Hound Description
Hound serves as a remarkably swift engine for searching source code. Its foundation is derived from an article and accompanying code by Russ Cox, which discusses regular expression matching utilizing a trigram index. The application itself features a static React frontend that communicates with a Go backend. This backend is responsible for maintaining an up-to-date index for every repository and processes searches via a streamlined API. Although Hound has primarily been tested on MacOS and CentOS, it is designed to operate on any Unix-like system. While Hound does not officially support Windows, reports indicate that it compiles and functions adequately; however, it is advisable to exclude your data folder from the Windows Search Indexer for optimal performance. Users have expressed enthusiasm for its capabilities, and developers are continually working on enhancing its compatibility across various platforms.
Hound Alternatives
UnForm is a powerful enterprise document management and process automation solution that seamlessly integrates with any application. Our platform-independent, fully browser-based solutions provide the ability to create, deliver, capture, index, route, and store documents from start to finish so that a transaction’s entire life cycle can be accessed with one easy search. Our data extraction and workflow capabilities enable the automation of data entry-intensive processes.
UnForm.Cloud, a hosting service for UnForm Document Management, is a perfect fit for those who are running cloud-based ERP systems or looking for a solution with no hardware to purchase, manage, or maintain. Implementing UnForm has never been easier. Backed by a proven hosting vendor, Oracle, you have the peace of mind knowing your data is safe and secure with well-managed data centers and cross-region backups, ensuring reliable and continues access to your data when you need it.
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RaimaDB, an embedded time series database that can be used for Edge and IoT devices, can run in-memory. It is a lightweight, secure, and extremely powerful RDBMS. It has been field tested by more than 20 000 developers around the world and has been deployed in excess of 25 000 000 times.
RaimaDB is a high-performance, cross-platform embedded database optimized for mission-critical applications in industries such as IoT and edge computing. Its lightweight design makes it ideal for resource-constrained environments, supporting both in-memory and persistent storage options. RaimaDB offers flexible data modeling, including traditional relational models and direct relationships through network model sets. With ACID-compliant transactions and advanced indexing methods like B+Tree, Hash Table, R-Tree, and AVL-Tree, it ensures data reliability and efficiency. Built for real-time processing, it incorporates multi-version concurrency control (MVCC) and snapshot isolation, making it a robust solution for applications demanding speed and reliability.
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SymbolHound
SymbolHound is a specialized search engine designed to recognize and accommodate special characters, allowing users to effortlessly search for symbols such as &, %, and π. Our aim with SymbolHound is to assist programmers in easily locating relevant information about their preferred programming languages and frameworks. While it serves primarily as a programming-focused search tool, the ability to search for symbols may not hold significant value for the average internet user. Although Google could potentially incorporate this feature, doing so might not enhance their search engine for the majority of users and could even deteriorate the search experience. Nevertheless, for the niche group of individuals who require the ability to search for special characters, Google falls short, making SymbolHound the ideal solution for their needs. In essence, SymbolHound stands out as a dedicated resource specifically tailored for symbol searches, catering to a unique user requirement.
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livegrep
Livegrep is a tool inspired by Google Code Search that facilitates interactive regex searches across source repositories that can be several gigabytes in size. To utilize livegrep, you must start both the codesearch backend, which handles indexing and searching, and the livegrep web interface. After building both codesearch and livegrep, you can run the sample web interface to test livegrep itself. The codesearch binary plays a crucial role by reading the source code, maintaining an index, and processing search requests. Notably, livegrep operates without state and depends solely on its TCP connection to the codesearch component. By default, codesearch generates an in-memory index for the repositories listed in its configuration file, but it also offers the option to save the index to a file on disk. This functionality not only accommodates indexes that exceed the available RAM but also allows for the reuse of index files across different sessions. Consequently, this feature enhances efficiency and resource management when dealing with extensive codebases.
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Integrations
Company Details
Company:
Etsy
Website:
github.com/hound-search/hound
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Product Details
Platforms
Web-Based
Types of Training
Training Docs
Hound Features and Options
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