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Move content to correct locations: - AGENTS.md → .agents/AGENTS.md (with orchestrator reference update) - third_party/docs/ (8 reference docs) → wiki/concepts/ - manuals/ (12 Node-RED docs) → wiki/manuals/ Delete 23 unreferenced one-off scripts from scripts/ (keeping 5 active). Delete stale Dockerfile.e2e, docker-compose.e2e.yml, test/e2e/. Remove empty third_party/ directory. Root is now: README, CLAUDE.md, LICENSE, package.json, Makefile, Dockerfile, docker-compose.yml, docker/, scripts/ (5), nodes/, wiki/, plus dotfiles (.agents, .claude, .gitea). Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
123 lines
4.7 KiB
Markdown
123 lines
4.7 KiB
Markdown
# InfluxDB Time-Series Best Practices
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> **Used by**: `telemetry-database` agent, `dashboardAPI` node
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> **Validation**: Verified against InfluxDB official documentation (v1, v2, v3)
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## Tag vs. Field Decision Framework
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| Criterion | Use Tag | Use Field |
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|-----------|---------|-----------|
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| Queried in WHERE clause frequently | Yes | No |
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| Used in GROUP BY | Yes | No |
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| Low cardinality (< 100 distinct values) | Yes | Acceptable |
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| High cardinality (IDs, timestamps, free text) | **Never** | Yes |
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| Numeric measurement values | No | Yes |
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| Needs aggregation (mean, sum, etc.) | No | Yes |
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| Node/station/machine identifier | Yes | No |
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| Actual sensor reading | No | Yes |
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| Setpoint value | No | Yes |
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| Quality flag | Depends* | Yes |
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*Quality flags: If you have ≤5 quality levels (good/uncertain/bad), a tag is acceptable. If quality is a numeric score, use a field.
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## EVOLV Tag/Field Convention
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### Standard Tags (low cardinality, indexed)
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```
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locationId — Site identifier (e.g., "wwtp-brabant-01")
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nodeType — Node type (e.g., "rotatingMachine", "reactor")
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nodeName — Instance name (e.g., "pump-01", "reactor-A")
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machineType — Equipment type (e.g., "pump", "blower", "valve")
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stationId — Parent station identifier
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measurementType — Sensor type (e.g., "flow", "pressure", "temperature")
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```
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### Standard Fields (not indexed, high cardinality)
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```
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value — Primary measurement value
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setpoint — Control setpoint
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quality — Data quality score (0.0-1.0)
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state — Machine state (numeric code)
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power — Power consumption (W)
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efficiency — Current efficiency (0.0-1.0)
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speed — Rotational speed (RPM or fraction)
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position — Valve position (0.0-1.0)
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```
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## Cardinality Management
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### What Is Cardinality?
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Series cardinality = unique combinations of (measurement_name × tag_key_1 × tag_key_2 × ... × tag_key_n)
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### Cardinality Limits
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- **InfluxDB v1/v2 (TSM engine)**: High cardinality degrades query performance and increases memory usage. Keep below ~1M series per database.
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- **InfluxDB v3**: Supports infinite series cardinality (new storage engine), but keeping cardinality low still improves query speed.
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### Anti-Patterns (NEVER do these)
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- Encoding timestamps in tag values
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- Using UUIDs or session IDs as tags
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- Free-text strings as tags
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- Unbounded enum values as tags
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- One measurement per sensor (use tags to differentiate instead)
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### Good Patterns
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- Use a single measurement name per data category
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- Differentiate by tags, not by measurement name
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- Keep tag value sets bounded and predictable
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- Document all tag values in a schema registry
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## Retention Policies
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### Three-Tier Strategy
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| Tier | Retention | Resolution | Purpose |
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|------|-----------|------------|---------|
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| Hot | 7-30 days | Full resolution (1s-10s) | Real-time dashboards, control loops |
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| Warm | 90-365 days | Downsampled (1min-5min) | Trending, troubleshooting |
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| Cold | 2-10 years | Heavily aggregated (1h-24h) | Compliance reporting, long-term trends |
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### EVOLV Recommended Defaults
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- Port 1 data at full resolution: 30 days
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- 1-minute aggregates: 1 year
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- 1-hour aggregates: 5 years (matches regulatory retention requirements)
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## Continuous Queries / Tasks (Downsampling)
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### InfluxDB v1: Continuous Queries
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```sql
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CREATE CONTINUOUS QUERY "downsample_1m" ON "evolv"
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BEGIN
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SELECT mean("value") AS "value", max("value") AS "max", min("value") AS "min"
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INTO "rp_warm"."downsampled_1m"
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FROM "telemetry"
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GROUP BY time(1m), *
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END
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```
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### InfluxDB v2: Tasks
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```flux
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option task = {name: "downsample_1m", every: 1m}
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from(bucket: "telemetry")
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|> range(start: -task.every)
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|> aggregateWindow(every: 1m, fn: mean, createEmpty: false)
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|> to(bucket: "telemetry-warm")
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```
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## Query Performance Tips
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1. **Always filter by time range first** — time is the primary index
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2. **Use tag filters in WHERE** — tags are indexed, fields are not
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3. **Avoid regex on tag values** — use exact matches when possible
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4. **Limit series scanned** — filter by specific nodeType/nodeName
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5. **Use aggregation** — let the database aggregate rather than fetching raw points
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6. **Batch writes** — write in batches of 5,000-10,000 points for optimal throughput
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## Authoritative References
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1. InfluxDB Documentation — "Schema Design and Data Layout" (https://docs.influxdata.com/influxdb/v1/concepts/schema_and_data_layout/)
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2. InfluxDB Documentation — "Schema Design Recommendations and Best Practices" (v2/v3)
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3. InfluxData Blog — "Time Series Data, Cardinality, and InfluxDB"
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4. InfluxDB Documentation — "Resolve High Series Cardinality" (https://docs.influxdata.com/influxdb/v2/write-data/best-practices/resolve-high-cardinality/)
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5. InfluxData (2023). "InfluxDB Best Practices" — Official technical guides
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