Integration of vectorwise with ingres

@article{Inkster2011IntegrationOV,
  title={Integration of vectorwise with ingres},
  author={Douglas Inkster and Marcin Zukowski and Peter A. Boncz},
  journal={SIGMOD Rec.},
  year={2011},
  volume={40},
  pages={45-53}
}
Actian Corporation recently entered into a cooperative relationship with VectorWise BV to integrate its Vector-Wise technology into the Ingres RDBMS server. The resulting commercial product has already achieved phenomenal performance results with the TPC-H industry standard benchmark, and has been well received in the analytical RDBMS market. This paper describes the integration of the VectorWise technology with Ingres, some of the design decisions made as part of the integration project, and… 

Figures and Tables from this paper

From x100 to vectorwise: opportunities, challenges and things most researchers do not think about
TLDR
Some of the interesting aspects of the work performed by the Vectorwise development team in the process are described, and the opportunities and challenges resulting from the decision of integrating a prototype-quality kernel with Ingres, an established commercial product are discussed.
Vectorwise: Beyond Column Stores
textabstractThis paper tells the story of Vectorwise, a high-performance analytical database system, from multiple perspectives: its history from academic project to commercial product, the evolution
From Cooperative Scans to Predictive Buffer Management
TLDR
PBM is based on the observation that in a workload with long-running scans, the buffer manager has quite a bit of information on the workload in the immediate future, such that an approximation of the ideal OPT algorithm becomes feasible.
Efficient Processing of Window Functions in Analytical SQL Queries
TLDR
This work presents an efficient and general algorithm for the window operator that is optimized for high-performance main-memory database systems and has excellent performance on modern multi-core CPUs.
Enhancements to SQL server column stores
TLDR
This paper gives an overview of SQL Server's column stores and batch processing, in particular the enhancements introduced in the upcoming release.
Autonomic physical database design – from indexing to multidimensional clustering
TLDR
A survey on the state of the art in autonomic physical design in database design, starting with the classic index tuning problem and possible solutions, and describing further design problems such as choosing materializations of aggregations for OLAP and multidimensional clustering schemes.
Business Analytics in (a) Blink
TLDR
The Blink project is working on the next generation of Blink, which will expand the “sweet spot” of the Blink technology to much larger, disk-based warehouses and allow Blink to “own” the data, rather than copies of it.
Columnar Storage in SQL Server 2012
TLDR
The design of column store indexes and batch-mode processing is outlined and the key benefits this technology provides to customers are summarized.
Using Vectorized Execution to Improve SQL Query Performance on Spark
TLDR
VEE, a thorough vectorized execution engine designed for SQL query processing on Spark, is presented and the performance speedup of VEE against Spark is up to 72.7% and 25.0% on average for OLAP workloads (TPC-H).
Automatic schema design for co-clustered tables
TLDR
An automatic schema design approach for a table co-clustering scheme called Bitwise Dimensional Co-Clustering, aimed at schemas with a moderate amount dimensions, but not limited to typical star and snowflake schemas.
...
...

References

SHOWING 1-10 OF 13 REFERENCES
Balancing vectorized query execution with bandwidth-optimized storage
TLDR
A new database system architecture is presented, realized in the MonetDB/X100 prototype, that combines a coherent set of new architecture-conscious techniques that are designed to work well together and achieves in-memory performance often one or two orders of magnitude higher than the existing approaches.
MonetDB/X100: Hyper-Pipelining Query Execution
TLDR
An in-depth investigation to the reason why database systems tend to achieve only low IPC on modern CPUs in compute-intensive application areas, and a new set of guidelines for designing a query processor for the MonetDB system that follows these guidelines.
The Optimization of Queries in Relational Databases
A fully implemented system for optimizing and executing queries for relational databases is described. The system optimizes n-table, equi-join queries written in QUEL, the query language supported by
The INGRES Papers: Anatomy of a Relational Database System
TLDR
When you read more every page of this the ingres papers anatomy of a relational database system, what you will obtain is something great.
Vectorization vs. compilation in query execution
TLDR
A careful merging of vectorized and compiled strategies inside the Ingres VectorWise database system is proposed for optimal performance: either by incorporating vectorized execution principles into compiled query plans or using query compilation to create building blocks for vectorized processing.
Positional update handling in column stores
TLDR
A new data structure for maintaining such positional updates to columnar databases, called the Positional Delta Tree (PDT), is described, and detailed algorithms for PDT/column merging, updating PDTs, and for using PDTs in transaction management are described.
Cooperative Scans: Dynamic Bandwidth Sharing in a DBMS
TLDR
This paper analyzes the performance of concurrent (index) scan operations in both record (NSM/PAX) and column (DSM) disk storage models and proposes the Cooperative Scans framework that enhances performance in such scenarios by improving data-sharing between concurrent scans.
Super-Scalar RAM-CPU Cache Compression
TLDR
This work proposes three new versatile compression schemes (PDICT, PFOR, and PFOR-DELTA) that are specifically designed to extract maximum IPC from modern CPUs and compares these algorithms with compression techniques used in (commercial) database and information retrieval systems.
Pathfinder: XQuery Compilation Techniques for Relational Database Targets
TLDR
A purely relational XQuery processor that handles huge amounts of XML data in an efficient and scalable manner and devise staircase join, a novel join operator that encapsulates knowledge on the underlying tree encoding to provide efficient support for XPath navigation primitives.
The Optimization of Queries in Relational Database Systems
  • The Optimization of Queries in Relational Database Systems
...
...