Fast Column Scans: Paged Indices for In-Memory Column Stores

Faust, Martin; Schwalb, David; Krueger, Jens

doi:10.1007/978-3-319-13960-9_2

Cited by 3 publications

(3 citation statements)

References 7 publications

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“…A more suitable approach is discussed by Faust et al [9]. They propose a Paged Index to reduce the amount of data to be processed during column scans in main-memory column stores.…”

Section: Related Workmentioning

confidence: 98%

Gratin

Paradies

Rudolf

Bornhövd

et al. 2014

Proceedings of Workshop on GRAph Data Management Experiences and Systems

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Native graph query and processing capabilities have become indispensable for modern business applications in enterprise-critical operations on data that is stored in relational database management systems. Traversal operations are a basic ingredient of graph algorithms and graph queries. As a consequence, they are fundamental for querying graph data in a relational database management system.In this paper we present GRATIN, a concise secondary index structure to speedup graph traversals in main-memory column stores. Conventional approaches for graph traversals rely on repeated full column scans, making it an inefficient approach for deep traversals on very large graphs. To tackle this challenge, we devise a novel and adaptive block-based index to handle graphs efficiently. Most importantly, GRATIN is updateable in constant time and allows supporting evolving graphs with frequent updates to the graph topology.We conducted an extensive evaluation on real-world data sets from different domains for a large variety of traversal queries. Our experiments show improvements of up to an order of magnitude compared to a scan-based traversal algorithm.

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“…A more suitable approach is discussed by Faust et al [9]. They propose a Paged Index to reduce the amount of data to be processed during column scans in main-memory column stores.…”

Section: Related Workmentioning

confidence: 98%

Gratin

Paradies

Rudolf

Bornhövd

et al. 2014

Proceedings of Workshop on GRAph Data Management Experiences and Systems

View full text Add to dashboard Cite

show abstract

“…Other questions such as how the usage of an index structure changes the susceptibility of a workload to modifications in the placement strategies and decisions at runtime can be studied. The index types we have implemented are the B‐Plus Tree 36 Index as commonly found in traditional relational databases, as well as the Group Key Index as found in emerging in‐memory database systems 37 . We use native operating system features to instrument the execution and report and aggregate the collected counters and to automate the measurements and make them reproducible.…”

Section: Developer Experiencementioning

confidence: 99%

“…Tree 36 Index as commonly found in traditional relational databases, as well as the Group Key Index as found in emerging in-memory database systems. 37 We use native operating system features to instrument the execution and report and aggregate the collected counters and to automate the measurements and make them reproducible.…”

Section: Page Replication For Scale-up Systems (Presley)mentioning

confidence: 99%

Improving the accessibility of NUMA‐aware C++ application development based on the PGASUS framework

Plauth

Eberhardt

Grapentin

et al. 2022

Concurrency and Computation

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Certain workloads such as in-memory databases are inherently hard to scale-out and rely on cache-coherent scale-up non-uniform memory access (NUMA) systems to keep up with the ever-increasing demand for compute resources. However, many parallel programming frameworks such as OpenMP do not make efficient use of large scale-up NUMA systems as they do not consider data locality sufficiently. In this work, we present PGASUS, a C++ framework for NUMA-aware application development that provides integrated facilities for NUMA-aware task parallelism and data placement.The framework is based on an extensive review of parallel programming languages and frameworks to incorporate the best practices of the field. In a comprehensive evaluation, we demonstrate that PGASUS provides average performance improvements of 1.56× and peak performance improvements of up to 4.67×across a wide range of workloads. K E Y W O R D Snon-uniform memory access, programming model, scale-up computing INTRODUCTIONThe ever-increasing demand for compute resources necessitates continuous improvements in computer technology. Even though accelerators such as graphics processing units (GPUs) and field-programmable gate arrays (FPGAs) are commonly used in many data-intensive applications, the majority of workloads still rely on the flexibility and versatility of multicore central processing units (CPUs). 1 Many of those CPU-based workloads can be adapted to scale-out across multiple systems to provide sufficient compute resources. Still, certain workloads such as in-memory databases 2 or de Novo genome assembly 3 are inherently hard to scale out and therefore require as many resources as possible in a single scale-up system.The most basic multi-CPU systems have employed uniform memory access (UMA) architectures, where multiple multicore CPUs are attached to a shared memory subsystem through facilities such as a front-side bus (FSB). All ×86-based systems until the introduction of the SledgeHammer and Nehalem micro architecture in 2009 were built with this memory architecture. From a software developers' perspective, UMA systems align conveniently with the shared memory programming model. Unfortunately, sharing the memory subsystem with all other multicore CPUs severely limits the scalability of multiprocessor systems, both in the number of multicore CPUs as well as in the amount of memory that can be accommodated in a single system.Non-uniform memory access (NUMA) systems avoid this bottleneck, as each multicore CPU is equipped with dedicated memory controllers.Memory attached to other multicore CPUs can still be accessed transparently through inter-CPU interconnects such as Ultra Path Interconnect (UPI), Infinity Fabric (IF), and Power with A-bus, X-bus, OpenCAPI, and NVLink (PowerAXON). However, remote memory access operations incur increased latencies and reduced bandwidth, especially on systems with more than four multicore CPUs where fully meshed connectivity among CPUs is no longer feasible. State-of-the-art NUMA systems support up to 32 multicore C...

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