Kafka Consumer App

There are a lot of performance knobs and it is important to have an understanding of the semantics of the consumer and how Kafka is designed to scale. 0 or newer protocols can publish. Create a topic 4. (Step-by-step) So if you're a Spring Kafka beginner, you'll love this guide. I am working on improving a streambase application for safe and unexpected shutdown. Answer: for general consumer apps just consuming messages, no. As you can see in the first chapter, Kafka Key Metrics to Monitor, the setup, tuning, and operations of Kafka require deep insights into performance metrics such as consumer lag, I/O utilization, garbage collection and many more. With SSL it is not working for me but with out SSL it is working fine. Apache Kafka is a popular distributed message broker designed to efficiently handle large volumes of real-time data. We will cover following things. Topic-partitions: the unit of parallelism. The following code examples show how to use org. This is why we make our data, deliberations, and decisions, accessible to consumers, developers and stakeholders, in accordance with the Open Government Initiative. If you have been working with Kafka for sometime, you might need to purge data in a particular topic. py from kafka import KafkaConsumer. I am new with Kafka, can you please provide an example of reading message one by one, and only commiting once you have processed the message. Well, it can be done by calculating the difference between the last offset the consumer has read and the latest offset which has been produced by the producer in the Kafka source topic. Kafka is fast, scalable, and durable. In this tutorial, you are going to create simple Kafka Consumer. Kafka is the leading open-source, enterprise-scale data streaming technology. The messages published into topics are then utilized by Consumers apps. GitHub Gist: instantly share code, notes, and snippets. 7 and shows how you can publish messages to a topic on IBM Message Hub and consume messages from that topic. It uses a high-level Kafka consumer to fetch the data from the source cluster, and then it feeds that data into a Kafka producer to dump it into the destination cluster. , when the message is replicated to all the in-sync replicas. A sample Spark Streaming app that reads data from Kafka secured by Kerberos, with SSL is available here. As with any other stream processing framework, it's capable of doing stateful and/or stateless processing on real-time data. You may start using the Kafka endpoint from your applications with no code change but a minimal configuration change. js with below script. In this blog, we will show how Structured Streaming can be leveraged to consume and transform complex data streams from Apache Kafka. Kafka records are stored within topics, and consist of a category to which the records are published. Introducing Apache Kafka on Heroku: Event-Driven Architecture for the Cloud Era. Apache Kafka - Simple Producer Example - Let us create an application for publishing and consuming messages using a Java client. The following code examples show how to use org. …And within that data directory,…I'm going to create a data ZooKeeper and…this is to hold the ZooKeeper. In this article, let us explore setting up a test Kafka broker on a Windows machine, create a Kafka producer, and create a Kafka consumer using the. In this post, we will be taking an in-depth look at Kafka Producer and Consumer in Java. It is horizontally scalable, fault-tolerant, wicked fast, and runs in production in thousands of companies. The producer and consumer components in this case are your own implementations of kafka-console-producer. The Anypoint Connector for Apache Kafka allows you to interact with the Apache Kafka messaging system, and enable seamless integration between your Mule app and an Apache Kafka cluster, using Mule runtime. What is a Kafka Consumer ? A Consumer is an application that reads data from Kafka Topics. Writing a small app that connects Kafka to a data store sounds simple, but there are many little details you will need to handle around data types and configuration that make the task non-trivial - Kafka connect handles most of this for you, allowing you to focus on transporting data to and from the external stores. Consumer Product Safety Commission (CPSC) believes in the power of open data and being accessible to the public. Step by step guide to realize a Kafka Consumer is provided for understanding. 0 or higher) The Spark Streaming integration for Kafka 0. This is great—it's a major feature of Kafka. The unit of parallelism in Kafka is the topic-partition. Today we are pleased to announce the initial release of Kafdrop, our open source Kafka UI for monitoring your Kafka cluster. This metric tells us, how far behind the consumer is with fetching messages from the inScalingTopic topic per partition. 1 release 1 and later include a Kafka integration feature that uses the new Kafka consumer API. 0, I do not find the "mqsichangeproperties" command to set the broker keystore and truststores, and the documentation of ACE 11. Sample use case is topic created in kafka cluster need to be available for multiple consumers other than pega connector Trying to understand pega kafka client implements consumer group. If using Kafka authorization (via Apache Sentry), you'd have to ensure that the consumer groups specified in your application are authorized in. We will cover following things. Kafka Offset Monitor. If you go to Global Elements, you will find “Apache Kafka. Then, you will breakdown this architecture into individual components and learn about each in great detail. Uses docker to containerize the app. In this post I am just doing the Consumer and using built in Producer. Python client for the Apache Kafka distributed stream processing system. The application flow map shows the tier receiving data from the Kafka queue. Alpakka Kafka connector (akka-stream-kafka) example. Mitra - Thanks for the A2A. Consumer code basically connects to the Zookeeper nodes and pulls from the specified topic during connect. If you have been working with Kafka for sometime, you might need to purge data in a particular topic. Streaming Salesforce notifications to Kafka topics Salesforce CRM's Streaming API allows for receiving real-time notifications of changes to records stored in Salesforce. Kafka Connect for MapR Streams is a utility for streaming data between MapR Streams and Apache Kafka and other storage systems. Kafka Producer and Consumer Examples Using Java In this article, a software engineer will show us how to produce and consume records/messages with Kafka brokers. Technically, Kafka consumer code can run in any client including a mobile. Whatever may the reason, our aim for this post is to find how much our consumer lags behind in reading data/records from the source topic. the first being "payloads" which is an array. [[email protected] nodejs]$ node producer_nodejs. Let's begin by grabbing the KafkaConsumer: # detector/app. The Kafka cluster stores data in topics. Broker sometimes refer to more of a logical system or as Kafka as a whole. Kafka Architecture: Topics, Producers and. In order to add, remove or list acls you can use the Kafka authorizer CLI. It is a lightweight library designed to process data from and to Kafka. yarn add kafkajs Or npm:. Kafka SimpleConsumer Entry Points. Nov 30, 2016 · Kafka is trying to register MBeans for application monitoring and is using the client. By the App I mean - the application which runs on Android device and connects directly to the Kafka. Kafka replicates its logs over multiple servers for fault-tolerance. Cognitive Class Simplifying Data Pipelines with Apache Kafka. However, it's important to note that this can only provide you with Kafka's exactly once semantics provided that it stores the state/result/output of your consumer(as is the case with Kafka Streams). Before going to best practices, lets understand what is Kafka. Kafka only exposes a message to a consumer after it has been committed, i. A message is sent to all the consumers in a consumer group. It brings the Apache Kafka community together to share best practices, write code, and discuss the future of streaming technologies. Kafka offers two separate consumer implementations, the old consumer and the new consumer. Net Core, I have used Confluent. This is because all messages are written using the same 'Key'. When Kafka was originally created, it shipped with a Scala producer and consumer client. Goal is to collect all data and show the average reaction time in real-time in the App. It will log all the messages which are getting consumed, to a file. Kafka broker; Kafka topic created; Once you have that, you can simply open the code in IntelliJ and right click and run the 2 apps (or set up new configurations) Producer. Add support for Kafka Streams from HD Insight Azure Functions should be able to be triggered from Apache Kafka. The tool enables you to create a setup and test it outside of the IIB/ACE environment and once you have it working, then to adopt the same configurations to IIB/ACE. Here is a example how to use Apache Kafka as a messaging system running on Linux Ubuntu. The browser tree in Kafka Tool allows you to view and navigate the objects in your Apache Kafka cluster -- brokers, topics, partitions, consumers -- with a couple of mouse-clicks. Apache Kafka is a distributed streaming platform that is used to build real time streaming data pipelines and applications that adapt to data streams. Note that you would not get the [IKI_CODE] metric from consumers using a consumer library other than the Java one. Let's start by creating a Producer. Kafka Producer and Consumer Examples Using Java In this article, a software engineer will show us how to produce and consume records/messages with Kafka brokers. Kafka clients include any application that uses the Apache Kafka client API to connect to Kafka brokers, such as custom client code or any service that has embedded producers or consumers, such as Kafka Connect, KSQL, or a Kafka Streams application. Site Search. The important part, for the purposes of demonstrating distributed tracing with Kafka and Jaeger, is that the example project makes use of a Kafka Stream (in the stream-app), a Kafka Consumer/Producer (in the consumer-app), and a Spring Kafka Consumer/Producer (in the spring-consumer-app). Apache Kafka: Low Level api Consumer If you want greater control over partition consumption then High Level api consumer you may implement it in low level api. Apache Kafka is a distributed and fault-tolerant stream processing system. The Confluent. We’re going to look at one particular metric: kafka. There are few concepts we need to know: Producer: an app that publish messages to a topic in Kafka cluster. What I want is to be able to store the last known processed offset so that the application safely can be restarted after failure and pickup where it left off. Kafdrop provides a lot of the same functionality that the Kafka command line tools offer, but in a more convenient and human friendly web front end. kafka kafka-producer kafka-consumer Updated Sep 27, 2019. Let’s configure this Kafka Connector first. If a consumer dies, its partitions are split among the remaining live consumers in the consumer group. Go to the Kafka home directory. kafka » kafka-clients Apache Kafka. The main way we scale data consumption from a Kafka topic is by adding more consumers to a consumer group. bytes configurations. Kafka integration - issue related with fetching consumer/producer metrics over JMX broken pipe [ERR. Apache Kafka has emerged as a next generation event streaming system to connect our distributed systems through fault tolerant and scalable event-driven architectures. EnableAutoCommit: Kafka uses a concept called OFFSET to track each consumer position in the entire topic consumption. MirrorMaker (as part of Kafka 0. Apache Kafka clusters are challenging to setup, scale, and manage in production. Hi Readers, If you are planning or preparing for Apache Kafka Certification then this is the right place for you. All the applications connecting to the Kafka core either act as a producer or consumer. If you have been working with Kafka for sometime, you might need to purge data in a particular topic. In fact, when I put together information for this blog post, I joked that getting all this data would be like drinking from a waterfall. Welcome to Kafka Summit San Francisco 2019!. These examples are extracted from open source projects. Finally yes, Kafka can scale further than RabbitMQ, but most of us deal with a message volume that both can handle comfortably. So we'll need a consumer and a producer. Check out a demo of using Kafka to stream property view events from the DreamHouse web app and then consume those events in another app that processes the data and sends aggregates through a web socket to a real-time dashboard. Kafka is popular among developers because it is easy to pick up and provides a powerful event streaming platform complete with just 4 APIs: — Producer — Consumer — Streams — Connect. Execute this command to see the list of all topics. Hi Rahul,I have tried mirror maker with SSL enabled within all kafka brokers in DC1 and DC2. In this tutorial, you will install and use Apache Kafka 1. By default, whenever a consumer enters or leaves a consumer group, the brokers rebalance the partitions across consumers, meaning Kafka handles load balancing with respect to the number of partitions per application instance for you. On Fri, Jun 17, When I view the MBean properties in jConsole, I only see the following for `kafka. Integrating disparate data silos is one of the essential functions of an enterprise system. is introducing a free tier to its cloud-based Apache Kafka offering, giving enterprises an easy way to get started with the popular open source data streaming platform. Kafka's exactly once semantics is a huge improvement over the previously weakest link in Kafka's API: the Producer. Microservices With AngularJS, Spring Boot, and Kafka – by DZone Microservices architecture has become dominant in technology for building scalable web applications that can be hosted on the cloud. Node: A node is a single computer in the Apache Kafka cluster. connect and used to create the TLS Secure Context, all options are accepted. Having seen the history of Kafka, let us move onto its architecture. Kafka Consumer Subscription Committable Source provides Kafka offset storage committing semantics Transform and produce a new message with reference to offset of consumed message Create ProducerMessage with reference to consumer offset it was processed from Produce ProducerMessage and automatically commit the consumed message once it’s been. Asynchronous end-to-end calls starting from the view layer to the backend is important in a microservices architecture because there is no. Kafka is used for building real-time data pipelines and streaming apps. In this post, we will dive into the consumer side of this application ecosystem, which means looking closely at Kafka consumer group monitoring. Consumer code basically connects to the Zookeeper nodes and pulls from the specified topic during connect. The system is a consumer for a service oriented platform, that reads protocol buffers from a Kafka topic and sends push notifications to all the different platforms: apns2, fcm and web-push. As explained in a previous post. Kafka's exactly once semantics is a huge improvement over the previously weakest link in Kafka's API: the Producer. But the Kafka brokers sit in the middle of an ecosystem, with Kafka producers on one side writing data, and Kafka consumers on the other side reading data. There are many Apache Kafka Certifications are available in the market but CCDAK (Confluent Certified Developer for Apache Kafka) is the most known certification as Kafka is now maintained by Confluent. We have our Java client (Spring boot micro service), running on kubernetes cluser, which is a consumer to Kafka topic. With Amazon MSK, you can use Apache Kafka APIs to populate data lakes, stream changes to and from databases, and power machine learning and analytics applications. Kafka is a distributed streaming platform created by LinkedIn in 2011 to handle high throughput, low latency transmission, and processing of streams of records in real time. To enable this functionality, the Salesforce developer creates a PushTopic channel backed by a SOQL query that defines the changes the developer wishes to be notified of. consumer`-. I am wondering if there is an alternative to Kafkabeat app to perform Kafka consumer lag monitoring. Though using some variant of a message queue is common when building event/log analytics pipeliines, Kafka is uniquely suited to Parse. This Jira has been LDAP enabled, if you are an ASF Committer, please use your LDAP Credentials to login. Kafka ecosystem needs to be covered by Zookeeper, so there is a necessity to download it, change its. Goal is to collect all data and show the average reaction time in real-time in the App. Together, you can use Apache Spark and Kafka to transform and augment real-time data read from Apache Kafka and integrate data read from Kafka with information stored in other systems. This enables you to create new types of architectures for incremental processing of immutable event streams. Where you set. 0 (MEP) is coming with some new features related to MapR Event Store for Apache Kafka: Kafka REST Proxy for MapR Event Store provides a RESTful interface to MapR Event Store and Kafka clusters, making it easy to consume and produce messages as well as perform administrative operations. If you need assistance with Kafka, spring boot or docker which are used in this article, or want to checkout the sample application from this post please check the References section below. Connect tens of millions of devices Create an event mesh to connect devices, enterprises app and user interfaces. For these reasons and more, we took matters into our own hands. These processes can either be running on the same machine or, as is more likely, they can be distributed over many machines to provide scalability and fault tolerance for processing. kafka kafka-producer kafka-consumer Updated Sep 27, 2019. Kafka is an asynchronous messaging queue. KafkaConsumer. Kafka's exactly once semantics is a huge improvement over the previously weakest link in Kafka's API: the Producer. This blog describes the integration between Kafka and Spark. Cognitive Class Simplifying Data Pipelines with Apache Kafka. The unit of parallelism in Kafka is the topic-partition. consumer:type=consumer-fetch-manager-metrics,client-id=ks-scaling-app-app-id-*-StreamThread-1-consumer,topic=inScalingTopic,partition=[0,1,2] records-lag. Kafka consumers belonging to the same consumer group share a group id. Apache Kafka is an open-source platform for building real-time streaming data pipelines and applications. I can check the pods and services in the Kubernetes Dashboard as well as through kubectl on the command line. Apache Kafka Monitoring. Let's skim through the code real quick. You can vote up the examples you like and your votes will be used in our system to product more good examples. Kafka nuget package. A consumer group, identified by a string of your choosing, is the cluster-wide identifier for a logical consumer application. This metric tells us, how far behind the consumer is with fetching messages from the inScalingTopic topic per partition. Apache Kafka This enables new types of intelligent and engagement applications, especially those that are powered by the new Salesforce Einstein technologies which brings AI to everyone. Custom management apps for Kafka User account / ACLs manage • Operating kafka from CLI - Add consumer permission to user - Add producer permission to user. Each consumer belongs to a consumer group. The mainApp is the objects that extends the App trait, here I just setup the logger and load the properties form environment. Kafka is the leading open-source, enterprise-scale data streaming technology. Configuring Confluent’s. In this post, I’m not going to go through a full tutorial of Kafka Streams but, instead, see how it behaves as regards to scaling. Kafka is an asynchronous messaging queue. You can see the current consumer groups, for each group the topics that they are consuming and the position of the group in each topic log. By the App I mean - the application which runs on Android device and connects directly to the Kafka. It is horizontally scalable. If a consumer fails before sending commit. consumer JMX metrics are only present on the consumer processes themselves, not on the Kafka broker processes. From no experience to actually building stuff. Provide support for Kafka in a microservice environment, when using Docker. For these reasons and more, we took matters into our own hands. Execute this command to see the list of all topics. The contents of the REST call could be - App name, Event Type and the allocated consumer group-id. After the Splunk platform indexes the events, you can analyze the data using the prebuilt panels included with the add-on. A Broker is a Kafka server that runs in a Kafka Cluster. kafka-python is designed to function much like the official java client, with a sprinkling of pythonic interfaces (e. As a Splunkbase app developer, you will have access to all Splunk development resources and receive a 10GB license to build an app that will help solve use cases for customers all over the world. Before proceeding further, let's make sure we understand some of the important terminologies related to Kafka. py from kafka import KafkaConsumer. This enables you to create new types of architectures for incremental processing of immutable event streams. Spring Kafka Consumer Producer Example 10 minute read In this post, you're going to learn how to create a Spring Kafka Hello World example that uses Spring Boot and Maven. It builds upon important stream processing concepts such as properly distinguishing between event time and processing time, windowing support, exactly-once processing semantics and simple yet efficient management of application state. The consumers in a group then divides the topic partitions as fairly amongst themselves as possible by establishing that each partition is only consumed by a single consumer from the group. This is great—it’s a major feature of Kafka. All the applications connecting to the Kafka core either act as a producer or consumer. Apache Kafka is a publish/subscribe messaging system with many advanced configurations. During development & testing of Kafka consumers you may need to reset the current offset for a consumer so that the consumer can start from the first message. Further processing is done on kafka. To simulate over consumption, we will use Kafka's consumer offset reset tool to set the offset of the consumer group app to an earlier offset, thereby forcing the consumer group to reconsume messages it has previously read. Consumers notify the Kafka broker when they have successfully processed a record, which advances the offset. The sample app assumes no Kafka authorization is being used. Kafka will record which messages (offset) were delivered to which consumer group, so that it doesn't serve it up again. Kafka integration - issue related with fetching consumer/producer metrics over JMX broken pipe [ERR. Kafka Connect is a framework for connecting Kafka with external systems such as databases, key-value stores, search indexes, and file systems, using so-called Connectors. We will provide a very brief overview of some of the most notable applications of Kafka in this cha. Kafka consumers are typically part of a consumer group. bytes and socket. Net Core Producer. tcpping 10. Finally I will type a message "Hello booking consumer" and press enter. HUAWEI AppGallery is HUAWEI official Android Application store. I am going to review our experience and try to write the advantages and disadvantages of both technologies in this short article. The main way we scale data consumption from a Kafka topic is by adding more consumers to a consumer group. We use this default on nearly all our services. , Fronting Kafka and Consumer Kafka. Consumer: Consumes records from the broker. id in group A. Ultimately what this project accomplishes is arbitrary. Integration with Apache Sentry. One fundamental problem we've encountered involves Kafka's consumer auto commit configuration—specifically, how data loss or data duplications can occur when the consumer service experiences an out of memory (OOM) kill or some other type of hard shutdown. If you are still not sure, I recommend to go for CCDAK as it is more comprehensive exam as compared to CCOAK. This is how Kafka does fail over of consumers in a consumer group. Group: Apache Kafka. Producer output. Install KafkaJS using yarn:. Well, it can be done by calculating the difference between the last offset the consumer has read and the latest offset which has been produced by the producer in the Kafka source topic. Learn more about how Kafka works, the benefits, and how your business can begin using Kafka. Building an Apache Kafka Messaging Consumer on Bluemix The Message Hub service on Bluemix is based on Apache Kafka, which is a fast, scalable, and durable real-time messaging engine. Micronaut applications built with Kafka can be deployed with or without the presence of an HTTP server. Afterward, you are able to configure your consumer with the Spring wrapper DefaultKafkaConsumerFactory or with the Kafka Java API. Actually, it is a bit more complex than that, because you have a bunch of configuration options available to control this, but we don't need to explore the options fully just to understand Kafka at a high level. This is exactly the opposite of what we just discussed above, consumer or subscriber app will pull or request the broker/server for all available messages after its current position in the log (or up to some configurable max size), and in case of fall behind with broker or if the app (consumer) is down, it will try to catch up later. Consumers notify the Kafka broker when they have successfully processed a record, which advances the offset. It is working fine. Asynchronous end-to-end calls starting from the view layer to the backend is important in a microservices architecture because there is no. Series Introduction. Since app and. 0 or newer protocols can publish. It keeps feeds of messages in topics. This Jira has been LDAP enabled, if you are an ASF Committer, please use your LDAP Credentials to login. The application flow map shows the tier receiving data from the Kafka queue. Configuring Confluent’s. You can optionally specify delimiter (-D). This will send This is the First Message I am sending Message to the Kafka consumer. It keeps feeds of messages in topics. In this tutorial, you are going to create simple Kafka Consumer. This is exactly the opposite of what we just discussed above, consumer or subscriber app will pull or request the broker/server for all available messages after its current position in the log (or up to some configurable max size), and in case of fall behind with broker or if the app (consumer) is down, it will try to catch up later. The system is designed to be multi-tenant, so it can hold connections for different applications and react to changes without needing to restart the whole. Try to monitor the consumer client app. Kafka – Creating Simple Producer & Consumer Applications Using Spring Boot We had already seen producing messages into a Kafka topic and the messages being processed by a consumer. It uses a high-level Kafka consumer to fetch the data from the source cluster, and then it feeds that data into a Kafka producer to dump it into the destination cluster. The old consumer is the Consumer class written in Scala. Processing in Kafka Streams apps are just another Kafka consumer, but they can maintain computed state they materialize off the stream. 0, I do not find the "mqsichangeproperties" command to set the broker keystore and truststores, and the documentation of ACE 11. Alpakka Kafka connector (akka-stream-kafka) example. Hi Rahul,I have tried mirror maker with SSL enabled within all kafka brokers in DC1 and DC2. If a consumer fails before sending commit. Few details: Our kafka broker is on cloud (multi-tenant), so it cannot expose metrics, hence we are not looking for broker metrics. Kafka is a system that is designed to run on a Linux machine. Our two-factor authentication app demonstrates the communication pattern between only two microservices using Apache Kafka (there are other systems like RabbitMQ, ZeroMQ), but by decoupling communication between those services, we add flexibility for the future. In this post I am just doing the Consumer and using built in Producer. Note that you would not get the [IKI_CODE] metric from consumers using a consumer library other than the Java one. There are a lot of performance knobs and it is important to have an understanding of the semantics of the consumer and how Kafka is designed to scale. servers you provide to Kafka clients (producer/consumer). After the Splunk platform indexes the events, you can analyze the data using the prebuilt panels included with the add-on. What I want is to be able to store the last known processed offset so that the application safely can be restarted after failure and pickup where it left off. Step by step guide to realize a Kafka Consumer is provided for understanding. Kafka Producer/Consumer Example in Scala. Let's now build and run the simples example of a Kafka Consumer and then a Kafka Producer using spring-kafka. Kafka Consumer Failover. After sending i am doing commitoffsets manually instead of automatically. If you are just interested to consume the messages after running the consumer then you can just omit --from-beginning switch it and run. 10 [Optional] Group ID to use while reading from Kafka. Just think of a stream as a sequence of events. Apache Kafka is an open source project that provides a messaging service capability, based upon a distributed commit log, which lets you publish and subscribe data to streams of data records (messages). How Kafka supports microservices. Producer output. Apache Kafka Tutorial - Learn about Apache Kafka Consumer with Example Java Application working as a Kafka consumer. You can see the current consumer groups, for each group the topics that they are consuming and the position of the group in each topic queue. Consumer Group: A consumer group includes the set of consumer processes that are subscribing to a specific topic. If a consumer fails before sending commit. The Kafka Consumer API is used to consume a stream of records from Kafka. id: A Kafka consumer group ID. kafka-python is designed to function much like the official java client, with a sprinkling of pythonic interfaces (e. A consumer group is a set of consumers sharing a common group identifier. The problem is all messages are ended up in one partition. , Fronting Kafka and Consumer Kafka. The unit of parallelism in Kafka is the topic-partition. Kafka Producer/Consumer Example in Scala. It is a lightweight library designed to process data from and to Kafka. Consumer Group: A consumer group includes the set of consumer processes that are subscribing to a specific topic. You can optionally specify delimiter (-D). 54 Use the right tool for the job (and combine them where it makes sense!) 54. Starting with a consideration of design principles and best practices for distributed applications, we'll explore various practical tips to improve your client. Kafka's exactly once semantics is a huge improvement over the previously weakest link in Kafka's API: the Producer. This article describes the new Kafka Nodes, KafkaProducer and KafkaConsumer, in IBM Integration Bus 10. Spring Kafka brings the simple and typical. Kafka Streams is a client library for processing and analyzing data stored in Kafka. This article explains how to write Kafka messages to Kafka topic (producer) and read messages from topic (consumer) using Scala example; producer sends messages to Kafka topics in the form of records, a record is a key-value pair along with topic name and consumer receives a messages from a topic. Check out a demo of using Kafka to stream property view events from the DreamHouse web app and then consume those events in another app that processes the data and sends aggregates through a web socket to a real-time dashboard. Further processing is done on kafka. Kafka Streams is a Java client library that uses underlying components of Apache Kafka to process streaming data. To enable this functionality, the Salesforce developer creates a PushTopic channel backed by a SOQL query that defines the changes the developer wishes to be notified of. Pay for parking with PayByPhone - the smart cashless parking app that'll help you avoid tickets & remind you when it's time to go. Uses docker to containerize the app. Kafka uses the concept of consumer groups to allow a pool of processes to divide the work of consuming and processing records. Technically, Kafka consumer code can run in any client including a mobile. The fact-checkers, whose work is more and more important for those who prefer facts over lies, police the line between fact and falsehood on a day-to-day basis, and do a great job. Today, my small contribution is to pass along a very good overview that reflects on one of Trump’s favorite overarching falsehoods. Namely: Trump describes an America in which everything was going down the tubes under  Obama, which is why we needed Trump to make America great again. And he claims that this project has come to fruition, with America setting records for prosperity under his leadership and guidance. “Obama bad; Trump good” is pretty much his analysis in all areas and measurement of U.S. activity, especially economically. Even if this were true, it would reflect poorly on Trump’s character, but it has the added problem of being false, a big lie made up of many small ones. Personally, I don’t assume that all economic measurements directly reflect the leadership of whoever occupies the Oval Office, nor am I smart enough to figure out what causes what in the economy. But the idea that presidents get the credit or the blame for the economy during their tenure is a political fact of life. Trump, in his adorable, immodest mendacity, not only claims credit for everything good that happens in the economy, but tells people, literally and specifically, that they have to vote for him even if they hate him, because without his guidance, their 401(k) accounts “will go down the tubes.” That would be offensive even if it were true, but it is utterly false. The stock market has been on a 10-year run of steady gains that began in 2009, the year Barack Obama was inaugurated. But why would anyone care about that? It’s only an unarguable, stubborn fact. Still, speaking of facts, there are so many measurements and indicators of how the economy is doing, that those not committed to an honest investigation can find evidence for whatever they want to believe. Trump and his most committed followers want to believe that everything was terrible under Barack Obama and great under Trump. That’s baloney. Anyone who believes that believes something false. And a series of charts and graphs published Monday in the Washington Post and explained by Economics Correspondent Heather Long provides the data that tells the tale. The details are complicated. Click through to the link above and you’ll learn much. But the overview is pretty simply this: The U.S. economy had a major meltdown in the last year of the George W. Bush presidency. Again, I’m not smart enough to know how much of this was Bush’s “fault.” But he had been in office for six years when the trouble started. So, if it’s ever reasonable to hold a president accountable for the performance of the economy, the timeline is bad for Bush. GDP growth went negative. Job growth fell sharply and then went negative. Median household income shrank. The Dow Jones Industrial Average dropped by more than 5,000 points! U.S. manufacturing output plunged, as did average home values, as did average hourly wages, as did measures of consumer confidence and most other indicators of economic health. (Backup for that is contained in the Post piece I linked to above.) Barack Obama inherited that mess of falling numbers, which continued during his first year in office, 2009, as he put in place policies designed to turn it around. By 2010, Obama’s second year, pretty much all of the negative numbers had turned positive. By the time Obama was up for reelection in 2012, all of them were headed in the right direction, which is certainly among the reasons voters gave him a second term by a solid (not landslide) margin. Basically, all of those good numbers continued throughout the second Obama term. The U.S. GDP, probably the single best measure of how the economy is doing, grew by 2.9 percent in 2015, which was Obama’s seventh year in office and was the best GDP growth number since before the crash of the late Bush years. GDP growth slowed to 1.6 percent in 2016, which may have been among the indicators that supported Trump’s campaign-year argument that everything was going to hell and only he could fix it. During the first year of Trump, GDP growth grew to 2.4 percent, which is decent but not great and anyway, a reasonable person would acknowledge that — to the degree that economic performance is to the credit or blame of the president — the performance in the first year of a new president is a mixture of the old and new policies. In Trump’s second year, 2018, the GDP grew 2.9 percent, equaling Obama’s best year, and so far in 2019, the growth rate has fallen to 2.1 percent, a mediocre number and a decline for which Trump presumably accepts no responsibility and blames either Nancy Pelosi, Ilhan Omar or, if he can swing it, Barack Obama. I suppose it’s natural for a president to want to take credit for everything good that happens on his (or someday her) watch, but not the blame for anything bad. Trump is more blatant about this than most. If we judge by his bad but remarkably steady approval ratings (today, according to the average maintained by 538.com, it’s 41.9 approval/ 53.7 disapproval) the pretty-good economy is not winning him new supporters, nor is his constant exaggeration of his accomplishments costing him many old ones). I already offered it above, but the full Washington Post workup of these numbers, and commentary/explanation by economics correspondent Heather Long, are here. On a related matter, if you care about what used to be called fiscal conservatism, which is the belief that federal debt and deficit matter, here’s a New York Times analysis, based on Congressional Budget Office data, suggesting that the annual budget deficit (that’s the amount the government borrows every year reflecting that amount by which federal spending exceeds revenues) which fell steadily during the Obama years, from a peak of $1.4 trillion at the beginning of the Obama administration, to $585 billion in 2016 (Obama’s last year in office), will be back up to $960 billion this fiscal year, and back over $1 trillion in 2020. (Here’s the New York Times piece detailing those numbers.) Trump is currently floating various tax cuts for the rich and the poor that will presumably worsen those projections, if passed. As the Times piece reported: