Parallel architectures and Row oriented vs colum oriented approach

Parallel Architectures

Parallel or concurrent operation has many different forms within a computer system. Using a model based on the different streams used in the computation process, we represent some of the different kinds of parallelism available. A stream is a sequence of objects such as data, or of actions such as instructions. Each stream is independent of all other streams, and each element of a stream can consist of one or more objects or actions. We thus have four combinations that describe most familiar parallel architectures:

1. (1)  SISD: single instruction, single data stream. This is the traditional uni- processor.
   
2. (2)  SIMD: single instruction, multiple data stream. This includes vector processors as well as massively parallel processors.
   
3. (3)  MISD: multiple instruction, single data stream. These are typically systolic arrays.
   
4. (4)  MIMD: multiple instruction, multiple data stream. This includes traditional multiprocessors as well as the newer networks of workstations.
   

Each of these combinations characterizes a class of architectures and a corresponding type of parallelism. 

Column Based Vs Row Based architecture

Sr No	Column Based	Row Based
1	A column oriented DBMS is a database management system that stores its content by column rather than the row.	A row oriented DBMS is a database management system that stores its content by row rather than the column.
2	Most data warehouse applications use only a few columns from a table during a typical single access, the resulting bandwidth savings can be substantial.	Whole row needs to be accessed
3	Column-based relational databases, on the other hand, have been de-signed from the ground up with that specify goal in mind.	Conventional approaches to data warehousing use traditional relational databases. How-ever, these were originally designed to sup-port transaction processing (OLTP) and do not have an architecture specifically designed for supporting queries.
4	Column-based approaches make complex queries feasible precisely because they opti-mise the capability of the warehouse in all of these other areas.	Complex queries tend to be slow or, in some cases, simply not achievable, not because of their complexity per se but because they com-bine elements of unpredictable queries and time-based or quantitative/qualitative queries and they frequently require whole table scans.
5	Because there is more data held within a specific space you can read more data with a single I/O, which means fewer I/Os per query and therefore better performance. Of course, the better the compression the greater the performance improvement and the smaller the overall warehouse, with all of the cost benefits that that implies.	Column based offer better performance than row based
6	The chief disadvantage of columnar databases is that they perform less satisfactorily in terms of import, export, bulk reporting and the efficient use of computer resources than do RDBMSs when required to carry out transactional processes.	Better for transactional processes.