Google Mangle: A New Language for Deductive Database Programming
▼ Summary
– Google has introduced Mangle, an open-source programming language that extends Datalog for modern deductive database programming and is implemented as a Go library.
– Mangle addresses data fragmentation by providing a unified, declarative framework to analyze information from multiple disparate sources cohesively.
– Key extensions in Mangle include support for recursive rules, uniform data access across various sources, and aggregation functions with external function calls.
– The language is well-suited for practical applications like vulnerability detection, software dependency analysis, and knowledge graph modeling.
– Implemented as an embeddable Go library, Mangle aims to make logic-based programming accessible to developers familiar with other paradigms.
Google has launched Mangle, an open-source programming language that builds upon Datalog to offer a modern solution for deductive database programming. Designed as a Go library, Mangle simplifies the process of querying and reasoning across diverse and distributed data sources, making it easier for developers to handle fragmented information environments.
The language addresses a common challenge in today’s software ecosystems: data scattered across multiple formats and locations. Whether it’s dependency details, security vulnerabilities, configuration settings, or infrastructure metadata, Mangle provides a unified declarative framework to analyze these varied data points cohesively.
At its foundation, Mangle extends Datalog, a well-established logic programming language rooted in database theory. While Datalog excels at expressing complex queries, it often falls short in real-world applications. Mangle introduces several enhancements to bridge this gap, all while preserving the simplicity and accessibility that made Datalog popular.
One of the standout features is its support for recursive rules, a capability inherited from Datalog that allows developers to model transitive relationships effortlessly. This is particularly useful for tracing dependency trees or mapping hierarchical access controls.
Another significant advantage is uniform data access. Mangle treats multiple data sources, whether files, APIs, or databases, as a single logical unit. Developers can write queries that join information seamlessly without concerning themselves with where the data resides.
The language also incorporates aggregation functions and external function calls, enabling more sophisticated computations. This allows users to perform counts, sums, and other operations, and even integrate custom logic from existing codebases.
Mangle is especially valuable in several practical domains. For vulnerability detection, security teams can model policies and codebases as logical facts and rules. For instance, a rule can identify projects that depend on libraries with known security flaws, recursively scanning entire dependency graphs to highlight risks.
In software dependency analysis, Mangle helps navigate complex supply chains. It can analyze Software Bill of Materials (SBOMs), enforce versioning policies, or identify projects affected by deprecated components.
The language also excels in knowledge graph modeling, allowing organizations to represent entities and relationships as logical facts. This makes it possible to uncover hidden connections and perform advanced reasoning across large, interconnected datasets.
Implemented as a Go library, Mangle is designed for easy integration into existing tools and applications. This approach avoids the need for a standalone database system and brings powerful deductive query capabilities directly to developers. The documentation focuses on practicality, making the language accessible even to those more familiar with imperative or object-oriented programming.
With its blend of declarative elegance and modern features, Mangle offers a robust tool for developers, site reliability engineers, and security professionals. It stands to simplify tasks ranging from security audits to infrastructure management, providing a cohesive way to reason about complex, distributed data.
(Source: Marktech Post)
