Specifies where bulk loading creates all intermediate files.
| Valid in: | DATA and PROC steps (when accessing DBMS data using SAS/ACCESS software) |
|---|---|
| Categories: | Bulk Loading |
| Data Set Control | |
| Default: | database-name [Oracle] |
| temporary-file-directory that is specified by the UTILLOC= system option [Amazon Redshift, Google BigQuery, Informix, Microsoft SQL Server, MySQL, Netezza, Oracle, PostgreSQL, Snowflake, Spark, Yellowbrick] | |
| Requirements: | To specify this option, you must first specify BULKLOAD=YES. |
| This option must end with a backslash on Windows or forward slash on UNIX. | |
| Supports: | CTL, DAT, LOG, BAD, DSC intermediate bulk-load files (Oracle) or CTL and DAT intermediate bulk-load files (PostgreSQL) |
| Data source: | Amazon Redshift, Google BigQuery, Informix, Microsoft SQL Server, MySQL, Netezza, Oracle, PostgreSQL, Snowflake, Spark, Yellowbrick |
| Notes: | Support for Microsoft SQL Server and Yellowbrick was added in SAS 9.4M7. |
| Support for Informix and Spark was added in SAS 9.4M9. | |
| See: | BULKLOAD= data set option |
| Examples: |
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Table of Contents
specifies the host-specific directory path where intermediate bulk-load files are created.
The value that you specify for this option is prepended to the file name. Be sure to provide the complete, host-specific directory path, including the file and directory separator character to accommodate all platforms.
Microsoft SQL Server: This option is used when Microsoft SQL Server loads data to Azure Synapse Analytics (SQL DW) with a Microsoft Azure Data Lake Storage Gen2 storage account. For more information, Bulk Loading to Azure Synapse Analytics.
Spark: This option is used when the Spark engine bulk loads or bulk unloads data in Databricks. For more information, see Bulk Loading and Unloading to Databricks in Azure.
In this example, bulk loading creates all related files in the c:\temp directory on a Windows system.
data x.test (bulkload=yes BL_DEFAULT_DIR="c:\temp\" bl_delete_files=no);
c1=1;
run;