Beginners Impala Tutorial

The Beginners Impala Tutorial covers key concepts of in-memory computation technology called Impala. It is developed by Cloudera. MapReduce based frameworks like Hive is slow due to excessive I/O operations. Cloudera offers a separate tool and that tool is what we call Apache Impala. This Beginners Impala Tutorial will cover the whole concept of Cloudera Impala and how this Massive Parallel Processing (MPP) engine is implemented. It includes Impala’s benefits, working as well as its features. Moreover, we will also learn about Daemons in Impala in this Impala Tutorials

What is Impala? – An Impala Overview

A tool which we use to overcome the slowness of Hive Queries (or similar other frameworks which interns uses MapReduce programming model) is what we call Cloudera Impala. This SQL engine was developed by Cloudera and comes by default with CDH distribution. Syntactically Impala queries run very faster than Hive Queries even after they are more or less the same as Hive Queries (syntax-wise) . It offers high-performance, low-latency SQL queries. Impala is the best option while we are dealing with medium sized datasets and we expect the real-time response from our queries.

MapReduce programming model stores intermediate results in the local file system (LFS), Apache Impala is not built on MapReduce and does not use the Hadoop Daemons.  Hence MapReduce, it is very slow for real-time query processing.

To make Impala SQL engine running fast, it uses its own execution engine. This engine stores the intermediate results in In-memory. Therefore, when compared to other tools & framework which uses MapReduce its query execution is very fast.

Some Key Points about Impala

  1. It offers high-performance SQL like syntax
  2. Low-latency SQL queries suites for the business analyst and functional analyst
  3. Share databases and tables between both Impala and Hive it integrates very well with the Hive Metastore
  4. It is Compatible with HiveQL Syntax, except a few exceptions.
  5. Integrate with HBase database system
  6. Can be used for Amazon Simple Storage System (S3)
  7. Provides SQL front-end access to these using Hue and impalaD (Impala Demon)

Using Impala, the user we can perform interactive, ad-hoc and batch queries together in the Hadoop system.  Impala’s MPP (M-P-P) style execution along with other Hadoop processing MapReduce frameworks.

Why Use Apache Impala?

  1. One of the biggest and longest-held complaints of MapReduce – Even a trivial job in Hadoop will 10+secods to complete.
  2. Data analyst used to work in ad-hoc mode in the database or OLAP system where the expectation is millisecond response times.
  3. Impala makes SQL a “first class citizen” – real time queries.
  4. Apache Impala uses its own set of daemons to execute queries
  5. MapReduce programming model is not used at all in Impala
  6. MapReduce is meant to be for parallel processing and not meant to be fast and it is certainly not fast in all cases.

Business Data was typically condensed into a manageable chunk of high-value information in Big Data storage (like Enterprise data Lake), before Impala. Also, this process is minimized with Impala. However, in Hadoop, the data arrives after fewer steps, whereas Impala queries it immediately. Also, the high-capacity and high-speed storage system of a Hadoop cluster let you bring in all the data.

Impala Vs Tez (Hortonwork Framework)

Apache Tez is another framework on the top of MapReduce programming model which is a very optimized solution to improve the query performance and works very similar to Impala. Since Tez an optimized abstraction over MapReduce and finally it runs over YARN as MapReduce program, it still lacks in-memory computing like Imapal.

Impala Design Architecture

  1. One Pool of data
  2. One metadata model
  3. One security framework
  4. One set of system resources
  5. Apache Impala Architecture

 

Apache Impala Features

  1. Impala offers support for most common SQL-92 features of Hive Query Language (HiveQL). This includes SELECT, joins, and aggregate functions.
  2. It also provides support for HDFS, HBase, and Amazon Simple Storage System (S3) storage.
  3. Supported HDFS file formats
    1. Delimited text files
    2. Parquet
    3. Avro
    4. SequenceFile
    5. RCFile.
  4. Supported Compression Codecs
    1. Snappy
    2. GZIP
    3. Deflate
    4. BZIP
  5.  Also, supports common data access interfaces
    1. JDBC driver
    2. ODBC driver
  6. It supports Hue Beeswax and the Impala Query UI.
  7. Supports impala-shell command-line interface.
  8. Supports Kerberos authentication.

 

 

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