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System Design > Web Application & Software Architecture 2 > Flashcards

Web Application & Software Architecture 2 Flashcards

1
Q

Working with NoSQL Databases:

A

Ease of Development:
Simplified Operations:
No stress of managing complex queries or relationships.
Efficiency:
Key-based object retrieval leads to faster operations.
Popular NoSQL Databases:
Industry Usage:
Examples include MongoDB, Redis, Neo4J, Cassandra, Memcache, etc.

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2
Q

Performance Comparison: SQL vs. NoSQL

Technology Performance:

A

Equality in Performance:
Relational and non-relational databases are equally performant from a technology benchmarking standpoint.
Dependence on System Design:
System design and architecture play a critical role in performance, more so than technology choice.

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3
Q

Performance Comparison: SQL vs. NoSQL

Tech Stack Choices:

A

Popular Tech Stack Preferences:
Tech stacks like MEAN (MongoDB, ExpressJS, AngularJS/ReactJS, NodeJS) often prefer NoSQL databases.
Reasons for Prevalence:
Convenience, availability of resources, and commercial factors influence tech stack choices.

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4
Q

Performance Comparison: SQL vs. NoSQL

Importance of Fit:

A

Use Case Alignment:
Focus on picking the technology that best suits the specific use case rather than following popular stacks blindly.

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5
Q

Performance Comparison: SQL vs. NoSQL

Performance Factors:

A

Application Architecture Impact:
Performance heavily reliant on architecture, database design, network latency, etc.
Complexity Impact:
Join-heavy relational databases may impact response times but can match NoSQL speed when simplified.

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6
Q

Real-World Examples:

A

Facebook and Quora:
Facebook’s MySQL Use:
Utilizes MySQL for storing user social graphs, making engine tweaks to suit its use case.
Quora’s Efficient MySQL Use:
Efficient partitioning of data in MySQL achieved at the application level.
Emphasis on Design:
Design Impact on Performance:
Well-designed SQL data stores often outperform less optimized NoSQL stores.

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7
Q

Polyglot Persistence:

A

Hybrid Database Use:
Polyglot Persistence Concept:
Leveraging multiple databases (SQL and NoSQL) in an application for varied persistence needs.
Common Practice:
Large-scale online services often use a mix of SQL and NoSQL for optimal persistence behavior.

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8
Q

Benefits of Polyglot Persistence:

A

Tailored Solutions:
Specific Data Needs:
Selecting the right database for each unique data storage and access requirement.
Enhanced Performance:
Optimized Performance:
Improved performance by leveraging specialized databases for different functionalities.
Diverse Features:
Utilizing Unique Features:
Accessing and utilizing specific database features tailored for distinct purposes.
Scalability and Flexibility:
Scalability Support:
Scalable solutions catered to varied data handling scenarios.

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9
Q

Drawbacks of Polyglot Persistence:

A

Complexity Concerns:
Increased Complexity:
Challenges in integrating, managing, and maintaining multiple databases together.
Learning Curve:
Diverse Skill Set:
Requires expertise across different database technologies.
Real-World Application:
Example Scenario:
Social Networking App Design:
Utilizing multiple databases (relational, key-value, wide-column, etc.) to serve different functionalities within the application (user relationships, session management, analytics, ads, search, etc.).

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10
Q

Multi-Model Databases

A

Integration of Different Models:
Support for Multiple Models:
Enable usage of various data models (graph, document-oriented, relational, etc.) within a single database system.
Unified Database System:
Eliminate the need for managing multiple databases or services for different data models.
Operational Simplification:
Reduced Complexity:
Minimize operational complexities associated with managing multiple persistence technologies.
Single API Integration:
Provides access to different data models through a unified API

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11
Q

Popular Multi-Model Databases:

A

Notable Examples:
ArangoDB:
Known for its multi-model capabilities supporting graph, document, and key-value data.
CosmosDB:
Microsoft’s offering providing multi-model support for various data types and APIs.
OrientDB:
Combines graph and document capabilities within a single database.

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12
Q

Eventual Consistency

A

Eventual consistency is a model where datastores prioritize high availability over immediate consistency across all nodes in a distributed system. It’s a fundamental concept in distributed systems, ensuring data eventually reaches a consistent state globally, even if momentarily inconsistent across different nodes or geographical regions.

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13
Q

Key Aspects of Eventual Consistency:

A

High Availability Focus:
Primary Objective:
Prioritize system availability and continuous write operations over immediate global consistency.
Data Propagation Delay:
Propagation Timeframe:
Data changes take time to propagate across distant nodes or geographic zones.
Momentary Inconsistencies:
Users may observe temporarily different or outdated data due to propagation delays.

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14
Q

Strong Consistency

A

Data must be consistently the same across all nodes at any given time, requiring locking nodes during updates to ensure synchronicity.

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15
Q

Strong Consistency

Social Application

A

In a microblogging site, implementing strong consistency would involve locking all nodes globally when a user in one zone updates a post, preventing concurrent updates until a consensus is reached.

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16
Q

Strong Consistency

Locking Nodes

A

Nodes are locked during updates to ensure only one user can modify data at a time until a global consensus is achieved.

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17
Q

Strong Consistency

Real-World Application: Stock Market System

A

In financial applications like stock markets, strong consistency ensures users across regions see the same stock prices, preventing chaos due to simultaneous updates.

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18
Q

Strong Consistency

Challenge of Strong Consistency: Scaling and Availability

A

Strong consistency impedes scalability and availability, limiting concurrent updates while ensuring data consistency.

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19
Q

Strong Consistency

Implementation Strategy: Queuing Write Requests

A

Managing write requests in a queue ensures strong consistency but can limit system scalability. Details on this are covered in a message queue chapter.

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20
Q

Strong Consistency

Impact on ACID Transactions:

A

Strong consistency enables ACID transactions but hinders the ability to scale globally due to concurrent update restrictions.

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21
Q

Strong Consistency

Tradeoff with NoSQL and Distributed Systems:

A

NoSQL databases prioritize scalability and availability, sacrificing global ACID transactions due to their inherent design.

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22
Q

Strong Consistency

Purpose of NoSQL Technology:

A

NoSQL was developed to scale and ensure high availability, compromising on strong consistency for these benefits.

Strong consistency ensures synchronized data but constrains system scalability and concurrent updates, contrasting with NoSQL’s emphasis on scalability and high availability.

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23
Q

CAP Theorem

A

In case of network failure, the system can prioritize either availability or consistency, not both simultaneously.

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24
Q

CAP

Trade-off Explanation

A

During node failures, prioritizing availability allows continued write operations, leading to potential inconsistency upon offline nodes’ return.

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25
Q

CAP

Consistency Priority

A

Prioritizing consistency requires locking nodes until all are online, ensuring data synchronization and strong consistency but impacting availability.

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26
Q

CAP

Decision Determinants

A

The choice between availability and consistency depends on use cases and business requirements, defining system behavior during failures.

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27
Q

CAP

Distributed System Impact

A

The CAP theorem mandates choosing between availability and consistency in distributed systems, impossible to achieve simultaneously.

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28
Q

CAP

Latency Acknowledgment

A

Nodes globally dispersed face latency issues, making instantaneous consensus impossible despite partition tolerance.

Designing systems that balance availability and consistency remains a fundamental challenge due to the CAP theorem’s implications.

The CAP theorem necessitates trade-offs between availability and consistency, reflecting the reality of distributed system design.

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29
Q

Different types of databases

A

Document-oriented database
Key-value datastore
Wide-column database
Relational database
Graph database
Time-series database
Databases dedicated to mobile apps and so on.

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30
Q

What defines document-oriented databases among NoSQL systems?

A

Document-oriented databases store data in a document-oriented model using independent documents, often in a JSON-like format.

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31
Q

Why are document-oriented databases seen as developer-friendly?

A

They align closely with the application code’s data model, making data storage and querying simpler for developers.

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32
Q

What are some popular document-oriented databases?

A

MongoDB, CouchDB, OrientDB, Google Cloud Datastore, and Amazon DocumentDB are among the popular choices for document-oriented databases.

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33
Q

In what scenarios should one consider using a document-oriented database?

A

Semi-structured data and an anticipated need for frequent schema changes.

Uncertainty about the initial schema with expectations of evolving requirements.
When rapid scalability and continuous high availability are crucial.

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34
Q

What are some typical use cases for document-oriented databases?

A

Real-time feeds, live sports apps, product catalogs, inventory management, user comments, and web-based multiplayer games are typical scenarios well-suited for document-oriented databases.

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35
Q

What defines a graph database in contrast to other types of databases?

A

Graph databases store data in nodes and edges representing relationships between entities, ideal for modeling complex relationships.

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36
Q

Why might developers prefer graph databases over relational databases for managing relationships?

A

Graph databases simplify querying complex relationships, eliminating the need for multiple joins often required in relational databases.

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37
Q

What are some real-world applications that leverage graph databases?

A

Examples include social networks (like Facebook’s graph search), recommendation engines, route planning (as seen in Google Maps), and NASA’s data storage for lessons learned from missions.

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38
Q

What distinguishes the graph data model from other database models?

A

Graph data models use vertices (nodes) and edges to represent entities and relationships, providing an efficient way to visualize and query complex data structures.

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39
Q

When should one consider using a graph database for a project?

A

Graph databases are ideal for scenarios involving complex relationships such as social networks, recommendation engines, fraud analysis, knowledge graphs, AI applications, and genetic data storage.

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40
Q

Name some popular graph databases used in the industry.

A

Neo4J is one of the prominent graph databases used, known for its efficient handling of complex relationships and real-time querying capabilities.

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41
Q

What is the primary feature that distinguishes key-value databases?

A

Key-value databases use a simple key-value pairing method, enabling quick data retrieval with minimal latency.

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42
Q

What are the typical use cases for key-value databases?

A

Use cases include caching, managing real-time data, persisting user sessions, implementing queues, creating leaderboards, and pub-sub systems.

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43
Q

Name some popular key-value databases.

A

Redis, Hazelcast, Riak, Voldemort, and Memcached are among the popular key-value data stores used in the industry.

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44
Q

Why are key-value databases suitable for caching application data?

A

They offer minimum latency with constant time O(1) for data fetching, making them ideal for use cases requiring super-fast data retrieval.

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45
Q

When should one consider choosing a key-value database for a project?

A

Key-value databases are best suited for scenarios where data needs to be fetched rapidly with minimal complexity in data retrieval operations.

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46
Q

Provide examples of real-world implementations using key-value databases.

A

Twitter utilizes Redis in its infrastructure, while Google Cloud implements caching using Memcached on its platform.

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47
Q

What type of data do time-series databases handle?

A

Time-series databases handle data associated with events occurring over time, often tracked from IoT devices, sensors, financial markets, etc.

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48
Q

Why is it essential to store massive amounts of time-series data?

A

Storing time-series data enables the study of user patterns, system behaviors, anomalies, and facilitates running analytics to derive insights for informed business decisions.

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49
Q

Name some popular time-series databases.

A

InfluxDB, TimescaleDB, and Prometheus are among the popular time-series databases used in the industry.

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50
Q

When should one consider using a time-series database?

A

Time-series databases are ideal for scenarios requiring continuous, real-time data management over extended periods, such as handling IoT device data or running real-time analytics.

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51
Q

What are some real-world implementations of time-series databases?

A

M3DB powers time-series metrics workflows at Uber, while Apache Druid is used for real-time analytics at Airbnb.

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52
Q

Define time-series data.

A

Time-series data consists of data points associated with events occurring over time, often collected from various sources like sensors, IoT devices, social networks, etc.

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53
Q

What type of databases belong to the NoSQL family and handle massive amounts of data, particularly Big Data?

A

Wide-column databases, also known as column-oriented databases, specialize in handling large volumes of data.

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54
Q

How are records structured in a wide-column database?

A

Records in a wide-column database consist of a dynamic number of columns and can hold billions of columns.

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55
Q

Name a few popular wide-column databases.

A

Cassandra, HBase, Google BigTable, and ScyllaDB are among the well-known wide-column databases used in various industries.

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56
Q

When is it recommended to use a wide-column database?

A

Wide-column databases are best suited for scenarios involving Big Data, offering scalability, high performance, and availability.

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57
Q

Can you provide examples of companies utilizing wide-column databases?

A

Netflix employs Cassandra in its analytics infrastructure, while Adobe and other major companies use HBase for processing large volumes of data.

58
Q

What type of use cases are wide-column databases most suitable for?

A

Wide-column databases are ideal for managing Big Data, offering scalability and high performance for analytical use cases.

59
Q

What are the use cases for a document-oriented database? Which of the following option(s) are correct?

A
  1. Web-based multiplayer games
  2. Product catalogues
  3. Real-time feeds
60
Q

Caching In Web Apps

A
61
Q

What is caching in the context of application performance?

A

Caching involves storing frequently accessed data from a database in RAM for faster response times, reducing latency, and improving throughput.

62
Q

Why is caching important for applications?

A

Caching ensures low latency and high throughput by intercepting and responding to frequent requests, allowing the database to focus on other operations.

63
Q

How does caching help in improving application performance?

A

By storing frequently requested data, caching reduces the need for database computations, especially for complex queries involving multiple table joins.

64
Q

What are the types of data that can be cached?

A

Caching can involve both static data (like images, CSS files) and dynamic data (such as frequently accessed database queries).

65
Q

```

~~~

How does caching handle dynamic data changes?

A

Dynamic data in a cache typically has a Time To Live (TTL), after which it’s purged and updated with new data. Cache invalidation ensures data remains current

66
Q

Can caching aid with data that changes too frequently?

A

Caching might not be as effective for data that changes too often, like real-time stock prices or live sports scores, as the constant updates could invalidate the cached data quickly.

67
Q

What’s the benefit of caching static data?

A

Caching static data reduces server load, speeds up content delivery, and minimizes data transfer by storing unchanging data closer to the user.

68
Q

Where can static data be cached?

A

Static data can be cached on the client-side (in browsers), content delivery networks (CDNs), or on servers depending on the nature and sensitivity of the data.

69
Q

What’s crucial in determining the need for caching in an application?

A

Understanding the nature of the application’s data, frequency of changes, and the balance between data volatility and potential performance improvements guides the decision to implement caching.

70
Q

What is a significant advantage of using caching in applications?

A

Caching helps in improving application performance by reducing data retrieval latency, especially with static data.

71
Q

Where can caching be implemented within an application architecture?

A

Caching can be utilized at various levels within an application, including client browsers for static data, with the database to intercept data requests, in REST API implementation, cross-module communication in microservices, and more.

72
Q

How does caching impact database performance?

A

Caching alleviates database load by intercepting data requests, reducing the need for repeated database queries and joins, leading to improved response times and reduced latency.

73
Q

What’s the significance of caching during database downtime?

A

Even if the database experiences downtime, caching mechanisms continue to serve data requests, ensuring users experience uninterrupted service.

74
Q

What role does caching play in the HTTP protocol?

A

Caching is fundamental in the HTTP protocol and can store user sessions, leveraging key-value data stores primarily for implementing caching in web applications.

75
Q

What’s essential to consider when implementing caching?

A

While caching is beneficial, it should be implemented wisely to prevent data inconsistency issues, and it can be applied at various layers within an application architecture.

76
Q

How does caching help with relational databases and joins?

A

Caching can circumvent the need for performing complex joins in relational databases, significantly improving response times and application speed.

77
Q

In which scenarios can caching benefit users even during database outages?

A

During database outages, the cached data can continue to serve user requests, ensuring a seamless experience even when the primary database is down.

78
Q

What type of data storage is commonly used for implementing caching in web applications?

A

Key-value data stores are predominantly utilized for implementing caching in web applications due to their ability to efficiently store and retrieve cached data.

79
Q

What strategy did the stock market-based multiplayer game implement to reduce costs associated with frequent database writes for stock prices?

A

The game used a caching mechanism (Memcache) to store updated stock prices every second, then scheduled batch operations to update the database at intervals, saving significantly on database write costs.

80
Q

How did Polyhaven, a 3D asset library, manage high traffic and data storage costs effectively?

A

Polyhaven managed 5 million page views and 80TB traffic monthly for less than 400 USD by leveraging caching. Without caching, storing that data on cloud object-based storage could have cost them around 4K USD monthly.

81
Q

What was the primary cost-saving approach employed by both the stock market game and Polyhaven?

A

Both applications saved on database storage costs by using caching mechanisms to store less critical or frequently updated data, reducing the frequency of database writes and associated expenses.

82
Q

What did the stock market game do differently regarding database writes to save costs?

A

Instead of persisting updated stock prices every second, it cached the changes using Memcache and performed batch updates periodically, significantly reducing database write costs.

83
Q

How did caching contribute to cost optimization in these examples?

A

Caching helped minimize database write operations, utilizing cheaper cache storage (Memcache) compared to database storage costs, resulting in substantial cost savings for both applications.

84
Q

What is an essential takeaway from these use cases about caching and cost-effectiveness?

A

Leveraging caching for non-critical or frequently updated data can significantly reduce storage costs, especially when using a more affordable cache storage solution compared to traditional database storage.

85
Q

How did caching contribute to lowering infrastructure costs in both applications?

A

Caching reduced the frequency of expensive database writes by storing and updating data in a cache, allowing less critical or frequently updated information to be managed more cost-effectively.

86
Q

What is the primary focus of the cache aside strategy in application caching?

A

Cache aside aims to minimize database hits by lazily loading data into the cache, fetching from the database if it’s a cache miss, and updating the cache for future requests.

87
Q

How does the read-through caching strategy differ from cache aside?

A

Read-through caching automatically maintains cache consistency with the database, handled by the cache library or framework, unlike cache aside where explicit logic updates the cache.

88
Q

What distinguishes the write-through caching strategy?

A

Write-through caching routes every write operation through the cache before updating the database, ensuring high data consistency between cache and database, albeit with a slight latency increase.

89
Q

How does the write-back caching strategy optimize database write operations?

A

Write-back caching directly writes data to the cache instead of the database, delaying database writes based on business logic, significantly reducing the frequency of database writes and associated costs.

90
Q

Which caching strategy is suitable for read-heavy workloads with less frequently updated data?

A

Cache aside is ideal for read-heavy workloads, caching data that doesn’t frequently change, such as customer data, with a longer TTL.

91
Q

What potential risk does the write-back caching strategy pose?

A

Write-back caching risks data loss if the cache fails before updating the database. This strategy is often combined with others to balance performance and data reliability.

92
Q

Which caching strategy maintains automatic cache consistency with the database without explicit logic for cache updates?

A

Read-through caching maintains cache consistency automatically through the cache library or framework, unlike cache aside where explicit logic is necessary for cache updates.

93
Q

What caching strategy works best for use cases requiring strict data consistency between the cache and the database?

A

Write-through caching ensures high data consistency between the cache and the database, making it suitable for scenarios requiring strict data integrity.

94
Q

What is the primary principle governing message queues?

A

Message queues follow the FIFO (First in, first out) principle, delivering messages in the order they are added to the queue.

95
Q

How do message queues facilitate cross-module communication in applications?

A

Message queues enable asynchronous communication between modules, allowing them to communicate in the background without hindering their primary tasks.

96
Q

What real-world analogy illustrates the concept of a message queue?

A

An email service stores messages in a queue until the recipient is online, allowing asynchronous communication between sender and receiver without both needing to be online simultaneously.

97
Q

How do message queues aid in implementing background processes in applications?

A

Message queues allow tasks like sending confirmation emails in the background while users can continue navigating the application, enhancing the user experience.

98
Q

What role did a message queue play in the stock market game’s database update process?

A

A message queue executed the batch job responsible for updating stock prices at regular intervals in the database, optimizing application hosting costs.

99
Q

What are the key roles in a message queue system?

A

The producer is responsible for sending messages, while the consumer receives and processes these messages. They don’t need to reside on the same machine to communicate.

100
Q

What is a characteristic feature of message queues that defines message routing based on business requirements?

A

Message queues enable defining rules for message processing, such as adding message priorities, acknowledgments, and handling failed messages.

101
Q

How does a message queue handle the size of messages it can contain?

A

Message queues can be infinite buffers, theoretically unlimited in size, depending on the business’s infrastructure resources.

102
Q

What messaging model is commonly used and is the foundation for consuming information on a large scale?

A

The publish-subscribe message routing model is widely used, allowing consumers to subscribe to specific message types and consume information as needed.

103
Q

What does the publish-subscribe (pub-sub) model allow in terms of message distribution?

A

It enables single or multiple producers to broadcast messages to multiple consumers interested in specific topics or types of information.

104
Q

Explain the analogy of a newspaper service in the context of the pub-sub model.

A

Similar to a newspaper service delivering news to multiple subscribers, the pub-sub model delivers messages to multiple consumers subscribed to specific topics or segments.

105
Q

What role do exchanges play in implementing the pub-sub pattern in message queues?

A

Exchanges in message queues route messages to queues based on their type and established rules, acting as intermediaries between producers and consumers.

106
Q

Name some common exchange types found in message queues.

A

Some common exchange types include direct, topic, headers, and fanout, each with distinct functionalities and use cases.

107
Q

Which exchange type best fits the implementation of a pub-sub pattern?

A

The fanout exchange type excels in the pub-sub pattern by broadcasting messages to all connected queues, allowing multiple consumers to receive the same message.

108
Q

How are message queues and the pub-sub pattern utilized in social applications?

A

They power real-time feeds and notification systems, enabling users to receive continuous updates from followed pages or topics of interest.

109
Q

What’s the upcoming topic after discussing the pub-sub model in message queues?

A

The next lesson will cover the point-to-point messaging model, an alternative to the pub-sub pattern in message queue communication.

110
Q

Explain the fundamental principle of the point-to-point messaging model.

A

The point-to-point model involves a single producer sending a message to be consumed by only one consumer, akin to a one-to-one relationship compared to the one-to-many relationship in the publish-subscribe model.

111
Q

What distinguishes the point-to-point model from other messaging models?

A

Unlike broadcast-style messaging, the point-to-point model facilitates direct communication between entities, allowing only one consumer to receive and process a message from a producer.

112
Q

Name two popular messaging protocols used in message queues.

A

The widely used protocols in message queues are AMQP (Advanced Message Queue Protocol) and STOMP (Simple Text Oriented Messaging Protocol), each having various implementations in specific messaging technologies.

113
Q

What’s the primary purpose of a message queue in the context of a social networking platform?

A

A message queue facilitates real-time updates by distributing new posts to user connections, enabling asynchronous communication without the need for frequent database polling.

114
Q

Outline the differences between a pull-based and a push-based approach to handling notifications.

A

The pull-based approach relies on frequent database polling by users to retrieve new updates, which can be resource-intensive and lacks real-time synchronization. The push-based approach, with the aid of a message queue, immediately distributes new posts to connected users without the need for frequent polling, enhancing system performance and providing real-time updates.

115
Q

What are the drawbacks of a pull-based approach in handling notifications?

A

The pull-based method leads to high database load due to frequent polling and doesn’t offer real-time updates since new posts are only displayed upon database polling.

116
Q

Explain how a push-based approach utilizing message queues resolves the drawbacks of a pull-based system.

A

By employing message queues, the push-based method ensures immediate distribution of new posts to connected users without database polling, enhancing performance and enabling real-time updates.

117
Q

How can system developers manage failure scenarios in distributed transactions involving database persistence and message queue pushes?

A

Developers can address failure scenarios by implementing distributed transactions as a single unit, rolling back the entire transaction if either database persistence or message queue push fails. Additionally, failed messages can be stored in the database for future retrieval.

118
Q

Notification System & Real-time Feed Via Message Queue

A
119
Q

How can a message queue handle a high number of concurrent update requests?

A

A message queue queues concurrent update requests, processing them sequentially in a FIFO approach, ensuring high availability and consistency.

120
Q

What is the role of a message queue in handling traffic surges in Facebook’s live video streaming service?

A

Facebook uses a message queue to manage surges in user requests during live streaming, fetching real-time data from the streaming server to populate the cache and serve queued requests efficiently.

121
Q

How does a message queue aid in system consistency during concurrent updates?

A

By queuing update requests and processing them sequentially, a message queue helps maintain system consistency while handling a high volume of concurrent updates.

122
Q

Why is using a pull-based approach not ideal in implementing the user notification feature in our applications?

A

A pull-based approach is resource-intensive. In this approach, the application servers have to deal with a lot of unnecessary requests. Also, the notification delivery via a pull-based approach is not in real-time.

123
Q

How can we achieve strong consistency with the help of a message queue?

A

We can queue all the incoming requests to update a particular resource and then process them one by one instead of letting all the requests update the resource in no particular order, subsequently making things inconsistent.

124
Q

What major technological advancements have led to a data-driven world?

A

The rise of the Internet of Things (IoT) has empowered entities to generate, transmit, and process vast amounts of data, enabling self-awareness and decision-making without human intervention.

125
Q

*

What are primary use cases for processing data streams from IoT devices?

A

IoT devices find extensive usage in industries, smart cities, wearables (like healthcare sensors, smartwatches), and gadgets (drones, cellphones), necessitating sophisticated backend systems to manage and derive meaningful insights from the data.

126
Q

How does data stream processing benefit businesses?

A

Processing streaming data aids businesses in making informed decisions, understanding customer needs and behavior, creating better products, executing effective marketing campaigns, and gaining deeper insights into the market, enhancing customer-centric approaches and loyalty.

127
Q

What role does stream processing play in tracking service efficiency?

A

Stream processing is instrumental in monitoring IoT device signals, ensuring correct functionality and uptime, providing critical metadata for businesses to assess product performance and service reliability.

128
Q

What does data ingestion refer to?

A

Data ingestion is the process of collecting data from diverse sources, preparing it for system processing, and routing it through data pipelines for analysis and archiving.

129
Q

What are the primary layers in a data processing setup?

A

The layers include Data Collection, Query, Processing, Visualization, Storage, and Security, each serving specific functions in the data processing ecosystem.

130
Q

Why is data standardization important in data ingestion?

A

Data arrives from various sources in different formats and speeds; standardization is crucial to convert this heterogeneous data into a uniform format for seamless processing and analysis.

131
Q

What role does the data processing layer play in the data processing setup?

A

The data processing layer routes standardized data, performs business-specific processing, and directs data flows to different destinations based on requirements.

132
Q

How does data visualization contribute to data analysis?

A

The data visualization layer presents insights obtained from data analysis in a comprehensible format using tools like Kibana, facilitating easy interpretation for stakeholders.

133
Q

What are the key responsibilities of the data security layer?

A

The data security layer ensures secure data movement and the prevention of security breaches throughout the data processing stages.

134
Q

What are the two primary ways of ingesting data mentioned in the text?

A

Real-time ingestion and batch ingestion are the two primary methods discussed. Real-time ingestion involves receiving data instantly, crucial for systems like medical or financial data analysis. Batch ingestion processes data at regular intervals and suits applications studying trends over time.

135
Q

Why is real-time data ingestion preferred in certain systems?

A

Real-time ingestion is favored in systems dealing with critical time-sensitive data, such as medical information (e.g., heartbeat monitoring) or financial data (e.g., stock market events). It ensures prompt availability of information crucial for decision-making.

136
Q

What challenges do developers face during data ingestion processes?

A

Developers encounter several challenges, including:
Data Variety: Data from different sources arrives in varying formats, demanding standardization, and transformation, leading to a resource-intensive process.
Resource Intensiveness: The process demands significant computing resources and time due to data transformation and authentication stages.
Security Concerns: Moving data across stages poses security risks, requiring continuous effort to meet organizational security standards.

137
Q

How does real-time data ingestion differ from batch processing regarding accuracy?

A

Real-time data ingestion provides immediate insights but might lack accuracy due to limited data availability for analysis. In contrast, batch processing considers the entire dataset over time, often resulting in more accurate results due to a comprehensive analysis.

138
Q

What challenges arise due to the resource-intensive nature of data flow processes?

A

Resource-intensive data flow processes demand substantial preparation before ingestion, requiring dedicated teams and, at times, the creation of custom solutions when available tools fail to meet specific needs.

139
Q

Can you elaborate on the security risks associated with moving data around?

A

Data movement across various stages poses vulnerability, necessitating continuous vigilance and additional resources to ensure the system meets security standards at every stage of data processing.

140
Q

Could you mention an example of a custom solution created for data ingestion?

A

LinkedIn developed “Goblin,” an in-house data ingestion tool, to address challenges faced by having fifteen data ingestion pipelines, highlighting instances where existing tools couldn’t meet their requirements.

141
Q

How does the evolution of IoT impact data processing?

A

The rapid evolution of IoT devices results in changing data semantics, necessitating frequent updates in backend data processing code to adapt to these changes.

System Design (5 decks)

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