Parquet is the most commonly used file format to store data in a columnar, binary structure. The format also supports storing nested data in this flattened columnar layout. However, many query engines either do not support nested data or process it with substantially worse performance than relational data. In this work, we close this gap and present a new way to leverage relational query engines for nested data that is stored in this flat columnar file format. Specifically, we demonstrate how to process nested Parquet files much more efficiently. Our approach does not store a copy of the data in an internal format but reads directly from the Parquet file. During query computation, the required flat columns are scanned independently and the nesting is reconstructed using joins with on-the-fly generated join keys. Our approach can be easily integrated into existing query engines to support querying nested Parquet files. Furthermore, we achieve orders of magnitude faster analytical query performance than existing solutions, which makes it a valuable addition.

Query performance prediction is used for scheduling, resource scaling, tenant placement, and various other use-cases. Here, the main goal is to estimate the execution time of a query without running it. To be effective, predictors need to be both accurate and fast. In contrast, neural networks that were used in recent work deliver very accurate predictions but suffer from high latency. In this work, we propose the Tuple Time Tree (T3), a new model that is both accurate and fast. It is orders of magnitude faster than comparable methods and has competitive accuracy to state-of-the-art approaches. Additionally, T3 works for new database instances without re-training because it generalizes across database instances. We achieve T3's speed by relying on a low-latency decision tree model that is compiled to native machine code. We maintain high accuracy with two novel techniques: pipeline-based query plan representation and tuple-centric prediction targets. In our pipeline-based query plan representation, T3 decomposes query plans into pipelines. Then, T3 predicts the execution time of each pipeline individually, instead of the whole query in one step. With tuple-centric prediction targets, T3 predicts the expected time it takes to push a single tuple through a pipeline. It then multiplies this predicted value by the input cardinality of the pipeline to estimate its execution time. As a result, T3 achieves state-of-the-art accuracy with a low-latency decision tree model.