Time Bucket Aggregation for Storage Performance Analysis
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Solution Overview
Problem
Current systems face inefficiencies in calculating top contributors to a storage system's performance due to high communication overhead between data processors and databases, as they often require frequent and scattered data point retrieval, leading to gaps in metric data analysis.
Innovation Solution
A method that groups contiguous data points into time buckets based on timestamps, minimizing database queries by processing and storing these buckets, which reduces communication and enhances calculation efficiency for identifying top contributors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If frequent and scattered data point retrieval is performed to calculate top contributors, then calculation accuracy is improved, but communication overhead between data processors and databases increases
Solution Approach 1:
The patent combines multiple scattered data point retrievals into a single bulk retrieval operation by aggregating time bucket requests. Instead of querying the database separately for each data point needed for top contributor calculations, the system merges these into one comprehensive query that fetches all required time buckets in advance, thereby maintaining calculation accuracy while dramatically reducing communication overhead.
Solution Approach 2:
The system performs preliminary action by pre-fetching and caching time bucket data before top contributor calculations are needed. Data processors retrieve and store time bucket information in advance, so when calculation requests arrive, the data is already available in memory rather than requiring database queries at calculation time. This eliminates the need for frequent scattered retrievals while ensuring accurate calculations can proceed.
2Loss of information
If multiple separate data point queries are executed to ensure complete metric data coverage, then data completeness is improved, but processing time increases
Solution Approach 1:
The patent merges multiple separate data point queries into a single bulk query operation. Instead of executing individual queries for each metric data point across different time buckets, the system combines these into one comprehensive query that retrieves all necessary data in a single database round-trip, thereby maintaining complete metric data coverage while significantly reducing total processing time.
Solution Approach 2:
The system performs preliminary data retrieval by fetching complete time bucket information in advance before calculations are needed. This pre-fetching ensures all metric data points are available in memory when processing begins, eliminating the need for time-consuming sequential queries during the actual calculation phase while guaranteeing data completeness.
3Measurement precision
If individual data points are retrieved and processed separately, then data processing accuracy is improved, but system productivity decreases
Solution Approach 1:
The patent merges individual data point processing operations into bulk processing of time buckets. Instead of retrieving, validating, and processing each data point separately through multiple database round-trips, the system retrieves complete time buckets in bulk and processes all data points within memory using efficient array operations, thereby maintaining processing accuracy while dramatically improving system productivity.
Solution Approach 2:
The system performs preliminary bulk retrieval of time bucket data before processing begins. By fetching all required data points in advance and loading them into memory, the system enables subsequent processing operations to work with complete datasets without repeated database access, maintaining accuracy through comprehensive data availability while boosting productivity through efficient in-memory processing.
Data Source
AI summary
A method for managing data includes obtaining, by a data processor, a time bucket calculation request, and in response to the time bucket calculation request: obtaining a plurality of data points from a database based on a period of time, wherein the period of time is specified in the time bucket calculation request, processing each data point in the plurality of data points based on a timestamp of each data point and a data point interval, generating a plurality of time buckets based on the processing, and storing the plurality of time buckets in the database.