Add the five Ledgerline application metrics

- ledgerline.transactions.total (counter: kind, status, currency) and
  ledgerline.transaction.duration (histogram, seconds: kind) in gateway,
  the only place that sees the final outcome, failures included. currency
  is client input, so values outside the seeded set are recorded as
  "other".
- ledgerline.fraud.score (histogram, buckets in 0.1 steps to 1.0) in fraud.
- ledgerline.stream.lag (observable gauge: consumer_group) in audit and
  notifier, read from XINFO GROUPS through a separate sync Redis client,
  because callbacks run on the scrape thread; the call is untraced so
  scrapes don't create traces.
- ledgerline.db.pool.in_use (observable up-down counter: pool) in every
  service with a DB engine. The SQLAlchemy instrumentation's own pool
  metric names a pool by DSN, which merges the normal and starved engines.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>

services/insights/app.py

@@ -11,6 +11,7 @@ from opentelemetry.trace import StatusCode
 
 from common.chaos_flags import flags_fingerprint
 from common.logging_config import configure_logging
+from common.metrics import observe_db_pools
 from common.redis_client import redis_client
 from common.settings import settings
 from common.telemetry import setup_telemetry
@@ -34,6 +35,7 @@ FastAPIInstrumentor.instrument_app(
 SQLAlchemyInstrumentor().instrument(
     engines=[normal_engine.sync_engine, starved_engine.sync_engine]
 )
+observe_db_pools({"normal": normal_engine, "starved": starved_engine})
 
 # Cache-stampede protection for the Redis-cached branch below: a burst of
 # concurrent requests for the same not-yet-cached (account, flags) key would