Streaming Rules Engine for Real-Time Log Data Processing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Manufacturers face difficulties in processing, storing, and utilizing log data from complex systems to detect bugs, analyze performance, and predict failures due to the unstructured and semi-structured nature of the data generated continuously in real-time.
Innovation Solution
A message bus-based streaming rules engine processes log data using Finite State Machines and domain-specific languages, compiling rules into executable actors that apply business rules asynchronously and in parallel, enabling efficient handling of unstructured data streams.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If log data is processed continuously in real-time from complex systems, then the ability to detect bugs and predict failures improves, but the difficulty of processing and storing unstructured data increases
Solution Approach 1:
The system segments unstructured log data into structured events with standardized fields (timestamp, source, event type, parameters). Each log entry is parsed and divided into discrete components that can be independently processed, stored, and queried, reducing the complexity of handling raw unstructured data while maintaining real-time processing capability for bug detection.
2Productivity
If rules are applied to process log data streams, then the ability to analyze performance and detect issues improves, but the complexity of managing and modifying rules increases
Solution Approach 1:
The rule engine enables non-technical users to create, modify, and deploy analysis rules through a graphical interface without requiring programming knowledge. Users can define custom events, set thresholds, and configure alerts through point-and-click operations, allowing business users to self-service their analytical needs without involving IT staff, thus improving productivity while simplifying rule management.
3Speed
If real-time processing of unstructured log data is implemented, then the speed of issue detection improves, but the resources required for processing and storage increase
Solution Approach 1:
The system applies selective processing by parsing only the portions of log data that are relevant to configured rules and events. Rather than processing every byte of incoming log streams uniformly, the engine identifies and processes only the specific fields and patterns that match active monitoring rules, reducing overall resource consumption while maintaining fast detection speed for critical issues.
4Ease of operation
If structured events are created from unstructured log data, then the ease of querying and analyzing data improves, but the time required to parse and transform data increases
Solution Approach 1:
The system performs preliminary parsing and structuring of log data as it arrives, converting unstructured entries into standardized events with predefined schemas before the data needs to be queried or analyzed. This upfront transformation creates an optimized data structure that enables fast querying and analysis later, reducing the time penalty of initial processing by preparing the data in advance in a query-friendly format.
Data Source
AI summary
A message bus-based streaming rules engine is disclosed. In various embodiments, a data analysis system as disclosed herein includes a communication interface configured to receive log data; and a processor coupled to the communication interface and configured to: parse the log data to generate a data unit comprising a data value; and send the data unit via a message bus to a rule actor configured to apply a rule based at least in part on the data value.