Cloudflare Releases Logpush Transformers for SQL-Based Log Filtering and Transformation
Cloudflare has made Transformers generally available for Logpush, allowing engineers to filter, redact, and reshape log data using SQL queries before export.
Cloudflare has announced the general availability of Transformers for its Logpush data pipeline service. While Logpush previously focused on near-real-time batch log streaming, this update enables users to apply SQL queries directly within the log pipeline. Developers can now filter out unwanted records, redact sensitive information, and modify schema fields prior to log delivery. From an operational perspective, pre-export transformation allows engineering teams to normalize log data before it reaches downstream analytics systems. Filtering redundant logs directly at the edge reduces total transfer volume and helps lower downstream ingestion and storage costs across observability destinations. Alongside the Transformers rollout, Cloudflare has made Logpush available across all self-service plans. Transformers are configured per dataset using standard SQL syntax, leveraging WHERE clauses for record filtering and SELECT statements for field manipulation and output schema control.
Primary source
Open Cloudflare Blog ↗This article summarizes the source. Check the original for full details and conditions.
Comparison
| Aspect | Before / Alternative | After / This |
|---|---|---|
| Log Processing Pipeline | Exported bulk raw logs directly to downstream destinations | Applies SQL-based filtering, redaction, and transformation before export |
| Data Volume and Schema Control | Required downstream systems to drop unwanted logs and reshape schemas | Filters unneeded records and reshapes fields at the edge using WHERE and SELECT clauses |
Action Checklist
- Configure a SQL query for target Logpush datasets to filter unwanted records Use the WHERE clause to exclude logs that do not need to be ingested downstream
- Define output field schemas using SELECT statements Mask sensitive data and add, remove, or rename log fields before export
