TxEventQ Stream Binder

docs-source/transactional-event-queues/content/spring-boot/jms.md

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+title = "Spring JMS"
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docs-source/transactional-event-queues/content/spring-boot/stream-binder.md

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+title = "Spring Cloud Stream Binder"
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+
+[Spring Cloud Stream](https://spring.io/projects/spring-cloud-stream) is a Java framework that is designed for building event-driven microservices backed by a scalable, fault-tolerant messaging system. The [Oracle Database Transactional Event Queues stream binder implementation](https://github.com/oracle/spring-cloud-oracle/tree/main/database/spring-cloud-stream-binder-oracle-txeventq) allows developers to leverage Oracle’s database messaging platform within the Spring Cloud Stream ecosystem.
+
+This section covers the key features of the Spring Cloud Stream Binder for Oracle Database Transactional Event Queues and getting started examples for developers.
+
+## Key Features of the Transactional Event Queues Stream Binder
+
+The Spring Cloud Stream Binder for Oracle Database Transactional Event Queues provides a high-throughput, reliable messaging platform built directly into the database.
+
+- Real-time messaging with multiple publishers, consumers, and topics — all with a simple functional interface. 
+- Convergence of data: Your messaging infrastructure integrates directly with the database, eliminating the need for external brokers.
+- Integration with Spring Cloud Stream provides an interface that's easy-to-use and quick to get started.
+
+## Configuring the Transactional Event Queues Stream Binder
+
+In this section, we'll cover how to configure the Transactional Event Queues Stream Binder for a Spring Boot project.
+
+### Project Dependencies
+
+To start developing with the Stream Binder, add the [spring-cloud-stream-binder-oracle-txeventq](https://central.sonatype.com/artifact/com.oracle.database.spring.cloud-stream-binder/spring-cloud-stream-binder-oracle-txeventq) dependency to your Maven project:
+
+```xml
+<dependency>
+    <groupId>com.oracle.database.spring.cloud-stream-binder</groupId>
+    <artifactId>spring-cloud-stream-binder-oracle-txeventq</artifactId>
+    <version>${txeventq-stream-binder.version}</version>
+</dependency>
+```
+
+For Gradle users, add the following dependency to your project:
+
+```groovy
+implementation "com.oracle.database.spring.cloud-stream-binder:spring-cloud-stream-binder-oracle-txeventq:${txeventqStreamBinderVersion}"
+```
+
+### Stream Binder Permissions
+
+The database user producing/consuming events with the Stream Binder requires the following database permissions. Modify the username and tablespace grant as appropriate for your application:
+
+```sql
+grant unlimited tablespace to testuser;
+grant select_catalog_role to testuser;
+grant execute on dbms_aq to testuser;
+grant execute on dbms_aqadm to testuser;
+grant execute on dbms_aqin to testuser;
+grant execute on dbms_aqjms_internal to testuser;
+grant execute on dbms_teqk to testuser;
+grant execute on DBMS_RESOURCE_MANAGER to testuser;
+grant select on sys.aq$_queue_shards to testuser;
+grant select on user_queue_partition_assignment_table to testuser;
+```
+
+### Stream Binder Database Connection
+
+The Stream Binder uses a standard JDBC connection to produce and consume messages. With YAML-style Spring application properties, it'll look something like this:
+
+```yaml
+
+spring:
+  datasource:
+    username: ${USERNAME}
+    password: ${PASSWORD}
+    url: ${JDBC_URL}
+    driver-class-name: oracle.jdbc.OracleDriver
+    type: oracle.ucp.jdbc.PoolDataSourceImpl
+    oracleucp:
+      initial-pool-size: 1
+      min-pool-size: 1
+      max-pool-size: 30
+      connection-pool-name: StreamBinderSample
+      connection-factory-class-name: oracle.jdbc.pool.OracleDataSource
+```
+
+## Suppliers, Functions and Consumers
+
+Spring Cloud Stream uses Java suppliers, functions, and consumers to abstract references to the underlying messaging system (in this case, Transactional Event Queues). To illustrate how this works, we'll create a basic Supplier, Function, and Consumer with Spring Cloud Stream.
+
+Our example workflow will use three functional interfaces:
+
+- A Supplier that streams a phrase word-by-word
+- A Function that processes each word from supplier and capitalizes it
+- A Consumer that receives each capitalized word from the function and prints it to `stdout`
+
+Once we've implemented the Java interfaces, we'll wire them together with Spring Cloud Stream.
+
+### Supplier Implementation
+
+The following Supplier implementation supplies a phrase word-by-word, indicating when it has processed the whole phrase.
+
+```java
+import java.util.concurrent.atomic.AtomicBoolean;
+import java.util.concurrent.atomic.AtomicInteger;
+import java.util.function.Supplier;
+
+public class WordSupplier implements Supplier<String> {
+    private final String[] words;
+    private final AtomicInteger idx = new AtomicInteger(0);
+    private final AtomicBoolean done = new AtomicBoolean(false);
+
+    public WordSupplier(String phrase) {
+        this.words = phrase.split(" ");
+    }
+
+    @Override
+    public String get() {
+        int i = idx.getAndAccumulate(words.length, (x, y) -> {
+            if (x < words.length - 1) {
+                return x + 1;
+            }
+            done.set(true);
+            return 0;
+        });
+        return words[i];
+    }
+
+    public boolean done() {
+        return done.get();
+    }
+}
+```
+
+Next, let’s add Spring beans for our Supplier, a Function, and a Consumer. The `toUpperCase` Function takes a string and capitalizes it, and the `stdoutConsumer` Consumer prints each string it receives to `stdout`.
+```java
+import java.util.function.Consumer;
+import java.util.function.Function;
+
+import org.springframework.beans.factory.annotation.Value;
+import org.springframework.context.annotation.Bean;
+import org.springframework.context.annotation.Configuration;
+
+@Configuration
+public class StreamConfiguration {
+
+    // Input phrase for the producer
+    @Value("${phrase}")
+    private String phrase;
+
+    // Function, Capitalizes each input
+    @Bean
+    public Function<String, String> toUpperCase() {
+        return String::toUpperCase;
+    }
+
+    // Consumer, Prints each input
+    @Bean
+    public Consumer<String> stdoutConsumer() {
+        return s -> System.out.println("Consumed: " + s);
+    }
+
+    // Supplier, WordSupplier
+    @Bean
+    public WordSupplier wordSupplier() {
+        return new WordSupplier(phrase);
+    }
+}
+```
+
+### Configure Beans with Spring Cloud Stream
+
+Returning to the application properties, let’s configure the Spring Cloud Stream bindings for each bean defined previously.
+
+In our binding configuration, the `wordSupplier` Supplier has the `toUpperCase` Function as a destination, and the `stdoutConsumer` Consumer reads from toUpperCase. The result of this acyclic configuration is that each word from the phrase is converted to uppercase and sent to stdout.
+
+```yaml
+# Input phrase for the wordSupplier
+phrase: "Spring Cloud Stream simplifies event-driven microservices with powerful messaging capabilities."
+
+spring:
+  cloud:
+    stream:
+      bindings:
+        wordSupplier-out-0:
+          # wordSupplier output
+          destination: toUpperCase-in-0
+          group: t1
+          producer:
+            required-groups:
+              - t1
+        stdoutConsumer-in-0:
+          # stdoutConsumer input
+          destination: toUpperCase-out-0
+          group: t1
+    function:
+      # defines the stream flow, toUppercase bridges
+      # wordSupplier and stdoutConsumer
+      definition: wordSupplier;toUpperCase;stdoutConsumer
+```
+
+If you’ve added the prior code to a Spring Boot application, you should see the following messages sent to `stdout` when it is run:
+
+```
+Consumed: SPRING
+Consumed: CLOUD
+Consumed: STREAM
+Consumed: SIMPLIFIES
+Consumed: EVENT-DRIVEN
+Consumed: MICROSERVICES
+Consumed: WITH
+Consumed: POWERFUL
+Consumed: MESSAGING
+Consumed: CAPABILITIES.
+```