Database systems often rely on historical query traces to perform workload-based performance tuning. However, real production workloads are time-evolving, making historical queries ineffective for optimizing future workloads. To address this challenge, we propose SIBYL, an end-to-end machine learning-based framework that accurately forecasts a sequence of future queries, with the entire query statements, in various prediction windows. Drawing insights from real-workloads, we propose template-based featurization techniques and develop a stacked-LSTM with an encoder-decoder architecture for accurate forecasting of query workloads. We also develop techniques to improve forecasting accuracy over large prediction windows and achieve high scalability over large workloads with high variability in arrival rates of queries. Finally, we propose techniques to handle workload drifts. Our evaluation on four real workloads demonstrates that SIBYL can forecast workloads with an 87.3% median F1 score, and can result in 1.7× and 1.3× performance improvement when applied to materialized view selection and index selection applications, respectively.

This paper presents MLOS (ML Optimized Systems), a flexible framework that bridges the gap between benchmarking, experimentation, and optimization of software systems. It allows users to create one-click benchmarking and experimentation scenarios for multi-VM setups in the cloud with optional standard and custom metrics collection and data management of the results. MLOS provides a collection of pluggable optimizers (ML or otherwise) for efficiently exploring the configuration space and finding optimal values for parameters across the entire software stack, including VM, OS kernel, and userland applications. It has a convenient lightweight interface for data storage, access, and visualization for a user-friendly notebook experience. These features make it a useful platform for both systems developers and auto-tuning researchers. MLOS is an active open-source project and is being used within Azure Data. A video demonstrating MLOS is available at https://aka.ms/MLOS/VLDB-2024-demo-video.

Large scale analytics engines have become a core dependency for modern data-driven enterprises to derive business insights and drive actions. These engines support a large number of analytic jobs processing huge volumes of data on a daily basis, and workloads are often inundated with overlapping computations across multiple jobs. Reusing common computation is crucial for efficient cluster resource utilization and reducing job execution time. Detecting common computation is the first and key step for reducing this computational redundancy. However, detecting equivalence on large-scale analytics engines requires efficient and scalable solutions that are fully automated. In addition, to maximize computation reuse, equivalence needs to be detected at the semantic level instead of just the syntactic level (i.e., the ability to detect semantic equivalence of seemingly different-looking queries). Unfortunately, existing solutions fall short of satisfying these requirements. In this paper, we take a major step towards filling this gap by proposing GEqO, a portable and lightweight machine-learning-based framework for efficiently identifying semantically equivalent computations at scale. GEqO introduces two machine-learning-based filters that quickly prune out nonequivalent subexpressions and employs a semi-supervised learning feedback loop to iteratively improve its model with an intelligent sampling mechanism. Further, with its novel database-agnostic featurization method, GEqO can transfer the learning from one workload and database to another. Our extensive empirical evaluation shows that, on TPC-DS-like queries, GEqO yields significant performance gains-up to 200x faster than automated verifiers-and finds up to 2x more equivalences than optimizer and signature-based equivalence detection approaches.

Enterprises are creating domain-specific knowledge graphs by curating and integrating their business data from multiple sources. Ontologies provide a semantic abstraction for such knowledge graphs to describe their data in terms of the entities involved and their relationships. There has been a lot of effort to build systems that enable efficient querying over knowledge graphs, represented as property graphs. However the problem of schema optimization in the property graph setting has been largely ignored. In this work, we show that graph schema design has significant impact on query performance, and propose two algorithms to generate an optimized property graph schema from the domain ontology. To the best of our knowledge, we are the first to present an ontology-driven approach for property graph schema optimization. The rich semantic relationships in an ontology contain a variety of opportunities to reduce edge traversals and consequently improve the graph query performance. Our experimental study with two real-world knowledge graphs shows that our algorithms produce high-quality schemas, achieving up to 2 orders of magnitude speed-up compared to alternative schema designs.

Hybrid complex analytics workloads typically include (i) data management tasks (joins, selections, etc. ), easily expressed using relational algebra (RA)-based languages, and (ii) complex analytics tasks (regressions, matrix decompositions, etc.), mostly expressed in linear algebra (LA) expressions. Such workloads are common in many application areas, including scientific computing, web analytics, and business recommendation. Existing solutions for evaluating hybrid analytical tasks - ranging from LA-oriented systems, to relational systems (extended to handle LA operations), to hybrid systems - either optimize data management and complex tasks separately, exploit RA properties only while leaving LA-specific optimization opportunities unexploited, or focus heavily on physical optimization, leaving semantic query optimization opportunities unexplored. Additionally, they are not able to exploit precomputed (materialized) results to avoid recomputing (part of) a given mixed (RA and/or LA) computation.

Big data applications increasingly involve diverse datasets, conforming to different data models. Such datasets are routinely hosted in heterogeneous stores, each capable of handling one or a few data models, and each efficient for some, but not all, kinds of data processing. Systems capable of exploiting disparate data in this fashion are usually termed polystores. A current limitation of polystores is that applications are written taking into account which part of the data is stored in which store and how. This fails to take advantage of ( i ) possible redundancy, when the same data may be accessible (with different performance) from distinct data stores; ( ii ) previous query results (in the style of materialized views), which may be available in the stores. We propose to demonstrate ESTOCADA [4], a novel approach that can be used in a polystore setting to transparently enable each query to benefit from the best combination of stored data and available processing capabilities. The system leverages recent advances in the area of view-based query rewriting under constraints, which we use to describe the various data models and stored data.

Big data applications routinely involve diverse datasets: relations flat or nested, complex-structure graphs, documents, poorly structured logs, or even text data. To handle the data, application designers usually rely on several data stores used side-by-side, each capable of handling one or a few data models, and each very efficient for some, but not all, kinds of processing on the data. A current limitation is that applications are written taking into account which part of the data is stored in which store and how. This fails to take advantage of (i) possible redundancy, when the same data may be accessible (with different performance) from distinct data stores; (ii) partial query results (in the style of materialized views) which may be available in the stores. We present ESTOCADA, a novel approach connecting applications to the potentially heterogeneous systems where their input data resides. ESTOCADA can be used in a polystore setting to transparently enable each query to benefit from the best combination of stored data and available processing capabilities. ESTOCADA leverages recent advances in the area of view-based query rewriting under constraints, which we use to describe the various data models and stored data. Our experiments illustrate the significant performance gains achieved by ESTOCADA.