Pains

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.

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.

Failing to implement connection pooling is a common mistake in PostgreSQL deployments. Each connection consumes approximately 10MB of RAM, and applications that create new connections for each database operation can quickly exhaust server resources, leading to performance degradation and application failures.

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.

As pipelines grow, developers must add numerous `if` statements to handle different triggers (push, manual, etc.). Reusing workflows requires boilerplate duplication and managing 30+ YAML files. This creates maintenance burden and makes it difficult to refactor without breaking `needs` clauses.

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.

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.

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.

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.

Performance optimization has emerged as the #1 operational challenge (46%), displacing earlier basic adoption concerns. Organizations struggle to optimize performance across databases, AI/ML, and traditional containerized workloads simultaneously.

Implementing flexible filtering in GraphQL requires awkward nested input types and bespoke filter designs for AND/OR logic, range filters, and custom operators. Filter inputs balloon in complexity with duplicate logic across different entity types and no cross-schema reuse.

GraphQL does not support map/table/dictionary return types or associative arrays keyed by dynamic values. Developers must use workarounds like custom scalars, key-value object arrays, or pre-transformed responses, adding unnecessary complexity to both schema and client logic.

Different components define their own ad hoc query shapes, resulting in fragmented chaos where the same resource is requested in dozens of slightly different structures. This defeats GraphQL's promise of reusability and creates brittle client logic with no unified contract.

GraphQL adds significant complexity to projects through schema management, type system duplication, tooling, and infrastructure costs. This overhead is not justified for small projects, simple APIs, or cases where use cases are well-known and static. 90% of typical API use cases can be handled by simpler alternatives.

99.94% of Kubernetes clusters are over-provisioned with CPU utilization at ~10% and memory at ~23%, meaning nearly three-quarters of allocated cloud spend sits idle. More than 65% of workloads run under half their requested resources, and 82% are overprovisioned.

High disk space consumption is a significant non-trivial problem affecting Rust developers' productivity. 22.24% of developers report it as a big problem limiting their work.

Authorization code written for GraphQL contexts often cannot be reused in background jobs or HTML endpoints, because Dataloaders (the common solution to N+1 problems) are GraphQL-specific. This forces developers to implement separate authorization logic for different execution contexts.

Repeated authentication handshakes and authentication logic on critical paths kill API speed. Developers struggle with token reuse, unnecessary redirects, and overhead that should be removed from hot paths.

GraphQL queries may not fetch sufficient data in a single request, necessitating additional round trips to enrich data and build complete models. This wastes bandwidth, consumes resources, adds latency, and increases complexity for API consumers.

Developers lack Kubernetes expertise and want to consume infrastructure without delays, but provisioning new clusters is time-consuming and expensive. This creates bottlenecks where developers wait for ops to provision infrastructure rather than focusing on feature development.

Large complex models achieve high accuracy but require excessive computing resources, resulting in higher costs, slower response times, and infrastructure overhead. Finding the right balance between sophistication and practicality is a persistent challenge.

In large, complex GraphQL applications, tracking how clients query the API is burdensome and requires significant engineering effort. Maintaining consistency and preventing the API from devolving into a REST-like structure as the product evolves demands considerable discipline.