Introduction to external tables

An external table is a Snowflake feature that allows you to query data stored in an external stage as if the data were inside a table in Snowflake. The external stage is not part of Snowflake, so Snowflake does not store or manage the stage.

External tables let you store (within Snowflake) certain file-level metadata, including filenames, version identifiers, and related properties. External tables can access data stored in any format that the COPY INTO <table> command supports.

External tables are read-only. You cannot perform data manipulation language (DML) operations on them. However, you can use external tables for query and join operations. You can also create views against external tables.

Querying data in an external table might be slower than querying data that you store natively in a table within Snowflake. To improve query performance, you can use a materialized view based on an external table.

Note

If Snowflake encounters an error while scanning a file in cloud storage during a query operation, the file is skipped and scanning continues on the next file. A query can partially scan a file and return the rows scanned before the error was encountered.

Planning the schema of an external table

This section describes the options available for designing external tables.

Schema on read

All external tables include the following columns:

VALUE:

A VARIANT type column that represents a single row in the external file.

METADATA$FILENAME:

A pseudocolumn that identifies the name of each staged data file included in the external table, including its path in the stage.

METADATA$FILE_ROW_NUMBER:

A pseudocolumn that shows the row number for each record in a staged data file.

To create external tables, you are only required to have some knowledge of the file format and record format of the source data files. Knowing the schema of the data files is not required.

Note that SELECT * always returns the VALUE column, in which all regular or semi-structured data is cast to variant rows.

Virtual columns

If you are familiar with the schema of the source data files, you can create additional virtual columns as expressions using the VALUE column and/or the METADATA$FILENAME or METADATA$FILE_ROW_NUMBER pseudocolumns. When the external data is scanned, the data types of any specified fields or semi-structured data elements in the data file must match the data types of these additional columns in the external table. This allows strong type checking and schema validation over the external data.

General file sizing recommendations

To optimize the number of parallel scanning operations when querying external tables, we recommend the following file or row group sizes per format:

Format

Recommended Size Range

Notes

Parquet files

256 - 512 MB

Parquet row groups

16 - 256 MB

Note that when Parquet files include multiple row groups, Snowflake can operate on each row group in a different server. For improved query performance, we recommend sizing Parquet files in the recommended range; or, if large file sizes are necessary, including multiple row groups in each file.

All other supported file formats

16 - 256 MB

For optimal performance when querying large data files, create and query materialized views over external tables.

Partitioned external tables

We strongly recommend partitioning your external tables, which requires that your underlying data is organized using logical paths that include date, time, country, or similar dimensions in the path. Partitioning divides your external table data into multiple parts using partition columns.

An external table definition can include multiple partition columns, which impose a multi-dimensional structure on the external data. Partitions are stored in the external table metadata.

Benefits of partitioning include improved query performance. Because the external data is partitioned into separate slices/parts, query response time is faster when processing a small part of the data instead of scanning the entire data set.

Based on your individual use cases, you can either:

  • Add new partitions automatically by refreshing an external table that defines an expression for each partition column.

  • Add new partitions manually.

Partition columns are defined when an external table is created, using the CREATE EXTERNAL TABLE … PARTITION BY syntax. After an external table is created, the method by which partitions are added cannot be changed.

The following sections explain the different options for adding partitions in greater detail. For examples, see CREATE EXTERNAL TABLE.

Partitions added automatically

An external table creator defines partition columns in a new external table as expressions that parse the path and/or filename information stored in the METADATA$FILENAME pseudocolumn. A partition consists of all data files that match the path and/or filename in the expression for the partition column.

The CREATE EXTERNAL TABLE syntax for adding partitions automatically based on expressions is as follows:

CREATE EXTERNAL TABLE
  <table_name>
     ( <part_col_name> <col_type> AS <part_expr> )
     [ , ... ]
  [ PARTITION BY ( <part_col_name> [, <part_col_name> ... ] ) ]
  ..
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Snowflake computes and adds partitions based on the defined partition column expressions when an external table metadata is refreshed. By default, the metadata is refreshed automatically when the object is created. In addition, the object owner can configure the metadata to refresh automatically when new or updated data files are available in the external stage. The owner can alternatively refresh the metadata manually by executing the ALTER EXTERNAL TABLE … REFRESH command.

Partitions added manually

An external table creator determines the partition type of a new external table as user-defined and specifies only the data types of partition columns. Use this option when you prefer to add and remove partitions selectively rather than automatically adding partitions for all new files in an external storage location that match an expression.

This option is generally chosen to synchronize external tables with other metastores (e.g. AWS Glue or Apache Hive).

The CREATE EXTERNAL TABLE syntax for manually added partitions is as follows:

CREATE EXTERNAL TABLE
  <table_name>
     ( <part_col_name> <col_type> AS <part_expr> )
     [ , ... ]
  [ PARTITION BY ( <part_col_name> [, <part_col_name> ... ] ) ]
  PARTITION_TYPE = USER_SPECIFIED
  ..
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Include the required PARTITION_TYPE = USER_SPECIFIED parameter.

The partition column definitions are expressions that parse the column metadata in the internal (hidden) METADATA$EXTERNAL_TABLE_PARTITION column.

The object owner adds partitions to the external table metadata manually by executing the ALTER EXTERNAL TABLE … ADD PARTITION command:

ALTER EXTERNAL TABLE <name> ADD PARTITION ( <part_col_name> = '<string>' [ , <part_col_name> = '<string>' ] ) LOCATION '<path>'
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Automatically refreshing an external table with user-defined partitions is not supported. Attempting to manually refresh this type of external table produces a user error.

Delta Lake support

Note

To query a Delta table, consider using an Apache Iceberg™ table instead. Iceberg tables use an external volume to connect to Delta table files in your cloud storage. Iceberg tables created from Delta tables files are currently in preview.

For more information, see Iceberg tables and CREATE ICEBERG TABLE (Delta files in object storage). You can also Migrate a Delta external table to Apache Iceberg™.

Delta Lake is a table format on your data lake that supports ACID (atomicity, consistency, isolation, durability) transactions among other features. All data in Delta Lake is stored in Apache Parquet format. Create external tables that reference your cloud storage locations enhanced with Delta Lake.

To create an external table that references a Delta Lake, set the TABLE_FORMAT = DELTA parameter in the CREATE EXTERNAL TABLE statement.

When this parameter is set, the external table scans for Delta Lake transaction log files in the [ WITH ] LOCATION location. Delta log files have names like _delta_log/00000000000000000000.json, _delta_log/00000000000000000010.checkpoint.parquet, etc. When the metadata for an external table is refreshed, Snowflake parses the Delta Lake transaction logs and determines which Parquet files are current. In the background, the refresh performs add and remove file operations to keep the external table metadata in sync.

For more information, including examples, see CREATE EXTERNAL TABLE.

Note

  • External tables that reference Delta Lake files don’t support deletion vectors.

  • Automated refreshes aren’t supported for this feature because the ordering of event notifications triggered by DDL operations in cloud storage is not guaranteed. To register any added or removed files, periodically execute an ALTER EXTERNAL TABLE … REFRESH statement.

Migrate a Delta external table to Apache Iceberg™

To migrate one or more external tables that reference a Delta Lake to Apache Iceberg™ tables, complete the following steps:

  1. Use the SHOW EXTERNAL TABLES command to retrieve the location (external stage and folder path) for the external table(s).

    For example, the following command returns information for external tables and filters on names like my_delta_ext_table:

    SHOW EXTERNAL TABLES LIKE 'my_delta_ext_table';
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  2. Create an external volume. Specify the location that you retrieved in the previous step as the STORAGE_BASE_URL.

    To create a single external volume for multiple Delta tables under the same storage location, set the external volume’s active location (STORAGE_BASE_URL) as the common root directory.

    For example, consider the following locations for three Delta tables that branch from the same storage location:

    • s3://my-bucket/delta-ext-table-1/

    • s3://my-bucket/delta-ext-table-2/

    • s3://my-bucket/delta-ext-table-3/

    Specify the bucket as the STORAGE_BASE_URL when you create the external volume. Later, you can specify the relative path to the table files (for example, delta-ext-table-1/) as the BASE_LOCATION when you create an Iceberg table.

    CREATE OR REPLACE EXTERNAL VOLUME delta_migration_ext_vol
STORAGE_LOCATIONS = (
  (
    NAME = storage_location_1
    STORAGE_PROVIDER = 'S3'
    STORAGE_BASE_URL = 's3://my-bucket/'
    STORAGE_AWS_ROLE_ARN = 'arn:aws:iam::123456789123:role/my-storage-role' )
);
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  3. Create a catalog integration for Delta tables.

  4. Create an Iceberg table using the CREATE ICEBERG TABLE (Delta files in object storage) command. The BASE_LOCATION of the external volume must point to the existing external table location.

    The following example creates an Iceberg table based on external table files located in s3://my-bucket/delta-ext-table-1/, and references the external volume created previously.

    To determine the full storage location for the table, Snowflake appends the BASE_LOCATION to the STORAGE_BASE_URL of the external volume.

    CREATE ICEBERG TABLE my_delta_table_1
  BASE_LOCATION = 'delta-ext-table-1'
  EXTERNAL_VOLUME = 'delta_migration_ext_vol'
  CATALOG = 'delta_catalog_integration';
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  5. Drop the external table:

    DROP EXTERNAL TABLE my_delta_ext_table_1;
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Adding or dropping columns

Alter an existing external table to add or remove columns using the following ALTER TABLE syntax:

  • Add columns: ALTER TABLE … ADD COLUMN.

  • Remove columns: ALTER TABLE … DROP COLUMN.

Note

The default VALUE column and METADATA$FILENAME and METADATA$FILE_ROW_NUMBER pseudocolumns cannot be dropped.

See the example in ALTER TABLE.

Protecting external tables

You can protect an external table using a masking policy and a row access policy. For details, see:

Materialized views over external tables

In many cases, materialized views over external tables can provide performance that is faster than equivalent queries over the underlying external table. This performance difference can be significant when a query is run frequently or is sufficiently complex.

Refresh the file-level metadata in any queried external tables in order for your materialized views to reflect the current set of files in the referenced cloud storage location.

You can refresh the metadata for an external table automatically using the event notification service for your cloud storage service or manually using ALTER EXTERNAL TABLE … REFRESH statements.

Automatically refreshing external table metadata

The metadata for an external table can be refreshed automatically using the event notification service for your cloud storage service.

The refresh operation synchronizes the metadata with the latest set of associated files in the external stage and path, i.e.:

  • New files in the path are added to the table metadata.

  • Changes to files in the path are updated in the table metadata.

  • Files no longer in the path are removed from the table metadata.

For more information, see Refreshing external tables automatically.

Billing for external tables

An overhead to manage event notifications for the automatic refreshing of external table metadata is included in your charges. This overhead increases in relation to the number of files added in cloud storage for the external stages and paths specified for your external tables. This overhead charge appears as Snowpipe charges in your billing statement because Snowpipe is used for event notifications for the automatic external table refreshes. You can estimate this charge by querying the PIPE_USAGE_HISTORY function or examining the Account Usage PIPE_USAGE_HISTORY view.

In addition, a small maintenance overhead is charged for manually refreshing the external table metadata (using ALTER EXTERNAL TABLE … REFRESH). This overhead is charged in accordance with the standard cloud services billing model, like all similar activity in Snowflake. Manual refreshes of standard external tables are cloud services operations only; however, manual refreshes of external tables enhanced with Delta Lake rely on user-managed compute resources (i.e. a virtual warehouse).

Users with the ACCOUNTADMIN role, or a role with the global MONITOR USAGE privilege, can query the AUTO_REFRESH_REGISTRATION_HISTORY table function to retrieve the history of data files registered in the metadata of specified objects and the credits billed for these operations.

Workflow

Amazon S3

This section provides a high-level overview of the setup and load workflow for external tables that reference Amazon S3 stages. For complete instructions, see Refreshing external tables automatically for Amazon S3.

  1. Create a named stage object (using CREATE STAGE) that references the external location (i.e. S3 bucket) where your data files are staged.

  2. Create an external table (using CREATE EXTERNAL TABLE) that references the named stage.

  3. Manually refresh the external table metadata using ALTER EXTERNAL TABLE … REFRESH to synchronize the metadata with the current list of files in the stage path. This step also verifies the settings in your external table definition.

  4. Configure an event notification for the S3 bucket. Snowflake relies on event notifications to continually refresh the external table metadata to maintain consistency with the staged files.

  5. Manually refresh the external table metadata one more time using ALTER EXTERNAL TABLE … REFRESH to synchronize the metadata with any changes that occurred since Step 3. Thereafter, the S3 event notifications trigger the metadata refresh automatically.

  6. Configure Snowflake access control privileges for any additional roles to grant them query access to the external table.

Google Cloud Storage

This section provides a high-level overview of the setup and load workflow for external tables that reference Google Cloud Storage (GCS) stages.

  1. Configure a Google Pub/Sub subscription for GCS events.

  2. Create a notification integration in Snowflake. A notification integration is a Snowflake object that provides an interface between Snowflake and third-party cloud message queuing services such as Pub/Sub.

  3. Create a named stage object (using CREATE STAGE) that references the external location (i.e. GCS bucket) where your data files are staged.

  4. Create an external table (using CREATE EXTERNAL TABLE) that references the named stage and integration.

  5. Manually refresh the external table metadata once using ALTER EXTERNAL TABLE … REFRESH to synchronize the metadata with any changes that occurred since Step 4. Thereafter, the Pub/Sub notifications trigger the metadata refresh automatically.

  6. Configure Snowflake access control privileges for any additional roles to grant them query access to the external table.

Microsoft Azure

This section provides a high-level overview of the setup and load workflow for external tables that reference Azure stages. For complete instructions, see Refreshing external tables automatically for Azure Blob Storage.

  1. Configure an Event Grid subscription for Azure Storage events.

  2. Create a notification integration in Snowflake. A notification integration is a Snowflake object that provides an interface between Snowflake and third-party cloud message queuing services such as Microsoft Event Grid.

  3. Create a named stage object (using CREATE STAGE) that references the external location (i.e. Azure container) where your data files are staged.

  4. Create an external table (using CREATE EXTERNAL TABLE) that references the named stage and integration.

  5. Manually refresh the external table metadata once using ALTER EXTERNAL TABLE … REFRESH to synchronize the metadata with any changes that occurred since Step 4. Thereafter, the Event Grid notifications trigger the metadata refresh automatically.

  6. Configure Snowflake access control privileges for any additional roles to grant them query access to the external table.

Querying external tables

Query external tables just as you would standard tables.

Snowflake omits query results for records that contain invalid UTF-8 data. After encountering invalid data, Snowflake continues to scan the file without returning an error message.

To avoid missing records in your query results caused by invalid UTF-8 data, specify REPLACE_INVALID_CHARACTERS = TRUE for your file format. Doing so replaces any invalid UTF-8 characters with the Unicode replacement character (�) when you query the table.

For Parquet files, you can also set BINARY_AS_TEXT = FALSE for your file format so that Snowflake interprets the columns with no defined logical data type as binary data instead of as UTF-8 text.

Filtering records in Parquet files

To take advantage of row group statistics to prune data in Parquet files, a WHERE clause can include either partition columns or regular columns, or both. The following limitations apply:

In addition, queries in the form "value:<path>::<data type>" (or the GET/ GET_PATH , : function equivalent) take advantage of the vectorized scanner. Queries in the form "value" or simply "value:<path>" are processed using the non-vectorized scanner. Convert all time zone data to a standard time zone using the CONVERT_TIMEZONE function for queries that use the vectorized scanner.

When files are sorted by a key included in a query filter, and if there are multiple row groups in the files, better pruning results are possible.

The following table shows similar query structures that illustrate the behaviors in this section, where et is an external table and c1, c2, and c3 are virtual columns:

Optimized

Not optimized

SELECT c1, c2, c3 FROM et;

SELECT value:c1, c2, c3 FROM et;

SELECT c1, c2, c3  FROM et WHERE c1 = 'foo';

SELECT c1, c2, c3 FROM et WHERE value:c1::string = 'foo';

SELECT c1, c2, c3 FROM et WHERE value:c1 = 'foo';

Persisted query results

Similar to tables, the query results for external tables persist for 24 hours. Within this 24-hour period, the following operations invalidate and purge the query result cache for external tables:

  • Any DDL operation that modifies the external table definition. This includes explicitly modifying the external table definition (using ALTER EXTERNAL TABLE) or recreating the external table (using CREATE OR REPLACE EXTERNAL TABLE).

  • Changes in the set of files in cloud storage that are registered in the external table metadata. Either automatic refresh operations using the event notification service for the storage location or manual refresh operations (using ALTER EXTERNAL TABLE … REFRESH) invalidate the result cache.

Note that changes in the referenced files in cloud storage do not invalidate the query results cache in the following circumstances, leading to outdated query results:

  • The automated refresh operation is disabled (i.e. AUTO_REFRESH = FALSE) or is not configured correctly.

  • The external table metadata is not refreshed manually.

Removing older staged files from external table metadata

A stored procedure can remove older staged files from the metadata in an external table using an ALTER EXTERNAL TABLE … REMOVE FILES statement. The stored procedure would remove files from the metadata based on their last modified date in the stage.

For example:

  1. Create the stored procedure using a CREATE PROCEDURE statement:

    CREATE or replace PROCEDURE remove_old_files(external_table_name varchar, num_days float)
  RETURNS varchar
  LANGUAGE javascript
  EXECUTE AS CALLER
  AS
  $$
  // 1. Get the relative path of the external table
  // 2. Find all files registered before the specified time period
  // 3. Remove the files


  var resultSet1 = snowflake.execute({ sqlText:
    `call exttable_bucket_relative_path('` + EXTERNAL_TABLE_NAME + `');`
  });
  resultSet1.next();
  var relPath = resultSet1.getColumnValue(1);


  var resultSet2 = snowflake.execute({ sqlText:
    `select file_name
     from table(information_schema.EXTERNAL_TABLE_FILES (
         TABLE_NAME => '` + EXTERNAL_TABLE_NAME +`'))
     where last_modified < dateadd(day, -` + NUM_DAYS + `, current_timestamp());`
  });

  var fileNames = [];
  while (resultSet2.next())
  {
    fileNames.push(resultSet2.getColumnValue(1).substring(relPath.length));
  }

  if (fileNames.length == 0)
  {
    return 'nothing to do';
  }


  var alterCommand = `ALTER EXTERNAL TABLE ` + EXTERNAL_TABLE_NAME + ` REMOVE FILES ('` + fileNames.join(`', '`) + `');`;

  var resultSet3 = snowflake.execute({ sqlText: alterCommand });

  var results = [];
  while (resultSet3.next())
  {
    results.push(resultSet3.getColumnValue(1) + ' -> ' + resultSet3.getColumnValue(2));
  }

  return results.length + ' files: \n' + results.join('\n');

  $$;

  CREATE or replace PROCEDURE exttable_bucket_relative_path(external_table_name varchar)
  RETURNS varchar
  LANGUAGE javascript
  EXECUTE AS CALLER
  AS
  $$
  var resultSet = snowflake.execute({ sqlText:
    `show external tables like '` + EXTERNAL_TABLE_NAME + `';`
  });

  resultSet.next();
  var location = resultSet.getColumnValue(10);

  var relPath = location.split('/').slice(3).join('/');
  return relPath.endsWith("/") ? relPath : relPath + "/";

  $$;
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  2. Call the stored procedure:

    -- Remove all files from the exttable external table metadata:
call remove_old_files('exttable', 0);

-- Remove files staged longer than 90 days ago from the exttable external table metadata:
call remove_old_files('exttable', 90);
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    Alternatively, create a task using CREATE TASK that calls the stored procedure periodically to remove older files from the external table metadata.

Apache Hive metastore integration

Snowflake supports integrating Apache Hive metastores with Snowflake using external tables. The Hive connector detects metastore events and transmits them to Snowflake to keep the external tables synchronized with the Hive metastore. This allows users to manage their data in Hive while querying it from Snowflake.

For instructions, see Integrating Apache Hive metastores with Snowflake.

External table DDL

To support creating and managing external tables, Snowflake provides the following set of special DDL commands:

Required access privileges

Creating and managing external tables requires a role with a minimum of the following role permissions:

Object

Privilege

Database

USAGE

Schema

USAGE, CREATE STAGE (if creating a new stage), CREATE EXTERNAL TABLE

Stage (if using an existing stage)

USAGE

Information Schema

The Snowflake Snowflake Information Schema includes views and table functions you can query to retrieve information about your external tables and their staged data files.

View

EXTERNAL_TABLES view

Displays information for external tables in the specified (or current) database.

Table functions

AUTO_REFRESH_REGISTRATION_HISTORY

Retrieve the history of data files registered in the metadata of specified objects and the credits billed for these operations.

EXTERNAL_TABLE_FILES

Retrieve information about the staged data files included in the metadata for a specified external table.

EXTERNAL_TABLE_FILE_REGISTRATION_HISTORY

Retrieve information about the metadata history for an external table, including any errors found when refreshing the metadata.

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