6 个仓库
Systems that use bytecode compilation and virtual machines to execute dynamic logic with minimal overhead.
Distinct from High Performance Task Execution: Existing candidates focus on build scripts, game tasks, or web rendering, whereas this is for general-purpose expression evaluation in application logic.
Explore 6 awesome GitHub repositories matching programming languages & runtimes · High-Performance Expression Evaluators. Refine with filters or upvote what's useful.
Expr is a statically typed expression engine and domain-specific language for parsing and evaluating dynamic logic strings within Go applications. It functions as a bytecode expression evaluator that executes compiled logic with guaranteed termination and memory safety. The engine utilizes a specialized compiler and virtual machine to achieve high-performance execution. It employs static type validation during the compilation phase to ensure operand compatibility and prevent runtime crashes. The project provides a data transformation engine capable of mapping, filtering, and aggregating coll
Running complex logic via a bytecode compiler and virtual machine to achieve faster evaluation than standard interpreters.
Vespa is a distributed search engine, vector database, and machine learning ranking engine. It serves as an AI search platform designed to handle large-scale document indexing and complex query processing across a cluster of nodes, combining keyword retrieval with high-dimensional embedding storage for semantic similarity search. The platform distinguishes itself by integrating machine learning models directly into the search pipeline to perform real-time inference and ranking. It converts these models into ranking expressions to score and order results based on relevance, while providing a s
Executes high-performance ranking and scoring logic by converting ML models and expressions into efficient evaluation code.
Aviatorscript is a high-performance scripting language and expression evaluation engine designed for integration within Java applications. It enables the dynamic execution of mathematical and logical formulas by compiling script instructions directly into Java bytecode at runtime. The engine distinguishes itself by transforming dynamic logic into machine-readable instructions, allowing for near-native execution speeds. It includes a built-in caching mechanism that stores compiled bytecode to eliminate redundant parsing and compilation overhead during repeated formula execution. This library
Transforms dynamic formulas into executable machine instructions at runtime to achieve high-performance calculation speeds.
Mapster 是一个 .NET 对象到对象映射器和高性能映射引擎,用于在不同对象模型之间转换数据。它作为类型安全的数据传输工具和 LINQ 查询投影工具,用于在内部逻辑和外部数据结构之间移动数据。 该引擎利用编译后的映射表达式和机器码来减少对象转换过程中的开销。它特别允许将可查询的数据源直接投影到目标类型中,以优化数据库检索。 该工具集涵盖了数据传输对象(DTO)生成、不可变数据转换和查询结果投影。它包括映射不可变集合、保留对象引用以及将运行时参数注入转换过程的功能。
Utilizes compiled mapping expressions and machine code to maximize throughput during object conversions.
cel-go is a Go implementation of the Common Expression Language, providing a type-safe engine for the evaluation of non-Turing complete, side-effect-free expressions. It functions as an embedded expression engine that converts human-readable strings into type-checked abstract syntax trees to ensure safe execution. The system utilizes a compile-once evaluate-many model, parsing and validating expressions into cached programs for repeated execution across different datasets. It further distinguishes itself as a partial state evaluator, using commutative logic to compute results and return resid
Parses expressions into cached programs for repeated high-performance execution across different input datasets.
Flags is a feature management platform designed to synchronize application configurations and feature toggles across server and client environments. It provides the infrastructure to evaluate and distribute settings in real-time, allowing for the control of application behavior without requiring code redeployments. The system is built to support isomorphic flag resolution, ensuring that evaluation logic remains consistent during transitions between server-side and client-side rendering. By utilizing server-side state hydration and local fallback caching, the platform prevents visual layout sh
Optimizes flag evaluation performance by utilizing local caching and infrastructure-level lookups to minimize latency.