Concurrent workloads often extract insights from high-throughput, real-time data streams. Existing stream processing engines isolate each query's resources, ensuring robust performance but incurring high infrastructure costs. In contrast, sharing work reduces the amount of necessary resources but introduces inter-query interference, leading to performance degradation for some queries. We introduce FunShare, a stream-processing system that improves resource efficiency without compromising performance by dynamically grouping queries based on their performance characteristics. FunShare strategically relaxes query interdependencies and minimizes redundant computation while preserving individual query performance. It achieves this by using an adaptive optimization framework that monitors execution metrics, accurately estimates computation overlaps, and reconfigures execution plans on the fly in response to changes in the underlying data streams. Our evaluation demonstrates that FunShare minimizes resource consumption compared to isolated execution while maintaining or improving throughput for all queries.

Interactive applications require processing tens to hundreds of concurrent analytical queries within tight time constraints. In such setups, where high concurrency causes contention, work-sharing databases are critical for improving scalability and for bounding the increase in response time. However, as such databases share data access using full scans and expensive shared filters, they suffer from a data-access bottleneck that jeopardizes interactivity. We present SH2O: a novel data-access operator that addresses the data-access bottleneck of work-sharing databases. SH2O is based on the idea that an access pattern based on judiciously selected multidimensional ranges can replace a set of shared filters. To exploit the idea in an efficient and scalable manner, SH2O uses a three-tier approach: i) it uses spatial indices to efficiently access the ranges without overfetching, ii) it uses an optimizer to choose which filters to replace such that it maximizes cost-benefit for index accesses, and iii) it exploits partitioning schemes and independently accesses each data partition to reduce the number of filters in the access pattern. Furthermore, we propose a tuning strategy that chooses a partitioning and indexing scheme that minimizes SH2O's cost for a target workload. Our evaluation shows a speedup of 1.8-22.2 for batches of hundreds of data-access-bound queries.

The growing demand for data-intensive decision support and the migration to multi-tenant infrastructures put databases under the stress of high analytical query load. The requirement for high throughput contradicts the traditional design of query-at-a-time databases that optimize queries for efficient serial execution. Sharing work across queries presents an opportunity to reduce the total cost of processing and therefore improve throughput with increasing query load. Systems can share work either by assessing all opportunities and restructuring batches of queries ahead of execution, or by inspecting opportunities in individual incoming queries at runtime: the former strategy scales poorly to large query counts, as it requires expensive sharing-aware optimization, whereas the latter detects only a subset of the opportunities. Both strategies fail to minimize the cost of processing for large and ad-hoc workloads. This paper presents RouLette, a specialized intelligent engine for multi-query execution that addresses, through runtime adaptation, the shortcomings of existing work-sharing strategies. RouLette scales by replacing sharing-aware optimization with adaptive query processing, and it chooses opportunities to explore and exploit by using reinforcement learning. RouLette also includes optimizations that reduce the adaptation overhead. RouLette increases throughput by 1.6-28.3x, compared to a state-of-the-art query-at-a-time engine, and up to 6.5x, compared to sharing-enabled prototypes, for multi-query workloads based on the schema of TPC-DS.

Traditionally, analytical database engines have used task parallelism provided by modern multisocket multicore CPUs for scaling query execution. Over the past few years, GPUs have started gaining traction as accelerators for processing analytical queries due to their massively data-parallel nature and high memory bandwidth. Recent work on designing join algorithms for CPUs has shown that carefully tuned join implementations that exploit underlying hardware can outperform naive, hardware-oblivious counterparts and provide excellent performance on modern multicore servers. However, there has been no such systematic analysis of hardware-conscious join algorithms for GPUs that systematically explores the dimensions of partitioning (partitioned versus non-partitioned joins), data location (data fitting and not fitting in GPU device memory), and access pattern (skewed versus uniform). In this paper, we present the design and implementation of a family of novel, partitioning-based GPU-join algorithms that are tuned to exploit various GPU hardware characteristics for working around the two main limitations of GPUs–limited memory capacity and slow PCIe interface. Using a thorough evaluation, we show that: i) hardware-consciousness plays a key role in GPU joins similar to CPU joins and our join algorithms can process 1 Billion tuples/second even if no data is GPU resident, ii) radix partitioning-based GPU joins that are tuned to exploit GPU hardware can substantially outperform non-partitioned hash joins, iii) hardware-conscious GPU joins can effectively overcome GPU limitations and match, or even outperform, state-of-the-art CPU joins.

Database systems serve a wide range of use cases efficiently, but require data to be loaded and adapted to the system's execution engine. This pre-processing step is a bottleneck to the analysis of the increasingly large and heterogeneous datasets. Therefore, numerous research efforts advocate for querying each dataset in situ,i.e., without pre-loading it in a DBMS. On the other hand, performing analysis over raw data entails numerous overheads because of the potentially inefficient data representations. In this paper, we investigate the effect of vector processing on raw data querying. We enhance the operators of a query engine to use SIMD operations. Specifically, we examine the effect of SIMD on two different cases: the scan operators that perform the CPU-intensive task of input parsing, and the part of the query pipeline that performs a selection and computes an aggregate. We show that a vectorized approach has a lot of potential to improve performance, which nevertheless comes with trade-offs.