nosql

name	nosql
description	NoSQL database design (Mongo, DynamoDB, etc).

Activate When

• /godmode:nosql, "mongodb schema", "dynamodb design"
• "cassandra model", "neo4j graph", "time-series"
• "which database?", "nosql vs sql?"

Workflow

1. Requirements Assessment

Data model: Document|Key-Value|Wide-Column|Graph|TimeSeries
Access patterns: <list read/write patterns + frequency>
Scale: <expected documents, queries/sec>
Consistency: eventual|strong|tunable

# Check for NoSQL drivers
cat package.json 2>/dev/null | grep -iE "mongo|dynamo|neo4j"
pip list 2>/dev/null | grep -iE "pymongo|boto3|cassandra"

2. Database Selection

IF hierarchical/nested data + flexible schema: MongoDB
IF key-based access, extreme scale: DynamoDB
IF time-series writes, wide rows: Cassandra
IF relationship traversal: Neo4j
IF metrics/IoT at scale: InfluxDB/TimescaleDB
DEFAULT: Start with PostgreSQL, move to NoSQL when
  a specific technical requirement demands it.

IF access patterns unknown: do NOT choose NoSQL yet. IF < 1M records and relational: PostgreSQL is better.

3. MongoDB Document Modeling

Embed when: read together, 1:1 or 1:few, bounded size
Reference when: independent, large, shared across docs
IF subdocument > 16MB: must reference (Mongo limit)
IF array grows unbounded: use bucketing pattern

// Compound index (covers multiple query patterns)
db.orders.createIndex({ customer_id: 1, created_at: -1 })
// Partial index (smaller, faster)
db.orders.createIndex(
  { status: 1 }, { partialFilterExpression: { active: true } }
)

4. DynamoDB Single-Table Design

Design for access patterns FIRST, entity model second.
PK: high cardinality, even distribution
SK: enables range queries within partition
GSI: different PK+SK for alternate access patterns

IF partition > 10GB: add write sharding. IF PK cardinality < 100 on high-volume: use composite. IF hot partition > 30% traffic: redistribute.

5. Cassandra Partition Design

One table per query (no JOINs).
Target: < 100MB per partition, < 100K rows.
Use time-bucketed keys for time-series data.

6. Neo4j Graph Modeling

Nodes = entities (nouns), Relationships = verbs
IF "find connections" or "shortest path": graph DB
  runs 100-1000x faster than SQL JOINs.

7. Time-Series (InfluxDB/TimescaleDB)

IF already using PostgreSQL: TimescaleDB extension
IF high-cardinality pure metrics: InfluxDB
Tags = indexed metadata (strings), Fields = values

Hard Rules

1. NEVER design schema before listing ALL access patterns.
2. ALWAYS set schema validation in MongoDB production.
3. NEVER allow unbounded document/partition growth.
4. ALWAYS add indexes matching query patterns.
5. Partition size: DynamoDB < 10GB, Cassandra < 100MB.

TSV Logging

Append .godmode/nosql-results.tsv:

timestamp	database	action	collection	index_count	status

Keep/Discard

KEEP if: tests pass AND quality improved AND no regression.
DISCARD if: tests fail OR performance regressed.

Stop Conditions

STOP when FIRST of:
  - All access patterns covered without scans
  - Partition sizes within limits
  - User requests stop

Autonomous Operation

On failure: git reset --hard HEAD~1. Never pause.

Error Recovery

Failure	Action
Query degrades with growth	Check explain(), add indexes
Data inconsistency	Check write concern, use majority
Connection exhaustion	Increase pool, check leaks