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What is LRU cache and how to implement it in scala?

Reading Time: 2 minutes In this blog, we are going to know about LRU Cache and how to implement it in Scala Language. What is LRU Cache? Least Recently Used Cache is an algorithm used for cache eviction. As the name is suggesting Least Recently Used item will be evicted here. Basically, it removes the least recently used page/frame when the capacity or size of the cache is full Continue Reading

Basics of Reactive Programming

Reading Time: 4 minutes Introduction In this blog, we’ll explore some fundamental notions of reactive programming in an effort to better comprehend what it is. What is Reactive Programming? Reactive is a style of programming that encourages an event-driven, asynchronous approach to data processing. “Asynchronous data streams and change propagation are dealt with in reactive programming.“ Now let’s take a moment and understand what the above statement actually means Continue Reading

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Introduction to ZIO Fibers and Fiber Data Type

Reading Time: 3 minutes In this blog post, we will discuss fibers in ZIO, and how are they different from threads. Introduction ZIO is a highly concurrent framework powered by fibers that are lightweight virtual threads. They enable tremendous scalability compared to threads and are reinforced with resource-safe cancellation, which supports several features in ZIO. What are Fibers? Fibers are lightweight equivalents of OS threads which represent an ongoing Continue Reading

Axon vs Kafka

Reading Time: 4 minutes Introduction One of the most common discussion points that come up regularly in interactions with Customers/Prospects/Forums is how does Axon compare to  Apache Kafka? Can one do the job of the other? Are they complementary to each other? Can they work together? Does Axon provide any capabilities to work with Kafka? Apache Kafka is a very popular system for publishing and consuming events. Its architecture Continue Reading

How Kafka Relates to Axon Framework?

Reading Time: 3 minutes Axon and Kafka are used for different purposes, Axon is used for Event-Driven Architecture and provides the application-level support for domain modeling and Event Sourcing, as well as the routing of Commands and Queries, while Kafka serves as an Event Streaming system. The basic fundamental of Axon is to implement CQRS and Event Sourcing-based architecture.  With the help of this, we can design & develop Continue Reading

Deploy modes in Apache Spark

Reading Time: 2 minutes Spark is an open-source framework engine that has high-speed and easy-to-use nature in the field of big data processing and analysis. Spark has some built-in modules for graph processing, machine learning, streaming, SQL, etc. The spark execution engine supports in-memory computation that makes it faster and cyclic data flow and it can run either on cluster mode or standalone mode and can also access diverse Continue Reading

Spring Cloud Pub/Sub

Reading Time: 2 minutes Cloud Pub/Sub Google Cloud Pub/Sub allows services to communicate asynchronously, with latency on the order of 100 milliseconds. Pub/Sub is used for streaming analytics and data integration pipelines to ingest and distribute data. It is equally effective as a messaging- oriented middleware for service integration or as a queue to parallelised tasks. Pub/Sub enables to create systems of event producers and consumers, called publishers and subscribers. Publishers communicate Continue Reading

Different Types of JOIN in Spark SQL

Reading Time: 3 minutes Join in Spark SQL is the functionality to join two or more datasets that are similar to the table join in SQL based databases. Spark works as the tabular form of datasets and data frames. The Spark SQL supports several types of joins such as inner join, cross join, left outer join, right outer join, full outer join, left semi-join, left anti join. Joins scenarios Continue Reading

The ecosystem of Apache Spark

Reading Time: 4 minutes Apache Spark is a powerful alternative to Hadoop MapReduce, with several, rich functionality features, like machine learning, real-time stream processing, and graph computations. It is an open-source distributed cluster-computing framework. It is designed to cover a wide range of workloads such as batch applications, iterative algorithms, interactive queries, and streaming. Apart from supporting all these workloads in a respective system. It reduces the management burden of Continue Reading

Introduction to Akka Streams

Reading Time: 3 minutes Introduction Lets discuss about streams first. Streams help us to ingest, process, analyze and store data in a quick and responsive manner. Also, it provides us a declarative way of describing, handling and hiding details that we don’t care about in the data. As we know, actors are the core of the Akka toolkit. Akka Streams are built on top of Akka actors which makes Continue Reading

Kalix.io – Platform-as-a-Service: Server less, Database less

Reading Time: 2 minutes Lightbend, comes with the new product that will meet the current developer problems and reduce the efforts while coding. Kalix.io comes with the advanced features that will compete the feature problems we face while developing Applications. Kalix combines the scalability and cost benefits of serverless infrastructure with the data management and responsiveness of stateful services. This adds up to one managed, cloud-based environment. By bringing Server, Continue Reading

What are Zio Effect Constructors?

Reading Time: 3 minutes In this blog post, we will discuss about ZIO effect constructors and how we can use them. Then we’ll take a look at Effect constructors for pure computations and side-effecting computations. Zio Effect Constructors A functional effect is a template for a concurrent workflow. The template which is mostly descriptive in nature, used to test for any side effects. Such as database interaction, logging, data Continue Reading

Spark 3.0 – Adaptive Query Execution With Example

Reading Time: 4 minutes Introduction Adaptive Query Execution (AQE) is one of the greatest features of Spark 3.0 which reoptimizes and adjusts query plans based on runtime statistics collected during the execution of the query. Need of AQE With each major release of Spark, it’s been introducing new optimization features in order to better execute the query to achieve greater performance. Before spark 3.0, cost-based optimization uses table statistics to determine the Continue Reading