational Databases with Sqoop
REST Interfaces
Best Practices for Importing Data

YARN and MapReduce

What Is MapReduce?
Basic MapReduce Concepts
YARN Cluster Architecture
Resource Allocation
Failure Recovery
Using the YARN Web UI
MapReduce Version 1

Planning Your Hadoop Cluster

General Planning Considerations
Choosing the Right Hardware
Network Considerations
Configuring Nodes
Planning for Cluster Management

Hadoop Installation and Initial Configuration

Deployment Types
Installing Hadoop
Specifying the Hadoop Configuration
Performing Initial HDFS Configuration
Performing Initial YARN and MapReduce Configuration
Hadoop Logging

Installing and Configuring Hive, Impala, and Pig

Hive
Impala
Pig

Hadoop Clients

What is a Hadoop Client?
Installing and Configuring Hadoop Clients
Installing and Configuring Hue
Hue Authentication and Authorization

Cloudera Manager

The Motivation for Cloudera Manager
Cloudera Manager Features
Express and Enterprise Versions
Cloudera Manager Topology
Installing Cloudera Manager
Installing Hadoop Using Cloudera Manager
Performing Basic Administration Tasks Using Cloudera Manager

Advanced Cluster Configuration

Advanced Configuration Parameters
Configuring Hadoop Ports
Explicitly Including and Excluding Hosts
Configuring HDFS for Rack Awareness
Configuring HDFS High Availability

Hadoop Security

Why Hadoop Security Is Important
Hadoop’s Security System Concepts
What Kerberos Is and How it Works
Securing a Hadoop Cluster with Kerberos

Managing and Scheduling Jobs

Managing Running Jobs
Scheduling Hadoop Jobs
Configuring the Fair Scheduler
Impala Query Scheduling

Cluster Maintenance

Checking HDFS Status
Copying Data Between Clusters
Adding and Removing Cluster Nodes
Rebalancing the Cluster
Cluster Upgrading

Cluster Monitoring and Troubleshooting

General System Monitoring
Monitoring Hadoop Clusters
Common Troubleshooting Hadoop Clusters
Common Misconfigurations

Introduction to Pig

What Is Pig?
Pig’s Features
Pig Use Cases
Interacting with Pig

Basic Data Analysis with Pig

Pig Latin Syntax
Loading Data
Simple Data Types
Field Definitions
Data Output
Viewing the Schema
Filtering and Sorting Data
Commonly-Used Functions

Processing Complex Data with Pig

Storage Formats
Complex/Nested Data Types
Grouping
Built-In Functions for Complex Data
Iterating Grouped Data

Multi-Dataset Operations with Pig

Techniques for Combining Data Sets
Joining Data Sets in Pig
Set Operations
Splitting Data Sets

Pig Troubleshooting and Optimization

Troubleshooting Pig
Logging
Using Hadoop’s Web UI
Data Sampling and Debugging
Performance Overview
Understanding the Execution Plan
Tips for Improving the Performance of Your Pig Jobs

Introduction to Hive and Impala

What Is Hive?
What Is Impala?
Schema and Data Storage
Comparing Hive to Traditional Databases
Hive Use Cases

Querying with Hive and Impala

Databases and Tables
Basic Hive and Impala Query Language Syntax
Data Types
Differences Between Hive and Impala Query Syntax
Using Hue to Execute Queries
Using the Impala Shell

Data Management

Data Storage
Creating Databases and Tables
Loading Data
Altering Databases and Tables
Simplifying Queries with Views
Storing Query Results

Data Storage and Performance

Partitioning Tables
Choosing a File Format
Managing Metadata
Controlling Access to Data

Relational Data Analysis with Hive and Impala

Joining Datasets
Common Built-In Functions
Aggregation and Windowing

Working with Impala

How Impala Executes Queries
Extending Impala with User-Defined Functions
Improving Impala Performance

Analyzing Text and Complex Data with Hive

Complex Values in Hive
Using Regular Expressions in Hive
Sentiment Analysis and N-Grams
Conclusion

Hive Optimization

Understanding Query Performance
Controlling Job Execution Plan
Bucketing
Indexing Data

Extending Hive

SerDes
Data Transformation with Custom Scripts
User-Defined Functions
Parameterized Queries

Importing Relational Data with Apache Sqoop

Sqoop Overview
Basic Imports and Exports
Limiting Results
Improving Sqoop’s Performance
Sqoop 2

Introduction to Impala and Hive

Introduction to Impala and Hive
Why Use Impala and Hive?
Comparing Hive to Traditional Databases
Hive Use Cases

Modelling and Managing Data with Impala and Hive

Data Storage Overview
Creating Databases and Tables
Loading Data into Tables
HCatalog
Impala Metadata Caching

Data Formats

Selecting a File Format
Hadoop Tool Support for File Formats
Avro Schemas
Using Avro with Hive and Sqoop
Avro Schema Evolution
Compression

Data Partitioning

Partitioning Overview
Partitioning in Impala and Hive

Capturing Data with Apache Flume

What is Apache Flume?
Basic Flume Architecture
Flume Sources
Flume Sinks
Flume Channels
Flume Configuration

Spark Basics

What is Apache Spark?
Using the Spark Shell
RDDs (Resilient Distributed Datasets)
Functional Programming in Spark

Working with RDDs in Spark

A Closer Look at RDDs
Key-Value Pair RDDs
MapReduce
Other Pair RDD Operations

Writing and Deploying Spark Applications

Spark Applications vs. Spark Shell
Creating the SparkContext
Building a Spark Application (Scala and Java)
Running a Spark Application
The Spark Application Web UI
Configuring Spark Properties
Logging

Parallel Programming with Spark

Review: Spark on a Cluster
RDD Partitions
Partitioning of File-based RDDs
HDFS and Data Locality
Executing Parallel Operations
Stages and Tasks

Spark Caching and Persistence

RDD Lineage
Caching Overview
Distributed Persistence

Common Patterns in Spark Data Processing

Common Spark Use Cases
Iterative Algorithms in Spark
Graph Processing and Analysis
Machine Learning
Example: k-means

Preview: Spark SQL

Spark SQL and the SQL Context
Creating DataFrames
Transforming and Querying DataFrames
Saving DataFrames
Comparing Spark SQL with Impala

Hadoop Testing

Hadoop Application Testing
Roles and Responsibilities of Hadoop Testing Professional
Framework MRUnit for Testing of MapReduce Programs
Unit Testing
Test Execution
Test Plan Strategy and Writing Test Cases for Testing Hadoop Application

Big Data Testing

BigData Testing
Unit Testing
Integration Testing
Functional Testing
Non-Functional Testing
Golden Data Set

System Testing

Building and Set up
Testing SetUp
Solary Server
Non-Functional Testing
Longevity Testing
Volumetric Testing

Security Testing

Security Testing
Non-Functional Testing
Hadoop Cluster
Security-Authorization RBA
IBM Project

Automation Testing

Query Surge Tool

Oozie

Why Oozie
Installation Engine
Oozie Workflow Engine
Oozie security
Oozie Job Process
Oozie terminology
Oozie bundle

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

430