May 2025 Summaries

Building a scalable and secure multi-tenant asset management system is enhanced by Harper's advanced blob storage capabilities, which offer significant performance improvements through its edge-native architecture and integrated compute and data model. Harper reduces latency by deploying nodes closer to users across global regions, facilitating rapid asset delivery and retrieval with minimal latency. The system is designed to handle unstructured binary data, with streaming capabilities and schema-level tenant isolation ensuring both efficiency and security. Harper's architecture supports high-throughput processing, allowing for fast global replication and real-time collaboration on large files like videos and documents. Additionally, the platform's tiered storage and caching strategies optimize resource allocation and performance, while edge deployment reduces latency for global applications. As tenant bases grow, Harper's architecture provides flexible scaling options that target specific regions, ensuring responsive and consistent service delivery across distributed teams.

In today's competitive e-commerce landscape, AI-driven personalization of Product Detail Pages (PDPs) is emerging as a key strategy for enhancing customer loyalty, conversion rates, and satisfaction. Companies like Harper are leveraging AI to move beyond traditional A/B testing, enabling dynamic, micro-optimizations tailored to specific customer segments. This involves fine-tuning small portions of content on PDPs to resonate more deeply with individual shoppers, thereby reducing the number of visits needed for a purchase. Performance concerns are addressed by creating intelligent cohorts and pre-generating content, which ensures instant page availability without impacting site speed. To control costs, retailers are adopting open-source large language models (LLMs) and techniques like Retrieval-Augmented Generation (RAG), allowing them to personalize experiences without incurring high API fees. By using server-side rendering and regionally retrained models, brands can optimize for SEO and maintain cultural relevance. Harper offers an e-commerce template to help brands integrate AI personalization incrementally, allowing them to scale efficiently and unlock new revenue streams through tailored customer experiences.

Redis is a popular tool for speeding up applications by caching frequently used data in memory, but it introduces complexity by requiring separate system management and special coding. While Redis is effective, it can become cumbersome for small projects or rapidly scaling teams due to the need for additional infrastructure and maintenance. HarperDB presents an alternative by integrating caching directly into its high-performance database, eliminating the need for a separate caching service like Redis. HarperDB's approach simplifies the process by allowing developers to define data structures that inherently include caching, with features such as expiration directives and automatic refreshing of stale data. This built-in solution is particularly advantageous for global e-commerce apps, internal dashboards, startups, and APIs that benefit from easy-to-manage caching without the overhead of additional services. HarperDB not only provides caching but also offers diverse functionalities like SQL, NoSQL, GraphQL queries, persistent data storage, and real-time messaging, all within a single platform, making it a versatile choice for growing applications.

A Library Management System is developed using Harper, demonstrating its efficiency in creating robust APIs through a simplified process of building and managing a catalog of books and library members. The system's core components include Book Management and Member Management, each utilizing CRUD operations for handling data. Books and members are defined through a GraphQL schema, automatically creating database tables and exposing them via REST and GraphQL endpoints. The functionality is implemented using resource classes that manage HTTP requests for creating, retrieving, updating, and deleting both books and members. The implementation showcases Harper's ability to seamlessly integrate database and API layers, providing a scalable, extensible, and maintainable solution that can be expanded with additional features like reservations or late fees. The system is tested by running the Harper instance, which sets up the necessary infrastructure, allowing HTTP calls to the service. This project exemplifies the power of Harper in building efficient and straightforward APIs, suitable for various library management needs.

Microsoft's announcement to end support for Dynamics 365 Fraud Protection (DFP) by February 3, 2026, has prompted enterprises to seek alternative fraud prevention methods, particularly within Azure Active Directory B2C architectures. Akamai's Account Protector (APR) remains a viable option with its device fingerprinting and risk scoring capabilities; however, Azure AD B2C's limitations on header propagation present challenges. To address this, Risk Query emerges as a solution by providing a lightweight, distributed key-value service that allows APR to write and B2C to read fraud protection data, mitigating the impact of header restrictions. This integration enables brands to maintain fraud protection without disruption, offering sub-100 ms user experiences and rapid migration facilitated by REST calls, thus ensuring global performance through distributed deployments.

Harper's Distributed Query Delivery solution enhances performance by moving APIs and their data to the edge, creating a multi-protocol query delivery layer near users, which reduces latency and cloud costs by resolving queries at the edge instead of relying on microservices. This innovation has led to Harper being recognized as a Sample Vendor in the Gartner Hype Cycle for Digital Commerce 2026 and winning two Data Breakthrough Awards for its open-source data platform and in-memory solution. Additionally, Harper's advancements in web personalization architecture and AI integration, such as their int8 quantization method, significantly improve search throughput and latency while maintaining high data recall accuracy. Harper also demonstrates its capabilities through applications like building a comprehensive nature-restoration game using a single Harper component for the backend, showcasing the versatility and efficiency of its unified runtime in various use cases.

Harper Redirector is a specialized redirect management tool designed to handle millions of redirects with sub-millisecond lookup times, eliminating the need for additional databases, caches, or edge workers. It addresses the limitations of legacy systems like Apache and Nginx by using horizontally sharded tables and in-memory indexes to efficiently manage redirects, providing a seamless solution for scaling and latency issues. The tool simplifies operations by consolidating redirects into a single CSV/JSON import or via REST API calls, supporting host-aware lookups, regex paths, and query-string operations. Harper Redirector offers instant metrics tracking and versioning capabilities, allowing atomic swaps of redirect sets, making it ideal for platform transitions. It can be deployed on a single VM, a Kubernetes cluster, or across geo-distributed edge nodes, offering flexibility in architecture design and enabling quick scaling by simply adding more nodes. Users can test the system locally with a straightforward setup process and deploy it to various environments without downtime using the Harper CLI.