Rajbinder Singh

So, yeah, I am a developer, and I worked on back end. I worked on making algorithms, and I worked on AI with computer vision. So in my last company, I was handling 2 projects, basically, in which first was a DCS tool, which actually have the whole bunch of data into Excel with respect to the player who are playing table tennis. And from that Excel, I have to generate new data with this particular comment using PyPANDAS So that, uh, that data can be helpful to the player to view the insight. Apart from that, I also work into Brazil table tennis protection back end. So I designed the architecture back into microservices. So they have the requirement was they already have a mold of the architecture Return into some other language, uh, .net. So they need to move to Python based, and they also want to To the scaling. So microservices, they could approach to go with with respect to that. Fast API, doc creation, these Tools get involved to scale that thing and to to the development with the team. Apart from that, I worked on the AI computer vision, as I mentioned. And in computer vision, I was basically Generating or you can see the data which needs to be given With respect to the television format of table tennis and the ball contours, which needs to be detected when the ball move from left right and right to left with respect to the AI models, which is there Helping to get the output from the data, and that data is getting used later on. So that is what I am. And apart from that, I know Docker, Redis, back end development, Python. I also know c plus plus, I'm good at oops concept, TSA. So, yeah, pretty much the same, and

In Python, how would you handle database and transaction to ensure AC compliance? So To ensure compliance and maintenance data integrated in Python application when dealing with database, you can use the concept of this transaction. Uh, like, the example can be by using SQLite 3, and The AC property, to begin with, will begin the transaction, initiate transaction, and, like, Theory. So the AC property define the key, Basically, which is like atomicity, and atomicity is defined as a transaction are atomic, meaning they are treated as an individual unit. All the operations within a transaction are either completely Lee successful or if an error occurred, they are all rolled back, ensuring that the database maintains in a consistent state. Then consistency, uh, which is transaction preserve the consistency of database. It ensures that it moves from One consistent state to another, consistent state after the actual transaction, constrained validation, and rule Find on the data I enforce within a transaction. And third is isolation. So Transaction executed concurrently are isolated from each other until they are completed. And isolation shows that the Intermediate state of transaction are not visible to other transaction until they are Uh, submitted. Durability. Once the transaction is completed, the changes made by the transaction are permanently saved in the database, And they persist even in the event of system failures. So this ensures the durability of Committed transaction. Handling transaction in a database involve explicitly, uh, starting uh, transaction excluding the city operations and then either communicating committing the transaction to apply the changes or ruling back the transaction to scan any changes in case of failure. So, yeah, by the database modules such as SQLite 3, by 2 or PostgreSQL, uh, MySQL connector Python. The more other provide method to manage transaction, allowing developer to ensure that, uh, data consistency and integrated by Adhering to the AC property, these transition play a crucial role in maintaining the reliability and occurrence I placed your data within the database.

How can you optimize a sequel query in Python to improve its execution speed? So optimizing SQL query in Python involves various strategies to These their execution speed, so some approaches can be first will be, Uh, Yeah. 1st will be use indexes. So proper indexing on columns providing, uh, proper indexing on columns involved in filtering, Joining or ordering can significantly speed up query execution. It allows the database engine to efficiently locate and retrieve 2nd would be limit the result set, retrieve only the necessary data by using the select statement with the specific column rather than using Asterisk. And, initially, applying limit or top clause to restrict the number of rows returned. 3rd will be optimized join. Use appropriate joins type that is inner join, left join, and ensure join Conditions are indexed and efficiently structured, avoiding joining unnecessary cables or using exclusive join. 4th would be Optimize where clause. So structure your where clause affects efficiently by placing the most restrictive condition first. Use appropriate comparison operator and avoid using functions or calculations On columns in the like, where clause as in can handle hinder the index users. 5th would be avoid Select, um, distinct. So minimize the use of select distinct as it can be resource Instensive. Ensure it's necessary for you your query and cannot be replaced by other technologies or techniques. Uh, 6th would be used query execution plan. So this and this will analyze the query execution Plan generated by the database engine. This helps identify insufficient parts of the query and suggest potential improvement, like tools like explain MySQL not explain query plan. SQLite can assist this. Uh, 7th would be batch processing and Prepared statement. So use batch processing for multiple similar queries and a prepared statement to avoid, Uh, the complying SQL query improving performance in scenarios where the same query is executed multiple times. And 8 would be database server optimization, which will ensure that a database server is properly configured and optimized to perf for performance. This includes Setting related to memory, allocation, caching, and other server specific optimization. And 9th would be Data normalization and denominalization. So normalization, the database schema to reduce redundancy and Improve data integrity on the other hand. Like, in certain scenarios, denormalization can be Beneficial to optimize, uh, query performance by reducing complex joints. So I think, yeah, these should.

Your team needs to write an efficient data migration script in Python that transfer large volumes of data between different SQL databases with 0 data loss. Describe your approach to designing and testing the script. So designing an efficient and reliable data migration chip involved, like careful planning, execution, and rigorous testing to ensure zero data loss. So the approach would be the design approach would be, first, should be the requirement analysis, like, uh, understand the source and destination database, their schemas, the types and any transformation needs during migration. 2nd should be script architecture. So this will involve designing the script architecture with modular component for extraction, transformation, if required, and loading data into destination database, utilize a library or framework like SQL, Kami, Pandas, or database specific connection for efficient data handling. 3rd would be data extraction. Efficiently extract data from the source database using, uh, optimized SQL query or batch processing technique to handle large volumes of data, utilizing or chunking method to prevent memory issues. Of so yeah. And 4th would be data transformation if required. I mean so it should apply the necessary formation to ensure data compatibility between source and destination. Databases handle data type conversion format adjustment or data cleansing. Data loading, we should also be there, which will do the load, extract dead and transformed data into the destination database, which will ensure proper error handling, transaction management, and integrity check. Can you also use bulk loading technologies, like copy method, copy command in post SQL for faster faster insertion? And, uh, one more thing that we need should we should also imply, which is logging and error handling. Implement the comprehensive logging to capture migration process, error, and warning, handle exception gracefully to ensure the script continues execution our rollbacks transaction without data loss. And the testing approaches that should be doing should have testing unit testing, which should include test individual component of the migration script using unit test to verify the correctness of data extraction, of transformation and loading functionality. It should also have integration testing, which will include conduct end to end testing by simulating the migration process using a smaller subset of data, verify that data is migrated accurately without loss or corruption. And 3rd should be performance testing. Test the script script's performance by gradually increasing data volumes and measuring execution time, memory usage, and CPU utilization optimized code and SQL query for better performance. And error and recovery testing should also be there, which will simulate various failures, values, network interruption. Server downtime during migration to ensure the script had an error gracefully and can recover without data loss or corruption and should also have scale testing, which will have the test to test the script's scalability by executing the migration process with large volumes of data, ensuring it perform efficiently without comprising data integrity. And, also, documentation and version control should also be there, which will maintain detail of condition of the script usage of and configure the troubleshoot steps utilized version control like Git for tracking the changes and maintaining script version. So, yeah, of by following these approaches and conduct through testing at various levels, our team can develop a robust and efficient data migration script capable of transferring large volumes of data between different SQL database and zero

A client is facing issues with data inconsistency in their SQL database after multiple concurrent updates from a Python script, how would you go about diagnosing and rectifying the issue? So diagnosing and rectifying data inconsistency issue resulting from multiple concurrent update in a Python script should involve a systematic approach to identify, analyze, and resolve the problem. And the steps should have like, the first the most important one is identify the issue. Uh, refuse a problem. Try to Replicate the issue by running the Python script that perform concurrent update on a test environment with similar conditions. Collect information like gather logs, error message, or any available information related to the inconsistent data or error encountered during the concurrent updates. 2nd should involve investigate concurrent update. Review script logic and lies the Python scripts code that perform concurrent update. Check for race condition, locking mechanism or transaction handling to identify potential causes of data inconsistency. Uh, then database transaction, which should ensure proper use of transaction in the Script to maintain data integrity, verify if transactions are committed or rolled back correctly after update. 3rd should involve data analysis. So check database configuration, which will review the database setting related to isolation levels, uh, locking mechanism, and concurrent concurrency control to ensure they are appropriately configured and also need to examine logs and time stamps, which will check Database logs or time stamp column in affected tables to identify the sequence and timing of concurrent update. 4th, shouldn't, uh, involve resolution steps. Transaction isolation, if not already in use, Consider using appropriate transaction isolation level. Example, serializable, repeatable read to control concurrent access and prevent Inconsistency. Locking strategies. Improve locking mechanism. Example, row level lock, prismatic locking to stick simulate simultaneous access to critical data during update. Retry mechanism can be applied, which involve implemented retry logic to optimize concurrent control to handle conflicts and retry in case of data modification errors and can also do audit the rollback. Consider auditing changes made by to track modification and facilitate rollback if necessary. The 5th step is testing and deployment. Uh, testing changes, implement modification based on the analysis, and conduct thorough testing in a controlled environment to verify their resolution and deploy fixes. Deploy the updated Python script with modifications to address the data inconsistency issues. And post resolution, which should involve monitoring and validating The system after deploying fixes to ensure data consistency and validate that the issue has been resolved and also should have documentation. The document the root cause step taken for resolution and preventative measure to avoid similar issues in the future. And continuous, uh, 7th step should be continuous improvement, which will have review the feedback, implementing preventive measures. So, yeah, following these steps Sure. Can diagnose, rectify, and prevent data inconsistency issue resulting from concurrent update in his Python script, ensuring provided data integrity and system reliability.

What Python design pattern are particularly useful for scalable API development and why? So several design pattern, Python can be, like, particularly useful for deploying scalable API due Ability to enhance maintainability, flexibility, and scalability of the code base. Some of these patterns include Factory method pattern and why it is import is useful because facilitates this, uh, creation of object Specifying the extract class in API development, this pattern allows you to create different types of a pinpoint to handle Dynamically based on user request or configuration promoting scalability and extensibility. 2nd should be fake add pattern. It will provide a simplified interface to Subsystem in API development. In the development, this pattern can encapsulate multiple API calls or into a single interface, simplifying interaction for client and allowing for easy scalability or modifications of the underlying component. 3rd should have decorator pattern. So it will allow adding new functionality to object dynamically. In API development, decorators can be used to add features such as authentication, rate limiting, logging, or caching to a different points without modifying their core implementation and making it easier to scale by Producing or modifying functionalities. 4th should should be single tone pattern, which will ensure a class has only 1 instance and provides a global point of access to it. In API development, singleton pattern can be applied to manage shared resource, like database, Connections, caching, mechanism, or configuration ensuring scalability by controlling access to these resolution. 5th should have observer pattern. It will define a one to many dependency between Objects to that, when one object changes, state all its dependent are notified and update automatically. In API development, this can be used for handling events or notification across different parts of the system, Which facilitating scalability by enabling loosely coupled communication? And 6 should be Strategy pattern. And that strategy pattern defines a family of algorithm encapsulated each one and make them interchangeable. Like, in API development, this pattern allow you to switch between different algorithms and implementation dynamically, which is useful for scaling by adapting to various client comment or press releases. And then we should also consider builder pattern, which will separate the construction of a complex object from its representation. And then it should be, like, async pattern for our asynchronous APIs. So, yeah, by incorporating these design patterns into architecture and code base for an API developers can ensure scalability, mentality, and flexibility Uh, then the API cloud app and grow efficiently as the system

Even a Python function that is meant to return the URL constructed from various parts provided as parameters? Protocol, host name, port, Return f. Protocol host name port path. Build URL, http, sample.com, port, API data. This function is called with the values as shown. However, the some user reported that they occasionally get malformed URL? Example, missing protocol report without changing the parameters, pass the function identify, possible reason for these malformed URL and suggest how you would debug the issue. So it seems like there might be inconsistencies in the way of function. So, Like, the possible strategies that I I should, uh, follow will be, Like, Whatever missing default value handling is there, I should check that. Apart from that, input data inconsistency, uh, I will handle that as well. And to address these issues, first, I will go with the default values. Ensure that the, Build URL handles to fall values for the protocol and port in case they are not provided explicitly. Input data validation, I will also implement the input to validation to ensure that the provided parameter protocols or port, etcetera, the in the correct format. Apart from that, uh, I will modify the code a bit because in the last line, I can see, uh, constructed URL, like, a protocol, host port, name, path, result returning the constructed URL. So by, like, handling these things, I can easily track

A piece of code is meant to integrate with internally, but to fetch user information at the users. They request and Python identify potential issues that could arise from this method and explain how you would debug them non Intrussively. So okay. 1st, what I will do, like, Like, the ways to handle them could include security concerns, uh, which is issues exposing sensitive user data on vulnerabilities due to improper handling or insecure communication channel. And noninclusive resolution will implement robust encryption, authentication and authorization mechanism, which will use secure communication protocols, STTPs, regularly update and patch software to migrate security risk. And, also, we'll check for data in data consistency and integration. And I will check the issues, potential inconsistency error, non intrusive resolution, performance bottlenecks, Dependency failures, I will check for that, and I will also check for compliance and privacy concurrence. And I will also do for Google error handling and log in.

How might you implement a connection pool in Python to manage and reuse SQL database connection in a web service? So to manage and reuse SQL database connection efficiently in our web Service, I will implement a connection pool. A connection pool help manage a set of reusable database connection, reducing overhead by reducing extending connection instead of establishing new one for one request. Python provides various libraries and framework that support connections. Pulling for different database. Uh, example is psych of g two. So by using Secop G2 for postgres, like, import Secop G2 dot poll, then creating a connection poll will be like DB connection pool equals to psych of g 2 dot pool dot symbol connection pool, which will which will have Main connection value equals to 1. Max connection value, it will be 10. Then DB name, user, password, host, and functions To get a connection from the pool, which will like, uh, def get connection, which will return d b connection pool dot get Connection function, then a function to release a connection back to the pool, which will be def release connection con. DB connection pool dot to put. So, like, Secop g 2 pool symbol connection creates a pool of post SQL connection with us, minimum of 1 connection. And, yeah, apart from that, um, by using SQL for connection pulling, What can be done is by, like, using SQL with the connection pool. Q pool allows connection pooling for various database, not limited to Postgres. So, yeah, that is like, by implementing a connection pool, can efficiently manage and reuse database and in our web service optimizing resource usage and providing performance when handling multiple database

Your Python web service has experienced a spike in load leading to slow SQL query response. What steps would you take to diagnose and solve the performance issue? Uh, diagnosing performance issue, uh, will have certain steps to diagnose and resolve the performance issue, which will have like, the first point should have to monitor system metric. We'll check system resource, CPU memory, disk input output input output using tools like, uh, h top Top on monitoring software to identify resource bottlenecks. 2nd Step will be database performance metric, uh, which will use database specific monitoring tools to tools or queries, which, like, explained in this Postgres MySQL to analyze query execution plan, identify slow query, and examine index uses. 3rd point will include application logs and profiling, which utilize application logs to identify any error warning or unusually high response times, utilize Python profiling tools, which will have c profiling, line profiler to identify performance bottlenecks in the code. 4th point will include load testing profiling. Simulate the spike in load using load testing tools like Locust The Apache JMeter to understand how the system behaves under stress. Profile the application during the high load to identify performance critical areas. And, yeah, resolving performance issues will include optimized SQL query to identify and optimize slow SQL queries by adding or modifying index optimizing query structure or refracting queries for better performance. And we'll show we should also include caching mechanism. Implement caching mechanism. Example, caching query results using a memory caching, like, Redis to store frequent access data and reduce database load. 3rd will include database scaling. So which will scale the database infrastructure horizontally Or, vertically, to handle increased load, can consider database replication for red heavy workload. We'll also code optimization, which will include code optimization of Python code by implementing bottlenecks, improving algorithms, reducing unnecessary computations, and optimizing data retrieval method. It should also look for the point like asynchronous processing, utilize asynchronous programming, Async await in Python async IO to handle concrete requests and perform non blocking, high operation improving, responsive under high load. Six points should be, like, load balancing. Implement load balancing techniques to, uh, distribute incoming requests across multiple server incenses. Reduce The load on individual servers and should also go with the circuit breaker pattern, connection action, pooling, and resource management, vertical scaling, uh, of infrastructure, and code ripping and refactoring. So by using these, I think we can easily implement necessary optimization that we need to do.

What is your approach in unit testing Python code that depend on a SQL database data and schema? So When, like, you're testing Python code that depend on database and schema, It's necessary to create a controlled testing moment to ensure reliable and repetitive tests without affecting the actual database. So an approach for unit testing code that interacts with the database and schemas should include like, first point will have use mocking and mock databases, mock database connection. 2nd should have mock database itself. So these 2 will use mocking libraries like unit test dot mock, I test mock, grid mock objects or function that similarly database interaction without actually connecting to a real database? And it will generate mock data or use fixture to simulate different scenarios and test each case without affecting the production database? This ensured that, uh, independent from the actual database state and use in memory database or test database? So in memory database, utilizing memory database solution like SQLite, which will provide by Python SQL three module for Unit testing. So these data base are lightweighted, fast, and don't require a separate server facilitating quick and isolated testing. Second point will include test database. Set up a separate test database that mirror the schema, and structure of the production database, but contains test specific or sanitized to data? Perform Test against this specify this the dedicated test database. That should include, uh, fixture setup and tier down? It should have fixture management, test data, seeding, It should also include transaction rollback so that the use can use transaction to wrap each test in a transaction and rollback changes. And can also include database road map for cleaning. We should also check for test scenarios, which will have, uh, test different scenarios, like, to write tests to cover various scenarios, including edge cases, boundary condition, error handling, and normal operations to ensure comprehensive test coverage? And should also have integrated integrating testing? So concern integration testing where feasible, which involves testing code against the real database but in an isolated test environment? And should also continuous integration set like CICD pipelines to automate data, test in CICD pipeline integrated unit tests into CICD to automate testing, ensuring that database dependent code and continuous tested and validated, and test isolation, which will ensure test isolation prevent test from impacting each other and from relying on the state of other test auditors?

What are the hallmarks of well optimized SQL statements in Python application? How do you measure them? Them? So optimizing SQL statement in a Python application involves several That's that contribute to, um, prove performance. Uh, so some hallmarks of optimized SQL statement And ways to measure their efficiency will be, like, first is query performance. Uh, its hallmark will be efficient Statement execute quickly and retrieve the necessary data without unnecessary overhead. Measurement, use profiling tools To analyze query execution plans, monitors, uh, query execution time, and optimize for faster execution, Like, Postgres, MySQL, SQL Server Profiler, and MSC SQL Server. Second point would be, Uh, index utilization, its hallmark will be, like, well, optimized query, leverage appropriate index to speed, update and retrieval. Measurement, check index usage and query execution plan, monitor the ratio of index seeks to scan, Use tools to identify missing or unused indexes. Example, p g stat missing index in post SQL. 3rd 1, 4, should include reduce, uh, resource consumption. Its hallmark will be optimized SQL statements, Consume fewer resources such as CPUs, memory, and input output. This measurement will be monitored system resource Usage during query execution, the system monitoring tools, which is top desktop database specific performance monitoring tool to Trace resource consumption. 4th point will have minimized locking and blocking. Its hallmark will have optimized queries, Minimize locking contention and blocking issues, improving concurrency measurement, analyze lock weights, and contention using Data based specific monitoring tool use isolation levels appropriate for applications like, The requirement to minimize locking. And 5th point should include parameterization, which will have parameterization queries, Preventing SQL injection and improved query plan, caching, and to use. And should also use proper joins and predicates, Which will have its hallmarks hallmark, like, well optimized SQL uses, appropriate join types, like inner join, left join, and Efficient where clause. 7th will have, like point should have batch processing and data retrieval. So its hallmark is, like, Of device SQL statement often use back processing for multiple operations and retrieve only the necessary data, And 8 point should have response time and throughput. And, also, we should consider Consistency and, uh, predictability, and 8 10th point should have maintenance and scalability. So, yeah, we should to that process?