Indexer Deletion Event Handling Across Archived Data Stores
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Solution Overview
Problem
Analyzing massive quantities of heterogeneous performance data at search time is challenging due to the unstructured nature of the data and the difficulty of indexing and querying, especially in systems like SPLUNK® ENTERPRISE, which requires flexible schema application at search time rather than ingestion time.
Innovation Solution
Implementing a late-binding schema in the SPLUNK® ENTERPRISE system allows for flexible schema development during search time, enabling extraction rules to be defined or refined as needed, and using techniques like map-reduce computations, keyword indices, and high-performance analytics stores to optimize data processing and retrieval.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data is archived from the first data store to the second data store, then storage capacity is optimized and data retention policy is enforced, but data availability may be compromised if deletion events are not properly coordinated
Solution Approach 1:
The system implements a feedback mechanism where deletion events from the first data store are communicated to the second data store. The archive manager monitors deletion events in the first data store and responds by deleting corresponding archived data from the second data store, ensuring data availability is maintained while optimizing storage capacity
Solution Approach 2:
The archive manager acts as an intermediary between the first data store and the second data store. It receives deletion events from the first data store and translates them into appropriate deletion actions in the second data store, coordinating data availability across both storage locations without requiring direct communication between the data stores
2Productivity
If schema is applied at ingestion time, then data processing is efficient and structured, but flexibility to adapt to heterogeneous data formats is reduced
Solution Approach 1:
The system implements dynamic schema application where the schema is not fixed at ingestion time but can be adapted and refined during search time. This allows the system to efficiently process structured data while maintaining the flexibility to handle heterogeneous data formats by adjusting the schema based on actual data characteristics and search requirements
3Loss of information
If massive quantities of heterogeneous performance data are analyzed at search time, then comprehensive analysis is achieved, but processing time and computational resources increase significantly
Solution Approach 1:
The system performs preliminary actions by maintaining data in both the first data store and the second data store with proper indexing and deletion event coordination. This preliminary organization of data allows for faster retrieval and analysis during search operations, reducing processing time while maintaining analysis completeness
Data Source
AI summary
In embodiments, a computer-implemented method may entail receiving a search request. A first data store and a second data store, that contains data archived from the first data store, may be identified. Data from the first data store may remain available in the first data store for a limited period of time once archived to the second data store. The first data store storing data in a first format and the second data store storing data in a second format, the first format and the second format being different from one another. Determining that a subset of data that has been archived into the second data store and is to be searched as part of the search request is still available from the first data store, and executing the search request on the subset of data utilizing the first data store. Additional embodiments are described and/or claimed.