Cloudflare R2 SQL Skill Reference

Expert guidance for Cloudflare R2 SQL - serverless distributed query engine for Apache Iceberg tables.

Reading Order

New to R2 SQL? Start here:

Read "What is R2 SQL?" and "When to Use" below
configuration.md - Enable catalog, create tokens
patterns.md - Wrangler CLI and integration examples
api.md - SQL syntax and query reference
gotchas.md - Limitations and troubleshooting

Quick reference? Jump to:

What is R2 SQL?

R2 SQL is Cloudflare's serverless distributed analytics query engine for querying Apache Iceberg tables in R2 Data Catalog. Features:

Serverless - No clusters to manage, no infrastructure
Distributed - Leverages Cloudflare's global network for parallel execution
SQL interface - Familiar SQL syntax for analytics queries
Zero egress fees - Query from any cloud/region without data transfer costs
Open beta - Free during beta (standard R2 storage costs apply)

What is Apache Iceberg?

Open table format for large-scale analytics datasets in object storage:

ACID transactions - Safe concurrent reads/writes
Metadata optimization - Fast queries without full table scans
Schema evolution - Add/rename/drop columns without rewrites
Partitioning - Organize data for efficient pruning

When to Use

Use R2 SQL for:

Log analytics - Query application/system logs with WHERE filters and aggregations
BI dashboards - Generate reports from large analytical datasets
Fraud detection - Analyze transaction patterns with GROUP BY/HAVING
Multi-cloud analytics - Query data from any cloud without egress fees
Ad-hoc exploration - Run SQL queries on Iceberg tables via Wrangler CLI

Don't use R2 SQL for:

Workers/Pages runtime - R2 SQL has no Workers binding, use HTTP API from external systems
Real-time queries (<100ms) - Optimized for analytical batch queries, not OLTP
Complex joins/CTEs - Limited SQL feature set (no JOINs, subqueries, CTEs currently)
Small datasets (<1GB) - Setup overhead not justified

Decision Tree: Need to Query R2 Data?

Do you need to query structured data in R2?
├─ YES, data is in Iceberg tables
│  ├─ Need SQL interface? → Use R2 SQL (this reference)
│  ├─ Need Python API? → See r2-data-catalog reference (PyIceberg)
│  └─ Need other engine? → See r2-data-catalog reference (Spark, Trino, etc.)
│
├─ YES, but not in Iceberg format
│  ├─ Streaming data? → Use Pipelines to write to Data Catalog, then R2 SQL
│  └─ Static files? → Use PyIceberg to create Iceberg tables, then R2 SQL
│
└─ NO, just need object storage → Use R2 reference (not R2 SQL)

Architecture Overview

Query Planner:

Top-down metadata investigation with multi-layer pruning
Partition-level, column-level, and row-group pruning
Streaming pipeline - execution starts before planning completes
Early termination with LIMIT - stops when result complete

Query Execution:

Coordinator distributes work to workers across Cloudflare network
Workers run Apache DataFusion for parallel query execution
Parquet column pruning - reads only required columns
Ranged reads from R2 for efficiency

Aggregation Strategies:

Scatter-gather - simple aggregations (SUM, COUNT, AVG)
Shuffling - ORDER BY/HAVING on aggregates via hash partitioning

Quick Start

# 1. Enable R2 Data Catalog on bucket
npx wrangler r2 bucket catalog enable my-bucket

# 2. Create API token (Admin Read & Write)
# Dashboard: R2 → Manage API tokens → Create API token

# 3. Set environment variable
export WRANGLER_R2_SQL_AUTH_TOKEN=<your-token>

# 4. Run query
npx wrangler r2 sql query "my-bucket" "SELECT * FROM default.my_table LIMIT 10"

Important Limitations

CRITICAL: No Workers Binding

R2 SQL cannot be called directly from Workers/Pages code
For programmatic access, use HTTP API from external systems
Or query via PyIceberg, Spark, etc. (see r2-data-catalog reference)

SQL Feature Set:

No JOINs, CTEs, subqueries, window functions
ORDER BY supports aggregation columns (not just partition keys)
LIMIT max 10,000 (default 500)
See gotchas.md for complete limitations

In This Reference

configuration.md - Enable catalog, create API tokens
api.md - SQL syntax, functions, operators, data types
patterns.md - Wrangler CLI, HTTP API, Pipelines, PyIceberg
gotchas.md - Limitations, troubleshooting, performance tips

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