apache/calcite
Calcite
Calcite ist ein Framework zum Parsen, Optimieren und Übersetzen von SQL-Abfragen in relationale Algebra für die Ausführung über verschiedene Datenquellen hinweg. Es fungiert als Cross-Source-Query-Engine, SQL-Parsing-Bibliothek und Optimierer für relationale Algebra.
Das Projekt bietet eine kostenbasierte Optimierungs-Engine, die logische Abfragepläne mittels pluggbarer Regeln in effiziente physische Ausführungspläne umwandelt. Es nutzt Übersetzungsadapter, um Standard-SQL-Anfragen in die nativen Formate externer Datenbanken und Messaging-Systeme zu konvertieren, was Datenföderation über heterogene Speichersysteme hinweg ermöglicht.
Das System deckt den gesamten Abfrage-Lebenszyklus ab, einschließlich SQL-Parsing und Validierung gegen Schemata, die Übersetzung von Ausdrücken in algebraische Operatoren sowie die Auswahl effizienter Ausführungspläne. Es enthält zudem ein Command-Line-Interface zum Ausführen von Abfragen und Verwalten von Verbindungen zu Datenquellen.
Features
- Cross-Source Querying - Implements a query engine that retrieves and processes data from multiple heterogeneous backend storage systems using a unified SQL dialect.
- Federated Data Query Engines - Provides a federated query engine that translates relational operators into native backend formats to query diverse storage systems.
- Cross-Source Data Integration - Connects diverse storage systems through a common interface, joining datasets from external sources into a single result.
- Cost-Based Optimizers - Implements a cost-based optimizer that estimates resource costs to select the most efficient physical execution plans.
- Query Plan Optimizations - Transforms logical plans into efficient physical plans using pluggable rules and cost-based functions.
- Query Optimizer Frameworks - Offers a complete framework for parsing, optimizing, and translating SQL queries into relational algebra for diverse data sources.
- Relational Query Optimizers - Translates text into relational algebra and applies optimization rules to improve efficiency across different data sources.
- Rule-Based Plan Optimizations - Transforms logical query plans into efficient physical plans by applying a series of rewrite rules.
- Relational Algebra Translation - Converts SQL query strings into a tree of relational algebraic operators for logical manipulation and optimization.
- SQL Abstract Syntax Tree Parsing - Converts SQL strings into abstract syntax trees (ASTs) to facilitate programmatic analysis and optimization.
- SQL Query Execution - Runs SQL statements and retrieves results as structured data from connected data backends.
- SQL to Relational Algebra Translation - Translates SQL expressions into relational algebraic operators to facilitate logical and physical query planning.
- Schema Validation - Provides comprehensive SQL parsing and validation of queries against defined metadata schemas to ensure correctness.
- External Store Integrations - Retrieves information from multiple external data stores using a set of pre-built translation adapters.
- Pluggable Rules - Allows the query transformation process to be extended by injecting custom logic into the optimization pipeline.
- Schema-Based Query Validation - Verifies the structural correctness of parsed queries against a defined metadata model before plan generation.
- Native Query Translation - Translates standard SQL expressions into optimized proprietary formats for various underlying storage engines.
- SQL Query Validators - Converts SQL strings into structured formats and verifies them against a schema to ensure correctness.
- Data Engineering - Dynamic data management framework for SQL optimization.
