Hybrid row-column partitioning in teradata _®

Abstract

Data partitioning is an indispensable ingredient of database systems due to the performance improvement it can bring to any given mixed workload. Data can be partitioned horizontally or vertically. While some commercial proprietary and open source database systems have one flavor or mixed flavors of these partitioning forms, Teradata Database offers a unique hybrid row-column store solution that seamlessly combines both of these partitioning schemes. The key feature of this hybrid solution is that either row, column, or combined partitions are all stored and handled in the same way internally by the underlying file system storage layer. In this paper, we present the main characteristics and explain the implementation approach of Teradata's row-column store. We also discuss query optimization techniques applicable specifically to partitioned tables. Furthermore, we present a performance study that demonstrates how different partitioning options impact the performance of various queries.

Application of Dynamic Fragmentation Methods in Multimedia Databases: A Review

“…Figure 5 shows the number of proposals related to each type of fragmentation. Articles [ 1 , 2 , 3 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 ] focus on horizontal fragmentation; while [ 7 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 ] consider vertical partitioning, [ 67 , 68 , 69 , 70 , 71 , 72 ,…”

“…Al-Kateb et al [ 72 ] presented the main features of the Teradata approach and explained in detail a new approach to implementing row-column storage. Subsequently, a performance study was presented that demonstrates how different fragmentation options affect query performance, and different query optimization techniques are proposed specifically applicable to fragmented tables.…”

Real-Time Analytics, Hybrid Transactional/Analytical Processing, In-Memory Data Management, and Non-Volatile Memory

“…Кажется, это общая цель любого поставщика, предоставляющего аналитические решения. В частности, для достижения этой цели помогают хранение таблиц по столбцам и различные виды разделения базы данных, используемые в специализированных аналитических СУБД, таких как, например, Teradata [7] или Vertica [2]. Второе утверждение в приведенной выше цитате дает более близкую к нашему пониманию, более конкретную и слегка противоречивую интерпретацию концепции аналитики в реальном времени.…”

A Simple Semantic-Based Data Storage Layout for Querying Point Clouds

“…To show that the basic idea in SDBL can also be used with a RDBMS, we implemented SDBL via PostgreSQL using the exact same three datasets. So as to use a data layout with tables that bears similarity to the basic idea behind SDBL, we use horizontal or row partitioning [69] to partition the data into a set of tables T_S ID where S ID = 0, 1, 2, . .…”