Database Management Systems (DBMSs) are the backbone for managing large volumes of data efficiently in the cloud. For providing high performance, many of the most complex DBMS components such as query optimizers or schedulers involve solving non-trivial problems. To tackle such problems, recent work has outlined a new direction of so-called learned DBMSs where core parts of DBMSs are being replaced by machine learning (ML) models which have shown to provide significant performance benefits. However, a major drawback of the current approaches to enabling learned DBMS components is that they not only cause very high overhead for training an ML model to replace a DBMS component but that the overhead occurs repeatedly which renders these approaches far from practical. In the first part talk, I will present our vision of zero-shot learned DBMSs to tackle these issues. In the second part, I will then outline very recent work on ML-augmented DBMSs to extend DBMS with new capabilities such as seamless querying of multimodal data which is composed of tables, text, and images.

Are these approaches successful?

For data-driven learning, Prof. Binning showed two works where significant improvements were measured. The multi-set convolutional network by Kipf et al. showed significantly better accuracy than classical techniques for cardinality estimation. Unfortunately, this network required a lot of queries to obtain the training data and had to be retrained if the data changed. Professor Binning’s Chair developed DeepDB, a data-driven approach that learns from the data distribution in and across tables. Traditionally, histograms are used to observe the data distribution, which lacks the inclusion of correlations between columns of a table. With their model, the researchers at Binning's Chair trained models to give cardinality estimations on a table, which can also be used collectively to estimate query cardinalities spanning multiple tables. The results are also significant, being four times more precise than PostgreSQL, only having an error of around 30% all whilst having a small model of a few megabytes.

The core challenge for zero-shot learning is to find ways to create generalized input data with which the model can be trained and later applied to unknown databases. In their approach, Binning and Hilprecht found a way to encode the data for query runtime prediction. Instead of using the query, they used the query plan. It includes more data on table size, like the row count and table width. They had hoped that the model learns, that the query runs longer the larger the input data is. This would then make the model also work on unknown databases, as this metadata is also available for other databases’ query plans. In their tests, they were able to show that their runtime estimation is much more accurate than the ones of PostgreSQL. Furthermore, they have tested this on real-world data instead of synthetically created data that many benchmarks in this field use.

So, what should I take away from this?

As databases move to the cloud, manual optimization will no longer be feasible. Instead, automated ways to adapt to the customers’ workload are needed. Machine learning offers multiple ways to do this, with Prof. Binning having presented three: Workload-driven, Data-driven, and Zero-shot learning. The first requires a lot of training data and, therefore, has too high costs to be trained (and retrained). Data-driven learning works only for use cases where the required knowledge can be learned from the data itself, and Zero-Shot learning uses generalized encoding of the input data to make its models applicable to unknown databases. Significant improvements were found in all approaches compared to traditional database techniques.

What should I be excited about?

Binning is excited about making further steps to use machine learning not only to make databases faster, but now also smarter. Using language models like GPT, further research will try to allow querying data that is not present in the table. As GPT models have a lot of common knowledge encoded in them, they can be seen as databases as well. Using these, queries that overreach the data stored in the table could be queried, or data that is present in datatypes not yet storable for databases (like images) can be queried and interpreted. Lots of exciting things to look forward to and reasons to stay updated on the topic of ML-Augmented Cloud Database Systems.