Observability Data Merging for Unified Time-Range Visualization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing data management systems face challenges in efficiently storing and visualizing observability data across long time intervals, making it difficult to debug performance issues in node clusters due to storage constraints and lack of unified data interaction.
Innovation Solution
A data management system retrieves and merges observability data sets from cloud storage, selecting and retaining relevant time-partitioned data blocks to generate a unified visualization of operations, enabling cohesive analysis of node performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Duration of action of stationary object
If observability data sets are stored in cloud storage for long time intervals, then the time range for analysis is extended, but storage constraints make it difficult to manage and visualize the data efficiently
Solution Approach 1:
The system segments observability data into time-partitioned data blocks organized by time intervals. This segmentation allows the system to manage long-term storage by dividing large datasets into manageable units, enabling efficient retrieval and visualization of specific time ranges without managing the entire dataset at once.
Solution Approach 2:
The triage service extracts and selects only the relevant time-partitioned data blocks corresponding to the requested time interval from cloud storage. This extraction mechanism reduces the data processing burden by retrieving only necessary portions of the dataset, simplifying storage management while maintaining extended time range analysis capability.
2Quantity of substance
If multiple observability data sets are retrieved from cloud storage, then comprehensive data coverage is achieved, but data merging becomes complex and time-consuming
Solution Approach 1:
By segmenting data into time-partitioned blocks with consistent naming conventions and structures, the system enables efficient merging through systematic processing. The segmentation allows the triage service to identify and combine relevant blocks from multiple data sets without manual intervention, reducing merging time while maintaining comprehensive coverage.
Solution Approach 2:
The system implements automated merging of time-partitioned data blocks from multiple observability data sets. The triage service combines selected blocks based on their time interval relationships, creating a unified data structure that maintains comprehensive coverage while significantly reducing the time required compared to manual merging approaches.
3Reliability
If overlapping time-partitioned data blocks are retained from multiple data sets, then data completeness is improved, but storage space is wasted due to redundancy
Solution Approach 1:
The triage service extracts and identifies overlapping time-partitioned data blocks from multiple data sets. By recognizing which blocks are duplicates or redundant based on their time interval overlap, the system can selectively retain only necessary blocks while discarding redundant copies, maintaining data completeness while reducing storage space consumption.
Solution Approach 2:
The system discards redundant overlapping data blocks while retaining unique or complementary blocks that enhance data completeness. This selective discarding approach eliminates storage waste from duplicate data while preserving the necessary information for complete and accurate observability analysis.
4Ease of operation
If tracing data is merged from multiple observability data sets, then unified visualization is achieved, but processing complexity increases
Solution Approach 1:
Segmenting data into standardized time-partitioned blocks with consistent metadata structures enables automated merging and simplifies processing. The segmentation provides a uniform interface that reduces processing complexity while achieving unified visualization across multiple data sets.
Solution Approach 2:
The triage service acts as an intermediary that automates the merging process and prepares unified data for visualization. This intermediary layer handles the complexity of data integration, presenting a simplified unified data structure to the visualization system and reducing the complexity burden on the visualization component.
Data Source
AI summary
A method for data processing is described. The method may include retrieving, from a cloud storage environment accessible to a data management system (DMS), two or more observability data sets associated with a set of time intervals within a selected time range. The observability data sets may contain a set of time-partitioned data blocks that include tracing data associated with operations performed by one or more nodes of a node cluster of the DMS during the selected time range. The method may further include merging the tracing data by selecting a first subset of overlapping time-partitioned data blocks, omitting a second subset of the overlapping time-partitioned data blocks, and retaining one or more non-overlapping time-partitioned data blocks. The method may further include transmitting data corresponding to a visualization of the merged tracing data.