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Search with the Where Clause

A where clause is a condition which references columns of a data table where the query is sent. For any given query Backendless returns the data records which match the condition in the where clause. A where clause is a query string which must conform to a subset of the SQL-92 standard. The subset of the syntax supported by Backendless is the part that goes into the WHERE clause of an SQL query. This is the reason why we refer to it as the where clause. For example, consider the following data objects, specifically notice the columns name and age:

sample-table.zoom70

Values for columns of types STRING and TEXT must be wrapped in single quotes. Suppose you need to retrieve an object where the value of the name property is "Joe". This can be accomplished with the following where clause:

name = 'Joe'

If the value itself has a single quote, it can be "escaped" by adding an additional single quote. For example:

name = 'Charlie''s Angels'

If you need all objects where the age column contains a value less than 30:

age < 30

Just like in SQL, columns can be combined in a single where clause. The following condition would retrieve all objects containing letter J in the name and the age column's value is greater than 20:

name LIKE 'J%' and age > 20
Condition
Sample where clause
Column type
Value in a column equals a string literal.
name = 'foo'
STRING, TEXT or EXTENDED STRING
Value in a column does not equal a string literal.
name != 'foo'
STRING, TEXT or EXTENDED STRING
Value in a column is not assigned
name is null
STRING, TEXT or EXTENDED STRING
Value in a column is assigned
name is not null
STRING, TEXT or EXTENDED STRING
Value in a column contains a substring literal
name LIKE '%foo%'
STRING, TEXT or EXTENDED STRING
Value in a column ends with a substring literal
name LIKE '%foo'
STRING, TEXT or EXTENDED STRING
Value in a column starts with a substring literal
name LIKE 'foo%'
STRING, TEXT or EXTENDED STRING
Value in a column is one of enumerated string literals.
name IN ('value1', 'value2', 'value3)

or

name = 'value1' OR name = 'value2' or name = 'value3'
STRING, TEXT or EXTENDED STRING
Value in a column equals a numeric literal
age = 21
INT or DOUBLE
Value in a column is greater than a numeric literal
age > 21
INT or DOUBLE
Value in a column is greater than or equal a numeric literal
age >= 21
INT or DOUBLE
Value in a column is less than a numeric literal
age < 21
INT or DOUBLE
Value in a column is less than or equal a numeric literal
age <= 21
INT or DOUBLE

Examples

You do not need to declare/write any custom classes when using the Map approach. Data records are represented as Map objects. Column names become map property names and the object values are corresponding property values. === "Custom Class"

Consider the following class:

import 'package:backendless_sdk/backendless_sdk.dart';

@reflector
class Contact {
  String objectId;
  String name;
  int age;
  String phone;
  String title;
}

Find all contacts where the value of the "age" property equals 47:

DataQueryBuilder queryBuilder = DataQueryBuilder()
  ..whereClause = "age = 47";
Backendless.data.of("Contact").find(queryBuilder).then((foundContacts) {
  // every loaded object from the "Contact" table is now an individual Map
});
DataQueryBuilder queryBuilder = DataQueryBuilder()
  ..whereClause = "age = 47";
Backendless.data.withClass<Contact>().find(queryBuilder).then((foundContacts) {
    // the "foundContact" list now contains instances of the Contact class.
    // each instance represents an object stored on the server.
});

Find all contacts where the value of the "age" property is greater than 21:

DataQueryBuilder queryBuilder = DataQueryBuilder()
  ..whereClause = "age > 21";
Backendless.data.of("Contact").find(queryBuilder).then((foundContacts) {
  // every loaded object from the "Contact" table is now an individual Map
});
DataQueryBuilder queryBuilder = DataQueryBuilder()
  ..whereClause = "age > 21";
Backendless.data.withClass<Contact>().find(queryBuilder).then((foundContacts) {
    // the "foundContacts" collection now contains instances of the Contact class.
    // each instance represents an object stored on the server.
});

Find all contacts by name:

DataQueryBuilder queryBuilder = DataQueryBuilder()
  ..whereClause = "name = 'Jack Daniels'";
Backendless.data.of("Contact").find(queryBuilder).then((foundContacts) {
  // every loaded object from the "Contact" table is now an individual Map
});
DataQueryBuilder queryBuilder = DataQueryBuilder()
  ..whereClause = "name = 'Jack Daniels'";
Backendless.data.withClass<Contact>().find(queryBuilder).then((foundContacts) {
    // the "foundContact" collection now contains instances of the Contact class.
    // each instance represents an object stored on the server.
});

Find all contacts by partial name match:

DataQueryBuilder queryBuilder = DataQueryBuilder()
  ..whereClause = "name LIKE 'Jack%'";
Backendless.data.of("Contact").find(queryBuilder).then((foundContacts) {
  // every loaded object from the "Contact" table is now an individual Map
});
DataQueryBuilder queryBuilder = DataQueryBuilder()
  ..whereClause = "name LIKE 'Jack%'";
Backendless.data.withClass<Contact>().find(queryBuilder).then((foundContacts) {
    // the "foundContact" collection now contains instances of the Contact class.
    // each instance represents an object stored on the server.
});

Codeless Reference

Consider the following data table:
sample-table.zoom70

For demonstration purposes, this Codeless Reference section has only one example which describes all use-cases of the where clause presented in the Examples section of this topic: Refer to the Examples section to find description for every Codeless use-case presented below:

The numbering in the list presented below is also referenced in the screenshot of the Codeless logic example:

1. Find all contacts where the value of the "age" property equals 20.

2. Find all contacts where the value of the "age" property is greater than 21.

3. Find all contacts where the value of the "age" property is between 21 and 40.

4. Find all contacts whose "age" property is 30 or 10.

5. Find all contacts by name 'Joe'.

6. Find all contacts whose name is 'Joe' or 'Kevin':

7. Find all contacts by partial name match.

data_service_search_with_the_where_clause

Important

For a detailed description of all input parameters see the Basic Object Retrieval topic of this guide.