How to Analyze Execution Plans with ParadeDB in PostgreSQL

How do I analyze ParadeDB execution plans in PostgreSQL?

EXPLAIN and EXPLAIN ANALYZE display detailed execution plans, letting you inspect cost, row estimates, runtimes, and index usage for ParadeDB-backed queries.

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Description

Example H2

Example H3

Why analyze ParadeDB execution plans?

Execution plans uncover slow joins, missing indexes, and mis-estimated cardinality so you can optimize queries before they hurt production latency.

How do I show an execution plan?

Run EXPLAIN for estimates or EXPLAIN ANALYZE for real runtimes. ParadeDB injects its own operator names (e.g., ParadeScan) so you know which parts run inside the extension.

Which plan metrics matter most?

Focus on cost (planner estimate), rows, loops, actual time, and ParadeDB-specific "gpu_time" when GPU acceleration is enabled. Large gaps between estimated and actual rows signal statistics issues.

How to locate expensive steps fast?

Sort the JSON plan: SELECT jsonb_path_query(plan,'$[*] ? (@."Actual Total Time" > 10)'). Anything above your SLA time threshold needs attention.

What are common ParadeDB plan operators?

ParadeScan (GPU scan), ParadeJoin (GPU hash join), and ParadeAggregate appear in place of standard Seq Scan, Hash Join, and Aggregate. Treat them similarly when reasoning about cost and rows.

When should I use BUFFERS and SETTINGS?

Add BUFFERS to surface shared-hit vs. read-blocks, crucial for IO tuning. Use SETTINGS to log per-session parameters that affected the plan.

How to export plans for team review?

Wrap the command in EXPLAIN (FORMAT JSON) and share the output inside a Galaxy Collection so teammates can comment and endorse fixes.

Best practices for ParadeDB plan analysis

1) Always start with EXPLAIN ANALYZE in staging. 2) Compare JSON plans between ParadeDB on/off to measure GPU benefit. 3) Keep statistics current with ANALYZE. 4) Benchmark one change at a time.

Can I force ParadeDB indexes?

Set SET enable_parade_seqscan = off; before EXPLAIN to ensure the planner considers ParadeDB index scans, then revert the setting.

How to read parallelism indicators?

Look for Workers Planned and Workers Launched. ParadeDB cooperates with PostgreSQL parallel workers; skewed worker utilization implies data skew.

Key takeaway

Regularly inspecting ParadeDB execution plans shortens optimization cycles and maximizes GPU acceleration benefits.

Why How to Analyze Execution Plans with ParadeDB in PostgreSQL is important

How to Analyze Execution Plans with ParadeDB in PostgreSQL Example Usage


-- Inspect last-month revenue with real runtimes and ParadeDB operators
EXPLAIN (ANALYZE, VERBOSE, BUFFERS)
SELECT c.name,
       SUM(oi.quantity * p.price) AS lifetime_value
FROM Customers c
JOIN Orders o      ON o.customer_id = c.id
JOIN OrderItems oi ON oi.order_id   = o.id
JOIN Products  p   ON p.id          = oi.product_id
WHERE c.created_at < CURRENT_DATE - INTERVAL '90 days'
GROUP BY c.name
ORDER BY lifetime_value DESC
LIMIT 10;

How to Analyze Execution Plans with ParadeDB in PostgreSQL Syntax


EXPLAIN [ ( option [, ...] ) ] <br>  option = ANALYZE | VERBOSE | COSTS | BUFFERS | SETTINGS | FORMAT { TEXT | JSON }<br><br>Example options string:<br>EXPLAIN (ANALYZE, BUFFERS, FORMAT JSON) \<br>SELECT p.id, p.name, oi.quantity<br>FROM Orders o<br>JOIN OrderItems oi  ON oi.order_id = o.id<br>JOIN Products p     ON p.id = oi.product_id<br>WHERE o.order_date >= CURRENT_DATE - INTERVAL '30 days';

Common Mistakes

Running EXPLAIN without ANALYZE in production and assuming the estimates match reality. Fix by using EXPLAIN ANALYZE in a safe environment to capture actual runtimes.
Ignoring ParadeDB operator names and treating the plan like vanilla PostgreSQL. Fix by learning ParadeScan, ParadeJoin, and ParadeAggregate semantics to tune GPU settings accurately.