What is Cloudera Impala?
Cloudera Impala is an open-source, interactive, distributed SQL query engine that enables users to conduct real-time queries on data stored in Hadoop Distributed File System (HDFS), Apache HBase, and Amazon Simple Storage Service (S3). It provides high-performance, low-latency SQL queries and complements the batch processing capabilities of Apache Hadoop.
History
Impala was introduced by Cloudera in October 2012 as a beta version and its stable release was out in May 2013. Initially, Impala only supported data stored in HDFS and HBase, but later extended its support to data stored in Amazon S3 and other cloud storage systems.
Functionality and Features
Impala supports a broad spectrum of SQL syntax, including joins, nested queries, aggregate functions, and user-defined functions. It uses the same metadata, SQL syntax, ODBC driver, and user interface as Apache Hive, providing users with more flexibility. The key features of Cloudera Impala include:
- Scalability and flexibility
- Real-time, interactive analysis of data
- Integration with Hadoop ecosystem
Architecture
Impala follows a massively parallel processing architecture. It consists of Impala Daemons running on data nodes within the Hadoop cluster, complemented by a StateStore and Catalog Service. This architecture allows for high-speed, distributed queries.
Benefits and Use Cases
Impala enables businesses to perform quick and interactive SQL queries directly on their Hadoop data. This capability helps in various use cases like data exploration, data warehousing, and providing an SQL interface to existing Hadoop applications. The primary benefits are:
- Immediate query results for Hadoop data
- Tight integration with the existing Hadoop ecosystem
- Offers a familiar SQL interface for business analysts
Challenges and Limitations
While Impala offers several benefits, it does have some limitations such as lack of support for complex data types and MapReduce functions. Additionally, Impala requires substantial memory resources and may not be ideal for small setups.
Integration with Data Lakehouse
In the context of a data lakehouse, Impala can act as the query engine, enabling SQL access to the vast amounts of data stored in the lakehouse. However, Dremio, a data lake engine, presents a more feature-rich, performant and efficient solution with its ability to run complex queries directly on your cloud data lake storage.
Security Aspects
Impala integrates with Apache Sentry for role-based access control, providing secure, fine-grained authorization to data stored in Hadoop.
Performance
Impala is known for its high-speed SQL queries and its performance is often comparable to that of traditional DBMS. However, performance can be a challenge when dealing with complex queries or large volumes of data.
FAQs
How does Cloudera Impala differ from Apache Hive? - While both are SQL query engines for Hadoop, Impala is known for its speed and efficiency in handling real-time, interactive analysis, whereas Hive is more suitable for batch processing.
Does Impala support all SQL operations? - Impala supports many SQL features, but does not support the full range of SQL. For instance, it lacks support for complex data types and MapReduce functions.
Glossary
SQL Query Engine: Software that interprets and executes SQL commands.
Hadoop Distributed File System (HDFS): A distributed file system for storing large volumes of data in a scalable way.
Apache HBase: A distributed, scalable, and open-source database built on Hadoop.
Data Lakehouse: A modern data architecture that combines the best features of data warehouses and data lakes.
Dremio: An open-source data lake engine that simplifies and accelerates data exploration and analysis.
