Hue, an open-source SQL Assistant and query editor, interacts with and supports multiple types of SQL. It's important to differentiate between the database system Hue uses internally for its own metadata and the various SQL dialects and engines Hue allows users to query data with.
Hue's Internal Database System
For its internal operations, such as storing user configurations, history, and saved queries, Hue relies on a backend relational database.
- Default Configuration: By default, Hue is configured to use an embedded SQLite database for quick setup and initial startup. While convenient for getting started, this default configuration often leads to errors in production environments due to SQLite's limitations, particularly its lack of robust transaction management required for high-volume or concurrent operations.
- Recommended Production Databases: For stable and reliable production deployments, it is highly recommended to configure Hue to use more robust external databases. Common choices include:
- PostgreSQL: A powerful, open-source object-relational database system known for its reliability and feature set.
- MySQL: A widely used open-source relational database management system.
- Oracle Database: A proprietary enterprise-grade relational database.
These external databases provide the necessary transactional integrity and performance for Hue's backend metadata store.
SQL Dialects and Engines Hue Supports for Data Querying
Hue's primary function is to provide a user-friendly web interface for interacting with various big data SQL engines and traditional relational databases. It doesn't "use" one specific type of SQL in the sense of a query language; rather, it's a platform that enables users to write and execute queries in the specific SQL dialect understood by the connected data engine.
Hue acts as a bridge, allowing users to leverage their SQL skills across a diverse ecosystem. Here are some of the prominent SQL types and engines Hue supports:
- Apache Hive (HiveQL): Used for querying data stored in Apache Hadoop, often for batch processing and data warehousing tasks. HiveQL is a SQL-like language that translates queries into MapReduce, Tez, or Spark jobs.
- Apache Impala (Impala SQL): Provides high-performance, interactive SQL queries on data stored in Hadoop. Impala SQL is designed for real-time analytics.
- Apache Spark SQL: A module within Apache Spark for structured data processing. It allows users to query data using SQL or the DataFrame API, supporting a wide range of data sources and complex analytical workloads.
- Presto / Trino: Distributed SQL query engines designed for high-performance interactive analytics over diverse data sources, including Hadoop, S3, Cassandra, and relational databases.
- Apache Kudu: A columnar storage manager developed for the Hadoop ecosystem, often queried via Impala SQL.
- Traditional RDBMS: Hue can also connect to and query standard relational databases like:
- MySQL
- PostgreSQL
- Oracle
- Microsoft SQL Server
This broad support makes Hue a versatile tool for data analysts and engineers working across different data platforms. The type of SQL you write in Hue depends entirely on the data engine you are connected to at that moment.
