Can Apache Spark handle OLTP?

Apache Spark is designed primarily for batch processing, real-time analytics, and data processing at scale, making it highly suitable for Online Analytical Processing (OLAP). However, it is not designed for Online Transaction Processing (OLTP). Here are the key reasons why Spark is not suitable for OLTP: 1. **Latency**: Spark is optimized for high-throughput, large-scale data processing tasks that can tolerate latency. OLTP systems require very low latency to handle a large number of short online transactions (such as inserts, updates, and deletes) quickly. 2. **Concurrency**: OLTP systems need to handle a high number of concurrent transactions with robust ACID (Atomicity, Consistency, Isolation, Durability) properties. Spark, while capable of handling many parallel tasks, is not optimized for the kind of fine-grained concurrency control needed in OLTP systems. 3. **State Management**: OLTP systems need to maintain and manage transactional state in a highly efficient manner. Spark processes data in a more stateless manner and is not designed for the kind of stateful operations that are common in OLTP. 4. **Architecture**: Spark's architecture is built around the concept of Resilient Distributed Datasets (RDDs) and DataFrames for batch processing and stream processing. In contrast, OLTP systems are often built around traditional database engines that are optimized for row-level operations. For OLTP, traditional relational databases like MySQL, PostgreSQL, and specialized NoSQL databases like MongoDB, Cassandra, or NewSQL databases like Google Spanner are more appropriate choices. They are optimized for the high-frequency, low-latency transactions typical of OLTP workloads. read less