SQL vs NoSQL
Decided by access patterns and query flexibility, not by data volume.
5 to work through
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intermediate
A team proposes a document database for an order management system because "orders are documents". Assess.
2 min answer -
advanced
A platform must choose between a relational database and a wide-column store for its highest-volume data. What decides it, and what is the common error?
2 min answer -
advanced
A startup with a few thousand users is choosing between a single-region Postgres and a geo-distributed serializable database. What evidence should drive the decision, and what would trigger revisiting it?
3 min answer -
advanced
By 2021 Notion's single Postgres database had reached the point where VACUUM stalled and transaction ID wraparound became a genuine risk. They sharded Postgres into 480 logical shards rather than moving block data to a NoSQL store. What forced the change, what decided the destination, and where would copying it be a mistake?
2 min answer -
advanced Multiple choice
Figma's Postgres stack grew roughly 100x between 2020 and 2024. The team had already partitioned tables vertically and now needed horizontal scale for the largest tables. They evaluated distributed SQL engines including CockroachDB and TiDB and rejected all of them, then spent about nine months building their own sharding layer with a query proxy written in Go. Which fact best explains that choice?
2 min answer
2 terms in this topic
Access Pattern Driven Modelling
Designing the data model from the queries the application must serve, rather than from an abstract normalised representation of the entities.
conceptQuery Surface Volatility
How fast a product invents query shapes it did not have before - the workload fact that decides whether a purpose-built physical layout or a general …
Neighbouring topics
Architecture Decision-Making
General material on making and recording architectural decisions.
Architecture Decision Records
One decision, its context, alternatives and consequences, kept immutable.
Reversibility
One-way and two-way doors, and buying optionality deliberately.
Build vs Buy
Differentiation, five-year TCO, and the exit cost of each option.
Monolith vs Microservices
A team-topology decision far more often than a technology one.
Sync vs Async
Whether the caller's outcome depends on the callee's response.
Strong vs Eventual Consistency
A per-operation decision, resolved by what a stale read would cost.
Managed vs Self-Managed
Trading control and unit cost against operational attention.
Serverless vs Containers
Spiky and event-driven versus sustained throughput.
Single vs Multi-Region
Driven by RTO, RPO and residency rather than by ambition.
Centralised vs Distributed
Shared platform leverage against team autonomy.
Performance vs Cost
Buying latency, and knowing what the last millisecond is worth.
Reliability vs Complexity
Mechanisms that add availability and add failure modes.
Security vs Usability
Varying control by the value of the action rather than uniformly.
Delivery vs Maintainability
Fast in the cheap places, careful in the expensive ones.
Deciding Under Uncertainty
Bounding the downside and buying information cheaply.
Trade-off Analysis Methods
ATAM, scenarios, and naming the points where qualities conflict.
Technology Selection
Evaluating options against drivers rather than against enthusiasm.
Decision Practice
Thresholds, review, supersession and keeping the log alive.