Documentation Experience Platform Query Service Guide

SQL syntax in Query Service

Last update: Wed Dec 20 2023 00:00:00 GMT+0000 (Coordinated Universal Time)

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You can use standard ANSI SQL for SELECT statements and other limited commands in Adobe Experience Platform Query Service. This document covers the SQL syntax supported by Query Service.

SELECT queries select-queries

The following syntax defines a SELECT query supported by Query Service:

[ WITH with_query [, ...] ]
SELECT [ ALL | DISTINCT [( expression [, ...] ) ] ]
    [ * | expression [ [ AS ] output_name ] [, ...] ]
    [ FROM from_item [, ...] ]
    [ SNAPSHOT { SINCE start_snapshot_id | AS OF end_snapshot_id | BETWEEN start_snapshot_id AND end_snapshot_id } ]
    [ WHERE condition ]
    [ GROUP BY grouping_element [, ...] ]
    [ HAVING condition [, ...] ]
    [ WINDOW window_name AS ( window_definition ) [, ...] ]
    [ { UNION | INTERSECT | EXCEPT | MINUS } [ ALL | DISTINCT ] select ]
    [ ORDER BY expression [ ASC | DESC | USING operator ] [ NULLS { FIRST | LAST } ] [, ...] ]
    [ LIMIT { count | ALL } ]
    [ OFFSET start ]

The tabs section below provides the available options for the FROM, GROUP, and WITH keywords.

`from_item`

code language-sql
`table_name [ * ] [ [ AS ] alias [ ( column_alias [, ...] ) ] ]`

code language-sql
`[ LATERAL ] ( select ) [ AS ] alias [ ( column_alias [, ...] ) ]`

code language-sql
`with_query_name [ [ AS ] alias [ ( column_alias [, ...] ) ] ]`

code language-sql
`from_item [ NATURAL ] join_type from_item [ ON join_condition \| USING ( join_column [, ...] ) ]`

`grouping_element`

code language-sql
`( )`

code language-sql
`expression`

code language-sql
`( expression [, ...] )`

code language-sql
`ROLLUP ( { expression \| ( expression [, ...] ) } [, ...] )`

code language-sql
`CUBE ( { expression \| ( expression [, ...] ) } [, ...] )`

code language-sql
`GROUPING SETS ( grouping_element [, ...] )`

`with_query`

code language-sql
`with_query_name [ ( column_name [, ...] ) ] AS ( select \| values )`

The following subsections provide details on additional clauses that you can use in your queries, provided they follow the format outlined above.

SNAPSHOT clause

This clause can be used to incrementally read data on a table based on snapshot IDs. A snapshot ID is a checkpoint marker represented by a Long-type number that is applied to a data lake table every time data is written to it. The SNAPSHOT clause attaches itself to the table relation that it is used next to.

    [ SNAPSHOT { SINCE start_snapshot_id | AS OF end_snapshot_id | BETWEEN start_snapshot_id AND end_snapshot_id } ]

Example

SELECT * FROM Customers SNAPSHOT SINCE 123;

SELECT * FROM Customers SNAPSHOT AS OF 345;

SELECT * FROM Customers SNAPSHOT BETWEEN 123 AND 345;

SELECT * FROM Customers SNAPSHOT BETWEEN HEAD AND 123;

SELECT * FROM Customers SNAPSHOT BETWEEN 345 AND TAIL;

SELECT * FROM (SELECT id FROM CUSTOMERS BETWEEN 123 AND 345) C

SELECT * FROM Customers SNAPSHOT SINCE 123 INNER JOIN Inventory AS OF 789 ON Customers.id = Inventory.id;

A SNAPSHOT clause works with a table or table alias but not on top of a subquery or view. A SNAPSHOT clause works anywhere a SELECT query on a table can be applied.

Also, you can use HEAD and TAIL as special offset values for snapshot clauses. Using HEAD refers to an offset before the first snapshot, while TAIL refers to an offset after the last snapshot.

NOTE

If you are querying between two snapshot IDs, the following two scenarios can occur if the start snapshot is expired and the optional fallback behavior flag (resolve_fallback_snapshot_on_failure) is set:

If the optional fallback behavior flag is set, Query Service chooses the earliest available snapshot, set it as the start snapshot, and return the data between the earliest available snapshot and the specified end snapshot. This data is inclusive of the earliest available snapshot.
If the optional fallback behavior flag is not set, an error is returned.

WHERE clause

By default, matches produced by a WHERE clause on a SELECT query are case-sensitive. If you want matches to be case-insensitive, you can use the keyword ILIKE instead of LIKE.

    [ WHERE condition { LIKE | ILIKE | NOT LIKE | NOT ILIKE } pattern ]

The logic of the LIKE and ILIKE clauses are explained in the following table:

Clause

Operator

WHERE condition LIKE pattern

~~

WHERE condition NOT LIKE pattern

!~~

WHERE condition ILIKE pattern

~~*

WHERE condition NOT ILIKE pattern

!~~*

Example

SELECT * FROM Customers
WHERE CustomerName ILIKE 'a%';

This query returns customers with names beginning in “A” or “a”.

JOIN

A SELECT query that uses joins has the following syntax:

SELECT statement
FROM statement
[JOIN | INNER JOIN | LEFT JOIN | LEFT OUTER JOIN | RIGHT JOIN | RIGHT OUTER JOIN | FULL JOIN | FULL OUTER JOIN]
ON join condition

UNION, INTERSECT, and EXCEPT

The UNION, INTERSECT, and EXCEPT clauses are used to combine or exclude like rows from two or more tables:

SELECT statement 1
[UNION | UNION ALL | UNION DISTINCT | INTERSECT | EXCEPT | MINUS]
SELECT statement 2

CREATE TABLE AS SELECT create-table-as-select

The following syntax defines a CREATE TABLE AS SELECT (CTAS) query:

CREATE TABLE table_name [ WITH (schema='target_schema_title', rowvalidation='false', label='PROFILE') ] AS (select_query)

Parameters

Description

schema

The title of XDM schema. Use this clause only if you wish to use an existing XDM schema for the new dataset created by the CTAS query.

rowvalidation

(Optional) Specifies if the user wants row level validation of every new batch ingested for the newly created dataset. The default value is true.

label

When you create a dataset with a CTAS query, use this label with the value of profile to label your dataset as enabled for profile. This means that your dataset automatically gets marked for profile as it gets created. See the derived attribute extension document for more information on the use of label.

select_query

A SELECT statement. The syntax of the SELECT query can be found in the SELECT queries section.

Example

CREATE TABLE Chairs AS (SELECT color, count(*) AS no_of_chairs FROM Inventory i WHERE i.type=="chair" GROUP BY i.color)

CREATE TABLE Chairs WITH (schema='target schema title', label='PROFILE') AS (SELECT color, count(*) AS no_of_chairs FROM Inventory i WHERE i.type=="chair" GROUP BY i.color)

CREATE TABLE Chairs AS (SELECT color FROM Inventory SNAPSHOT SINCE 123)

NOTE

The SELECT statement must have an alias for the aggregate functions such as COUNT, SUM, MIN, and so on. Also, the SELECT statement can be provided with or without parentheses (). You can provide a SNAPSHOT clause to read incremental deltas into the target table.

INSERT INTO

The INSERT INTO command is defined as follows:

INSERT INTO table_name select_query

Parameters

Description

table_name

The name of the table that you want to insert the query into.

select_query

A SELECT statement. The syntax of the SELECT query can be found in the SELECT queries section.

Example

NOTE

The following is a contrived example and simply for instructional purposes.

INSERT INTO Customers SELECT SupplierName, City, Country FROM OnlineCustomers;

INSERT INTO Customers AS (SELECT * from OnlineCustomers SNAPSHOT AS OF 345)

INFO

Do not enclose the SELECT statement in parentheses (). Also, the schema of the result of the SELECT statement must conform to that of the table defined in the INSERT INTO statement. You can provide a SNAPSHOT clause to read incremental deltas into the target table.

Most fields in a real XDM schema are not found at the root level and SQL does not permit the use of dot notation. To achieve a realistic result using nested fields, you must map each field in your INSERT INTO path.

To INSERT INTO nested paths, use the following syntax:

INSERT INTO [dataset]
SELECT struct([source field1] as [target field in schema],
[source field2] as [target field in schema],
[source field3] as [target field in schema]) [tenant name]
FROM [dataset]

Example

INSERT INTO Customers SELECT struct(SupplierName as Supplier, City as SupplierCity, Country as SupplierCountry) _Adobe FROM OnlineCustomers;

DROP TABLE

The DROP TABLE command drops an existing table and deletes the directory associated with the table from the file system if it is not an external table. If the table does not exist, an exception occurs.

DROP TABLE [IF EXISTS] [db_name.]table_name

Parameters

Description

IF EXISTS

If this is specified, no exception is thrown if the table does not exist.

CREATE DATABASE

The CREATE DATABASE command creates an Azure Data Lake Storage (ADLS) database.

CREATE DATABASE [IF NOT EXISTS] db_name

DROP DATABASE

The DROP DATABASE command deletes the database from an instance.

DROP DATABASE [IF EXISTS] db_name

Parameters

Description

IF EXISTS

If this is specified, no exception is thrown if the database does not exist.

DROP SCHEMA

The DROP SCHEMA command drops an existing schema.

DROP SCHEMA [IF EXISTS] db_name.schema_name [ RESTRICT | CASCADE]

Parameters

Description

IF EXISTS

If this parameter is specified and the schema does not exist, no exception is thrown.

RESTRICT

The default value for the mode. If specified, the schema only drops if it does not contain any tables.

CASCADE

If specified, the schema is dropped along with all the tables present in the schema.

CREATE VIEW

The following syntax defines a CREATE VIEW query for a dataset. This dataset can be an ADLS or accelerated store dataset.

CREATE VIEW view_name AS select_query

Parameters

Description

view_name

The name of view to be created.

select_query

A SELECT statement. The syntax of the SELECT query can be found in the SELECT queries section.

Example

CREATE VIEW V1 AS SELECT color, type FROM Inventory

CREATE OR REPLACE VIEW V1 AS SELECT model, version FROM Inventory

The following syntax defines a CREATE VIEW query which creates a view in the context of a database and schema.

Example

CREATE VIEW db_name.schema_name.view_name AS select_query
CREATE OR REPLACE VIEW db_name.schema_name.view_name AS select_query

Parameters

Description

db_name

The name of the database.

schema_name

The name of the schema.

view_name

The name of the view to be created.

select_query

A SELECT statement. The syntax of the SELECT query can be found in the SELECT queries section.

Example

CREATE VIEW <dbV1 AS SELECT color, type FROM Inventory;

CREATE OR REPLACE VIEW V1 AS SELECT model, version FROM Inventory;

SHOW VIEWS

The following query shows the list of views.

SHOW VIEWS;

 Db Name  | Schema Name | Name  | Id       |  Dataset Dependencies | Views Dependencies | TYPE
----------------------------------------------------------------------------------------------
 qsaccel  | profile_agg | view1 | view_id1 | dwh_dataset1          |                    | DWH
          |             | view2 | view_id2 | adls_dataset          | adls_views         | ADLS
(2 rows)

DROP VIEW

The following syntax defines a DROP VIEW query:

DROP VIEW [IF EXISTS] view_name

Parameters

Description

IF EXISTS

If this is specified, no exception is thrown if the view does not exist.

view_name

The name of the view to be deleted.

Example

DROP VIEW v1
DROP VIEW IF EXISTS v1

Anonymous block anonymous-block

An anonymous block consists of two sections: executable and exception-handling sections. In an anonymous block, the executable section is mandatory. However, the exception-handling section is optional.

The following example shows how to create a block with one or more statements to be executed together:

$$BEGIN
  statementList
[EXCEPTION exceptionHandler]
$$END

exceptionHandler:
      WHEN OTHER
      THEN statementList

statementList:
    : (statement (';')) +

Below is an example using anonymous block.

$$BEGIN
   SET @v_snapshot_from = select parent_id  from (select history_meta('email_tracking_experience_event_dataset') ) tab where is_current;
   SET @v_snapshot_to = select snapshot_id from (select history_meta('email_tracking_experience_event_dataset') ) tab where is_current;
   SET @v_log_id = select now();
   CREATE TABLE tracking_email_id_incrementally
     AS SELECT _id AS id FROM email_tracking_experience_event_dataset SNAPSHOT BETWEEN @v_snapshot_from AND @v_snapshot_to;

EXCEPTION
  WHEN OTHER THEN
    DROP TABLE IF EXISTS tracking_email_id_incrementally;
    SELECT 'ERROR';
$$END;

Conditional statements in an anonymous block conditional-anonymous-block-statements

The IF-THEN-ELSE control structure enables the conditional execution of a list of statements when a condition is evaluated as TRUE. This control structure is only applicable within an anonymous block. If this structure is used as a standalone command, it results in a syntax error (“Invalid command outside Anonymous Block”).

The code snippet below demonstrates the correct format for an IF-THEN-ELSE conditional statement in an anonymous block.

IF booleanExpression THEN
   List of statements;
ELSEIF booleanExpression THEN
   List of statements;
ELSEIF booleanExpression THEN
   List of statements;
ELSE
   List of statements;
END IF

Example

The example below executes SELECT 200;.

$$BEGIN
    SET @V = SELECT 2;
    SELECT @V;
    IF @V = 1 THEN
       SELECT 100;
    ELSEIF @V = 2 THEN
       SELECT 200;
    ELSEIF @V = 3 THEN
       SELECT 300;
    ELSE
       SELECT 'DEFAULT';
    END IF;

 END$$;

This structure can be used with raise_error(); to return a custom error message. The code block seen below terminates the anonymous block with “custom error message”.

Example

$$BEGIN
    SET @V = SELECT 5;
    SELECT @V;
    IF @V = 1 THEN
       SELECT 100;
    ELSEIF @V = 2 THEN
       SELECT 200;
    ELSEIF @V = 3 THEN
       SELECT 300;
    ELSE
       SELECT raise_error('custom error message');
    END IF;

 END$$;

Nested IF statements

Nested IF statements are supported within anonymous blocks.

Example

$$BEGIN
    SET @V = SELECT 1;
    IF @V = 1 THEN
       SELECT 100;
       IF @V > 0 THEN
         SELECT 1000;
       END IF;
    END IF;

 END$$;

Exception blocks

Exception blocks are supported within anonymous blocks.

Example

$$BEGIN
    SET @V = SELECT 2;
    IF @V = 1 THEN
       SELECT 100;
    ELSEIF @V = 2 THEN
       SELECT raise_error(concat('custom-error for v= ', '@V' ));

    ELSEIF @V = 3 THEN
       SELECT 300;
    ELSE
       SELECT 'DEFAULT';
    END IF;
EXCEPTION WHEN OTHER THEN
  SELECT 'THERE WAS AN ERROR';
 END$$;

Auto to JSON auto-to-json

Query Service supports an optional session-level setting to return top-level complex fields from interactive SELECT queries as JSON strings. The auto_to_json setting allows for data from complex fields to be returned as JSON then parsed into JSON objects using standard libraries.

SET the feature flag auto_to_json to true before executing your SELECT query that contains complex fields.

set auto_to_json=true;

Before setting the `auto_to_json` flag

The following table provides an example query result before the auto_to_json setting is applied. The same SELECT query (as seen below) that targets a table with complex fields was used in both scenarios.

SELECT * FROM TABLE_WITH_COMPLEX_FIELDS LIMIT 2;

The results are as follows:

                _id                |                                _experience                                 | application  |                   commerce                   | dataSource |                               device                               |                       endUserIDs                       |                                                                                                environment                                                                                                |                     identityMap                     |                              placeContext                               |   receivedTimestamp   |       timestamp       | userActivityRegion |                                         web                                          | _adcstageforpqs
-----------------------------------+----------------------------------------------------------------------------+--------------+----------------------------------------------+------------+--------------------------------------------------------------------+--------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-----------------------------------------------------+-------------------------------------------------------------------------+-----------------------+-----------------------+--------------------+--------------------------------------------------------------------------------------+-----------------
 31892EE15DE00000-401D52664FF48A52 | ("("("(1,1)","(1,1)")","(-209479095,4085488201,-2105158467,2189808829)")") | (background) | (NULL,"(USD,NULL)",NULL,NULL,NULL,NULL,NULL) | (475341)   | (32,768,1024,205202,https://ns.adobe.com/xdm/external/deviceatlas) | ("("(31892EE080007B35-E6CE00000000000,"(AAID)",t)")")  | ("(en-US,f,f,t,1.6,"Mozilla/5.0 (iPhone; U; CPU iPhone OS 4_1 like Mac OS X; ja-jp) AppleWebKit/532.9 (KHTML, like Gecko) Version/4.0.5 Mobile/8B117 Safari/6531.22.7",490,1125)",xo.net,64.3.235.13)     | [AAID -> "{(31892EE080007B35-E6CE00000000000,t)}"]  | ("("(34.01,-84.0)",lawrenceville,US,524,30043,ga)",600)                 | 2022-09-02 19:47:14.0 | 2022-09-02 19:47:14.0 | (UT1)              | ("(f,Search Results,"(1.0)")","(http://www.google.com/search?ie=UTF-8&q=,internal)") |
 31892EE15DE00000-401B92664FF48AE8 | ("("("(1,1)","(1,1)")","(-209479095,4085488201,-2105158467,2189808829)")") | (background) | (NULL,"(USD,NULL)",NULL,NULL,NULL,NULL,NULL) | (475341)   | (32,768,1024,205202,https://ns.adobe.com/xdm/external/deviceatlas) | ("("(31892EE100007BF3-215FE00000000001,"(AAID)",t)")") | ("(en-US,f,f,t,1.5,"Mozilla/5.0 (iPhone; U; CPU iPhone OS 4_1 like Mac OS X; ja-jp) AppleWebKit/532.9 (KHTML, like Gecko) Version/4.0.5 Mobile/8B117 Safari/6531.22.7",768,556)",ntt.net,219.165.108.145) | [AAID -> "{(31892EE100007BF3-215FE00000000001,t)}"] | ("("(34.989999999999995,138.42)",shizuoka,JP,392005,420-0812,22)",-240) | 2022-09-02 19:47:14.0 | 2022-09-02 19:47:14.0 | (UT1)              | ("(f,Home - JJEsquire,"(1.0)")","(NULL,typed_bookmarked)")                           |
(2 rows)

After setting the `auto_to_json` flag

The following table demonstrates the difference in results that the auto_to_json setting has on the resulting dataset. The same SELECT query was used in both scenarios.

                _id                |   receivedTimestamp   |       timestamp       |                                                                                                                   _experience                                                                                                                   |           application            |             commerce             |    dataSource    |                                                                  device                                                                   |                                                   endUserIDs                                                   |                                                                                                                                                                                           environment                                                                                                                                                                                            |                             identityMap                              |                                                                                            placeContext                                                                                            |      userActivityRegion      |                                                                                     web                                                                                      | _adcstageforpqs
-----------------------------------+-----------------------+-----------------------+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+----------------------------------+----------------------------------+------------------+-------------------------------------------------------------------------------------------------------------------------------------------+----------------------------------------------------------------------------------------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+----------------------------------------------------------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+-----------------
 31892EE15DE00000-401D52664FF48A52 | 2022-09-02 19:47:14.0 | 2022-09-02 19:47:14.0 | {"analytics":{"customDimensions":{"eVars":{"eVar1":"1","eVar2":"1"},"props":{"prop1":"1","prop2":"1"}},"environment":{"browserID":-209479095,"browserIDStr":"4085488201","operatingSystemID":-2105158467,"operatingSystemIDStr":"2189808829"}}} | {"userPerspective":"background"} | {"order":{"currencyCode":"USD"}} | {"_id":"475341"} | {"colorDepth":32,"screenHeight":768,"screenWidth":1024,"typeID":"205202","typeIDService":"https://ns.adobe.com/xdm/external/deviceatlas"} | {"_experience":{"aaid":{"id":"31892EE080007B35-E6CE00000000000","namespace":{"code":"AAID"},"primary":true}}}  | {"browserDetails":{"acceptLanguage":"en-US","cookiesEnabled":false,"javaEnabled":false,"javaScriptEnabled":true,"javaScriptVersion":"1.6","userAgent":"Mozilla/5.0 (iPhone; U; CPU iPhone OS 4_1 like Mac OS X; ja-jp) AppleWebKit/532.9 (KHTML, like Gecko) Version/4.0.5 Mobile/8B117 Safari/6531.22.7","viewportHeight":490,"viewportWidth":1125},"domain":"xo.net","ipV4":"64.3.235.13"}     | {"AAID":[{"id":"31892EE080007B35-E6CE00000000000","primary":true}]}  | {"geo":{"_schema":{"latitude":34.01,"longitude":-84.0},"city":"lawrenceville","countryCode":"US","dmaID":524,"postalCode":"30043","stateProvince":"ga"},"localTimezoneOffset":600}                 | {"dataCenterLocation":"UT1"} | {"webPageDetails":{"isHomePage":false,"name":"Search Results","pageViews":{"value":1.0}},"webReferrer":{"URL":"http://www.google.com/search?ie=UTF-8&q=","type":"internal"}} |
 31892EE15DE00000-401B92664FF48AE8 | 2022-09-02 19:47:14.0 | 2022-09-02 19:47:14.0 | {"analytics":{"customDimensions":{"eVars":{"eVar1":"1","eVar2":"1"},"props":{"prop1":"1","prop2":"1"}},"environment":{"browserID":-209479095,"browserIDStr":"4085488201","operatingSystemID":-2105158467,"operatingSystemIDStr":"2189808829"}}} | {"userPerspective":"background"} | {"order":{"currencyCode":"USD"}} | {"_id":"475341"} | {"colorDepth":32,"screenHeight":768,"screenWidth":1024,"typeID":"205202","typeIDService":"https://ns.adobe.com/xdm/external/deviceatlas"} | {"_experience":{"aaid":{"id":"31892EE100007BF3-215FE00000000001","namespace":{"code":"AAID"},"primary":true}}} | {"browserDetails":{"acceptLanguage":"en-US","cookiesEnabled":false,"javaEnabled":false,"javaScriptEnabled":true,"javaScriptVersion":"1.5","userAgent":"Mozilla/5.0 (iPhone; U; CPU iPhone OS 4_1 like Mac OS X; ja-jp) AppleWebKit/532.9 (KHTML, like Gecko) Version/4.0.5 Mobile/8B117 Safari/6531.22.7","viewportHeight":768,"viewportWidth":556},"domain":"ntt.net","ipV4":"219.165.108.145"} | {"AAID":[{"id":"31892EE100007BF3-215FE00000000001","primary":true}]} | {"geo":{"_schema":{"latitude":34.989999999999995,"longitude":138.42},"city":"shizuoka","countryCode":"JP","dmaID":392005,"postalCode":"420-0812","stateProvince":"22"},"localTimezoneOffset":-240} | {"dataCenterLocation":"UT1"} | {"webPageDetails":{"isHomePage":false,"name":"Home - JJEsquire","pageViews":{"value":1.0}},"webReferrer":{"type":"typed_bookmarked"}}                                        |
(2 rows)

Resolve fallback snapshot on failure resolve-fallback-snapshot-on-failure

The resolve_fallback_snapshot_on_failure option is used to resolve the issue of an expired snapshot ID. Snapshot metadata expires after two days and an expired snapshot can invalidate the logic of a script. This can be a problem when using anonymous blocks.

Set the resolve_fallback_snapshot_on_failure option to true to override a snapshot with a previous snapshot ID.

SET resolve_fallback_snapshot_on_failure=true;

The following line of code overrides the @from_snapshot_id with the earliest available snapshot_id from metadata.

$$ BEGIN
    SET resolve_fallback_snapshot_on_failure=true;
    SET @from_snapshot_id = SELECT coalesce(last_snapshot_id, 'HEAD') FROM checkpoint_log a JOIN
                            (SELECT MAX(process_timestamp)process_timestamp FROM checkpoint_log
                                WHERE process_name = 'DIM_TABLE_ABC' AND process_status = 'SUCCESSFUL' )b
                                on a.process_timestamp=b.process_timestamp;
    SET @to_snapshot_id = SELECT snapshot_id FROM (SELECT history_meta('DIM_TABLE_ABC')) WHERE  is_current = true;
    SET @last_updated_timestamp= SELECT CURRENT_TIMESTAMP;
    INSERT INTO DIM_TABLE_ABC_Incremental
     SELECT  *  FROM DIM_TABLE_ABC SNAPSHOT BETWEEN @from_snapshot_id AND @to_snapshot_id WHERE NOT EXISTS (SELECT _id FROM DIM_TABLE_ABC_Incremental a WHERE _id=a._id);

Insert Into
   checkpoint_log
   SELECT
       'DIM_TABLE_ABC' process_name,
       'SUCCESSFUL' process_status,
      cast( @to_snapshot_id AS string) last_snapshot_id,
      cast( @last_updated_timestamp AS TIMESTAMP) process_timestamp;
EXCEPTION
  WHEN OTHER THEN
    SELECT 'ERROR';
END
$$;

Data asset organization

It is important to logically organize your data assets within the Adobe Experience Platform data lake as they grow. Query Service extends SQL constructs that enable you to logically group data assets within a sandbox. This method of organization allows for the sharing of data assets between schemas without the need to move them physically.

The following SQL constructs using standard SQL syntax are supported for you to logically organize your data.

CREATE DATABASE dg1;
CREATE SCHEMA dg1.schema1;
CREATE table t1 ...;
CREATE view v1 ...;
ALTER TABLE t1 ADD PRIMARY KEY (c1) NOT ENFORCED;
ALTER TABLE t2 ADD FOREIGN KEY (c1) REFERENCES t1(c1) NOT ENFORCED;

See the logical organization of data assets guide for a more detailed explanation on Query Service best practices.

Table exists

The table_exists SQL command is used to confirm whether a table currently exists in the system. The command returns a boolean value: true if the table does exist, and false if the table does not exist.

By validating whether a table exists before running the statements, the table_exists feature simplifies the process of writing an anonymous block to cover both the CREATE and INSERT INTO use cases.

The following syntax defines the table_exists command:

$$
BEGIN

#Set mytableexist to true if the table already exists.
SET @mytableexist = SELECT table_exists('target_table_name');

#Create the table if it does not already exist (this is a one time operation).
CREATE TABLE IF NOT EXISTS target_table_name AS
  SELECT *
  FROM   profile_dim_date limit 10;

#Insert data only if the table already exists. Check if @mytableexist = 'true'
 INSERT INTO target_table_name           (
                     select *
                     from   profile_dim_date
                     WHERE  @mytableexist = 'true' limit 20
              ) ;
EXCEPTION
WHEN other THEN SELECT 'ERROR';

END $$;

Inline inline

The inline function separates the elements of an array of structs and generates the values into a table. It can only be placed in the SELECT list or a LATERAL VIEW.

The inline function cannot be placed in a select list where there are other generator functions.

By default, the columns produced are named “col1”, “col2”, and so on. If the expression is NULL then no rows are produced.

TIP

Column names can be renamed using the RENAME command.

Example

> SELECT inline(array(struct(1, 'a'), struct(2, 'b'))), 'Spark SQL';

The example returns the following:

1  a Spark SQL
2  b Spark SQL

This second example further demonstrates the concept and application of the inline function. The data model for the example is illustrated in the image below.

A schema diagram for productListItems.

Example

select inline(productListItems) from source_dataset limit 10;

The values taken from the source_dataset are used to populate the target table.

SKU

_experience

quantity

priceTotal

product-id-1

(“(”(“(A,pass,B,NULL)”)“)”)

10.5

product-id-5

(“(”(“(A, pass, B,NULL)”)“)”)

product-id-2

(“(”(“(AF, C, D,NULL)”)“)”)

product-id-4

(“(”(“(BM, pass, NA,NULL)”)“)”)

Spark SQL commands

The subsection below covers the Spark SQL commands supported by Query Service.

SET

The SET command sets a property and either returns the value of an existing property or lists all the existing properties. If a value is provided for an existing property key, the old value is overridden.

SET property_key = property_value

Parameters

Description

property_key

The name of the property that you want to list or alter.

property_value

The value that you want the property to be set as.

To return the value for any setting, use SET [property key] without a property_value.

PostgreSQL commands

The subsections below cover the PostgreSQL commands supported by Query Service.

ANALYZE TABLE analyze-table

The ANALYZE TABLE command performs a distribution analysis and statistical calculations for the named table or tables. The use of ANALYZE TABLE varies depending on whether the datasets are stored on the accelerated store or the data lake. See their respective sections for more information on its use.

COMPUTE STATISTICS on the accelerated store compute-statistics-accelerated-store

The ANALYZE TABLE command computes statistics for a table on the accelerated store. The statistics are calculated on executed CTAS or ITAS queries for a given table on the accelerated store.

Example

ANALYZE TABLE <original_table_name>

The following is a list of statistical calculations that are available after using the ANALYZE TABLE command:-

Calculated values

Description

field

The name of the column in a table.

data-type

The acceptable type of data for each column.

count

The number of rows that contain a non-null value for this field.

distinct-count

The number of unique or distinct values for this field.

missing

The number of rows that have a null value for this field.

max

The maximum value from the analyzed table.

min

The minimum value from the analyzed table.

mean

The average value of the analyzed table.

stdev

The standard deviation of the analyzed table.

COMPUTE STATISTICS on the data lake compute-statistics-data-lake

You can now calculate column-level statistics on Azure Data Lake Storage (ADLS) datasets with the COMPUTE STATISTICS SQL command. Compute column statistics on either the entire dataset, a subset of a dataset, all columns, or a subset of columns.

COMPUTE STATISTICS extends the ANALYZE TABLE command. However, the COMPUTE STATISTICS, FILTERCONTEXT, and FOR COLUMNS commands are not supported on accelerated store tables. These extensions for the ANALYZE TABLE command are currently only supported for ADLS tables.

Example

ANALYZE TABLE tableName FILTERCONTEXT (timestamp >= to_timestamp('2023-04-01 00:00:00') and timestamp <= to_timestamp('2023-04-05 00:00:00')) COMPUTE STATISTICS  FOR COLUMNS (commerce, id, timestamp);

The FILTER CONTEXT command calculates statistics on a subset of the dataset based on the filter condition provided. The FOR COLUMNS command targets specific columns for analysis.

NOTE

The Statistics ID and the statistics generated are only valid for each session and cannot be accessed across different PSQL sessions.

Limitations:

Statistics generation is not supported for array or map data types
Computed statistics are not persisted across sessions.

skip_stats_for_complex_datatypes

SET skip_stats_for_complex_datatypes = false

The console output appears as seen below.

|     Statistics ID      |
| ---------------------- |
| adc_geometric_stats_1  |
(1 row)

You can then query the computed statistics directly by referencing the Statistics ID. Use the the Statistics ID or the alias name as shown in the example statement below, to view the output in full. To learn more about this feature, see the alias name documentation.

-- This statement gets the statistics generated for `alias adc_geometric_stats_1`.
SELECT * FROM adc_geometric_stats_1;

Use the SHOW STATISTICS command to display the metadata for all the temporary statistics generated in the session. This command can help you refine the scope of your statistical analysis.

SHOW STATISTICS;

An example output of SHOW STATISTICS is seen below.

      statsId         |   tableName   | columnSet |         filterContext       |      timestamp
----------------------+---------------+-----------+-----------------------------+--------------------
adc_geometric_stats_1 | adc_geometric |   (age)   |                             | 25/06/2023 09:22:26
demo_table_stats_1    |  demo_table   |    (*)    |       ((age > 25))          | 25/06/2023 12:50:26
age_stats             | castedtitanic |   (age)   | ((age > 25) AND (age < 40)) | 25/06/2023 09:22:26

See the dataset statistics documentation for more information.

TABLESAMPLE tablesample

Adobe Experience Platform Query Service provides sample datasets as part of its approximate query processing capabilities.

Data set samples are best used when you do not need an exact answer for an aggregate operation over a dataset. To conduct more efficient exploratory queries on large datasets by issuing an approximate query to return an approximate answer, use the TABLESAMPLE feature.

Sample datasets are created with uniform random samples from existing Azure Data Lake Storage (ADLS) datasets, using only a percentage of records from the original. The dataset sample feature extends the ANALYZE TABLE command with the TABLESAMPLE and SAMPLERATE SQL commands.

In the example below, line one demonstrates how to compute a 5% sample of the table. Line two demonstrates how to compute a 5% sample from a filtered view of the data within the table.

Example

ANALYZE TABLE tableName TABLESAMPLE SAMPLERATE 5;
ANALYZE TABLE tableName FILTERCONTEXT (timestamp >= to_timestamp('2023-01-01')) TABLESAMPLE SAMPLERATE 5:

See the dataset samples documentation for more information.

BEGIN

The BEGIN command, or alternatively the BEGIN WORK or BEGIN TRANSACTION command, initiates a transaction block. Any statements that are inputted after the begin command will be executed in a single transaction until an explicit COMMIT or ROLLBACK command is given. This command is the same as START TRANSACTION.

BEGIN
BEGIN WORK
BEGIN TRANSACTION

CLOSE

The CLOSE command frees the resources associated with an open cursor. After the cursor is closed, no subsequent operations are allowed on it. A cursor should be closed when it is no longer needed.

CLOSE name
CLOSE ALL

If CLOSE name is used, name represents the name of an open cursor that must be closed. If CLOSE ALL is used, all open cursors are closed.

DEALLOCATE

To deallocate a previously prepared SQL statement, use the DEALLOCATE command. If you did not explicitly deallocate a prepared statement, it is deallocated when the session ends. More information about prepared statements can be found in the PREPARE command section.

DEALLOCATE name
DEALLOCATE ALL

If DEALLOCATE name is used, name represents the name of the prepared statement that must be deallocated. If DEALLOCATE ALL is used, all the prepared statements are deallocated.

DECLARE

The DECLARE command allows a user to create a cursor, which can be used to retrieve a small number of rows out of a larger query. After the cursor is created, rows are fetched from it using FETCH.

DECLARE name CURSOR FOR query

Parameters

Description

name

The name of the cursor to be created.

query

A SELECT or VALUES command which provides the rows to be returned by the cursor.

EXECUTE

The EXECUTE command is used to execute a previously prepared statement. Since prepared statements only exist during a session, the prepared statement must have been created by a PREPARE statement executed earlier in the current session. More information about using prepared statements can be found in the PREPARE command section.

If the PREPARE statement that created the statement specified some parameters, a compatible set of parameters must be passed to the EXECUTE statement. If these parameters are not passed in, an error is raised.

EXECUTE name [ ( parameter ) ]

Parameters

Description

name

The name of the prepared statement to execute.

parameter

The actual value of a parameter to the prepared statement. This must be an expression yielding a value that is compatible with the data type of this parameter, as determined when the prepared statement was created. If there are multiple parameters for the prepared statement, they are separated by commas.

EXPLAIN

The EXPLAIN command displays the execution plan for the supplied statement. The execution plan shows how the tables referenced by the statement will be scanned. If multiple tables are referenced, it shows what join algorithms are used to bring together the required rows from each input table.

EXPLAIN statement

To define the format of the response, use the FORMAT keyword with the EXPLAIN command.

EXPLAIN FORMAT { TEXT | JSON } statement

Parameters

Description

FORMAT

Use the FORMAT command to specify the output format. The available options are TEXT or JSON. Non-text output contains the same information as the text output format, but is easier for programs to parse. This parameter defaults to TEXT.

statement

Any SELECT, INSERT, UPDATE, DELETE, VALUES, EXECUTE, DECLARE, CREATE TABLE AS, or CREATE MATERIALIZED VIEW AS statement, whose execution plan you want to see.

IMPORTANT

Any output that a SELECT statement might return is discarded when run with the EXPLAIN keyword. Other side effects of the statement happen as usual.

Example

The following example shows the plan for a simple query on a table with a single integer column and 10000 rows:

EXPLAIN SELECT * FROM foo;


                       QUERY PLAN
---------------------------------------------------------
 Seq Scan on foo (dataSetId = "6307eb92f90c501e072f8457", dataSetName = "foo") [0,1000000242,6973776840203d3d,6e616c58206c6153,6c6c6f430a3d4d20,74696d674c746365]
(1 row)

FETCH

The FETCH command retrieves rows using a previously created cursor.

FETCH num_of_rows [ IN | FROM ] cursor_name

Parameters

Description

num_of_rows

The number of rows to fetch.

cursor_name

The name of the cursor you’re retrieving information from.

PREPARE prepare

The PREPARE command lets you create a prepared statement. A prepared statement is a server-side object that can be used to templatize similar SQL statements.

Prepared statements can take parameters, which are values that are substituted into the statement when it is executed. Parameters are referred by position, using $1, $2, and so on, when using prepared statements.

Optionally, you can specify a list of parameter data types. If a parameter’s data type isn’t listed, the type can be inferred from the context.

PREPARE name [ ( data_type [, ...] ) ] AS SELECT

Parameters

Description

name

The name for the prepared statement.

data_type

The data types of the prepared statement’s parameters. If a parameter’s data type isn’t listed, the type can be inferred from the context. If you must add multiple data types, you can add them in a comma-separated list.

ROLLBACK

The ROLLBACK command undoes the current transaction and discards all the updates made by the transaction.

ROLLBACK
ROLLBACK WORK

SELECT INTO

The SELECT INTO command creates a new table and fills it with data computed by a query. The data is not returned to the client, as it is with a normal SELECT command. The new table’s columns have the names and data types associated with the output columns of the SELECT command.

[ WITH [ RECURSIVE ] with_query [, ...] ]
SELECT [ ALL | DISTINCT [ ON ( expression [, ...] ) ] ]
    * | expression [ [ AS ] output_name ] [, ...]
    INTO [ TEMPORARY | TEMP | UNLOGGED ] [ TABLE ] new_table
    [ FROM from_item [, ...] ]
    [ WHERE condition ]
    [ GROUP BY expression [, ...] ]
    [ HAVING condition [, ...] ]
    [ WINDOW window_name AS ( window_definition ) [, ...] ]
    [ { UNION | INTERSECT | EXCEPT } [ ALL | DISTINCT ] select ]
    [ ORDER BY expression [ ASC | DESC | USING operator ] [ NULLS { FIRST | LAST } ] [, ...] ]
    [ LIMIT { count | ALL } ]
    [ OFFSET start [ ROW | ROWS ] ]
    [ FETCH { FIRST | NEXT } [ count ] { ROW | ROWS } ONLY ]
    [ FOR { UPDATE | SHARE } [ OF table_name [, ...] ] [ NOWAIT ] [...] ]

More information about the standard SELECT query parameters can be found in the SELECT query section. This section only lists parameters that are exclusive to the SELECT INTO command.

Parameters

Description

TEMPORARY or TEMP

An optional parameter. If the parameter is specified, the created table is a temporary table.

UNLOGGED

An optional parameter. If the parameter is specified, the created table is an unlogged table. More information about unlogged tables can be found in the PostgreSQL documentation.

new_table

The name of the table to be created.

Example

The following query creates a new table films_recent consisting of only recent entries from the table films:

SELECT * INTO films_recent FROM films WHERE date_prod >= '2002-01-01';

SHOW

The SHOW command displays the current setting of runtime parameters. These variables can be set using the SET statement, by editing the postgresql.conf configuration file, through the PGOPTIONS environmental variable (when using libpq or a libpq-based application), or through command-line flags when starting the Postgres server.

SHOW name
SHOW ALL

Parameters

Description

name

The name of the runtime parameter you want information about. Possible values for the runtime parameter include the following values:
SERVER_VERSION: This parameter shows the server’s version number.
SERVER_ENCODING: This parameter shows the server-side character set encoding.
LC_COLLATE: This parameter shows the database’s locale setting for collation (text ordering).
LC_CTYPE: This parameter shows the database’s locale setting for character classification.
IS_SUPERUSER: This parameter shows if the current role has superuser privileges.

ALL

Show the values of all configuration parameters with descriptions.

Example

The following query shows the current setting of the parameter DateStyle.

SHOW DateStyle;

 DateStyle
-----------
 ISO, MDY
(1 row)

COPY

The COPY command duplicates the output of any SELECT query to a specified location. The user must have access to this location for this command to succeed.

COPY query
    TO '%scratch_space%/folder_location'
    [  WITH FORMAT 'format_name']

Parameters

Description

query

The query that you want to copy.

format_name

The format that you want to copy the query in. The format_name can be one of parquet, csv, or json. By default, the value is parquet.

NOTE

The complete output path is adl://<ADLS_URI>/users/<USER_ID>/acp_foundation_queryService/folder_location/<QUERY_ID>

ALTER TABLE alter-table

The ALTER TABLE command lets you add or drop primary or foreign key constraints and add columns to the table.

ADD or DROP CONSTRAINT

The following SQL queries show examples of adding or dropping constraints to a table. Primary key and foreign key constraints can be added to multiple columns with comma-separated values. You can create composite keys by passing two or more column name values as seen in the examples below.

Define primary or composite keys


ALTER TABLE table_name ADD CONSTRAINT PRIMARY KEY ( column_name ) NAMESPACE namespace

ALTER TABLE table_name ADD CONSTRAINT PRIMARY KEY ( column_name1, column_name2 ) NAMESPACE namespace

Define a relationship between tables based on one or more keys

ALTER TABLE table_name ADD CONSTRAINT FOREIGN KEY ( column_name ) REFERENCES referenced_table_name ( primary_column_name )

ALTER TABLE table_name ADD CONSTRAINT FOREIGN KEY ( column_name1, column_name2 ) REFERENCES referenced_table_name ( primary_column_name1, primary_column_name2 )

Define an identity column

ALTER TABLE table_name ADD CONSTRAINT PRIMARY IDENTITY ( column_name ) NAMESPACE namespace

ALTER TABLE table_name ADD CONSTRAINT IDENTITY ( column_name ) NAMESPACE namespace

Drop a constraint/relationship/identity

ALTER TABLE table_name DROP CONSTRAINT PRIMARY KEY ( column_name )

ALTER TABLE table_name DROP CONSTRAINT PRIMARY KEY ( column_name1, column_name2 )

ALTER TABLE table_name DROP CONSTRAINT FOREIGN KEY ( column_name )

ALTER TABLE table_name DROP CONSTRAINT FOREIGN KEY ( column_name1, column_name2 )

ALTER TABLE table_name DROP CONSTRAINT PRIMARY IDENTITY ( column_name )

ALTER TABLE table_name DROP CONSTRAINT IDENTITY ( column_name )

Parameters

Description

table_name

The name of the table which you are editing.

column_name

The name of the column that you are adding a constraint to.

referenced_table_name

The name of the table that is referenced by the foreign key.

primary_column_name

The name of the column that is referenced by the foreign key.

NOTE

The table schema should be unique and not shared among multiple tables. Also, the namespace is mandatory for primary key, primary identity, and identity constraints.

Add or drop primary and secondary identities

To add or delete constraints for both primary and secondary identity table columns, use the ALTER TABLE command.

The following examples add a primary identity and a secondary identity by adding constraints.

ALTER TABLE t1 ADD CONSTRAINT PRIMARY IDENTITY (id) NAMESPACE 'IDFA';
ALTER TABLE t1 ADD CONSTRAINT IDENTITY(id) NAMESPACE 'IDFA';

Identities can also be removed by dropping constraints, as seen in the example below.

ALTER TABLE t1 DROP CONSTRAINT PRIMARY IDENTITY (c1) ;
ALTER TABLE t1 DROP CONSTRAINT IDENTITY (c1) ;

For more detailed information, see the document on setting identities in an ad hoc datasets.

ADD COLUMN

The following SQL queries show examples of adding columns to a table.

ALTER TABLE table_name ADD COLUMN column_name data_type

ALTER TABLE table_name ADD COLUMN column_name_1 data_type1, column_name_2 data_type2

Supported data types

The following table lists the accepted data types for adding columns to a table with Postgres SQL, XDM, and the Accelerated Database Recovery (ADR) in Azure SQL.

—

PSQL client

XDM

ADR

Description

bigint

int8

bigint

A numerical data type used to store large integers ranging from –9,223,372,036,854,775,807 to 9,223,372,036,854,775,807 in 8 bytes.

integer

int4

integer

A numerical data type used to store integers ranging from -2,147,483,648 to 2,147,483,647 in 4 bytes.

smallint

int2

smallint

A numerical data type used to store integers ranging from -32,768 to 215-1 32,767 in 2 bytes.

tinyint

int1

tinyint

A numerical data type used to store integers ranging from 0 to 255 in 1 byte.

varchar(len)

string

varchar(len)

A character data type that is of variable-size. varchar is best used when the sizes of the column data entries vary considerably.

double

float8

double precision

FLOAT8 and FLOAT are valid synonyms for DOUBLE PRECISION. double precision is a floating-point data type. Floating-point values are stored in 8 bytes.

double precision

float8

double precision

FLOAT8 is a valid synonym for double precision.double precision is a floating-point data type. Floating-point values are stored in 8 bytes.

date

The date data types are 4-byte stored calendar date values without any timestamp information. The range of valid dates is from 01-01-0001 to 12-31-9999.

datetime

A data type used to store an instant in time expressed as a calendar date and time of day. datetime includes the qualifiers of: year, month, day, hour, second, and fraction. A datetime declaration can include any subset of these time units that are joined in that sequence, or even comprise only a single time unit.

char(len)

string

char(len)

The char(len) keyword is used to indicate that the item is fixed-length character.

ADD SCHEMA

The following SQL query shows an example of adding a table to a database / schema.

ALTER TABLE table_name ADD SCHEMA database_name.schema_name

NOTE

ADLS tables and views cannot be added to DWH databases / schemas.

REMOVE SCHEMA

The following SQL query shows an example of removing a table from a database / schema.

ALTER TABLE table_name REMOVE SCHEMA database_name.schema_name

NOTE

DWH tables and views cannot be removed from physically linked DWH databases / schemas.

Parameters

Parameters

Description

table_name

The name of the table which you are editing.

column_name

The name of the column you want to add.

data_type

The data type of the column you want to add. Supported data types include the following: bigint, char, string, date, datetime, double, double precision, integer, smallint, tinyint, varchar.

SHOW PRIMARY KEYS

The SHOW PRIMARY KEYS command lists all the primary key constraints for the given database.

SHOW PRIMARY KEYS

    tableName | columnName    | datatype | namespace
------------------+----------------------+----------+-----------
 table_name_1 | column_name1  | text     | "ECID"
 table_name_2 | column_name2  | text     | "AAID"

SHOW FOREIGN KEYS

The SHOW FOREIGN KEYS command lists all the foreign key constraints for the given database.

SHOW FOREIGN KEYS

    tableName   |     columnName      | datatype | referencedTableName | referencedColumnName | namespace
------------------+---------------------+----------+---------------------+----------------------+-----------
 table_name_1   | column_name1        | text     | table_name_3        | column_name3         |  "ECID"
 table_name_2   | column_name2        | text     | table_name_4        | column_name4         |  "AAID"

SHOW DATAGROUPS

The SHOW DATAGROUPS command returns a table of all associated databases. For each database, the table includes schema, group type, child type, child name, and child ID.

SHOW DATAGROUPS

   Database   |      Schema       | GroupType |      ChildType       |                     ChildName                       |               ChildId
  -------------+-------------------+-----------+----------------------+----------------------------------------------------+--------------------------------------
   adls_db     | adls_scheema      | ADLS      | Data Lake Table      | adls_table1                                        | 6149ff6e45cfa318a76ba6d3
   adls_db     | adls_scheema      | ADLS      | Accelerated Store | _table_demo1                                       | 22df56cf-0790-4034-bd54-d26d55ca6b21
   adls_db     | adls_scheema      | ADLS      | View                 | adls_view1                                         | c2e7ddac-d41c-40c5-a7dd-acd41c80c5e9
   adls_db     | adls_scheema      | ADLS      | View                 | adls_view4                                         | b280c564-df7e-405f-80c5-64df7ea05fc3

SHOW DATAGROUPS FOR table

The SHOW DATAGROUPS FOR 'table_name' command returns a table of all associated databases that contain the parameter as its child. For each database, the table includes schema, group type, child type, child name, and child ID.

SHOW DATAGROUPS FOR 'table_name'

Parameters

table_name: The name of the table that you want to find associated databases for.

   Database   |      Schema       | GroupType |      ChildType       |                     ChildName                      |               ChildId
  -------------+-------------------+-----------+----------------------+----------------------------------------------------+--------------------------------------
   dwh_db_demo | schema2           | QSACCEL   | Accelerated Store | _table_demo2                                       | d270f704-0a65-4f0f-b3e6-cb535eb0c8ce
   dwh_db_demo | schema1           | QSACCEL   | Accelerated Store | _table_demo2                                       | d270f704-0a65-4f0f-b3e6-cb535eb0c8ce
   qsaccel     | profile_aggs      | QSACCEL   | Accelerated Store | _table_demo2                                       | d270f704-0a65-4f0f-b3e6-cb535eb0c8ce

recommendation-more-help

ccf2b369-4031-483f-af63-a93b5ae5e3fb

SQL syntax in Query Service

SELECT queries select-queries

SNAPSHOT clause

Example

WHERE clause

JOIN

UNION, INTERSECT, and EXCEPT

CREATE TABLE AS SELECT create-table-as-select

INSERT INTO

DROP TABLE

CREATE DATABASE

DROP DATABASE

DROP SCHEMA

CREATE VIEW

SHOW VIEWS

DROP VIEW

Anonymous block anonymous-block

Conditional statements in an anonymous block conditional-anonymous-block-statements

Nested IF statements

Exception blocks

Auto to JSON auto-to-json

Before setting the auto_to_json flag

After setting the auto_to_json flag

Resolve fallback snapshot on failure resolve-fallback-snapshot-on-failure

Data asset organization

Table exists

Inline inline

Spark SQL commands

SET

PostgreSQL commands

ANALYZE TABLE analyze-table

COMPUTE STATISTICS on the accelerated store compute-statistics-accelerated-store

COMPUTE STATISTICS on the data lake compute-statistics-data-lake

TABLESAMPLE tablesample

BEGIN

CLOSE

DEALLOCATE

DECLARE

EXECUTE

EXPLAIN

FETCH

PREPARE prepare

ROLLBACK

SELECT INTO

SHOW

COPY

ALTER TABLE alter-table

ADD or DROP CONSTRAINT

Add or drop primary and secondary identities

ADD COLUMN

Supported data types

ADD SCHEMA

REMOVE SCHEMA

SHOW PRIMARY KEYS

SHOW FOREIGN KEYS

SHOW DATAGROUPS

SHOW DATAGROUPS FOR table

Before setting the `auto_to_json` flag

After setting the `auto_to_json` flag