Kafka vs rest api


  • Kafka Msg VS REST Calls
  • 3 ways to connect Apache Kafka and MuleSoft for event-driven integrations
  • Redis, Kafka or RabbitMQ: Which MicroServices Message Broker To Choose?
  • Use Cases and Architectures for HTTP and REST APIs with Apache Kafka
  • Comparing REST and Streaming APIs
  • REST vs Messaging for Microservices – Which One is Best?
  • Kafka Msg VS REST Calls

    It enables Developers to create Applications that consistently produce and consume streams of Data Records, relying on a message broker that relays messages from the Publishers systems that transform data into the desired format from Data Producers to the Subscribers systems that manipulate or analyse data in order to find alerts and insights and deliver them to Data Consumers.

    Apache Kafka is super fast and maintains a high level of accuracy for the Data Records. These Data Records are maintained in order of their occurrence inside Clusters that can span multiple Servers or even multiple Data Centers.

    Apache Kafka replicates these records and partitions them in such a way that allows for a high volume of users to use the Application simultaneously. Kafka copies the partitions to other Brokers also known as replicas from the elected Broker leader to ensure robustness.

    All writes and reads to a Topic are routed through the leader, who organises the updating of replicas with new data. Furthermore, in the case of a breakdown or maintenance, it immediately balances Customers. That is, it is more dependable than other existing Messaging Systems.

    A common production setting in Kafka is a Replication factor of 3, i. This replication is performed at the level of Topic-partitions. Kafka is designed to cope with ingesting massive amounts of Streaming Data, with Data Persistence and Replication also handled by design. Data Processing with Real-time Streaming In the financial arena, for example, it is critical to detect and prevent fraudulent transactions as soon as they occur.

    Predictive maintenance models should continually analyse streams of data from functioning equipment and trigger alerts as soon as variations are identified. IoT devices are frequently rendered ineffective in the absence of real-time data processing capability.

    Hevo not only loads the data onto the desired Data Warehouse but also enriches the data and transforms it into an analysis-ready form without having to write a single line of code.

    Get Started with Hevo for Free Its completely Automated Data Pipeline offers data to be delivered in real-time without any loss from source to destination. Its fault-tolerant and scalable architecture ensure that the data is handled in a secure, consistent manner with zero data loss and supports different forms of data.

    The solutions provided are consistent and work with different BI tools as well. Check out why Hevo is the Best: Secure: Hevo has a fault-tolerant architecture that ensures that the data is handled in a secure, consistent manner with zero data loss.

    Minimal Learning: Hevo, with its simple and interactive UI, is extremely simple for new customers to work on and perform operations. Hevo Is Built To Scale: As the number of sources and the volume of your data grows, Hevo scales horizontally, handling millions of records per minute with very little latency.

    Incremental Data Load: Hevo allows the transfer of data that has been modified in real-time. This ensures efficient utilization of bandwidth on both ends. Live Support: The Hevo team is available round the clock to extend exceptional support to its customers through chat, email, and support calls.

    Live Monitoring: Hevo allows you to monitor the data flow and check where your data is at a particular point in time. This means that the time it takes for a Data Record produced to Kafka to be retrieved by the Consumer is quite quick. It is because it decouples the message, allowing the Consumer to retrieve it at any moment. Seamless Messaging and Streaming Functionality Apache Kafka provides a unique capacity to publish, subscribe, store, and process Data Records in real-time, thanks to its special ability to decouple messages and store them in a highly efficient manner.

    With such seamless messaging functionality, dealing with huge volumes of data becomes simple and easy, giving Business Communications and Scalability a considerable edge over conventional communication approaches. Consumer Friendly Kafka may be used to integrate with a wide range of Consumers.

    The best thing about Kafka is that it may behave or act differently depending on the Consumer with whom it connects because each Customer has a varied ability to manage the messages that come out of Kafka. Furthermore, Kafka integrates nicely with a wide range of Consumers written in a wide range of languages. Kafka Connect is a tool for scalably and reliably streaming data between Apache Kafka and other systems. Kafka Connect may import whole Databases or metrics from all of your Application Servers into Kafka Topics, making the data available for low-latency Stream Processing.

    This setup eliminates the need to build Custom Producer or Consumer Applications. It also eliminates the need for integrating or subscribing to a third-party Data Collector that can provide Connectors to these Data Stores. This breakthrough has far-reaching implications for streamlining IoT systems.

    Kafka Connect currently supports two modes of execution: Standalone single process Mode and Distributed Mode Standalone Mode In Standalone Mode all work is performed in a single process. It is easier to set up and helpful in cases when just one worker is required e. This mode is helpful for obtaining status information, adding and deleting Connectors without interrupting the operation, as well as for testing and troubleshooting.

    Distributed Mode The other mode of execution, Distributed mode handles automatic balancing of work, allows you to scale up or down dynamically, and offers fault tolerance both in the active tasks and for configuration and offset commit data. Clients should check the HTTP status, especially before attempting to parse and use response entities.

    3 ways to connect Apache Kafka and MuleSoft for event-driven integrations

    ICO design There can be many ways, you can achieve this requirement to consume messages from consumer. You can handle everything in adapter module or java mapping or using separate ico to orchestrate the api. ICO 1 This will be scheduled to run once in 10 mins and sender adapter reads the dummy file in test mode. Rest receiver adapter will call the read records api and send the response to ICO2 via bridge.

    Readrecords api will produce 3 possible outputs in json format. Kafka will return the records in json format and it can have 1 to n xml messages which are base64 encoded. Client has to be do the splitting Response will contain no data empty json file.

    When no data is present in kafka to consumer, it will return empty payload. Error message incase any exceptions Using response code, you can handle which kind exceptions you would like to handle and not handle. Based on the incoming payload type it will do the mapping and send to 3 different receiver.

    Using receiver determination condition, input will be routed to respective receiver. Empty : Create empty file in receiver with overwrite option. With Records: I have used two mappings.

    First mapping will do lookup to commit records in kafka and once commit is success, second mapping will split and send it to receiver. SAP PO will decode and create an xml file. Exception : When exceptions occurs, This receiver channel will perform consumer delete operation and then call ICO3 via bridge. You could also add one more receiver interface to send email notification.

    So that you can find out if error happens in loop. Based on input payload, ICO2 will determine the receiver which will delete the consumer and call ICO3 to initiate consumer creation. Delete call will not succeed as initially no consumer is present. You can ingore this by handling custom exception handling. More complexities in my implementation.

    In my case, i had to store the cookie value in value mapping and use it for consecutive calls. In all API call except Create consumer instance api cookie has to be used in header.

    This is because, rest calls will be handled in kafka rest proxy, using two nodes and consumer instance will always be created with particulate node. Incoming api call will be routed to node using dispatcher.

    Without cookie, dispatcher will not know, in which node Either node 1 or node 2 the instance is created. Let us say, consumer is created with node1 and when incoming api call is directed to node 2, it will throw error saying that consume does not exist. But if we make api call with cookie set in header, then It will be able to route to correct node and we dont get error like consumer does not exist.

    Due to handling cookie and cookie is valid for only 1 day, i had to refresh it once in a day. That is the reason, When ICO 3 runs, it will get the cookie from create consumer api and will store in value mapping. In ICO1, We will check the cookie date, if date is equal to current day, then proceed with read api call else do a loopback to initiate the consumer creation process.

    We are in the process of moving to cpi, hopefully, we can make use of cpi kafka adapter. Few points We could have used sender rest adapter with polling option and It comes with inbuilt split and duplicate handling features. To handle, duplicate, we could use offset value as identifier. But read records api returns multiple offsets, hence would be difficult to use duplicate check. And there is no custom error handling in REST polling message protocol. In our case if rest call ends with error, we have to initiate create consumer api call.

    If we use split, then it will be difficult to make commit call for each record. There is no option to set headers dynamically in sender poll option. We can use adapter, module, but since we are in the process to move interfaces to cpi, We dont want to use modules. Our kafka server is not available in public. Why should a consumer take care of all creation reading and commiting. This is the fundamental constraint kafka has.

    Redis, Kafka or RabbitMQ: Which MicroServices Message Broker To Choose?

    It enables Developers to create Applications that consistently produce and consume streams of Data Records, relying on a message broker that relays messages from the Publishers systems that transform data into the desired format from Data Producers to the Subscribers systems that manipulate or analyse data in order to find alerts and insights and deliver them to Data Consumers.

    Apache Kafka is super fast and maintains a high level of accuracy for the Data Records.

    Use Cases and Architectures for HTTP and REST APIs with Apache Kafka

    These Data Records are maintained in order of their occurrence inside Clusters that can span multiple Servers or even multiple Data Centers. Apache Kafka replicates these records and partitions them in such a way that allows for a high volume of users to use the Application simultaneously. Kafka copies the partitions to other Brokers also known as replicas from the elected Broker leader to ensure robustness.

    All writes and reads to a Topic are routed through the leader, who organises the updating of replicas with new data. Furthermore, in the case of a breakdown or maintenance, it immediately balances Customers.

    That is, it is more dependable than other existing Messaging Systems. A common production setting in Kafka is a Replication factor of 3, i. This replication is performed at the level of Topic-partitions. Kafka is designed to cope with ingesting massive amounts of Streaming Data, with Data Persistence and Replication also handled by design. Data Processing with Real-time Streaming In the financial arena, for example, it is critical to detect and prevent fraudulent transactions as soon as they occur.

    Predictive maintenance models should continually analyse streams of data from functioning equipment and trigger alerts as soon as variations are identified.

    Comparing REST and Streaming APIs

    IoT devices are frequently rendered ineffective in the absence of real-time data processing capability. The same type of issue occurs when directly invoking a RESTful service.

    If it does the service is even more tightly coupled to other services — again violating the key principle of keeping microservices architecture single in purpose and small in size. Event-Driven Microservices EDM are inherently asynchronous and are notified when it is time to perform work. In many cases, asynchronous communications is how many of our daily interactions take place.

    Take Facebook: It would be incredibly inefficient to navigate to each friend and check to see if they have a status update. Instead we are notified when a friend has updated their status so we can go see that cute new picture of their cat. Obviously, that makes us more productive as individuals. Loose coupling allows microservices to be ready for the never-ending changes that occur within enterprises.

    Non-Blocking — Microservices should perform as efficiently as possible, and it is a waste of resources to have many threads blocked and waiting for a response.

    With asynchronous messaging applications can send a request and process another request instead of waiting for a response. This becomes clear when revisiting the bartender analogy. Bartenders are complex individuals and can service multiple patrons and interleave the execution of multiple tasks at the same time.

    Simple to scale — As applications and enterprises grow, the ability to increase capacity or dynamically scale to optimize costs becomes one of the most important advantages of microservice architecture. Since each service is small and only performs one task, each service should be able to grow or shrink as needed.

    This also makes it easy to determine which service is the bottleneck and add additional instances of just that service, instead of blindly scaling up all services, which can be the case when microservices are chained together with synchronous communications. The ability to scale using event driven architecture has been proven by companies such as Linked-in and Netflix so you can rest assured it will work for your enterprise.

    Greater Resiliency and Error Handling —In the past few months major airlines have experienced data center issues that resulted a cascade of application synchronization problems. The impact of these problems was massive: flight cancellations, angry customers and the loss of millions of dollars, not counting damage to their reputations.

    Microservices failure scenarios become tricky when considering in-progress transactions. In all API call except Create consumer instance api cookie has to be used in header. This is because, rest calls will be handled in kafka rest proxy, using two nodes and consumer instance will always be created with particulate node.

    Incoming api call will be routed to node using dispatcher. Without cookie, dispatcher will not know, in which node Either node 1 or node 2 the instance is created.

    Let us say, consumer is created with node1 and when incoming api call is directed to node 2, it will throw error saying that consume does not exist. But if we make api call with cookie set in header, then It will be able to route to correct node and we dont get error like consumer does not exist. Due to handling cookie and cookie is valid for only 1 day, i had to refresh it once in a day.

    REST vs Messaging for Microservices – Which One is Best?

    That is the reason, When ICO 3 runs, it will get the cookie from create consumer api and will store in value mapping. In ICO1, We will check the cookie date, if date is equal to current day, then proceed with read api call else do a loopback to initiate the consumer creation process.

    We are in the process of moving to cpi, hopefully, we can make use of cpi kafka adapter. Few points We could have used sender rest adapter with polling option and It comes with inbuilt split and duplicate handling features. To handle, duplicate, we could use offset value as identifier.

    But read records api returns multiple offsets, hence would be difficult to use duplicate check. And there is no custom error handling in REST polling message protocol.


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