azure-data-tables-java

tarafından microsoft

Build table storage applications with Azure Tables SDK for Java. Use when working with Azure Table Storage or Cosmos DB Table API for NoSQL key-value data, schemaless storage, or structured data at scale.

npx skills add https://github.com/microsoft/skills --skill azure-data-tables-java

Azure Tables SDK for Java

Build table storage applications using the Azure Tables SDK for Java. Works with both Azure Table Storage and Cosmos DB Table API.

Installation

<dependency>
  <groupId>com.azure</groupId>
  <artifactId>azure-data-tables</artifactId>
  <version>12.6.0-beta.1</version>
</dependency>

Client Creation

With Connection String

import com.azure.data.tables.TableServiceClient;
import com.azure.data.tables.TableServiceClientBuilder;
import com.azure.data.tables.TableClient;

TableServiceClient serviceClient = new TableServiceClientBuilder()
    .connectionString("<your-connection-string>")
    .buildClient();

With Shared Key

import com.azure.core.credential.AzureNamedKeyCredential;

AzureNamedKeyCredential credential = new AzureNamedKeyCredential(
    "<account-name>",
    "<account-key>");

TableServiceClient serviceClient = new TableServiceClientBuilder()
    .endpoint("<your-table-account-url>")
    .credential(credential)
    .buildClient();

With SAS Token

TableServiceClient serviceClient = new TableServiceClientBuilder()
    .endpoint("<your-table-account-url>")
    .sasToken("<sas-token>")
    .buildClient();

With DefaultAzureCredential (Storage only)

import com.azure.core.credential.TokenCredential;
import com.azure.identity.AzureIdentityEnvVars;
import com.azure.identity.DefaultAzureCredentialBuilder;
import com.azure.identity.ManagedIdentityCredentialBuilder;

// Local dev: DefaultAzureCredential. Production: set AZURE_TOKEN_CREDENTIALS=prod or AZURE_TOKEN_CREDENTIALS=<specific_credential>
TokenCredential credential = new DefaultAzureCredentialBuilder()
    .requireEnvVars(AzureIdentityEnvVars.AZURE_TOKEN_CREDENTIALS)
    .build();
// Or use a specific credential directly in production:
// See https://learn.microsoft.com/java/api/overview/azure/identity-readme?view=azure-java-stable#credential-classes
// TokenCredential credential = new ManagedIdentityCredentialBuilder().build();

TableServiceClient serviceClient = new TableServiceClientBuilder()
    .endpoint("<your-table-account-url>")
    .credential(credential)
    .buildClient();

Key Concepts

  • TableServiceClient: Manage tables (create, list, delete)
  • TableClient: Manage entities within a table (CRUD)
  • Partition Key: Groups entities for efficient queries
  • Row Key: Unique identifier within a partition
  • Entity: A row with up to 252 properties (1MB Storage, 2MB Cosmos)

Core Patterns

Create Table

// Create table (throws if exists)
TableClient tableClient = serviceClient.createTable("mytable");

// Create if not exists (no exception)
TableClient tableClient = serviceClient.createTableIfNotExists("mytable");

Get Table Client

// From service client
TableClient tableClient = serviceClient.getTableClient("mytable");

// Direct construction
TableClient tableClient = new TableClientBuilder()
    .connectionString("<connection-string>")
    .tableName("mytable")
    .buildClient();

Create Entity

import com.azure.data.tables.models.TableEntity;

TableEntity entity = new TableEntity("partitionKey", "rowKey")
    .addProperty("Name", "Product A")
    .addProperty("Price", 29.99)
    .addProperty("Quantity", 100)
    .addProperty("IsAvailable", true);

tableClient.createEntity(entity);

Get Entity

TableEntity entity = tableClient.getEntity("partitionKey", "rowKey");

String name = (String) entity.getProperty("Name");
Double price = (Double) entity.getProperty("Price");
System.out.printf("Product: %s, Price: %.2f%n", name, price);

Update Entity

import com.azure.data.tables.models.TableEntityUpdateMode;

// Merge (update only specified properties)
TableEntity updateEntity = new TableEntity("partitionKey", "rowKey")
    .addProperty("Price", 24.99);
tableClient.updateEntity(updateEntity, TableEntityUpdateMode.MERGE);

// Replace (replace entire entity)
TableEntity replaceEntity = new TableEntity("partitionKey", "rowKey")
    .addProperty("Name", "Product A Updated")
    .addProperty("Price", 24.99)
    .addProperty("Quantity", 150);
tableClient.updateEntity(replaceEntity, TableEntityUpdateMode.REPLACE);

Upsert Entity

// Insert or update (merge mode)
tableClient.upsertEntity(entity, TableEntityUpdateMode.MERGE);

// Insert or replace
tableClient.upsertEntity(entity, TableEntityUpdateMode.REPLACE);

Delete Entity

tableClient.deleteEntity("partitionKey", "rowKey");

List Entities

import com.azure.data.tables.models.ListEntitiesOptions;

// List all entities
for (TableEntity entity : tableClient.listEntities()) {
    System.out.printf("%s - %s%n",
        entity.getPartitionKey(),
        entity.getRowKey());
}

// With filtering and selection
ListEntitiesOptions options = new ListEntitiesOptions()
    .setFilter("PartitionKey eq 'sales'")
    .setSelect("Name", "Price");

for (TableEntity entity : tableClient.listEntities(options, null, null)) {
    System.out.printf("%s: %.2f%n",
        entity.getProperty("Name"),
        entity.getProperty("Price"));
}

Query with OData Filter

// Filter by partition key
ListEntitiesOptions options = new ListEntitiesOptions()
    .setFilter("PartitionKey eq 'electronics'");

// Filter with multiple conditions
options.setFilter("PartitionKey eq 'electronics' and Price gt 100");

// Filter with comparison operators
options.setFilter("Quantity ge 10 and Quantity le 100");

// Top N results
options.setTop(10);

for (TableEntity entity : tableClient.listEntities(options, null, null)) {
    System.out.println(entity.getRowKey());
}

Batch Operations (Transactions)

import com.azure.data.tables.models.TableTransactionAction;
import com.azure.data.tables.models.TableTransactionActionType;
import java.util.Arrays;

// All entities must have same partition key
List<TableTransactionAction> actions = Arrays.asList(
    new TableTransactionAction(
        TableTransactionActionType.CREATE,
        new TableEntity("batch", "row1").addProperty("Name", "Item 1")),
    new TableTransactionAction(
        TableTransactionActionType.CREATE,
        new TableEntity("batch", "row2").addProperty("Name", "Item 2")),
    new TableTransactionAction(
        TableTransactionActionType.UPSERT_MERGE,
        new TableEntity("batch", "row3").addProperty("Name", "Item 3"))
);

tableClient.submitTransaction(actions);

List Tables

import com.azure.data.tables.models.TableItem;
import com.azure.data.tables.models.ListTablesOptions;

// List all tables
for (TableItem table : serviceClient.listTables()) {
    System.out.println(table.getName());
}

// Filter tables
ListTablesOptions options = new ListTablesOptions()
    .setFilter("TableName eq 'mytable'");

for (TableItem table : serviceClient.listTables(options, null, null)) {
    System.out.println(table.getName());
}

Delete Table

serviceClient.deleteTable("mytable");

Typed Entities

public class Product implements TableEntity {
    private String partitionKey;
    private String rowKey;
    private OffsetDateTime timestamp;
    private String eTag;
    private String name;
    private double price;
    
    // Getters and setters for all fields
    @Override
    public String getPartitionKey() { return partitionKey; }
    @Override
    public void setPartitionKey(String partitionKey) { this.partitionKey = partitionKey; }
    @Override
    public String getRowKey() { return rowKey; }
    @Override
    public void setRowKey(String rowKey) { this.rowKey = rowKey; }
    // ... other getters/setters
    
    public String getName() { return name; }
    public void setName(String name) { this.name = name; }
    public double getPrice() { return price; }
    public void setPrice(double price) { this.price = price; }
}

// Usage
Product product = new Product();
product.setPartitionKey("electronics");
product.setRowKey("laptop-001");
product.setName("Laptop");
product.setPrice(999.99);

tableClient.createEntity(product);

Error Handling

import com.azure.data.tables.models.TableServiceException;

try {
    tableClient.createEntity(entity);
} catch (TableServiceException e) {
    System.out.println("Status: " + e.getResponse().getStatusCode());
    System.out.println("Error: " + e.getMessage());
    // 409 = Conflict (entity exists)
    // 404 = Not Found
}

Environment Variables

# Storage Account
AZURE_TABLES_CONNECTION_STRING=DefaultEndpointsProtocol=https;AccountName=...  # Alternative to Entra ID auth
AZURE_TABLES_ENDPOINT=https://<account>.table.core.windows.net  # Required for all auth methods
AZURE_TOKEN_CREDENTIALS=prod  # Required only if DefaultAzureCredential is used in production

# Cosmos DB Table API
COSMOS_TABLE_ENDPOINT=https://<account>.table.cosmosdb.azure.com  # Alternative endpoint for Cosmos DB Table API

Best Practices

  1. Partition Key Design: Choose keys that distribute load evenly
  2. Batch Operations: Use transactions for atomic multi-entity updates
  3. Query Optimization: Always filter by PartitionKey when possible
  4. Select Projection: Only select needed properties for performance
  5. Entity Size: Keep entities under 1MB (Storage) or 2MB (Cosmos)

Trigger Phrases

  • "Azure Tables Java"
  • "table storage SDK"
  • "Cosmos DB Table API"
  • "NoSQL key-value storage"
  • "partition key row key"
  • "table entity CRUD"

microsoft tarafından daha fazla skill

oss-growth
microsoft
OSS büyüme korsanı kişiliği
agent-framework-azure-ai-py
microsoft
Microsoft Agent Framework Python SDK'sini (agent-framework-azure-ai) kullanarak Azure AI Foundry aracıları oluşturun. AzureAIAgentsProvider ile kalıcı aracılar oluştururken, barındırılan araçları (kod yorumlayıcı, dosya arama, web araması) kullanırken, MCP sunucularını entegre ederken, konuşma iş parçacıklarını yönetirken veya akış yanıtları uygularken kullanın. Fonksiyon araçlarını, yapılandırılmış çıktıları ve çok araçlı aracıları kapsar.
development
airunway-aks-setup
microsoft
Set up AI Runway on AKS — from bare cluster to running model. Covers cluster verification, controller install, GPU assessment, provider setup, and first deployment. WHEN: "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
Azure Application Insights ile web uygulamalarını enstrümante etme rehberi. Telemetri desenleri, SDK kurulumu ve yapılandırma referansları sağlar. NE ZAMAN: uygulama nasıl enstrümante edilir, App Insights SDK, telemetri desenleri, App Insights nedir, Application Insights rehberliği, enstrümantasyon örnekleri, APM en iyi uygulamaları.
devops
applicationinsights-web-ts
microsoft
Tarayıcı/web uygulamalarını Application Insights JavaScript SDK'sı (@microsoft/applicationinsights-web) ile izleyin. Gerçek Kullanıcı İzleme (RUM) için kullanın — sayfa görünümleri, tıklamalar, AJAX/fetch bağımlılıkları, özel durumlar, özel olaylar ve arka uç OpenTelemetry izleriyle ilişkilendirilen tarayıcı tarafı GenAI aracı izleri. SDK Loader Script ve npm kurulumunu, çerçeve uzantılarını (React, React Native, Angular), Tıklama Analitiğini, telemetri başlatıcılarını ve tarayıcıdan yayılan aracı/araç/model yayılımları için OTel GenAI anlamsal kurallarını kapsar.
devops
azure-ai-anomalydetector-java
microsoft
Azure AI Anomaly Detector SDK for Java ile anomali tespiti uygulamaları oluşturun. Tek değişkenli/çok değişkenli anomali tespiti, zaman serisi analizi veya yapay zeka destekli izleme uygularken kullanın.
development
azure-ai-language-conversations-py
microsoft
azure-ai-language-conversations Python SDK'sini kullanarak Konuşma Dili Anlama (CLU) uygulayın. ConversationAnalysisClient ile konuşma niyetini ve varlıklarını analiz etmek, NLP özellikleri oluşturmak veya dil anlamayı uygulamalara entegre etmek için kullanın.
development
azure-ai-ml-py
microsoft
Azure Machine Learning SDK v2 for Python. Makine öğrenimi çalışma alanları, işler, modeller, veri kümeleri, bilgi işlem ve iş akışları için kullanın. Tetikleyiciler: "azure-ai-ml", "MLClient", "workspace", "model registry", "training jobs", "datasets".
development