Good presentation on Database partitioning. Being a developer that was a new learning for me. I will post here a breif about what exactly this partitioning is.
A partition is a division of a logical database or its constituting elements into distinct independent parts. Database partitioning is normally done for manageability, performance or availability reasons.
A popular and favourable application of partitioning is in a distributed database management system. Each partition may be spread over multiple nodes, and users at the node can perform local transactions on the partition. This increases performance for sites that have regular transactions involving certain views of data, whilst maintaining availability and security.
The partitioning can be done by either building separate smaller databases (each with its own tables, indices, and transaction logs), or by splitting selected elements, for example just one table.
Horizontal partitioning (also see shard) involves putting different rows into different tables. Perhaps customers with ZIP codes less than 50000 are stored in CustomersEast, while customers with ZIP codes greater than or equal to 50000 are stored in CustomersWest. The two partition tables are then CustomersEast and CustomersWest, while a view with a union might be created over both of them to provide a complete view of all customers.
Vertical partitioning involves creating tables with fewer columns and using additional tables to store the remaining columns. Normalization also involves this splitting of columns across tables, but vertical partitioning goes beyond that and partitions columns even when already normalized. Different physical storage might be used to realize vertical partitioning as well; storing infrequently used or very wide columns on a different device, for example, is a method of vertical partitioning. Done explicitly or implicitly, this type of partitioning is called "row splitting" (the row is split by its columns). A common form of vertical partitioning is to split (slow to find) dynamic data from (fast to find) static data in a table where the dynamic data is not used as often as the static. Creating a view across the two newly created tables restores the original table with a performance penalty, however performance will increase when accessing the static data e.g. for statistical analysis.
Current high end relational database management systems provide for different criteria to split the database. They take a partitioning key and assign a partition based on certain criteria. Common criteria are:
Selects a partition by determining if the partitioning key is inside a certain range. An example could be a partition for all rows where the column zipcode has a value between 70000 and 79999.
A partition is assigned a list of values. If the partitioning key has one of these values, the partition is chosen. For example all rows where the column Country is either Iceland, Norway, Sweden, Finland or Denmark could build a partition for the Nordic countries.
The value of a hash function determines membership in a partition. Assuming there are four partitions, the hash function could return a value from 0 to 3.
Composite partitioning allows for certain combinations of the above partitioning schemes, by for example first applying a range partitioning and then a hash partitioning. Consistent hashing could be considered a composite of hash and list partitioning where the hash reduces the key space to a size that can be listed.
Visit http://www.oracle.com/us/products/database/options/partitioning/index.htm to know more about Oracle partitioning.
For more insight simply Google the term. The ultimate source :)
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