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Create and alter Azure Storage external tables

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The following command describes how to create an external table located in Azure Blob Storage, Azure Data Lake Store Gen1, or Azure Data Lake Store Gen2.

For an introduction to the external Azure Storage tables feature, see Query data in Azure Data Lake using Azure Data Explorer.

Permissions

To .create requires at least Database User permissions and to .alter requires at least Table Admin permissions.

To .create-or-alter an external table using managed identity authentication requires AllDatabasesAdmin permissions.

Authentication and authorization

The authentication method to access an external table is based on the connection string provided during its creation, and the permissions required to access the table vary depending on the authentication method.

The following table lists the supported authentication methods for Azure Storage external tables and the permissions needed to read or write to the table.

Authentication method Azure Blob Storage / Data Lake Storage Gen2 Data Lake Storage Gen1
Impersonation Read permissions: Storage Blob Data Reader
Write permissions:Storage Blob Data Contributor		 Read permissions: Reader
Write permissions: Contributor
Managed identity Read permissions: Storage Blob Data Reader
Write permissions: Storage Blob Data Contributor		 Read permissions: Reader
Write permissions: Contributor
Shared Access (SAS) token Read permissions: List + Read
Write permissions: Write		 This authentication method isn't supported in Gen1.
Azure AD access token
Storage account access key This authentication method isn't supported in Gen1.

.create or .alter external table

Syntax

(.create | .alter | .create-or-alter) external table tableName (schema) kind = storage [partition by (partitions) [pathformat = (path-format)]] dataformat = format (storageConnectionString [, ...] ) [with (propertyName = propertyValue [, ...])]

Creates or alters a new external table in the database in which the command is executed.

Note

  • If the table exists, .create command will fail with an error. Use .create-or-alter or .alter to modify existing tables.
  • The external table is not accessed during creation time. It will only be accessed during query / export. You can use the validateNotEmpty (optional) property during creation time to make sure the external table definition is valid and that the underlying storage is accessible.

Parameters

Name Type Required Description
tableName string ✓ An external table name that adheres to the entity names rules. An external table can't have the same name as a regular table in the same database.
schema string ✓ The external data schema is a comma-separated list of one or more column names and data types, where each item follows the format: ColumnName : ColumnType. If the schema is unknown, use infer_storage_schema to infer the schema based on external file contents.
kind string ✓ The type of the external table. In this case, storage should be used instead of sql. Deprecated terms: blob for Blob Azure Storage or Azure Data Lake Gen 2 Storage, and adl for Azure Data Lake Gen 1 Storage.
partitions string A comma-separated list of columns by which the external table is partitioned. Partition column can exist in the data file itself, or as part of the file path. See partitions formatting to learn how this value should look.
pathFormat string An external data folder URI path format to use with partitions. See partitions formatting.
format string ✓ The data format, which can be any of the ingestion formats. It's recommended to use the Parquet format for external tables to improve query and export performance, unless you use JSON paths mapping. When using an external table for export scenario, you're limited to the following formats: CSV, TSV, JSON and Parquet.
storageConnectionString string ✓ One or more comma-separated paths to Azure Blob Storage blob containers, Azure Data Lake Gen 2 file systems or Azure Data Lake Gen 1 containers, including credentials. The external table storage type is determined by the provided connection strings. See storage connection strings for details.
propertyName, propertyValue string A comma-separated list of key-value property pairs. See optional properties.

Tip

Provide more than a single storage account to avoid storage throttling while exporting large amounts of data to the external table. Export will distribute the writes between all accounts provided.

Tip

For CSV data files, having files with non-identical schema under the same storage container might result in data appearing shifted or missing. If some CSV files miss columns or have extra columns, move them to a different storage container(s) and define another external table(s) matching their schema, so that each external table covers a set of storage containers containing files of an identical schema.

Partitions formatting

The partitions list is any combination of partition columns, specified using one of the forms described in the following table.

Partition Type Syntax Notes
Virtual column PartitionName : (datetime | string) Read more on virtual columns.
String column value PartitionName : string = ColumnName
String column value hash PartitionName : long = hash(ColumnName, Number) The hash is modulo Number.
Truncated datetime column (value) PartitionName : datetime = (startofyear | startofmonth | startofweek | startofday) ( ColumnName ) See documentation on startofyear, startofmonth, startofweek, or startofday functions.
Truncated Datetime Column Value (bin) PartitionName : datetime = bin ( ColumnName , TimeSpan ) Read more about the bin function.

PathFormat is a way to specify the format for the external data folder URI path in addition to partitions. It consists of a sequence of partition elements and text separators. A partition element refers to a partition that is declared in the partition by clause, and the text separator is any text enclosed in quotes. Consecutive partition elements must be set apart using the text separator.

[ StringSeparator ] Partition [ StringSeparator ] [Partition [ StringSeparator ] ...]

To construct the original file path prefix, partition elements are rendered as strings and separated with corresponding text separators. You can use the datetime_pattern macro (datetime_pattern(DateTimeFormat, PartitionName)) to specify the format used for rendering a datetime partition value. The macro adheres to the .NET format specification, and allows format specifiers to be enclosed in curly brackets. For example, the following two formats are equivalent:

  • 'year='yyyy'/month='MM
  • year={yyyy}/month={MM}

By default, datetime values are rendered using the following formats:

Partition function Default format
startofyear yyyy
startofmonth yyyy/MM
startofweek yyyy/MM/dd
startofday yyyy/MM/dd
bin(Column, 1d) yyyy/MM/dd
bin(Column, 1h) yyyy/MM/dd/HH
bin(Column, 1m) yyyy/MM/dd/HH/mm

Tip

To check Partitions and PathFormat definition correctness, use the property sampleUris or filesPreview when creating an external table.

Optional properties

Property Type Description
folder string Table's folder
docString string String documenting the table
compressed bool If set, indicates whether the files are compressed as .gz files (used in export scenario only)
includeHeaders string For delimited text formats (CSV, TSV, ...), indicates whether files contain a header. Possible values are: All (all files contain a header), FirstFile (first file in a folder contains a header), None (no files contain a header).
namePrefix string If set, indicates the prefix of the files. On write operations, all files will be written with this prefix. On read operations, only files with this prefix are read.
fileExtension string If set, indicates file extensions of the files. On write, files names will end with this suffix. On read, only files with this file extension will be read.
encoding string Indicates how the text is encoded: UTF8NoBOM (default) or UTF8BOM.
sampleUris bool If set, the command result provides several examples of simulated external data files URI as they're expected by the external table definition. This option helps validate whether the Partitions and PathFormat parameters are defined properly.
filesPreview bool If set, one of the command result tables contains a preview of .show external table artifacts command. Like sampleUri, the option helps validate the Partitions and PathFormat parameters of external table definition.
validateNotEmpty bool If set, the connection strings are validated for having content in them. The command will fail if the specified URI location doesn't exist, or if there are insufficient permissions to access it.
dryRun bool If set, the external table definition isn't persisted. This option is useful for validating the external table definition, especially in conjunction with the filesPreview or sampleUris parameter.

Tip

To learn more about the role namePrefix and fileExtension properties play in data file filtering during query, see file filtering logic section.

Examples

A non-partitioned external table. Data files are expected to be placed directly under the container(s) defined:

.create external table ExternalTable (x:long, s:string)  
kind=storage 
dataformat=csv 
( 
   h@'https://storageaccount.blob.core.windows.net/container1;secretKey' 
)

An external table partitioned by date. Data files are expected to be placed under directories of the default datetime format yyyy/MM/dd:

.create external table ExternalTable (Timestamp:datetime, x:long, s:string) 
kind=storage
partition by (Date:datetime = bin(Timestamp, 1d)) 
dataformat=csv 
( 
   h@'abfss://filesystem@storageaccount.dfs.core.windows.net/path;secretKey'
)

An external table partitioned by month, with a directory format of year=yyyy/month=MM:

.create external table ExternalTable (Timestamp:datetime, x:long, s:string) 
kind=storage 
partition by (Month:datetime = startofmonth(Timestamp)) 
pathformat = (datetime_pattern("'year='yyyy'/month='MM", Month)) 
dataformat=csv 
( 
   h@'https://storageaccount.blob.core.windows.net/container1;secretKey' 
)

An external table partitioned first by customer name, then by date. Expected directory structure is, for example, customer_name=Softworks/2019/02/01:

.create external table ExternalTable (Timestamp:datetime, CustomerName:string) 
kind=storage 
partition by (CustomerNamePart:string = CustomerName, Date:datetime = startofday(Timestamp)) 
pathformat = ("customer_name=" CustomerNamePart "/" Date)
dataformat=csv 
(  
   h@'https://storageaccount.blob.core.windows.net/container1;secretKey' 
)

An external table partitioned first by customer name hash (modulo ten), then by date. Expected directory structure is, for example, customer_id=5/dt=20190201. Data file names end with .txt extension:

.create external table ExternalTable (Timestamp:datetime, CustomerName:string) 
kind=storage 
partition by (CustomerId:long = hash(CustomerName, 10), Date:datetime = startofday(Timestamp)) 
pathformat = ("customer_id=" CustomerId "/dt=" datetime_pattern("yyyyMMdd", Date)) 
dataformat=csv 
( 
   h@'https://storageaccount.blob.core.windows.net/container1;secretKey'
)
with (fileExtension = ".txt")

To filter by partition columns in a query, specify original column name in query predicate:

external_table("ExternalTable")
 | where Timestamp between (datetime(2020-01-01) .. datetime(2020-02-01))
 | where CustomerName in ("John.Doe", "Ivan.Ivanov")

Sample Output

TableName TableType Folder DocString Properties ConnectionStrings Partitions PathFormat
ExternalTable Blob ExternalTables Docs {"Format":"Csv","Compressed":false,"CompressionType":null,"FileExtension":null,"IncludeHeaders":"None","Encoding":null,"NamePrefix":null} ["https://storageaccount.blob.core.windows.net/container1;*******"] [{"Mod":10,"Name":"CustomerId","ColumnName":"CustomerName","Ordinal":0},{"Function":"StartOfDay","Name":"Date","ColumnName":"Timestamp","Ordinal":1}] "customer_id=" CustomerId "/dt=" datetime_pattern("yyyyMMdd",Date)

Virtual columns

When data is exported from Spark, partition columns (that are provided to the dataframe writer's partitionBy method) aren't written to data files. This process avoids data duplication because the data is already present in the folder names (for example, column1=<value>/column2=<value>/), and Spark can recognize it upon read.

External tables support reading this data in the form of virtual colums. Virtual columns can be of either type string or datetime, and are specified using the following syntax:

.create external table ExternalTable (EventName:string, Revenue:double)  
kind=storage  
partition by (CustomerName:string, Date:datetime)  
pathformat = ("customer=" CustomerName "/date=" datetime_pattern("yyyyMMdd", Date))  
dataformat=parquet
( 
   h@'https://storageaccount.blob.core.windows.net/container1;secretKey'
)

To filter by virtual columns in a query, specify partition names in query predicate:

external_table("ExternalTable")
 | where Date between (datetime(2020-01-01) .. datetime(2020-02-01))
 | where CustomerName in ("John.Doe", "Ivan.Ivanov")

File filtering logic

When querying an external table, the query engine improves performance by filtering out irrelevant external storage files. The process of iterating files and deciding whether a file should be processed is as follows:

  1. Build a URI pattern that represents a place where files are found. Initially, the URI pattern equals a connection string provided as part of the external table definition. If there are any partitions defined, they're rendered using PathFormat, then appended to the URI pattern.

  2. For all files found under the URI pattern(s) created, check that:

    • Partition values match predicates used in a query.
    • Blob name starts with NamePrefix, if such a property is defined.
    • Blob name ends with FileExtension, if such a property is defined.

Once all the conditions are met, the file is fetched and processed by the query engine.

Note

Initial URI pattern is built using query predicate values. This works best for a limited set of string values as well as for a closed time ranges.

.show external table artifacts

Returns a list of all files that will be processed when querying a given external table.

Note

The operation requires database user permission.

Syntax:

.show external table tableName artifacts [limit MaxResults]

where MaxResults is an optional parameter, which can be set to limit the number of results.

Output

Output parameter Type Description
Uri string URI of external storage data file
Size long File length in bytes
Partition dynamic Dynamic object describing file partitions for partitioned external table

Tip

Iterating over all files referenced by an external table can be quite costly, depending on the number of files. Make sure to use limit parameter if you just want to see some URI examples.

Examples:

.show external table T artifacts

Output:

Uri Size Partition
https://storageaccount.blob.core.windows.net/container1/folder/file.csv 10743 {}

For partitioned table, Partition column will contain extracted partition values:

Output:

Uri Size Partition
https://storageaccount.blob.core.windows.net/container1/customer=john.doe/dt=20200101/file.csv 10743 {"Customer": "john.doe", "Date": "2020-01-01T00:00:00.0000000Z"}

.create external table mapping

.create external table ExternaltableName mapping MappingName MappingInJsonFormat

Creates a new mapping. For more information, see Data Mappings.

Example

.create external table MyExternalTable mapping "Mapping1" '[{"Column": "rownumber", "Properties": {"Path": "$.rownumber"}}, {"Column": "rowguid", "Properties": {"Path": "$.rowguid"}}]'

Example output

Name Kind Mapping
mapping1 JSON [{"ColumnName":"rownumber","Properties":{"Path":"$.rownumber"}},{"ColumnName":"rowguid","Properties":{"Path":"$.rowguid"}}]

.alter external table mapping

.alter external table ExternaltableName mapping MappingName MappingInJsonFormat

Alters an existing mapping.

Example

.alter external table MyExternalTable mapping "Mapping1" '[{"Column": "rownumber", "Properties": {"Path": "$.rownumber"}}, {"Column": "rowguid", "Properties": {"Path": "$.rowguid"}}]'

Example output

Name Kind Mapping
mapping1 JSON [{"ColumnName":"rownumber","Properties":{"Path":"$.rownumber"}},{"ColumnName":"rowguid","Properties":{"Path":"$.rowguid"}}]

.show external table mappings

.show external table ExternaltableName mapping MappingName

.show external table ExternaltableName mappings

Show the mappings (all or the one specified by name).

Example

.show external table MyExternalTable mapping "Mapping1" 

.show external table MyExternalTable mappings

Example output

Name Kind Mapping
mapping1 JSON [{"ColumnName":"rownumber","Properties":{"Path":"$.rownumber"}},{"ColumnName":"rowguid","Properties":{"Path":"$.rowguid"}}]

.drop external table mapping

.drop external table ExternaltableName mapping MappingName

Drops the mapping from the database.

Example

.drop external table MyExternalTable mapping "Mapping1"

Next steps