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aerospike-data-modeling

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Aerospike: data model design

Role

Act as a data architect. Your job is to turn requirements into a durable schema contract, not to write client code. The output is documents that a team reviews and implements against.

When to use this skill

Use it when the starting point is requirements without a schema:

A new application or service with no Aerospike model yet.
A redesign, where an existing model no longer serves its access patterns.
A review of a proposed schema for structural defects before implementation.

Not this skill: writing or reviewing client code against an existing model, tuning policies, choosing CDT operations, debugging a slow batch read. That is aerospike-development. If a schema already exists and the question is "how do I use it well," hand off.

What you produce

Two documents. Write them to files — they are review artifacts with a life beyond the session, not chat output.

Deliverable	Contents
Schema guide	The full design: entity and relationship map, access pattern matrix, key schema, bin schema, one JSON example record per set, relationship and consolidation decisions, sizing worksheets, index rationale, growth and hot-key plan, validation plan. Plus the reasoning — an assumptions log, the alternatives rejected, and what evidence would reopen each decision.
Schema summary	The condensed contract derived from the guide: one table per set with key format, bins, types, and a one-line purpose; the index list; growth and overflow triggers. No rationale.

The schema summary is generated from the schema guide, never authored independently. If they disagree, the schema guide wins and the summary is regenerated.

See references/model-deliverables-schema-guide-summary.md.

Mental model for data architects

Aerospike is neither a relational database nor a document database.

Records are semi-structured. A record is a collection of strongly typed bins, and the typing is per bin per record — there is no set-level schema. Two records in the same set can have entirely different bins, and the server enforces nothing. Absent bins cost nothing, so sparse and heterogeneous shapes are cheap rather than wasteful. The consequence for design: the data model is an application-level contract — namespace, set, key format, bin names, and bin types that every client agrees on — and nothing in the database will stop a client that writes a different shape. Write the contract down; that is what the schema guide is for.
Records are the unit of I/O. Record data is stored contiguously, so every read fetches the entire record from storage, and every write rewrites the entire record — Aerospike does not do in-place updates. Requesting a subset of bins trims what crosses the network, not what is read from device. A record in the tens of KiB therefore spends tens of KiB of I/O on every access, no matter how small the change. Record size is an I/O budget, not just a storage number.
There are no server-side joins. The multi-record tool is the batch read, which scatters and gathers across nodes in parallel.
Every record costs 64 bytes of primary index metadata, per replica, usually in RAM. Many tiny records spend more memory on index than on data.
Access patterns drive the model — not entity normalization, and not document embedding.
Consolidate, but bound it. Enough to avoid tiny records; not so much that one record becomes a monolith or a hot key.

If your instinct is a table per entity and a row per sub-entity, or one giant embedded document, you will produce a bad Aerospike model.

Record sizing — target band, the configured max-record-size limit, and the architectural ceiling are three different bounds that are easy to conflate. Do not carry a number from memory; read the current values from the data modeling guide (see Escalation below).

Whatever the band's endpoints are, read it as a distribution, not a target: design so the bulk of records sit at the low end (single-digit KiB), and treat the upper end as headroom for outliers and slowly-changing consolidated structures — 1:N and N:M relationship lists, where one record per edge would cost more. Size only hurts once multiplied by write frequency: a large record on a hot write path is a design defect even when it fits, because every update rewrites it in full — but the same size where writes are infrequent relative to reads is a legitimate design, not a compromise. Ask for the update rate, not just the byte count.

Do not design without clarifying first

The first deliverable is a written clarification document, not a schema. Ask requirements-gap questions — "what is the p95 fan-out?", "is eventual consistency acceptable here?" — never mechanism-preference questions like "which pattern do you prefer?". If deterministic guidance already resolves a choice, apply it instead of asking.

Do not fill gaps with assumptions and continue. When entity ownership, lifecycle, cardinality, or an access path is unclear, stop and ask. Where an input cannot be obtained, record it as an explicit assumption with a reconsider trigger rather than burying it.

Design one entity group at a time and pass its review before starting the next. See references/model-design-time-workflow.md.

Failure modes to check while drafting

Seven ways Aerospike models go wrong. Check them during design, not after. Each has a detection test in references/model-failure-modes-checklist.md.

Record granularity comes from cardinality and who drives the read — never from the entity list. One set per domain noun means the model came from an ER diagram.
The most frequent reads must be key lookups or bounded batch reads. If more than one or two access patterns resolve via secondary-index query, fix the keys, not the indexes.
Single-element mutations happen server-side, in place. Any read-modify-write of a whole bin should have been a CDT operation.
A bin is a container, not a field. Bin counts that scale with data rather than schema belong in one CDT bin. Bin names cap at 15 characters.
Duplicate data deliberately when two access patterns need it in two shapes. A second round trip purely to assemble a response is a normalization you should have collapsed.
Every collection bin needs a growth ceiling and a decided behavior at it. If element count is driven by user behavior rather than a design decision, it is unbounded.
Small independent entities still need an explicit sizing decision. Index overhead against a small payload is real cost; consolidating all of them into one record is the opposite error.

Escalation: use the data modeling guide

This skill covers the decision layer. The full workflow — the clarification gates, the per-relationship decision packs, the sizing worksheets, the stakeholder checkpoints — lives in the https://github.com/aerospike/data-modeling-guide repository.

For a new application, fetch the guide and follow its checklist. Do not design a complete model from this skill alone.

gh repo clone aerospike/data-modeling-guide

Then read its AGENTS.md first — it carries the routing table and the hard rules — followed by new-app-modeling-checklist.md.

Task	Guide file
New model from scratch (required first read)	`new-app-modeling-checklist.md`
Core concepts, record sizing, indexes, applied patterns	`concepts-and-patterns.md`
1:N pattern selection	`one-to-many-relationships.md`
A list that grows very large; sharding and overflow	`follow-relationship-scale.md`
List vs map, ordering, persisted indexes	`cdt-api.md`
Server-side filtering, computed bins, expression indexes	`expressions.md`
Nested CDT querying, list-of-structs	`path-expressions.md`
Matching a workload to a known shape and its sizing profile	`workload-archetypes.md`
Reviewing a drafted model	`modeling-failure-modes.md`
Identifier format / timestamp naming	`id-selection-guidance.md`, `timestamp-bin-naming-guidance.md`

If you cannot reach the guide — no access, no gh auth, offline — say so plainly and state what that limits. Deliver what this skill supports (the mental model, the failure-mode checks, a clarification document) and flag that the sizing worksheets and decision packs were not applied. Do not improvise a complete model and present it as if the full process ran.

Version-gated features

Several patterns depend on server version. Confirm the target version and client support before recommending any of them; the guide's checklist has a version gate table with fallbacks for each.

Path expressions — nested CDT filtering and indexing.
Expression indexes — sparse or computed-value indexing.
Multi-record transactions — atomic multi-record updates; require a strong-consistency namespace, and carry limits that rule them out for wide cascades. See concepts-and-patterns.md § Multi-record consistency.

Do not state a specific minimum version from memory. Read it from the guide or the client matrix.

name: aerospike-data-modeling
description: >-
  Designs a new Aerospike data model from requirements, producing a schema guide
  and schema summary: record granularity, key design, bin structure, relationship
  and consolidation decisions, and index strategy. Use when no schema exists yet,
  when redesigning an existing model, or when reviewing a proposed schema for
  structural defects. This is design-time work that precedes code. For writing or
  reviewing client code against a model that already exists, or for client APIs,
  policies, CDT operations, and expression usage, use aerospike-development
  instead. Core database only; not Aerospike Graph.
license: Apache-2.0
metadata:
  last_verified: "2026-08-06"
  server_versions: "7.0+"

View original text

---
name: aerospike-data-modeling
description: >-
Designs a new Aerospike data model from requirements, producing a schema guide
and schema summary: record granularity, key design, bin structure, relationship
and consolidation decisions, and index strategy. Use when no schema exists yet,
when redesigning an existing model, or when reviewing a proposed schema for
structural defects. This is design-time work that precedes code. For writing or
reviewing client code against a model that already exists, or for client APIs,
policies, CDT operations, and expression usage, use aerospike-development
instead. Core database only; not Aerospike Graph.
license: Apache-2.0
metadata:
last_verified: "2026-08-06"
server_versions: "7.0+"
---

# Aerospike: data model design

## Role

Act as a data architect. Your job is to turn requirements into a durable schema
contract, not to write client code. The output is documents that a team reviews
and implements against.

## When to use this skill

Use it when the starting point is **requirements without a schema**:

- A new application or service with no Aerospike model yet.
- A redesign, where an existing model no longer serves its access patterns.
- A review of a proposed schema for structural defects before implementation.

**Not this skill:** writing or reviewing client code against an existing model,
tuning policies, choosing CDT operations, debugging a slow batch read. That is
`aerospike-development`. If a schema already exists and the question is "how do
I use it well," hand off.

## What you produce

Two documents. Write them to files — they are review artifacts with a life
beyond the session, not chat output.

| Deliverable | Contents |
|---|---|
| **Schema guide** | The full design: entity and relationship map, access pattern matrix, key schema, bin schema, one JSON example record per set, relationship and consolidation decisions, sizing worksheets, index rationale, growth and hot-key plan, validation plan. Plus the **reasoning** — an assumptions log, the alternatives rejected, and what evidence would reopen each decision. |
| **Schema summary** | The condensed contract derived from the guide: one table per set with key format, bins, types, and a one-line purpose; the index list; growth and overflow triggers. No rationale. |

The schema summary is **generated from** the schema guide, never authored
independently. If they disagree, the schema guide wins and the summary is
regenerated.

See [references/model-deliverables-schema-guide-summary.md](references/model-deliverables-schema-guide-summary.md).

## Mental model for data architects

Aerospike is neither a relational database nor a document database.

- **Records are semi-structured.** A record is a collection of **strongly typed
bins**, and the typing is per bin per record — there is no set-level schema.
Two records in the same set can have entirely different bins, and the server
enforces nothing. Absent bins cost nothing, so sparse and heterogeneous shapes
are cheap rather than wasteful. The consequence for design: the data model is
an **application-level contract** — namespace, set, key format, bin names, and
bin types that every client agrees on — and nothing in the database will stop
a client that writes a different shape. Write the contract down; that is what
the schema guide is for.
- **Records are the unit of I/O.** Record data is stored **contiguously**, so
every read fetches the **entire record** from storage, and every write
**rewrites the entire record** — Aerospike does not do in-place updates.
Requesting a subset of bins trims what crosses the *network*, not what is read
from *device*. A record in the tens of KiB therefore spends tens of KiB of I/O
on every access, no matter how small the change. Record size is an I/O budget,
not just a storage number.
- **There are no server-side joins.** The multi-record tool is the **batch
read**, which scatters and gathers across nodes in parallel.
- **Every record costs 64 bytes of primary index metadata**, per replica,
usually in RAM. Many tiny records spend more memory on index than on data.
- **Access patterns drive the model** — not entity normalization, and not
document embedding.
- **Consolidate, but bound it.** Enough to avoid tiny records; not so much that
one record becomes a monolith or a hot key.

If your instinct is a table per entity and a row per sub-entity, or one giant
embedded document, you will produce a bad Aerospike model.

**Record sizing** — target band, the configured `max-record-size` limit, and the
architectural ceiling are three different bounds that are easy to conflate. Do
not carry a number from memory; read the current values from the data modeling
guide (see Escalation below).

Whatever the band's endpoints are, read it as a **distribution, not a target**:
design so the **bulk of records sit at the low end** (single-digit KiB), and
treat the upper end as headroom for **outliers** and **slowly-changing
consolidated structures** — 1:N and N:M relationship lists, where one record per
edge would cost more. Size only hurts once multiplied by **write frequency**: a large record on a
**hot write path** is a design defect even when it fits, because every update
rewrites it in full — but the same size where writes are infrequent relative to
reads is a legitimate design, not a compromise. Ask for the **update rate**, not
just the byte count.

## Do not design without clarifying first

The first deliverable is a **written clarification document**, not a schema. Ask
requirements-gap questions — "what is the p95 fan-out?", "is eventual
consistency acceptable here?" — never mechanism-preference questions like "which
pattern do you prefer?". If deterministic guidance already resolves a choice,
apply it instead of asking.

Do not fill gaps with assumptions and continue. When entity ownership,
lifecycle, cardinality, or an access path is unclear, stop and ask. Where an
input cannot be obtained, record it as an explicit assumption with a reconsider
trigger rather than burying it.

Design **one entity group at a time** and pass its review before starting the
next. See [references/model-design-time-workflow.md](references/model-design-time-workflow.md).

## Failure modes to check while drafting

Seven ways Aerospike models go wrong. Check them **during** design, not after.
Each has a detection test in
[references/model-failure-modes-checklist.md](references/model-failure-modes-checklist.md).

1. **Record granularity comes from cardinality and who drives the read** — never
from the entity list. One set per domain noun means the model came from an ER
diagram.
2. **The most frequent reads must be key lookups or bounded batch reads.** If
more than one or two access patterns resolve via secondary-index query, fix
the keys, not the indexes.
3. **Single-element mutations happen server-side, in place.** Any
read-modify-write of a whole bin should have been a CDT operation.
4. **A bin is a container, not a field.** Bin counts that scale with data rather
than schema belong in one CDT bin. Bin names cap at 15 characters.
5. **Duplicate data deliberately** when two access patterns need it in two
shapes. A second round trip purely to assemble a response is a normalization
you should have collapsed.
6. **Every collection bin needs a growth ceiling and a decided behavior at it.**
If element count is driven by user behavior rather than a design decision, it
is unbounded.
7. **Small independent entities still need an explicit sizing decision.** Index
overhead against a small payload is real cost; consolidating all of them into
one record is the opposite error.

## Escalation: use the data modeling guide

This skill covers the decision layer. The full workflow — the clarification
gates, the per-relationship decision packs, the sizing worksheets, the
stakeholder checkpoints — lives in the **`https://github.com/aerospike/data-modeling-guide`**
repository.

**For a new application, fetch the guide and follow its checklist. Do not design
a complete model from this skill alone.**

```bash
gh repo clone aerospike/data-modeling-guide
```

Then read its `AGENTS.md` first — it carries the routing table and the hard
rules — followed by `new-app-modeling-checklist.md`.

| Task | Guide file |
|---|---|
| New model from scratch (required first read) | `new-app-modeling-checklist.md` |
| Core concepts, record sizing, indexes, applied patterns | `concepts-and-patterns.md` |
| 1:N pattern selection | `one-to-many-relationships.md` |
| A list that grows very large; sharding and overflow | `follow-relationship-scale.md` |
| List vs map, ordering, persisted indexes | `cdt-api.md` |
| Server-side filtering, computed bins, expression indexes | `expressions.md` |
| Nested CDT querying, list-of-structs | `path-expressions.md` |
| Matching a workload to a known shape and its sizing profile | `workload-archetypes.md` |
| Reviewing a drafted model | `modeling-failure-modes.md` |
| Identifier format / timestamp naming | `id-selection-guidance.md`, `timestamp-bin-naming-guidance.md` |

**If you cannot reach the guide** — no access, no `gh` auth, offline — say so
plainly and state what that limits. Deliver what this skill supports (the mental
model, the failure-mode checks, a clarification document) and flag that the
sizing worksheets and decision packs were not applied. Do not improvise a
complete model and present it as if the full process ran.

## Version-gated features

Several patterns depend on server version. Confirm the target version and client
support before recommending any of them; the guide's checklist has a version
gate table with fallbacks for each.

- **Path expressions** — nested CDT filtering and indexing.
- **Expression indexes** — sparse or computed-value indexing.
- **Multi-record transactions** — atomic multi-record updates; require a
strong-consistency namespace, and carry limits that rule them out for wide
cascades. See `concepts-and-patterns.md` § Multi-record consistency.

Do not state a specific minimum version from memory. Read it from the guide or
the [client matrix](https://aerospike.com/docs/develop/client-matrix).

## See also

- [reference.md](reference.md) — external links.
- `aerospike-development` — implementation-time work against an existing model.

Review the source

Skill source recorded

Skill instructions are recorded. This is not a runtime test, safety guarantee or compatibility certification.

Review before install: Avoid automatic install

License: Apache-2.0

Dependency or permission surface needs review
Permission surface may require sandboxing
Skill instructs the agent to clone and follow instructions from an external repository (aerospike/data-modeling-guide), which is an injection surface if that repository is ever compromised; SKILL.md contains no explicit guardrails for treating fetched content as untrusted.
Metadata timestamps are inconsistent with the current date (last_verified 2026-08-06, repository last updated 2026-08-27) and should be corrected or verified.
Parsed frontmatter shows description as '>-' rather than the expanded description, indicating either a parsing quirk or malformed YAML that should be checked.
Low GitHub adoption signal
Quality score needs review
Permission surface needs review: secrets or environment access, shell or command execution
GitHub adoption: 14 GitHub stars
Stars/forks activity: 14 stars, 7 forks; issue activity unavailable in current metadata
Dependency/runtime risk: command execution surface, credential or environment access
Permission surface: secrets or environment access, shell or command execution

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This page exposes the same decision, trust, audit, use-case, and install signals through the Registry API, so agents can rank this skill without scraping the UI.

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      "humanReviewRequired": 0,
      "uniqueAgents": 0,
      "lastOutcomeAt": null
    },
    "signals": [],
    "penalties": [
      "No real agent outcome evidence yet"
    ]
  },
  "audit": {
    "score": 68,
    "risk_level": "needs_review",
    "risk_label": "Needs review",
    "warnings": [
      "Dependency or permission surface needs review",
      "Permission surface may require sandboxing",
      "Skill instructs the agent to clone and follow instructions from an external repository (aerospike/data-modeling-guide), which is an injection surface if that repository is ever compromised; SKILL.md contains no explicit guardrails for treating fetched content as untrusted.",
      "Metadata timestamps are inconsistent with the current date (last_verified 2026-08-06, repository last updated 2026-08-27) and should be corrected or verified.",
      "Parsed frontmatter shows description as '>-' rather than the expanded description, indicating either a parsing quirk or malformed YAML that should be checked.",
      "Low GitHub adoption signal",
      "Quality score needs review",
      "Permission surface needs review: secrets or environment access, shell or command execution"
    ]
  },
  "safety_gate": {
    "tier": "blocked",
    "label": "Blocked for auto-install",
    "auto_install_policy": "block",
    "auto_install_allowed": false,
    "human_review_required": true,
    "blocked": true,
    "recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first."
  },
  "quality": {
    "score": 58,
    "label": "Promising"
  },
  "supply": {
    "track": "Data, BI, and analytics",
    "scenario": "Research agents",
    "maintenance": "28d since push",
    "risk": "Needs review"
  },
  "alternative_skills": [],
  "do_not_use_when": [
    "teams that need a vendor-supported SLA",
    "production agents without a repository review",
    "Low GitHub adoption signal",
    "Skill instructs the agent to clone and follow instructions from an external repository (aerospike/data-modeling-guide), which is an injection surface if that repository is ever compromised; SKILL.md contains no explicit guardrails for treating fetched content as untrusted.",
    "High-risk permission hints: Shell or command execution, Secrets or environment access",
    "Dependency or permission surface needs review",
    "Permission surface may require sandboxing",
    "Metadata timestamps are inconsistent with the current date (last_verified 2026-08-06, repository last updated 2026-08-27) and should be corrected or verified."
  ],
  "agent_contract": {
    "task_input": "Use aerospike-data-modeling in an agent workflow",
    "recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first.",
    "install_policy": "block",
    "minimum_review_before_use": [
      "Trust: 55/100 High review required",
      "Audit: 68/100 Needs review",
      "Safety: 24/100 Avoid automatic install",
      "Review repository, license, install command, and permission surface before production use."
    ],
    "expected_agent_output": {
      "selected_skill": "aerospike-aerospike-data-modeling (aerospike-data-modeling)",
      "install_command": "npx skills add aerospike/agent-skills --skill aerospike-data-modeling",
      "risk_summary": "Needs review; Blocked for auto-install; High review required",
      "verification_result": "Report the smallest successful task, files touched, warnings, and any missing setup."
    }
  },
  "outcome_feedback": {
    "endpoint": "https://www.openagentskill.com/api/agent/outcome",
    "method": "POST",
    "requires_resolve_event_id": true,
    "event_id_source": "Use install_receipt.outcome_feedback.event_id or feedback.event_id returned by /api/agent/resolve for the current task.",
    "expected_outcomes": [
      "success",
      "failed",
      "not_relevant",
      "blocked_by_risk",
      "setup_required"
    ],
    "payload_template": {
      "event_id": "<install_receipt.outcome_feedback.event_id or feedback.event_id from /api/agent/resolve>",
      "skill_slug": "aerospike-aerospike-data-modeling",
      "task": "Use aerospike-data-modeling in an agent workflow",
      "agent": "codex",
      "outcome": "success",
      "install_used": true,
      "risk_blocked": false,
      "setup_required": false,
      "task_success": true,
      "output_quality": 4,
      "error_type": null,
      "human_review_required": false,
      "workspace": "sandbox",
      "time_to_useful_ms": 120000,
      "notes": "Report the smallest successful task, setup friction, files touched, and risk notes."
    }
  },
  "endpoints": {
    "web": "https://www.openagentskill.com/skills/aerospike-aerospike-data-modeling",
    "api": "https://www.openagentskill.com/api/agent/skills/aerospike-aerospike-data-modeling",
    "audit": "https://www.openagentskill.com/skills/aerospike-aerospike-data-modeling/audit",
    "eval": "https://www.openagentskill.com/api/agent/evals?slug=aerospike-aerospike-data-modeling&task=Use%20aerospike-data-modeling%20in%20an%20agent%20workflow&max_risk=medium",
    "resolve": "https://www.openagentskill.com/api/agent/resolve?task=Use%20aerospike-data-modeling%20in%20an%20agent%20workflow&agent=codex&max_risk=medium",
    "receipt": "https://www.openagentskill.com/api/agent/receipt?task=Use%20aerospike-data-modeling%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text",
    "install": "https://www.openagentskill.com/api/skills/aerospike-aerospike-data-modeling/install",
    "manifest": "https://www.openagentskill.com/api/registry/manifest/aerospike-aerospike-data-modeling"
  }
}

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Creator: aerospike
Source: aerospike/agent-skills
Indexed by: OpenAgentSkill community index

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aerospike-data-modeling

Overview

Aerospike: data model design

Role

When to use this skill

What you produce

Mental model for data architects

Do not design without clarifying first

Failure modes to check while drafting

Escalation: use the data modeling guide

Version-gated features

See also

Review the source

Source & usage notes

Agent access

For the creator

Registry indexed

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Community signal

Start with one small task

Overview

Aerospike: data model design

Role

When to use this skill

What you produce

Mental model for data architects

Do not design without clarifying first

Failure modes to check while drafting

Escalation: use the data modeling guide

Version-gated features

See also

Review the source

Source & usage notes

Agent access

Related skills

For the creator

Registry indexed

Claim this skill listing

Add the evidence badges to your README

Community signal

Start with one small task