Optimizing Intermittent Query Processing for Efficient Resource Utilization and Deadline Satisfaction

Core Concepts

Scheduling intermittent query processing in batches can significantly reduce the overall computation cost while meeting the query deadlines, compared to eager tuple-by-tuple or micro-batch processing.

Abstract

The paper presents scheduling schemes for processing queries in batches to minimize the overall computation cost while meeting the query deadlines. The key highlights are: For a single query scenario with known parameters, the paper proposes an algorithm to determine the optimal batch sizes that minimize the computation cost while satisfying the deadline. This is done by processing all the tuples in the last batch at the deadline, and processing the remaining tuples in one or more batches before the window end time. For a dynamic scenario with multiple queries, each with its own deadline, the paper proposes a scheme to determine a minimum batch size for each query. This minimum batch size is chosen such that the overall computation cost does not exceed a predefined factor of the minimum cost for processing all tuples in a single batch. The paper then presents a non-preemptive scheduling algorithm based on Least Laxity First (LLF) to schedule the batches of different queries, considering their deadlines and available input tuples. The proposed scheduling schemes have been implemented on top of Apache Spark and evaluated using TPC-H data and queries. The results show orders of magnitude improvement in computation cost compared to naive Spark streaming. The paper also discusses how the intermittent processing avoids memory issues that can arise in stream processing engines when processing large windows.

Stats

The total number of tuples to be processed for a query is denoted as numTupleTotal. The minimum computation cost for processing all the tuples in a single batch is denoted as minCompCost. The slack time available for a query, defined at the window end time, is denoted as slackTime.

Quotes

"For such queries with a longer deadline, as the results of the query are needed only at the deadline, tuples can be processed in larger batches." "Large window operations in streaming mode, often require data to be kept in memory for efficient processing. There is thus a risk of encountering insufficient memory errors and the same was observed in our experiments presented in Section 7. Such memory issues are avoided in our processing scheme as processing is done intermittently on larger batches, allowing the use of algorithms that do not require the entire data to be memory resident."

Key Insights Distilled From

Scheduling of Intermittent Query Processing

by Saranya C,Su... at arxiv.org 04-23-2024

https://arxiv.org/pdf/2306.06678.pdf

Deeper Inquiries

How can the scheduling algorithms be extended to handle queries with dependencies, such as those involving joins between multiple input streams

To handle queries with dependencies, such as those involving joins between multiple input streams, the scheduling algorithms can be extended by incorporating a dependency graph. Each query can be represented as a node in the graph, and the dependencies between queries can be represented as edges. By analyzing the dependencies, the scheduler can ensure that queries with dependencies are executed in the correct order to meet the deadline constraints. When a query with dependencies is added to the system, the scheduler can check the dependency graph to determine the order in which the queries should be processed. This ensures that queries dependent on the results of other queries are executed only after the required data is available. By considering dependencies, the scheduler can optimize the execution order to minimize the overall computation cost while meeting the query deadlines.

How can the Resource Slack Factor (δRSF) be automatically adjusted based on the system load to dynamically balance computation cost and deadline satisfaction

To automatically adjust the Resource Slack Factor (δRSF) based on the system load, a feedback control mechanism can be implemented. The system can monitor key performance metrics such as CPU utilization, memory usage, and query execution times. Based on these metrics, the system can dynamically adjust the δRSF to balance the computation cost and deadline satisfaction. The feedback control mechanism can continuously analyze the system load and performance metrics. If the system is under heavy load and queries are struggling to meet their deadlines, the δRSF can be increased to allow for more flexibility in scheduling. Conversely, if the system load is low and queries are consistently meeting their deadlines with a lower δRSF, the system can decrease the δRSF to optimize resource utilization. By dynamically adjusting the δRSF based on real-time system load and performance metrics, the scheduler can adapt to changing conditions and ensure efficient query processing while meeting deadline constraints.

What other techniques can be explored to further reduce the computation cost, such as leveraging partial results from previous windows or exploiting query similarities across recurring queries

To further reduce the computation cost, additional techniques can be explored, such as leveraging partial results from previous windows and exploiting query similarities across recurring queries. Leveraging Partial Results: Instead of recomputing the entire query from scratch in each window, the scheduler can store and reuse partial results from previous windows. By incrementally updating the results and only processing new data, the computation cost can be significantly reduced. This approach is particularly effective for queries with overlapping windows or incremental updates. Exploiting Query Similarities: For recurring queries that share similar computation patterns, the scheduler can identify common subqueries or operations and optimize their execution. By recognizing similarities across queries, the scheduler can reuse intermediate results, share computation resources, and streamline the overall processing pipeline. This approach can lead to substantial savings in computation cost and improved performance for recurring query workloads. By implementing these techniques in the scheduling algorithms, the system can achieve further optimization in terms of computation cost, resource utilization, and deadline satisfaction.