Upstore Search

To solve the consistency latency problem, UpStore Search utilizes Event Sourcing. Every mutation (create, update, delete) is captured as an immutable event. The search index is technically a "read model" or projection derived from these events. This allows the system to be eventually consistent for search queries while remaining strongly consistent for file access.

Before diving into search techniques, it is crucial to understand what Upstore is and is not.

Upstore is primarily a premium file hosting service. Its business model is built on two pillars: upstore search

Unlike search engines that crawl and index metadata, Upstore does not provide a public API or an internal search catalog. Why? Because the platform is often used for private or semi-private sharing. The files are not meant to be universally discoverable; instead, they rely on external links shared via forums, blogs, social media, and direct marketing.

This means that performing an Upstore search is not about typing a query into Upstore’s homepage. It is about using indirect methods to find URLs that point to Upstore-hosted files. To solve the consistency latency problem, UpStore Search

While UpStore Search demonstrates superior performance, there are trade-offs. The asynchronous nature of the indexing pipeline means that a file might be uploaded but not immediately searchable ("soft real-time"). However, in most consumer storage use cases (e.g., file sharing services like Upstore.net), a latency of less than 2 seconds is imperceptible to users and acceptable for business logic.

Another consideration is the complexity of the maintenance infrastructure. Managing a distributed message queue and a sharded index cluster requires robust DevOps monitoring. Future work will focus on implementing "Zero-Copy" indexing to further reduce CPU overhead during text extraction. Unlike search engines that crawl and index metadata,

We deployed UpStore Search on a cluster of 10 nodes (16 vCPU, 64GB RAM each). We generated a synthetic dataset of 50 million files totaling 10TB of data. We compared UpStore Search against a standard Elasticsearch deployment integrated with a standard S3 backend.

The Query Layer acts as the interface for end-users. It utilizes a custom query parser that translates user requests (e.g., "filename:report AND date:2023") into distributed queries. The query layer aggregates results from the relevant shards, ranks them based on relevance, and returns the paginated result to the client.

Since Upstore itself doesn’t index its files, you need to search the places where people share Upstore links. Here are the three most effective methods: