Impala is an MPP (Massive Parallel Processing) query execution engine that runs on variety of systems within the Hadoop cluster. Unlike traditional storage systems, impala is decoupled from its storage engine. it’s three main components namely, Impala daemon (Impalad), Impala Statestore, and Impala metadata or metastore.

Impala daemon(Impalad)

Impala daemon (also referred to as impalad) runs on each node where Impala is installed. It accepts the queries from various interfaces like impala shell, hue browser, etc.… and processes them.

Whenever a question is submitted to an impalad on a specific node, that node is a “coordinator node” for that question. Multiple queries are served by Impalad running on other nodes also . After accepting the query, Impalad reads and writes to data files and parallelizes the queries by distributing the work to the opposite Impala nodes within the Impala cluster. When queries are processing on various Impalad instances, all of them return the result to the central coordinating node.

Depending on the need , queries are often submitted to a fanatical Impalad or during a load balanced manner to a different Impalad in your cluster.

Impala State Store

Impala has another important component called Impala State store, which is liable for checking the health of every Impalad then relaying each Impala daemon health to the opposite daemons frequently. this will run on same node where Impala server or another node within the cluster is running.

The name of the Impala State store daemon process is State stored. Impalad reports its health status to the Impala State store daemon, i.e., State stored.

In the event of a node failure thanks to any reason, State store updates all other nodes about this failure and once such a notification is out there to the opposite impalad, no other Impala daemon assigns any longer queries to the affected node.

Impala Metadata & Meta Store

Impala metadata & meta store is another important component. Impala uses traditional MySQL or PostgreSQL databases to store table definitions. The important details like table & column information & table definitions are stored during a centralized database referred to as a meta store.

Each Impala node caches all of the metadata locally. When handling a particularly great deal of knowledge and/or many partitions, getting table specific metadata could take a big amount of your time. So, a locally stored metadata cache helps in providing such information instantly.

When a table definition or table data is updated, other Impala daemons must update their metadata cache by retrieving the newest metadata before issuing a replacement query against the table in question.

Query Processing Interfaces

To process queries, Impala provides three interfaces as listed below.

Impala-shell − After fixing Impala using the Cloudera VM, you’ll start the Impala shell by typing the command impala-shell within the editor. we’ll discuss more about the Impala shell.
Hue interface − you’ll process Impala queries using the Hue browser. within the Hue browser, you’ve got Impala query editor where you’ll type and execute the impala queries. To access this editor, first of all, you would like to logging to the Hue browser.
ODBC/JDBC drivers − a bit like other databases, Impala provides ODBC/JDBC drivers. Using these drivers, you’ll hook up with impala through programming languages that supports these drivers and build applications that process queries in impala using those programming languages.

Query Execution Procedure

Whenever users pass a question using any of the interfaces provided, this is often accepted by one among the Impalads within the cluster. This Impalad is treated as a coordinator for that specific query.

After receiving the query, the query coordinator verifies whether the query is acceptable, using the Table Schema from the Hive meta store. Later, it collects the knowledge about the situation of the info that’s required to execute the query, from HDFS name node and sends this information to other impalads so as to execute the query.

All the opposite Impala daemons read the required data block and processes the query. As soon all the daemons complete their tasks, the query coordinator collects the result back and delivers it to the user.

So, this brings us to the end of blog. This Tecklearn ‘Detailed understanding of Architecture of 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

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