Enhancements to SQL server column stores

Larson, Per-Åke; Clinciu, Cipri; Fraser, Campbell; Hanson, Eric N.; Mokhtar, Mostafa; Nowakiewicz, Michal; Papadimos, Vassilis; Price, Susan; Rangarajan, Srikumar; Rusanu, Remus; Saubhasik, Mayukh

doi:10.1145/2463676.2463708

Cited by 43 publications

(30 citation statements)

References 7 publications

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“…[9,17,19,26,27]. It maps the value domain of one or more columns to a contiguous integer range [7,9,10,12,18,20]. This mapping replaces column values with unique integer codes and is stored in a separate data structure, the dictionary, which supports two access methods:…”

Section: Dictionaries In Column Storesmentioning

confidence: 99%

Interleaving with coroutines

et al. 2017

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Index join performance is determined by the efficiency of the lookup operation on the involved index. Although database indexes are highly optimized to leverage processor caches, main memory accesses inevitably increase lookup runtime when the index outsizes the last-level cache; hence, index join performance drops. Still, robust index join performance becomes possible with instruction stream interleaving: given a group of lookups, we can hide cache misses in one lookup with instructions from other lookups by switching among their respective instruction streams upon a cache miss.In this paper, we propose interleaving with coroutines for any type of index join. We showcase our proposal on SAP HANA by implementing binary search and CSB + -tree traversal for an instance of index join related to dictionary compression. Coroutine implementations not only perform similarly to prior interleaving techniques, but also resemble the original code closely, while supporting both interleaved and non-interleaved execution. Thus, we claim that coroutines make interleaving practical for use in real DBMS codebases.

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Section: Dictionaries In Column Storesmentioning

confidence: 99%

Interleaving with coroutines

et al. 2017

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“…Many DBMS that do not mention advanced NUMA capabilities, indirectly rely on the first-touch policy, e.g., Vectorwise [36], IBM DB2 BLU [30], and the column-store of Microsoft SQL Server [21].…”

Section: Related Workmentioning

confidence: 99%

“…The data of an uncompressed column can be stored sequentially in a vector in main-memory [14,18,21,23]. Compression techniques are typically employed to reduce the amount of consumed memory, and potentially speed up processing.…”

Section: Main-memory Column-storesmentioning

confidence: 99%

Scaling up concurrent main-memory column-store scans

et al. 2015

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Main-memory column-stores are called to efficiently use modern non-uniform memory access (NUMA) architectures to service concurrent clients on big data. The efficient usage of NUMA architectures depends on the data placement and scheduling strategy of the column-store. Most column-stores choose a static strategy that involves partitioning all data across the NUMA architecture, and employing a stealingbased task scheduler. In this paper, we implement different strategies for data placement and task scheduling for the case of concurrent scans. We compare these strategies with an extensive sensitivity analysis. Our most significant findings include that unnecessary partitioning can hurt throughput by up to 70%, and that stealing memory-intensive tasks can hurt throughput by up to 58%. Based on our analysis, we envision a design that adapts the data placement and task scheduling strategy to the workload.

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“…Recently, however, a few DBMSs have begun to realize substantial performance improvement by frugally keeping values encoded and performing query processing on the encoded values [1,7,10,15,18,22]. In the above-mentioned example, a predicate such as State = "California" can be applied on the encoded value as State = 000101.…”

Section: Introductionmentioning

confidence: 99%

Joins on encoded and partitioned data

et al. 2014

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Compression has historically been used to reduce the cost of storage, I/Os from that storage, and buffer pool utilization, at the expense of the CPU required to decompress data every time it is queried. However, significant additional CPU efficiencies can be achieved by deferring decompression as late in query processing as possible and performing query processing operations directly on the still-compressed data. In this paper, we investigate the benefits and challenges of performing joins on compressed (or encoded) data. We demonstrate the benefit of independently optimizing the compression scheme of each join column, even though join predicates relating values from multiple columns may require translation of the encoding of one join column into the encoding of the other. We also show the benefit of compressing "payload" data other than the join columns "on the fly," to minimize the size of hash tables used in the join. By partitioning the domain of each column and defining separate dictionaries for each partition, we can achieve even better overall compression as well as increased flexibility in dealing with new values introduced by updates. Instead of decompressing both join columns participating in a join to resolve their different compression schemes, our system performs a light-weight mapping of only qualifying rows from one of the join columns to the encoding space of the other at run time. Consequently, join predicates can be applied directly on the compressed data. We call this procedure encoding translation. Two alternatives of encoding translation are developed and compared in the paper. We provide a comprehensive evaluation of these alternatives using product implementations of each on the TPC-H data set, and demonstrate that performing joins on encoded and partitioned data achieves both superior performance and excellent compression.

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Enhancements to SQL server column stores

Cited by 43 publications

References 7 publications

Interleaving with coroutines

Interleaving with coroutines

Scaling up concurrent main-memory column-store scans

Joins on encoded and partitioned data

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