v0.14.0 — Tiered Scheduling, UNLOGGED Buffers, and a TUI Dashboard

Full technical details: v0.14.0.md-full.md

Status: ✅ Released | Scope: Medium (~4 weeks)

Priority-based tiered scheduling that ensures the most latency-sensitive stream tables refresh first, faster change buffers using unlogged tables, rich diagnostic output for refresh mode decisions, and an interactive terminal dashboard.

What problem does this solve?

In a deployment with many stream tables of varying importance, all tables were treated equally — a low-priority background summary would compete for refresh resources with a high-priority user-facing dashboard. Change buffers were WAL-logged (the PostgreSQL default) even though they are transient data that does not need to survive a crash. And operators needed a better way to see what was happening without writing custom queries.

Tiered Scheduling

Stream tables are now assigned to one of several tiers based on their required freshness:

  • Hot tier — refreshes as fast as possible, ahead of all other tables
  • Warm tier — refreshes at the normal configured interval
  • Cold tier — refreshes only when hot and warm tiers are idle

You can assign a stream table to a tier explicitly, or let AUTO mode determine the appropriate tier based on the configured sla (staleness deadline). A stream table with an SLA of “never more than 10 seconds stale” is automatically promoted to the hot tier during periods of high activity.

In plain terms: the customer-facing leaderboard gets refreshed before the nightly background rollup — without any manual tuning.

UNLOGGED Change Buffers

Change buffers are the temporary tables where pg_trickle stores captured row changes before they are applied to stream tables during the next refresh. These buffers are transient — they do not need to survive a database crash, because the change capture triggers will replay the changes from the source tables on restart.

By making change buffers UNLOGGED (a PostgreSQL table option that skips write-ahead logging), write operations to the change buffer are significantly faster. The tradeoff — that the buffer is empty after a crash — is acceptable because the data can be recaptured.

In plain terms: capturing changes into the buffer is now faster, with no correctness impact.

Refresh Mode Diagnostics

A new diagnostic function pgtrickle.explain_refresh_decision(name) returns a detailed explanation of why the last refresh used DIFFERENTIAL, FULL, or AUTO, including the specific metrics that drove the decision:

  • The delta size as a fraction of the total table size
  • The measured cost of the last DIFFERENTIAL refresh
  • The measured cost of the last FULL refresh
  • The AUTO mode threshold and how far the delta was from the threshold

In plain terms: you can now understand exactly why pg_trickle chose to do a full refresh instead of a differential one, and tune accordingly.

Error-State Circuit Breaker Improvements

The circuit breaker from v0.11.0 received improvements: the error-state information is now surfaced in pg_stat_stream_tables with the specific error message, and the breaker can be reset manually with pgtrickle.reset_fuse(name) without needing to drop and recreate the stream table.

TUI Dashboard

A terminal user interface (TUI) dashboard — accessible via the pgtrickle-tui command-line tool — shows a live overview of all stream tables: their current status, last refresh time, refresh latency, error state, and tier assignment. It updates in real time.

In plain terms: run pgtrickle-tui in a terminal and see at a glance what all your stream tables are doing, without writing any SQL.

GHCR Docker Images

Official Docker images are now published to the GitHub Container Registry (ghcr.io), making it easier to run pg_trickle in container environments without building from source.

Scope

v0.14.0 introduces tiered scheduling (the biggest operational improvement since the event-driven scheduler in v0.11.0), UNLOGGED buffers for performance, and the TUI dashboard for observability. Together these represent a significant step toward production-friendly operations.