What Makes a System Read Heavy or Write Heavy?

Definition
Workload shape determines fundamental architectural decisions. A system is read-heavy when fetch operations outnumber mutations by 10:1 or higher. A system is write-heavy when it continuously ingests high volumes of mutations, often with bursty patterns.
Read-Heavy Systems
Most production applications are read-heavy. A product catalog serves millions of page views against hundreds of inventory updates daily. For these workloads, optimize the read path aggressively. Add read replicas (copies of the database that serve read-only queries) to multiply query capacity. Use caching layers that absorb 80-95% of reads. Denormalize data into shapes matching common query patterns. The goal: reduce load on your primary database to only authoritative writes, serving reads from replicas and caches.
Write-Heavy Systems
Write-heavy systems face different challenges. Telemetry pipelines ingesting events at 100,000+/sec, financial transaction logs, and real-time analytics with continuous streaming updates need architectures that absorb sustained write throughput. Prioritize append-only structures like LSM trees (Log-Structured Merge trees, which batch writes in memory then flush to sorted files on disk). Use horizontal partitioning to spread write load across nodes. Buffer and batch writes to smooth bursts. Read latency often becomes secondary to write durability and throughput.
Hybrid Workloads
Many systems have hybrid workloads with distinct hot paths. An e-commerce platform might handle 10,000 read queries/sec for product browsing (read-heavy) while simultaneously processing 500 order writes/sec (write-heavy bursts during sales). Recognizing which paths need which optimization lets you apply targeted solutions: cache the product catalog, use queues for order processing, rather than making global compromises that hurt one workload to help another.
Measuring Workload Shape
Profile your actual traffic. Measure read:write ratio, query patterns, and peak throughput for each endpoint. A 100:1 read:write ratio justifies aggressive caching and replication. A 2:1 ratio needs more balanced optimization. Track metrics over time since patterns shift: marketing campaigns spike reads, new feature launches spike writes.

💡 Key Takeaways

✓Read to write ratio above 10:1 signals read heavy optimization: invest in caching, replication, and precomputation to achieve sub 50ms p95 latency

✓Write heavy systems with sustained high ingest rates (100K+ writes/sec) require buffering, sharding, and eventual consistency to smooth bursty load

✓Mixed workloads are common but critical path drives bias: Meta optimizes for read latency on social graph despite constant writes, Netflix optimizes edge delivery despite personalization writes

✓Storage overhead trade off: read optimization can increase storage by 3x to 10x through denormalization and materialized views in exchange for faster queries

✓Consistency trade off: write heavy systems accepting eventual consistency achieve 5x to 20x higher throughput compared to strongly consistent writes that block on replication

📌 Interview Tips

1Meta TAO serves 1-2 million ops/sec per cache node with sub millisecond latency, handling 100:1 read:write ratio across social graph with asynchronous replication

2Netflix delivers over 90% of traffic from Open Connect edge caches at hundreds of Tbps globally, keeping origin load minimal with p95 UI loads under 20-50ms

3LinkedIn Kafka handles tens of millions of messages per second at peak, decoupling write streams from read views with asynchronous consumers

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