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Observability Done Right: Best Practices and Anti-Patterns for Effective System Monitoring

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  WHAT Observability is a concept that refers to the ability to gain insights into the behavior and performance of complex systems. In the context of software engineering, observability involves the collection, analysis, and visualization of data from software applications, infrastructure, and other components of a system. In the animal kingdom, observability plays a critical role in survival, allowing animals to monitor their surroundings, detect threats, and find food. Dolphins use echolocation to observe their surroundings. They emit high-frequency sounds that bounce off objects, allowing them to create a 3D map of their environment. Thanks for reading Knowledge Cafe! Subscribe for free to receive new posts and support my work. Subscribed WHY In today's era, architectures are becoming increasingly large, complex, and fast-paced due to the faster development and deployment of software by distributed teams with the help of DevOps, continuous delivery, and agile development methodo...
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Chaos Engineering | Type of Attacks

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  Today’s advance distributed software systems must be tested for potential weaknesses and faults. Chaos engineering is the process of testing a distributed computing system to ensure that it can tolerate unexpected disruptions. It relies on concepts underlying chaos theory, which focus on random and unpredictable behavior. If you are interested in knowing more about Chaos Engineering and History please refer this article from Gremlin  In this article we will discuss about various categories of attack and some usecases.  Resource Attack Generate load across CPU, Memory and Storage devices Help in preparation for sudden load change, validating auto scaling, test monitoring and alerting config. Its like preparing our system for Black Friday sale in advance.  CPU Attack CPU attack sends heavy traffic on system which can help to identify stability and performance undrer stress. We can also validate auto scaling and alerting mechanism.  Memory Attack Memory leak is t...
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Chaos Engineering : Game Day

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  What is chaos engineering: Chaos engineering is a methodology that helps developers attain consistent reliability by hardening distributed services against failures in production. Another way to think about chaos engineering is that it's about embracing the inherent chaos in complex systems and, through experimentation, growing confidence in your solution's ability to handle it. A common way to introduce chaos is to deliberately inject faults that cause system components to fail. The goal is to observe, monitor, respond to, and improve your system's reliability under adverse circumstances. Why Chaos Engineering? Contrary to what the name may indicate, chaos events are not performed in a chaotic fashion. The goal of chaos engineering is to identify weakness in a system through controlled experiments that introduce random and unpredictable behavior. A main benefit of chaos engineering is that organizations can use it to identify vulnerabilities before a hacker does or befor...
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Container Patterns

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  Why we need Container Patterns: Due to popularity of microservices and distributed computing, containerization has become a major trend in software development.  It involves encapsulating or packaging software code and all its dependencies so that it can run uniformly and consistently on any infrastructure.  In distributed architecture which consists of many microservices, we want our microservices business focused and keep non functional aspects like security, service discovery, proxy, logging and platform configuration etc out of our microservices code, container patterns evolved.  There are 2 popular patterns: Sidecar Ambassadors Sidecar pattern:  In this pattern, we schedule a workload on the same hosts which is intended for specific things that don’t concern your application. There are various use cases for sidecar patterns like request authentication/authorization, service discovery, adding HTTPS to legacy service.  Usecase : We will d...
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Distributed Transactions in Microservices

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  What is a distributed transaction? Microservices architecture has been very popular architecture pattern in recent time. However, one common problem is how to manage distributed transactions across multiple microservices.  When a microservice architecture decomposes a monolithic system into self-encapsulated services, it can break transactions. This means a local transaction in the monolithic system is now distributed into multiple services that will be called in a sequence. Lets try to understand this concept with hypothetical train ticket booking system. Consider below ticket booking monolith application.  In the train ticket booking example above, if a actor sends a book ticket action to a monolithic system, the system will create a  local database transaction that works over multiple database tables (account table, booking table). If any step fails, the transaction can roll back and data consistency is guaranteed by database's ACID (Atomicity, Consistency, Isol...
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API Benchmarking with Gatling

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  What and Why Performance Measurement: The downtime of a mission-critical application can be very costly in recent time. It can lead to customer loss or financial loss. In today's world most of the web applications are built using APIs.  APIs should be functionally correct, as well as available, fast, secure and reliable. It is very important to gather the data about API performance periodically. This will give us information about health and hygiene of our application.  It doesn’t matter how beautiful your front end applications are if the API data sources take several seconds to respond or even worse if API performance is not consistent. API Performance matters very much in a world of microservices, which means the source of what a client application shows is probably being aggregated from multiple APIs behind the scenes. How to Measure API Performance: There are many tools available for performance measurement. This page list open source tools for performance measurem...
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Cassandra Admin Quiz 1

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Cassandra Foundation Quiz 2

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Cassandra Foundation Quiz-1

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Cassandra DataModelling -2

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Cassandra Data modelling Quiz - Part 1

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Is Java Dynamically Typed Language?

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  Lets understand difference between Dynamically Typed and Statically Typed Language. Dynamically Typed Language Statically Typed Language Perform variable type checking at runtime perform variable type checking at compile time Not Mandatory Need to declare the data types of your variables before you use them // Java example int roleNumber;  roleNumber = 5; // Groovy example roleNumber = 5 Dynamically typed language if not used properly it may lead to run to errors or exception due to type mismatch or typo.  Java 7 has introduced diamond operator and Lambda (Java 8) expression can infer variable types, see below example.   //Diamond Operator List<String> nameList = new ArrayList<>(); //Lambda Expr Function<String, String> convertLower= (s) -> s. toLowerCase (); Prior to Java 10, we used to declare variable like below String ...
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