# Considerations for DB cluster exports
<a name="export-cluster-data.Considerations"></a>

Use the following sections to learn about the limitations, file naming conventions, and data conversion and storage when exporting DB cluster data to Amazon S3.

**Topics**
+ [Limitations](#export-cluster-data.Limits)
+ [File naming convention](#export-cluster-data.FileNames)
+ [Data conversion and storage format](#export-cluster-data.data-types)

## Limitations
<a name="export-cluster-data.Limits"></a>

Exporting DB cluster data to Amazon S3 has the following limitations:
+ You can't run multiple export tasks for the same DB cluster simultaneously. This applies to both full and partial exports.
+ You can have up to five concurrent DB snapshot export tasks in progress per AWS account.
+ Aurora MySQL and Aurora PostgreSQL support exports to S3 only for the provisioned engine mode.
+ Exports to S3 don't support S3 prefixes containing a colon (:).
+ The following characters in the S3 file path are converted to underscores (\$1) during export:

  ```
  \ ` " (space)
  ```
+ If a database, schema, or table has characters in its name other than the following, partial export isn't supported. However, you can export the entire DB cluster.
  + Latin letters (A–Z)
  + Digits (0–9)
  + Dollar symbol (\$1)
  + Underscore (\$1)
+ Spaces ( ) and certain characters aren't supported in database table column names. Tables with the following characters in column names are skipped during export:

  ```
  , ; { } ( ) \n \t = (space)
  ```
+ Tables with slashes (/) in their names are skipped during export.
+ Aurora PostgreSQL temporary and unlogged tables are skipped during export.
+ If the data contains a large object, such as a BLOB or CLOB, that is close to or greater than 500 MB, then the export fails.
+ If a table contains a large row that is close to or greater than 2 GB, then the table is skipped during export.
+ For partial exports, the `ExportOnly` list has a maximum size of 200 KB.
+ We strongly recommend that you use a unique name for each export task. If you don't use a unique task name, you might receive the following error message:

  ExportTaskAlreadyExistsFault: An error occurred (ExportTaskAlreadyExists) when calling the StartExportTask operation: The export task with the ID *xxxxx* already exists.
+ Because some tables might be skipped, we recommend that you verify row and table counts in the data after export.

## File naming convention
<a name="export-cluster-data.FileNames"></a>

Exported data for specific tables is stored in the format `base_prefix/files`, where the base prefix is the following:

```
export_identifier/database_name/schema_name.table_name/
```

For example:

```
export-1234567890123-459/rdststcluster/mycluster.DataInsert_7ADB5D19965123A2/
```

Output files use the following naming convention, where *partition\$1index* is alphanumeric:

```
partition_index/part-00000-random_uuid.format-based_extension
```

For example:

```
1/part-00000-c5a881bb-58ff-4ee6-1111-b41ecff340a3-c000.gz.parquet
    a/part-00000-d7a881cc-88cc-5ab7-2222-c41ecab340a4-c000.gz.parquet
```

The file naming convention is subject to change. Therefore, when reading target tables, we recommend that you read everything inside the base prefix for the table.

## Data conversion and storage format
<a name="export-cluster-data.data-types"></a>

When you export a DB cluster to an Amazon S3 bucket, Amazon Aurora converts, exports, and stores data in the Parquet format. For more information, see [Data conversion when exporting to an Amazon S3 bucket](aurora-export-snapshot.Considerations.md#aurora-export-snapshot.data-types).