Kaushik Rao

Could you help me understand more about your background by giving a brief introduction? Yeah, sure. So, hi, I'm Kaushik Rao. I'm currently working in Capgemini since the past three years as a three years of experience in software engineering, specifically in Java backend development. So, I'm expertise in Java, Spring Boot, overall Spring Framework, and building RESTful APIs, like maintain the scalable web-based applications, Spring Boot applications. So, and I'm originally from Chandrapur, Maharashtra, from India. And I've done my, I've done my graduation from Sant Gargai Baba Amravati University with CGP of 9.97 out of 10. And I have extensively like worked in the Java itself. And moreover, I've just worked in two projects. Basically, the domain was financial services. So basically, we need to look into the backend stuff for the transaction that will be happening in the backend and the reports will be generated for specific users or the bunch of users or bulk, it will be in millions of 5 millions, 10 millions, depending on the region that we are in, like in India, we have some users. So that in Indian, in India users, we have a different sort of database postgres. And for another like any other region, we have Oracle SQL database. So basically, yeah, that's about me. And yeah.

When would you choose to use a NoSQL database over a SQL database in a Java backend? So basically, when we can use this, okay, let me think. So we can use it for, you know, like scalability. We can, we might choose NoSQL database over SQL database in this, like in the backend web applications or backend specific applications. So NoSQL will provide the scalability to database that are, you know, designed to scale out by distributing data across multiple servers. And this is ideal for application with large scale data and like high transaction volumes. So that's the first one, like, second is to like, we have the flexible schema, okay, like NoSQL databases such as like MongoDB itself that I have used in my project. So it will allow for a flexible schema, like making it easier to understand and making it, sorry, making it easier to handle the unstructured or semi-structured data that is NoSQL basically. So this is useful when dealing with rapidly changing data structures and also the performance. So, you know, the performance can offer like NoSQL databases can offer the better performance as compared to SQL within the like specific use cases we have such as like real-time analytics we have or caching and we need high throughput transactions. So for that, NoSQL databases is obviously better than SQL ones. And again, like complex data models is there or horizontal scaling is there. So when we need a horizontal scale, horizontally scale our application. So NoSQL databases applications, sorry, NoSQL databases provides a, you know, a simpler path as they are designed for distributed architectures. So basically performance, scalability, and these all things are there. And yeah, like these are the things that are, that will be there to, you know, that will align with specific use cases and requirements, which will make NoSQL databases or like NoSQL databases. So the choice is yours, like it, it will depends on the specific requirements actually. So yeah.

How do you streamline the development process of a Java backend application using Git workflows and branching strategies? So okay, in this like, so like, like streamlining the development process of a Java backend application, like, like we're saying using Git workflows, it can improve significantly the efficiency and the collaboration and the code quality basically. And like, what we can do, like, we'll define a clear branching strategies like main branches is there. So main always contains the production ready code, okay, and the developer branch is there, which will integrate feature for the next release that we will be having. And and supporting branches will be there like, if we need to, if we are assigning a feature, so feature branches will be there for developing the new features created from the developer branch. And what else we have, the release branches that I talked about, the release branches will be like, for it will be preparation for the new production release that we'll be having. That will be also created from the developer branch, and we will merge that branch back into both developer main set so that it will be the production ready release branch, production ready branch, okay, and hotfix branches for fixing the bugs in the production code and creating from this is also creating from the main and merge back into the both main and developer branch. So this is the first one. And second is like we can implement the Git workflows like Git flow is basically a robust workflow that uses all the branches that I have mentioned just now. So this will help in managing large projects basically with multiple releases we have. So it will be good and efficient, okay. And talking about the feature branch workflow, it's simple and effective for like smaller projects, sorry, for smaller teams in working in a project, like focusing on just a feature branch and like, and will be, you know, regularly merging into the developer branch, it will be small and I mean, it will be simple and effective. So now talking about the PRs that we are having full request. So for that, the required code reviews for all PRs to ensure like code quality and sharing the knowledge among the team members will be there. So and like automatic checks will be there in the PR process such as like running the test, code style checks and like that we will have the static analysis tools. So yeah, that will be there. And we can also automate with the CICD setup as Jenkins, we can use the Jenkins or GitHub actions, we can use that or enforcing the coding standards, you know. So there are various ways to do that. Also the documentation and communication is really important for that. So yeah, we need to maintain the clear communication within the team about the status of the branches and like whatever the features or releases that will be there. So yeah, these are the strategies we can use.

uh discuss uh how would you um how would you use uh hibernate's caching mechanism okay caching caching mechanism let me uh let me think uh okay so sql database interaction so basically like there is um um hibernate's caching mechanism like it can um basically significantly enhance the performance of the sql database like interactions uh like it will interact by it will interact by like reducing the number of databases we will be having uh number of database queries sorry not databases database queries and improving the data retrieval efficiency okay when we want to retrieve the data it will like efficiently retrieve the data when we are using like hibernate's caching mechanism so first one was like a first level cache that is a session cache you can say uh it is associated with the hibernate session and it is enabled by default and it it caches the entities uh within a session uh meaning that if we if an entity is retrieved um multiple times uh within the same session it will not result in like multiple database queries it will just remain the same so the usage is like when we load an entity hibernate okay uh uh entity entity in the hibernate to hibernate first it checks the session cache before querying the database okay so like this is this is the um useful way like for uh there is a repeated data so we can use this so within a single session context we can use this so like the mini minimizing the database uh hits so this is the useful one second one is like second level cache obviously uh it is associated with the session factory and is shared across the session okay and um like um it it um like it enables like no not enables like it needs to be explicitly enabled okay um and configured using a cache provider like ehcache we can give or hazelcast we can use so the usage for this second level cache is for caching the entities collections and query results across multiple sessions okay so as i said like it can be have a multiple sessions and it reduces the database access by storing frequently accessed data in memory okay and what uh so first level is done second level and i think yeah the third one is around a query cache is there yeah query cache is there so the query cache caches the that specific result of queries which can be um which can be beneficial when the same query is executed uh frequently with the same parameters okay so so the query cache use will be like requires the second level cache to be enabled okay so it is useful for uh so that is the read heavy applications um where the same queries are repeated so the use case will be for there so yeah like these are the three ways that i know like it will be considered and um it will be like considerations will be for cache invalidation memory usage and the concurrency will be having so by strategy strategically leveraging this hibernate's um caching mechanism we can like significantly enhance the performance of our applications interaction so

Which diagnostic steps would you take if a docker containerized application exhibits Docker Containerized Java Applications, we can we can monitor like memory leaks using like some steps will be there like first we need to monitor the monitor the container resources okay so using Docker monitoring tools like Docker stats will be there to check the containers memory usage and like the second one will be the in a we have to enable the enable and analyze that garbage collection logs okay so that is the important one so we will enable the GC logging in our app Java applications by adding some JVM options we need to add hyphen X log will be there GC and some yeah some kind of good I mean the statement will be added into the Docker file or the or we can add into the container runtime configuration also so after that we need to analyze the GC logs for signs of frequent of full garbage collections which indicate memory pressure okay so thirdly like we can diagnose it by using Java profiling or tools we are having like attach a Java profile profiler such as like example we can use a visual VM or your kit is there to you know running container to monitor heap usage and identifying the memory hotspots okay so like since these tools are connecting to a JVM machine so we we need to we like we need to or you may need to expose the necessary ports and configure security settings also and another one is like we can heap dumps is there so configure our application we need to configure our application to generate a heap dump on out of memory error okay using some again JVM options you need to use so we need to write it down in that file itself so after writing it down we need to analyze the heap dump with tools like Eclipse mat we can use so this is the memory analyzer tool basically to identify memory leaks so after that we need to inspect the application logs then review the code for common memory leak patterns then then update the dependencies libraries and dependencies and libraries like whatever will be there we need to ensure that all dependencies and libraries are up-to-date and container we need to check the like implement auto scaling for that and automated container it starts to like mitigate the impact of memory leaks also the environment configuration we need to check like we need to ensure that the docker environment you know is properly configured for resource limits and health checks so after like one will be like

okay that one was done i guess so what sort of load balancing strategies might implement load balancing strategies will be i think okay in in my project itself i have some um configured i think but i'm okay yeah yeah yeah in in there i have configured so like when we are using docker swarm um for so like there are there are several strategies against so there is a round robin load balancing is there so in the docker swarm we we will use a round algorithm by you know default to distribute requests across the services early as sorry evenly and this will help us to you know in balancing the load by sending each new request to the new available service instance okay and um so again another strategy will be one sticky session will be there so for like applications that require session persistence we can configure sticky sessions for that so that we can ensure that a client's request are like always sent to the same service instance so like this can be a setup we can set up this using an external load balancer that supports the session affinity okay yeah like for that this is a session affinity yeah we can use that again like third strategy we can increase the load balancing like we are using a swarm but like the swarm uses ingress load balancing to distribute you know incoming external traffic to the appropriate services so this is useful like for handling peak loads from the outside the cluster and one more strategy we can use like the overlay networks we can use like we can create an overlay network to you know enable services running on different docker nodes to communicate with each other this will help to distribute the load across multiple nodes and improve the fault tolerance again we can there is one more strategy like when during the peak loads we can manually or automatically scale services by adding more replicas so like docker swarm will makes it easier to you know scale up or scale down by simply changing the number of replicas so that will be the good good one to you know good strategy to implement and one more like to by setting that resource limits and reservations for our services we need to ensure okay or we can ensure that no single service consumes all the resources okay which will like allowing for better distribution of load and preventing any single service from becoming a bottleneck so like combining these many strategies we can we can help like effectively manage and distribute load ensuring like high availability and responsiveness of our backend system like during peak times high times so yeah

uh below is the java code snippet using a docker container initialization script it's common pitfall um so we have our docker service um we're starting the container uh okay debug the issue and explain what would the breakdowns do so okay let me do this start container with your right so um uh here the issue provided a just a code then so like uh specifically that the code is related to the way uh the um so like uh specifically that the code is related to the way a docker run command is structured basically because the start container method is there like basically um specifically hyphen hyphen name option should be named hyphen hyphen hyphen name okay without a space okay that will be that will be it but following the um command that is the result will be like invalid docker command only uh causing the container to fail to start um the best practice we can do is like command validation always validate and test the commands separately in a shell like before embedding them in our code so this will ensure that the command syntax is correct and avoids runtime issues okay again the error handling um need to enhance the error handling to provide more informative error messages for you know for an instance for instance like capturing and logging the error stream uh from the process that can provide a better insights we can do and the output handling like considering the handling the output and streams of the process to capture and log useful information it will be there also the resource management ensure we need to ensure that resources are properly managed such as closing the streams and associated with the process okay so like uh like like we can improve this version um by considering these practices we can like create a class for this like your docker service then we will include the start container method but we need to you know use the try catch block to execute our specific method so like this version will provide the better error reporting and ensuring that the command output is captured and logged will be make it easier to diagnose the future issues so yeah that will be good

All design pattern would be most effective for implementing a new RESTful API feature in a scalable Java system. So specifically when we are implementing a builder pattern for constructing the complex objects such as we are constructing an HTTP request and responses in a flexible and readable way. So builder pattern will be there. Single pattern we are using to ensure a single instance of a class, sample, service or repository is used throughout the application which is useful for managing shared resources like database connections. So the most effective will be, these all are the effective ones only, the observer pattern or proxy pattern or strategy pattern or decorator pattern we have. So decorator pattern is also good for adding additional responsibility to objects dynamically such as logging, validation and transforming of API requests and responses. So yeah, decorator pattern will be also effective for that. And there is factory pattern which is also good for creating the instances of classes based on the specific criteria and which can help in managing the object creation. Similarly when dealing with different types of requests, again there are various patterns that are effective in their own way. So one more like we have used in our projecting also, the template method pattern. So to define the skeleton of an algorithm in a method, you need to defer some steps to subclasses which can help in reusing some common API processing logic while we need to like allowing the customizing of specific steps. So like this is the template method pattern. So like these are the patterns that will help in creating a scalable and maintainable flexible REST API feature in our Java system. So yeah.

How could you leverage the principles of the cap theorem in the design of distributed systems with spring and hibernate leverage principles of the cap theorem. Cap theorem is basically consistency, availability and partition tolerance. So basically how I will approach is like we will understand the cap theorem first like what will do like as I said consistency like every read that will receives the most recent write that will be the consistency and availability like every request receives a response. So without the guarantee that it contains the most recent write but it will be there. So that is availability. So partition tolerance cap. So for that the system continues to operate despite the network partitions. And so we need to understand this cap first. Second we need to identify our priorities. We will determine which two of the three cap properties are most critical for our application. So we have a consistency availability and partition tolerance. So for example an e-commerce platform is here that will might prioritize availability availability and partition tolerance over consistency because of obvious reasons. So that is the case. And thirdly like when design when we designing like based on the design the decisions based on our priorities to there is a list called based on our priorities. So it will be divided in two parts like consistency and partition tolerance will be there or availability and partition tolerance will be there or consistency and availability will be there. So we'll just talk about first two like consistency and partition tolerance. So for that we need to prioritize these two by considering our distributed databases like MongoDB we can use. It is in a strong consistency mode. So like three of these then we can use and again moving forward like we will implement this implementing the Spring and Hibernate. So Spring data and repository pattern will be there. So use we will use the Spring data to extract the persistence layer and depending on our cap priorities we choose the appropriate database and configure our repository accordingly. And transaction management will be also there. So we'll use the Springs manager transaction management and there is a transaction annotation will be there to handle consistency. So Hibernate as the question asked earlier Hibernate second level cache can also help maintain the consistency across distributed nodes. So that will be there and resilience and fault tolerance like implementing the resilience patterns example like circuit breaker now retry using we can use resilience 4G for that or Spring Cloud Netflix is also there.

Okay Describe your approach for monitoring RESTful APS for the performance Of its request and responses using Grafana. Oh Okay Frankly speaking like Grafana I have extreme. I mean I have knowledge for that but in project I won't Explicitly Implement, but yeah like for setting up the data collection. We have Prometheus matrix Exporter or expose matrix will be there. So if we like to monitor the performance of It is RESTful APS using Grafana. I will Use Prometheus as our time series database to you know, collect metrics Prometheus can scrap Metrics from our API service. So it is good and for for configuration Yeah Prometheus configuration is it's pretty simple to you know Set up the Prometheus to scrape our API matrix then update the Prometheus.yaml file To include script configurations for our API services and Like service discovery Will be there like use static configurations or service take a discovery mechanism to find our API endpoints For setting up the Grafana. We need to install the Grafana and add data source in Grafana so in the data source We need to add the Prometheus with the appropriate URL for that and we'll create the dashboards We need to define the dashboards. We need to use queries for that. We need visual metrics for that and Afterwards we can set up the alerts. We need to define it. We need to configure the alert manager and The Grafana alerts will be there like additionally we can set up Grafana alerts for specific panels directly within Grafana itself For continuous improvement we can monitor and adjust Adjust it like continuously monitoring the dashboards and alerts Adjusting the thresholds and queries as necessary To you know, ensure we will get accurate and actionable insights. So also the optimizing the performance Use we will use the insights from Grafana to optimize the performance of our APIs. So yeah Like by following like these steps like setting up configuration We can you know effectively monitor the performance of our RESTful API according to me using Grafana Internet is gone. I don't think I am visible or not. Sorry for that.