# How Bitsets Supercharged Our Backend: Faster String Overlap Checks at Scale

DevFeed: [How Bitsets Supercharged Our Backend: Faster String Overlap Checks at Scale](<https://devfeed.tech/articles/how-bitsets-supercharged-our-backend-faster-string-overlap-checks-at-scale-22541.md>)

Original publisher: [Read original article](<https://medium.com/walmartglobaltech/how-bitsets-supercharged-our-backend-faster-string-overlap-checks-at-scale-412911bfc3d5?source=rss----905ea2b3d4d1---4>)

Author: Jiaqi Zhu

Published: 2026-03-25T19:02:18Z

Content type: article

Language: en

Sources: [Walmart Global Tech](<https://devfeed.tech/sources/walmart-global-tech.md>)

Topics: [Optimization](<https://devfeed.tech/topics/optimization.md>), [Back end](<https://devfeed.tech/topics/backend.md>), [Database](<https://devfeed.tech/topics/database.md>), [data](<https://devfeed.tech/topics/data.md>), [Caching](<https://devfeed.tech/topics/caching.md>)

Tags: [access-control](<https://devfeed.tech/tags/access-control.md>), [backend](<https://devfeed.tech/tags/backend.md>), [backend-development](<https://devfeed.tech/tags/backend-development.md>), [caching](<https://devfeed.tech/tags/caching.md>), [complexity](<https://devfeed.tech/tags/complexity.md>), [database](<https://devfeed.tech/tags/database.md>), [distributed-systems](<https://devfeed.tech/tags/distributed-systems.md>), [memory](<https://devfeed.tech/tags/memory.md>), [optimization](<https://devfeed.tech/tags/optimization.md>), [performance](<https://devfeed.tech/tags/performance.md>), [performance-optimization](<https://devfeed.tech/tags/performance-optimization.md>), [real-time](<https://devfeed.tech/tags/real-time.md>), [software-engineering](<https://devfeed.tech/tags/software-engineering.md>)

## AI overview

This article describes a backend performance problem involving overlap checks between large sets of strings. It explains that nested loops and hash-based comparisons became bottlenecks as data volumes and request loads increased, while local caching improved lookup speed but created significant memory pressure. The article introduces bitset optimization as the approach used to transform these overlap checks.

## Source excerpt

Introduction Ever wondered why your backend slows to a crawl when comparing massive sets of strings? Here's how we turned a bottleneck into a lightning-fast operation using bitsets. Efficiently checking for overlaps between large sets of strings is a common challenge in high-throughput backend services. Traditional approaches -- such as nested loops or hash-based comparisons -- can quickly become performance bottlenecks, especially as data volumes scale and both memory and response time are critical. This is particularly true in environments like Walmart's, where backend systems routinely process millions of records per second to power search, recommendation, and fraud detection features. As our applications grew, we observed that even well-optimized hash set operations struggled to keep up with the demands of real-time processing. In this blog, I'll share how we leveraged bitset optimization to transform our string set overlap checks. The Problem: When "Fast Enough" Isn't Enough Our service is designed to handle high volumes of requests, each containing multiple sets of strings mapped to unique keys -- such as user IDs, product SKUs, or transaction identifiers. For every incoming key, we must efficiently retrieve the corresponding set of strings from our backend database and determine if there is any overlap with the provided set in the request. This overlap check is a critical step in workflows like deduplication, access control, and real-time validation. However, as the number of keys and the size of each string set grow, the computational and memory demands of these operations can escalate rapidly. The Memory Bottleneck To mitigate performance bottlenecks, we initially explored caching the database string sets locally within each pod. While this approach did improve lookup speed, it introduced a new challenge: memory consumption. As our dataset grew, the memory footprint of these in-memory caches ballooned, reaching as high as an unsustainable level in the worst-ca