Apache Impala, Cloudera Impala, Impala Architecture, Impala Features, Impala Query Engine

Apache Impala- Features and Architecture

Introduction to Impala

Apache Impala is an open source distributed SQL query engine for running interactive analytic queries against data stored in a variety of databases and file systems. It is used in large Hadoop deployments and developed by Cloudera in 2012. Impala acts as a bridge between the data stored in Hadoop clusters and the applications that access them. It brings the familiar SQL dialect to Hadoop, allowing SQL developers to query high volumes of data quickly and easily.

Features of Apache Impala

• Scalability and Interoperability: Impala can scale to thousands of nodes and petabytes of data. It works with all popular file formats and systems, including HDFS, HBase, Apache Parquet, and Apache Avro.
• Query Optimization: Impala intelligently optimizes queries at runtime to make execution faster. This allows it to run queries on complex data sets quickly and efficiently.
• Real-Time Performance: Impala processes queries and responds to user requests in real-time. This makes it ideal for applications that require speed and interactivity.
• Security: Impala uses several security measures, including Authentication, authorization, encryption, and auditing.

Architecture of Apache Impala

The Apache Impala architecture consists of four main components.

• Impala Daemon: This component is responsible for accepting and processing queries. It is comprised of two components: a statestore and a coordinator.
• Impala Query Executor: This component is responsible for executing queries received from the impala daemon. It is responsible for taking the query plan, optimizing it, and executing it on the selected database.
• HDFS Namenode: This component is responsible for organizing the storage and access of data stored in HDFS.
• HDFS Datanode: This component is responsible for storing the actual data blocks on the hard disk.

Apache Impala is a powerful distributed query engine for processing large datasets. With its scalability, interoperability, real-time performance, and security measures, it is an ideal tool for dealing with complex data sets.