changelog: Update for last two prs.

CHANGELOG.md

@@ -32,6 +32,8 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ### Performance
 
+- **EPG programme parse now streams through PostgreSQL staging instead of holding the full catalogue in Python.** `parse_programs_for_source` writes parsed rows into a session-scoped temp table in batches (`_EPG_PARSE_BATCH_SIZE=2500`), then atomically swaps them into `ProgramData` with batched `DELETE ... RETURNING` + `INSERT` (`_EPG_SWAP_BATCH_SIZE=5000`) so Postgres never materializes the entire guide in one statement. Peak Celery memory during large XMLTV refreshes drops sharply compared with building a monolithic in-memory list before bulk insert. The byte-offset programme index build is deferred until after the swap completes so index construction no longer competes with parse for memory and I/O. `refresh_epg_data` closes its DB connection in `finally`, and `build_programme_index_task` is registered as a memory-intensive Celery task. SQLite/dev installs keep an in-memory fallback path.
+- **Pooled PostgreSQL connections now rotate on a bounded lifetime.** psycopg3 client-side cache grows on handles kept open indefinitely by `django-db-geventpool`; recycling uWSGI workers would interrupt live streams. A thin custom backend (`dispatcharr.db.backends.postgresql_psycopg3`) closes and replaces pool connections after `DATABASE_POOL_CONN_MAX_LIFETIME` seconds (default 600, env-overridable; set `0` to disable) on checkout and return while preserving warm-pool reuse within the window. (Fixes #1343)
 - **Schedules Direct refresh only fetches guide data for mapped channels.** Schedule MD5 checks and schedule downloads now target mapped lineup stations instead of the entire lineup, and schedule MD5 cache for unmapped stations is pruned each refresh. Unmapped lineup entries no longer trigger wasted schedule API calls when their MD5 changes.
 - **EPG auto-match memory and throughput improvements.**
   - Single-channel matching streams active EPG rows and keeps only the best match plus the top 20 candidates in memory; ML validates at most 21 names per channel instead of embedding the full catalog.