--- title: Cache consistency: strategies to keep data fresh | daily.dev description: Cache consistency is about keeping cached values in sync with the source database. Caches drift due to TTL timing windows, write-ordering races, and... canonical: https://daily.dev/posts/cache-consistency-strategies-to-keep-data-fresh-o2gww6sna twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: Cache consistency: strategies to keep data fresh | daily.dev og:description: Cache consistency is about keeping cached values in sync with the source database. Caches drift due to TTL timing windows, write-ordering races, and... og:url: https://daily.dev/posts/cache-consistency-strategies-to-keep-data-fresh-o2gww6sna og:image: https://api.daily.dev/og/posts/O2gwW6sNa.png og:image:alt: Cache consistency: strategies to keep data fresh og:image:width: 1200 og:image:height: 630 og:locale: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # Cache consistency: strategies to keep data fresh **[Redis](https://daily.dev/sources/redislabs)** · 11 min read · 6 upvotes · 0 comments ## Summary Cache consistency is about keeping cached values in sync with the source database. Caches drift due to TTL timing windows, write-ordering races, and multi-instance population races. The post covers three classic patterns: cache-aside (lazy loading), write-through, and write-behind, explaining their consistency trade-offs. For changes originating outside the app, event-driven approaches like Redis keyspace notifications and change data capture (CDC) via tools like Debezium provide near-real-time invalidation. A practical decision framework maps workload characteristics (read-heavy vs. write-heavy, staleness tolerance) to the appropriate strategy. The post concludes by introducing Redis Data Integration (RDI) as a managed CDC solution that handles ordering, deduplication, and at-least-once delivery. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Questions this post answers ### What causes a Redis cache to drift out of sync with the source database? Cache drift comes from three main mechanisms: TTL windows that outlast the underlying data change, write-ordering races where concurrent updates get reordered and leave the cache holding a stale value, and multi-instance population races where one instance repopulates a cache with an old value right after another instance updates the database. Each mechanism leaves the cache serving wrong data until the next write or expiry. _Compare caching failure modes and mitigation patterns on daily.dev before your next architecture review._ ### What is the difference between cache-aside and write-through caching patterns? Cache-aside has the application read from the cache first, fall back to the database on a miss, then populate the cache; writes update the database and invalidate the cache entry, but it doesn't guarantee consistency and is prone to multi-instance races. Write-through writes to both cache and database synchronously, giving read-your-writes behavior at the cost of higher write latency and cache entries for data that may never be read. _Weigh cache-aside against write-through tradeoffs on daily.dev when picking a caching strategy._ ### How did Uber's CacheFront system fix stale cache reads? Uber's CacheFront originally paired TTLs with change data capture but still hit read-your-writes problems where a cached row could keep serving stale values until invalidated or expired. Uber added a write-through protocol where each node validates data freshness against the database before serving a cached value, which later allowed a higher cache hit rate even with longer TTLs. _Track real-world cache consistency fixes like Uber's CacheFront on daily.dev while designing your own system._ ## Similar posts on daily.dev - [Redis vs Other Distributed Caches: How to Choose the Right Caching Technology](https://daily.dev/posts/redis-vs-other-distributed-caches-how-to-choose-the-right-caching-technology-xjitqqnaj) · C\# Corner · 1 upvotes · 0 comments - [Caching and Latency \[Book Excerpt\]](https://daily.dev/posts/caching-and-latency-book-excerpt--xatnprmvu) · P99 Conf · 6 upvotes · 0 comments - [Copying data is not an anti-pattern. Copying data without a strategy is.](https://daily.dev/posts/copying-data-is-not-an-anti-pattern-copying-data-without-a-strategy-is--f86im5ezw) · ITNEXT · 1 upvotes · 0 comments - [Dual-Read Cache Consistency in Monolith DB Migrations](https://daily.dev/posts/dual-read-cache-consistency-in-monolith-db-migrations-yoqhsvloq) · SitePoint · 3 upvotes · 0 comments - [Why Strong Consistency?](https://daily.dev/posts/why-strong-consistency--qk4prqx2z) · Lobsters · 4 upvotes · 0 comments --- Tags: [#backend](https://daily.dev/tags/backend), [#redis](https://daily.dev/tags/redis), [#change-data-capture](https://daily.dev/tags/change-data-capture) [View this post on daily.dev](https://daily.dev/posts/cache-consistency-strategies-to-keep-data-fresh-o2gww6sna) ```json {"@context":"https://schema.org","@graph":[{"@type":"Organization","@id":"https://daily.dev/#organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180},"sameAs":["https://twitter.com/dailydotdev","https://github.com/dailydotdev","https://www.linkedin.com/company/daily-dev-ltd"]},{"@type":"WebSite","@id":"https://daily.dev/#website","url":"https://daily.dev","name":"daily.dev","publisher":{"@id":"https://daily.dev/#organization"},"potentialAction":{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https://daily.dev/search?q={search_term_string}"},"query-input":"required name=search_term_string"}}]} {"@context":"https://schema.org","@type":"TechArticle","headline":"Cache consistency: strategies to keep data fresh","url":"https://daily.dev/posts/cache-consistency-strategies-to-keep-data-fresh-o2gww6sna","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/cache-consistency-strategies-to-keep-data-fresh-o2gww6sna"},"datePublished":"2026-07-23T00:08:24.565Z","dateModified":"2026-09-14T06:27:13.137Z","description":"Cache consistency is about keeping cached values in sync with the source database. 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