01.06.2026 Problem:
A production Patroni-managed PostgreSQL 15 cluster experienced periodic heavy queries that threatened availability. An example slow job ran for ~84 seconds and performed a full scan of a 1.8 TB partitioned table (arbor.CDR_DATA) that uses daily partitions starting in early April. Most clients connect through generic application users rather than distinct personal accounts. The environment is a 2-node Patroni cluster (leader + streaming replica) and the supplied postgresql.conf showed high max_connections (3000), a global work_mem of 2MB, and enabled query logging extensions (pg_stat_statements, auto_explain, log_min_duration_statement, log_temp_files, track_io_timing).
Process:
Step 1: Receipt of artifacts and example query
Client supplied the cluster configuration file and an example slow query that aggregated recent rows from arbor.CDR_DATA with filters on msg_id_serv, TRANS_DT (last 5 minutes), and other_parameters1. The artifact review established the query did a full-table scan because there were no indexes supporting the filter expressions. This clarified the immediate cause of the long duration for that specific job and framed whether resource-limiting or schema/index work was the primary fix.
Step 2: Cluster layout and baseline resource settings
Configuration inspection revealed a 2-node Patroni PostgreSQL 15 cluster with near-zero replica lag and Patroni-managed dynamic config. Key PostgreSQL settings were noted: max_connections=3000, work_mem=2MB, and logging/statistics extensions enabled. This mattered because high connection counts increase concurrency risk and Patroni requires DCS-mediated configuration changes, which constrained how safely changes could be applied in production.
Step 3: Logging and telemetry looked sufficient
Enabled pg_stat_statements, auto_explain, and log_temp_files were confirmed in the config. That discovery meant actionable telemetry already existed to identify the worst offenders and to quantify per-query temp file and execution-time impact—reducing guesswork and enabling targeted rather than global changes.
Step 4: Assessment of query vs. resource-control options
EXPLAIN had not yet been provided for the specific query, but the example’s filter expressions and the table’s partitioning suggested missing useful indexes and potential partition-pruning gaps. Because the query could be fixed by indexing or rewriting, this shifted the remediation priority from pure OS-level throttling toward database-level mitigations that would address the root cause and avoid collateral application breakage.
Step 5: Candidate database-level controls identified
A set of PostgreSQL role- and session-level controls were selected as viable, Patroni-safe mitigations: per-role statement_timeout to cancel long queries, role-specific work_mem to limit per-operation memory, temp_file_limit to bound spill-to-disk per session/transaction, and per-role connection limits (or an external pooler) to limit concurrent pressure. The high max_connections value also surfaced as a reduction candidate to limit aggregate concurrency. The Patroni constraint (use patronictl or the DCS REST API for dynamic changes and patronictl restart for parameters requiring a restart) was documented as part of safe rollout instructions.
Step 6: Implementation plan and immediate customer action
Client indicated the sample query was addressed at the application level. A controlled configuration change plan was prepared for staging: apply per-role statement_timeout and temp_file_limit, adjust work_mem for analytical/generic application roles, lower max_connections to a realistic operational value, and use pg_stat_statements to validate the impact. The plan included executing changes via Patroni’s dynamic configuration and validating on the replica before rolling to the leader, to avoid unscheduled failover or downtime. This implementation approach transitioned into the final Solution below.
Solution:
Implemented changes (validated in staging and recommended for production) centered on PostgreSQL role-level controls plus operational constraints enforced through the Patroni-managed configuration. Specific actions: set per-role statement_timeout values to cancel runaway statements, assign conservative role-specific work_mem values to bound per-query memory usage, configure temp_file_limit per role/transaction to abort queries that spill beyond safe disk usage, and reduce max_connections to cap total concurrent sessions. All configuration edits that affect the running PostgreSQL were applied using Patroni’s dynamic config APIs / patronictl workflows and tested on the replica before promotion.
These controls work architecturally because PostgreSQL enforces timeouts and per-session resource limits inside the server process, preventing individual queries from consuming unbounded CPU, RAM, or disk I/O. Using role-scoped settings isolates limits to application or monitoring accounts (the environment uses generic users), avoiding global impact. Combining this with telemetry from pg_stat_statements enables iterative tuning and targeted index/schema fixes for truly high-cost queries.
Conclusion:
After applying the role-level limits in a controlled rollout and addressing the immediate bad query at the application level, the risk window for cluster-wide resource exhaustion was reduced. The environment now has automated visibility into top offenders, per-role guards to stop runaway queries, and a safe Patroni-approved change process—improving stability and making future performance tuning surgical rather than disruptive.