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The Cache-aside pattern can 10x your application performance. Another name for it is "Lazy Loading". Here's the entire Cache aside logic summarized: 1. Try to read data from the cache first 2. Cache hit - return the cached data directly 3. Cache miss - query the database and cache the result The Cache-aside pattern comes with a few interesting advantages. The cache only contains the frequently requested data in the application. This is helpful to control the cache size. Another advantage is that this approach is simple to implement. The only significant disadvantage is that the first request is often slow. This is because of the cache miss and having to query the database.

🚀 Evolving Database Interaction: From Manual JDBC to Seamless Spring Data JPA Integration 🚀 In the dynamic world of software development, efficient database interaction is crucial. Let's explore the evolution of database access through three key technologies: JDBC, Hibernate, and Spring Data JPA. 🔄 JDBC (Java Database Connectivity): 💪 Strengths: - Low-level control: Direct SQL queries and transactions. - Efficient for small to medium-sized projects with simple requirements. 🛑 Considerations: - Tedious manual handling of database connections, transactions, and result sets. - Prone to SQL injection if not handled carefully. Hibernate (Object-Relational Mapping - ORM): 💪 Strengths: - Automated mapping of Java objects to database tables. - Improved productivity with less boilerplate code. - Portable across different database vendors. 🛑 Considerations: - Steeper learning curve due to configuration complexities. - Performance concerns in certain scenarios. Spring Data JPA: 💪 Strengths: - Building on top of Hibernate, it simplifies data access with JPA. - Reduces boilerplate code through annotations and conventions. - Enhanced productivity with CRUD operations and custom queries. - Seamless integration with the Spring ecosystem. 🛑 Considerations: - May introduce some overhead compared to manual JDBC for simple queries. - Requires understanding of JPA concepts for optimal usage. The Evolution: As technology advances, so does the way we interact with databases. While JDBC provides ultimate control, Hibernate and Spring Data JPA add layers of abstraction, easing the development process. Moving from manual JDBC to Spring Data JPA represents a paradigm shift, allowing developers to focus more on business logic and less on database intricacies. Recommendation: Choose the right tool for the job. For small projects with simple requirements, JDBC might suffice. As complexity grows, Hibernate and eventually Spring Data JPA offer a smoother and more maintainable path. In conclusion, embracing the evolution of database interaction from manual JDBC to Spring Data JPA signifies a commitment to efficiency, maintainability, and adaptability in the ever-evolving landscape of software development. 🌐💻

𝗢𝗽𝘁𝗶𝗺𝗶𝘀𝗲 𝗬𝗼𝘂𝗿 𝗗𝗼𝗰𝗸𝗲𝗿 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄 🚀💻 Supercharge your Docker skills with these key commands: 𝟭. 𝗿𝘂𝗻: Launch containers from images. 𝟮. 𝘀𝘁𝗼𝗽: Gracefully halt running containers. 𝟯. 𝘀𝘁𝗮𝗿𝘁: Revitalize stopped containers. 𝟰. 𝗲𝘅𝗲𝗰: Execute commands within containers. 𝟱. 𝗽𝗼𝗿𝘁: Identify a container's public-facing port. 𝟲. 𝗿𝗲𝗻𝗮𝗺𝗲: Give containers new identities. 𝟳. 𝗽𝗮𝘂𝘀𝗲/𝘂𝗻𝗽𝗮𝘂𝘀𝗲: Suspend/resume container processes. 𝟴. 𝗸𝗶𝗹𝗹: Halt running containers. 𝟵. 𝗯𝘂𝗶𝗹𝗱: Create custom Docker images. 𝟭𝟬. 𝘁𝗼𝗽: Display running processes of a container. 𝟭𝟭. 𝗰𝗽: Copy files between containers and local system. 𝟭𝟮. 𝗿𝗺: Remove stopped containers. 𝟭𝟯. 𝘀𝘁𝗮𝘁𝘀: Monitor real-time container resource usage. 𝟭𝟰. 𝗽𝘀: View container processes. 𝟭𝟱. 𝗵𝗶𝘀𝘁𝗼𝗿𝘆: View image evolution history. 𝟭𝟲. 𝗶𝗺𝗮𝗴𝗲 𝗹𝘀: List available Docker images. 𝟭𝟳. 𝗹𝗼𝗴𝘀: Retrieve and analyze container logs. 𝟭𝟴. 𝘃𝗲𝗿𝘀𝗶𝗼𝗻: Check Docker version. 𝟭𝟵. 𝗶𝗻𝗳𝗼: Fetch crucial Docker system information. 𝟮𝟬. 𝗶𝗻𝘀𝗽𝗲𝗰𝘁: Explore details of containers, images, networks.

System Design Acronyms

CAP, BASE, SOLID, KISS, What do these acronyms mean? The diagram below explains the common acronyms in system designs.    🔹 CAP  CAP theorem states that any distributed data store can only provide two of the following three guarantees:  1. Consistency - Every read receives the most recent write or an error.  2. Availability - Every request receives a response.  3. Partition tolerance - The system continues to operate in network faults.    🔹 BASE  The ACID (Atomicity-Consistency-Isolation-Durability) model used in relational databases is too strict for NoSQL databases. The BASE principle offers more flexibility, choosing availability over consistency. It states that the states will eventually be consistent.    🔹 SOLID  SOLID principle is quite famous in OOP. There are 5 components to it.    1. SRP (Single Responsibility Principle) 2. OCP (Open Close Principle)  3. LSP (Liskov Substitution Principle)  4. ISP (Interface Segregation Principle)  5. DIP (Dependency Inversion Principle)    🔹 KISS  "Keep it simple, stupid!" is a design principle first noted by the U.S. Navy in 1960. It states that most systems work best if they are kept simple. 

Atleast we are expecting 100 votes for above poll

𝟰 𝗧𝘆𝗽𝗲𝘀 𝗼𝗳 𝗡𝗼𝗦𝗤𝗟 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲𝘀 𝘄𝗶𝘁𝗵 𝗲𝘅𝗮𝗺𝗽𝗹𝗲𝘀: [𝟭] 𝗗𝗼𝗰𝘂𝗺𝗲𝗻𝘁 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲𝘀 This is probably the most popular category of NoSQL databases. Data is stored in JSON, BSON, or XML format. A table is a collection of records. Records are known as documents Documents can closely mimic the application domain object. Examples - MongoDB, Couchbase, etc. [𝟮] 𝗞𝗲𝘆 𝗩𝗮𝗹𝘂𝗲 𝗦𝘁𝗼𝗿𝗲𝘀 Each element is stored as a key-value pair. It's similar to relational DB but with only two columns - key & value. The value can be an object. Examples - Redis, Dynamo [𝟯] 𝗖𝗼𝗹𝘂𝗺𝗻-𝗢𝗿𝗶𝗲𝗻𝘁𝗲𝗱 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲𝘀 In RDBMS, Data is stored row by row. But in Column-Oriented Database, data is stored as a set of columns. More efficient for aggregation queries. For example, find the total number of employees in a department. Examples - MonetDB, Apache HBase [𝟰] 𝗚𝗿𝗮𝗽𝗵 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲𝘀 Graph DBs deal with relationships between data elements. Each element is a node and it is connected to other elements. They support different data formats such as JSON and key-value. Not used as commonly as Document or Key-Value storage. Examples - Neo4J, Amazon Neptune. 👉 𝗪𝗵𝗲𝗻 𝘁𝗼 𝘂𝘀𝗲 𝘄𝗵𝗮𝘁 𝘁𝘆𝗽𝗲 𝗼𝗳 𝗡𝗼𝗦𝗤𝗟 𝗱𝗮𝘁𝗮𝗯𝗮𝘀𝗲? ✅ 𝗗𝗼𝗰𝘂𝗺𝗲𝗻𝘁 𝗗𝗕𝘀: Great for almost all types of applications that relied on SQL databases. ✅ 𝗞𝗲𝘆-𝗩𝗮𝗹𝘂𝗲: Shopping carts, user profiles & caching ✅ 𝗖𝗼𝗹𝘂𝗺𝗻-𝗢𝗿𝗶𝗲𝗻𝘁𝗲𝗱: Analytics and aggregation ✅ 𝗚𝗿𝗮𝗽𝗵: Map applications and knowledge graphs