What is parallel execution?

Parallel execution is a database performance optimization where parts of query execution happen simultaneously across multiple processors or servers. By using parallel computing resources, query operations complete faster compared to serial execution.

Database engines utilize parallel execution frameworks and algorithms to coordinate query plan operations like scans, aggregations, sorts, etc in parallel. Goals include maximizing resource utilization and minimizing response times.

Parallel execution works in conjunction with query execution, distributed execution across clustered nodes, and partitioning strategies to achieve high performance at scale. Sophisticated engines can adaptively tune the degree of parallelism based on query complexity, data volume and system resources.

How does it work?

Database engines decompose query plans into stages or steps that can run concurrently like:

• Scanning partitioned data in parallel with multiple threads.
• Building aggregation hash tables in a distributed fashion.
• Performing sorts by partitioning data across cores.
• Processing joins by dividing work across join workers.

The query coordinator oversees dispatching work units to available resources and combining results.

Why is it important?

With single-threaded serial execution, long running operations in a query can only use one CPU core leading to underutilization of modern multi-core hardware. Parallel execution enables efficiently leveraging all hardware resources.

By working in parallel, queries can see order-of-magnitude speedup compared to serial plans. This improves application performance and reduces user latency.

FAQ

When is parallel execution beneficial?

Scenarios where parallel execution helps:

• Queries involving large scans, joins, aggregations.
• Multi-core servers for intra-query parallelism.
• Distributed databases for inter-query parallelism.
• OLAP workloads with large data processing.
• Long batch jobs with independent parallelizable steps.

What are some challenges with parallel execution?

Some downsides to enabling parallelism include:

• Increased CPU and resource usage requirements.
• Overhead of coordinating parallel workers.
• Diminishing returns beyond a point.
• Potential race conditions and deadlocks.
• Re-tuning queries optimized for serial plans.

What are examples of parallel execution frameworks?

Popular parallel execution frameworks:

• Oracle Parallel Server - For RAC clusters and Exadata Systems.
• SQL Server Parallelism - Leverages multiple CPUs on a server.
• Amazon Redshift Workload Management - Manages user queries.
• Apache Spark Dynamic Allocation - For distributed Spark workloads.

References:

• [Book] PostgreSQL Query Optimization: The Ultimate Guide to Building Efficient Queries, by Apress
• [Documentation] Apache Arrow Datafusion - Configuration Settings
• [Article] An overview of query optimization in relational systems
• [Article] Query evaluation techniques for large databases
• [Post] Parallel Query Processing and Optimization in DBMS