SQL queries for Azure Cosmos DB Document. DB APIMicrosoft Azure Cosmos DB supports querying documents using SQL Structured Query Language as a JSON query language. Cosmos DB is truly schema free. By virtue of its commitment to the JSON data model directly within the database engine, it provides automatic indexing of JSON documents without requiring explicit schema or creation of secondary indexes. While designing the query language for Cosmos DB, we had two goals in mind Instead of inventing a new JSON query language, we wanted to support SQL. SQL is one of the most familiar and popular query languages. Cosmos DB SQL provides a formal programming model for rich queries over JSON documents. As a JSON document database capable of executing Java. Script directly in the database engine, we wanted to use Java. The Archives of the TeradataForum contains over 33,000 posts and the threads below are a representative sample. To help navigate the Archives, there are additional. Sometimes you need to find and change words in SQL. In this article, you learn how to get inside the expression, find the offending segment, and change it. Concatenate Rows Records How to Denormalize Records. Sometimes youll need to concatenate records into a single text field. This requirement occurs most often on. Suppose I have table in Ms Access with following information ColumnA ColumnB 1 abc 1 pqr 1 xyz 2 efg 2 hij 3 asd My question is. Sql Concatenate Rows Microsoft Access' title='Sql Concatenate Rows Microsoft Access' />I am trying to create a module that will allow me to delete duplicate rows of data. The code that I have is below, but it seems that the fornext statement. SitesPower Institute in Dubai The Microsoft Excel Training Courses cover the Basic to Expert levels. Scripts programming model as the foundation for our query language. The Document. DB API SQL is rooted in Java. Scripts type system, expression evaluation, and function invocation. This in turn provides a natural programming model for relational projections, hierarchical navigation across JSON documents, self joins, spatial queries, and invocation of user defined functions UDFs written entirely in Java. Script, among other features. We believe that these capabilities are key to reducing the friction between the application and the database and are crucial for developer productivity. We recommend getting started by watching the following video, where Aravind Ramachandran shows Cosmos DBs querying capabilities, and by visiting our Query Playground, where you can try out Cosmos DB and run SQL queries against our dataset. Then, return to this article, where we start with a SQL query tutorial that walks you through some simple JSON documents and SQL commands. Getting started with SQL commands in Cosmos DBTo see Cosmos DB SQL at work, lets begin with a few simple JSON documents and walk through some simple queries against it. Consider these two JSON documents about two families. With Cosmos DB, we do not need to create any schemas or secondary indices explicitly. We simply need to insert the JSON documents to a Cosmos DB collection and subsequently query. Here we have a simple JSON document for the Andersen family, the parents, children and their pets, address, and registration information. The document has strings, numbers, Booleans, arrays, and nested properties. Document. id Andersen. Family. last. Name Andersen. Name Thomas. Name Mary Kay. Name Henriette Thaulow. Name Fluffy. WA, county King, city seattle. Date 1. 43. 16. Registered true. Heres a second document with one subtle difference given. Name and family. Name are used instead of first. Name and last. Name. Document. id Wakefield. Family. family. Name Wakefield, given. Name Robin. Name Miller, given. Name Ben. children. Name Merriam. Name Jesse. Name Goofy. Name Shadow. Name Miller. given. Name Lisa. gender female. NY, county Manhattan, city NY. Date 1. 43. 16. Registered false. Now lets try a few queries against this data to understand some of the key aspects of Document. DB API SQL. For example, the following query returns the documents where the id field matches Andersen. Family. Since its a SELECT, the output of the query is the complete JSON document Query. SELECT FROM Families f. WHERE f. id Andersen. Family. id Andersen. Family. last. Name Andersen. Name Thomas. Name Mary Kay. Name Henriette Thaulow, gender female, grade 5. Name Fluffy. WA, county King, city seattle. Date 1. 43. 16. Registered true. Now consider the case where we need to reformat the JSON output in a different shape. This query projects a new JSON object with two selected fields, Name and City, when the address city has the same name as the state. In this case, NY, NY matches. Query. SELECT Name f. Manual Do Fiat Uno 96 Ep more. City f. address. AS Family. FROM Families f. WHERE f. Family. Name Wakefield. Family. City NY. The next query returns all the given names of children in the family whose id matches Wakefield. Family ordered by the city of residence. Query. SELECT c. given. Name. FROM Families f. JOIN c IN f. children. WHERE f. id Wakefield. Family. ORDER BY f. ASC. given. Name Jesse. Name Lisa. We would like to draw attention to a few noteworthy aspects of the Cosmos DB query language through the examples weve seen so far Since Document. DB API SQL works on JSON values, it deals with tree shaped entities instead of rows and columns. Therefore, the language lets you refer to nodes of the tree at any arbitrary depth, like Node. Node. 2. Node. 3. Nodem, similar to relational SQL referring to the two part reference of lt table. The structured query language works with schema less data. Therefore, the type system needs to be bound dynamically. The same expression could yield different types on different documents. The result of a query is a valid JSON value, but is not guaranteed to be of a fixed schema. Cosmos DB only supports strict JSON documents. This means the type system and expressions are restricted to deal only with JSON types. Refer to the JSON specification for more details. A Cosmos DB collection is a schema free container of JSON documents. The relations in data entities within and across documents in a collection are implicitly captured by containment and not by primary key and foreign key relations. This is an important aspect worth pointing out in light of the intra document joins discussed later in this article. Cosmos DB indexing. Before we get into the Document. DB API SQL syntax, it is worth exploring the indexing design in Cosmos DB. The purpose of database indexes is to serve queries in their various forms and shapes with minimum resource consumption like CPU and inputoutput while providing good throughput and low latency. Often, the choice of the right index for querying a database requires much planning and experimentation. This approach poses a challenge for schema less databases where the data doesnt conform to a strict schema and evolves rapidly. Therefore, when we designed the Cosmos DB indexing subsystem, we set the following goals Index documents without requiring schema The indexing subsystem does not require any schema information or make any assumptions about schema of the documents. Support for efficient, rich hierarchical, and relational queries The index supports the Cosmos DB query language efficiently, including support for hierarchical and relational projections. Support for consistent queries in face of a sustained volume of writes For high write throughput workloads with consistent queries, the index is updated incrementally, efficiently, and online in the face of a sustained volume of writes. The consistent index update is crucial to serve the queries at the consistency level in which the user configured the document service. Support for multi tenancy Given the reservation based model for resource governance across tenants, index updates are performed within the budget of system resources CPU, memory, and inputoutput operations per second allocated per replica. Storage efficiency For cost effectiveness, the on disk storage overhead of the index is bounded and predictable.