- Composite SQL Queries
- User-Defined Functions
- JavaScript Integration
- Linq to SQL Translation
- DocumentDB SQL - Built-in Function
- DocumentDB SQL - Parameterized
- DocumentDB - Scalar Expressions
- DocumentDB SQL - Array Creation
- DocumentDB SQL - Aliasing
- DocumentDB SQL - Joins
- DocumentDB SQL - Iteration
- DocumentDB SQL - Order By Clause
- DocumentDB SQL - Value Keyword
- DocumentDB SQL - In Keyword
- DocumentDB - Between Keyword
- DocumentDB SQL - Operators
- DocumentDB SQL - Where Clause
- DocumentDB SQL - From Clause
- DocumentDB SQL - Select Clause
- DocumentDB SQL - Overview
- DocumentDB SQL - Home
DocumentDB SQL Useful Resources
Selected Reading
- Who is Who
- Computer Glossary
- HR Interview Questions
- Effective Resume Writing
- Questions and Answers
- UPSC IAS Exams Notes
DocumentDB SQL - Array Creation
In DocumentDB SQL, Microsoft has added a key feature with the help of which we can easily create an array. It means when we run a query, then as a result it will create an array of collection similar to JSON object as a result of query.
Let’s consider the same documents as in the previous examples.
Following is the AndersenFamily document.
{ "id": "AndersenFamily", "lastName": "Andersen", "parents": [ { "firstName": "Thomas", "relationship": "father" }, { "firstName": "Mary Kay", "relationship": "mother" } ], "children": [ { "firstName": "Henriette Thaulow", "gender": "female", "grade": 5, "pets": [ { "givenName": "Fluffy", "type": "Rabbit" } ] } ], "location": { "state": "WA", "county": "King", "city": "Seattle" }, "isRegistered": true }
Following is the SmithFamily document.
{ "id": "SmithFamily", "parents": [ { "familyName": "Smith", "givenName": "James" }, { "familyName": "Curtis", "givenName": "Helen" } ], "children": [ { "givenName": "Michelle", "gender": "female", "grade": 1 }, { "givenName": "John", "gender": "male", "grade": 7, "pets": [ { "givenName": "Tweetie", "type": "Bird" } ] } ], "location": { "state": "NY", "county": "Queens", "city": "Forest Hills" }, "isRegistered": true }
Following is the WakefieldFamily document.
{ "id": "WakefieldFamily", "parents": [ { "familyName": "Wakefield", "givenName": "Robin" }, { "familyName": "Miller", "givenName": "Ben" } ], "children": [ { "familyName": "Merriam", "givenName": "Jesse", "gender": "female", "grade": 6, "pets": [ { "givenName": "Charpe Brown", "type": "Dog" }, { "givenName": "Tiger", "type": "Cat" }, { "givenName": "Princess", "type": "Cat" } ] }, { "familyName": "Miller", "givenName": "Lisa", "gender": "female", "grade": 3, "pets": [ { "givenName": "Jake", "type": "Snake" } ] } ], "location": { "state": "NY", "county": "Manhattan", "city": "NY" }, "isRegistered": false }
Let’s take a look at an example.
Following is the query which will return the family name and address of each family.
SELECT f.id AS FamilyName, [f.location.city, f.location.county, f.location.state] AS Address FROM Famipes f
As can be seen city, county and state fields are enclosed in square brackets, which will create an array and this array is named Address. When the above query is executed, it produces the following output.
[ { "FamilyName": "WakefieldFamily", "Address": [ "NY", "Manhattan", "NY" ] }, { "FamilyName": "SmithFamily", "Address": [ "Forest Hills", "Queens", "NY" ] }, { "FamilyName": "AndersenFamily", "Address": [ "Seattle", "King", "WA" ] } ]
The city, county, and state information are added in the Address array in the above output.
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