SQL server column store indexes

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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“…[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].…”

Section: Dictionaries In Column Storesmentioning

confidence: 99%

“…In main memory column stores that employ dictionary encoding [9,17,19,26,27], we encounter relations that are always indexed: the dictionaries. Each dictionary holds the mapping between a value and its encoding, and every query for a list of values requires a sequence of lookups on the dictionary.…”

Section: Introductionmentioning

confidence: 99%

Interleaving with coroutines

et al. 2017

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“…Eventually, several modern MMDBs adopted the column oriented approach of MonetDB and made further development on it. Some well known examples include Vectorwise [20], SAP HANA [21], [22], [23], Hyper [11], DB2 BLU [24], SQL Server Column Store [25], etc. A significant body of research work was dedicated to make column-wise query processing more efficient.…”

Section: E E E P R O O Fmentioning

confidence: 99%

Virtual denormalization via array index reference for main memory OLAP

Zhang

Zhou

Zhang

et al. 2016

2016 IEEE 32nd International Conference on Data Engineering (ICDE)

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Abstract-Denormalization is a common tactic for enhancing performance of data warehouses, though its side-effect is quite obvious. Besides being confronted with update abnormality, denormalization has to consume additional storage space. As a result, this tactic is rarely used in main memory databases, which regards storage space, i.e., RAM, as scarce resource. Nevertheless, our research reveals that main memory database can benefit enormously from denormalization, as it is able to remarkably simplify the query processing plans and reduce the computation cost. In this paper, we present A-Store, a main memory OLAP engine customized for star/snowflake schemas. Instead of generating fully materialized denormalization, A-Store resorts to virtual denormalization by treating array indexes as primary keys. This design allows us to harvest the benefit of denormalization without sacrificing additional RAM space. A-Store uses a generic query processing model for all SPJGA queries. It applies a number of state-of-the-art optimization methods, such as vectorized scan and aggregation, to achieve superior performance. Our experiments show that A-Store outperforms the most prestigious MMDB systems significantly in star/snowflake schema based query processing.

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“…In 2005(re-birth of column store), read optimized DSM came in existence which focused on huge data warehouses to improve disk/CPU efficiency. A research paper on C-store by Stonebraker et al (in 2005) was first comprehensive design structure of column store [3,4,5]. Microsoft SQL Server, first general purpose DBMS, fully integrates columnar storage and processing into the system [5].…”

Section: Introductionmentioning

confidence: 99%

“…A research paper on C-store by Stonebraker et al (in 2005) was first comprehensive design structure of column store [3,4,5]. Microsoft SQL Server, first general purpose DBMS, fully integrates columnar storage and processing into the system [5]. Our paper discusses the effects of different types of DML queries on performance of row and column based storage in data warehouse.…”

Section: Introductionmentioning

confidence: 99%

Query based performance analysis of row and column storage data warehouse

Kamal

Gupta

2014

2014 9th International Conference on Industrial and Information Systems (ICIIS)

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Row based relational database management systems (R-RDBMS) are the most common choice for data warehouse implementations. Row based storage is useful in online transaction processing (OLTP) to insert and update data while column oriented RDBMS (C-RDBMS) in online analytical processing (OLAP) to access data from data warehouse. This paper analyses the best suited mode of storage (row or column based storage) for different type of data manipulation language (DML) queries in data warehouse. It also evaluates and compares the performance of row and column oriented data structures based on elapsed time, CPU, I/O and operator costs; for different types of DML queries. A data warehouse has been designed for simulation purpose using Red Gate Data Generator tool along with Microsoft SQL Server 2012. Index Terms-Row and columnar storage data warehouse, DML queries, Star Schema Benchmark (SSB), Microsoft SQL Server 2012

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SQL server column store indexes

Cited by 59 publications

References 8 publications

Interleaving with coroutines

Interleaving with coroutines

Virtual denormalization via array index reference for main memory OLAP

Query based performance analysis of row and column storage data warehouse

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