Processing Pipeline Definition for Low-Latency Discrete Workloads
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Solution Overview
Problem
Existing database systems face inefficiencies in workload distribution, high latency, and inefficient allocation of compute resources during data processing, particularly in handling diverse file types and workloads.
Innovation Solution
A continuous file processing service is configured using a processing pipeline definition (PPD) to manage workload processing, which includes a file processing manager (FPM) that discovers workloads, generates query plans, and allocates compute resources efficiently, enabling scalable and low-latency processing of discrete work items.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional database systems process workloads, then data storage and access are provided, but workload distribution is inefficient and latency is high
Solution Approach 1:
The system segments workloads into discrete work items that can be independently processed. Each work item is assigned to specific compute nodes based on workload characteristics, enabling parallel processing and reducing latency. The processing pipeline divides complex workloads into manageable stages (ingestion, processing, output) that can be executed concurrently.
Solution Approach 2:
The system dynamically allocates compute nodes to workloads based on real-time demands and workload characteristics. Compute resource allocation is flexible and adaptive, allowing the system to scale resources up or down as needed. The processing pipeline can dynamically route work items to appropriate compute nodes based on current system state.
2Power
If compute resources are allocated for data processing, then processing capability is provided, but resource allocation is inefficient
Solution Approach 1:
The system assigns specific compute nodes to specific workloads based on local characteristics and requirements. Different compute nodes can be optimized for different types of processing tasks (e.g., batch processing vs. streaming). Resource allocation is tailored to match workload-specific needs rather than using uniform resource allocation.
Solution Approach 2:
The system changes resource allocation parameters based on workload characteristics and system state. Compute resource allocation is adjusted dynamically based on factors such as data size, processing complexity, and node availability. This allows optimal resource utilization by matching resource parameters to workload requirements.
3Productivity
If a processing pipeline is implemented, then workload distribution is improved, but system complexity increases
Solution Approach 1:
The processing pipeline is designed as a universal framework that can handle multiple types of workloads and data formats through a common architecture. The same pipeline infrastructure supports diverse processing tasks by configuring different processing steps and compute node assignments. This multi-functionality reduces overall system complexity despite handling varied workloads.
Solution Approach 2:
The system introduces intermediary components that simplify the interaction between workloads and compute resources. A workload manager acts as an intermediary that handles workload dispatching, compute node selection, and result collection. This abstraction layer simplifies the overall system architecture by isolating complexity in dedicated management components.
Data Source
AI summary
Provided herein are systems and methods for discrete workload processing using a file processing service. An example method includes retrieving a manifest file from a work queue. The manifest file includes metadata associated with a plurality of workloads. A plurality of processing configurations corresponding to the plurality of workloads is generated. A processing configuration of the plurality of processing configurations is associated with scheduling execution of one or more tasks for a workload of the plurality of workloads. A processing pipeline definition of the manifest file is generated. The processing pipeline definition includes the plurality of processing configurations. The processing pipeline definition is registered with a pipeline definition registry of a network-based database system to generate a definition registration.