HARDCODE

       Imagine going out to buy bread. And you know that there are two types of bakeries. One that gives you the bread right away because they know exactly when it's going to be done. The other kind takes the order from you and tells you to go home and it will deliver the bread to you once it's ready because it's unclear for them when it's going to be done. Then it's clear for you what to expect when dealing with both sorts of bakeries. Now, imagine if there were a third type of bakeries. One that tells you that it's going to give it to you right away while it's actually unclear for them when the bread is going to done. So, you expect the bakery to behave in a certain way but it will actually behave in another. Well, imagine working in such a bakery. Eventual 📨 VS Transactional⚡ In my humble opinion, the problem that I'm discussing in this article is probably the most critical one when designing a system. It's a problem about the identity of your so...

      When designing a system, you should not limit yourself with using only one type of databases. Some business needs might require having a more flexible sort of databases like NoSQL / Document databases.  One of these databases is Elasticsearch ,   which is a topic that I have talked about in a previous post that you should definitely check out. However, if you use more than one database for your system, making sure that all your databases are in sync is crucial . How It Started 🚩 As I said in my previous post . The other sort of problems that we have faced while working on the microservices project is data inconsistency. To explain this problem I have to tell you why the system needed an Elasticsearch index. In microservices projects you usually have more than one database, one for each service/domain. The client-side needed an aggregated document of the data from all the databases. Which in microservices is called The Aggregate Root ,   and that is ...

     People always want the newest things. The more trendy something is, the more it appeals to most people. Same applies to technology. Adding the word microservices to the CV can help it shine a little bit. These are some of the thoughts I was having while I was 2 hours deep into debugging an issue in a project that involved .Net, Kafka, SignalR, React js, Elasticsearch, ECS, MediatR and more. Beware of Microservices  ⚠️ What I'm about to share is a cautionary tale for people who are on the verge of shifting their solution into a microservices architecture. Before you start implementing such an architecture, please read carefully the decisions that others have made along with their consequences so that you can see for yourself the outcome of each design decision instead of falling for the same mistakes. Before anything : The case study that I’m about to discuss might have had its problems. However, the people who made the design decisions are very good software e...

  Confluent has built some useful tools that rely on Kafka . One of these tools is Kafka Connect . And as I have shown you before  one of the ways to use Kafka Connect as a consumer of Kafka Topics, I will show you how to use Kafka Connect as a producer to Kafka topics Kafka Connect offers a very powerful feature which is connecting to databases using certain connectors as JDBC (Java Database Connectivity).  The Kafka Connect JDBC source connector allows you to import data from any relational database with a JDBC driver into an Apache Kafka topic. This connector supports a wide variety of databases. Data is loaded by periodically executing a SQL query and creating an output record for each row in the result set. It enables you to pull data (source) from a database into Kafka , and also push data (sink) from a Kafka topic to a database. In this tutorial we'll be focusing on pulling data from a SQL database. I will be reusing the same setup as my p...

 Suppose you need to store all the messages published on a certain topic. Normally you would create a consumer that listens to that topic, receives the message then stores it in the dataset that you want. What if I told you that Confluent has created a shortcut. Instead of doing this, you can just create a Kafka connector using Confluent Kafka Connect and this connector will automatically store any published message in your database. Kafka Connect is a tool for scalably and reliably streaming data between Apache Kafka and other data systems. It makes it simple to quickly define connectors that move large collections of data into and out of Kafka . Kafka Connect can ingest entire databases or collect metrics from all your application servers into Kafka topics, making the data available for stream processing with low latency. It can also deliver data from Kafka topics into secondary indexes like Elasticsearch or batch systems such as Hadoop for offline analysis. In this tutorial we...

 There are two keywords that you must understand when publishing a message: latency and throughput.  Throughput is a measure of how much data can be processed in a given amount of time. It's usually measured in bits per second (bit/s), or data packets per second. High throughput means the system can process a large amount of data quickly, which is often desirable in high-load scenarios. For example, if a Kafka producer can send 1000 messages per second to a broker, the throughput is 1000 messages per second. Latency, on the other hand, is a measure of time delay experienced in a system, the time it takes for a bit of data to travel from one point to another in a network. It is usually measured in milliseconds. Low latency means that data can be transferred quickly from source to destination. For example, if a message takes 10 milliseconds from the time it's sent by a Kafka producer until it's received by a broker, the latency is 10 milliseconds. In all systems, there's ...

 People say that Kafka is a dumb broker. It just holds data under some defined topics and forward messages from producers to consumers. It doesn't do much processing on the messages, doesn't route them based on content, and doesn't transform them. But that actually isn't entirely true. Kafka does much more than just storing messages. It keeps count of which consumer group consumed which message by tracking its offset. The consumer group is the group Id you give your consumer once you initialize it. Having more than one consumer helps you avoid consumer failures and scale better. If a message is consumed by one consumer in a group, no other consumers in the same group will receive it. However, all other consumers with other group Ids will receive the message. It's important to note that the offset of each message is basically the id of the message related to the group. So, if "message A" was published on a brand-new group its offset will be 0. But if anoth...

    Exceptions can happen anytime and if you don't know how to handle them, then you have got a problem. Or maybe you just haven't checked out my post about exception handling. NET Global Exception Handling: 3 Techniques Beyond Try/Catch Blocks . Anyway, because of that you need to plan what to do if an exception happens during processing a message that you received from a  RabbitMQ queue. You have two options: Negatively Acknowledge (Nack) Using BasicNack:  This method is more flexible. It allows you to negatively acknowledge one or more messages. It takes three parameters: the delivery tag of the message to nack, a boolean indicating whether to nack multiple messages, and a boolean indicating whether to requeue the message.  If the multiple parameter is true, all messages up to and including the one with the specified delivery tag are nacked. If the requeue parameter is true, the nacked messages will be requeued. If it's false, the messages will be dis...