Data manipulation language

GoogleSQL supports the following statements for manipulating data:

INSERT
UPDATE
DELETE
MERGE

Example data

The sections in this topic use the following table schemas.

Singers table

Column Name	Data Type	Default Value
SingerId	`INT64 NOT NULL`	`<auto-increment>`
FirstName	`STRING`
LastName	`STRING`
BirthDate	`DATE`
Status	`STRING`	`"active"`
SingerInfo	`PROTO<SingerMetadata>`
Albums	`PROTO<Album>`

The proto, SingerMetadata, has the following definition:

message SingerMetadata {
  optional string    nationality = 1;
  repeated Residence residence   = 2;

  message Residence {
    required int64  start_year   = 1;
    optional int64  end_year     = 2;
    optional string city         = 3;
    optional string country      = 4 [default = "USA"];
  }
}

The proto, Album, has the following definition:

message Album {
  optional string title = 1;
  optional int32 tracks = 2;
  repeated string comments = 3;
  repeated Song song = 4;

  message Song {
    optional string songtitle = 1;
    optional int32 length = 2;
    repeated Chart chart = 3;

    message Chart {
      optional string chartname = 1;
      optional int32 rank = 2;
    }
  }
}

Concerts table

Column Name	Data Type	Default Value
VenueId	`INT64 NOT NULL`	`<auto-increment>`
SingerId	`INT64 NOT NULL`
ConcertDate	`DATE NOT NULL`
BeginTime	`TIMESTAMP`
EndTime	`TIMESTAMP`
TicketPrices	`ARRAY<INT64>`

Inventory table

Column Name	Data Type	Default Value
product	`STRING NOT NULL`
quantity	`INT64 NOT NULL`	0
supply_constrained	`BOOL`	false

NewArrivals table

Column Name	Data Type	Default Value
product	`STRING NOT NULL`
quantity	`INT64 NOT NULL`	0
warehouse	`STRING NOT NULL`

`INSERT` statement

Use the INSERT statement when you want to add new rows to a table.

INSERT [[OR] IGNORE | REPLACE | UPDATE] [INTO] target_name
 (column[, ...])
 input [ASSERT_ROWS_MODIFIED m] [ then_return_clause ]

input:
  {
    VALUES (column_values) [, ...]
    | SELECT_QUERY
  }

column_values:
  { value_expression | DEFAULT } [, ...]

then_return_clause:
  THEN RETURN
  [ WITH ACTION [ AS alias ]  ]
  { value_expression | expression [  [ AS ] alias ] } [, ...]

value_expression:
  [expression.]* [ except_clause ] [ replace_clause ]

except_clause:
  EXCEPT ( column_name [, ...] )

replace_clause:
  REPLACE ( expression [ AS ] column_name [, ...] )

INSERT statements must comply with the following rules:

Column names must be specified.
Duplicate names aren't allowed in the list of target columns.
Values must be added in the same order as the specified columns.
The number of values added must match the number of specified columns.
Values must have a type that's compatible with the target column.
All non-null columns must appear in the column list, and have a non-null value specified.

Statements that don't follow these rules will raise an error.

The following statement inserts a new row to the Singers example table:

INSERT INTO Singers
    (SingerId, FirstName, LastName, BirthDate, Status, SingerInfo)
VALUES (5, "Catalina", "Smith", "1990-08-17", "active", "nationality:'U.S.A.'");

This next statement inserts multiple rows into the table:

INSERT INTO Singers
    (SingerId, FirstName, LastName, BirthDate, Status, SingerInfo)
VALUES (6, "Zak", "Sterling", "1996-03-12", "active", "nationality:'U.S.A.'"),
       (7, "Edie", "Silver", "1998-01-23", "active", "nationality:'U.S.A.'");

You can construct INSERT statements to insert the results of a query. For example, the following statement inserts a new concert for a singer into the Concerts table.

INSERT INTO Concerts
  (VenueId, SingerId, ConcertDate, BeginTime, EndTime, TicketPrices)
SELECT c.VenueId, c.SingerId, DATE "2015-06-01", c.BeginTime, c.EndTime, c.TicketPrices
  FROM Concerts c WHERE c.SingerId = 1;

Value type compatibility

Values added with an INSERT statement must be compatible with the target column's type. A value's type is considered compatible with the target column's type if one of the following criteria are met:

The value type matches the column type exactly. (For example, inserting a value of type INT32 in a column that also has a type of INT32.)
The value type is one that can be implicitly coerced into another type. See the Coercion of the Expressions, Functions, and Operators topic for more information.
The value can be assigned after applying a narrowing integer or floating point cast. For example, if you attempt to add a value of INT64 into a column that supports INT32, GoogleSQL automatically adds a narrowing cast to INT32.

Default values

If the target table supports a default value for a column, you can omit the column from the INSERT statement. Alternatively, you can insert the default value explicitly using the DEFAULT keyword.

Note: If a column doesn't require a value, the default value is typically NULL.

The following example inserts default values for all unspecified columns. For example, the Singers table has one column that has a defined default value—the Status column, which has a default value of active. The SingerId column is defined NOT NULL and so has no implicit default value. For all other columns, the default value is NULL.

INSERT INTO Singers
  (FirstName)
VALUES ("Neil");

If this was the first row added to the table, a SELECT statement querying this row would return the following:

SingerId	FirstName	LastName	BirthDate	Status	SingerInfo	Albums
1	Neil	NULL	NULL	active	NULL	NULL

If you construct an INSERT statement to use a default value for a column and that column doesn't support a default value, the statement fails. For example, the Singers table contains a column, SingerId, which can't be NULL. Consequently, the following statement fails, because the SingerId doesn't support a default value:

INSERT INTO Singers
  (SingerId, FirstName, LastName, BirthDate, Status, SingerInfo)
VALUES (DEFAULT, "Andrew", "Duneskipper", "2000-01-15", "active", NULL);

Duplicate rows

By default, an INSERT statement fails when it tries to add a duplicate row. A duplicate row is defined as one for which the primary key or a unique key already exists in the table.

You can change this behavior by choosing one of the following options for handling duplicate rows:

INSERT OR IGNORE: The existing or first row is inserted, and the rest are ignored.
INSERT OR REPLACE: All but the last inserted row are replaced.
INSERT OR UPDATE: Updates are applied sequentially, ending with the last update.

Your statement would resemble the following:

INSERT [OR] { IGNORE | REPLACE | UPDATE } <rest of statement>

These keywords require that your table includes a primary key. If the table doesn't have a primary key, the statement fails.

If a table is unordered, the first row, last row, and sequence of rows are arbitrary.

`INSERT OR IGNORE`

You can instruct your INSERT statement to skip any rows that have duplicate primary keys by using the IGNORE keyword.

For example, the following statement instructs GoogleSQL to skip the row if it already exists in the table:

INSERT OR IGNORE INTO Singers
    (FirstName, LastName, BirthDate, Status, SingerInfo)
VALUES ("Catalina", "Smith", "1990-08-17", DEFAULT, "nationality:'U.S.A.'");

INSERT OR IGNORE ignores only duplicate rows that have the same primary key. GoogleSQL raises an error if any other constraint violation occurs, including duplicate keys in unique indexes.

`INSERT OR REPLACE`

You can instruct your INSERT statement to replace any rows that have duplicate primary keys by using the REPLACE statement. Replaced rows have the same primary key as the original row.

In the following example, the REPLACE keyword is used instead of IGNORE. This time, the duplicate row replaces the existing row in the table.

INSERT OR REPLACE INTO Singers
    (FirstName, LastName, BirthDate, Status, SingerInfo)
VALUES ("Zak", "Sterling", "1996-03-12", "active", "nationality:'U.S.A.'");

Using REPLACE is the same as deleting the existing row and inserting a replacement one. As a result, any columns that you don't mention in the replacement row are cleared and replaced with their default values. If you want your statement to preserve the values of unspecified columns, use INSERT OR UPDATE.

`INSERT OR UPDATE`

INSERT OR UPDATE updates the columns specified for one or more rows. Columns that aren't listed in the INSERT statement remain unchanged.

The following example illustrates how INSERT OR UPDATE changes an existing row. In this case, the status is changed from active to inactive.

INSERT OR UPDATE INTO Singers
    (SingerId, Status)
VALUES (5, "inactive");

Notice that if the row doesn't exist, the previous statement inserts a new row with values in the specified fields, and all other values set to their corresponding defaults.

When you use the UPDATE keyword, any column that you don't specify remains unchanged.

`INSERT` and `ASSERT_ROWS_MODIFIED`

With GoogleSQL, you can confirm how many rows are added each time you use an INSERT statement by using the ASSERT_ROWS_MODIFIED keyword.

With the ASSERT_ROWS_MODIFIED keyword, you specify the number of rows you expect the command to insert, and GoogleSQL compares that number against the number of rows actually modified. This check occurs before GoogleSQL commits the change to the database. If the row count matches, GoogleSQL commits the changes. Otherwise, GoogleSQL returns an error and rolls back the statement.

When calculating the number of rows modified, GoogleSQL includes rows that were inserted, updated, or replaced. It doesn't count rows that were skipped through the IGNORE keyword. To illustrate this, consider the following example of a Singers table:

SingerId	FirstName	LastName	BirthDate	SingerInfo	Albums
5	Alice	Trentor	1991-10-2	"nationality: 'U.S.A.'"	NULL

INSERT OR UPDATE INTO Singers
    (SingerId, FirstName, LastName, Birthdate, Status, SingerInfo)
VALUES (5, "Zak", "Sterling", "1996-03-12", "active", "nationality:'U.S.A.'"),
       (6, "Edie", "Silver", "1998-01-23", "active", "nationality:'U.S.A.'")
ASSERT_ROWS_MODIFIED 2;

One of the value expressions in the INSERT statement adds a new row, while the second updates an existing one. Both result in a total of 2 modified rows, which matches the ASSERT_ROWS_MODIFIED 2 clause of the statement.

The following statement uses INSERT OR IGNORE to add rows to the table.

INSERT OR IGNORE INTO Singers
    (SingerId, FirstName, LastName, Birthdate, Status, SingerInfo)
VALUES (5, "Zak", "Sterling", "1996-03-12", "active", "nationality:'U.S.A.'"),
       (7, "Edie", "Silver", "1998-01-23", "active", "nationality:'U.S.A.'")
ASSERT_ROWS_MODIFIED 1;

In this case, there is a collision when inserting a new row with a SingerId of 5, because a row with that SingerId value already exists. This statement inserts one row, which matches the ASSERT_ROWS_MODIFIED 1 statement.

`INSERT` and `THEN RETURN`

Use the THEN RETURN clause to return the results of the INSERT operation and selected data from the newly inserted rows. This is especially useful for retrieving values of columns with default values, generated columns, and auto-generated keys, without issuing additional SELECT statements.

THEN RETURN supports a returning clause to capture expressions based on newly inserted rows that include these parts:

WITH ACTION: An optional clause that adds a string column called ACTION to the result row set. Each value in this column represents the type of action that was applied to the column during statement execution. Values include "INSERT", "REPLACE", and "UPDATE". The ACTION column is appended as the last output column.
*: Returns all columns.
expression.*: Returns all columns from range variables, like a table or proto field. The dot star expression can't be applied on other expressions, including field access.
except_clause: Specifies the columns to exclude from the result. All matching column names are omitted from the output.
replace_clause: Specifies the columns to replace in the result. The column that matches the identifier in a replace_clause is replaced by the expression in that replace_clause. A replace_clause doesn't change the names or order of columns. However, it can change the value and the value type of a column.
expression: Represents a column name of the table specified by target_name or an expression that uses any combination of such column names. Column names are valid if they belong to columns of the table being modified. Excluded expressions include aggregate and window functions.
alias: A temporary name for an expression in the query.

The following query inserts three rows into a table, and uses THEN RETURN to fetch these rows and compute a new column called new_quantity.

INSERT Inventory (product, quantity)
VALUES ('washer', 20),
       ('dryer', 30),
       ('oven', 5)
THEN RETURN *, quantity * 10 AS new_quantity;

/*---------+----------+--------------------+--------------+
 | product | quantity | supply_constrained | new_quantity |
 +---------+----------+--------------------+--------------+
 | washer  | 20       | false              | 200          |
 | dryer   | 30       | false              | 300          |
 | oven    | 10       | false              | 50           |
 +---------+----------+--------------------+--------------*/

The following query tries to insert two rows into a table, but ignores a duplicated row. It uses THEN RETURN to fetch the inserted row and WITH ACTION to show the modified row action type.

INSERT IGNORE Inventory (product, quantity)
VALUES ('desk', 40),
       ('desk', 45)
THEN RETURN WITH ACTION * EXCEPT(supply_constrained);

/*----------+----------+--------+
 | product  | quantity | ACTION |
 +----------+----------+--------+
 | desk     | 40       | INSERT |
 +----------+----------+--------*/

The following query tries to insert or update a row into a table. It uses THEN RETURN to fetch the modified row and WITH ACTION to show the modified row action type.

INSERT OR UPDATE Inventory (product, quantity)
VALUES ('oven', 100)
THEN RETURN WITH ACTION product, quantity * 10 AS new_quantity;

/*---------+--------------+--------+
 | product | new_quantity | ACTION |
 +---------+--------------+--------+
 | oven    | 1000         | UPDATE |
 +---------+--------------+--------*/

`INSERT` examples

Add a new row to the Singers table.

INSERT INTO Singers
    (SingerId, FirstName, LastName, BirthDate, Status, SingerInfo)
VALUES (5, "Catalina", "Smith", "1990-08-17", DEFAULT, "nationality:'U.S.A.'");

RESULT: New singer, Catalina Smith, added to table.

Try to add a singer, but only if the SingerId isn't already in the table.

INSERT OR IGNORE INTO Singers
    (SingerId, FirstName, LastName, BirthDate, Status, SingerInfo)
VALUES (5, "Zak", "Sterling", "1996-03-12", "active", "nationality:'U.S.A.'");

RESULT: Table unaffected. Catalina Smith remains in the table.

Try to add another singer and replace an existing row if it has the same SingerId.

INSERT OR REPLACE INTO Singers
    (SingerId, FirstName, LastName, BirthDate, Status, SingerInfo)
VALUES (5, "Zak", "Sterling", "1996-03-12", "active", "nationality:'U.S.A.'");

RESULT: Singer, Catalina Smith, replaced with Zak Sterling.

Add a singer to the table, or update a singer if they already exist.

INSERT OR UPDATE INTO Singers
    (SingerId, FirstName, LastName, Status)
VALUES (5, "Zak", "Sterling", "inactive");

RESULT: The database updated the existing row Zak Sterling. His status is now inactive. All other values, such as BirthDate, remain unchanged.

Add a new row to the Singers table and return the DEFAULT column value.

INSERT INTO Singers (SingerId, FirstName,
LastName, BirthDate, Status, SingerInfo) VALUES (6, "Michael", "Leon",
"1958-08-29", DEFAULT, "nationality:'U.S.A.'") THEN RETURN Status;

RESULT: The database adds a new row to the Singer table and returns the value active of its Status column.

`DELETE` statement

Use the DELETE statement when you want to delete rows from a table.

DELETE [FROM] target_name
WHERE condition [ASSERT_ROWS_MODIFIED n] [ then_return_clause ]

then_return_clause:
  THEN RETURN
  [ WITH ACTION [ AS alias ]  ]
  { value_expression | expression [  [ AS ] alias ] } [, ...]

value_expression:
  [expression.]* [ except_clause ] [ replace_clause ]

except_clause:
  EXCEPT ( column_name [, ...] )

replace_clause:
  REPLACE ( expression [ AS ] column_name [, ...] )

Note: DELETE statements must comply with all statement rules.

`WHERE` keyword

Each time you construct a DELETE statement, you must use the WHERE keyword, followed by a condition. For example:

DELETE FROM Singers
WHERE LastName = "Sterling";

You can use the identifier for the target of the update as a range variable within the WHERE clause. For example:

DELETE FROM Singers s
WHERE s.LastName = "Sterling";

Using an identifier can help make your DELETE statement more explicit with regards to what data GoogleSQL should update.

The WHERE keyword is mandatory for any DELETE statement. However, you can set the condition for the WHERE keyword to be true, which results in the statement deleting all rows in the table.

DELETE FROM Singers s
WHERE true;

`DELETE` and `ASSERT_ROWS_MODIFIED`

With GoogleSQL, you can confirm how many rows are deleted each time you use a DELETE statement. You implement this confirmation through the ASSERT_ROWS_MODIFIED keyword.

DELETE FROM Singers
WHERE LastName = "Sterling"
ASSERT_ROWS_MODIFIED 1;

With the ASSERT_ROWS_MODIFIED keyword, you specify the number of rows you expect the command to delete, and compare that number against the number of rows actually deleted. This check occurs before GoogleSQL commits the change to the database. If the row count matches, GoogleSQL commits the changes. Otherwise, GoogleSQL returns an error and rolls back the statement.

`DELETE` and `THEN RETURN`

With THEN RETURN, you can obtain data from rows that are being removed from a table. To learn more about the values you can use in this clause, see INSERT and THEN RETURN

Example:

The following query deletes all rows in a table that contains a product called washer and then returns the deleted rows.

DELETE FROM Inventory
WHERE product = 'washer'
THEN RETURN *;

/*---------+----------+--------------------+
 | product | quantity | supply_constrained |
 +---------+----------+--------------------+
 | washer  | 20       | false              |
 +---------+----------+--------------------*/

`UPDATE` statement

Use the UPDATE statement when you want to update existing rows within a table.

UPDATE target_name
SET set_clause
[FROM from_clause]
WHERE condition [ASSERT_ROWS_MODIFIED n] [ then_return_clause ]

set_clause:
  update_item[, ...]

update_item:
  {
    path_expression = expression
    | path_expression = DEFAULT
    | (dml_stmt)
  }

then_return_clause:
  THEN RETURN
  [ WITH ACTION [ AS alias ]  ]
  { value_expression | expression [  [ AS ] alias ] } [, ...]

value_expression:
  [expression.]* [ except_clause ] [ replace_clause ]

except_clause:
  EXCEPT ( column_name [, ...] )

replace_clause:
  REPLACE ( expression [ AS ] column_name [, ...] )

dml_stmt:
  { insert_statement | update_statement | delete_statement }

Note: UPDATE statements must comply with all statement rules and use compatible types.

`FROM` keyword

An UPDATE statement can optionally include a FROM clause.

You can use the FROM clause to specify the rows to update in the target table. You can also use columns from joined tables in a SET clause or WHERE condition.

The FROM clause join can be a cross join if no condition is specified in the WHERE clause, otherwise it's an inner join. In either case, rows from the target table can join with at most one row from the FROM clause.

To specify the join predicate between the table to be updated and tables in the FROM clause, use the WHERE clause.

Caveats:

The SET clause can reference columns from a target table and columns from any FROM item in the FROM clause. If there is a name collision, unqualified references are treated as ambiguous.
If the target table is present in the FROM clause as a table name, it must have an alias if you would like to perform a self-join.
If a row in the table to be updated joins with zero rows from the FROM clause, then the row isn't updated.
If a row in the table to be updated joins with exactly one row from the FROM clause, then the row is updated.
If a row in the table to be updated joins with more than one row from the FROM clause, then the query generates a runtime error.

The following example generates a table with inventory totals that include existing inventory and inventory from the NewArrivals table, and marks supply_constrained as false.

UPDATE dataset.Inventory
SET quantity = quantity +
  (SELECT quantity FROM dataset.NewArrivals
   WHERE Inventory.product = NewArrivals.product),
    supply_constrained = false
WHERE product IN (SELECT product FROM dataset.NewArrivals)

Alternately, you can join the tables.

UPDATE dataset.Inventory i
SET quantity = i.quantity + n.quantity,
    supply_constrained = false
FROM dataset.NewArrivals n
WHERE i.product = n.product

Note: The join predicate between Inventory and NewArrivals is specified using the WHERE clause.

Before:

-- Inventory
/*-------------------+----------+--------------------+
 |      product      | quantity | supply_constrained |
 +-------------------+----------+--------------------+
 | dishwasher        |       30 |               NULL |
 | dryer             |       30 |               NULL |
 | front load washer |       20 |               NULL |
 | microwave         |       20 |               NULL |
 | oven              |        5 |               NULL |
 | refrigerator      |       10 |               NULL |
 | top load washer   |       10 |               NULL |
 +-------------------+----------+--------------------*/

-- NewArrivals
/*-----------------+----------+--------------+
 |     product     | quantity |  warehouse   |
 +-----------------+----------+--------------+
 | dryer           |      200 | warehouse #2 |
 | oven            |      300 | warehouse #3 |
 | top load washer |      100 | warehouse #1 |
 +-----------------+----------+--------------*/

After:

/*-------------------+----------+--------------------+
 |      product      | quantity | supply_constrained |
 +-------------------+----------+--------------------+
 | dishwasher        |       30 |               NULL |
 | dryer             |      230 |              false |
 | front load washer |       20 |               NULL |
 | microwave         |       20 |               NULL |
 | oven              |      305 |              false |
 | refrigerator      |       10 |               NULL |
 | top load washer   |      110 |              false |
 +-------------------+----------+--------------------*/

`WHERE` keyword

Each UPDATE statement must include the WHERE keyword, followed by a condition. For example, the following statement illustrates an UPDATE statement that modifies the row with the primary key, 5.

UPDATE Singers s
SET s.Status = "inactive"
WHERE s.SingerId = 5;

To update all rows in a table, use WHERE true. The following statement sets the SingerInfo value in all rows to NULL (the default value for the field).

UPDATE Singers s
SET s.SingerInfo = DEFAULT
WHERE true;

`UPDATE` and `ASSERT_ROWS_MODIFIED`

With GoogleSQL, you can confirm how many rows are updated each time you use an UPDATE statement. You implement this confirmation through the ASSERT_ROWS_MODIFIED keyword.

Note: A row still counts as modified even if the updated values are identical to the previous values.

With the ASSERT_ROWS_MODIFIED keyword, you specify the number of rows you expect the command to update. If the row count matches, GoogleSQL commits the changes. Otherwise, GoogleSQL returns an error and rolls back the statement.

The ASSERT_ROWS_MODIFIED keyword is helpful when you want to be sure your statement changes only a specific number of rows. For example, the following statement verifies that exactly one row was found and updated.

UPDATE Singers s
SET s.SingerInfo = DEFAULT
WHERE s.SingerId = 5
ASSERT_ROWS_MODIFIED 1;

`UPDATE` and `THEN RETURN`

With THEN RETURN, you can obtain data from rows that are being updated in a table. To learn more about the values you can use in this clause, see INSERT and THEN RETURN

Example:

The following query updates the supply_constrained column in this Inventory table when the quantity of product is zero, and returns all updated product with an out of stock message.

UPDATE Inventory
SET supply_constrained = true
WHERE quantity = 0
THEN RETURN product, product || " is out of stock" AS message;

Setting new values

Each UPDATE statement requires a SET keyword. This keyword identifies the columns that you want to update.

You specify values for specific columns using the syntax column_name = expression. Expressions can see all the columns within a table. For example:

UPDATE Singers s
SET s.SingerId = s.SingerId + 1
WHERE true

Any assignments within the expression happen simultaneously. For example, the following statement swaps the values in the FirstName and LastName column.

UPDATE Singers s
SET s.FirstName = s.LastName,
    s.LastName = s.FirstName
WHERE true;

Within the SET clause, you can use the identifier for the target of the update as a table alias. For example:

UPDATE Singers s
SET s.LastName = "Solo"
WHERE s.SingerId = 5;

Using an identifier can help make your UPDATE statement more explicit with regards to what data GoogleSQL should update.

Specifying columns

An UPDATE statement must explicitly state which columns you want to update. Any column not included in the UPDATE statement remains unmodified.

In the following example, notice that only the Status column is specified — consequently, the other columns (such as SingerId, FirstName, LastName, and Birthdate) remain unchanged.

UPDATE Singers s
SET s.Status = "inactive"
WHERE s.SingerId = 5;

You can specify the columns to update in any order; however, you can list each column only once. For example, the following statement is invalid and would be rejected by GoogleSQL:

UPDATE Singers s
SET s.LastName = "Solo", s.LastName = "Organa"
WHERE s.SingerId = 5;

UPDATE statements must use values that are compatible with the corresponding column's type. For example, a value of 1991-10-02 works for a column of type Date, but a value of October 2, 1991 wouldn't. You can, however, use values that can be cast to the type of the corresponding column. Casting happens automatically for numerical values (this is also referred to as coercion).

If you attempt to set a column with an invalid value, the statement fails. For example, the following statement doesn't work:

UPDATE Singers s
SET s.Birthdate = "October 2, 1991"
WHERE s.SingerId = 5;

Returning columns to default values

You can use an UPDATE statement to return any value to its corresponding default by using the DEFAULT keyword. In cases where the column contains a protocol buffer field, that field is cleared.

For example, consider the following Singers table:

SingerId	FirstName	LastName	BirthDate	SingerInfo	Albums
5	Alice	Trentor	1991-10-2	"nationality: 'USA'"	NULL
6	Zak	Sterling	1989-1-13	"nationality: 'Australia'"	NULL

If you run the following statement:

UPDATE Singers s
SET s.SingerInfo = DEFAULT
WHERE SingerId = 6;

The table is updated as follows:

SingerId	FirstName	LastName	BirthDate	SingerInfo	Albums
5	Alice	Trentor	1991-10-2	"nationality:'USA'"	NULL
6	Zak	Sterling	1989-1-13	NULL	NULL

Notice that the row for Zak Sterling now has NULL in the SingerInfo column, because NULL is the default value for that column. The following example has the same effect:

UPDATE Singers s
SET s.SingerInfo = NULL
WHERE SingerId = 6;

Updating fields

GoogleSQL allows you to update non-repeating or repeating fields within protocol buffers. To illustrate how to update a non-repeating field, consider the Singers example table. It contains a column, Albums, of type PROTO<Albums>, and Albums contains a non-repeating field tracks. The following statement updates the value of tracks:

UPDATE Singers s
SET s.Albums.tracks = 15
WHERE s.SingerId = 5 AND s.Albums.title = "Fire is Hot";

An update can replace a repeated field using an array of values, as shown below.

UPDATE Singers s
SET s.Albums.comments = ["A good album!", "Hurt my ears!", "Totally inedible."]
WHERE s.SingerId = 5 AND s.Albums.title = "Fire is Hot";

Nested updates

Inside a parent update statement you can construct DML statements that modify a repeated field of a protocol buffer or an array. Such statements are called nested updates.

For example, the Albums column contains a repeated field comments. This nested update statement adds a comment to an album:

UPDATE Singers s
SET (INSERT s.Albums.comments
     VALUES ("Groovy!"))
WHERE s.SingerId = 5 AND s.Albums.title = "Fire is Hot";

Albums also contains a repeated protocol buffer, Song, which provides information about a song on an album. This nested update statement updates the album with a new song:

UPDATE Singers s
SET (INSERT s.Albums.Song(Song)
     VALUES ("songtitle: 'Bonus Track', length: 180"))
WHERE s.SingerId = 5 AND s.Albums.title = "Fire is Hot";

If the repeated field is another protocol buffer, you can provide the protocol buffer as a string literal. For example, the following statement adds a new song to an album and updates the number of tracks. Notice that this statement uses ASSERT_ROWS_MODIFIED to ensure that only one Singer is updated.

UPDATE Singers s
SET (INSERT s.Albums.Song
     VALUES ('''songtitle: 'Bonus Track', length:180''')),
     s.Albums.tracks = 16
WHERE s.SingerId = 5 and s.Albums.title = "Fire is Hot"
ASSERT_ROWS_MODIFIED 1;

You can also nest a nested update statement in another nested update statement. For example, the Song protocol buffer itself has another repeated protocol buffer, Chart, which provides information on what chart the song appeared on, and what rank it had.

The following statement adds a new chart to a song.

UPDATE Singers s
SET (UPDATE s.Albums.Song so
    SET (INSERT INTO so.Chart
         VALUES ("chartname: 'Galaxy Top 100', rank: 5"))
    WHERE so.songtitle = "Bonus Track")
WHERE s.SingerId = 5
ASSERT_ROWS_MODIFIED 1;

This next statement updates the chart to reflect a new rank for the song. Notice that each inner UPDATE statement uses ASSERT_ROWS_MODIFIED 1 to ensure that only one update is made.

UPDATE Singers s
SET (UPDATE s.Albums.Song so
     SET (UPDATE so.Chart c
          SET c.rank = 2
          WHERE c.chartname = "Galaxy Top 100"
          ASSERT_ROWS_MODIFIED 1)
     WHERE so.songtitle = "Bonus Track"
     ASSERT_ROWS_MODIFIED 1)
WHERE s.SingerId = 5
ASSERT_ROWS_MODIFIED 1;

GoogleSQL treats an array or a repeated field inside a row that matches an UPDATE WHERE clause as a table, with individual elements of the array or field treated like rows. These rows can then have nested DML statements run against them, allowing you to delete, update, and insert data as needed.

Modifying multiple fields

The previous sections demonstrated how to update a single value within a compound data type. You can also perform multiple updates to a compound data type within a single statement. For example:

UPDATE Singers s
SET
    (DELETE FROM s.SingerInfo.Residence r WHERE r.City = 'Seattle'),
    (UPDATE s.Albums.Song song SET song.songtitle = 'No, This Is Rubbish' WHERE song.songtitle = 'This Is Pretty Good'),
    (INSERT s.Albums.Song VALUES ("songtitle: 'The Second Best Song'"))
WHERE SingerId = 3 AND s.Albums.title = 'Go! Go! Go!';

Nested queries are processed as follows:

Delete all rows that match a WHERE clause of a DELETE statement.
Update any remaining rows that match a WHERE clause of an UPDATE statement. Each row must match at most one UPDATE WHERE clause, or the statement fails due to overlapping updates.
Insert all rows in INSERT statements.

You must construct nested statements that affect the same field in the following order:

DELETE
UPDATE
INSERT

For example:

UPDATE Singers s
SET
    (DELETE FROM s.SingerInfo.Residence r WHERE r.City = 'Seattle'),
    (UPDATE s.SingerInfo.Residence r SET r.end_year = 2015 WHERE r.City = 'Eugene'),
    (INSERT s.Albums.Song VALUES ("songtitle: 'The Second Best Song'"))
WHERE SingerId = 3 AND s.Albums.title = 'Go! Go! Go!';

The following statement is invalid, because the UPDATE statement happens after the INSERT statement.

UPDATE Singers s
SET
    (DELETE FROM s.SingerInfo.Residence r WHERE r.City = 'Seattle'),
    (INSERT s.Albums.Song VALUES ("songtitle: 'The Second Best Song'")),
    (UPDATE s.SingerInfo.Residence r SET r.end_year = 2015 WHERE r.City = 'Eugene')
WHERE SingerId = 3 AND s.Albums.title = 'Go! Go! Go!';

Note: In nested queries, you can't use INSERT OR statements. These types of statements don't work because arrays and other compound data types don't always have a primary key, so there is no applicable definition of duplicate rows.

You can also add ASSERT_ROWS_MODIFIED to nested statements, as shown here:

UPDATE Singers
SET
    (DELETE FROM AlbumTitles a WHERE a.AlbumTitles = "Too Many Vegetables"
        ASSERT_ROWS_MODIFIED 1),
    (UPDATE AlbumTitles a SET a.AlbumTitles = "Album IV"
        WHERE a.AlbumTitles = "Album Four"),
    (INSERT AlbumTitles VALUES ("The Sloth and the Tiger"));

`MERGE` statement

Use the MERGE statement when you want to merge rows from a source table or subquery into a target table.

MERGE [INTO] target_name [[AS] alias]
USING source_name
ON merge_condition
when_clause [...]

when_clause:
  {
    matched_clause
    | not_matched_by_target_clause
    | not_matched_by_source_clause
  }

matched_clause:
  WHEN MATCHED [ AND search_condition ]
  THEN { merge_update_clause | merge_delete_clause }

not_matched_by_target_clause:
  WHEN NOT MATCHED [BY TARGET] [ AND search_condition ]
  THEN merge_insert_clause

not_matched_by_source_clause:
  WHEN NOT MATCHED BY SOURCE [ AND search_condition ]
  THEN { merge_update_clause | merge_delete_clause }

merge_condition:
  bool_expression

search_condition:
  bool_expression

merge_update_clause:
  UPDATE SET update_item [, ...]

update_item:
  path_expression = expression

merge_delete_clause:
  DELETE

merge_insert_clause:
  INSERT [(column [, ... ])] input

input:
  {
    VALUES (expr [, ... ])
    | ROW
  }

expr:
  { expression | DEFAULT }

A MERGE statement is a DML statement that can combine INSERT, UPDATE, and DELETE operations into a single statement and perform the operations atomically.

target_name: The name of the table you're changing.
source_name: A table name or subquery.
merge_condition: A MERGE statement performs a JOIN between the target and the source. Then, depending on the match status (row matched, only in source table, only in destination table), the corresponding WHEN clause is executed. The merge condition is used by the JOIN to match rows between source and target tables. Depending on the combination of WHEN clauses, different INNER and OUTER JOIN types are applied.

If the merge condition is FALSE, the query optimizer avoids using a JOIN. This optimization is referred to as a constant false predicate. A constant false predicate is useful when you perform an atomic DELETE on the target plus an INSERT from a source (DELETE with INSERT is also known as a REPLACE operation).
when_clause: The WHEN clause has three options: MATCHED, NOT MATCHED BY TARGET and NOT MATCHED BY SOURCE. There must be at least one WHEN clause in each MERGE statement.

Each WHEN clause can have an optional search condition. The WHEN clause is executed for a row if both the merge condition and search condition are satisfied. When there are multiple qualified clauses, only the first WHEN clause is executed for a row.
matched_clause: The WHEN MATCHED clause defines how to update or delete a row in the target table if that row matches a row in the source table.

If there is at least one matched clause performing an UPDATE operation, a runtime error is returned when multiple rows from the source table match one row from the target table, and you are trying to update or delete that row in the target table.
not_matched_by_target_clause: The WHEN NOT MATCHED clause defines how to insert into the target table if a row from the source table doesn't match any row in the target table.

When the column names of the target table are omitted, all columns in the target table are included in ascending order based on their ordinal positions.

ROW can be used to include all the columns of the source in the ascending sequence of their ordinal positions. Note that none of the pseudo column of the source table is included.
not_matched_by_source_clause: The WHEN NOT MATCHED BY SOURCE clause defines how to update or delete a row in the target table if that row doesn't match any row in the source table.

In the following example, the query merges items from the NewArrivals table into the Inventory table. If an item is already present in Inventory, the query increments the quantity field. Otherwise, the query inserts a new row.

MERGE dataset.Inventory T
USING dataset.NewArrivals S
ON T.product = S.product
WHEN MATCHED THEN
  UPDATE SET quantity = T.quantity + S.quantity
WHEN NOT MATCHED THEN
  INSERT (product, quantity) VALUES(product, quantity)

These are the tables before you run the query:

-- NewArrivals
/*-----------------+----------+--------------+
 |     product     | quantity |  warehouse   |
 +-----------------+----------+--------------+
 | dryer           |       20 | warehouse #2 |
 | oven            |       30 | warehouse #3 |
 | refrigerator    |       25 | warehouse #2 |
 | top load washer |       10 | warehouse #1 |
 +-----------------+----------+--------------*/

--Inventory
/*-------------------+----------+
 |      product      | quantity |
 +-------------------+----------+
 | dishwasher        |       30 |
 | dryer             |       30 |
 | front load washer |       20 |
 | microwave         |       20 |
 | oven              |        5 |
 | top load washer   |       10 |
 +-------------------+----------*/

This is the Inventory table after you run the query:

--Inventory
/*-------------------+----------+
 |      product      | quantity |
 +-------------------+----------+
 | dishwasher        |       30 |
 | dryer             |       50 |
 | front load washer |       20 |
 | microwave         |       20 |
 | oven              |       35 |
 | refrigerator      |       25 |
 | top load washer   |       20 |
 +-------------------+----------*/

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Data manipulation language

Example data

Singers table

Concerts table

Inventory table

NewArrivals table

`INSERT` statement

Value type compatibility

Default values

Duplicate rows

`INSERT OR IGNORE`

`INSERT OR REPLACE`

`INSERT OR UPDATE`

`INSERT` and `ASSERT_ROWS_MODIFIED`

`INSERT` and `THEN RETURN`

`INSERT` examples

`DELETE` statement

`WHERE` keyword

`DELETE` and `ASSERT_ROWS_MODIFIED`

`DELETE` and `THEN RETURN`

`UPDATE` statement

`FROM` keyword

`WHERE` keyword

`UPDATE` and `ASSERT_ROWS_MODIFIED`

`UPDATE` and `THEN RETURN`

Setting new values

Specifying columns

Returning columns to default values

Updating fields

Nested updates

Modifying multiple fields

`MERGE` statement

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Data manipulation language

Example data

Singers table

Concerts table

Inventory table

NewArrivals table

INSERT statement

Value type compatibility

Default values

Duplicate rows

INSERT OR IGNORE

INSERT OR REPLACE

INSERT OR UPDATE

INSERT and ASSERT_ROWS_MODIFIED

INSERT and THEN RETURN

INSERT examples

DELETE statement

WHERE keyword

DELETE and ASSERT_ROWS_MODIFIED

DELETE and THEN RETURN

UPDATE statement

FROM keyword

WHERE keyword

UPDATE and ASSERT_ROWS_MODIFIED

UPDATE and THEN RETURN

Setting new values

Specifying columns

Returning columns to default values

Updating fields

Nested updates

Modifying multiple fields

MERGE statement

`INSERT` statement

`INSERT OR IGNORE`

`INSERT OR REPLACE`

`INSERT OR UPDATE`

`INSERT` and `ASSERT_ROWS_MODIFIED`

`INSERT` and `THEN RETURN`

`INSERT` examples

`DELETE` statement

`WHERE` keyword

`DELETE` and `ASSERT_ROWS_MODIFIED`

`DELETE` and `THEN RETURN`

`UPDATE` statement

`FROM` keyword

`WHERE` keyword

`UPDATE` and `ASSERT_ROWS_MODIFIED`

`UPDATE` and `THEN RETURN`

`MERGE` statement