Getting Started with NoSQL

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What is database partitioning?

Partitioning is where we split data into distinct individual parts, to improve manageability, performace etc.

Partitioning is performed using a consitent partitioning criteria. There are 4 such criterias.

One! Range partitioning, 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 3000 and 3999.

Two! List partitioning, 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.

Three! Hash partitioning, 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.

Four! 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.


What are the benefits of partitioning?


Partitioning can provide tremendous benefits to a wide variety of applications by improving manageability, performance, and availability. It is not unusual for partitioning to improve the performance of certain queries or maintenance operations by an order of magnitude. Moreover, partitioning can greatly reduce the total cost of data ownership, using a “tiered archiving” approach of keeping older relevant information still online on low cost storage devices. Oracle Partitioning enables an efficient and simple, yet very powerful approach when considering Information Lifecycle Management for large environments.
Partitioning also enables database designers and administrators to tackle some of the toughest problems posed by cutting-edge applications. Partitioning is a key tool for building multi-terabyte systems or systems with extremely high availability requirements.

So When is partitioning is not required?
There are some scenarios where partitioning is not required. These scenarios include,
one! If your Dataset is too small.
two! When traffic to your website or application is not too much and you are not expecting it to peak, and few dataset is not more than few Gigs of data.

Horizontal versus Vertical partitioning!

Horizontal partitioning also known as Sharding, involves putting different rows into different tables. Perhaps customers with ZIP codes less than 3000 are stored in Melbourne East, while customers with ZIP codes greater than or equal to 3000 are stored in Melbourne City. A view can be created with a union between 2 tables to provide user with a complete view.
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. In vertical partitioning, different physical storage might be used.

With regards to partitioning in NoSQL and RDBMS, NoSQL and RDBMS partitioning is very similar, Most NoSQL databases are designed with partitioning in mind. Knowing how partitioning works is key to making informed decisions about your DB performance.