Redis

Scaling Redis horizontally requires implementing complex sharding techniques across multiple instances. This is time-consuming, requires careful planning for data consistency and high availability, and demands specialized operational knowledge separate from traditional database operations.

Redis lacks robust security features out of the box and is accessible to anyone who can connect by default. Developers must manually implement firewalls, ACLs, SSL/TLS encryption, and other security measures.

Redis persistence through RDB snapshots and AOF (Append-Only Files) can fail to prevent data loss during crashes or unexpected failures. These mechanisms are unreliable for mission-critical workloads where data loss is unacceptable, especially when persistence is disabled for performance.

Redis master-slave replication has only one master handling writes, creating a critical single point of failure. The clustering solution needed for redundancy was not production-ready at the time of these reports.

Redis provides only eventual consistency through replication, which can introduce latency and inconsistency during network partitions. Replication mechanisms designed for basic redundancy fall short for applications demanding strong consistency or transactional guarantees in real-time scenarios.

Running large datasets (>25GB or 25K ops/sec per shard) on a single Redis instance means failover, backup, and recovery all take significantly longer. If the instance fails, the entire dataset blast radius and recovery time are unacceptable for production systems.

When many clients connect directly to Redis instances without a proxy, network disruptions trigger reconnect floods that overwhelm the single-threaded Redis process, forcing cascading failovers and loss of availability.

Developers frequently fail to implement proper error handling and retry logic for Redis connection failures, leading to data loss, inconsistent application state, and cascading failures.

Redis' single-threaded design cannot effectively utilize modern multi-core processors, requiring additional instances to scale horizontally. This increases hardware costs, operational complexity, and leaves CPU cores underutilized even on commodity servers.

As an in-memory store, Redis requires all data to reside in RAM, limiting total dataset size by available memory. Large datasets consume significant memory overhead per instance, creating cost and performance pressure when data grows beyond infrastructure limits.

Redis Cluster cannot execute atomic transactions when keys are distributed across different nodes, requiring developers to carefully plan key distribution strategies or use alternative solutions for applications requiring transactional integrity.

Managing Redis Cluster at scale involves complex resharding, partition rebalancing, and data coordination. Online migrations and scaling require careful orchestration and are prone to errors, with automation still requiring multiple manual steps and risk of downtime or data inconsistencies.

Improper use of Redis commands that modify data (INCR, HSET, etc.) without proper locking mechanisms can lead to race conditions where concurrent updates from multiple clients overwrite each other, causing data inconsistency.

Misconfigurations in Redis settings (maxmemory policies, timeout settings, binding IP addresses) can cause unexpected behavior, security vulnerabilities, premature key eviction, and data loss.

Storing cache keys without expiration causes indefinite accumulation over time, leading to unbounded memory growth, increased eviction pressure, and out-of-memory errors. Keys added without TTLs because "data never changes" persist even after assumptions change, causing unpredictable eviction behavior.

Developers frequently fail to properly serialize/deserialize data when working with Redis, causing data corruption, integrity issues, and unexpected behavior, especially with complex data types.

Frequently accessed data that isn't distributed across multiple shards becomes a bottleneck, concentrating load on a single node. This defeats horizontal scaling benefits and creates performance ceiling for the application.

Redis does not natively support advanced features like joins, aggregations, full-text search, time-series data management, or graph processing. This limits use cases to simpler key-value and caching scenarios, blocking applications requiring sophisticated data processing.

Complex Lua scripts that run for long durations or depend on data size block the entire Redis instance while executing. Moving business logic into Lua for atomic operations can cause Redis to block under load, reducing availability.

Including user input, URLs, or search queries directly in Redis keys without predictability controls causes unbounded cardinality growth. Redis has no warnings when cardinality explodes, simply allocating memory until exhausted, resulting in unpredictable and chaotic eviction behavior.

Combining cache data, locks, queues, sessions, and feature flags in a single Redis instance causes eviction pressure and performance characteristics to collide. Cache evictions unintentionally affect critical locks, and memory pressure impacts essential queues.

Keys in Redis don't expire by default, leading to memory leaks and performance degradation when developers forget to set TTL values. This is a recurring developer mistake that requires constant vigilance.

Commercial licensing changes in Redis have created future uncertainty concerns. Combined with high operational costs for maintaining Sentinel/Cluster configurations and version upgrades, organizations are reconsidering Redis investments.

Redis operates over a network, and network latency—especially in distributed or geo-distributed environments—can cause increased response times, timeouts, and severely impact performance.

Using the KEYS command for searching in Redis blocks the entire process during a full-scan operation with O(N) complexity. This causes severe performance degradation and should be replaced with SCAN or Redis Search alternatives.

Developers frequently fail to use Redis pipelining or batch operations, causing unnecessary round trips to the server and significant performance overhead.

Redis Community Edition lacks enterprise-level support and advanced features. Organizations struggle to address performance bottlenecks, troubleshoot issues, or receive critical security updates without paid support.

Teams often aggressively tune Redis before understanding their actual workload, tweaking memory policies, persistence settings, clustering, and sharding prematurely. This adds complexity and failure modes without solving real problems.

Storing large JSON blobs in string format increases serialization/deserialization costs, network traffic latency, and eviction overhead. It prevents atomic field-level updates and makes tuning and scaling harder.

When Redis performance degrades, multiple potential causes exist (system load, memory pressure, poorly structured requests). Effective troubleshooting requires correlating and analyzing diverse data points across the system.

Redis does not automatically manage memory like relational databases. Developers must manually configure eviction policies to handle out-of-memory scenarios, adding operational complexity and risk of data loss.

Redis lacks proper native monitoring and alerting mechanisms. Without adequate monitoring tools and manual setup, it is difficult to identify performance issues or potential failures before they impact production applications.

Developers fail to properly model data for Redis's key-value paradigm, making poor decisions about data organization and access patterns that lead to inefficiencies and performance issues.

Redis uses a gossip protocol and majority voting for failure detection instead of formal consensus algorithms like Raft or Paxos, reducing implementation complexity but increasing latency during failover operations.