What is an execution framework?

An execution framework is a distributed system infrastructure that provides automation, scaling and resilience for executing computational jobs across clusters of commodity servers. It abstracts infrastructure complexities like fault tolerance, resource allocation and job scheduling.

Execution frameworks power large scale data processing workloads and applications requiring coordination of distributed computation, storage and network I/O. For example, Apache Spark and Flink are popular distributed execution engines.

Database query execution engines like Apache DataFusion also rely on execution frameworks to evaluate optimized query plans efficiently at scale. This includes managing cluster resources, memory, parallelism, user defined functions, and intermediate state across nodes.

Reliable, performant execution frameworks are essential building blocks for scalable data-intensive applications.

How do execution frameworks work?

Execution frameworks handle details like provisioning servers, scheduling tasks, managing memory/disk, balancing load, replicating data, recovering from failures and coordinating dependencies automatically.

Developers focus on application logic while the framework handles infrastructure aspects transparently. Popular frameworks include Hadoop, Spark, Flink, AWS Batch etc.

Why are execution frameworks useful? Where are they applied?

Execution frameworks enable scalable distributed computing on clusters of commodity hardware. They power large scale batch and stream data pipelines, machine learning applications, ETL workflows and general purpose parallel computational jobs that need to leverage distributed resources.

FAQ

How do execution frameworks contrast with traditional distributed computing?

They automate and optimize complex low-level aspects like fault tolerance, task scheduling and resource management that otherwise have to be handled manually.

What capabilities do execution frameworks provide?

Typical capabilities:

• Automated provisioning and cluster management
• Task scheduling and execution
• Data locality optimization
• Fault tolerance and failure recovery
• Exposing job and system metrics
• Scalability and throughput

What are examples of common execution frameworks?

Popular frameworks used today:

• Apache Spark
• Apache Flink
• Hadoop MapReduce
• AWS Batch
• Apache Mesos
• Kubeflow on Kubernetes

What are challenges in building execution frameworks?

Some key challenges include:

• Performance, scalability and latency
• Abstraction vs control and visibility
• Debugging and monitoring
• Versioning and compatibility
• Integration with data sources and infrastructure

References:

• [Paper] An adaptive query execution system for data integration
• [Book] In-Memory Analytics with Apache Arrow
• [Book] Rust Data Engineering

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