Query performance basics

Practical levers for faster, cheaper queries in e6data - sizing, caching, execution mode, the transpiler, and reading execution details.

A handful of levers cover most query-performance work in e6data. This page is a practical starting point; for the underlying mechanics, see How e6data works.

Start with the query

• Filter early and prune partitions. Predicates on partition columns let the engine skip data it never reads. Queries that scan whole fact tables without filters are the most common cause of slowness.
• Select only the columns you need. Wide SELECT * over columnar data reads more than necessary.
• Set a row limit while exploring. Use a LIMIT to preview results without running unbounded scans - see Run queries and view results.

Read the execution details

After a run, open the query's execution details to see the Total Elapsed Time broken into planning, queueing, and execution, plus the Query Profile showing the most expensive steps. This tells you whether time is going to queueing (cluster under-provisioned), planning, or execution (the query itself). See Run queries and view results.

Cluster-side levers

Lever	When it helps	Where
Right-size the cluster	Queries queue or run slowly under concurrency	Cluster sizing and scaling
Autoscaling	Variable or spiky load	Cluster sizing and scaling
Result caching	The same queries run repeatedly against unchanged data	Cluster sizing and scaling
Native execution mode	Scan-heavy queries with simple filters, joins, and aggregations	Cluster sizing and scaling
SQL transpiler	You're running SQL written for another dialect (Snowflake, Trino, Databricks)	Cluster settings - see Create and manage clusters

A simple workflow

1. Run the query and open its execution details.
2. If queueing dominates, scale the cluster up or enable autoscaling.
3. If execution dominates, look at the Query Profile - add filters, prune partitions, or try native execution for scan-heavy patterns.
4. For repeated reports, enable result caching.
5. Measure again.

See also

• Run queries and view results
• Cluster sizing and scaling