azure-eventhub-java
Build real-time streaming applications with Azure Event Hubs SDK for Java. Use when implementing event streaming, high-throughput data ingestion, or building event-driven architectures.
What this skill does
# Azure Event Hubs SDK for Java
Build real-time streaming applications using the Azure Event Hubs SDK for Java.
## Installation
```xml
<dependency>
<groupId>com.azure</groupId>
<artifactId>azure-messaging-eventhubs</artifactId>
<version>5.19.0</version>
</dependency>
<!-- For checkpoint store (production) -->
<dependency>
<groupId>com.azure</groupId>
<artifactId>azure-messaging-eventhubs-checkpointstore-blob</artifactId>
<version>1.20.0</version>
</dependency>
```
## Client Creation
### EventHubProducerClient
```java
import com.azure.messaging.eventhubs.EventHubProducerClient;
import com.azure.messaging.eventhubs.EventHubClientBuilder;
// With connection string
EventHubProducerClient producer = new EventHubClientBuilder()
.connectionString("<connection-string>", "<event-hub-name>")
.buildProducerClient();
// Full connection string with EntityPath
EventHubProducerClient producer = new EventHubClientBuilder()
.connectionString("<connection-string-with-entity-path>")
.buildProducerClient();
```
### With DefaultAzureCredential
```java
import com.azure.identity.DefaultAzureCredentialBuilder;
EventHubProducerClient producer = new EventHubClientBuilder()
.fullyQualifiedNamespace("<namespace>.servicebus.windows.net")
.eventHubName("<event-hub-name>")
.credential(new DefaultAzureCredentialBuilder().build())
.buildProducerClient();
```
### EventHubConsumerClient
```java
import com.azure.messaging.eventhubs.EventHubConsumerClient;
EventHubConsumerClient consumer = new EventHubClientBuilder()
.connectionString("<connection-string>", "<event-hub-name>")
.consumerGroup(EventHubClientBuilder.DEFAULT_CONSUMER_GROUP_NAME)
.buildConsumerClient();
```
### Async Clients
```java
import com.azure.messaging.eventhubs.EventHubProducerAsyncClient;
import com.azure.messaging.eventhubs.EventHubConsumerAsyncClient;
EventHubProducerAsyncClient asyncProducer = new EventHubClientBuilder()
.connectionString("<connection-string>", "<event-hub-name>")
.buildAsyncProducerClient();
EventHubConsumerAsyncClient asyncConsumer = new EventHubClientBuilder()
.connectionString("<connection-string>", "<event-hub-name>")
.consumerGroup("$Default")
.buildAsyncConsumerClient();
```
## Core Patterns
### Send Single Event
```java
import com.azure.messaging.eventhubs.EventData;
EventData eventData = new EventData("Hello, Event Hubs!");
producer.send(Collections.singletonList(eventData));
```
### Send Event Batch
```java
import com.azure.messaging.eventhubs.EventDataBatch;
import com.azure.messaging.eventhubs.models.CreateBatchOptions;
// Create batch
EventDataBatch batch = producer.createBatch();
// Add events (returns false if batch is full)
for (int i = 0; i < 100; i++) {
EventData event = new EventData("Event " + i);
if (!batch.tryAdd(event)) {
// Batch is full, send and create new batch
producer.send(batch);
batch = producer.createBatch();
batch.tryAdd(event);
}
}
// Send remaining events
if (batch.getCount() > 0) {
producer.send(batch);
}
```
### Send to Specific Partition
```java
CreateBatchOptions options = new CreateBatchOptions()
.setPartitionId("0");
EventDataBatch batch = producer.createBatch(options);
batch.tryAdd(new EventData("Partition 0 event"));
producer.send(batch);
```
### Send with Partition Key
```java
CreateBatchOptions options = new CreateBatchOptions()
.setPartitionKey("customer-123");
EventDataBatch batch = producer.createBatch(options);
batch.tryAdd(new EventData("Customer event"));
producer.send(batch);
```
### Event with Properties
```java
EventData event = new EventData("Order created");
event.getProperties().put("orderId", "ORD-123");
event.getProperties().put("customerId", "CUST-456");
event.getProperties().put("priority", 1);
producer.send(Collections.singletonList(event));
```
### Receive Events (Simple)
```java
import com.azure.messaging.eventhubs.models.EventPosition;
import com.azure.messaging.eventhubs.models.PartitionEvent;
// Receive from specific partition
Iterable<PartitionEvent> events = consumer.receiveFromPartition(
"0", // partitionId
10, // maxEvents
EventPosition.earliest(), // startingPosition
Duration.ofSeconds(30) // timeout
);
for (PartitionEvent partitionEvent : events) {
EventData event = partitionEvent.getData();
System.out.println("Body: " + event.getBodyAsString());
System.out.println("Sequence: " + event.getSequenceNumber());
System.out.println("Offset: " + event.getOffset());
}
```
### EventProcessorClient (Production)
```java
import com.azure.messaging.eventhubs.EventProcessorClient;
import com.azure.messaging.eventhubs.EventProcessorClientBuilder;
import com.azure.messaging.eventhubs.checkpointstore.blob.BlobCheckpointStore;
import com.azure.storage.blob.BlobContainerAsyncClient;
import com.azure.storage.blob.BlobContainerClientBuilder;
// Create checkpoint store
BlobContainerAsyncClient blobClient = new BlobContainerClientBuilder()
.connectionString("<storage-connection-string>")
.containerName("checkpoints")
.buildAsyncClient();
// Create processor
EventProcessorClient processor = new EventProcessorClientBuilder()
.connectionString("<eventhub-connection-string>", "<event-hub-name>")
.consumerGroup("$Default")
.checkpointStore(new BlobCheckpointStore(blobClient))
.processEvent(eventContext -> {
EventData event = eventContext.getEventData();
System.out.println("Processing: " + event.getBodyAsString());
// Checkpoint after processing
eventContext.updateCheckpoint();
})
.processError(errorContext -> {
System.err.println("Error: " + errorContext.getThrowable().getMessage());
System.err.println("Partition: " + errorContext.getPartitionContext().getPartitionId());
})
.buildEventProcessorClient();
// Start processing
processor.start();
// Keep running...
Thread.sleep(Duration.ofMinutes(5).toMillis());
// Stop gracefully
processor.stop();
```
### Batch Processing
```java
EventProcessorClient processor = new EventProcessorClientBuilder()
.connectionString("<connection-string>", "<event-hub-name>")
.consumerGroup("$Default")
.checkpointStore(new BlobCheckpointStore(blobClient))
.processEventBatch(eventBatchContext -> {
List<EventData> events = eventBatchContext.getEvents();
System.out.printf("Received %d events%n", events.size());
for (EventData event : events) {
// Process each event
System.out.println(event.getBodyAsString());
}
// Checkpoint after batch
eventBatchContext.updateCheckpoint();
}, 50) // maxBatchSize
.processError(errorContext -> {
System.err.println("Error: " + errorContext.getThrowable());
})
.buildEventProcessorClient();
```
### Async Receiving
```java
asyncConsumer.receiveFromPartition("0", EventPosition.latest())
.subscribe(
partitionEvent -> {
EventData event = partitionEvent.getData();
System.out.println("Received: " + event.getBodyAsString());
},
error -> System.err.println("Error: " + error),
() -> System.out.println("Complete")
);
```
### Get Event Hub Properties
```java
// Get hub info
EventHubProperties hubProps = producer.getEventHubProperties();
System.out.println("Hub: " + hubProps.getName());
System.out.println("Partitions: " + hubProps.getPartitionIds());
// Get partition info
PartitionProperties partitionProps = producer.getPartitionProperties("0");
System.out.println("Begin sequence: " + partitionProps.getBeginningSequenceNumber());
System.out.println("Last sequence: " + partitionProps.getLastEnqueuedSequenceNumber());
System.out.println("Last offset: " + partitionProps.getLastEnqueuedOffset());
```
## Event Positions
```java
// Related in Backend & APIs
jfrog
IncludedInteract with the JFrog Platform via the JFrog CLI and REST/GraphQL APIs. Use this skill when the user wants to manage Artifactory repositories, upload or download artifacts, manage builds, configure permissions, manage users and groups, work with access tokens, configure JFrog CLI servers, search artifacts, manage properties, set up replication, manage JFrog Projects, run security audits or scans, look up CVE details, query exposures scan results from JFrog Advanced Security, manage release bundles and lifecycle operations, aggregate or export platform data, or perform any JFrog Platform administration task. Also use when the user mentions jf, jfrog, artifactory, xray, distribution, evidence, apptrust, onemodel, graphql, workers, mission control, curation, advanced security, exposures, or any JFrog product name.
cupynumeric-migration-readiness
IncludedPre-migration readiness assessor for porting NumPy to cuPyNumeric. Use BEFORE substantial porting work begins when the user asks whether code will scale on GPU, whether they should migrate to cuPyNumeric, which NumPy patterns transfer cleanly, what must be refactored before porting, or mentions pre-port assessment, scaling analysis, or refactor planning. Inspect the user's source code, look up NumPy usage, cross-reference the cuPyNumeric API support manifest, and distinguish distributed-scaling-friendly patterns from blockers such as unsupported APIs, scalar synchronization, host round-trips, Python/object-heavy control flow, shape/data-dependent branching, and in-place mutation hazards. Produce a verdict of READY, LIGHT REFACTOR, SIGNIFICANT REFACTOR, or NOT RECOMMENDED, with concrete refactor pointers.
alibabacloud-data-agent-skill
IncludedInvoke Alibaba Cloud Apsara Data Agent for Analytics via CLI to perform natural language-driven data analysis on enterprise databases. Data Agent for Analytics is an intelligent data analysis agent developed by Alibaba Cloud Database team for enterprise users. It automatically completes requirement analysis, data understanding, analysis insights, and report generation based on natural language descriptions. This tool supports: discovering data resources (instances/databases/tables) managed in DMS, initiating query or deep analysis sessions, real-time progress tracking, and retrieving analysis conclusions and generated reports. Use this Skill when users need to query databases, analyze data trends, generate data reports, ask questions in natural language, or mention "Data Agent", "data analysis", "database query", "SQL analysis", "data insights".
token-optimizer
IncludedReduce OpenClaw token usage and API costs through smart model routing, heartbeat optimization, budget tracking, and native 2026.2.15 features (session pruning, bootstrap size limits, cache TTL alignment). Use when token costs are high, API rate limits are being hit, or hosting multiple agents at scale. The 4 executable scripts (context_optimizer, model_router, heartbeat_optimizer, token_tracker) are local-only — no network requests, no subprocess calls, no system modifications. Reference files (PROVIDERS.md, config-patches.json) document optional multi-provider strategies that require external API keys and network access if you choose to use them. See SECURITY.md for full breakdown.
resend-cli
IncludedUse this skill when the task is specifically about operating Resend from an AI agent, terminal session, or CI job via the official resend CLI: installing/authenticating the CLI, sending/listing/updating/cancelling emails, batch sends, domains and DNS, webhooks and local listeners, inbound receiving, contacts, topics, segments, broadcasts, templates, API keys, profiles, or debugging Resend CLI/API failures. Trigger on mentions of Resend CLI, `resend`, `resend doctor`, `resend emails send`, `resend domains`, `resend webhooks listen`, `resend emails receiving`, or agent-friendly terminal automation.
alibabacloud-odps-maxframe-coding
IncludedUse this skill for MaxFrame SDK development and documentation navigation on Alibaba Cloud MaxCompute (ODPS). Helps answer MaxFrame API, concept, official example, and supported pandas API questions; create data processing programs; read/write MaxCompute tables; debug jobs (remote or local); and build custom DPE runtime images. Trigger when users mention MaxFrame, MaxCompute with MaxFrame, ODPS table processing, DPE runtime, MaxFrame docs/examples, DataFrame/Tensor operations, or GPU runtime setup. Works for both English and Chinese queries about Alibaba Cloud data processing with MaxFrame.