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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 directly into text files stored in an Amazon S3 bucket. You can use this functionality to skip bringing the data down to the client first, and then copying it from the client to Amazon S3. The LOAD DATA FROM S3 statement can use the files created by this 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.

If you are using encryption, the Amazon S3 bucket must be encrypted with an Amazon managed key. Currently, you can't save data to a bucket that is encrypted with a customer managed key.

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

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.

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'

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 master 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 SET GLOBAL sql_slave_skip_counter statement (Aurora MySQL version 2) or SET GLOBAL sql_replica_skip_counter statement (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:

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.

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 are not supported. Encrypted files are supported starting in Aurora MySQL 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}]

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

Following, you can find a list of the required and optional parameters used by the SELECT INTO OUTFILE S3 statement that are specific to Aurora.

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:

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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