azure-cosmos-rust

Pustaka Azure Cosmos DB untuk Rust (API NoSQL). CRUD dokumen, kontainer, dan data yang didistribusikan secara global. Pemicu: "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

Lebih banyak skill dari microsoft

oss-growth
microsoft
Persona peretas pertumbuhan OSS
agent-framework-azure-ai-py
microsoft
Bangun agen Azure AI Foundry menggunakan Microsoft Agent Framework Python SDK (agent-framework-azure-ai). Gunakan saat membuat agen persisten dengan AzureAIAgentsProvider, menggunakan alat yang dihosting (code interpreter, file search, web search), mengintegrasikan server MCP, mengelola utas percakapan, atau mengimplementasikan respons streaming. Mencakup alat fungsi, keluaran terstruktur, dan agen multi-alat.
development
airunway-aks-setup
microsoft
Siapkan AI Runway di AKS — dari klaster kosong hingga model berjalan. Mencakup verifikasi klaster, instalasi controller, penilaian GPU, penyiapan penyedia, dan deployment pertama. KAPAN: "setup AI Runway", "onboard AKS cluster", "install AI Runway", "airunway setup", "deploy model to AKS", "GPU inference on AKS", "KAITO setup on AKS", "run LLM on AKS", "vLLM on AKS", "set up model serving on AKS", "AI Runway controller".
devops
appinsights-instrumentation
microsoft
Panduan untuk instrumentasi aplikasi web dengan Azure Application Insights. Menyediakan pola telemetri, pengaturan SDK, dan referensi konfigurasi. KAPAN: cara menginstrumentasi aplikasi, SDK App Insights, pola telemetri, apa itu App Insights, panduan Application Insights, contoh instrumentasi, praktik terbaik APM.
devops
applicationinsights-web-ts
microsoft
Instrumentasi aplikasi browser/web dengan Application Insights JavaScript SDK (@microsoft/applicationinsights-web). Digunakan untuk Real User Monitoring (RUM) — tampilan halaman, klik, dependensi AJAX/fetch, pengecualian, peristiwa kustom, dan jejak agen GenAI sisi browser yang dikorelasikan dengan jejak OpenTelemetry backend. Mencakup pengaturan SDK Loader Script dan npm, ekstensi kerangka kerja (React, React Native, Angular), Click Analytics, inisialisasi telemetri, dan konvensi semantik OTel GenAI untuk span agen/alat/model yang dipancarkan dari browser.
devops
azure-ai-anomalydetector-java
microsoft
Bangun aplikasi deteksi anomali dengan Azure AI Anomaly Detector SDK untuk Java. Gunakan saat mengimplementasikan deteksi anomali univariat/multivariat, analisis deret waktu, atau pemantauan bertenaga AI.
development
azure-ai-language-conversations-py
microsoft
Implementasikan Pemahaman Bahasa Percakapan (CLU) menggunakan SDK Python azure-ai-language-conversations. Gunakan saat bekerja dengan ConversationAnalysisClient untuk menganalisis maksud dan entitas percakapan, membangun fitur NLP, atau mengintegrasikan pemahaman bahasa ke dalam aplikasi.
development
azure-ai-ml-py
microsoft
Azure Machine Learning SDK v2 untuk Python. Gunakan untuk ruang kerja ML, pekerjaan, model, kumpulan data, komputasi, dan pipeline. Pemicu: "azure-ai-ml", "MLClient", "ruang kerja", "registri model", "pekerjaan pelatihan", "kumpulan data".
development