Flexible Schema Binding for Streaming Data Analysis
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Solution Overview
Problem
Current tools lack the capability to efficiently and flexibly search and analyze large sets of raw machine data from diverse sources, particularly in IT environments, as they often require pre-processing that discards significant amounts of data and do not provide intuitive visual interfaces for identifying data subsets of interest.
Innovation Solution
A data intake and query system that utilizes a flexible schema for extracting information from events, allowing for late-binding schema application during search time, and includes a graphical programming system for designing data processing pipelines, enabling unified processing of queries for indexed and streaming data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If pre-processing is applied to data, then data analysis efficiency is improved, but data flexibility and completeness deteriorate due to schema restrictions and data discarding
Solution Approach 1:
The system performs preliminary actions by collecting and storing raw data before schema binding occurs. The flexible schema collector gathers data from multiple sources with diverse formats, and the flexible schema binder later applies appropriate schemas to each data type without restricting the collection process. This allows efficient analysis while maintaining data flexibility.
Solution Approach 2:
The patent implements dynamic schema binding where schemas are applied at runtime rather than being fixed in advance. The flexible schema binder dynamically determines and applies schemas to data based on the actual data format and type, allowing the system to adapt to different data structures while maintaining analysis efficiency through automated schema application.
2Speed
If pre-processing with fixed schemas is used, then data processing speed is improved, but adaptability to diverse data sources deteriorates
Solution Approach 1:
The system segments the data processing function into separate components: a flexible schema collector that handles diverse data sources, a flexible schema binder that applies appropriate schemas, and a data processor that executes queries. This segmentation allows each component to optimize for its specific function while maintaining overall adaptability and speed.
Solution Approach 2:
The flexible schema binder acts as an intermediary between the diverse data sources and the data processor. It receives raw data from various sources with different formats, applies the appropriate schemas dynamically, and outputs standardized data structures that can be efficiently processed, thus maintaining both adaptability and processing speed.
3Adaptability or versatility
If all raw data is stored for later analysis, then data flexibility and analysis completeness are improved, but system complexity and resource requirements worsen
Solution Approach 1:
The system performs preliminary data collection and storage of raw data in a structured manner, then applies schemas dynamically during query execution. This preliminary organization of data without immediate processing allows complete analysis capability while managing complexity through structured storage and automated processing.
Solution Approach 2:
The system changes the parameter of schema application timing from fixed pre-processing to dynamic runtime binding. By deferring schema application until needed and using flexible schema binders to automatically determine appropriate schemas, the system manages complexity through automated parameter determination rather than manual configuration for each data source.
4Ease of operation
If traditional search tools are used, then ease of operation is maintained, but capability to analyze large sets of raw data from diverse sources deteriorates
Solution Approach 1:
The system implements a universal data processing platform that can handle multiple data sources and formats through a single unified interface. The flexible schema collector and binder work together to process diverse data types (logs, metrics, events, etc.) using the same query language and operational interface, thus maintaining ease of use while dramatically improving analytical capability.
Solution Approach 2:
The flexible schema binder operates autonomously to determine and apply appropriate schemas to incoming data without requiring manual configuration. The system self-determines the correct schema based on data characteristics, automatically binds schemas to data streams, and prepares data for processing, thereby maintaining operational simplicity while enabling comprehensive data analysis capability.
Data Source
AI summary
Systems and methods are described for implementation by a streaming data processing system of a processing pipeline that obtains data items from one or more data sources. The one or more data sources may include multiple partitions of a topic or multiple topics. The one or more data sources are defined by a data pattern, a group of tenants, or a process. The implementation of the processing pipeline can include identifying the one or more data sources to obtain data items. The processing pipeline can include a plurality of processing tasks to obtain the data items. The streaming data processing system can utilize a workload coordinator to dynamically assign particular data sources to particular processing tasks. The implementation of the processing pipeline can further include executing the plurality of processing tasks to cause processing tasks to obtain data items from assigned data sources and process the data items.