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Introduction to Impala

Oct 14, 2019 Big Data, Impala, 496 Views
In this article, we will discuss Impala

Introduction to Impala

Apache Impala is an open-source software written in Java and C++. It is a Massive Parallel Processing SQL query engine for processing a huge volume of data stored Hadoop cluster. It delivers low latency and high performance compared to the other SQL engines for Hadoop.

Impala mixes the SQL feature of a traditional database system with the scalability and flexibility of Hadoop, by exploiting the components such as HDFS, Hbase, YARN.

  • Impala can read almost any type of file formats such as Avro, Parquet.
  • In Impala users can communicate with HDFS or Hbase using SQL queries much faster was as compared to other SQL engines.

Features of Impala

  • It is an open-source Apache software.
  • It supports in-memory data processing that means it analyzes data stored in Hadoop with any movement of the data.
  • Data can be accessed using SQL like queries.
  • It supports various file formats like Avro, Parquet, Sequence File, RCFile.
  • It provides faster data access to the data stored in HDFS as compared to other SQL engines.

Impala vs RDBMS

The following table shows some of the key differences between Impala and RDBMS systems.

Impala RDBMS
  • It does not support transactions.
  • It supports transactions.
  • It does not support indexing
  • It supports indexing.
  • It stores and manages a huge amount of data.
  • It manages a smaller amount of data when compared with Impala.
  • We cannot delete and update the individual records in Impala.
  • It is possible to delete and update the individual records in RDBMS.

Advantages of impala

  • Using Impala, we can access the data at a very high speed compared to the other SQL engines.
  • Data transformation and data movement are not required for the data stored in Hadoop while working with Impala as the data processing is carried where the data resides.
  • We can access the data stored in HDFS with the help of Impala without any knowledge of MapReduce jobs and access them with a basic idea of SQL queries.
  • It follows the relational model and it supports all the languages supporting ODBC/JDBC.

 Limitations of Impala

  • It does not support Serialization and Deserialization.
  • It only read text files and cannot read any custom binary files.
  • Triggers are not supported in Impala.
  • It does not support indexing.
  • It does not support transactions.
  • We need to refresh the table whenever we add new records to the data directory in HDFS.