Learning a Partitioning Advisor with Deep Reinforcement Learning

Commercial data analytics products such as Microsoft Azure SQL Data Warehouse or Amazon Redshift provide ready-to-use scale-out database solutions for OLAP-style workloads in the cloud. While the provisioning of a database cluster is usually fully automated by cloud providers, customers typically still have to make important design decisions which were traditionally made by the database administrator such as selecting the partitioning schemes. In this paper we introduce a learned partitioning advisor for analytical OLAP-style workloads based on Deep Reinforcement Learning (DRL). The main idea is that a DRL agent learns its decisions based on experience by monitoring the rewards for different workloads and partitioning schemes. We evaluate our learned partitioning advisor in an experimental evaluation with different databases schemata and workloads of varying complexity. In the evaluation, we show that our advisor is not only able to find partitionings that outperform existing approaches for automated partitioning design but that it also can easily adjust to different deployments. This is especially important in cloud setups where customers can easily migrate their cluster to a new set of (virtual) machines

Data-Efficient Learned Database Components

While databases are the backbone of many software systems, database components such as query optimizers often have to be redesigned to meet the increasing variety in workloads, data and hardware designs, which incurs significant engineering efforts to adapt their design. Recently, it was thus proposed to replace DBMS components such as optimizers, cardinality estimators, etc. by ML models, which not only eliminates the engineering efforts but also provides superior performance for many components. The predominant approach to derive such learned components is workload-driven learning where ten thousands of queries have to be executed first to derive the necessary training data. Unfortunately, the training data collection, which can take days even for medium-sized datasets, has to be repeated for every new database (i.e., the combination of dataset, schema and workload) a component should be deployed for. This is especially problematic for cloud databases such as Snowflake or Redshift since this effort has to be incurred for every customer. This dissertation thus proposes data-efficient learned database components, which either reduce or fully eliminate the high costs of training data collection for learned database components. In particular, three directions are proposed in this dissertation, namely (i) we first aim to reduce the number of training queries needed for workload-driven components before we (ii) propose data-driven learning, which uses the data stored in the database as training data instead of queries, and (iii) introduce zero-shot learned components, which can generalize to new databases out-of-the-box, s.t. no training data collection is required. First, we strive to reduce the number of training queries required for workload-driven components by using simulation models to convey the basic tradeoffs of the underlying problem, e.g., that in database partitioning the network costs of shuffling tuples over the network for joins is the dominating factor. This substantially reduces the number of training queries since the basic principles are already covered by the simulation model and thus only subtleties not covered in the simulation model have to be learned by observing query executions, which we will demonstrate for the problem of database partitioning. An alternative direction is to incorporate domain knowledge (e.g., in a cost model we could encode that scan costs increase linearly with the number of tuples) into components by designing them using differentiable programming. This significantly reduces the number of learnable parameters and thus also the number of required training queries. We demonstrate the feasibility of the approach for the problem of cost estimation in databases. While both approaches reduce the number of training queries, there is still a significant number of training queries required for unseen databases. This motivates our second approach of data-driven learning. In particular, we propose to train the database component by learning the data distribution present in a database instead of observing query executions. This not only completely eliminates the need to collect training data queries but can even improve the state-of-the-art in problems such as cardinality estimation or AQP. While we demonstrate the applicability to a wide range of additional database tasks such as the completion of incomplete relational datasets, data-driven learning is only useful for problems where the data distribution provides sufficient information for the underlying database task. However, for tasks where observations of query executions are indispensable such as cost estimation, data-driven learning cannot be leveraged. In a third direction, we thus propose zero-shot learned database components, which are applicable to a broader set of tasks including those that require observations of queries. In particular, motivated by recent advances in transfer learning, we propose to pretrain a model once on a variety of databases and workloads and thus allow the component to generalize to unseen databases out-of-the-box. Hence, similar to data-driven learning no training queries have to be collected. In this dissertation, we demonstrate that zero-shot learning can indeed yield learned cost models which can predict query latencies on entirely unseen databases more accurately than state-of-the-art workload-driven approaches, which require ten thousands of query executions on every unseen database. Overall, the proposed techniques yield state-of-the-art performance for many database tasks while significantly reducing or completely eliminating the expensive training data collection for unseen databases. However, while the proposed directions address the prevalent data-inefficiency of learned database components, there are still many opportunities to improve learned components in the future. First, the robustness and debuggability of learned components should be improved since as of today they do not offer the same transparency as standard code in databases, which can render the components less attractive to be deployed in production systems. Moreover, to increase the applicability of data-driven models it is desirable to increase the coverage of supported queries, e.g., queries involving wildcard predicates on string columns, which are currently not supported by data-driven learning. Finally, we envision that a broader set of tasks should be supported in the future by zero-shot models (e.g., query optimization) potentially converging towards complete zero-shot learned systems

DeepDB: Learn from Data, not from Queries!

The typical approach for learned DBMS components is to capture the behavior by running a representative set of queries and use the observations to train a machine learning model. This workload-driven approach, however, has two major downsides. First, collecting the training data can be very expensive, since all queries need to be executed on potentially large databases. Second, training data has to be recollected when the workload and the data changes. To overcome these limitations, we take a different route: we propose to learn a pure data-driven model that can be used for different tasks such as query answering or cardinality estimation. This data-driven model also supports ad-hoc queries and updates of the data without the need of full retraining when the workload or data changes. Indeed, one may now expect that this comes at a price of lower accuracy since workload-driven models can make use of more information. However, this is not the case. The results of our empirical evaluation demonstrate that our data-driven approach not only provides better accuracy than state-of-the-art learned components but also generalizes better to unseen queries

ReStore - Neural Data Completion for Relational Databases

Classical approaches for OLAP assume that the data of all tables is complete. However, in case of incomplete tables with missing tuples, classical approaches fail since the result of a SQL aggregate query might significantly differ from the results computed on the full dataset. Today, the only way to deal with missing data is to manually complete the dataset which causes not only high efforts but also requires good statistical skills to determine when a dataset is actually complete. In this paper, we propose an automated approach for relational data completion called ReStore using a new class of (neural) schema-structured completion models that are able to synthesize data which resembles the missing tuples. As we show in our evaluation, this efficiently helps to reduce the relative error of aggregate queries by up to 390 on real-world data compared to using the incomplete data directly for query answering

One Model to Rule them All: Towards Zero-Shot Learning for Databases

In this paper, we present our vision of so called zero-shot learn- ing for databases which is a new learning approach for database components. Zero-shot learning for databases is inspired by recent advances in transfer learning of models such as GPT-3 and can support a new database out-of-the box without the need to train a new model. Furthermore, it can easily be extended to few-shot learning by further retraining the model on the unseen database. As a first concrete contribution in this paper, we show the feasi- bility of zero-shot learning for the task of physical cost estimation and present very promising initial results. Moreover, as a second contribution we discuss the core challenges related to zero-shot learning for databases and present a roadmap to extend zero-shot learning towards many other tasks beyond cost estimation or even beyond classical database systems and workloads

One Model to Rule them All: Towards Zero-Shot Learning for Databases

In this paper, we present our vision of so called zero-shot learn- ing for databases which is a new learning approach for database components. Zero-shot learning for databases is inspired by recent advances in transfer learning of models such as GPT-3 and can support a new database out-of-the box without the need to train a new model. Furthermore, it can easily be extended to few-shot learning by further retraining the model on the unseen database. As a first concrete contribution in this paper, we show the feasi- bility of zero-shot learning for the task of physical cost estimation and present very promising initial results. Moreover, as a second contribution we discuss the core challenges related to zero-shot learning for databases and present a roadmap to extend zero-shot learning towards many other tasks beyond cost estimation or even beyond classical database systems and workloads

Towards Foundation Models for Relational Databases Vision Paper

Tabular representation learning has recently gained a lot of attention. However, existing approaches only learn a representation from a single table, and thus ignore the potential to learn from the full structure of relational databases, including neighboring tables that can contain important information for a contextualized representation. Moreover, current models are significantly limited in scale, which prevents that they learn from large databases. In this paper, we thus introduce our vision of relational representation learning, that can not only learn from the full relational structure, but also can scale to larger database sizes that are commonly found in real-world. Moreover, we also discuss opportunities and challenges we see along the way to enable this vision and present initial very promising results. Overall, we argue that this direction can lead to foundation models for relational databases that are today only available for text and images