Teradata Architecture and Components

Major Components of a Teradata Architecture

 NODE:

A node is made up of various hardware and software components.

components that make up a node are

1.             Parsing Engine (PE)

2.             BYNET

3.             Access Module Processor (AMP)

4.             Disks

Parsing Engine

The Parsing Engine (PE) is a component that interprets SQL requests, receives input records, and passes data. To do that it sends the messages through the BYNET to the AMPs.

BYNET

The BYNET is the message passing layer. It determines which AMP(s) (Access Module Processor) should receive a message.

Access Module Processor (AMP)

The AMP is a virtual processor designed for and dedicated to managing a portion of the entire database. It performs all database management functions such as sorting, aggregating, and formatting data. The AMP receives data from the PE, formats rows, and distributes them to the disk storage units it controls. The AMP also retrieves the rows requested by the Parsing Engine.

Disks

Disks are disk drives associated with an AMP that store the data rows. On current systems, they are implemented using a disk array

All applications run under UNIX, Windows NT or Windows 2000 and all Teradata software runs under PDE. All share the resources of CPU and memory on the node.

AMPs and PEs are virtual processors running under control of the PDE. Their numbers are software configurable. In addition to user applications, gateway software and channel driver support may also be running.

The Teradata RDBMS has a “shared-nothing” architecture, which means that the vprocs (which are the PEs and AMPs) do not share common components. For example, each AMP manages its own dedicated memory space (taken from the memory pool) and the data on its own vdisk — these are not shared with other AMPs. Each AMP uses system resources independently of the other AMPs so they can all work in parallel for high system performance overall.

Symmetric Multi-Processor (SMP): A single node is a Symmetric Multi-Processor (SMP)

Massively Parallel Processing (MPP): When multiple SMP nodes are connected to form a larger configuration, we refer to this as a Massively Parallel Processing (MPP) system.