---
description: Troubleshoot common R2 SQL errors including query structure, type, and timeout issues.
title: Troubleshooting guide
image: https://developers.cloudflare.com/og-docs.png
---

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# Troubleshooting guide

Last updated Aug 24, 2026|Copy as Markdown|[View as Markdown](https://mchen-ai-models-sync.previews.developers.cloudflare.com/r2-sql/troubleshooting/index.md)|[Agent setup](https://mchen-ai-models-sync.previews.developers.cloudflare.com/agent-setup/)

This guide covers potential errors and limitations you may encounter when using R2 SQL. R2 SQL is in open beta, and supported functionality will evolve and change over time.

## Query structure errors

### Missing required clauses

**Error**: `expected exactly 1 table in FROM clause`

**Problem**: R2 SQL requires a `FROM` clause in your query.

```sql
-- Invalid - Missing FROM clause
SELECT user_id WHERE status = 200;

-- Valid
SELECT user_id
FROM my_namespace.http_requests
WHERE status = 200 AND timestamp BETWEEN '2025-09-24T01:00:00Z' AND '2025-09-25T01:00:00Z';
```

**Solution**: Always include `FROM` with a fully qualified table name (`namespace_name.table_name`).

---

## FROM clause issues

### Join performance issues

**Symptom**: Query returns 502 Bad Gateway or times out.

**Problem**: Multi-way joins across large tables can exceed resource limits, especially with `COUNT(DISTINCT)` or other memory-intensive aggregations.

```sql
-- May timeout: cross-joining two large fact tables
SELECT COUNT(DISTINCT h.ray_id), COUNT(DISTINCT f.event_id)
FROM my_namespace.http_requests h
INNER JOIN my_namespace.firewall_events f ON h.zone_id = f.zone_id
```

**Solution**:

* Add `WHERE` filters to reduce intermediate result sizes.
* Join through dimension tables instead of directly joining fact tables.
* Use `approx_distinct()` instead of `COUNT(DISTINCT)` for approximate counts.
* Break complex multi-way joins into smaller queries using CTEs or sequential queries.

```sql
-- Better: filter both sides and use approx_distinct
SELECT z.plan,
       approx_distinct(h.ray_id) AS unique_requests
FROM my_namespace.zones z
INNER JOIN my_namespace.http_requests h ON z.zone_id = h.zone_id
WHERE z.plan = 'enterprise'
  AND h.status_code >= 400
GROUP BY z.plan
```

### `NOT IN` on nullable columns

**Symptom**: `NOT IN` subquery returns unexpected results or errors.

**Problem**: `NOT IN` subqueries are not supported when the subquery column can contain `NULL` values.

```sql
-- Fails: nullable_col may contain NULLs
SELECT zone_id
FROM my_namespace.http_requests
WHERE zone_id NOT IN (
    SELECT nullable_col FROM my_namespace.other_table
)
LIMIT 20
```

**Solution**: Use `NOT EXISTS` with a correlated subquery instead.

```sql
-- Works: NOT EXISTS handles NULLs correctly
SELECT h.zone_id
FROM my_namespace.http_requests h
WHERE NOT EXISTS (
    SELECT 1 FROM my_namespace.other_table o
    WHERE o.nullable_col = h.zone_id
)
LIMIT 20
```

### Correlated subquery performance

**Symptom**: `EXISTS` or `NOT EXISTS` subquery runs slowly.

**Problem**: Correlated subqueries with complex conditions can be slow because the inner query is evaluated for each row of the outer query.

```sql
-- Slower: multiple filter conditions in correlated subquery
SELECT z.domain
FROM my_namespace.zones z
WHERE EXISTS (
    SELECT 1 FROM my_namespace.firewall_events f
    WHERE f.zone_id = z.zone_id
      AND f.risk_score > 0.9
      AND f.colo = 'SJC'
)
LIMIT 20
```

**Solution**:

* Simplify correlated conditions where possible.
* Consider rewriting as a `JOIN` with `GROUP BY` instead of `EXISTS`.
* Use an `IN` subquery with pre-aggregated results instead of `EXISTS`.

---

## WHERE clause issues

### JSON object filtering

**Error**: `unsupported binary operator` or `Error during planning: could not parse compound`

**Problem**: JSON functions are not yet implemented. You cannot filter on fields inside JSON objects using JSON path operators.

```sql
-- Invalid - JSON path operators not supported
SELECT * FROM my_namespace.requests WHERE json_data->>'level' = 'error'

-- Valid - Filter on the entire JSON column
SELECT * FROM my_namespace.logs WHERE json_data IS NOT NULL LIMIT 100
```

**Solution**:

* Denormalize frequently queried JSON fields into separate columns.
* Filter on the entire JSON field, and handle parsing in your application.

Note

Struct columns are supported and can be filtered using bracket notation. Refer to [Complex types](https://mchen-ai-models-sync.previews.developers.cloudflare.com/r2-sql/sql-reference/complex-types/) for details.

```sql
SELECT * FROM my_namespace.products WHERE pricing['price'] > 50 LIMIT 100
```

---

## LIMIT clause issues

### Invalid limit values

**Error**: `maximum LIMIT is 10000`

**Problem**: LIMIT values must be between 1 and 10,000.

```sql
-- Invalid - Out of range
SELECT * FROM my_namespace.events LIMIT 50000

-- Valid
SELECT * FROM my_namespace.events LIMIT 10000
```

**Solution**: Use LIMIT values between 1 and 10,000.

### Pagination attempts

**Error**: `unsupported feature: OFFSET clause is not supported`

**Problem**: OFFSET is not supported.

```sql
-- Invalid - Pagination not supported
SELECT * FROM my_namespace.events LIMIT 100 OFFSET 200

-- Valid - Use cursor-based pagination with ORDER BY and WHERE
-- Page 1
SELECT * FROM my_namespace.events
WHERE timestamp >= '2024-01-01'
ORDER BY timestamp
LIMIT 100

-- Page 2 - Use the last timestamp from the previous page
SELECT * FROM my_namespace.events
WHERE timestamp > '2024-01-01T10:30:00Z'
ORDER BY timestamp
LIMIT 100
```

**Solution**: Implement cursor-based pagination using `ORDER BY` and `WHERE` conditions.

---

## Schema issues

### DDL and DML operations

**Error**: `only read-only queries are allowed`

**Problem**: R2 SQL is a read-only query engine. DDL and DML statements are not supported.

```sql
-- Invalid - Schema changes not supported
ALTER TABLE my_namespace.events ADD COLUMN new_field STRING
UPDATE my_namespace.events SET status = 200 WHERE user_id = '123'
CREATE TABLE my_namespace.test (id INT)
DROP TABLE my_namespace.events
```

**Solution**: Manage your schema through your data ingestion pipeline and R2 Data Catalog.

---

## Performance optimization

### Query performance issues

If your queries are running slowly:

1. **Always include partition (timestamp) filters**: This is the most important optimization.  
```sql
-- Good - Narrows data scan to one day  
SELECT * FROM my_namespace.events  
WHERE timestamp BETWEEN '2024-01-01' AND '2024-01-02'  
LIMIT 100  
```
2. **Use selective filtering**: Include specific conditions to reduce result sets.  
```sql
-- Good - Multiple filters reduce scanned data  
SELECT * FROM my_namespace.events  
WHERE status = 200 AND region = 'US' AND timestamp > '2024-01-01'  
LIMIT 100  
```
3. **Select specific columns**: Avoid `SELECT *` when you only need a few fields.  
```sql
-- Good - Only reads the columns you need  
SELECT user_id, status, timestamp  
FROM my_namespace.events  
WHERE timestamp > '2024-01-01'  
LIMIT 100  
```
4. **Use EXPLAIN to inspect the execution plan**: Verify that predicate pushdown and file pruning are working.  
```sql  
EXPLAIN SELECT user_id, status  
FROM my_namespace.events  
WHERE timestamp > '2024-01-01' AND status = 200  
```
5. **Enable compaction**: Enable compaction in R2 Data Catalog to reduce the number of small files scanned per query.

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