Tag: Redis

1. maxmemory-policy Compared: Which Eviction Fits When

   Sep 29, 2025

   A poorly chosen maxmemory-policy leads either to write commands being rejected in the middle of production or to the silent loss of important data that was actually meant to persist. Understanding how Redis manages memory under pressure lets you pick the right policy deliberately instead of by accident, avoiding both outcomes reliably.

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2. Using the Slowlog for Targeted Performance Diagnostics

   Sep 27, 2025

   Redis runs as a single-threaded event loop, where every slow command blocks all other clients at once. The slowlog logs exactly these commands with timestamp, duration, and arguments, making it the most direct tool for tracing a latency problem back to a specific command instead of a vague guess.

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3. Cache Warmup Strategies After Deployments in Magento

   Sep 23, 2025

   After every Magento deployment the Redis cache is empty, and the first requests hit the database unchecked. This article explains why that traffic spike happens, how warmup crawlers and cache preload scripts prevent it, and how warmup can be cleanly integrated into a deployment pipeline.

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4. Planning Redis Memory Sizing for Magento Shops Correctly

   Sep 22, 2025

   An undersized Redis server leads to frequent eviction, dropping hit rates, and in the worst case out of memory errors right in the middle of checkout. Proper memory sizing means realistically estimating the memory needs of cache, session and full page cache based on catalog size and traffic, instead of relying on roughly guessed defaults that account for neither growth nor traffic peaks.

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5. Persistence-Performance Tradeoffs: Durability vs. Latency

   Sep 7, 2025

   Every form of persistence in Redis costs latency, whether through fsync calls on every write or through the fork process behind a snapshot. Anyone who does not understand this tradeoff ends up choosing either a configuration that causes latency spikes in production, or one that loses more data than planned when something actually breaks.

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6. Avoiding Split-Brain Scenarios in Redis Clusters

   Aug 25, 2025

   A network partition can cause two Redis nodes to simultaneously believe they are the only valid master and accept conflicting writes. Quorum-based decisions in Sentinel, safeguarding through min-replicas-to-write, and a clear understanding of failover mechanics prevent such a split-brain situation from ever occurring, or from silently costing you data.

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7. Setting Up Redis Sentinel for Automatic Failover

   Aug 17, 2025

   When the master of a Redis setup fails without automation, a human's reaction time determines how long an application stays down. Redis Sentinel continuously monitors master and replicas, coordinates with other Sentinel instances about the real state of the system, and automatically promotes a replica to the new master on a genuine outage, without anyone stepping in manually.

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8. Redis Lua Scripting for Atomic Operations

   Aug 17, 2025

   Redis Lua scripting runs a complete script as a single atomic command, without any other client being able to interleave. Anyone who implements increment-with-limit, conditional delete or other read-then-write patterns without real server-side logic risks race conditions that Lua scripts structurally rule out.

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9. Building Leaderboards with Sorted Sets

   Aug 10, 2025

   A ranking with thousands of players sounds like a sorting and scaling problem, but with Redis Sorted Sets it is a data structure built for exactly that. ZADD, ZRANK and ZREVRANGE deliver ranking, neighborhood queries and top-N lists in logarithmic time, without the application ever having to sort or cache anything itself.

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10. Redis Data Types Explained: Strings, Hashes, Lists, Sets

    Aug 5, 2025

    Redis is far more than a simple key value store for strings. Anyone who understands the four core data types, strings, hashes, lists and sets, and their internal memory encoding can build data models that use noticeably less memory and respond significantly faster than a naive implementation that leans on a single data type.

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