/Protocol
Slow Query Proof Before Index Changes
A daily research protocol for turning a slow-query complaint into a proof packet before recommending a production index.
Postgres slow queryindex safetyHypoPG
Open note Daily Postgres research
Original Postgres notes from the QueryRook proof engine.
Short, evidence-backed research notes on slow-query proof, index safety, database agents, AI boundaries, burn-in testing, and production readiness. The daily cadence is for useful field notes, not filler.
Topic clusters
Research clusters for search and buyer diligence.
Each cluster collects published notes and the upcoming editorial queue around one durable Postgres problem space.
Slow Query Analysis
Daily notes on pg_stat_statements, query fingerprints, EXPLAIN plans, workload evidence, and proof-backed diagnosis.
Postgres slow querypg_stat_statementsEXPLAIN analysis
Postgres Indexing
Research on index safety, HypoPG shadow planning, write amplification, lock posture, and rollback-ready recommendations.
Postgres indexesHypoPGCREATE INDEX CONCURRENTLY
Database Agents
Evidence standards for autonomous database agents, governed actions, action capsules, rollback contracts, and human approval.
database agentsautonomous databasePostgres automation
Postgres AI Security
Buyer-facing AI boundaries for Postgres products: prompt context, secrets, BYOK, private model options, and audit trails.
Postgres AIAI database securityBYOK
Production Readiness
Burn-in testing, readiness gates, evidence freshness, restore drills, benchmark disclosures, and operational launch criteria.
Postgres production readinessburn-in testingdatabase reliability
Publishing standard
Every note needs a question, evidence, and a caveat.
QueryRook will publish daily when there is something specific to teach. Notes can be protocols, hypotheses, or field notes, but each one must say what it proves and what it does not.
/Field note
pg_stat_statements as a Trust Boundary
Why QueryRook treats pg_stat_statements as a safer first evidence source than raw query logs or copied application data.
pg_stat_statementsPostgres monitoringquery fingerprints
Open note /Hypothesis
Why Autonomous DDL Needs Rollback Contracts
A safe autonomous Postgres agent should prove how it backs out before it asks to change a production database.
Postgres DDLautonomous database agentrollback
Open note /Protocol
Burn-In Testing for Database Agents
A database agent should survive boring repeated work before it earns permission to touch serious Postgres environments.
database agentPostgres reliabilityburn-in testing
Open note /Field note
Postgres AI Boundaries for Secure Buyers
The useful question is not whether AI helps with Postgres. It is what the AI can see, decide, and execute.
Postgres AIAI securitydatabase copilot
Open note Seven-day queue
Next notes already have an evidence plan.
These queued topics are visible so the daily cadence is a product habit, not a last-minute content scramble.
May 6
Postgres Indexing
When CREATE INDEX CONCURRENTLY Is Still Risky
Explain why concurrent index builds reduce blocking risk but still need lock, bloat, storage, and cancellation evidence.
May 7
Production Readiness
Reading Postgres WAL Pressure Before Tuning
Connect write-ahead log pressure to optimization safety and show why write-heavy systems need different proof.
May 8
Postgres AI Security
Why AI Query Advice Needs Human Approval
Define the approval boundary between AI diagnosis, recommendation, dry-run evidence, and production mutation.
May 9
Slow Query Analysis
Detecting Plan Regression With Small Samples
Show how small-sample query analysis should avoid overclaiming while still catching obvious plan regressions.
May 10
Database Agents
Action Capsules for Postgres Automation
Introduce action capsules as bounded, signed units of database work that separate suggestion from execution.
May 11
Production Readiness
Restore Drills as a Database Agent Gate
Argue that autonomous change authority should wait for recent restore-drill evidence.
May 12
Postgres Indexing
Ranking Index Candidates by Proof, Not Hunch
Describe a scoring model for prioritizing index candidates by observed workload and shadow-plan evidence.