volume is very low

Pic 2 is crazy

love it

Proud of you!🎉

🇬🇧♥️

Shit hole for evil

👿

please explain the title more

It's not English any more London as fell

You made this.

Promo sm 😃

The scaffolding company have just made a ton of money on that building. An absolute ton of it.

awesome

🥲🥲🥲🥲

Relax?? Those are red belly piranhas

youtube.com/@MindfullHeartbeats?si=E_oSp4042TIVx2XL Follow this Channel for Unique and Awesome motivational Videos & Music.

👍👍👍👍👍👍👍💪💪💪💪💪

I need to state something extremly important for the ranges of reciprocal functions, since we are focusing on the y axis, the ranges are actually going to be different. I apologize for the mistake. That should never be the case. I am working on revising the videos to make the full correction. In the meantime, I have to make this extremley clear. Reciprocal Functions: 3:33 Practice Problem 1: the Range is (-∞, 0)U(0,∞) 8:36 Practice Problem 2: the Range is (-∞, 0)U(0,∞)

I need to state something extremly important for the ranges of reciprocal functions, since we are focusing on the y axis, the ranges are actually going to be different. I apologize for the mistake. That should never be the case. I am working on revising the videos to make the full correction. In the meantime, I have to make this extremley clear. Reciprocal Functions: 41:53 Practice Problem 1: the Range is (-∞, 0)U(0,∞) 47:06 Practice Problem 2: the Range is (-∞, 0)U(0,∞)

Good stuff! Very helpful. 🙏 Will watch other 2 videos as well.

Please upload the all the course Your teaching is awesome

Life is a challenge! You got it go!! And faith

Opening this masterpiece with the best speech ever! 🔥🔥🔥

Vanilla Eye of The Tiger was already badass enough , the one you have here is PURE FIRE! My PR is trembling already )

Talk about Far Out⚡⚡✨✨☯️☯️ Woww💫💫✅✅✴️✴️💯💯🔥🔥👍👍♨️♨️

This is awesome! Thanks for putting it together. I just found a new workout playlist. :)

Outstanding!

I just found your channel. Thank you for taking the time to upload all these videos. And thank you for not being an influencer! It’s always the smaller channels that have the most helpful content.

Thank you Gunnar, Will you be sharing this blackboard as notes?

i literally completed my work with this rock masterpiece , thank u bro, its fire

This is my morning inspiration to keep moving forward...........

Practice Problem 1: Imagine you're developing a neural network to predict stock prices. The data system behind this network must process historical price data, company performance indicators, and market sentiment analysis to forecast future prices. A neural network could effectively capture the complex, non-linear relationships between these variables. Step-by-Step Solution Guide: 1. Data Preprocessing: Before feeding the data into the network, it's crucial to preprocess the data. This includes normalizing the data to a specific scale, filling in missing values, and possibly transforming categorical data into a numerical format. For stock price prediction, this might mean scaling price data so that all values are between 0 and 1, making it easier for the neural network to process. This step ensures that the network isn't biased towards variables with larger magnitudes and can learn more effectively from the data. 2. Network Architecture Design: Deciding on the network architecture involves choosing the number of hidden layers and neurons in each layer. A more complex problem might require a deeper network with more layers. For stock prediction, a network might start with two hidden layers. The first layer might focus on short-term trends and indicators, while the second layer could learn longer-term patterns. The architecture decision is crucial because it affects the network's ability to learn and generalize from the data without becoming too complex or overfitting to the training set. 3. Training the Network: With the architecture set, training involves feeding the preprocessed data into the network and adjusting the weights and biases using backpropagation and gradient descent. For stock price prediction, the network would learn to weigh various factors like historical prices and company performance to minimize the difference between its predictions and the actual stock prices. This step is iterative and may require multiple passes through the data (epochs) to ensure the model accurately learns the underlying patterns. 4. Evaluation and Adjustment: After training, the model's performance is evaluated using a separate validation set not seen by the model during training. This helps to gauge how well the model generalizes to new, unseen data. For our stock prediction model, this could mean testing its predictions against recent stock prices not included in the training set. Based on this evaluation, adjustments might be made to the model's architecture or training process to improve accuracy. 5. Deployment: Once the model is trained and validated, it can be deployed in a real-time trading application. Here, it will receive live data, process it through the trained neural network, and output stock price predictions. Continuous monitoring is necessary to ensure the model remains accurate over time, and retraining may be required as new data becomes available or market conditions change. import numpy as np import pandas as pd from sklearn.preprocessing import MinMaxScaler from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Dense, LSTM from tensorflow.keras.optimizers import Adam # Load and preprocess data data = pd.read_csv('stock_prices.csv') # Ensure you have a CSV file with historical stock prices features = data[['Open', 'High', 'Low', 'Volume']] # Example features target = data['Close'] # Target variable scaler = MinMaxScaler(feature_range=(0, 1)) scaled_features = scaler.fit_transform(features) scaled_target = scaler.fit_transform(target.values.reshape(-1,1)) # Split data into training and testing sets train_size = int(len(scaled_features) * 0.8) train_features, test_features = scaled_features[:train_size], scaled_features[train_size:] train_target, test_target = scaled_target[:train_size], scaled_target[train_size:] # Define the neural network architecture model = Sequential([ LSTM(50, return_sequences=True, input_shape=(train_features.shape[1], 1)), LSTM(50), Dense(1) ]) model.compile(optimizer=Adam(learning_rate=0.01), loss='mean_squared_error') # Reshape features for LSTM layer train_features = np.reshape(train_features, (train_features.shape[0], 1, train_features.shape[1])) # Train the model model.fit(train_features, train_target, epochs=100, batch_size=32, validation_split=0.1) # Make predictions (don't forget to inverse the scaling to interpret results) predictions = model.predict(np.reshape(test_features, (test_features.shape[0], 1, test_features.shape[1]))) predictions = scaler.inverse_transform(predictions) # Here, you could add code to visualize the predictions vs. actual prices, or calculate error metrics. Practice Problem 2: Now, let's apply a neural network to a different scenario: predicting the energy consumption of a building based on historical usage data, weather conditions, and time of year. This is crucial for optimizing energy use and reducing costs. Step-by-Step Solution Guide: 1. Data Preprocessing: The first step remains the same. For energy consumption prediction, this involves normalizing historical energy usage data and weather conditions, ensuring the network receives this information in a format it can efficiently process. This step helps the model accurately capture the influence of external factors like temperature and humidity on energy use. 2. Network Architecture Design: The complexity of the problem dictates the architecture. For energy prediction, a similar two-layer design could be used, with the first layer focusing on immediate factors like current weather conditions and the second on more complex, longer-term trends like seasonal changes. The design must balance depth (number of layers) and breadth (number of neurons) to capture the nuanced relationship between weather conditions and energy use without overfitting. 3. Training the Network: This step involves adjusting the model to minimize the error between its predictions and actual energy usage. Through backpropagation, the model learns how different factors contribute to energy use, refining its predictions over successive training epochs. This iterative process allows the network to uncover the intricate patterns linking weather conditions and historical usage to future energy needs. 4. Evaluation and Adjustment: Evaluating the model's performance against a set of unseen data ensures it can generalize well to new situations. For energy consumption prediction, this might involve using data from a different time period or a different building to test the model's accuracy. Adjustments are made based on performance metrics to improve the model's predictive ability. 5. Deployment: The final step is to integrate the trained model into a building management system, where it can predict energy needs in real-time. Continuous monitoring and periodic retraining with new data ensure the model adapts to changing patterns in energy use or weather conditions, maintaining its accuracy over time. # Import necessary libraries import numpy as np import pandas as pd from sklearn.preprocessing import MinMaxScaler from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Dense from tensorflow.keras.optimizers import Adam # Load and preprocess data # Read data from a CSV file which contains historical records of energy consumption and relevant weather conditions data = pd.read_csv('energy_consumption.csv') # Select features which are considered relevant for predicting energy consumption features = data[['Temperature', 'Humidity', 'Time_of_Year']] # The target variable is what we want to predict, in this case, energy consumption target = data['Energy_Consumption'] # Initialize the MinMaxScaler to scale features/target to the range [0, 1] scaler = MinMaxScaler(feature_range=(0, 1)) # Compute min and max values for scaling features and store these parameters scaled_features = scaler.fit_transform(features) # Compute min and max values for scaling the target and store these parameters scaled_target = scaler.fit_transform(target.values.reshape(-1,1)) # Split data into training and testing sets (80% train, 20% test) # It is essential that the scaling parameters are derived from the training set to prevent information leak from the test set train_size = int(len(scaled_features) * 0.8) train_features, test_features = scaled_features[:train_size], scaled_features[train_size:] train_target, test_target = scaled_target[:train_size], scaled_target[train_size:] # Neural network architecture # Define a Sequential model with three Dense layers model = Sequential([ # Input layer with 64 neurons and 'relu' activation, shape matches number of features Dense(64, activation='relu', input_shape=(train_features.shape[1],)), # Hidden layer with 64 neurons and 'relu' activation for non-linear feature extraction Dense(64, activation='relu'), # Output layer with a single neuron for regression output since we are predicting a continuous value Dense(1) ]) # Compile the model with Adam optimizer and mean squared error loss function # Mean squared error is a common choice for regression problems model.compile(optimizer=Adam(learning_rate=0.01), loss='mean_squared_error') # Train the model on the training data # The model will learn to predict energy consumption from features over 100 iterations of the entire dataset model.fit(train_features, train_target, epochs=100, batch_size=32, validation_split=0.1) # Make predictions on the test set predictions = model.predict(test_features) # Invert the scaling on the predictions to transform them back to the original scale of energy consumption predictions = scaler.inverse_transform(predictions)

Practice Problem 1: An Academic Research Network Let's conjure an academic research network as our practice canvas. Here, researchers, papers, and citations are subjects, predicates are the relationships such as "authored by," "cites," or "reviews," and objects could be other researchers, papers, or affiliations. Step 1: Establishing the Network We begin by mapping researchers to their works and citations, employing triple-stores to represent each discrete fact - a paper, its author, and its influences. Step 2: Querying Connections Using SPARQL, we can craft queries to reveal the most influential papers in a field, trace the evolution of a theory, or even identify potential peer reviewers based on their expertise and previous citations. Step 3: Interpreting the Network Dynamics The resulting web of connections offers us a bird's-eye view of the academic landscape, highlighting key players and pivotal works, much as we would map out a city's landmarks and thoroughfares. # Step 1: Establishing the Network PREFIX dc: <purl.org/dc/elements/1.1/> PREFIX foaf: <xmlns.com/foaf/0.1/> CONSTRUCT { ?paper dc:creator ?author . ?paper dc:title ?title . ?author foaf:name ?name . } WHERE { ?paper dc:creator ?author . ?author foaf:name ?name . ?paper dc:title ?title . } # Step 2: Querying Connections SELECT ?paper (COUNT(?cites) AS ?numCitations) WHERE { ?paper dc:creator ?author . ?cites dc:references ?paper . } GROUP BY ?paper ORDER BY DESC(?numCitations) Practice Problem 2: Ecological Interaction Network Now, consider an ecological interaction network where species (subjects) are connected by interactions (predicates) like "preys on," "pollinates," or "competes with," and the objects may be other species or environmental features. Step 1: Mapping the Ecosystem We construct a triple-store that encapsulates each interaction, forming a complex web of life within our data model. Step 2: Querying Interactions SPARQL enables us to ask which species are keystone to a habitat, understand the flow of energy through food webs, or assess the impact of an invasive species. Step 3: Understanding the Ecosystem's Balance The insights we gain from these queries help us comprehend the delicate balance of ecosystems, guiding conservation efforts and policy-making to preserve biodiversity. PREFIX eco: <example.org/ecology/> # Step 1: Mapping the Ecosystem CONSTRUCT { ?species eco:interactsWith ?otherSpecies . } WHERE { ?species eco:interactsWith ?otherSpecies . } # Step 2: Querying Interactions SELECT ?species (COUNT(?interactsWith) AS ?importance) WHERE { ?species eco:interactsWith ?interactsWith . } GROUP BY ?species ORDER BY DESC(?importance) # Step 3: Understanding the Ecosystem's Balance # This query might look for species without interactions, which could indicate an issue in the ecosystem. SELECT ?species WHERE { FILTER NOT EXISTS { ?species eco:interactsWith ?other . } FILTER NOT EXISTS { ?other eco:interactsWith ?species . } }

bro your channel is so underrated, it's very helpful while I am preparing for Microsoft SDE-2 Interview. it's helping me revise the blind 75 really quickly. Appreciate your efforts a lot and one nice thing is documentation as I can run through the documentation you do before every solution without going through the whole video.

Should you count search inside of the hashmap too when you estimate time complexity? For each element you make a search which in case of unordered_map is O(N) in the worse case (but O(1) on average).

Hey Gunnar will there be more lessons for this coming out ?

Please make more and entire playlist of just system design! Thank you

Golden, thanks✌️

Nice I like it

//Input /* Two strings, s and t */ //What to DS/Algo/Technique /* One map, to keep track of the frequency of characters for both s and t */ //What to do with the data /* We will keep track of each character in our maps to determine if they both have the same number of characters and the same type 1. Initialize our map to keep track of the frequency of similar characters between variables s and t 2. We are going to loop through the s string and increment the frequency of characters for s 3. We will loop through each character in string t, and doing so we will decrement each character in the ferequency map 4. Finally, if there is a non-zero number, we will return false, if the frequency map didnt turn to zero 5. Return true if an anagram */ //Output /* We return true if we found an anagram */ class Solution { public: bool isAnagram(string s, string t) { //Step 1-We setup our map to keep track unordered_map<char, int> freqA; //Step 2: looping through string s for(char ch: s){ freqA[ch]++; } //Step 3: loop through string t for(char ch: t){ freqA[ch]--; } //Step 4: If any count in freqA is non-zero, return false for(auto entry: freqA){ if(entry.second != 0){ return false; } } //Step 5: Return true if s and t are anagrams, and false otherwise return true; } };

Full Course Playlist: kzbin.info/aero/PLPERBdDHWLi2uNVqv7u0b5oRCng3KDnoz&si=dLiSJUv42Oh06LB3

Full Course Playlist: kzbin.info/aero/PLPERBdDHWLi2uNVqv7u0b5oRCng3KDnoz

Another really good performance my friend!!🎸

Full Playlist: kzbin.info/aero/PLPERBdDHWLi2uNVqv7u0b5oRCng3KDnoz For today's lesson-- Practice Problem 1: Imagine you are tasked with designing a database system for an e-commerce platform that needs to efficiently handle product data, customer information, and transaction records. The products have diverse attributes and require a document database's flexibility, while customer information and transaction records are best handled by a relational database. Design a workflow diagram illustrating how you would leverage a hybrid database to handle an order process from the moment a customer places an order to the final transaction completion. Determine Data Requirements Assess the nature of the data. Product details like descriptions, images, and specifications are well-suited to a document-oriented model because of their varying attributes. Customer data, such as names, addresses, and purchase history, and the transactional data are more structured and relational. Creating a hybrid database involves integrating and managing multiple types of databases to meet specific requirements for an organization's data needs. Here's a general guide on how to create a hybrid database: 1. Identify Requirements: - Understand the data needs of your organization. Determine what types of data you need to store, how it will be accessed, and any regulatory or compliance requirements. 2. Choose Database Types: - Select the types of databases that best suit your needs. Common types include: - Relational databases (e.g., MySQL, PostgreSQL, SQL Server) - NoSQL databases (e.g., MongoDB, Cassandra, Couchbase) - In-memory databases (e.g., Redis, Memcached) - Graph databases (e.g., Neo4j, Amazon Neptune) - Time-series databases (e.g., InfluxDB, Prometheus) 3. Design Data Architecture: - Determine how data will be structured and stored across different database types. - Decide which data will be stored in each type of database based on factors such as data model, scalability, performance, and query requirements. 4. Integrate Databases: - Implement mechanisms to enable communication and data transfer between different database types. This could involve: - Using ETL (Extract, Transform, Load) tools to transfer data between databases. - Implementing APIs or middleware to facilitate communication between databases. - Utilizing built-in features of certain databases (e.g., foreign data wrappers in PostgreSQL) to access data stored in other databases. 5. Data Synchronization: - Establish processes for keeping data synchronized across different database types to ensure consistency. - Implement replication, mirroring, or synchronization mechanisms to update data in real-time or at scheduled intervals. 6. Security and Access Control: - Implement security measures to protect data across all database types. - Configure access controls and permissions to restrict unauthorized access to sensitive data. 7. Monitoring and Maintenance: - Set up monitoring tools to track the performance and health of the hybrid database environment. - Establish maintenance procedures to ensure databases are regularly updated, optimized, and backed up. 8. Testing and Optimization: - Conduct thorough testing to ensure the hybrid database meets performance, scalability, and reliability requirements. - Continuously optimize the database configuration and data distribution to improve performance and efficiency. 9. Documentation and Training: - Document the architecture, configuration, and maintenance procedures of the hybrid database. - Provide training to staff members responsible for managing and using the database to ensure they understand its operation and best practices. 10. Scalability and Flexibility: - Design the hybrid database with scalability and flexibility in mind to accommodate future growth and changes in data requirements. - Regularly review and update the database architecture to adapt to evolving business needs and technological advancements. Practice Problem 2: Imagine that you are responsible for creating a hybrid database system for a healthcare management application. This system must efficiently handle sensitive patient records, facilitate appointment scheduling, and securely maintain medical histories. Your design must comply with HIPAA regulations, ensuring the protection of personal health information (PHI). Create a plan that utilizes a hybrid database model, combining the strengths of both document-based and relational databases. Step 1: Compliance and Data Requirements: Conduct a HIPAA Risk Assessment to identify potential risks to PHI and implement measures to mitigate those risks. - Identify the types of data such as personal health information, appointment details, and medical histories, and determine how they will be stored, accessed, and protected. Step 2: Database Type Selection: - Choose a document database, like MongoDB, for flexible patient record storage due to its varied and complex structure. - Select a relational database, such as PostgreSQL, for appointment scheduling and medical histories, which require structured data storage and complex queries. * Verify that chosen databases offer features to support HIPAA-required audit controls, access logs, and data integrity. * Establish Business Associate Agreements (BAAs) with database providers if they will handle PHI. Step 3: Architectural Design: - Map out how each type of data will be stored, ensuring that PHI is encrypted and access is logged. - Structure the relational database to support efficient scheduling and historical data retrieval, employing relationships and indexing strategies. * Design the system to include mechanisms for automatic log-off and encryption key management as per HIPAA’s technical safeguards. * Ensure data retention policies comply with HIPAA’s requirements for record retention. Step 4: Integration and Communication: - Develop secure APIs that allow the two database systems to communicate while maintaining data integrity and security. - Use middleware for data transformation and ensure that any exchange of PHI is encrypted and trackable. * Design APIs to enforce minimum necessary use, ensuring only the least amount of PHI required for a task is accessed. * Implement secure communication channels according to the HIPAA Security Rule's transmission security standards. Step 5: Data Synchronization and Security: - Implement real-time data synchronization mechanisms with audit trails for PHI access and updates. - Develop robust security protocols including encryption, access controls, and frequent security audits. * Data synchronization protocols must include mechanisms for verifying the integrity of PHI during transfer. * Create a policy for the regular review and updating of encryption protocols to remain in compliance with HIPAA. Step 6: Monitoring, Maintenance, and Disaster Recovery: - Install monitoring tools for anomaly detection and performance issues. - Create regular backup schedules and a disaster recovery plan that complies with HIPAA’s contingency planning requirements. * ncorporate mechanisms for continuous monitoring of PHI access, with automated alerts for any unauthorized activities. * Test disaster recovery plans annually to ensure they meet HIPAA Time-based objectives for recovery. Step 7: Testing, Optimization, and Scalability: - Perform comprehensive testing to verify HIPAA compliance, database performance, and security. - Continuously review database performance and scalability to support an increasing number of patient records and expanding healthcare services. Step 8: Documentation, Training, and Auditing: - Document all processes, policies, and procedures in relation to the database system. - Train staff on HIPAA compliance and the proper use of the database system. - Schedule regular audits to ensure ongoing compliance with HIPAA and database performance standards. Step 9: Fault Tolerance and Redundancy Planning: - Design the system with fault tolerance in mind to handle hardware failures seamlessly. - Implement redundant hardware and automate failover processes to minimize downtime. - Use redundant array of independent disks (RAID) for data storage to protect against data loss from drive failures. - Deploy clustered database environments that provide high availability and load balancing. Step 10: Error Handling and Human Factors: - Develop comprehensive error-logging and handling mechanisms to catch and mitigate software glitches. - Design user interfaces with error prevention in mind, including confirmation dialogs for critical actions and undo functionality where appropriate. - Provide comprehensive user training to reduce the risk of human error and establish protocols for quickly correcting user mistakes. - Implement strict change management procedures for system updates to prevent software-related outages or issues.

Practice Problem 1 with a Step-by-Step Solution Guide: Practice Problem 1: Consider an online bookstore application. The system must manage a large inventory of books, customer orders, and reviews. A document database is used to store details about books and reviews, while a relational database manages customer and order information. A cache is employed for quick access to book data, and a message queue handles the stream of incoming orders. Your task is to create a workflow diagram that illustrates the process from when a customer places an order to the completion of the transaction. The diagram should depict the interactions with the book details in the document database, customer data in the relational database, and the order processing through the message queue. Additionally, consider failure points such as a database going offline and how the system will maintain consistency and recover from such events. Step 1: Identify Components and Relationships Start by listing all the components involved: the document database for book details and reviews, the relational database for customer and order data, the cache, and the message queue. Next, understand the relationships and dependencies between these components. The document database must be queried to retrieve book details, which are then stored in the cache for rapid access. The relational database is queried for customer data when an order is placed, which is then sent to the message queue for processing. Step 2: Workflow Diagram Creation With a clear understanding of the system components and their interactions, begin drafting the workflow diagram. Illustrate the customer placing an order, the system querying the document database for the book details, checking the cache, and then retrieving the customer data from the relational database. Show how the order is sent to the message queue and processed. Use arrows to denote the direction of data flow and include any intermediate steps, such as validation checks or inventory updates. Step 3: Handling Failures Incorporate potential failure points into the diagram. For instance, if the document database fails, show how the cache serves as a read-only backup for book details. If the message queue fails, illustrate how the system stores orders temporarily and retries sending them once the queue is back online. Indicate backup procedures and recovery mechanisms to ensure that no data is lost and consistency is maintained throughout the system. Practice Problem 2 with the Step-by-Step Solution Guide after the Given Problem: Practice Problem 2: Imagine an event management application that handles registrations, schedules, and participant feedback. A NoSQL database is utilized for flexible data storage of event details and participant feedback, while a relational database maintains registrations and scheduling. A caching system provides instant access to schedule data, and a message queue is in place for processing feedback and updates. Develop a scenario where a new feature allows participants to sign up for event updates. Design the data flows and interactions to implement this feature, considering how registrations are tracked, schedules are updated in the database, and the cache reflects these changes. Also, address what happens if an update process is interrupted, and how the system handles partial failures to avoid miscommunications or data inconsistency. Step 1: Identify Components and Relationships Begin by identifying all the key components: the NoSQL database for event details and feedback, the relational database for registration and scheduling, the cache for quick schedule access, and the message queue for updates. Understanding how these parts interact is crucial. The NoSQL database will handle the dynamic information like participant feedback, while the relational database manages the structured data of registrations and schedules. Step 2: Workflow Diagram Creation For the workflow diagram, depict participants signing up for updates. Show the process of their registration information being stored in the relational database and how the sign-up triggers an update in the schedule stored both in the database and cache. Illustrate the data flow from the registration form to the databases and the subsequent cache update. Ensure the diagram reflects the sequence of these actions and how they interlink. Step 3: Handling Failures Include potential points of failure and their solutions in the diagram. If a participant signs up and the relational database is temporarily down, illustrate a fallback procedure where the sign-up information is queued and later processed. Similarly, show how the cache temporarily holds the latest schedule if the NoSQL database is unavailable, ensuring that participants still receive the most current information. Highlight fail-safes that maintain data integrity, like transaction logs or rollback mechanisms, to ensure the system can recover without data loss or inconsistency after a failure.

Practice Problem 1 Now, let's delve into a practice problem to deepen our understanding of scalability through the lens of a real-time stock trading application. This application must handle the monumental task of processing thousands of transactions per second, instantly updating account balances, and providing real-time feedback to users. Imagine a robust database serving as the financial ledger, meticulously recording trades and account movements. This is complemented by a distributed cache, like Hazelcast, offering up-to-the-second stock prices and account balances with minimal latency, empowering traders to make swift, informed decisions. Furthermore, a message queue, such as Apache Kafka, orchestrates the flood of trade orders, ensuring each is processed accurately and in sequence. Solution Guide: Workflow Diagram The process begins with "load balancing," aimed at the equitable distribution of trade orders throughout the system to avert overload. Here's the streamlined approach: 1. User Interface Interaction: Traders place trade orders via the application's interface, where a seamless user experience is critical. The system immediately acknowledges the order to uphold the trader's trust in the platform's efficiency. 2. Message Queue Handling: Upon order placement, it is routed to a message queue like Apache Kafka. This step is vital as the queue acts as both a buffer and organizer, ensuring trades are processed sequentially, thus maintaining transaction integrity. 3. Database Transaction: The order transitions from the queue to be executed by the system. The database then updates the ledger with the new trade, adjusting account balances accordingly. This phase highlights the importance of atomicity and consistency, ensuring the transaction is entirely successful or aborted. 4. Cache Update: Concurrently with the database transaction, the cache refreshes to display the latest account balance and stock price, ensuring traders see accurate, up-to-date information. 5. Failure Management and Recovery Protocol: The system is engineered to manage failures smoothly, with protocols to reroute or hold orders until functionality is restored, ensuring no trade is lost and consistency is maintained. Upon recovery, the system reconciles discrepancies across the cache, database, and message queue, accurately reflecting all accounts and transactions. --- Practice Problem 2 For our next exercise, consider an online multiplayer game with a vibrant virtual economy. Here, scalability is tested by the dynamic game world, including player interactions and transactions. The system manages game states, player actions, and the in-game economy efficiently. A NoSQL database is adept at handling the constantly evolving data model of player states and game items. A caching layer maintains the current game world state, offering rapid access for game servers. A message queue facilitates various tasks, such as matchmaking, event broadcasts, and transaction processing. Introducing a player-to-player item trading system adds complexity, necessitating trade validation, inventory updates, and cache reflection - all while preventing item loss or duplication, even amid trade interruptions. Solution Guide: Implementing the Trade System Applying 'consistency' as a guiding principle from the first problem: 1. User Interface Interaction: Players initiate trades, promptly recorded by the system to ensure real-time request notifications. This underscores the need for a responsive interface that mirrors real-time changes. 2. Message Queue and Database Transaction: Confirming trades triggers inventory updates in the NoSQL database for both players. This operation is atomic, ensuring either both inventories update or neither does, preserving transaction consistency. 3. Cache Update: Post-trade, the cache updates to reflect new inventory states, crucial for maintaining an accurate player experience and preventing exploitation. 4. Failure Management and Transaction Durability: The message queue logs the trade's progress, safeguarding against data loss or item duplication during interruptions. 5. Recovery Protocol and Validation: Following an interruption, the system checks the trade's integrity against logs, rectifying or finalizing the transaction as needed to maintain game state consistency. By applying a unified approach to scalability, emphasizing consistency and reliability across diverse systems, we demonstrate the concept's broad applicability and the critical role of standardized processes in scalable architectures.

Full Playlist: kzbin.info/aero/PLPERBdDHWLi2uNVqv7u0b5oRCng3KDnoz&si=UPzVTlvsswn9IIw3

Now, let's apply these concepts to some practice problems to solidify our understanding. Example Practice Problem 1: Workflow Diagram for a Stock Trading Application For our first practice problem, let's design a fault-tolerant workflow for a real-time stock trading application. This system must process numerous transactions swiftly, accurately reflect account balances, and provide immediate feedback. Step 1: Understanding the Data Flow Requirements: The first step is to understand the critical components. Our system needs a high-performance database to maintain a ledger of trades, a distributed cache like Hazelcast for real-time stock prices and account balances, and a robust message queue, such as Apache Kafka, to handle the flow of trade orders. Step 2: Designing for Reliability- With the components identified, we integrate fault tolerance. For the database, we implement redundancy, perhaps with a master-slave configuration, so if the master fails, a slave can take over without loss of service. The cache system should be distributed across multiple nodes to handle a node failure. For the message queue, we ensure that it's robust against message loss or duplication, even during faults, by using transaction logs and replication. Step 3: Handling Failures We then plan for handling failures. If a component goes down, the system should automatically reroute the tasks to healthy components. If the primary database fails, the system should failover to a replica with minimal downtime. A mechanism to retry or compensate for failed operations must be in place for the message queue. Step 4: Recovery In case of a fault, the system needs to recover gracefully. If the database server crashes, it should automatically restart and recover from logs. If a cache node fails, other nodes should take over its load while it's being restored. Step 5: Workflow Diagram Finally, we create a detailed workflow diagram illustrating how trade orders are received, processed through the system, and how the updates reflect in the database and cache. It should also include contingency paths for potential failures and the recovery process. Practice Problem 2: Data Flow for Online Multiplayer Game For our second practice problem, we're designing a data flow for a new feature in an online multiplayer game-a player-to-player item trading system. This requires managing game states, player interactions, and ensuring that the virtual economy remains intact. Step 1: Identifying Data Flow Requirements We first identify what data is required to facilitate a trade. We need information about the players involved, the items for trade, and the current state of each player's inventory. Step 2: Designing for Reliability We design the system to handle these trades reliably. A NoSQL database could be a good fit for the flexible data models of player states. For real-time aspects, like current state and inventory, we use a caching layer that reflects changes immediately. And for asynchronous tasks like processing trades, a message queue is appropriate. Step 3: Ensuring Consistency To ensure consistency, especially in cases of failure, each trade transaction must be atomic. If a failure occurs mid-trade, the system should roll back both players' states to the pre-trade conditions to avoid duplication or loss of items. Step 4: Recovery from Failures We also plan how the system recovers from partial failures. If the game play experience feature experiences a fault, it should log the incident, alert an operator, and attempt to complete or reverse any affected trades based on the last known good state. Step 5: Visualizing Data Interactions Finally, we develop a visualization of the data interactions involved in player-to-player trades. This will include checks for trade validation, database updates for inventory changes, and cache updates to reflect those changes in real-time. By designing for fault tolerance, ensuring consistent transactions, planning for recovery, and visualizing the process,

Practice Problem 1: Online Travel Booking Agency Imagine you're building a data system for an online travel agency. This system must manage hotel bookings, customer reviews, and real-time availability updates. A relational database stores the booking details and customer data, ensuring transactional integrity for reservations. A separate document-based database might hold user reviews for easy retrieval and analysis, while a cache ensures that the most frequently accessed hotel information is available for immediate display. A user books a hotel room, and the application code must execute several steps: first, it writes the booking details to the database, ensuring that no overbooking occurs. Next, it updates the cache, so the next search reflects the new availability status. Simultaneously, a message is published to a queue to process the payment and send a confirmation email. This queue ensures that the payment processing system is not overwhelmed during peak booking times. Example Practice: Online Travel Agency Data System Workflow Diagram: 1. User Action/Trade Initiation: A customer initiates a hotel booking via the agency's platform. 2. Initial Validation/Trade Validation: The application server validates the booking for completeness and room availability. 3. Queuing: The booking details are placed in a high-throughput message queue, like Apache Kafka, to manage the order of bookings and associated payment processing. 4. Transaction Execution/Database Update: The booking is dequeued, and a database transaction begins, locking the inventory record to prevent overbooking. 5. Order Fulfillment/Trade Execution: The booking transaction confirms the reservation and updates the customer's booking details. 6. Data Synchronization/Cache Update: After the transaction is committed, the cache is updated to reflect the new booking and availability status. 7. User Notification/Trade Confirmation: The customer receives a confirmation notification, which is also queued in the message system. Points of Failure and Recovery: - Validation Step: Prevents bookings without available rooms or invalid customer data. - Message Queue: Manages booking order and load, serving as a buffer during peak times and ensuring that payment processing is evenly distributed. - Database Transactions: Ensure that no room is double-booked and that customer information is correctly updated. - Cache: Provides immediate feedback to users regarding room availability and booking status. - Failure Handling: If a component goes offline, bookings in the message queue are paused or stored. The system can roll back incomplete transactions and re-queue them once stability is restored. ==================================================================================================================================================//////////// Practice Problem 2 (2-minute script): Lets consider an online multiplayer game with a virtual economy. The data system must manage the game state, player interactions, and virtual transactions. A NoSQL database could be ideal for storing the flexible data model of player states and game items. A caching layer would be responsible for holding the current state of a game world, providing low-latency access for the game server. Meanwhile, a message queue might be used to handle tasks such as matchmaking, broadcasting game events to players, and processing in-game purchases. Develop a scenario where a new game feature is being rolled out: a player-to-player item trading system. Design the data flows and interactions needed to implement this feature, considering how trades will be validated, how the players' inventory will be updated in the database, and how the cache must reflect these changes. Moreover, what happens if a trade operation is interrupted? How will the system handle partial failures to prevent item duplication or loss? This problem pushes you to consider how new features integrate into existing systems, ensuring that expansions do not compromise the system's integrity or performance. Practice Problem 2: Online Multiplayer Game with Virtual Economy Workflow for Player-to-Player Item Trading System: 1. Initiation: Player offers an item to another player, creating a trade within the game. 2. Validation: Game server validates the trade, ensuring agreement and item existence. 3. Queuing: Validated trades are queued if necessary to manage game event flow. 4. Database Transaction: The trade is processed, updating both players' inventories in the database. 5. Execution: Items are exchanged between players' inventories as per the trade details. 6. Synchronization: Game servers' cache is updated to reflect the new state of the game world. 7. Confirmation: Players receive trade confirmation and see updated inventories. Explanation and Points of Failure: - Validation: Ensures fair trade and game economy balance. - Queuing (if applicable): Manages the order of in-game events. - Database Transaction: Secures inventory accuracy and prevents item loss or duplication. - Synchronization: Provides immediate game world updates. Failure Handling: Trade operations are atomic; they fully complete or are entirely rolled back in case of interruption. The cache is synchronized post-database transaction to avoid desynchronization. If a system crash occurs, transactions can be recovered and re-executed. In both of these practice problems, the key to designing robust data systems lies in understanding and handling the potential points of failure and ensuring that all components of the system-databases, caches, and queues-work in concert to maintain data integrity and provide a responsive user experience.

🤯🤯