Database Replication
Replication involves copying data across multiple database servers to improve availability, reliability, and read performance.
Master-Slave Replication
One master handles writes, multiple slaves handle reads. Simple but master is a single point of failure.
Master-Master Replication
Multiple masters can handle both reads and writes. Better availability but requires conflict resolution.
Database Sharding
Sharding partitions data across multiple databases based on a shard key. Each shard operates independently.
Sharding Strategies
- Range-based: Partition by value ranges
- Hash-based: Partition using hash function
- Directory-based: Use lookup table to find shard
CAP Theorem
In distributed systems, you can only guarantee two of three properties:
- Consistency: All nodes see same data
- Availability: System remains operational
- Partition Tolerance: System works despite network failures
Next Steps
Understanding databases is crucial, but service boundaries add a different set of trade-offs. Continue with microservices architecture and distributed systems.
A Practical Mental Model for Database Design
Replication copies data for availability and read scale, while sharding divides data so storage and write load can grow beyond one database. The definition matters, but the more useful skill is connecting it to a user-visible goal and a measurable operating limit. A design is convincing when it explains what improves, what becomes more complex, and what evidence would trigger the next change.
Replication introduces lag, failover, and consistency choices. Sharding introduces routing, rebalancing, cross-shard queries, and hotspot risks. They solve different constraints and are often combined. Draw the critical request path first. For every hop, name the work performed, the state read or changed, and the way that hop can fail. This prevents a diagram full of boxes from hiding the actual behavior.
A simple way to reason about database design is to separate four concerns: correctness, performance, availability, and operability. Correctness protects user and business invariants. Performance defines latency and capacity. Availability describes degradation during failure. Operability covers deployment, observation, recovery, and cost. Improving one concern can make another harder, so every design choice needs a stated priority.
Worked Example and Capacity Reasoning
A global catalog can use one write primary and regional read replicas when slightly stale browsing is acceptable. Orders require read-your-writes after checkout, so route those reads to the primary or track a replication position. When one primary cannot hold all orders, shard by customer ID and replicate each shard.
Turn the narrative into numbers before selecting infrastructure. Estimate average and peak request rates, the read-to-write ratio, payload size, retained data, and acceptable response time. Add headroom for traffic bursts and failures, but show the arithmetic. The goal is not a perfect forecast; it is to distinguish a design that needs one machine from one that needs partitioning, replication, or asynchronous processing.
Next, trace one successful request and one failed request. The successful trace validates the normal data flow. The failed trace forces decisions about timeouts, retries, idempotency, stale data, and user feedback. If the system can only be explained while every dependency is healthy, the design is incomplete.
Design Decisions to Make Explicit
Start from invariants: which writes must be unique, which reads may be stale, and which transactions must remain atomic. In production, validate this with a small experiment or load test, then expose a metric and an alert that show whether the decision still holds. In an interview, state the trade-off plainly instead of presenting the choice as universally correct.
Choose a shard key that distributes both data volume and request volume while supporting common queries. In production, validate this with a small experiment or load test, then expose a metric and an alert that show whether the decision still holds. In an interview, state the trade-off plainly instead of presenting the choice as universally correct.
Plan resharding with virtual partitions or a routing directory instead of baking physical node counts into clients. In production, validate this with a small experiment or load test, then expose a metric and an alert that show whether the decision still holds. In an interview, state the trade-off plainly instead of presenting the choice as universally correct.
Specify replica promotion, split-brain prevention, backup restore, and recovery point objectives. In production, validate this with a small experiment or load test, then expose a metric and an alert that show whether the decision still holds. In an interview, state the trade-off plainly instead of presenting the choice as universally correct.
These decisions should appear next to the component they affect. A short annotation such as “p99 under 250 ms,” “eventual consistency under 30 seconds,” or “survives one availability-zone failure” makes the diagram testable. Without a target, terms such as fast, scalable, and highly available are only aspirations.
Common Failure Modes
- 1. Replica lag shows users stale state immediately after a successful write. For Database Design, document the expected behavior, the capacity assumption behind it, and the fallback when that assumption stops being true.
- 2. A time-based or geographic shard receives disproportionate current traffic. For Database Design, document the expected behavior, the capacity assumption behind it, and the fallback when that assumption stops being true.
- 3. Cross-shard joins and transactions turn ordinary queries into distributed coordination. For Database Design, document the expected behavior, the capacity assumption behind it, and the fallback when that assumption stops being true.
- 4. Automatic failover promotes an incomplete replica and loses acknowledged writes. For Database Design, document the expected behavior, the capacity assumption behind it, and the fallback when that assumption stops being true.
Do not try to eliminate every failure. Decide which failures must be masked, which can produce a degraded response, and which should reject new work quickly. Bounded queues, deadlines, bulkheads, and circuit breakers are often safer than unlimited retries. Recovery also needs verification: regularly test restores, failovers, rebalancing, and rollback paths before an incident makes them necessary.
Observability and Production Readiness
At minimum, monitor replication lag, shard size and request skew, failover duration, conflict and transaction-abort rate. Break metrics down by endpoint, dependency, region, or partition where an aggregate could conceal a hotspot. Pair metrics with structured logs for local detail and distributed traces for request paths that cross service boundaries.
Alerts should describe user impact or exhausted safety margin, not every small fluctuation. Use service-level objectives to connect telemetry to a promise: for example, 99.9% of valid requests succeed and 99% finish within the target latency over a rolling window. Add dashboards for traffic, errors, duration, saturation, and deployment markers so an operator can see whether a regression began with load, a dependency, or a release.
Capacity planning is continuous. Record the tested limit, current peak, growth rate, and time required to add capacity. If the system needs thirty minutes to scale safely, an alert at ninety-nine percent utilization is too late. Operational readiness is part of system design because a component that cannot be observed or recovered is not dependable.
How to Explain This in a System Design Interview
- Clarify the requirement. Ask which user action depends on database design and define the success target.
- Estimate demand. Calculate peak traffic, data size, and the ratio that drives the design.
- Start simple. Present the smallest architecture that meets the current requirement before adding distributed machinery.
- Find the limit. Explain which resource or failure domain breaks first and how you know.
- Evolve the design. Add the next mechanism, then state its cost, consistency effect, and operational burden.
- Close with failure handling. Walk through one dependency failure and the metrics that reveal it.
This sequence demonstrates judgment. Interviewers usually care less about naming a particular product than about whether you can defend boundaries and adapt when a requirement changes. If a managed service is useful, describe the capability you need first, then mention the product as one implementation.
Review Checklist
- Is the functional scope clear, including what is deliberately excluded?
- Are peak traffic, storage, bandwidth, and latency targets quantified?
- Does every important write have an owner, durability rule, and idempotency strategy?
- Are consistency and staleness visible to the user explained?
- Can the design tolerate one instance, zone, or dependency failure as required?
- Are queues and retries bounded, and is overload rejected or degraded intentionally?
- Can an operator detect, diagnose, roll back, and recover the system?
- Is the next scaling step identified without paying for it prematurely?
Want a guided way to practice these trade-offs? Continue in System Design Fundamentals for Interviews on Udemy, which connects the concepts through complete interview case studies.
Continue Learning
Use the complete System Design interview-preparation guide to place this topic in a four-week roadmap. Then apply the same reasoning to the System Design case-study collection, where requirements, estimates, bottlenecks, and failure modes are combined in end-to-end designs.