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Azure Cosmos DB, a globally distributed, multi-model database service provided by Microsoft Azure, offers various application programming interfaces (APIs) to facilitate seamless integration with different programming languages and frameworks. These APIs allow developers to interact with Azure Cosmos DB using their preferred programming languages, enabling them to build scalable, high-performance, and globally distributed applications. In this article, we will explore the different APIs offered by Azure Cosmos DB and their relevance to the Microsoft Azure Data Fundamentals exam.
The SQL API is the default and most widely used API for Azure Cosmos DB. It provides developers with a SQL-like query language to perform CRUD (create, read, update, delete) operations on JSON documents stored in Azure Cosmos DB containers. The SQL API supports rich query capabilities, including filtering, sorting, aggregation, and joining multiple documents. Here’s an example of executing a SQL query using the .NET SDK:
// Using the SQL API with .NET SDK
using Microsoft.Azure.Cosmos;
// Instantiate a Cosmos DB client with the connection string
CosmosClient cosmosClient = new CosmosClient("connection-string");
// Get a reference to the database and container
Database database = cosmosClient.GetDatabase("database-id");
Container container = database.GetContainer("container-id");
// Execute a SQL query
QueryDefinition queryDefinition = new QueryDefinition("SELECT * FROM c WHERE c.Type = 'Product'");
FeedIterator feedIterator = container.GetItemQueryIterator(queryDefinition);
while (feedIterator.HasMoreResults)
{
FeedResponse
foreach (Product product in response)
{
Console.WriteLine($"{product.Id} - {product.Name}");
}
}
Azure Cosmos DB’s MongoDB API allows developers to use their existing MongoDB application code and skills to interact with Azure Cosmos DB seamlessly. It provides full compatibility with the MongoDB wire protocol, enabling easy migration of MongoDB applications to Azure Cosmos DB without changing the code. The MongoDB API supports features like CRUD operations, indexes, queries, and aggregation pipelines. Here’s an example of using the MongoDB API with Node.js:
// Using the MongoDB API with Node.js
const { MongoClient } = require('mongodb');
// Connect to the Azure Cosmos DB MongoDB API endpoint
const uri = 'mongodb://:@:10255/admin?ssl=true';
// Create a MongoClient instance
const client = new MongoClient(uri);
// Connect to the MongoDB API
await client.connect();
// Get a reference to the database and collection
const database = client.db('database-id');
const collection = database.collection('collection-id');
// Insert a document
const result = await collection.insertOne({ name: 'John Doe', age: 30 });
console.log(`Inserted document with _id: ${result.insertedId}`);
// Query documents
const documents = await collection.find({ age: { $gte: 25 } }).toArray();
console.log(documents);
// Disconnect from the database
await client.close();
The Cassandra API in Azure Cosmos DB allows developers to build applications using the Cassandra Query Language (CQL) and leverage the benefits of the Azure Cosmos DB’s global distribution, scalability, and low latency. This API is ideal for migrating existing Cassandra applications to Azure Cosmos DB without any code changes. It supports features such as tunable consistency levels, materialized views, and automatic indexing. Here’s an example of using the Cassandra API with Java:
// Using the Cassandra API with Java
import com.datastax.oss.driver.api.core.CqlSession;
import com.datastax.oss.driver.api.core.cql.ResultSet;
// Create a CqlSession instance
try (CqlSession session = CqlSession.builder()
.withCloudSecureConnectBundle("secure-connect-bundle.zip")
.withAuthCredentials("username", "password")
.build()) {
// Execute a CQL query
ResultSet rs = session.execute("SELECT * FROM keyspace.table WHERE id = 123");
// Process the result
rs.forEach(row -> System.out.println(row.getString("column1")));
}
The Gremlin API in Azure Cosmos DB provides support for graph-based data models using the Apache TinkerPop™ graph traversal language, Gremlin. It allows developers to model, query, and analyze complex, interconnected data with ease. The Gremlin API supports traversing graphs, creating vertices and edges, executing graph queries, and aggregating data. Here’s an example of using the Gremlin API with Python:
# Using the Gremlin API with Python
from azure.cosmosdb.gremlin import GremlinClient
# Create a GremlinClient instance
client = GremlinClient('wss://:443/', 'database-id', 'graph-id',
username='/dbs/database-id/colls/graph-id',
password='your-password')
# Execute a Gremlin query
query = 'g.V().has("name", "Alice").out("knows").values("name")'
result_set = client.submit(query)
# Process the result
for result in result_set:
print(result)
In conclusion, Azure Cosmos DB offers multiple APIs, including the SQL API, MongoDB API, Cassandra API, and Gremlin API, to cater to diverse application requirements and developer preferences. These APIs allow developers to seamlessly interact with Azure Cosmos DB using their preferred programming languages and frameworks, providing flexibility and ease of integration. Understanding these APIs is essential for the Microsoft Azure Data Fundamentals exam and will enable you to leverage Azure Cosmos DB’s capabilities effectively in your application development endeavors.
Correct answer: b) Azure Cosmos DB supports multiple APIs, including SQL, Gremlin, Table, Cassandra, and MongoDB.
Correct answer: True
Correct answer: b) The Gremlin API is specifically designed for working with graph databases.
Correct answer: False
Correct answer: a) The Cassandra API supports a flexible schema.
Correct answer: True
Correct answer: d) Migrating data to Azure Cosmos DB requires manual scripting or custom tools.
Correct answer: True
Correct answer: b) Consistency levels in Azure Cosmos DB determine the trade-off between data consistency and availability.
Correct answer: True
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