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A message queue is an asynchronous communication component that stores messages sent by producers until consumers are ready to process them, enabling decoupled, reliable, and scalable system architecture. It acts as a buffer (shock absorber) allowing fast producers and slower consumers to operate at their own pace. Core Concepts: Producer (Sender): Applications or services that create and send data/messages to the queue. Consumer (Receiver): Applications or services that retrieve and process messages from the queue. Message Queue (Broker): A, durable, asynchronous buffer that holds messages. In this pattern, a message is typically consumed only once and removed after acknowledgment. Topic (Pub/Sub Model): Unlike a point-to-point queue, a Topic allows a message to be published once and received by multiple consumers (subscribers) simultaneously.

The Temporal Dead Zone (TDZ) in JavaScript refers to a specific period within a scope where variables declared with let or const exist but cannot be accessed or used until their declaration and initialization are reached in the code.

Top 10 Github Repositories For Web Developer 1. Web Developer-Roadmap : https://github.com/kamranahmedse/developer-roadmap 2. 30-Seconds-Of-Code : https://github.com/30-seconds/30-seconds-of-code 3. Awesome-Cheatsheets : https://github.com/LeCoupa/awesome-cheatsheets 4. CSS-Protips : https://github.com/AllThingsSmitty/css-protips 5. 33-JS-Concepts : https://github.com/leonardomso/33-js-concepts 6. You-Dont-Know-JS : https://github.com/getify/You-Dont-Know-JS/tree/2nd-ed 7. Front-End-Checklist : https://github.com/thedaviddias/Front-End-Checklist 8. Javascript-Questions : https://github.com/lydiahallie/javascript-questions 9. Clean-Code-Javascript : https://github.com/ryanmcdermott/clean-code-javascript Follow our Community❤️🌻 https://telegram.me/sign2tech

GET, POST, PUT... Common HTTP “verbs” in one figure. 1. HTTP GET This retrieves a resource from the server. It is idempotent. Multiple identical requests return the same result. 2. HTTP PUT This updates or Creates a resource. It is idempotent. Multiple identical requests will update the same resource. 3. HTTP POST This is used to create new resources. It is not idempotent, making two identical POST will duplicate the resource creation. 4. HTTP DELETE This is used to delete a resource. It is idempotent. Multiple identical requests will delete the same resource. 5. HTTP PATCH The PATCH method applies partial modifications to a resource. 6. HTTP HEAD The HEAD method asks for a response identical to a GET request but without the response body. 7. HTTP CONNECT The CONNECT method establishes a tunnel to the server identified by the target resource. 8. HTTP OPTIONS This describes the communication options for the target resource. 9. HTTP TRACE This performs a message loop-back test along the path to the target resource.

Placement toh ho kar rahegi! Padhai nahi rukni chahiye! Keep studying, stay focused! Your hard work will pay off! Reaction🙃😍

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25 Papers That Completely Transformed the Computer World. 1. Dynamo - Amazon’s Highly Available Key Value Store 2. Google File System: Insights into a highly scalable file system 3. Scaling Memcached at Facebook: A look at the complexities of Caching 4. BigTable: The design principles behind a distributed storage system 5. Borg - Large Scale Cluster Management at Google 6. Cassandra: A look at the design and architecture of a distributed NoSQL database 7. Attention Is All You Need: Into a new deep learning architecture known as the transformer 8. Kafka: Internals of the distributed messaging platform 9. FoundationDB: A look at how a distributed database 10. Amazon Aurora: To learn how Amazon provides high-availability and performance 11. Spanner: Design and architecture of Google’s globally distributed databas 12. MapReduce: A detailed look at how MapReduce enables parallel processing of massive volumes of data 13. Shard Manager: Understanding the generic shard management framework 14. Dapper: Insights into Google’s distributed systems tracing infrastructure 15. Flink: A detailed look at the unified architecture of stream and batch processing 16. A Comprehensive Survey on Vector Databases 17. Zanzibar: A look at the design, implementation and deployment of a global system for managing access control lists at Google 18. Monarch: Architecture of Google’s in-memory time series database 19. Thrift: Explore the design choices behind Facebook’s code-generation tool 20. Bitcoin: The ground-breaking introduction to the peer-to-peer electronic cash system 21. WTF - Who to Follow Service at Twitter: Twitter’s (now X) user recommendation system 22. MyRocks: LSM-Tree Database Storage Engine 23. GoTo Considered Harmful 24. Raft Consensus Algorithm: To learn about the more understandable consensus algorithm 25. Time Clocks and Ordering of Events: The extremely important paper that explains the concept of time and event ordering in a distributed system Follow our Community❤️🌻 https://whatsapp.com/channel/0029Va9pSbzBlHpU1fu6ff1Q https://telegram.me/sign2tech

8 Key Data Structures That Power Modern Databases 🔹Skiplist: a common in-memory index type. Used in Redis 🔹Hash index: a very common implementation of the “Map” data structure (or “Collection”) 🔹SSTable: immutable on-disk “Map” implementation 🔹LSM tree: Skiplist + SSTable. High write throughput 🔹B-tree: disk-based solution. Consistent read/write performance 🔹Inverted index: used for document indexing. Used in Lucene 🔹Suffix tree: for string pattern search 🔹R-tree: multi-dimension search, such as finding the nearest neighbor

95% of the companies ask the same SQL questions 😱 Check the questions list below 🔻 1.) Explain order of execution of SQL. 2.) What is difference between where and having? 3.) What is the use of group by? 4.) Explain all types of joins in SQL? 5.) What are triggers in SQL? 6.) What is stored procedure in SQL 7.) Explain all types of window functions? (Mainly rank, row_num, dense_rank, lead & lag) 8.) What is difference between Delete and Truncate? 9.) What is difference between DML, DDL and DCL? 10.) What are aggregate function and when do we use them? explain with few example. 11.) Which is faster between CTE and Subquery? 12.) What are constraints and types of Constraints? 13.) Types of Keys? 14.) Different types of Operators ? 15.) Difference between Group By and Where? 16.) What are Views? 17.) What are different types of constraints? 18.) What is difference between varchar and nvarchar? 19.) Similar for char and nchar? 20.) What are index and their types? 21.) What is an index? Explain its different types. 22.) List the different types of relationships in SQL. 23.) Differentiate between UNION and UNION ALL. 24.) How many types of clauses in SQL? 25.) What is the difference between UNION and UNION ALL in SQL? 26.) What are the various types of relationships in SQL? 27.) Difference between Primary Key and Secondary Key? 28.) What is the difference between where and having? 29.) Find the second highest salary of an employee? 30.) Write retention query in SQL? 31.) Write year-on-year growth in SQL? 32.) Write a query for cumulative sum in SQL? 33.) Difference between Function and Store procedure ? 34.) Do we use variable in views? 35.) What are the limitations of views? Follow our Community❤️🌻 https://telegram.me/sign2tech https://whatsapp.com/channel/0029Va9pSbzBlHpU1fu6ff1Q

💥Different Tools and their use cases 1. Selenium: 🌐 Automation testing that scales effortlessly. 2. Postman: 🔗 Simplify your API testing journey. 3. Appium: 📱 Master mobile app testing like a pro. 4. JIRA: 🐛 Track bugs & collaborate with ease. 5. JMeter: ⚡️ Unleash the power of performance testing. 6. TestRail: 🧾 Organize and manage your test cases seamlessly. 7. Cypress: 🕵️‍♀️ Take UI automation to the next level with modern tools. 8. SoapUI: 🛠 Comprehensive API testing and service virtualization. 9. Katalon Studio: 🔍 A versatile web, API, and mobile testing tool. 10. QTest: 📋 A reliable tool for test management and execution. 11. BrowserStack: 🌐 Test across multiple browsers and devices. 12. TestComplete: 🤖 Build reliable automated UI tests quickly. 13. Robot Framework: 🛠 Open-source automation for everyone. 14. LoadRunner: 🚀 Performance testing for heavy loads and stress tests. 15. Bugzilla: 🐞 Lightweight yet powerful bug tracking tool. 16. Ranorex: 🎯 User-friendly desktop, web, and mobile application automation. 17. Cucumber: 🥒 Simplify behavior-driven testing (BDD). 18. TestLink: 🖇 Manage test cases and requirements effectively. 19. Zephyr: 📊 Integrate testing into your Agile workflow seamlessly. 20. Charles Proxy: 🕵️ Debug and monitor HTTP/HTTPS traffic effortlessly.

🔐 Key Python Libraries for Data Science: Numpy: Core for numerical operations and array handling. SciPy: Complements Numpy with scientific computing features like optimization. Pandas: Crucial for data manipulation, offering powerful DataFrames. Matplotlib: Versatile plotting library for creating various visualizations. Keras: High-level neural networks API for quick deep learning prototyping. TensorFlow: Popular open-source ML framework for building and training models. Scikit-learn: Efficient tools for data mining and statistical modeling. Seaborn: Enhances data visualization with appealing statistical graphics. Statsmodels: Focuses on estimating and testing statistical models. NLTK: Library for working with human language data. These libraries empower data scientists across tasks, from preprocessing to advanced machine learning. Join for More https://whatsapp.com/channel/0029Va9pSbzBlHpU1fu6ff1Q https://telegram.me/sign2tech

Excel vs SQL vs Python (pandas): 1️⃣ Filtering Data ↳ Excel: =FILTER(A2:D100, B2:B100>50) (Excel 365 users) ↳ SQL: SELECT * FROM table WHERE column > 50; ↳ Python: df_filtered = df[df['column'] > 50] 2️⃣ Sorting Data ↳ Excel: Data → Sort (or =SORT(A2:A100, 1, TRUE)) ↳ SQL: SELECT * FROM table ORDER BY column ASC; ↳ Python: df_sorted = df.sort_values(by="column") 3️⃣ Counting Rows ↳ Excel: =COUNTA(A:A) ↳ SQL: SELECT COUNT(*) FROM table; ↳ Python: row_count = len(df) 4️⃣ Removing Duplicates ↳ Excel: Data → Remove Duplicates ↳ SQL: SELECT DISTINCT * FROM table; ↳ Python: df_unique = df.drop_duplicates() 5️⃣ Joining Tables ↳ Excel: Power Query → Merge Queries (or VLOOKUP/XLOOKUP) ↳ SQL: SELECT * FROM table1 JOIN table2 ON table1.id = table2.id; ↳ Python: df_merged = pd.merge(df1, df2, on="id") 6️⃣ Ranking Data ↳ Excel: =RANK.EQ(A2, $A$2:$A$100) ↳ SQL: SELECT column, RANK() OVER (ORDER BY column DESC) AS rank FROM table; ↳ Python: df["rank"] = df["column"].rank(method="min", ascending=False) 7️⃣ Moving Average Calculation ↳ Excel: =AVERAGE(B2:B4) (manually for rolling window) ↳ SQL: SELECT date, AVG(value) OVER (ORDER BY date ROWS BETWEEN 2 PRECEDING AND CURRENT ROW) AS moving_avg FROM table; ↳ Python: df["moving_avg"] = df["value"].rolling(window=3).mean() 8️⃣ Running Total ↳ Excel: =SUM($B$2:B2) (drag down) ↳ SQL: SELECT date, SUM(value) OVER (ORDER BY date) AS running_total FROM table; ↳ Python: df["running_total"] = df["value"].cumsum() Join for more https://telegram.me/sign2tech https://whatsapp.com/channel/0029Va9pSbzBlHpU1fu6ff1Q