Data Lake Column Layout and Compression for Faster Queries
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Solution Overview
Problem
Existing data lake table storage systems inefficiently manage file storage and compression, leading to wasted resources and suboptimal query performance due to manual trial and error in determining data layout and compression techniques.
Innovation Solution
A storage controller optimizes file storage by reordering columns based on historical access patterns and selecting compression techniques dynamically, using a combination of heuristic models, closed-form optimization, and machine learning to minimize waste and enhance query performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual trial and error is used to determine data layout and compression techniques, then flexibility in optimization is maintained, but resource waste increases and query performance becomes suboptimal
Solution Approach 1:
The system automatically optimizes data layout and compression techniques by analyzing historical access patterns and executing compaction operations without manual intervention. The storage controller autonomously determines column ordering based on access patterns and selects compression techniques dynamically, eliminating the need for manual trial and error while optimizing both query performance and resource utilization.
Solution Approach 2:
The system continuously monitors historical access patterns and uses this feedback to dynamically adjust data layout and compression strategies. By analyzing actual query patterns and adjusting the compaction plan accordingly, the system achieves optimal performance while minimizing resource waste through iterative improvement based on real usage data.
2Productivity
If columns are stored in initial schema ordering, then implementation simplicity is maintained, but query efficiency decreases due to non-optimal data access patterns
Solution Approach 1:
The system performs preliminary analysis of historical access patterns before executing compaction operations. By pre-determining the optimal column ordering based on past query patterns, the system prepares an optimized storage layout in advance, which then improves query efficiency without requiring complex real-time adjustments during data access operations.
Solution Approach 2:
The system changes the column ordering parameter from the initial schema-based arrangement to an access-pattern-driven arrangement. By dynamically adjusting this fundamental storage parameter based on historical access data, the system achieves significant query efficiency improvements while managing complexity through automated parameter optimization.
3Loss of energy
If compression techniques are selected manually, then control over storage optimization is maintained, but computational resources are wasted and optimal compression is not achieved
Solution Approach 1:
The storage controller automatically evaluates and selects compression techniques by analyzing the data characteristics and historical access patterns. The system self-determines which compression algorithm provides the best balance between compression ratio and computational cost, eliminating manual selection and optimizing storage efficiency without wasting computational resources on suboptimal compression choices.
Solution Approach 2:
The system dynamically changes the compression technique parameter based on data characteristics and access patterns. By automatically adjusting this parameter to match the specific needs of different datasets and query patterns, the system achieves optimal compression while minimizing computational overhead compared to manual or fixed compression strategies.
4Quantity of substance
If data is stored without compaction optimization, then storage simplicity is maintained, but file size increases and storage efficiency decreases
Solution Approach 1:
The system performs preliminary compaction operations that reorganize and compress data before it is fully written to storage. By executing compaction in advance based on predicted access patterns, the system reduces the final storage size and improves efficiency without requiring complex ongoing compaction operations during normal data access.
Solution Approach 2:
The compaction strategy dynamically adjusts based on historical access patterns and data characteristics. The system adapts the compaction operations to match actual usage patterns, achieving high storage efficiency while managing complexity through dynamic, data-driven compaction rather than static or overly complex compaction schemes.
Data Source
AI summary
A method for optimizing file storage includes receiving columnar data to store at a columnar data store with columns ordered with an initial ordering. The method includes determining, based on historical access patterns for the columnar data store, an updated ordering for the columns. The method includes storing the columnar data at a first location of the columnar data store using the updated ordering. The method includes determining that the stored columnar data is to be compacted and compressing at least a portion of the columnar data using each of a plurality of compression techniques. The method includes, based on compressing the at least a portion of the columnar data, selecting one of the plurality of compression techniques. The method includes storing the columnar data at a second location of the columnar data store using the selected one of the plurality of compression techniques.