apache/calcite
Calcite
Calcite este un framework pentru parsarea, optimizarea și traducerea interogărilor SQL în algebră relațională pentru execuție pe diverse surse de date. Funcționează ca un motor de interogare cross-source, o bibliotecă de parsare SQL și un optimizator de algebră relațională.
Proiectul oferă un motor de optimizare bazat pe costuri care transformă planurile logice de interogare în planuri de execuție fizică eficiente, folosind reguli pluggable. Utilizează adaptoare de traducere pentru a converti cererile SQL standard în formatele native ale bazelor de date externe și ale sistemelor de mesagerie, permițând federarea datelor între sisteme de stocare eterogene.
Sistemul acoperă întregul ciclu de viață al interogării, incluzând parsarea SQL și validarea pe baza schemelor, traducerea expresiilor în operatori algebrici și selectarea planurilor de execuție eficiente. Include, de asemenea, o interfață de linie de comandă pentru executarea interogărilor și gestionarea conexiunilor la sursele de date.
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.
