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Computer Science and Programming

Logo of telegram channel computer_science_and_programming — Computer Science and Programming
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Logo of telegram channel computer_science_and_programming — Computer Science and Programming
Topics from channel:
Categories: Technologies
Language: English
Subscribers: 150.32K
Description from channel

Channel specialized for advanced topics of:
* Artificial intelligence,
* Machine Learning,
* Deep Learning,
* Computer Vision,
* Data Science,
* Papers implementations
* Python
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The latest Messages

2023-11-24 22:22:01
5 books for Software Engineers

1. Clean Architecture (Martin)
2. Building Microservices (Newman)
3. Unit Testing (Khorikov)
4. Domain Driven Design (Evans)
5. Head First Design Patterns (Freeman & Robson)

What is your favourite? Comment bellow.
18.1K views19:22
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2023-11-21 16:14:01
6 API Architectural designs You Must Know

Representational State Transfer - REST is like a classic library where you request specific books and receive them as they are. It's simple and widely used for web APIs, like ordering a la carte from a menu .

2. GraphQL
GraphQL is like a customizable buffet where you ask for exactly what you want and get a tailored plate. It allows clients to request only the data they need, reducing over-fetching.

SOAP (Simple Object Access Protocol) is like sending a letter with detailed instructions, complete with a table of contents. It's more structured but can be heavier than REST or GraphQL.

4. gRPC :
gRPC is like a high-speed train for communication between services. It uses Protocol Buffers for efficient data exchange and supports streaming and bidirectional communication.

5. WebSockets
WebSockets are like real-time phone calls for the web. They enable two-way communication, perfect for chat apps and live updates.

MQTT (Message Queuing Telemetry Transport) is like a radio broadcast , designed for low-bandwidth, high-latency, or unreliable networks. Ideal for IoT devices and sensor data.
20.4K views13:14
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2023-11-18 21:34:01
6 Database Types You Must Know

1. Relational Database
- Data Model: Organizes data into tables with rows and columns.
- Examples: MySQL, PostgreSQL, Oracle, SQL Server.
- Key Features: ACID compliance, strong data consistency, structured data storage, support for SQL queries, well-suited for complex transactions and reporting.

2. Document Database
- Data Model: Stores data in semi-structured or JSON-like documents.
- Examples: MongoDB, CouchDB, Firebase Firestore.
- Key Features: Flexible schema, horizontal scalability, support for semi-structured data, well-suited for content management systems and real-time applications.

3. In-Memory Database
- Data Model: Stores data entirely in the system's main memory (RAM).
- Examples: Redis, Memcached, Apache Ignite.
- Key Features: Ultra-fast data retrieval, low-latency, suitable for caching, session management, and real-time analytics.

4. Graph Database
- Data Model: Represents data as nodes and edges to model relationships.
- Examples: Neo4j, Amazon Neptune, ArangoDB.
- Key Features: Efficient querying of complex relationships, graph traversal, suitable for social networks, recommendation systems, and fraud detection.

5. Time-Series Database
- Data Model: Optimized for time-ordered data points, like sensor readings or log files.
- Examples: InfluxDB, Prometheus, TimescaleDB.
- Key Features: Efficient storage and retrieval of time-series data, aggregations, retention policies, ideal for monitoring, IoT, and event data.

6. Spatial Database
- Data Model: Designed for storing and querying spatial or geographic data.
- Examples: PostGIS (extension for PostgreSQL), MongoDB Geospatial, Microsoft SQL Server Spatial.
- Key Features: Geospatial indexing, support for spatial data types (points, polygons, lines), useful for location-based services, GIS (Geographic Information Systems), and map applications.
12.7K views18:34
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2023-11-15 12:12:01
HTTP status codes are three-digit numbers that are returned by a web server in response to a client's request made to the server via HTTP (Hypertext Transfer Protocol).

These status codes provide information about the outcome of the request, indicating whether it was successful, encountered an error, or needs further action. They are an essential part of the HTTP protocol, helping both clients (e.g., web browsers) and servers communicate effectively.
14.0K views09:12
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2023-11-12 17:26:06
REST vs GraphQL

Stands for Representational State Transfer
Well-established and widely adopted
Uses predefined endpoints for data retrieval
Great for simple, stateless operations

A modern query language for APIs
Allows clients to request exactly what they need
Reduces over-fetching and under-fetching of data
Empowers front-end developers with data control

Which one is right for your project?

Use REST if:

Simplicity and Convention: REST is straightforward and relies on a set of conventions. If you have a simple API with well-defined endpoints and actions, REST might be a good choice.

Caching: RESTful APIs are typically easier to cache because the URLs for resources remain consistent. This can lead to better performance in scenarios where caching is crucial.

Existing Ecosystem: If you're working with legacy systems or integrating with third-party APIs that follow REST principles, it may make sense to stick with REST for consistency.

Use GraphQL if:

Flexibility: GraphQL allows clients to request exactly the data they need, which can lead to reduced over-fetching and under-fetching of data. This flexibility is especially beneficial for complex applications with varying data requirements.

Efficiency: With GraphQL, you can often make a single request to fetch related
data, reducing the number of API calls required compared to REST, which might require multiple requests to different endpoints.

Real-time Data: If you need real-time updates and subscriptions, GraphQL's ability to provide live data can be a significant advantage.

Team Expertise: If your development team is comfortable with GraphQL and prefers its query language, it might lead to faster development and easier maintenance.
13.2K views14:26
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2023-11-11 02:13:48
Prod Software Release

1. Planning:
- Identify the goals and features for the upcoming release.
- Prioritize tasks based on importance and dependencies.
- Define timelines and allocate resources accordingly.

2. Development:
- Programmers start coding based on the planned features.
- Regular code reviews and collaboration to maintain code quality.
- Version control systems track changes for better collaboration.

3. Building Artifact:
- Compile the source code into executable or deployable artifacts.
- Generate documentation and other necessary files.
- Automation tools can be used to streamline this process.

4. Testing:
- Conduct various levels of testing (unit, integration, system, etc.).
- Identify and fix bugs or issues.
- Ensure compatibility with different platforms and configurations.

5. Release:
- Once testing is successful, prepare for the release.
- Generate release notes documenting changes and updates.
- Coordinate with other teams for a smooth rollout.

- Set up different environments for development, testing, and production.
- Ensure consistency across environments to minimize deployment issues.
- Monitor and troubleshoot any discrepancies between environments.
16.7K views23:13
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2023-11-10 19:33:59
Is AI making a real impact in the way you work or is it all hype? Stack Overflow recaps some of the top insights from their 2023 Developer Survey.

Explore what developers are thinking about the benefits, accuracy, and use cases for GenAI here.
15.4K views16:33
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2023-11-10 09:23:34
Angular 17 and the new site has been officially released.

Here's a summary of what's new.
12.8K views06:23
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2023-11-09 14:12:01
Top 12 Tips for API Security:

- Use OAuth2
- Use WebAuthn
- Use Leveled API Keys
- Authorization
- Rate Limiting
- API Versioning
- Whitelisting
- Check OWASP API Security Risks
- Use API Gateway
- Error Handling
- Input Validation
16.0K views11:12
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2023-11-06 09:26:12
Docker Architecture and Components

1. Docker Daemon (dockerd):
- 𝗥𝗼𝗹𝗲: Manages Docker containers on a system.
- 𝗥𝗲𝘀𝗽𝗼𝗻𝘀𝗶𝗯𝗶𝗹𝗶𝘁𝗶𝗲𝘀: Building, running, and managing containers.

2. Docker Client (docker):
- 𝗥𝗼𝗹𝗲: Interface through which users interact with Docker.
- 𝗖𝗼𝗺𝗺𝗮𝗻𝗱𝘀: build, pull, run, etc.

3. Docker Images:
- 𝗗𝗲𝗳𝗶𝗻𝗶𝘁𝗶𝗼𝗻: Read-only templates used to create containers.
- 𝗥𝗼𝗹𝗲: Serve as the basis for creating containers.
- 𝗥𝗲𝗴𝗶𝘀𝘁𝗿𝘆/𝗛𝘂𝗯: A storage and distribution system for Docker images.

4. Docker Containers:
- 𝗗𝗲𝗳𝗶𝗻𝗶𝘁𝗶𝗼𝗻: Runnable instances of Docker images.
- 𝗥𝗼𝗹𝗲: Encapsulate the application and its environment.

5. Docker Registry:
- 𝗥𝗼𝗹𝗲: Store Docker images.
- 𝗣𝘂𝗯𝗹𝗶𝗰 𝗥𝗲𝗴𝗶𝘀𝘁𝗿𝘆: Docker Hub.
- 𝗣𝗿𝗶𝘃𝗮𝘁𝗲 𝗥𝗲𝗴𝗶𝘀𝘁𝗿𝘆: Can be hosted by users.
20.8K views06:26
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