Observability Pipeline Data Collectors for External Storage
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Solution Overview
Problem
Existing observability pipeline systems face challenges in efficiently processing and routing data from external data storage systems, leading to inefficiencies in data analysis and increased computing resource usage.
Innovation Solution
The implementation of data collectors within the observability pipeline system that can ingest data from various external data storage systems, apply filtering, aggregation, and analytics, and route the processed data to appropriate destinations, while also employing load balancing techniques to optimize resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data from external data storage systems is processed using traditional methods, then data can be analyzed, but processing efficiency is low and computing resource usage is high
Solution Approach 1:
The system performs preliminary actions by pre-processing and filtering data before it enters the main analysis pipeline. Data collectors retrieve and pre-process archived data in advance, applying filters and transformations before the data is needed for analysis, thereby reducing the computational burden during actual analysis operations
Solution Approach 2:
The system segments data processing into distinct stages: data retrieval from external storage, pre-processing and filtering, routing decisions, and final analysis. This segmentation allows each component to be optimized independently and enables parallel processing of different data streams, improving overall efficiency while reducing resource contention
2Adaptability or versatility
If archived data is processed in real-time alongside live streams, then data analysis flexibility is improved, but system complexity increases
Solution Approach 1:
The system introduces intermediary components including data collectors that act as mediators between external storage systems and the processing pipeline, and routing mechanisms that mediate between live and archived data streams. These intermediaries abstract the complexity of handling archived data, allowing real-time processing flexibility without directly increasing core system complexity
Solution Approach 2:
The routing system is designed to be universal, handling both live data streams and archived data through the same infrastructure. The same data collectors, processors, and destinations serve multiple purposes - processing live events and replaying archived data using identical components, thereby achieving versatility without proportional increases in system complexity
3Reliability
If data is retained for long-term storage and replay capability, then incident investigation capability is improved, but storage costs increase
Solution Approach 1:
The system extracts only the essential data elements needed for incident investigation from archived data. Data collectors retrieve specific data payloads based on filtering criteria rather than storing and retrieving entire datasets, thereby maintaining incident investigation capability while reducing the quantity of data that must be retained in expensive storage systems
Solution Approach 2:
The system changes parameters of data retention by storing archived data in a compressed or filtered state rather than full fidelity. Data is retained with modified parameters (filtered to include only relevant fields, aggregated where appropriate) that preserve incident investigation capability while significantly reducing storage capacity requirements
Data Source
AI summary
Data payloads from an external data storage system are processed in an observability pipeline system. In some aspects, the observability pipeline system defines a leader role and worker roles. The leader role generates a data discovery task based on configuration information for a data collection task. A worker role executes the data discovery task, which includes communicating with an external data storage system to identify a data payload that is stored on the external data storage system and contains event data that meet event filter criteria. The leader role generates data collection tasks based on the data payload. Worker roles execute the data collection tasks. Executing a data collection task includes communicating with the external data storage system to obtain a subset of filtered event data from the data payload; and streaming the subset of filtered event data to an observability pipeline process.