Apache Impala vs PostgreSQL: What are the differences?

Introduction: Apache Impala and PostgreSQL are two popular database management systems that have key differences in terms of their architecture, query processing, and performance.

1. Architecture: Apache Impala follows a distributed architecture, allowing it to enable real-time interactive query processing on large volumes of data. It utilizes a shared-nothing architecture with a distributed computing framework, which allows it to process and analyze data in parallel across multiple nodes. On the other hand, PostgreSQL follows a traditional client-server architecture, where a central server manages all the data and processes queries.

2. Query Processing: Apache Impala is designed specifically for fast and efficient processing of analytical queries. It uses a massively parallel processing (MPP) engine to parallelize query execution across multiple nodes, resulting in high-speed query performance. PostgreSQL, on the other hand, is more suitable for transactional workloads and supports a wider range of SQL features for complex queries.

3. Performance: Due to its distributed architecture and MPP engine, Apache Impala is known for its high-performance query processing. It is optimized for large-scale data analysis and can handle complex analytical queries efficiently. PostgreSQL, while also capable of handling analytical queries, may not perform as well as Impala when dealing with large volumes of data and complex analytical workloads.

4. Data Types: Impala and PostgreSQL have different sets of supported data types. Impala supports a wide range of data types including integers, floats, strings, dates, and timestamps, as well as more specialized types for geospatial data. PostgreSQL offers a broader range of data types, including array types, binary types, network address types, and JSON data types.

5. Concurrency Control: Concurrency control mechanisms differ between Impala and PostgreSQL. Impala does not support built-in row-level locking, and instead relies on the optimistic concurrency control strategy, which can lead to better performance in certain scenarios. PostgreSQL, being a full-featured relational database, offers more advanced locking mechanisms, such as row-level locking and multi-version concurrency control (MVCC).

6. Data Manipulation Language: PostgreSQL offers a comprehensive set of data manipulation language (DML) features, including advanced query capabilities, support for procedural languages like PL/pgSQL, and complex data manipulation operations like window functions and common table expressions. Impala, being optimized for analytical workloads, has limited support for DML operations and focuses primarily on fast query processing.

In summary, Apache Impala and PostgreSQL differ in terms of their architecture, query processing capabilities, performance, supported data types, concurrency control mechanisms, and data manipulation language features.