To begin as Martin Kleppman from Designing Data Intensive Applications outlines, there are simple queries that read and write a single key. Plus there are scatter gather queries, in the case of document partition secondary indexes.
Massive Parallel Processing (MPP)
This is about the level of access supported by most NoSQL distributed data stores…
- However Massive Parallel Processing (MPP) Relational Database Products…
- Often used for analytics are much more sophisticated in the types of queries they support
- A typical data warehouse query…
- Contains several joins
- Filterings
- Groupings
- Aggregation operations
- MPP Query Optimiser, breaks this complex query into a number of execution stages and partitions
- 👉 Many of which can be executed in parallel in different nodes on the database cluster
- Queries that involve scanning a over large parts of the dataset, particularly benefit from such parallel execution
Final Note
Fast parallel execution of data warehouse queries is a specialised topic, and given its importance of business analytics, it receives a lot of commercial interest! This will be covered more in my upcoming blogs.
📚 Further Reading & Related Topics
If you’re exploring parallel processing and optimizing performance in data-intensive applications, you might also find these articles valuable:
• Load Balancing Algorithms Every Developer Should Know – Understand key load-balancing strategies to optimize resource distribution and enhance system efficiency, complementing the principles of parallel execution.
• Understanding Market, Limit, and Stop Orders in Trading – If you’re applying parallel execution in financial systems, this guide provides insights into how order execution strategies impact performance in trading algorithms.
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