Vectorwise: Beyond Column Stores

Column Stores for Wide and Sparse Data

While it is generally accepted that data warehouses and OLAP workloads are excellent applications for column-stores, this paper speculates that column-stores may well be suited for additional applications. In particular we observe that column-stores do not see a performance degradation when storing extremely wide tables, and column-stores handle sparse data very well. These two properties lead ...

Column-store Databases: Approaches and Optimization Techniques

Column-Stores database stores data column-by-column. The need for Column-Stores database arose for the efficient query processing in read-intensive relational databases. Also, for read-intensive relational databases,extensive research has performed for efficient data storage and query processing. This paper gives an overview of storage and performance optimization techniques used in Column-Stores.

Data Compression in Database Query Processing

Row-oriented databases (or “row-store”) employ data compression methods (like dictionary encoding) to reduce the I/O cost by decreasing the data sizes. However, there are two limitations on row-stores when applying data compression schemes: (1) row-stores only allow encoding one single value at a time, and (2) they have to pay the decompression cost in query processing. The above shortcomings l...

Query Optimization and Execution in Vectorwise MPP

Column oriented Database Systems

Column-oriented database systems (column-stores) have attracted a lot of attention in the past few years. Column-stores, in a nutshell, store each database table column separately, with attribute values belonging to the same column stored contiguously, compressed, and densely packed, as opposed to traditional database systems that store entire records (rows) one after the other. Reading a subse...