ALTER VIEW

Modifies the properties for an existing view. Currently the only supported operations are:

  • Renaming a view.

  • Converting to (or reverting from) a secure view.

  • Adding, overwriting, removing a comment for a view.

Note that you cannot use this command to change the definition for a view. To change the view definition, you must drop the view and then recreate it.

See also:

CREATE VIEW , DROP VIEW , SHOW VIEWS , DESCRIBE VIEW

Syntax

ALTER VIEW [ IF EXISTS ] <name> RENAME TO <new_name>

ALTER VIEW [ IF EXISTS ] <name> SET COMMENT = '<string_literal>'

ALTER VIEW [ IF EXISTS ] <name> UNSET COMMENT

ALTER VIEW [ IF EXISTS ] <name> SET SECURE

ALTER VIEW [ IF EXISTS ] <name> SET CHANGE_TRACKING =  { TRUE | FALSE }

ALTER VIEW <name> UNSET SECURE

ALTER VIEW <name> dataMetricFunctionAction

ALTER VIEW [ IF EXISTS ] <name> dataGovnPolicyTagAction
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Where:

dataMetricFunctionAction ::=

    { ADD | DROP } DATA METRIC FUNCTION
      <metric_name> ON ( <col_name> [ , ... ] )
      [ , <metric_name_2> ON ( <col_name> [ , ... ] ) ]
      [ , ... ]
  |
    SET DATA_METRIC_SCHEDULE = {
        '<num> MINUTE'
      | 'USING CRON <expr> <time_zone>'
      | 'TRIGGER_ON_CHANGES'
    }

  | UNSET DATA_METRIC_SCHEDULE
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dataGovnPolicyTagAction ::=
  {
      SET TAG <tag_name> = '<tag_value>' [ , <tag_name> = '<tag_value>' ... ]
    | UNSET TAG <tag_name> [ , <tag_name> ... ]
  }
  |
  {
      ADD ROW ACCESS POLICY <policy_name> ON ( <col_name> [ , ... ] )
    | DROP ROW ACCESS POLICY <policy_name>
    | DROP ROW ACCESS POLICY <policy_name> ,
        ADD ROW ACCESS POLICY <policy_name> ON ( <col_name> [ , ... ] )
    | DROP ALL ROW ACCESS POLICIES
  }
  |
  {
      SET AGGREGATION POLICY <policy_name>
        [ ENTITY KEY ( <col_name> [, ... ] ) ]
        [ FORCE ]
    | UNSET AGGREGATION POLICY
  }
  |
  ADD [ COLUMN ] [ IF NOT EXISTS ] <col_name> <col_type>
    [ [ WITH ] MASKING POLICY <policy_name>
          [ USING ( <col1_name> , <cond_col_1> , ... ) ] ]
    [ [ WITH ] PROJECTION POLICY <policy_name> ]
    [ [ WITH ] TAG ( <tag_name> = '<tag_value>'
          [ , <tag_name> = '<tag_value>' , ... ] ) ]
  |
  {
    { ALTER | MODIFY } [ COLUMN ] <col1_name>
        SET MASKING POLICY <policy_name>
          [ USING ( <col1_name> , <cond_col_1> , ... ) ] [ FORCE ]
      | UNSET MASKING POLICY
  }
  |
  {
    { ALTER | MODIFY } [ COLUMN ] <col1_name>
        SET PROJECTION POLICY <policy_name>
          [ FORCE ]
      | UNSET PROJECTION POLICY
  }
  |
  { ALTER | MODIFY } [ COLUMN ] <col1_name> SET TAG
      <tag_name> = '<tag_value>' [ , <tag_name> = '<tag_value>' ... ]
      , [ COLUMN ] <col2_name> SET TAG
          <tag_name> = '<tag_value>' [ , <tag_name> = '<tag_value>' ... ]
  |
  { ALTER | MODIFY } [ COLUMN ] <col1_name> UNSET TAG <tag_name> [ , <tag_name> ... ]
                   , [ COLUMN ] <col2_name> UNSET TAG <tag_name> [ , <tag_name> ... ]
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Parameters

name

Specifies the identifier for the view to alter. If the identifier contains spaces or special characters, the entire string must be enclosed in double quotes. Identifiers enclosed in double quotes are also case-sensitive.

RENAME TO new_name

Specifies the new identifier for the view; must be unique for the schema.

For more details, see Identifier requirements.

You can move the object to a different database and/or schema while optionally renaming the object. To do so, specify a qualified new_name value that includes the new database and/or schema name in the form db_name.schema_name.object_name or schema_name.object_name, respectively.

Note

  • The destination database and/or schema must already exist. In addition, an object with the same name cannot already exist in the new location; otherwise, the statement returns an error.

  • Moving an object to a managed access schema is prohibited unless the object owner (that is, the role that has the OWNERSHIP privilege on the object) also owns the target schema.

When an object is renamed, other objects that reference it must be updated with the new name.

SET ...

Specifies the property to set for the view:

SECURE

Specifies a view as secure.

CHANGE_TRACKING = TRUE | FALSE

Specifies to enable or disable change tracking on the table.

  • TRUE enables change tracking on the view, and cascades the setting to all underlying tables.

  • FALSE disables change tracking on the view, and cascades the setting to all underlying tables.

COMMENT = 'string_literal'

Adds a comment or overwrites an existing comment for the view.

Note

You must set each view property individually.

UNSET ...

Specifies the property to unset for the view, which resets it to the default:

  • SECURE

  • COMMENT

When resetting a property, specify only the name; specifying a value for the property will return an error.

Note

You must reset each view property individually.

Data metric function actions (dataMetricFunctionAction)

{ ADD | DROP } DATA METRIC FUNCTION metric_name

Identifier of the data metric function to add to the table or view or drop from the table or view.

ON ( col_name [ , ... ] )

The table or view columns on which to associate the data metric function. The data types of the columns must match the data types of the columns specified in the data metric function definition.

[ , metric_name_2 ON ( col_name [ , ... ] ) [ , ... ] ]

Additional data metric functions to add to the table. Use a comma to separate each data metric function and its specified columns.

DATA_METRIC_SCHEDULE ...

Specifies the schedule to run the data metric function periodically.

'num MINUTE'

Specifies an interval (in minutes) of wait time inserted between runs of the data metric function. Accepts positive integers only.

Also supports num M syntax.

For data metric functions, use one of the following values: 5, 15, 30, 60, 720, or 1440.

'USING CRON expr time_zone'

Specifies a cron expression and time zone for periodically running the data metric function. Supports a subset of standard cron utility syntax.

For a list of time zones, see the list of tz database time zones.

The cron expression consists of the following fields, and the periodic interval must be at least 5 minutes:

# __________ minute (0-59)
# | ________ hour (0-23)
# | | ______ day of month (1-31, or L)
# | | | ____ month (1-12, JAN-DEC)
# | | | | _ day of week (0-6, SUN-SAT, or L)
# | | | | |
# | | | | |
  * * * * *
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The following special characters are supported:

*

Wildcard. Specifies any occurrence of the field.

L

Stands for “last”. When used in the day-of-week field, it allows you to specify constructs such as “the last Friday” (“5L”) of a given month. In the day-of-month field, it specifies the last day of the month.

/{n}

Indicates the nth instance of a given unit of time. Each quanta of time is computed independently. For example, if 4/3 is specified in the month field, then the data metric function is scheduled for April, July and October (i.e. every 3 months, starting with the 4th month of the year). The same schedule is maintained in subsequent years. That is, the data metric function is not scheduled to run in January (3 months after the October run).

Note

  • The cron expression currently evaluates against the specified time zone only. Altering the TIMEZONE parameter value for the account (or setting the value at the user or session level) does not change the time zone for the data metric function.

  • The cron expression defines all valid run times for the data metric function. Snowflake attempts to run a data metric function based on this schedule; however, any valid run time is skipped if a previous run has not completed before the next valid run time starts.

  • When both a specific day of month and day of week are included in the cron expression, then the data metric function is scheduled on days satisfying either the day of month or day of week. For example, DATA_METRIC_SCHEDULE = 'USING CRON 0 0 10-20 * TUE,THU UTC' schedules a data metric function at 0AM on any 10th to 20th day of the month and also on any Tuesday or Thursday outside of those dates.

  • The shortest granularity of time in cron is minutes.

'TRIGGER_ON_CHANGES'

Specifies that the DMF runs when a DML operation modifies the table, such as inserting a new row or deleting a row.

Changes to the table as a result of reclustering do not trigger the DMF to run.

For details about the access control requirements for these actions, see DMF privileges.

Data Governance policy and tag actions (dataGovnPolicyTagAction)

TAG tag_name = 'tag_value' [ , tag_name = 'tag_value' , ... ]

Specifies the tag name and the tag string value.

The tag value is always a string, and the maximum number of characters for the tag value is 256.

For information about specifying tags in a statement, see Tag quotas for objects and columns.

policy_name

Identifier for the policy; must be unique for your schema.

The following clauses apply to all table kinds that support row access policies, such as but not limited to tables, views, and event tables. To simplify, the clauses just refer to “table.”

ADD ROW ACCESS POLICY policy_name ON (col_name [ , ... ])

Adds a row access policy to the table.

At least one column name must be specified. Additional columns can be specified with a comma separating each column name. Use this expression to add a row access policy to both an event table and an external table.

DROP ROW ACCESS POLICY policy_name

Drops a row access policy from the table.

Use this clause to drop the policy from the table.

DROP ROW ACCESS POLICY policy_name, ADD ROW ACCESS POLICY policy_name ON ( col_name [ , ... ] )

Drops the row access policy that is set on the table and adds a row access policy to the same table in a single SQL statement.

DROP ALL ROW ACCESS POLICIES

Drops all row access policy associations from the table.

This expression is helpful when a row access policy is dropped from a schema before dropping the policy from an event table. Use this expression to drop row access policy associations from the table.

SET AGGREGATION POLICY policy_name
[ ENTITY KEY (col_name [ , ... ]) ] [ FORCE ]

Assigns an aggregation policy to the table.

Use the optional ENTITY KEY parameter to define which columns uniquely identity an entity within the table. For more information, see Implementing entity-level privacy with aggregation policies.

Use the optional FORCE parameter to atomically replace an existing aggregation policy with the new aggregation policy.

UNSET AGGREGATION POLICY

Detaches an aggregation policy from the table.

{ ALTER | MODIFY } [ COLUMN ] ...
USING ( col_name , cond_col_1 ... )

Specifies the arguments to pass into the conditional masking policy SQL expression.

The first column in the list specifies the column for the policy conditions to mask or tokenize the data and must match the column to which the masking policy is set.

The additional columns specify the columns to evaluate to determine whether to mask or tokenize the data in each row of the query result when a query is made on the first column.

If the USING clause is omitted, Snowflake treats the conditional masking policy as a normal masking policy.

FORCE

Replaces a masking or projection policy that is currently set on a column with a different policy in a single statement.

Note that using the FORCE keyword with a masking policy requires the data type of the policy in the ALTER TABLE statement (i.e. STRING) to match the data type of the masking policy currently set on the column (i.e. STRING).

If a masking policy is not currently set on the column, specifying this keyword has no effect.

For details, see: Replace a masking policy on a column or Replace a projection policy.

Usage notes: General

  • Moving a view to a managed access schema (using the ALTER VIEW … RENAME TO syntax) is prohibited unless the view owner (i.e. the role that has the OWNERSHIP privilege on the view) also owns the target schema.

  • For masking policies:

    • The USING clause and the FORCE keyword are both optional; neither are required to set a masking policy on a column. The USING clause and the FORCE keyword can be used separately or together. For details, see:

    • A single masking policy that uses conditional columns can be applied to multiple tables provided that the column structure of the table matches the columns specified in the policy.

    • When modifying one or more table columns with a masking policy or the table itself with a row access policy, use the POLICY_CONTEXT function to simulate a query on the column(s) protected by a masking policy and the table protected by a row access policy.

  • A single masking policy that uses conditional columns can be applied to multiple views provided that the column structure of the view matches the columns specified in the policy.

  • For row access policies:

    • Snowflake supports adding and dropping row access policies in a single SQL statement.

      For example, to replace a row access policy that is already set on a table with a different policy, drop the row access policy first and then add the new row access policy.

    • For a given resource (i.e. table or view), to ADD or DROP a row access policy you must have either the APPLY ROW ACCESS POLICY privilege on the schema, or the OWNERSHIP privilege on the resource and the APPLY privilege on the row access policy resource.

    • A table or view can only be protected by one row access policy at a time. Adding a policy fails if the policy body refers to a table or view column that is protected by a row access policy or the column protected by a masking policy.

      Similarly, adding a masking policy to a table column fails if the masking policy body refers to a table that is protected by a row access policy or another masking policy.

    • Row access policies cannot be applied to system views or table functions.

    • Similar to other DROP <object> operations, Snowflake returns an error if attempting to drop a row access policy from a resource that does not have a row access policy added to it.

    • If an object has both a row access policy and one or more masking policies, the row access policy is evaluated first.

  • Regarding metadata:

    Attention

    Customers should ensure that no personal data (other than for a User object), sensitive data, export-controlled data, or other regulated data is entered as metadata when using the Snowflake service. For more information, see Metadata Fields in Snowflake.

Usage notes: Data metric functions

Add a DMF to a table:

Prior to adding a data metric function to a table, you must:

  • Set the schedule for the data metric function to run. For details, see DATA_METRIC_SCHEDULE.

  • Configure the event table to store the results of calling the data metric function. For details, see View the DMF results.

  • Ensure that the table is view is not granted to a share because you cannot set a data metric function on a shared table or view.

Additionally:

  • You can add a data metric function to a table, view, or materialized view. You cannot set a data metric function on any other kind of table, such as a dynamic table or external table.

  • When you specify a column, Snowflake uses the ordinal position. If you rename a column after adding a data metric function to the table or view, the association of the data metric function to the column remains valid.

  • Only one data metric function of its kind can be added to a column. For example, a NULL_COUNT data metric function cannot be added to a single column twice.

  • If you drop a column after adding a data metric function that references the column, Snowflake cannot evaluate the data metric function.

  • Referencing a virtual column is not supported.

Drop a DMF from a table:
  • Drop the data metric function from the table before using the DROP FUNCTION command to remove the data metric function from the system.

  • You can use the DATA_METRIC_FUNCTION_REFERENCES function to identify the table and view objects that have a data metric function set on them.

Schedule a DMF

It takes ten minutes for the schedule to become effective once the schedule is set.

Similarly, it takes ten minutes once the DMF is unset for the scheduling changes to take effect. For more information, see Schedule your DMFs to run.

Examples

Rename view view1 to view2:

ALTER VIEW view1 RENAME TO view2;
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Convert a view to a secure view:

ALTER VIEW view1 SET SECURE;
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Revert a secure view to a regular view:

ALTER VIEW view1 UNSET SECURE;
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Apply a Column-level Security masking policy to a view column:

-- single column

ALTER VIEW user_info_v MODIFY COLUMN ssn_number SET MASKING POLICY ssn_mask_v;

-- multiple columns

ALTER VIEW user_info_v MODIFY
    COLUMN ssn_number SET MASKING POLICY ssn_mask_v
  , COLUMN dob SET MASKING POLICY dob_mask_v
;
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Unset a Column-level Security masking policy from a view column:

-- single column

ALTER VIEW user_info_v modify column ssn_number unset masking policy;

-- multiple columns

ALTER VIEW user_info_v modify
    column ssn_number unset masking policy
  , column dob unset masking policy
;
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The following example adds a row access policy on a view. After setting the policies, you can verify by checking the information schema.

alter view v1
  add row access policy rap_v1 on (empl_id);
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The following example drops a row access policy from a view. Verify the policies were dropped by querying the information schema.

alter view v1
  drop row access policy rap_v1;
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The following example shows how to combine adding and dropping row access policies in a single SQL statement for a view. Verify the results by checking the information schema.

alter view v1
  drop row access policy rap_v1_version_1,
  add row access policy rap_v1_version_2 on (empl_id);
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