Sharing Data and Work Across Concurrent Analytical Queries

Today's data deluge enables organizations to collect massive data, and analyze it with an ever-increasing number of concurrent queries. Traditional data warehouses (DW) face a challenging problem in executing this task, due to their query-centric model: each query is optimized and executed independently. This model results in high contention for resources. Thus, modern DW depart from the query-centric model to execution models involving sharing of common data and work. Our goal is to show when and how a DW should employ sharing. We evaluate experimentally two sharing methodologies, based on their original prototype systems, that exploit work sharing opportunities among concurrent queries at run-time: Simultaneous Pipelining (SP), which shares intermediate results of common sub-plans, and Global Query Plans (GQP), which build and evaluate a single query plan with shared operators. First, after a short review of sharing methodologies, we show that SP and GQP are orthogonal techniques. SP can be applied to shared operators of a GQP, reducing response times by 20%-48% in workloads with numerous common sub-plans. Second, we corroborate previous results on the negative impact of SP on performance for cases of low concurrency. We attribute this behavior to a bottleneck caused by the push-based communication model of SP. We show that pull-based communication for SP eliminates the overhead of sharing altogether for low concurrency, and scales better on multi-core machines than push-based SP, further reducing response times by 82%-86% for high concurrency. Third, we perform an experimental analysis of SP, GQP and their combination, and show when each one is beneficial. We identify a trade-off between low and high concurrency. In the former case, traditional query-centric operators with SP perform better, while in the latter case, GQP with shared operators enhanced by SP give the best results.

Reactive and Proactive Sharing Across Concurrent Analytical Queries

Anastasia Ailamaki, Manoussos Gavriil Athanassoulis, Matthaios Alexandros Olma, Iraklis Psaroudakis

Today an ever increasing amount of data is collected and analyzed by researchers, businesses, and scientists in data warehouses (DW). In addition to the data size, the number of users and applications querying data grows exponentially. The increasing concurrency is itself a challenge in query execution, but also introduces an opportunity favoring synergy between concurrent queries. Traditional execution engines of DW follows a query-centric approach, where each query is optimized and executed independently. On the other hand, workloads with increased concurrency have several queries with common parts of data and work, creating the opportunity for sharing among concurrent queries. Sharing can be reactive to the inherently existing sharing opportunities, or proactive by redesigning query operators to maximize the sharing opportunities. This demonstration showcases the impact of proactive and reactive sharing by comparing and integrating representative state-of-the-art techniques: Simultaneous Pipelining (SP), for reactive sharing, which shares intermediate results of common sub-plans, and Global Query Plans (GQP) for proactive sharing, which build and evaluate a single query plan with shared operators. We visually demonstrate, in an interactive interface, the behavior of both sharing approaches on top of a state-of-the-art storage engine using the original prototypes. We show that pull-based sharing for SP eliminates the serialization point imposed by the original push-based approach. Then, we compare, through a sensitivity analysis, the performance of SP and GQP. Finally, we show that SP can improve the performance of GQP for a query mix with common sub-plans.

2014

Scaling Up Concurrent Analytical Workloads on Multi-Core Servers

Iraklis Psaroudakis

Today, an ever-increasing number of researchers, businesses, and data scientists collect and analyze massive amounts of data in database systems. The database system needs to process the resulting highly concurrent analytical workloads by exploiting modern multi-socket multi-core processor systems with non-uniform memory access (NUMA) architectures and increasing memory sizes. Conventional execution engines, however, are not designed for many cores, and neither scale nor perform efficiently on modern multi-core NUMA architectures. Firstly, their query-centric approach, where each query is optimized and evaluated independently, can result in unnecessary contention for hardware resources due to redundant work found across queries in highly concurrent workloads. Secondly, they are unaware of the non-uniform memory access costs and the underlying hardware topology, incurring unnecessarily expensive memory accesses and bandwidth saturation. In this thesis, we show how these scalability and performance impediments can be solved by exploiting sharing among concurrent queries and incorporating NUMA-aware adaptive task scheduling and data placement strategies in the execution engine. Regarding sharing, we identify and categorize state-of-the-art techniques for sharing data and work across concurrent queries at run-time into two categories: reactive sharing, which shares intermediate results across common query sub-plans, and proactive sharing, which builds a global query plan with shared operators to evaluate queries. We integrate the original research prototypes that introduce reactive and proactive sharing, perform a sensitivity analysis, and show how and when each technique benefits performance. Our most significant finding is that reactive and proactive sharing can be combined to exploit the advantages of both sharing techniques for highly concurrent analytical workloads. Regarding NUMA-awareness, we identify, implement, and compare various combinations of task scheduling and data placement strategies under a diverse set of highly concurrent analytical workloads. We develop a prototype based on a commercial main-memory column-store database system. Our most significant finding is that there is no single strategy for task scheduling and data placement that is best for all workloads. In specific, inter-socket stealing of memory-intensive tasks can hurt overall performance, and unnecessary partitioning of data across sockets involves an overhead. For this reason, we implement algorithms that adapt task scheduling and data placement to the workload at run-time. Our experiments show that both sharing and NUMA-awareness can significantly improve the performance and scalability of highly concurrent analytical workloads on modern multi-core servers. Thus, we argue that sharing and NUMA-awareness are key factors for supporting faster processing of big data analytical applications, fully exploiting the hardware resources of modern multi-core servers, and for more responsive user experience.

EPFL2016

Scalable Multi-Query Execution using Reinforcement Learning

Anastasia Ailamaki, Panagiotis Sioulas

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.

Association for Computing Machinery2021

Scaling Up Concurrent Analytical Workloads on Multi-Core Servers

Iraklis Psaroudakis

EPFL2016