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Yeah. My name is Sashid Pratap Singh, and, uh, I have around 8 years of experience working in the software industry. And, uh, I have worked in different text tags, uh, including Golang and Java. And, uh, on the database front, I have worked in MySQL and Postgres and a little bit in in MongoDB. And, uh, I was working, uh, in a start up called as Cathayas Private Limited, where I was working as a staff software engineer for the start up mainly in very initial phase. So I was responsible for, you know, creating, uh, dashboards and all in database and, uh, contributing, uh, for new features, uh, directly reporting to the CTO of the company and, uh, uh, contributed in various projects as well. And, uh, I have, um, I have worked in various product based companies. So I started my career with NRC Document Solutions where I stayed there for around 1 year. And after that, I worked in an ecommerce based company called a ShopRoots, where I work around 2, two and a half years. And after that, I worked in Guava's, a networking company. But, um, its clients are, uh, telecom based clients. So I worked there for around 2 and a half years. And after that, I I joined the heart as a senior engineer where I where I stayed for around, uh, 2 years nearly. Yeah.

Yeah. So implementing distributed transactions across microservices and Kubernetes while ensuring data consistency involves several strategies and best practices. So so so the best practices that, uh, or pattern that we can use is the saga pattern. So the saga pattern is suitable for managing distributed transaction in microservices architecture. It breaks down a transaction into a series of smaller and and independent transaction. Each microservice, it executes its part of the transaction and compensating transactions are defined to undo changes if any step fails. So saga orchestration is the first step. A central coordinator or part of an API gateway manages the sequence of mark microservices involved in the saga. It initiates each step and tracks their progress. Then there is a service to service communication. Use reliable communication mechanisms such as gRPC or REST with idempotent operations to ensure that if a request fails and retry it, it does not call unintended effects. Then, uh, for distributed transactions and data consistency, uh, database transactions, We can ensure that each microservices manage its database transactions correctly, use asset compliant databases where necessary to maintain strong consistency within each microservice boundary. Then even driven architecture, use events to communicate between microservices to trigger the next step in the saga. Events should include transaction ID and payload to ensure that steps can be retried or compensated if needed. So service discovery and and load balancing. Use Kubernetes service discovery via DNS to locate microservices involved in the saga. Container orchestration. Kubernetes ensured that microservices are deployed and scaled efficiently, providing resilience and fault tolerance. Ensuring data consistency, item potent operations are needed, and then compensation actions to undo any changes made by previous steps.

So there are, uh, multiple ways in which we can avoid the race condition in Go while multiple Go routines are accessing the same MongoDB collection. So let me list them 1 by 1. So first is they use MongoDB driver with session management. When working with MongoDB and Go, it's crucial to use a MongoDB driver that supports session management. For example, uh, official MongoDB driver, Mongo Go driver provides a client and database truck that manage connection efficiently. Then what we can use is we can use synchronize access with mutexes. So to prevent risk condition when accessing MongoDB collection from multiple go routines, use sync.newtext or sync.rwnewtext. So the this ensures that each method accesses MongoDB uses lock before performing operation and unlock afterwards. And then there can be used context management. So always context dot context while interacting with MongoDB operations. This allows you to control the life cycle of operations and manage timeouts, uh, and test cancellations effectively.

So performing seamless schema migration on MongoDB during application deployment in go involves careful planning and execution to minimize downtime and ensure data consistency. So here is here is a strategy using Go and MongoDB to achieve this. So first is versioning and tracking. So version control, maintain a versioning system for your MongoDB schemers. This could be a simple integer or a time stamp based identifier stored in a dedicated collection or document with MongoDB itself. Tracking migration state. So store migration, such state store migration state in a persistent way such as another collection or document in MongoDB. Then we can use schema migration tool, write a migration tool, develop a Go application or a script that manages schema migration. This tool should read the current schema version, apply pending migrations, and update the schema version after each successful migration. Then 3rd is MongoDB driver and transactions. Use MongoDB Go driver. Utilize the, uh, MongoDB Go driver. Contract with MongoDB, then transactions. If your MongoDB deployment supports transactions, MongoDB 44.0 plus with replica sets or mongoDB 4.2 plus with sharded clusters use transactions to ensure atomicity of schema migrations. This helps in rolling back changes if a migration fails in the mid way. Then you have to, uh, handle the rollbacks also. So rollback mechanism should be there in case of any in case a a fallback is needed and changes needs to be revert, uh, in case of migration failure. Then there should be clear that some kind of a deployment strategy should be there. So pre deployment check should be there before deploying new code that includes schema changes. Ensure that the migration tool is tested thoroughly in a staging environment, and then you should use gradual rollout. If possible, deploy schema changes gradually across multiple instances or shards to minimize downtime and ensure that all instances are in sync. Then you should use monitoring and logging in order to, you know, that everything is going well after the deployment. So logging, implement detailed logging while within the migration tool to check the progress of each migration and detect any issues directly. And then monitoring, monitor MongoDB and your application during deployment to detect any performance issues or failures related to scheme schema migration.

Yeah. So, um, in order to design and optimize a Go service to handle last throughput of data incorporating both MongoDB and in memory cache like Redis or Memcache requires, gain careful consideration of concurrency, data retrieval strategies, caching mechanisms, and performance tuning. So here is a structural approach to achieve this. So first, you develop a Go service, Uh, develop a Go based micro service using a framework like game, echo, or plain HTTP handlers to handle incoming request and manage data operation. Then use MongoDB for position storage and in memory cache like Redis, uh, for caching that, uh, frequent for the frequently accessed data to reduce latency and input throughput. So if we talk of the component design, then there is a service layer. There is a data access layer. Okay. So if I talk of service layer, then HTTP handlers define HTTP handlers to handle incoming request and delegate operation to the service layer. Service logic, basically, will include all the business logic and data processing here and use asynchronous processing wherever possible to handle concurrent request efficiently. Then data access there. So MongoDB client, uh, use use the MongoDB Go driver to interact with MongoDB, ensure efficient connection management and connection pooling. Caching client integrate Redis or Memcached client libraries to manage in memory caching. In order to optimize data access, read operations. So base basically, use the caching strategy. Implement the caching strategy to fetch, uh, frequently accessed data from Redis or Memcached first. If data is not found in the cache, then fetch it from MongoDB and store it in the cache or subsequent request. Then cache invalidation, implement cache invalidation mechanism to remove or, uh, update cache whenever any data changes to MongoDB. Then there are write operations. So transaction handling with MongoDB transactions to maintain data condense consistency across multiple documents or collections. Then you can use batch operations, batch MongoDB write operations wherever possible to reduce the number of round trips to the database and improve, uh, and save the network operations. And then concurrency and scalability. So we can use go routines for concurrent processing of request and operations. Use channels for communication between proteins wherever necessary. And then connection pooling should be used. Use MongoDB's driver, built in connection pooling, and configure the maximum number of connections appropriately based on your deployment environment and expected load. Then Redis connection pooling. Configure the connection pooling for Redis or memcache clients to manage the connections efficiently. Then for performance, use monitoring, profiling, and query optimization.

Yeah. To so for designing a code system that utilizes both channels and mutexes to coordinate, uh, access to MongoDB in a high concurrent environment includes managing concurrent access to resources like database connections and data structures while ensuring data consistency and minimizing the contention. So there is a way to achieve this. So we can use the go services, develop multiple coroutines, concurrent processes that interact with MongoDB, Then you should use channels. Use channels for communication between coroutines to coordinate access to MongoDB and exchange data safely. Then mutexes, employ mutexes. For example, sync dot mutex, uh, to synchronize access to shared resources such as MongoDB client connections or shared data structures. And there should be components and responsibilities. Uh, so for MongoDB client management, so we can use singleton pattern, implement a new MongoDB client as a singleton to manage database connection efficiently and prevent multiple go routines from creating unnecessary connection that, uh, unnecessary creates, uh, load on the memory. It's unnecessary occupies an unnecessary memory. Then we should use connection pooling. Use the MongoDB Go drivers built in connection pooling to reuse connection across both trees. Then con in order to concurrent, uh, control with mutexes, a MongoDB client mutex, use a mutex to control access to the MongoDB client instance. This this ensures that only one more routine at a time can perform operations that require MongoDB interaction using channels for coordination. For asynchronous processing, use channels to decouple tasks and manage asynchronous processing of database operations. This allows Goru page to request database operations and receive results asynchronously.

Yeah. So for building a scalable, uh, event driven microservice architecture in go, leveraging channels and goroutines, involves designing a system where microservices communicate asynchronously through events. So this approach allows for loose coupling, scalability, and resilience. Here is a structured approach to achieve this. So component of event driven architecture. First of all, we will list all the components. So there is an event producer, microservices that in that generate events based on state changes or incoming request. Then there is an event broker, middleware like Kafka, RabbitMQ, that manages event distribution and ensures reliable delivery. Then there are event consumers. So microservices that react to events that are interested in performing an appropriate actions. Then some kind of design principle should be followed. So for example, some event definition should be defined in a format like JSON that include an that include necessary metadata and payload. Then producer microservices, goroutines at as producers, use goroutines to handle concurrent event production. Channel for event delivery. Use a channel to send events to the event broker. Event broker, uh, basically decouple producers and, uh, consumers receives event from producer and distribute them to interested consumers. And then scaling with topics. Topics or queues can be used to categorize events for specific consumers. Then consumer microservice, goroutines, as consumers can be used. We use goroutines to handle concurrent event consumption. Channel for event reception. Use a channel to receive events from the event group. Then in order to considerations for scalability and reliability, Con we need some pointers. For example, con con concurrency and control, error handling, scaling, and then monitoring and logging. And in order to deploy and and keep in mind the infrastructure, So we need containerization and orchestration. So you can deploy microservices as containers, docker, or easier scalability and deployment engagement. And for orchestration, use Kubernetes or Docker Swarm for orchestrating and managing containers in production

Uh, so monitoring and debugging go application along with its MongoDB interaction in Kubernetes, involves a combination of tools and practices to ensure observe observability, performance monitoring, and efficient debugging. So here here are some, uh, essential tools and approaches that that can be used. So in order to, uh, monitor, you can use Prometheus. So Prometheus operator deploy the Prometheus operator to manage and automate it. Prometheus deployments on Kubernetes. And Prometheus metrics instrument their co application with Prometheus client libraries to expose custom metrics such as HTTP request, database interactions, and application specific metrics. Then service discovery, you can use Kubernetes service discovery to dynamically discover and monitor your application instances. Then for visualization, we can use we can use Grafana. So for visualization, Grafana integrates well with Prometheus to create dashboard for, um, monitoring various metrics. Use use it to visualize the performance of your Go application, MongoDB database, and Kubernetes infrastructure. And in order for alerts, you can cert set up alerts in their panel based on Prometheus metrics to notify any of the performance issues or anomalies.

So configuring, uh, a Kubernetes cluster to automatically handle failover and recovery for straightforward go services using MongoDB for, uh, which are using MongoDB for data persistence involves several key steps and considerations. So here is a structured approach to achieve this. So use stateful set for stateful application. So stateful definition should be used. Use Kubernetes stateful sets to deploy stateful application like MongoDB. Stateful sets provide stable network identifiers and stable storage crucial for stateful application, and you can use persistent volumes. So configure a stateful sets with positive persistent volumes and persistent volume claims to ensure data persist beyond the life cycle of bots. Then in order to handle failover and recovery in port management, the port restart policy should be defined. So configure full set port specification with appropriate restart policies always or on failure to handle port port distarts automatically. Readiness and liveness probes. Define readiness and liveness probes to ensure Kubernetes knows when ports are ready to serve traffic and when they need to be restarted. Then mongodb configuration needs to be done. Replication and sharding. Configure MongoDB for replication and sharding to ensure data redundancy and high availability. Then MongoDB replica set. Use a MongoDB as a replica set to automatically handle failover. Kubernetes stateful sets are ideal for this setup because they provide stable DNS names that the MongoDB replica set can be used for discovery. Then, uh, we need monitoring and alerting. So we can use Prometheus and Grafana for monitoring and alerting, and we can use the alert manager in order to know if some who's in Prometheus and are violated and notified administration, administrators for any critical issues or failures. Then backup and restore strategy. Implement a backup strategy for MongoDB data using tools like MongoDump or third party solutions. And, uh, there should be scheduled backups. Use Kubernetes cron jobs to schedule regular backups of MongoDB data.

Yeah. In order to maximize query performance in Go application that combines data from MongoDB and GraphQL based API, you will need to optimize both your database interactions and GraphQL query executions. So in order to achieve that, uh, what you can do is we can optimize the MongoDB queries, use indexes whenever required. So MongoDB indexes, uh, ensure that MongoDB collections used in your queries are properly indexed. Use the MongoDB to ensure index method for index creation in the schema definition. Then field selection. So fetch only the necessary fields from MongoDB by specifying them explicitly in queries to reduce data transfer and processing overhead. Then aggregation pipelines utilize MongoDB aggregation pipelines for complex queries that involve data transformation, grouping, and joining. Then, uh, of in order to optimize GraphQL query execution, you we can use batching queries. So the data loader use data loader to batch and cache databases queries in GraphQL resolvers to minimize the number of database round trips. We can use pagination. So pagination support is there. Implement pagination in GraphQL resolvers to limit query results and improve response time, especially for queries written in large datasets. Then caching. Result caching can be used. Cache GraphQL queries results using a caching layer. For example, Redis to serve repeated queries faster and reduce load on the packing systems.

Yeah. So, uh, if we are using multiple databases like MongoDB and PostgreSQL, So what so in order to define the common functionalities, we can use the interface. So we can define a database interface. Start we can start by defining an interface that declares the common methods required for interacting with the databases. So this interface will include methods for operation, like connecting to the database, wiring data, inserting inserting, updating, deleting records, and closing connections. Then implement database handlers. So we can define the structures that implement the database interface for MongoDB. Similarly, for, uh, we can I mean, once the interface is defined, we we can, uh, actually implement those interfaces by defining the handlers? So for MongoDB, there will be MongoDB handler, which will implement all these methods that are required to interact with the database. And for postgreSQL, uh, handler, it will also, uh, differ it will also implement those methods required to connect with the post based DB.