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The Periodic Table of Software Architecture is a map of 92 architecture principles, patterns, and practices—not a formula that tells every team what to build. Stephan van Stekelenburg’s interactive reference groups the vocabulary into ten families, shows how choices relate, and prompts you to ask what each choice costs in operations, coordination, data management, resilience, security, and delivery. Use it to frame an architecture discussion, compare alternatives against your domain and team, and record a deliberate decision. Do not treat its rough complexity bars or its “sensible default” as measured evidence or universal rules.
What is the Periodic Table of Software Architecture?
It is an interactive, opinionated reference table created by Stephan van Stekelenburg. A text version appeared on DEV Community on October 12, 2026, and identifies the original as the author’s site. The table contains 92 entries covering principles, system shapes, domain boundaries, data, integration, resilience, observability, security, delivery, and architecture practices.
The table’s purpose is practical: software architecture has a vocabulary scattered across books, talks, and vendor documentation. A single map makes those terms easier to hold in mind, compare in a meeting, and connect to the trade-offs that follow from a design choice. Each entry is intended to explain what a concept is, when it may help, and what it costs.
It is not a standards document, a benchmark, or a catalog of every valid technique. Van Stekelenburg explicitly describes the placement as opinionated and the table as incomplete. Use it as a conversation aid and a checklist for questions, then validate decisions with the actual domain, constraints, operating model, and skills of your team.
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Which architecture topics does the table cover?
The 92 entries are arranged in ten families. The family names provide a fast way to find the kind of decision you are facing, while the individual entries supply the more specific vocabulary.
| Family | What it helps you discuss |
|---|---|
| Principles | General rules for keeping designs understandable, changeable, and aligned with responsibility. |
| Architectural Styles | Overall system shapes and deployment boundaries, such as monolithic or distributed arrangements. |
| Domain & Modularity | Business boundaries, modules, coupling, and ways to keep responsibilities coherent. |
| Data | Data ownership, storage choices, access patterns, and consistency concerns. |
| Integration | How components communicate and what contracts, dependencies, or coordination that communication creates. |
| Resilience & Scale | Ways to handle failure, load growth, and the operational consequences of distributing work. |
| Observability | Signals and practices used to understand system behavior in production. |
| Security | Controls and boundaries that reduce exposure and protect systems and data. |
| Delivery & Evolution | How architecture changes safely through releases, migrations, and incremental improvement. |
| Practices | Decision and governance techniques that help teams keep architecture intentional over time. |
The boundaries are deliberately debatable. For example, readers might disagree about where sharding belongs or whether hexagonal architecture and clean architecture deserve separate entries. That disagreement is useful: it reveals that categories are aids for thinking, not natural laws.
How should you read an entry?
Start with the entry’s definition, then ask when it fits your problem and what burden it introduces. The table generally places lower-cost choices above heavier choices within a family, but the visual order is only a rough guide. “Cost” can mean implementation effort, cognitive load, operational work, coordination, or the difficulty of changing course later.
- Identify the problem. Are you trying to isolate a domain, scale a workload, survive dependency failures, release more independently, or understand production behavior?
- Check the boundary. Does the concept change a code-module boundary, a data-ownership boundary, a deployment boundary, or a network boundary?
- List the compensating practices. A distributed choice may require stronger contracts, observability, automation, or failure handling.
- Ask what becomes harder. Look for new coordination, consistency, testing, security, or on-call responsibilities.
- Relate it to alternatives. Compare a simpler option and a heavier option using the same criteria rather than treating the table’s position as a ranking.
This reading method prevents a common error: selecting a pattern because its name sounds modern while ignoring the work required to operate it.
How can you use the table to make an architecture decision?
Use the table at the beginning of a design discussion and again when constraints change. The following process turns its vocabulary into a decision record without pretending that the table can choose for you.
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- Write the decision question. Phrase it as a choice with a context, such as “How should this product separate its billing responsibility while the team remains small?”
- Describe non-negotiable constraints. Capture regulatory needs, availability expectations, data residency, deployment environments, deadlines, and the team’s ability to run infrastructure.
- Select a small candidate set. Use the relevant families to find two or three plausible approaches. Avoid comparing every entry in the table.
- Map boundaries and dependencies. For each candidate, mark which modules, services, databases, queues, and external systems become separate responsibilities.
- Evaluate the trade-offs. Discuss operational and complexity cost, data ownership and consistency, failure handling, deployment independence, and the coordination the team can realistically sustain.
- Choose compensating measures. If a candidate introduces network or data-distribution risk, pair it with appropriate integration, resilience, observability, or security practices.
- Record the decision and a reversal signal. An architecture decision record should state the context, considered options, chosen approach, consequences, and the evidence that would justify revisiting it.
- Test the assumption. A prototype, migration slice, load exercise, or operational rehearsal can reveal whether the expected cost is acceptable before the design spreads.
The table also points toward “fitness functions”: automated checks that continuously test an architectural property. For example, a build can enforce module boundaries or a deployment pipeline can verify a required security control. These checks turn a decision from a one-time document into an observable constraint.
What is the difference between a monolith, a modular monolith, and microservices?
The useful distinction is not that one shape is always better. It is where code, data, deployment, and operational responsibility are separated, and whether your team can pay for that separation.
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| Shape | Typical boundary | Advantages to examine | Costs to examine |
|---|---|---|---|
| Monolith | Most capabilities share one deployable unit. | Fewer network interactions and a simpler operational surface can make an initial system easier to change. | Deployment and ownership boundaries may be broad; internal coupling can grow if modules are not disciplined. |
| Modular monolith | Capabilities are separated in code and domain responsibilities while remaining in one deployable unit. | It can preserve simpler operations while making boundaries explicit and enabling later extraction if that becomes justified. | Teams must enforce module rules; a single deployment still couples release timing and runtime resources. |
| Microservices | Capabilities are separated into independently deployable services, usually with explicit integration and data ownership. | Independent ownership and deployment can fit organizations that genuinely need those boundaries. | Network dependencies, distributed data, testing, observability, incident response, and coordination all become more demanding. |
Van Stekelenburg offers a modular monolith, combined with bounded contexts, ubiquitous language, architecture decision records, and fitness functions, as a “sensible default” example. That is the author’s recommendation for discussion, not an industry consensus or a promise that it suits every organization.
A microservices design is not simply a monolith split into smaller folders. Independent services need clear ownership, integration contracts, and a plan for partial failure. If those capabilities do not solve a real organizational or scaling problem, the additional operational surface may outweigh their benefits.
How do architecture patterns combine?
The table’s central lesson is that choices are composable. One decision often creates a condition that another practice must address. The combinations below are illustrative examples described by the author, not proven recipes.
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A sensible default
A modular monolith provides a single deployment boundary; bounded contexts and ubiquitous language clarify domain responsibilities; architecture decision records preserve why choices were made; and fitness functions help keep those boundaries intact.
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Independently owned services can communicate through events, but the design still needs explicit data ownership and integration conventions. Teams must decide how consumers handle duplicate, delayed, or unavailable messages and how operators will see the resulting flows.
Systems with network dependencies
When a design relies on remote calls, integration patterns should be considered alongside resilience and observability. Timeouts, retries, fallback behavior, and useful telemetry are architectural concerns because a remote dependency can fail independently of the caller.
Read-heavy systems
A read-heavy workload may combine a deliberate data-access pattern with scaling and observability techniques. The relevant question is not which storage pattern is fashionable, but which consistency, freshness, and operational obligations the workload permits.
Legacy modernization
Domain and modularity practices can help isolate a legacy responsibility before a team changes its deployment shape. Incremental delivery and recorded decisions reduce the risk of attempting a single irreversible rewrite.
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Deployments and security
Delivery practices should be evaluated with the security boundaries they create. More independent deployment can require stronger identity, authorization, secrets handling, auditability, and release controls rather than eliminating those responsibilities.
How much weight should you give the complexity bars?
Very little as a numerical estimate. The bars are explicitly rough, and their meaning depends on a team’s experience, tooling, domain, reliability expectations, and operating environment. They are best used to start a cost conversation, not to forecast a project schedule or claim that one pattern has a measured failure rate.
| Comparison axis | Questions to ask |
|---|---|
| System shape and deployment | How many independently released units will exist, and who owns their runtime? |
| Domain and module boundaries | Can responsibilities be named, tested, and changed without hidden coupling? |
| Operational complexity | What must be monitored, automated, secured, backed up, and supported at night? |
| Data ownership and consistency | Who is authoritative, and what stale or conflicting data can the business tolerate? |
| Failure handling and resilience | What happens when a dependency is slow, unavailable, or only partly successful? |
| Team capacity | Does the team have the skills, time, and on-call coverage to operate the proposed design? |
Write down the assumptions behind each answer. A design that is inexpensive for a platform team with mature automation may be expensive for a small product team that must build every operational capability itself.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What are the table’s limitations?
Its scope and arrangement are intentionally subjective. Ninety-two entries cannot capture every variation, and a family assignment may be arguable. Concepts also overlap: a data decision can affect resilience, integration, security, and observability at the same time.
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What mistakes does this reference help you avoid?
- Pattern collecting: adopting several named techniques without a problem statement or a way to operate them.
- Confusing physical and logical boundaries: assuming a separate module must become a separate service, or that one deployable unit has no meaningful internal boundaries.
- Ignoring compensating work: adding distribution without planning contracts, failure handling, data ownership, and telemetry.
- Using a visual order as a guarantee: treating a rough complexity bar as a price, benchmark, or reliability prediction.
- Skipping decision records: forgetting the context that made a choice reasonable, which makes later change look arbitrary.
- Designing for an imagined future: paying today for independent scaling or releases that the product, domain, or team does not yet require.
A disciplined team can use the table to make fewer accidental decisions: name the boundary, state the trade-off, assign ownership, and define how you will know whether the choice is working.
Where does the reference fit in an architecture practice?
Use it before a design review to establish terms, during the review to compare alternatives, and afterward to check that implementation still matches the decision. Pair it with architecture decision records for context and fitness functions for continuous enforcement. Revisit the record when the domain, team, deployment environment, or operating requirements change.
For deeper study, the author notes that architecture vocabulary is distributed across books, talks, and vendor documentation. The table is a starting index; selecting books or courses requires evaluating those materials separately rather than assuming an endorsement from the reference.
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Frequently Asked Questions
Who created the Periodic Table of Software Architecture?
Stephan van Stekelenburg created it and published a text version on DEV Community on October 12, 2026. The post identifies an original interactive version on the author’s own site.
Can a small engineering team use this table?
Yes, if the team uses it to narrow a decision rather than to adopt every pattern. Start with the team’s operational capacity and choose the simplest candidate that satisfies the actual domain and delivery constraints.
What is an architecture fitness function?
It is an automated check that verifies an architectural property continuously, such as a module-dependency rule or a required security control. The check runs with normal development or delivery workflows so drift is detected early.
Should the table be treated as a formal standard?
No. Its categories and placements are explicitly opinionated, and its complexity indicators are rough. Formal standards, contractual requirements, and your system’s measured behavior must take precedence when they apply.
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