Considerations for DB cluster exports - Amazon Aurora
LimitationsFile naming convention Data conversion

Considerations for DB cluster exports

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.

Limitations

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 Serverless v1 DB clusters don't support exports to S3.

  • 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 (_) 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 ($)

    • Underscore (_)

  • 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

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_index 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

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.