# caching-strategies Published articles for caching-strategies. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Improving performance in the layers panel DevFeed: [Improving performance in the layers panel]() Original publisher: [Read original article]() Author: Shannen Wu; Peter Hayes Published: 2026-06-11T17:30:00Z Content type: article Language: en Sources: [Figma Blog]() Topics: [Figma](), [Caching](), [ui](), [data](), [JavaScript]() Tags: [architecture](), [caching](), [caching-strategies](), [compute](), [data](), [figma](), [incremental](), [javascript](), [performance](), [ui]() ### AI overview Figma explains how it rebuilt the layers panel architecture to improve performance in very large, complex files. The changes use two-pass data gathering and caching strategies, producing 30-50% faster interactions in some cases. ### Source excerpt The layers panel is the central blueprint of a Figma file. We rearchitected it with new computation and caching strategies, resulting in 30-50% faster interactions in some of the largest, most complex files. ## Five Caching Strategies Every Backend Dev Should Know DevFeed: [Five Caching Strategies Every Backend Dev Should Know]() Original publisher: [Read original article]() Author: ratneshmaurya2311@gmail.com (Ratnesh Maurya) Published: 2026-02-24T00:00:00Z Content type: tutorial Language: en Sources: [Ratn Labs]() Topics: [Caching](), [Back end](), [Development]() Tags: [backend](), [beginner](), [caching](), [caching-strategies](), [guide](), [system-design](), [system-design-backend]() ### AI overview A beginner-friendly guide to five distributed caching strategies: Cache-Aside, Read-Through, Write-Through, Write-Back, and Write-Around, including when to use each. ### Source excerpt A beginner-friendly guide to Cache-Aside, Read-Through, Write-Through, Write-Back, and Write-Around, with when to use each. ## Backend Development Engineering in 2026: Skills, Systems and Career Paths DevFeed: [Backend Development Engineering in 2026: Skills, Systems and Career Paths]() Original publisher: [Read original article]() Author: Prachi Kothiyal Published: 2026-01-27T07:08:48Z Content type: article Language: en Sources: [Backend Archives | Talent500 blog]() Topics: [backend-development](), [Architecture & Design](), [API](), [Database](), [Microservice](), [Caching](), [Optimization]() Tags: [2026](), [api](), [backend](), [backend-development](), [caching-strategies](), [career-paths-and-learning-routes-for-backend-engineers](), [core-skills-for-backend-engineers-in-2026](), [databases](), [development](), [engineering](), [microservices](), [newsletters](), [optimization](), [trends-redefining-backend-engineering-in-2026](), [what-backend-development-engineering-means-in-2026](), [why-backend-engineering-matters-more-than-ever]() ### AI overview This article explains how backend engineering in 2026 extends beyond server-side coding to include scalable architectures, APIs, service communication, data management, caching, observability, reliability, security, and production-ready engineering skills. It presents backend engineering as an influence on application performance, user experience, search discoverability, and business outcomes. ### Source excerpt What backend development engineering means in 2026 Backend development engineering in 2026 is about far more than writing server-side code. [...] The post Backend Development Engineering in 2026: Skills, Systems and Career Paths appeared first on Talent500 blog. ## Scaling Nextdoor's Datastores: Part 5 DevFeed: [Scaling Nextdoor's Datastores: Part 5]() Original publisher: [Read original article]() Author: Slava Markeyev Published: 2025-03-19T15:09:15Z Content type: article Language: en Sources: [Nextdoor]() Topics: [Databases](), [Cache](), [consistency](), [Scalability](), [Usability](), [data]() Tags: [cache](), [cache-invalidation](), [caching-strategies](), [consistency](), [database-consistency](), [database-scalability](), [databases](), [rdbms](), [reconciliation](), [scalability](), [stream](), [usability]() ### AI overview The final installment of Nextdoor's datastore-scaling series explains how cache consistency can fail when a database writer misses its cache update. It presents a Change Data Capture stream and a reconciler that uses database changes to repair cache inconsistencies. ### Source excerpt In this final installment of the Scaling Nextdoor's Datastores blog series, we detail how the Core-Services team at Nextdoor solved cache consistency challenges as part of a holistic approach to improve our database and cache scalability and usability. In Part 4: Keeping the cache consistent, we highlighted a class of consistency issues arising from racing cache writes and introduced an approach for forward cache versioning as a mechanism to avoid inconsistencies. The cache is able to decide which write to persist and which to reject because it is aware of the version of data it currently has. However, this is only a partial solution because it assumes writers will always succeed in communicating with the cache in a timely manner, if at all. Missed Writes Let's consider the scenario where Writers A and B both performed an update to the same row in the database and have not yet updated the cache. Writer A holds Version 1 and Writer B holds Version 2. What happens if Writer B with Value 2 fails to talk with the cache? Writer B fails to write to the cache. In this case the result is that the cache becomes inconsistent and we can't rely on the writers to provide that consistency. A process must exist outside of this interaction to fix-up the cache when Version 2 is written to the database but fails to be written to the cache. Change Data Stream To solve this problem we tap into a common feature provided by most modern databases, a Change Data Capture (CDC) Stream. A CDC Stream is a mechanism to subscribe to row level changes in a database. The change stream contains a row's previous column values along with the new values. Here's a visual example of the change stream when the last_name field gets updated in the database. For visual clarity the changed values have been underlined in red. Reconciler Since the database is the source of truth and the CDC Stream emits all changes, a consumer of this stream can clean up any consistency issues in the cache. In our system we ca ## Caching Strategies in a Federated GraphQL Architecture DevFeed: [Caching Strategies in a Federated GraphQL Architecture]() Original publisher: [Read original article]() Author: Kyle Schrade Published: 2020-11-09T13:01:16Z Content type: article Language: en Sources: [Apollo Blog]() Topics: [Caching](), [GraphQL](), [Programming](), [API](), [Back end](), [gateway]() Tags: [api](), [backend](), [cache](), [caching](), [caching-strategies](), [code](), [gateway](), [graphql](), [optimization](), [programming](), [resolvers]() ### AI overview This article explains caching strategies for a federated GraphQL architecture, progressing from no caching to local in-memory caching and memoization, with distributed caching also identified as a possible approach. It notes that these are only some of the available strategies and links to code examples. ### Source excerpt Caching, along with naming variables and off-by-one errors, is one of the hardest problems in programming. In this post, we'll explore caching in a federated GraphQL context from local caching and memoization to distributed caching. The approaches described here are only one of many ways to cache in a federated GraphQL architecture. You can find the code describing each strategy here. ## Caching Strategies and How to Choose the Right One DevFeed: [Caching Strategies and How to Choose the Right One]() Original publisher: [Read original article]() Author: umer Published: 2017-08-11T00:00:00Z Content type: tutorial Language: en Sources: [Code Ahoy - Articles]() Topics: [Caching](), [Cache](), [Databases](), [Redis](), [systems]() Tags: [cache](), [caching](), [caching-strategies](), [database](), [how-to](), [performance](), [redis](), [strategy](), [systems]() ### AI overview This tutorial explains how caching strategies should be chosen based on data access patterns, including write frequency, read frequency, and whether returned data is unique. It describes cache-aside caching, including cache hits, cache misses, database fallback, and the use of Redis and Memcached. ### Source excerpt 👉 Read First: A Brief Overview of Caching Caching is one of the easiest ways to increase system performance. Databases can be slow (yes even the NoSQL ones) and as you already know, speed is the name of the game. If done right, caches can reduce response times, decrease load on database, and save costs. There are several strategies and choosing the right one can make a big difference. Your caching strategy depends on the data and data access patterns. In other words, how the data is written and read. For example: is the system write heavy and reads less frequently? (e.g. time based logs) is data written once and read multiple times? (e.g. User Profile) is data returned always unique? (e.g. search queries) A caching strategy for Top-10 leaderboard system for mobile games will be very different than a service which aggregates and returns user profiles. Choosing the right caching strategy is the key to improving performance. Let's take a quick look at various caching strategies. Cache-Aside This is perhaps the most commonly used caching approach, at least in the projects that I worked on. The cache sits on the side and the application directly talks to both the cache and the database. There is no connection between the cache and the primary database. All operations to cache and the database are handled by the application. This is shown in the figure below. Here's what's happening: The application first checks the cache. If the data is found in cache, we've cache hit. The data is read and returned to the client. If the data is not found in cache, we've cache miss. The application has to do some extra work. It queries the database to read the data, returns it to the client and stores the data in cache so the subsequent reads for the same data results in a cache hit. Use Cases, Pros and Cons Cache-aside caches are usually general purpose and work best for read-heavy workloads. Memcached and Redis are widely used. Systems using cache-aside are resilient to cache failures.