FireDAC Components for Couchbase

Build 21.0.7930

Query Mapping

The component maps SQL-92-compliant queries into corresponding N1QL or SQL++ queries. Although the mapping below is not complete, it should help you get a sense for the common patterns the component uses during this transformation.

SELECT Queries

The SELECT statements are translated to the appropriate N1QL SELECT query as shown below. Due to the similarities between SQL-92 and N1QL, many queries will simply be direct translations.

One major difference is that when the schema for a given Couchbase bucket exists in the component, a SELECT * query will be translated to directly select the individual fields in the bucket. The component will also automatically create a "Document.Id" column based on the primary key of each document in the bucket.

SQL Query N1QL Query
SELECT * FROM users SELECT META(`users`).id AS `id`, ... FROM `users`
SELECT [Document.Id], status FROM users SELECT META(`users`).id AS `Document.Id`, `users`.`status` FROM `users`
SELECT * FROM users WHERE status = 'A' OR age = 50 SELECT META(`users`).id AS `id`, ... FROM `users` WHERE TOSTRING(`users`.`status`) = "A" OR TONUMBER(`users`.`age`) = 50
SELECT * FROM users WHERE name LIKE 'A%' SELECT META(`users`).id AS `id`, ... FROM `users` WHERE TOSTRING(`users`.`name`) LIKE "A%"
SELECT * FROM users WHERE status = 'A' ORDER BY [Document.Id] DESC SELECT META(`users`).id AS `id`, ... FROM `users` WHERE TOSTRING(`users`.`status`) = "A" ORDER BY META(`users`).id DESC
SELECT * FROM users WHERE status IN ('A', 'B') SELECT META(`users`).id, ... FROM `users` WHERE TOSTRING(`users`.`status`) IN ["A", "B"]

Note that conditions can include extra type functions if the component detects that a type conversion may be necessary. You can disable these type conversions using the StrictComparison property. For clarity, the rest of the N1QL samples are shown without these extra conversion functions.

Child Tables

As long as all the child tables in a query share the same parent, and they are combined using INNER JOINs on their "Document.Id" columns, the component will combine the JOINs into a single UNNEST expression. Unlike N1QL UNNEST queries, you must explicitly JOIN with the base table if you want to access its fields.

SQL Query N1QL Query
SELECT * FROM users_posts SELECT META(`users`).id, `users_posts`.`text`, ... FROM `users` UNNEST `users`.`posts` AS `users_posts`
SELECT * FROM users INNER JOIN users_posts ON users.[Document.Id] = users_posts.[Document.Id] SELECT META(`users`).id, `users`.`name`, ..., `users_posts`.`text`, ... FROM `users` UNNEST `users`.`posts` AS `users_posts`
SELECT * FROM users INNER JOIN users_posts ... INNER JOIN users_comments ON ... SELECT ... FROM `users` UNNEST `users`.`posts` AS `users_posts` UNNEST `users`.`comments` AS `users_comments`

Flavor Tables

Flavored tables always have the appropriate condition included when you query, so that only documents from the flavor will be returned:

SQL Query N1QL Query
SELECT * FROM [users.subscriber] SELECT ... FROM `users` WHERE `docType` = "subscriber"
SELECT * FROM [users.subscriber] WHERE age > 50 SELECT ... FROM `users` WHERE `docType` = "subscriber" AND `age` > 50

Aggregate Queries

N1QL has several built-in aggregate functions. The component makes extensive use of this for various aggregate queries. See some examples below:

SQL QueryN1QL Query
SELECT Count(*) As Count FROM OrdersSELECT Count(*) AS `count` FROM `Orders`
SELECT Sum(price) As total FROM OrdersSELECT Sum(`price`) As `total` FROM `Orders`
SELECT cust_id, Sum(price) As total FROM Orders GROUP BY cust_id ORDER BY totalSELECT `cust_id`, Sum(`price`) As `total` FROM `Orders` GROUP BY `cust_id` ORDER BY `total`
SELECT cust_id, ord_date, Sum(price) As total FROM Orders GROUP BY cust_id, ord_date Having total > 250SELECT `cust_id`, `ord_date`, Sum(`price`) As `total` FROM `Orders` GROUP BY `cust_id`, `ord_date` Having `total` > 250

Insert Statements

The SQL INSERT statement is mapped to the N1QL INSERT statement as shown below. This works the same for both top-level fields as well as fields produced by Vertical Flattening:

SQL QueryN1QL Query
INSERT INTO users([Document.Id], age, status) VALUES ('bcd001', 45, 'A') INSERT INTO `users`(KEY, VALUE) VALUES ('bcd001', { "age" : 45, "status" : "A" })
INSERT INTO users([Document.Id], [metrics.posts]) VALUES ('bcd002', 0) INSERT INTO `users`(KEY, VALUE) VALUES ('bcd002', {"metrics': {"posts": 0}})

Child Table Inserts

Inserts on child tables are converted internally into N1QL UPDATEs using array operations. Since that this does not create the top-level document, the Document.Id provided must refer to a document that already exists.

Another limitation of child table inserts is that multi-valued inserts must all use the same Document.Id. The provider will verify this before modifying any data and raise an error if this constraint is violated.

SQL Query N1QL Query
INSERT INTO users_ratings([Document.Id], value) VALUES ('bcd001', 4.8), ('bcd001', 3.2) UPDATE `users` USE KEYS "bcd001" SET `ratings` = ARRAY_PUT(`ratings`, 4.8, 3.2)
INSERT INTO users_reviews([Document.Id], score) VALUES ('bcd002', 'Great'), ('bcd002', 'Lacking') UPDATE `users` USE KEYS "bcd001" SET `ratings` = ARRAY_PUT(`ratings`, {"score": "Great"}, {"score": "Lacking"})

Bulk Insert Statements

Bulk inserts are also supported the SQL Bulk Insert is converted as shown below:
INSERT INTO users#Temp([Document.Id], KEY, VALUE) VALUES('bcd001', 45, "A")
INSERT INTO users#Temp([Document.Id], KEY, VALUE) VALUES('bcd002', 24, "B")
INSERT INTO users SELECT * FROM users#Temp
is converted to:
INSERT INTO `users` (KEY, VALUE) VALUES
  ('bcd001', {"age": 45, "status": "A"}),
  ('bcd002', {"age": 24, "status": "B"})

Like multi-valued inserts on child tables, all the rows in a bulk insert must also have the same Document.Id.

Update Statements

The SQL UPDATE statement is mapped to the N1SQL UPDATE statement as shown below:

SQL QueryN1QL Query
UPDATE users SET status = 'C' WHERE [Document.Id] = 'bcd001' UPDATE `users` USE KEYS ["bcd001"] SET `status` = "C"
UPDATE users SET status = 'C' WHERE age > 45 UPDATE `users` SET `status` = "C" WHERE `age` > 45

Child Table Updates

When updating a child table, the SQL query is converted to an UPDATE query using either a "FOR" expression or an "ARRAY" expression:

SQL Query N1QL Query
UPDATE users_ratings SET value = 5.0 WHERE value > 5.0 UPDATE `users` SET `ratings` = ARRAY CASE WHEN `value` > 5.0 THEN 5 ELSE `value` END FOR `value` IN `ratings` END
UPDATE users_reviews SET score = 'Unknown' WHERE score = '' UPDATE `users` SET `$child`.`score` = 'Unknown' FOR `$child` IN `reviews` WHEN `$child`.`score` = "" END

Flavor Table Updates

Like flavor table SELECTs, UPDATEs on flavor tables always include the appropriate condition, so only docments belonging to the flavor are affected:

SQL Query N1QL Query
UPDATE [users.subscriber] SET status = 'C' WHERE age > 45 UPDATE `users` SET `status` = "C" WHERE `docType` = "subscriber" AND `age` > 45

Delete Statements

The SQL DELETE statement is mapped to the N1QL DELETE statement as shown below:

SQL QueryN1QL Query
DELETE FROM users WHERE [Document.Id] = 'bcd001' DELETE FROM `users` USE KEYS ["bcd001"]
DELETE FROM users WHERE status = 'inactive' DELETE FROM `users` WHERE `status` = "inactive"

Child Table Deletes

When deleting from a child table, the SQL query is converted to an UPDATE query using an "ARRAY" expression:

SQL Query N1QL Query
DELETE FROM users_ratings WHERE value < 0 UPDATE `users` SET `ratings` = ARRAY `value` FOR `value` IN `ratings` WHEN NOT (`value` < 0) END
DELETE FROM users_reviews WHERE score = '' UPDATE `users` SET `reviews` = ARRAY `$child` FOR `$child` IN `reviews` WHEN NOT (`$child`.`score` = "") END

Flavor Tables Deletes

Like flavor table SELECTs, DELETEs on flavor tables always include the appropriate condition, so only docments belonging to the flavor are affected:

SQL Query N1QL Query
DELETE FROM [users.subscriber] WHERE status = 'inactive' DELETE FROM `users` WHERE `docType` = "subscriber" AND status = "inactive"

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Build 21.0.7930