Bucket Index Retrieval for Flexible Machine Data Search
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Solution Overview
Problem
Analyzing and searching massive quantities of machine data from diverse sources is time-consuming and inefficient due to the challenges of handling different types and formats, particularly when storage capacity is abundant and minimally processed data is discarded during pre-processing.
Innovation Solution
An event-based data intake and query system with a late-binding schema that collects, indexes, and stores machine data as events, allowing flexible schema definition and extraction rules to be applied at search time, enabling efficient retrieval and analysis of raw data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If pre-processing data to extract specified data items is performed, then data retrieval efficiency is improved, but data flexibility and completeness are worsened due to discarding minimally processed data
Solution Approach 1:
The system segments data processing into two independent paths: a fast path for pre-processed data retrieval and a complete path for raw data analysis. The indexing system creates separate indexes for both pre-processed data items and raw data, allowing queries to be routed to the appropriate path based on requirements. This segmentation enables the system to maintain both efficient retrieval and complete data accessibility without forcing a single processing approach.
Solution Approach 2:
The system dynamically adapts its processing behavior based on query requirements. The query system can dynamically select between searching pre-processed data indexes for speed or raw data for completeness. The schema definition is also dynamic, allowing analysts to define extraction rules at search time rather than being locked into pre-defined schemas. This dynamic behavior resolves the contradiction by allowing the system to switch between efficiency and flexibility modes as needed.
2Adaptability or versatility
If massive quantities of raw data are stored for later analysis, then data flexibility is improved, but search and analysis performance deteriorates due to data volume
Solution Approach 1:
The system performs preliminary extraction and indexing of data items that are likely to be queried frequently. The indexing system creates inverted indexes for pre-processed data, enabling fast retrieval. This preliminary action does not discard the raw data, but prepares a optimized representation that can be quickly accessed when needed, thus maintaining both flexibility and performance.
Solution Approach 2:
The system extracts and separates the indexing function from the raw data storage. By creating separate indexes that point to raw data locations, the system can quickly search through indexed data without moving or processing the entire raw data set. This extraction of the search function into a separate indexing layer enables fast performance while preserving complete data access.
3Speed
If data is processed and extracted at pre-processing time, then data retrieval is faster, but loss of information occurs due to discarding minimally processed data
Solution Approach 1:
The system nests the pre-processed data index within the raw data framework. The indexing system stores references to raw data locations alongside the pre-processed data items. This nesting allows the fast pre-processed data to be accessed while simultaneously maintaining access to the complete raw data through the nested references, preventing information loss while preserving retrieval speed.
Solution Approach 2:
The indexing system acts as an intermediary between queries and raw data. Instead of directly accessing raw data for all queries, the intermediary index layer provides fast routing to relevant data. This intermediary can be configured to provide either pre-processed data for speed or raw data for completeness, mediating between the conflicting requirements without losing information.
Data Source
AI summary
Embodiments described herein are directed to facilitating efficient management of data storage and retrieval. In one embodiment, filter data associated with a bucket of data is obtained at a local data store from a remote data store. Based on analysis of the filter data, it is determined that the bucket of data is a candidate to contain data relevant to a search query. Based on such a determination, the index data associated with the bucket of data is obtained at the local data store from the remote data store. Thereafter, it may be determined that the bucket of data includes data relevant to the search query based on analysis of the index data. Based on the determination that the bucket of data includes data relevant to the search query, the journal data associated with the bucket of data is obtained at the local data store from the remote data store.