Skip to main content

PE - Parsing Engine


PE, acronym for "Parsing Engine," is the type of vproc (Virtual Processor) for session control, task dispatching and SQL parsing in the multi-tasking and possibly parallel-processing environment of the Teradata Database.


DEFINITION


PE, acronym for "Parsing Engine," is the type of vproc (Virtual Processor) for session control, task dispatching and SQL parsing in the multi-tasking and possibly parallel-processing environment of the Teradata Database. Vproc is a software process running in an SMP (Symmetric Multiprocessing) environment or a node.


COMPONENTS

The components of a PE vproc can be classified as the following:

   1. Parser: It desolves SQL statements into RDBMS processing steps;
   2. Query Optimizer: It decides the optimal way to access data;
   3. Step Generator: It 1) produces processing steps, and 2) encapsulates them into packages;
   4. Dispatcher: It 1) transmits the encapsulated steps from the parser to the pertinent AMPs, and 2) performs monitoring and error-handling functionalities during step processing;
   5. Session Controller: It 1) manipulates session-control activities (e.g., logon, authentication, and logoff), and 2) restores sessions after client or server failures.


OVERVIEW

PE is an instance of the database management software responsible for the communication between the requesting client and the relevant AMPs, usually via the BYNET. Each PE runs independently to handle sessions, parse SQL statements into processing steps with optimization, dispatching the steps to the relevant AMPs and sends the processing results back to the requesting client.

The PE vproc was invented in Teradata V2 to replace the following dedicated physical processors that performed the similar functions on the DBC 1012 systems:

    * The InterFace Processor(IFP) - The IFP was responsible for the communication between the DBC and the HOST. Its components included parser, dispatcher, session controller, client interface and YNET interface.
    * The COmmunication Processor (COP) - The COP was similar in function as IFP, but responsible for communication with network-attached hosts (DOS-PC/UNIX).

Comments

Popular posts from this blog

Informatica Powercenter Partitioning

Informatica PowerCenter Partitioning Option increases the performance of PowerCenter through parallel data processing. This option provides a thread-based architecture and automatic data partitioning that optimizes parallel processing on multiprocessor and grid-based hardware environments. Introduction: With the Partitioning Option, you can execute optimal parallel sessions by dividing data processing into subsets that are run in parallel and spread among available CPUs in a multiprocessor system. When different processors share the computational load,large data volumes can be processed faster. When sourcing and targeting relational databases, the Partitioning Option enables PowerCenter to automatically align its partitions with database table partitions to improve performance. Unlike approaches that require manual data partitioning, data integrity is automatically guaranteed because the parallel engine of PowerCenter dynamically realigns data partitions for set-oriented trans

Data virtualization

Data virtualization is a process of offering a data access interface that hides the technical aspects of stored data, such as location, storage structure, API, access language, and storage technology. Analogous to concept of views in databases Data virtualization tools come with capabilities of  data integration, data federation, and data modeling Requires more memory caching Can integrate several data marts or data warehouses through a  single data virtualization layer This concept and software is a subset of data integration and is commonly used within business intelligence, service-oriented architecture data services, cloud computing, enterprise search, and master data management. Composite, Denodo, and Informatica are the largest players in the area of data virtualization References for definition: http://www.b-eye-network.com/view/14815

Difference between server jobs and parallel jobs in Datastage

Server job stages do not have in built partitioning and parallelism mechanism for extracting and loading data between different stages. To enhance the speed and performance in server jobs is to     - Enable inter process row buffering through the administrator. This helps stages  to exchange data as soon as it is available in the link.     - Using IPC stage also helps one passive stage read data from another as soon as data is available. In other words, stages do not have to wait for the entire set of records to be read first and then transferred to the next stage.    - Link partitioner and link collector stages can be used to achieve a certain degree of partitioning paralellism. All of the above features which have to be explored in server jobs are built in datastage Parallel jobs . The Px engine runs on a multiprocessor sytem and takes full advantage of the processing nodes defined in the configuration file. Both SMP and MMP architecture is supported by datastage Px. Px ta