agnes-the-ai-analyst/cli/skills/agnes-data-querying.md
ZdenekSrotyr 79b55b6ff3 remove agnes query --register-bq from client CLI
The flag ran RemoteQueryEngine in-process on the caller's machine and
required local BigQuery credentials (BIGQUERY_PROJECT + ADC). Analysts
don't have those, so calling --register-bq from an analyst workspace
surfaced as a confusing not_configured error chain ("Could not load
static instance.yaml" + "BigQuery project not configured"). An agent
following CLAUDE.md's hybrid-queries guidance would land in exactly
that trap.

The underlying engine was originally designed server-side (commit
d180b201, "Step 28: Remote query architecture"); the CLI port (commit
d605e7d9) silently assumed parity with the server. Server-side hybrid
already exists as an admin-only POST /api/query/hybrid endpoint
(app/api/query_hybrid.py) and is untouched here.

Analysts combining local + remote data now have two documented paths:
agnes snapshot create a filtered slice and join locally, or run the
join server-side via agnes query --remote. CLAUDE.md, the agent skill,
docs/DATA_SOURCES.md, and connectors.md updated accordingly.
2026-05-12 18:18:13 +02:00

123 lines
5.6 KiB
Markdown

---
name: agnes-data-querying
description: Use when querying any data in Agnes — discovery first, estimate before fetch, materialize scoped subsets locally
---
# Querying Agnes data
When asked about ANY data in Agnes, follow this protocol: **discover → choose tool → fetch (with estimate) → query locally → clean up**.
## Discovery first
Before writing ANY query, understand what's available:
```bash
agnes catalog --json | jq <filter> # know what's available
agnes schema <table> # learn columns + types
agnes describe <table> -n 5 # see real values for shape
```
**Never** write `SELECT * FROM <table>` blindly. For local-mode tables it's wasteful; for remote-mode tables it can blow up at 225M+ rows.
## Choose the right tool
Tables in `agnes catalog` have a `query_mode`:
| Mode | Means | How to query |
|------|-------|--------------|
| `local` | parquet synced on laptop | `agnes query "SELECT …"` directly |
| `remote` (BigQuery) | parquet NOT on laptop | `agnes snapshot create` subset → snapshot, OR `agnes query --remote` one-shot |
For **remote tables**, you MUST either:
1. `agnes snapshot create` a filtered subset → query the local snapshot (preferred), OR
2. `agnes query --remote` for one-shot server-side execution
## The `agnes snapshot create` workflow (preferred for remote tables)
### 1. Estimate first
Always estimate before fetching:
```bash
agnes snapshot create web_sessions_example \
--select event_date,country_code,session_id \
--where "event_date >= DATE_SUB(CURRENT_DATE(), INTERVAL 30 DAY)
AND country_code = 'CZ'" \
--estimate
```
Output tells you scan cost, expected rows, and local bytes — so you know if it's reasonable.
### 2. If reasonable, fetch to snapshot
```bash
agnes snapshot create web_sessions_example \
--select event_date,country_code,session_id \
--where "event_date >= DATE_SUB(CURRENT_DATE(), INTERVAL 30 DAY)
AND country_code = 'CZ'" \
--as cz_recent
```
### 3. Query the local snapshot
```bash
agnes query "SELECT event_date, COUNT(*) FROM cz_recent GROUP BY 1 ORDER BY 1"
```
## Heuristics for `agnes snapshot create`
| Requirement | Why |
|-------------|-----|
| **Always `--select` specific columns** | Avoid implicit `SELECT *` on remote (expensive) |
| **Always `--where` for remote tables** | Otherwise add `--limit` to keep result bounded |
| **Always `--estimate` first if unsure** | Partition/clustering metadata + shape matters; dry runs are free |
| **Reuse snapshots across questions** | `agnes snapshot list` before fetching — existing snapshot? Skip the fetch |
## BigQuery SQL flavor for `--where`
For `source_type=bigquery` (per `agnes catalog`), use BigQuery SQL syntax:
| Syntax | Example |
|--------|---------|
| Date literal | `DATE '2026-01-01'` (NOT `'2026-01-01'::date`) |
| Timestamp literal | `TIMESTAMP '2026-01-01 00:00:00 UTC'` |
| Now | `CURRENT_DATE()`, `CURRENT_TIMESTAMP()` |
| Date arithmetic | `DATE_SUB(CURRENT_DATE(), INTERVAL 30 DAY)` |
| Regex | `REGEXP_CONTAINS(col, r'pattern')` (raw string!) |
| NULL check | `col IS NOT NULL` (standard) |
| Cast | `CAST(x AS INT64)` (NOT `INT`) |
For `source_type=keboola` / `source_type=jira` (local), use **DuckDB SQL** in your `agnes query` calls — there's no `--where` on local since fetch is implicit.
## Snapshot hygiene
- Reuse snapshots across questions in the same conversation
- Use descriptive names: `cz_recent`, `orders_q1_us`, `sessions_today`
- Drop with `agnes snapshot drop <name>` when done with a topic
- Check total cache size with `agnes disk-info`
## When NOT to use `agnes snapshot create`
| Scenario | Use instead |
|----------|------------|
| Single aggregate on remote BASE TABLE (`SELECT COUNT(*)`) | `agnes query --remote "SELECT COUNT(*) FROM web_sessions_example"` — cheap, no fetch needed (Storage Read API pushes the COUNT into BQ) |
| Single aggregate on remote VIEW/MATERIALIZED_VIEW | Same syntax works (#160) but the BQ jobs API can't push WHERE/COUNT into the view body. Cost guardrail (default 5 GiB) catches expensive scans → 400 `remote_scan_too_large` with `agnes snapshot create` suggestion. Pivot to `agnes snapshot create <id> --where '<predicate>'` if rejected. |
| Throwaway exploration with raw BQ syntax | `agnes query --remote "SELECT … FROM <registered_id>"` — direct `bq."<dataset>"."<table>"` paths are now registry-gated (403 `bq_path_not_registered` if not registered). Register first or use the catalog id. |
| Cross-table JOIN with both remote | Use `agnes snapshot create` for one side + `agnes query --remote` for the other; full cross-remote JOIN needs design (see #101) |
## When the table you need isn't in `agnes catalog`
The catalog reads from `system.duckdb::table_registry` — entries land there only via admin registration, not auto-discovery. If `agnes catalog` doesn't show what the user is asking about:
1. Tell the user the table isn't registered
2. Hand off to an admin (or, if you have admin role yourself, follow the **agnes-table-registration** skill)
3. Don't `agnes query --remote` your way around it — the catalog gap means the registry doesn't track this dataset, RBAC can't gate it, and quotas don't apply
## Protocol summary
1. **Discover**: `agnes catalog`, `agnes schema`, `agnes describe`
2. **Check query_mode**: local (direct) or remote (fetch or --remote)?
3. **For remote**: `--estimate` first, then `agnes snapshot create` with `--select` + `--where`
4. **Snapshot name**: descriptive (`cz_recent`), reuse across questions
5. **Query**: `agnes query` against snapshot; DuckDB SQL syntax
6. **Cleanup**: `agnes snapshot drop` when done; `agnes disk-info` to check size