Distributed partitioned joins are one of the most expensive operators in distributed DBMSs where a major part of the execution is attributed to network transfer costs. Although high-speed network technologies, such as RDMA, can lower this cost, they still come with significantly higher latency than local DRAM access. The emerging CXL interconnect protocol promises to provide direct and cache-coherent access to remote memory while offering byte-addressable memory access without CPU intervention. For short-distance communication in distributed DBMSs, CXL represents an interesting alternative for low-latency requirements. In this work, we explore how CXL can be leveraged for engine-internal communication and data exchange. We discuss and apply communication strategies to distributed joins. We emulate various CXL characteristics based on optimistic and pessimistic assumptions on the real performance of upcoming CXL devices and evaluate their impact on the execution of distributed joins. Our results show that CXL has the potential to improve distributed join performance.

The Python programming language has become very popular among data scientists because of its easy-to-learn syntax and rich ecosystem of libraries. Especially the Pandas framework is widely used for various data processing and analytics tasks. However, due to its memory management and eager evaluation Pandas does not scale and workstations quickly come to their limits even for moderate data set sizes. With Grizzly, we introduce a framework that produces SQL queries for operations on DataFrames, moving complexity from workstations to database servers. Grizzly allows to not only access data already stored in a database, but also to combine it with external data from files. Furthermore, users can use their own user-defined functions or use Grizzly’s model join feature to easily apply machine learning models to data, both being executed inside the database server. This allows for fast and scalable data analytics operations, even with a small workstation.

Modern big data applications integrate data from various sources. As a result, these datasets may not satisfy perfect constraints, leading to sparse schema information and non-optimal query performance. The existing approach of PatchIndexes enable the definition of approximate constraints and improve query performance by exploiting the materialized constraint information. As real world data warehouse workloads are often not limited to read-only queries, we enhance the PatchIndex structure towards an update-conscious design in this paper. Therefore, we present a sharded bitmap as the underlying data structure which offers efficient update operations, and describe approaches to maintain approximate constraints under updates, avoiding index recomputations and full table scans. In our evaluation, we prove that PatchIndexes provide more lightweight update support than traditional materialization approaches.

Abstract Cloud data warehouse systems lower the barrier to access data analytics. These applications often lack a database administrator and integrate data from various sources, potentially leading to data not satisfying strict constraints. Automatic schema optimization in self-managing databases is difficult in these environments without prior data cleaning steps. In this paper, we focus on constraint discovery as a subtask of schema optimization. Perfect constraints might not exist in these unclean datasets due to a small set of values violating the constraints. Therefore, we introduce the concept of a generic PatchIndex structure, which handles exceptions to given constraints and enables database systems to define these approximate constraints. We apply the concept to the environment of distributed databases, providing parallel index creation approaches and optimization techniques for parallel queries using PatchIndexes. Furthermore, we describe heuristics for automatic discovery of PatchIndex candidate columns and prove the performance benefit of using PatchIndexes in our evaluation.

Cloud infrastructures allow to dynamically adapt to workload changes by provisioning additional resources or deprovisioning resources to reduce costs. This offers also opportunities for scalable distributed data management. However, elastic scaling in databases requires to migrate or even repartition data. In this work, we present an approach implemented in Actian’s MPP solution VectorH that speeds up the elastic resizing process by minimizing partition reassignments while still achieving load balancing. Moreover, we describe a buffer matching and prefilling technique to further increase performance after the resize step. The experimental evaluation shows that our solution significantly outperforms the non-elastic way of scaling using a system restart by a factor of 2 up to 4 and reduces downtimes during resizing to less than one minute.