Spring Integration Channels :Lab-1

-

Here we will understand publisher and subscriber interaction b/w channels using java project with scenarios.

Pom.xml

<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"	xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">	<modelVersion>4.0.0</modelVersion>	<groupId>com.intertech.si</groupId>	<artifactId>lab1-channels</artifactId>	<version>0.0.1-SNAPSHOT</version>	<name>Spring Integration Lab 1</name>	<description>Lab 1 - using channels</description>	<properties>		<project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>		<springframework.version>4.0.4.RELEASE</springframework.version>		<spring.integration.version>4.0.0.RELEASE</spring.integration.version>		<spring.integration.xml.version>4.0.0.RELEASE</spring.integration.xml.version>		<log4j.version>1.2.17</log4j.version>	</properties>	<dependencies>		<dependency>			<groupId>org.springframework.integration</groupId>			<artifactId>spring-integration-core</artifactId>			<version>${spring.integration.version}</version>		</dependency>		<dependency>			<groupId>org.springframework.integration</groupId>			<artifactId>spring-integration-stream</artifactId>			<version>${spring.integration.version}</version>		</dependency>		<dependency>			<groupId>log4j</groupId>			<artifactId>log4j</artifactId>			<version>${log4j.version}</version>		</dependency>	</dependencies></project>

Spring integration channel registration

spring-integration-channels.xml

<?xml version="1.0" encoding="UTF-8"?><beans xmlns="http://www.springframework.org/schema/beans"	xmlns:int="http://www.springframework.org/schema/integration"	xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:int-stream="http://www.springframework.org/schema/integration/stream"	xsi:schemaLocation="	http://www.springframework.org/schema/beans http://www.springframework.org/schema/beans/spring-beans.xsd	http://www.springframework.org/schema/integration http://www.springframework.org/schema/integration/spring-integration.xsd	http://www.springframework.org/schema/integration/stream http://www.springframework.org/schema/integration/stream/spring-integration-stream.xsd">	<!-- message producer / a Spring Integration wrapped Java Standard input 		stream -->	<int-stream:stdin-channel-adapter id="producer"		channel="messageChannel" />	<!-- a pair of message consumers / a pair of Spring Integration wrapped 		Java Standard output streams -->	<int-stream:stdout-channel-adapter		id="consumer1" channel="messageChannel" append-newline="true" />	<int-stream:stdout-channel-adapter		id="consumer2" channel="messageChannel" append-newline="true" />	<int:poller id="defaultPoller" default="true"		max-messages-per-poll="5" fixed-rate="200" />	<!-- a pub/sub message channel -->	<int:publish-subscribe-channel id="messageChannel" /></beans>

Main container loader class which will load all the beans

Startup.java

import org.springframework.context.support.ClassPathXmlApplicationContext;public class Startup {	@SuppressWarnings({ "resource", "unused" })	// @SuppressWarnings({ "resource" })	public static void main(String[] args) {		ClassPathXmlApplicationContext context = new ClassPathXmlApplicationContext(				"/META-INF/spring/si-components.xml");		while (true) {		}			}}

Here If we run Sratup.java class then we have to enter message which means we are publishing message using console which is producer and If you see xml there are 2 consumers which are consuming messgae.

So if i enter deepak on console then deppak will be prited twice because there are 2 subscribers(consumers) here.

Output:

log4j:WARN No appenders could be found for logger (org.springframework.core.env.StandardEnvironment).
log4j:WARN Please initialize the log4j system properly.
log4j:WARN See http://logging.apache.org/log4j/1.2/faq.html#noconfig for more info.
Contanier started…..
Deepak
Deepak
Deepak

Please find below GIT URL for source code of this Lab

https://github.com/techstudioonline/spring-integration/commit/af6a29342c5a39fa4e56f96b6342f3faec1310be

Popular posts from this blog

What is Garbage collection in Spark and its impact and resolution

-
Image
Garbage Collection Spark runs on the Java Virtual Machine (JVM). Because Spark can store large amounts of data in memory, it has a major reliance on Java’s memory management and garbage collection (GC). Therefore, garbage collection  (GC) can be a major issue that can affect many Spark applications. Common symptoms of excessive GC in Spark are: Slowness of application Executor heartbeat timeout GC overhead limit exceeded error Spark’s memory-centric approach and data-intensive applications make it a more common issue than other Java applications. Thankfully, it’s easy to diagnose if your Spark application is suffering from a GC problem. The Spark UI marks executors in red if they have spent too much time doing GC. Spark executors are spending a significant amount of CPU cycles performing garbage collection. This can be determined by looking at the “Executors” tab in the Spark application UI. Spark will mark an executor in  red  if the executor has spent more than 10% of the time in gar...
Read more

How to change column name in Dataframe and selection of few columns in Dataframe using Pyspark with example

-
Input details: #● File has json records #● Each record has fields: #○ user_id #○ card_num #○ merchant #○ category #○ amount #○ ts ### Below analysis to be done Sample data: +------+--------+---------+--------+----------+-------+|amount|card_num| category|merchant| ts|user_id|+------+--------+---------+--------+----------+-------+| 243| C_108| food| M_102|1579532902| U_104|| 699| C_106|cosmetics| M_103|1581759040| U_103|| 228| C_104| children| M_110|1584161986| U_103| Solution: from pyspark.sql.functions import col # this can be done without using window function creditCardData = spark.read.json(“card_transactions.json”) useridMaxSpendDF = df.groupby(‘user_id’).max(‘amount’) useridMaxSpendDF=useridMaxSpendDF.withColumnRenamed(“max(amount)”,”max_amount”) useridMaxSpendDF=useridMaxSpendDF.withColumnRenamed(“user_id”,”m_user_id”) cond = [creditCardData.user_id == useridMaxSpen...
Read more

Window function in PySpark with Joins example using 2 Dataframes (inner join)

-
Input details: #● File has json records #● Each record has fields: #○ user_id #○ card_num #○ merchant #○ category #○ amount #○ ts ### Below analysis to be done Sample data: +------+--------+---------+--------+----------+-------+|amount|card_num| category|merchant| ts|user_id|+------+--------+---------+--------+----------+-------+| 243| C_108| food| M_102|1579532902| U_104|| 699| C_106|cosmetics| M_103|1581759040| U_103|| 228| C_104| children| M_110|1584161986| U_103| Solution: from pyspark.sql.functions import col # this can be done without using window function creditCardData = spark.read.json(“card_transactions.json”) useridMaxSpendDF = df.groupby(‘user_id’).max(‘amount’) useridMaxSpendDF=useridMaxSpendDF.withColumnRenamed(“max(amount)”,”max_amount”) useridMaxSpendDF=useridMaxSpendDF.withColumnRenamed(“user_id”,”m_user_id”) cond = [creditCardData.user_id == useridMaxSpen...
Read more