---
type: concept
x-llmwiki:
  schemaVersion: "0.1"
  contentHash: c833bc0ec1e20b4cf844c3e9e89ff9e6b4efe379034050e922bd50a112b1118a
  pageDirectory: concepts
  sources:
    - delta_duplicate_columns_found-error-condition-databricks-on-aws.md
  confidence: 1
  provenanceState: extracted
  freshnessStatus: unverified
  aliases:
    - delta-lake-duplicate-columns-in-schema-operations
    - DLDCISO
  citations:
    - file: delta_duplicate_columns_found-error-condition-databricks-on-aws.md
title: Delta Lake Duplicate Columns in Schema Operations
description: Scenarios where duplicate columns are detected during schema-related Delta Lake operations including adding columns, replacing columns, reading schemas, and updating metadata.
tags:
  - delta-lake
  - schema
  - error-messages
timestamp: "2026-06-19T10:05:08.909Z"
---

# Delta Lake Duplicate Columns in Schema Operations

**Delta Lake Duplicate Columns in Schema Operations** refers to a class of errors (`DELTA_DUPLICATE_COLUMNS_FOUND`) that occurs when Delta Lake detects duplicate column names during a schema operation. These errors prevent the operation from completing and indicate an inconsistency in the column definitions of the data being written, read, or altered. ^[delta_duplicate_columns_found-error-condition-databricks-on-aws.md]

## Error Condition

The error is raised with the SQLSTATE **42711** and the message: `Found duplicate column(s): <duplicateCols>`. The error is categorized into several sub‑conditions, each identifying the specific schema operation where the duplicate was encountered. ^[delta_duplicate_columns_found-error-condition-databricks-on-aws.md]

### Sub‑Conditions

Each sub‑condition pinpoints the exact operation that triggered the duplicate column detection:

| Sub‑Condition | Description |
|---------------|-------------|
| `ADDING_COLUMNS` | Duplicate found while adding columns. |
| `CLUSTER_BY` | Duplicate found in a `CLUSTER BY` clause. |
| `CONVERT_TO_DELTA` | Duplicate found during conversion to Delta format. |
| `DATA` | Duplicate found in the data being saved. |
| `EXISTING_SCHEMA` | Duplicate found in the existing table schema. |
| `PARTITION_COLUMNS` | Duplicate found in the partition columns. |
| `PARTITION_SCHEMA` | Duplicate found in the partition schema. |
| `READ_SCHEMA` | Duplicate found in the schema of the data being read. |
| `REPLACING_COLUMNS` | Duplicate found while replacing columns. |
| `SPECIFIED_COLUMNS` | Duplicate found in the specified columns. |
| `TABLE_SCHEMA` | Duplicate found in the table schema. |

All sub‑conditions derive from the same `DELTA_DUPLICATE_COLUMNS_FOUND` error class but provide context for diagnosing the root cause. ^[delta_duplicate_columns_found-error-condition-databricks-on-aws.md]

## Common Causes

Duplicate columns typically arise from:

- Manually specifying a column name more than once in a schema definition (e.g., in `CREATE TABLE`, `ALTER TABLE`, or a query's column list).
- Loading data files that contain columns with identical names, which can happen when concatenating datasets with overlapping column names without aliasing.
- Writing data with a schema that contains duplicate column names, often due to a poorly constructed DataFrame or a [schema inference](https://spark.apache.org/docs/latest/sql-reference.html#schema-inference) mismatch.
- Using partition columns that duplicate existing column names.
- Convert operations where the source schema has duplicate columns.

The error ensures that Delta Lake tables maintain a unique set of column names, which is required for consistent querying and schema evolution. ^[delta_duplicate_columns_found-error-condition-databricks-on-aws.md]

## Resolution

Resolving the error requires identifying the duplicate column(s) and removing or renaming them so that each column name is unique within the table, data frame, or schema expression. The specific sub‑condition message helps pinpoint the operation to correct:

- For **`DATA`** or **`READ_SCHEMA`**, review the source dataset for duplicate column names and deduplicate before writing or reading.
- For **`ADDING_COLUMNS`** or **`REPLACING_COLUMNS`**, ensure the new column list does not contain duplicates.
- For **`CLUSTER_BY`**, verify that the clustering columns are unique.
- For **`PARTITION_COLUMNS`**, check that partition columns are distinct from each other and from non‑partition columns.
- For **`EXISTING_SCHEMA`** or **`TABLE_SCHEMA`**, rebuild the table with a corrected schema.

In many cases, duplicating columns is unintentional; auditing the schema definition or the source data file resolves the issue. ^[delta_duplicate_columns_found-error-condition-databricks-on-aws.md]

## Related Concepts

- [Delta Lake Schema Evolution](/concepts/delta-lake-schema-migration.md) – How schemas can change over time, including the enforcement of unique column names.
- Delta Lake DDL Operations – Commands that modify table structure (e.g., `ALTER TABLE`, `CLUSTER BY`).
- [Delta Lake Error Conditions](/concepts/delta-error-sub-conditions.md) – Overview of Delta Lake error classes and their meanings.
- [SQLSTATE 42711](/concepts/sqlstate-42711.md) – The standard SQL state for duplicate column errors.
- Partitioning in Delta Lake – Guidelines for choosing partition columns without duplication.

## Sources

- delta_duplicate_columns_found-error-condition-databricks-on-aws.md

# Citations

1. [delta_duplicate_columns_found-error-condition-databricks-on-aws.md](/references/delta_duplicate_columns_found-error-condition-databricks-on-aws-42950ac1.md)
