system-design-estimation

Capacity Estimation

Priority: P1 (HIGH)

Estimate before you architect. One order of magnitude decides cache, shard, and queue choices.

Core Formulas

• average QPS = DAU x actions per user per day / 86,400
• peak QPS = average QPS x peak factor (default 5x; 20-100x for flash sale, ticket drop, or scheduled push)
• storage per year = writes per day x record size x 365 x replication factor
• bandwidth = QPS x payload size (compute ingress and egress separately)
• working set = hot records x record size, where hot is typically 20% of data serving 80% of reads
• connections = concurrent users x connections per user; compare against pool and file-descriptor limits

Method

1. Round every input to one significant figure. Precision here is false precision.
2. Compute average, then peak, then storage, then bandwidth, then memory.
3. Compare each result to a known ceiling from estimation numbers: single-node QPS, disk IOPS, NIC throughput, RAM per instance.
4. Name the shaping quantity - the first number that breaks a single-node ceiling. It dictates the first component added in high-level design.
5. Restate every assumed input beside the result so a wrong assumption is visible, not buried.

Cost

• Convert the sized capacity into monthly spend before recommending it: compute, storage plus egress, managed-service premiums, and the multiplier any redundancy applies.
• Cost is a design constraint, not an afterthought. A topology the budget cannot hold is not a design, it is a proposal to be rejected later.
• State cost per unit of value where it clarifies: cost per 1k requests, per GB retained, per nine of availability added.

Latency and Availability Budgets

• Allocate the end-to-end latency objective across work, queueing, and dependencies; make each allocation fit the same request deadline and reserve time for caller/network overhead.
• A measured per-hop p95 or p99 is not an additive end-to-end percentile. Percentiles describe distributions, and their joint tail depends on correlation and fan-out. Measure the end-to-end distribution, or use explicit per-hop bounds whose sum fits the deadline; do not add independently observed p95/p99 values and call the result the path percentile. Define latency at the user/request boundary as a measurable SLI, for example the proportion of requests completing within a threshold (Google SRE SLO guidance).
• For parallel fan-out, response latency is governed by the slowest required branch; budget that tail against the end-to-end objective.
• Serial dependency availability is the joint probability that every required dependency is available. Multiply their availability values only when independence is a justified assumption; common causes, shared infrastructure, and correlated failures change the result.
• Redundancy can reduce independent failure risk. Common-mode failures impose a ceiling on the gain, so include shared stores, configuration planes, and other correlated failures in the joint availability model; they do not necessarily erase all benefit.
• Translate the target into an error budget for the declared window before promising it.