OLTP

  Here’s a simplified breakdown of the key points from the paragraph:

**OLTP in Cloud Computing:**

- **Online Transaction Processing (OLTP)**: Used for fast transactions under strict latency requirements.

- **Challenges**: Traditional relational databases can't handle the huge data volumes and real-time demands of companies like Google, Amazon, Facebook, etc.

- **Solution**: Use **key-value databases** and caching to speed up data access by storing frequently used data in memory.

**Memcaching**:

- **Memcaching**: Caching system that stores data in memory across many servers, improving access speed.

- Uses a **hash table** that stores key-value pairs across multiple servers.

- The **LRU (Least Recently Used)** strategy is used to replace old data with new data.

**Scalability**:

- **Vertical Scaling**: More resources (processors, RAM, etc.) are added to a single system.

- **Horizontal Scaling**: More systems (servers) are added, each handling its own data without sharing resources.

**OLTP Overhead**:

- Four main sources of overhead in OLTP systems:

  1. **Logging**: Ensures transaction durability, but slows down the system.

  2. **Locking**: Locks records during transactions to ensure data integrity.

  3. **Latching**: Short-term holds on shared data structures.

  4. **Buffer Management**: Managing how data is temporarily stored.

- These operations consume significant system resources.

**Modern OLTP Improvements**:

- **In-Memory Databases**: Storing data in memory (RAM) rather than relying on disk storage speeds up operations.

- **Logless, single-threaded databases**: For some cloud applications, these could replace traditional OLTP databases.

**Data Replication**:

- Replication increases system **reliability and performance**.

- **Master-Slave Replication**: The master server copies data to slaves (can be done synchronously or asynchronously).

- **Homogeneous Replica Groups**: All members can update the data, leading to faster updates and higher availability.

**Flexible Approach**:

- Modern cloud systems don’t use a “one-size-fits-all” database. Different databases and storage models are used based on specific needs.

- **Example**: Oracle integrates multiple databases (NoSQL, HDFS, and Oracle Database) for different use cases.

This breakdown captures the key points in simpler terms.