Giving Aurora MySQL access to Amazon S3 Granting privileges to save data in Aurora MySQL Specifying a path to an Amazon S3 bucket Creating a manifest SELECT INTO OUTFILE S3

Saving data from an Amazon Aurora MySQL DB cluster into text files in an Amazon S3 bucket

You can use the SELECT INTO OUTFILE S3 statement to query data from an Amazon Aurora MySQL DB cluster and save it into text files stored in an Amazon S3 bucket. In Aurora MySQL, the files are first stored on the local disk, and then exported to S3. After the exports are done, the local files are deleted.

You can encrypt the Amazon S3 bucket using an Amazon S3 managed key (SSE-S3) or AWS KMS key (SSE-KMS: AWS managed key or customer managed key).

The LOAD DATA FROM S3 statement can use files created by the SELECT INTO OUTFILE S3 statement to load data into an Aurora DB cluster. For more information, see Loading data into an Amazon Aurora MySQL DB cluster from text files in an Amazon S3 bucket.

Note

This feature isn't supported for Aurora Serverless v1 DB clusters. It is supported for Aurora Serverless v2 DB clusters.

You can also save DB cluster data and DB cluster snapshot data to Amazon S3 using the AWS Management Console, AWS CLI, or Amazon RDS API. For more information, see Exporting DB cluster data to Amazon S3 and Exporting DB cluster snapshot data to Amazon S3.

Giving Aurora MySQL access to Amazon S3

Before you can save data into an Amazon S3 bucket, you must first give your Aurora MySQL DB cluster permission to access Amazon S3.

To give Aurora MySQL access to Amazon S3

Create an AWS Identity and Access Management (IAM) policy that provides the bucket and object permissions that allow your Aurora MySQL DB cluster to access Amazon S3. For instructions, see Creating an IAM policy to access Amazon S3 resources.

Note
In Aurora MySQL version 3.05 and higher, you can encrypt objects using AWS KMS customer managed keys. To do so, include the kms:GenerateDataKey permission in your IAM policy. For more information, see Creating an IAM policy to access AWS KMS resources.
You don't need this permission to encrypt objects using AWS managed keys or Amazon S3 managed keys (SSE-S3).
Create an IAM role, and attach the IAM policy you created in Creating an IAM policy to access Amazon S3 resources to the new IAM role. For instructions, see Creating an IAM role to allow Amazon Aurora to access AWS services.
For Aurora MySQL version 2, set either the aurora_select_into_s3_role or aws_default_s3_role DB cluster parameter to the Amazon Resource Name (ARN) of the new IAM role. If an IAM role isn't specified for aurora_select_into_s3_role, Aurora uses the IAM role specified in aws_default_s3_role.

For Aurora MySQL version 3, use aws_default_s3_role.

If the cluster is part of an Aurora global database, set this parameter for each Aurora cluster in the global database.

For more information about DB cluster parameters, see Amazon Aurora DB cluster and DB instance parameters.
To permit database users in an Aurora MySQL DB cluster to access Amazon S3, associate the role that you created in Creating an IAM role to allow Amazon Aurora to access AWS services with the DB cluster.

For an Aurora global database, associate the role with each Aurora cluster in the global database.

For information about associating an IAM role with a DB cluster, see Associating an IAM role with an Amazon Aurora MySQL DB cluster.
Configure your Aurora MySQL DB cluster to allow outbound connections to Amazon S3. For instructions, see Enabling network communication from Amazon Aurora to other AWS services.

For an Aurora global database, enable outbound connections for each Aurora cluster in the global database.

Granting privileges to save data in Aurora MySQL

The database user that issues the SELECT INTO OUTFILE S3 statement must have a specific role or privilege. In Aurora MySQL version 3, you grant the AWS_SELECT_S3_ACCESS role. In Aurora MySQL version 2, you grant the SELECT INTO S3 privilege. The administrative user for a DB cluster is granted the appropriate role or privilege by default. You can grant the privilege to another user by using one of the following statements.

Use the following statement for Aurora MySQL version 3:


GRANT AWS_SELECT_S3_ACCESS TO 'user'@'domain-or-ip-address'

Tip

When you use the role technique in Aurora MySQL version 3, you can also activate the role by using the SET ROLE role_name or SET ROLE ALL statement. If you aren't familiar with the MySQL 8.0 role system, you can learn more in Role-based privilege model. For more details, see Using roles in the MySQL Reference Manual.

This only applies to the current active session. When you reconnect, you must run the SET ROLE statement again to grant privileges. For more information, see SET ROLE statement in the MySQL Reference Manual.

You can use the activate_all_roles_on_login DB cluster parameter to automatically activate all roles when a user connects to a DB instance. When this parameter is set, you generally don't have to call the SET ROLE statement explicitly to activate a role. For more information, see activate_all_roles_on_login in the MySQL Reference Manual.

However, you must call SET ROLE ALL explicitly at the beginning of a stored procedure to activate the role, when the stored procedure is called by a different user.

Use the following statement for Aurora MySQL version 2:


GRANT SELECT INTO S3 ON *.* TO 'user'@'domain-or-ip-address'

The AWS_SELECT_S3_ACCESS role and SELECT INTO S3 privilege are specific to Amazon Aurora MySQL and are not available for MySQL databases or RDS for MySQL DB instances. If you have set up replication between an Aurora MySQL DB cluster as the replication source and a MySQL database as the replication client, then the GRANT statement for the role or privilege causes replication to stop with an error. You can safely skip the error to resume replication. To skip the error on an RDS for MySQL DB instance, use the mysql_rds_skip_repl_error procedure. To skip the error on an external MySQL database, use the slave_skip_errors system variable (Aurora MySQL version 2) or replica_skip_errors system variable (Aurora MySQL version 3).

Specifying a path to an Amazon S3 bucket

The syntax for specifying a path to store the data and manifest files on an Amazon S3 bucket is similar to that used in the LOAD DATA FROM S3 PREFIX statement, as shown following.


s3-region://bucket-name/file-prefix

The path includes the following values:

region (optional) – The AWS Region that contains the Amazon S3 bucket to save the data into. This value is optional. If you don't specify a region value, then Aurora saves your files into Amazon S3 in the same region as your DB cluster.
bucket-name – The name of the Amazon S3 bucket to save the data into. Object prefixes that identify a virtual folder path are supported.
file-prefix – The Amazon S3 object prefix that identifies the files to be saved in Amazon S3.

The data files created by the SELECT INTO OUTFILE S3 statement use the following path, in which 00000 represents a 5-digit, zero-based integer number.


s3-region://bucket-name/file-prefix.part_00000

For example, suppose that a SELECT INTO OUTFILE S3 statement specifies s3-us-west-2://bucket/prefix as the path in which to store data files and creates three data files. The specified Amazon S3 bucket contains the following data files.

s3-us-west-2://bucket/prefix.part_00000
s3-us-west-2://bucket/prefix.part_00001
s3-us-west-2://bucket/prefix.part_00002

Creating a manifest to list data files

You can use the SELECT INTO OUTFILE S3 statement with the MANIFEST ON option to create a manifest file in JSON format that lists the text files created by the statement. The LOAD DATA FROM S3 statement can use the manifest file to load the data files back into an Aurora MySQL DB cluster. For more information about using a manifest to load data files from Amazon S3 into an Aurora MySQL DB cluster, see Using a manifest to specify data files to load.

The data files included in the manifest created by the SELECT INTO OUTFILE S3 statement are listed in the order that they're created by the statement. For example, suppose that a SELECT INTO OUTFILE S3 statement specified s3-us-west-2://bucket/prefix as the path in which to store data files and creates three data files and a manifest file. The specified Amazon S3 bucket contains a manifest file named s3-us-west-2://bucket/prefix.manifest, that contains the following information.


{
  "entries": [
    {
      "url":"s3-us-west-2://bucket/prefix.part_00000"
    },
    {
      "url":"s3-us-west-2://bucket/prefix.part_00001"
    },
    {
      "url":"s3-us-west-2://bucket/prefix.part_00002"
    }
  ]
}

SELECT INTO OUTFILE S3

You can use the SELECT INTO OUTFILE S3 statement to query data from a DB cluster and save it directly into delimited text files stored in an Amazon S3 bucket.

Compressed files aren't supported. Encrypted files are supported starting in Aurora MySQL version 2.09.0.

Syntax


SELECT
    [ALL | DISTINCT | DISTINCTROW ]
        [HIGH_PRIORITY]
        [STRAIGHT_JOIN]
        [SQL_SMALL_RESULT] [SQL_BIG_RESULT] [SQL_BUFFER_RESULT]
        [SQL_CACHE | SQL_NO_CACHE] [SQL_CALC_FOUND_ROWS]
    select_expr [, select_expr ...]
    [FROM table_references
        [PARTITION partition_list]
    [WHERE where_condition]
    [GROUP BY {col_name | expr | position}
        [ASC | DESC], ... [WITH ROLLUP]]
    [HAVING where_condition]
    [ORDER BY {col_name | expr | position}
         [ASC | DESC], ...]
    [LIMIT {[offset,] row_count | row_count OFFSET offset}]
INTO OUTFILE S3 's3_uri'
[CHARACTER SET charset_name]
    [export_options]
    [MANIFEST {ON | OFF}]
    [OVERWRITE {ON | OFF}]
    [ENCRYPTION {ON | OFF | SSE_S3 | SSE_KMS ['cmk_id']}]

export_options:
    [FORMAT {CSV|TEXT} [HEADER]]
    [{FIELDS | COLUMNS}
        [TERMINATED BY 'string']
        [[OPTIONALLY] ENCLOSED BY 'char']
        [ESCAPED BY 'char']
    ]
    [LINES
        [STARTING BY 'string']
        [TERMINATED BY 'string']
]

Parameters

The SELECT INTO OUTFILE S3 statement uses the following required and optional parameters that are specific to Aurora.

s3-uri

Specifies the URI for an Amazon S3 prefix to use. Use the syntax described in Specifying a path to an Amazon S3 bucket.

FORMAT {CSV|TEXT} [HEADER]

Optionally saves the data in CSV format.

The TEXT option is the default and produces the existing MySQL export format.

The CSV option produces comma-separated data values. The CSV format follows the specification in RFC-4180. If you specify the optional keyword HEADER, the output file contains one header line. The labels in the header line correspond to the column names from the SELECT statement. You can use the CSV files for training data models for use with AWS ML services. For more information about using exported Aurora data with AWS ML services, see Exporting data to Amazon S3 for SageMaker model training (Advanced).

MANIFEST {ON | OFF}

Indicates whether a manifest file is created in Amazon S3. The manifest file is a JavaScript Object Notation (JSON) file that can be used to load data into an Aurora DB cluster with the LOAD DATA FROM S3 MANIFEST statement. For more information about LOAD DATA FROM S3 MANIFEST, see Loading data into an Amazon Aurora MySQL DB cluster from text files in an Amazon S3 bucket.

If MANIFEST ON is specified in the query, the manifest file is created in Amazon S3 after all data files have been created and uploaded. The manifest file is created using the following path:


s3-region://bucket-name/file-prefix.manifest

For more information about the format of the manifest file's contents, see Creating a manifest to list data files.

OVERWRITE {ON | OFF}

Indicates whether existing files in the specified Amazon S3 bucket are overwritten. If OVERWRITE ON is specified, existing files that match the file prefix in the URI specified in s3-uriare overwritten. Otherwise, an error occurs.

ENCRYPTION {ON | OFF | SSE_S3 | SSE_KMS ['cmk_id']}

Indicates whether to use server-side encryption with Amazon S3 managed keys (SSE-S3) or AWS KMS keys (SSE-KMS, including AWS managed keys and customer managed keys). The SSE_S3 and SSE_KMS settings are available in Aurora MySQL version 3.05 and higher.

You can also use the aurora_select_into_s3_encryption_default session variable instead of the ENCRYPTION clause, as shown in the following example. Use either the SQL clause or the session variable, but not both.


set session set session aurora_select_into_s3_encryption_default={ON | OFF | SSE_S3 | SSE_KMS};

The SSE_S3 and SSE_KMS settings are available in Aurora MySQL version 3.05 and higher.

When you set aurora_select_into_s3_encryption_default to the following value:

OFF – The default encryption policy of the S3 bucket is followed. The default value of aurora_select_into_s3_encryption_default is OFF.
ON or SSE_S3 – The S3 object is encrypted using Amazon S3 managed keys (SSE-S3).
SSE_KMS – The S3 object is encrypted using an AWS KMS key.

In this case, you also include the session variable aurora_s3_default_cmk_id, for example:
```
set session aurora_select_into_s3_encryption_default={SSE_KMS};
set session aurora_s3_default_cmk_id={NULL | 'cmk_id'};
```
- When aurora_s3_default_cmk_id is NULL, the S3 object is encrypted using an AWS managed key.
- When aurora_s3_default_cmk_id is a nonempty string cmk_id, the S3 object is encrypted using a customer managed key.
  
  The value of cmk_id can't be an empty string.

When you use the SELECT INTO OUTFILE S3 command, Aurora determines the encryption as follows:

If the ENCRYPTION clause is present in the SQL command, Aurora relies only on the value of ENCRYPTION, and doesn't use a session variable.
If the ENCRYPTION clause isn't present, Aurora relies on the value of the session variable.

For more information, see Using server-side encryption with Amazon S3 managed keys (SSE-S3) and Using server-side encryption withAWS KMS keys (SSE-KMS) in the Amazon Simple Storage Service User Guide.

You can find more details about other parameters in SELECT statement and LOAD DATA statement, in the MySQL documentation.

Considerations

The number of files written to the Amazon S3 bucket depends on the amount of data selected by the SELECT INTO OUTFILE S3 statement and the file size threshold for Aurora MySQL. The default file size threshold is 6 gigabytes (GB). If the data selected by the statement is less than the file size threshold, a single file is created; otherwise, multiple files are created. Other considerations for files created by this statement include the following:

Aurora MySQL guarantees that rows in data files are not split across file boundaries. For multiple files, the size of every data file except the last is typically close to the file size threshold. However, occasionally staying under the file size threshold results in a row being split across two data files. In this case, Aurora MySQL creates a data file that keeps the row intact, but might be larger than the file size threshold.
Because each SELECT statement in Aurora MySQL runs as an atomic transaction, a SELECT INTO OUTFILE S3 statement that selects a large data set might run for some time. If the statement fails for any reason, you might need to start over and issue the statement again. If the statement fails, however, files already uploaded to Amazon S3 remain in the specified Amazon S3 bucket. You can use another statement to upload the remaining data instead of starting over again.
If the amount of data to be selected is large (more than 25 GB), we recommend that you use multiple SELECT INTO OUTFILE S3 statements to save the data to Amazon S3. Each statement should select a different portion of the data to be saved, and also specify a different file_prefix in the s3-uri parameter to use when saving the data files. Partitioning the data to be selected with multiple statements makes it easier to recover from an error in one statement. If an error occurs for one statement, only a portion of data needs to be re-selected and uploaded to Amazon S3. Using multiple statements also helps to avoid a single long-running transaction, which can improve performance.
If multiple SELECT INTO OUTFILE S3 statements that use the same file_prefix in the s3-uri parameter run in parallel to select data into Amazon S3, the behavior is undefined.
Metadata, such as table schema or file metadata, is not uploaded by Aurora MySQL to Amazon S3.
In some cases, you might re-run a SELECT INTO OUTFILE S3 query, such as to recover from a failure. In these cases, you must either remove any existing data files in the Amazon S3 bucket with the same file prefix specified in s3-uri, or include OVERWRITE ON in the SELECT INTO OUTFILE S3 query.

The SELECT INTO OUTFILE S3 statement returns a typical MySQL error number and response on success or failure. If you don't have access to the MySQL error number and response, the easiest way to determine when it's done is by specifying MANIFEST ON in the statement. The manifest file is the last file written by the statement. In other words, if you have a manifest file, the statement has completed.

Currently, there's no way to directly monitor the progress of the SELECT INTO OUTFILE S3 statement while it runs. However, suppose that you're writing a large amount of data from Aurora MySQL to Amazon S3 using this statement, and you know the size of the data selected by the statement. In this case, you can estimate progress by monitoring the creation of data files in Amazon S3.

To do so, you can use the fact that a data file is created in the specified Amazon S3 bucket for about every 6 GB of data selected by the statement. Divide the size of the data selected by 6 GB to get the estimated number of data files to create. You can then estimate the progress of the statement by monitoring the number of files uploaded to Amazon S3 while the statement runs.

Examples

The following statement selects all of the data in the employees table and saves the data into an Amazon S3 bucket that is in a different region from the Aurora MySQL DB cluster. The statement creates data files in which each field is terminated by a comma (,) character and each row is terminated by a newline (\n) character. The statement returns an error if files that match the sample_employee_data file prefix exist in the specified Amazon S3 bucket.


SELECT * FROM employees INTO OUTFILE S3 's3-us-west-2://aurora-select-into-s3-pdx/sample_employee_data'
    FIELDS TERMINATED BY ','
    LINES TERMINATED BY '\n';

The following statement selects all of the data in the employees table and saves the data into an Amazon S3 bucket that is in the same region as the Aurora MySQL DB cluster. The statement creates data files in which each field is terminated by a comma (,) character and each row is terminated by a newline (\n) character, and also a manifest file. The statement returns an error if files that match the sample_employee_data file prefix exist in the specified Amazon S3 bucket.


SELECT * FROM employees INTO OUTFILE S3 's3://aurora-select-into-s3-pdx/sample_employee_data'
    FIELDS TERMINATED BY ','
    LINES TERMINATED BY '\n'
    MANIFEST ON;

The following statement selects all of the data in the employees table and saves the data into an Amazon S3 bucket that is in a different region from the Aurora DB cluster. The statement creates data files in which each field is terminated by a comma (,) character and each row is terminated by a newline (\n) character. The statement overwrites any existing files that match the sample_employee_data file prefix in the specified Amazon S3 bucket.


SELECT * FROM employees INTO OUTFILE S3 's3-us-west-2://aurora-select-into-s3-pdx/sample_employee_data'
    FIELDS TERMINATED BY ','
    LINES TERMINATED BY '\n'
    OVERWRITE ON;

The following statement selects all of the data in the employees table and saves the data into an Amazon S3 bucket that is in the same region as the Aurora MySQL DB cluster. The statement creates data files in which each field is terminated by a comma (,) character and each row is terminated by a newline (\n) character, and also a manifest file. The statement overwrites any existing files that match the sample_employee_data file prefix in the specified Amazon S3 bucket.


SELECT * FROM employees INTO OUTFILE S3 's3://aurora-select-into-s3-pdx/sample_employee_data'
    FIELDS TERMINATED BY ','
    LINES TERMINATED BY '\n'
    MANIFEST ON
    OVERWRITE ON;

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Document Conventions

Loading data from text files in Amazon S3

Invoking a Lambda function from Aurora MySQL

Saving data from an Amazon Aurora MySQL DB cluster into text files in an Amazon S3 bucket

Note

Contents

Giving Aurora MySQL access to Amazon S3

To give Aurora MySQL access to Amazon S3

Note

Granting privileges to save data in Aurora MySQL

Tip

Specifying a path to an Amazon S3 bucket

Creating a manifest to list data files

SELECT INTO OUTFILE S3

Syntax

Parameters

Considerations

Examples