Parallel Databases

A parallel database system seeks to improve performance through parallelization of various operations, such as loading data, building indexes and querying data. This is achieved by running multiple operations simultaneously across different processors or cores, usually in a distributed environment where data is partitioned across multiple disks on multiple machines.

Parallel databases use a parallel processing model to execute queries faster than would be possible using a single machine or processor. They can be classified based on their architecture into two main types:

Shared-Memory System: In this system, multiple CPUs share the main memory and disks. All CPUs can access all the memory, but data manipulation is done in parallel to improve performance.
Shared-Nothing System: In this architecture, each node (processor/CPU or computer) has its own memory and disk storage, but they're interconnected for communication. Data is distributed across the nodes, and each node works on its own portion of the total task. Most large-scale parallel database systems use this architecture due to its better scalability.

There are three key ways that parallelism is achieved in parallel databases:

Data Parallelism: This is when a large dataset is divided into smaller chunks and the same operation is performed on each chunk at the same time. This is common in "shared-nothing" architectures, where each node operates on its own portion of the data.
Pipeline Parallelism: This is when different operations of a query are performed by different processors on different data streams concurrently. It's like an assembly line, where each step of the process is handled by a different worker.
Query Parallelism: This is when a system can execute multiple queries concurrently. This type of parallelism is often used in OLTP (Online Transaction Processing) systems where many short queries are executed concurrently.

These techniques can also be combined. For instance, a system might use both data and pipeline parallelism to execute a single query.

Parallel databases can provide significant performance improvements for large-scale data processing tasks. They are often used in data warehousing environments, where large volumes of data are queried for business intelligence purposes.