azure-cosmos-rust

Azure Cosmos DB library for Rust (NoSQL API). Document CRUD, containers, and globally distributed data. Triggers: "cosmos db rust", "CosmosClient rust", "document crud rust", "NoSQL rust", "partition key rust".

npx skills add https://github.com/microsoft/skills --skill azure-cosmos-rust

Azure Cosmos DB library for Rust

Client library for Azure Cosmos DB NoSQL API — document CRUD, containers, and globally distributed data.

Use this skill when:

  • An app needs to store or query documents in Cosmos DB from Rust
  • You need CRUD operations on items with partition keys
  • You need key-based auth as an alternative to Entra ID

IMPORTANT: Only use the official azure_data_cosmos crate published by the azure-sdk crates.io user. Do NOT use the unofficial azure_cosmos or azure_sdk_for_rust community crates. Official crates use underscores in names and none have version 0.21.0.

Installation

cargo add azure_data_cosmos azure_identity serde serde_json tokio

If your code uses azure_core types directly (for example, azure_core::credentials::TokenCredential), add azure_core to Cargo.toml. If you only use azure_data_cosmos re-exports, direct azure_core dependency is optional.

Environment Variables

COSMOS_ENDPOINT=https://<account>.documents.azure.com/ # Required for all operations

Authentication

Rust Azure SDK code must not use DefaultAzureCredential. The Rust identity crate does not provide that type.

use azure_identity::DeveloperToolsCredential;
use azure_data_cosmos::{
    CosmosClient, AccountReference, AccountEndpoint, RoutingStrategy,
};

#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    // Local dev: DeveloperToolsCredential. Production: use ManagedIdentityCredential.
    let credential = DeveloperToolsCredential::new(None)?;
    let endpoint: AccountEndpoint = "https://<account>.documents.azure.com/"
        .parse()?;
    let account = AccountReference::with_credential(endpoint, credential);
    let client = CosmosClient::builder()
        .build(account, RoutingStrategy::ProximityTo("East US".into()))
        .await?;
    Ok(())
}

Prefer the crate README/examples when checking builder signatures and CRUD method shapes instead of reconstructing APIs from memory or generated internals.

Client Hierarchy

ClientPurposeAccess
CosmosClientAccount-level operationsCosmosClient::builder().build(account).await?
DatabaseClientDatabase operationsclient.database_client("db")
ContainerClientContainer/item operationsdatabase.container_client("c").await

Core Workflow

use serde::{Serialize, Deserialize};
use azure_data_cosmos::CosmosClient;

#[derive(Serialize, Deserialize)]
struct Item {
    pub id: String,
    pub partition_key: String,
    pub value: String,
}

async fn crud(client: CosmosClient) -> Result<(), Box<dyn std::error::Error>> {
    let container = client
        .database_client("myDatabase")
        .container_client("myContainer")
        .await;

    let item = Item {
        id: "1".into(),
        partition_key: "pk1".into(),
        value: "hello".into(),
    };

    // Create
    container.create_item("pk1", "1", item, None).await?;

    // Read
    let resp = container.read_item("pk1", "1", None).await?;
    let mut item: Item = resp.into_model()?;

    // Update
    item.value = "updated".into();
    container.replace_item("pk1", "1", item, None).await?;

    // Delete
    container.delete_item("pk1", "1", None).await?;
    Ok(())
}

Patch Item

use azure_data_cosmos::{PatchInstructions, PatchOperation};

let patch = PatchInstructions::from(vec![
    PatchOperation::set("/value", serde_json::json!("patched")),
]);
let patched: Item = container
    .patch_item("pk1", "1", patch, None)
    .await?
    .into_model()?;
println!("Patched value: {}", patched.value);

Key Auth (Optional)

Enable account key authentication with the feature flag:

cargo add azure_data_cosmos --features key_auth

RBAC Roles

For Entra ID auth, assign one of these built-in Cosmos DB roles:

RoleAccess
Cosmos DB Built-in Data ReaderRead-only
Cosmos DB Built-in Data ContributorRead/write

Best Practices

  1. Use cargo add to manage dependencies, never edit Cargo.toml directly. Add and remove Rust SDK dependencies with cargo commands instead of manual manifest edits.
  2. Add azure_core only when importing azure_core types directly. If your code imports azure_core::http::Url, azure_core::http::RequestContent, or azure_core::error::ErrorKind, include azure_core; otherwise a direct dependency is optional.
  3. Use DeveloperToolsCredential for local dev, ManagedIdentityCredential for production — Rust does not provide a single DefaultAzureCredential type
  4. Never hardcode credentials — use environment variables or managed identity
  5. Reuse CosmosClient — clients are thread-safe; create once, share across tasks
  6. Use RoutingStrategy::ProximityTo — route to the nearest region for lowest latency
  7. Always specify partition key for item operations — Cosmos DB requires it for all CRUD
  8. Run cargo clippy -- -D warnings when the prompt, eval, or CI expects lint-clean output
  9. Future-proof #[non_exhaustive] SDK models — when constructing SDK model/options structs, end the initializer with ..Default::default() (add #[allow(clippy::needless_update)]) and use a _ wildcard arm when matching SDK enums, so new service-added fields/variants don't break your build

Reference Links

ResourceLink
API Referencehttps://docs.rs/azure_data_cosmos/latest/azure_data_cosmos
crates.iohttps://crates.io/crates/azure_data_cosmos
Source Codehttps://github.com/Azure/azure-sdk-for-rust/tree/main/sdk/cosmos/azure_data_cosmos

Mais skills de microsoft

oss-growth
microsoft
Persona de growth hacker OSS
agent-framework-azure-ai-py
microsoft
Crie agentes do Azure AI Foundry usando o SDK Python do Microsoft Agent Framework (agent-framework-azure-ai). Use ao criar agentes persistentes com AzureAIAgentsProvider, usando ferramentas hospedadas (interpretador de código, pesquisa de arquivos, pesquisa na web), integrando servidores MCP, gerenciando threads de conversa ou implementando respostas em streaming. Abrange ferramentas de função, saídas estruturadas e agentes com múltiplas ferramentas.
development
airunway-aks-setup
microsoft
Configure o AI Runway no AKS — do cluster vazio ao modelo em execução. Abrange verificação do cluster, instalação do controlador, avaliação de GPU, configuração do provedor e primeira implantação. QUANDO: "configurar AI Runway", "integrar cluster AKS", "instalar AI Runway", "configuração do airunway", "implantar modelo no AKS", "inferência GPU no AKS", "configuração KAITO no AKS", "executar LLM no AKS", "vLLM no AKS", "configurar serviço de modelo no AKS", "controlador AI Runway".
devops
appinsights-instrumentation
microsoft
Orientação para instrumentar aplicações web com Azure Application Insights. Fornece padrões de telemetria, configuração de SDK e referências de configuração. QUANDO: como instrumentar o app, SDK do App Insights, padrões de telemetria, o que é App Insights, orientação sobre Application Insights, exemplos de instrumentação, melhores práticas de APM.
devops
applicationinsights-web-ts
microsoft
Instrumente aplicativos de navegador/web com o SDK JavaScript do Application Insights (@microsoft/applicationinsights-web). Use para Real User Monitoring (RUM) — visualizações de página, cliques, dependências AJAX/fetch, exceções, eventos personalizados e rastreamentos de agentes GenAI no lado do navegador correlacionados a rastreamentos OpenTelemetry no backend. Abrange o Script de Carregamento do SDK e a configuração via npm, extensões de frameworks (React, React Native, Angular), Click Analytics, inicializadores de telemetria e convenções semânticas GenAI do OTel para spans de agente/ferramenta/modelo emitidos pelo navegador.
devops
azure-ai-anomalydetector-java
microsoft
Crie aplicativos de detecção de anomalias com o SDK do Azure AI Anomaly Detector para Java. Use ao implementar detecção de anomalias univariada/multivariada, análise de séries temporais ou monitoramento com IA.
development
azure-ai-language-conversations-py
microsoft
Implemente o reconhecimento de linguagem conversacional (CLU) usando o SDK Python azure-ai-language-conversations. Use ao trabalhar com ConversationAnalysisClient para analisar intenção e entidades de conversas, criar recursos de NLP ou integrar o reconhecimento de linguagem em aplicativos.
development
azure-ai-ml-py
microsoft
SDK v2 do Azure Machine Learning para Python. Use para workspaces de ML, jobs, modelos, conjuntos de dados, computação e pipelines. Gatilhos: "azure-ai-ml", "MLClient", "workspace", "registro de modelos", "jobs de treinamento", "conjuntos de dados".
development