“There is no such thing as a one-size-fits-all database. Choosing the right one is less about features and more about understanding your data’s behavior.” — Alex DeBrie
Cache Me If You Can: Choosing the Right Database and Optimization Strategy for Your Workload
Introduction: Performance, Precision, and the Problem of Persistence
Once upon a time in the early days of the web, speed was measured in seconds, and databases were mostly relational. Caching meant slapping Memcached or Varnish in front of your app, and your main concern was making sure your SQL queries weren’t a dumpster fire. But today, we operate in a world of real-time interactions, machine learning inference, edge computing, and petabyte-scale data.
It’s no longer just about getting data fast—it’s about getting the right data fast. This requires not only thoughtful caching strategies but also the careful selection of data storage engines tailored to the specific shape and semantics of your workload.
Welcome to the modern world of database optimization.
Chapter 1: The Historical Backbone – From Flat Files to Data Fabrics
Relational databases like Oracle, MySQL, and PostgreSQL dominated from the 1980s through the early 2000s. Their ACID guarantees, normalized schemas, and structured query languages made them ideal for business applications—banking, ERP, CRMs—where consistency and relationships were paramount.
Then came the NoSQL revolution, driven by companies like Amazon (DynamoDB), Google (Bigtable), and Facebook (Cassandra). These systems offered flexibility, scalability, and performance for web-scale applications. Document databases like MongoDB and Couchbase catered to flexible schemas. Graph databases like Neo4j and ArangoDB helped model networks and relationships more naturally.
Today, with workloads becoming increasingly complex—ranging from ML pipelines and real-time analytics to LLM-driven apps—newer paradigms like vector databases (e.g., Pinecone, Weaviate, FAISS), semantic caching, and data lakehouses are gaining traction.
Chapter 2: Choosing the Right Database for the Right Job
Let’s break it down by database type and use case:
| Type | Best For | Not Ideal For |
| Relational (SQL) | Structured data with strong consistency (e.g., finance, inventory) | Flexible or large-scale unstructured datasets |
| Document | Flexible schemas (e.g., user profiles, product catalogs) | Complex joins, transactional operations |
| Key-Value | High-speed reads/writes, session storage (e.g., Redis, DynamoDB) | Complex queries, relational data |
| Columnar | OLAP, big data analytics (e.g., ClickHouse, Redshift) | High write throughput, row-level operations |
| Graph | Social networks, fraud detection, recommendation engines | Flat tabular data, large-scale writes |
| Vector | Semantic similarity, embeddings, LLM memory retrieval | Non-ML workloads, transactional consistency |
| Time-Series | IoT data, observability, metrics (e.g., InfluxDB, TimescaleDB) | General-purpose storage |
Chapter 3: Caching Done Right – From Layers to Semantics
Traditional Caching
At its most basic, caching accelerates repeated access to data. Examples:
- Page-level caching (e.g., CDN edge caches like Cloudflare)
- Query-level caching (e.g., MySQL query cache)
- Object-level caching (e.g., Redis or Memcached)
Done poorly? Imagine a stale cache delivering outdated prices on a trading app. Oops.
Semantic Caching
Now we’re talking meaning, not just values.
In LLM applications and search platforms, semantic caching stores intent or contextual embeddings rather than raw queries. When a new query is made, it’s compared to previously cached vectors (using cosine similarity, for example) to see if the system has already seen something similar.
🔧 Tools: FAISS, Annoy, Milvus
Used Well: A customer service chatbot that can instantly return past support answers when users ask slightly different questions.
Done Poorly: Caching based on keywords only, missing the fact that “I lost my luggage” and “my bag is missing” are semantically the same.
Chapter 4: Examples in the Wild
Good Example: Netflix
Netflix leverages a polyglot persistence strategy:
- Cassandra for high-availability content metadata
- Elasticsearch for search and indexing
- Redis for session data and user preferences
- S3 for large media storage
- Presto + Iceberg for analytics
They pair these with caching strategies at multiple layers: edge CDN, metadata cache, and semantic deduplication for recommendations.
Bad Example: A Single MongoDB for Everything
A fast-scaling startup once chose MongoDB for all their data: analytics, transactions, and content indexing. As workloads diversified, they began to experience:
- Poor query performance on large analytic queries
- Write locks during high-concurrency transactional events
- Data inconsistency issues due to overuse of flexible schemas
The result? A rushed and painful migration to PostgreSQL + ClickHouse + Redis.
Chapter 5: Evaluation Framework
To choose the right database and caching strategy, ask:
- What shape is your data?
- Structured tabular? → Relational
- Nested/JSON? → Document
- Relationships? → Graph
- Vectors/embeddings? → Vector DB
- How fresh must your data be?
- Seconds? → Cache heavily
- Real-time? → Stream + cache invalidation
- Batch okay? → Lakehouse with ETL
- How will you access it?
- Key-based lookups? → KV store
- Ad hoc analytics? → Columnar DB
- Full-text or semantic search? → Elastic + Vector DB
- What are your consistency and scale needs?
- Strong consistency? → RDBMS or newer ACID-compliant NoSQL (e.g., FoundationDB)
- High availability and partition tolerance? → Dynamo-style NoSQL or distributed SQL (e.g., CockroachDB)
Wrapping up…
Choosing a database and caching strategy is not about picking the shiniest new toy. It’s about designing a system that mirrors the way your users interact with your product.
Like all good engineering, it’s about tradeoffs. Fast and flexible vs. consistent and correct. Simple and scalable vs. complex and precise.
Modern architecture isn’t a monolith—it’s a mosaic.
References
- Martin Kleppmann, Designing Data-Intensive Applications – https://dataintensive.net/
- Netflix Tech Blog – https://netflixtechblog.com/
- Pinecone Semantic Caching – https://docs.pinecone.io/
- Milvus Vector Database – https://milvus.io/
- Faiss by Facebook AI – https://github.com/facebookresearch/faiss
