analyze-local-telemetry

name: analyze-local-telemetry description: Use when the user wants to analyze OTel traces/logs/metrics captured locally — inspect spans, find slow operations, correlate logs with traces, sanity-check metrics. Talks SQL to dev/local-telemetry over HTTP via curl. Trigger phrases include "look at the traces", "what spans did X emit", "why was this slow", "any errors in the logs", "check the metrics".

Analyze local telemetry

dev/local-telemetry ingests OTLP/HTTP, writes JSONL to dev/local-telemetry/data/, and exposes a DuckDB SQL endpoint at http://127.0.0.1:4319/sql. Query it with curl to investigate behavior. The same port also serves a sync-flow viewer at / (handy to glance at — point a browser there); for programmatic analysis stick to /sql.

Endpoint contract

curl -s -X POST http://127.0.0.1:4319/sql \
  -H 'Content-Type: application/json' \
  --data @- <<'EOF'
{"query":"SELECT ..."}
EOF

Use a heredoc (--data @- <<'EOF' ... EOF) — SQL needs single quotes ('$.field') and heredocs avoid shell escaping pain.

Success → {"columns": [...], "rows": [[...], ...]}. Error → {"error": "<DuckDB message>"} with HTTP 400.

Check it's running first: curl -s http://127.0.0.1:4319/health returns ok. If it doesn't, tell the user to start it: cd dev/local-telemetry && go run . (the README has flags).

Views

Created lazily — only exist once data has landed for the matching signal. If a view is missing, ask the user to exercise the code path that emits that signal.

spans columns

trace_id, span_id, parent_span_id, name, kind (1=INTERNAL, 2=SERVER, 3=CLIENT, 4=PRODUCER, 5=CONSUMER), start_time_unix_nano, end_time_unix_nano, duration_ns, service_name, scope_name, status_code (0=UNSET, 1=OK, 2=ERROR), attributes (JSON array), events (JSON array), raw_span (JSON).

Attributes are stored OTLP-shape — extract with json_extract_string(attributes, '$[0].value.stringValue') or filter:

SELECT name, (
  SELECT json_extract_string(a, '$.value.stringValue')
  FROM UNNEST(attributes::JSON[]) AS u(a)
  WHERE json_extract_string(a, '$.key') = 'http.route'
  LIMIT 1
) AS route
FROM spans WHERE service_name = 'my-service';

logs columns

time_unix_nano, observed_time_unix_nano, severity_number (1–24, 17+ is ERROR), severity_text, body (JSON — typically {"stringValue":"..."}), trace_id, span_id, service_name, scope_name, attributes (JSON), raw_record (JSON).

Body is typed: json_extract_string(body, '$.stringValue') for strings.

number_points columns

name, description, unit, service_name, scope_name, kind ('gauge' or 'sum'), time_unix_nano, value (DOUBLE), attributes (JSON).

Histograms aren't pre-flattened — go through raw_metrics.

Recipes

Top slowest spans

SELECT name, service_name, duration_ns / 1e6 AS ms
FROM spans
WHERE service_name = 'jazz-server'
ORDER BY duration_ns DESC
LIMIT 20;

Error spans

SELECT service_name, name, trace_id
FROM spans
WHERE status_code = 2
ORDER BY start_time_unix_nano DESC
LIMIT 50;

Full trace tree

WITH t AS (SELECT * FROM spans WHERE trace_id = '<trace_id>')
SELECT span_id, parent_span_id, name, duration_ns / 1e6 AS ms
FROM t ORDER BY start_time_unix_nano;

Recent error logs with their span

SELECT
  time_unix_nano,
  severity_text,
  json_extract_string(body, '$.stringValue') AS msg,
  trace_id, span_id, service_name
FROM logs
WHERE severity_number >= 17
ORDER BY time_unix_nano DESC
LIMIT 50;

Metric over time

SELECT
  to_timestamp(time_unix_nano / 1e9) AS ts,
  value,
  json_extract_string(attributes, '$[0].value.stringValue') AS first_attr
FROM number_points
WHERE name = 'jazz.sync.batch.size'
ORDER BY ts DESC
LIMIT 100;

Find which service.name values are present

SELECT DISTINCT service_name FROM spans;

Workflow tips

• Start narrow (LIMIT 20, filter by service_name) — the dataset can hold ~2 days of telemetry.
• Trace IDs are 32 hex chars, span IDs 16. Filter as strings.
• Times are unix nanoseconds. Convert with to_timestamp(n / 1e9).
• If a JSON extract path doesn't exist, you get SQL NULL — WHERE field IS NOT NULL is the right filter.
• The response is JSON — pipe through python3 -m json.tool for readability or jq -r '.rows[][]' to extract values.
• When investigating a regression, get the user to run the failing case after starting the collector, then query.

When to use the raw_* views

When the flattened views drop fields you need (custom attribute shapes, histogram buckets, span events with their attributes). Pattern:

WITH unfurled AS (
  SELECT
    UNNEST(json_extract(doc, '$.resourceSpans')::JSON[]) AS rs
  FROM raw_traces
)
SELECT json_extract(rs, '$.scopeSpans[0].spans[0].events') FROM unfurled;

Cast JSON arrays as ::JSON[] before UNNEST. After casting, schema-unified array elements may have JSON-null entries for missing fields — check with json_type(x, '$.field') = 'OBJECT' rather than IS NOT NULL.