If you are looking at adding another data store, here are some of the additional functions in Postgres that you may not be aware of.

Postgres offers several well-known powerful features:

  • In-memory caching.
  • Special data type enables rapid searched.
  • Postgres works well with heavy loads.
  • Specialized indexes – to search data, data sets and structured data.

If you are considering adding another data store it is worth noting that with Postgres this is often unnecessary and, in fact, adding more data stores can mean that data management becomes more cumbersome. With Postgres, you can add functionality and avoid some of the major pitfalls involved when adding different data stores.

Having a product that employees are familiar with, that new employees can learn easily are part of the reason why sticking with a single data store is always the best option. It also ameliorates some of the costs associated with updating, upgrading and maintaining security across multiple stores and the cost of having to deal with failures. There is always an added complexity of trying to maintain and manage several different types of data store.

Postgres is a lot more than an SQL database. Here are some of the other functions of Postgres that you may be unaware of:

  • Brilliantly designed caching: Postgres offers in-memory caching and compares well with other systems. Most queries happen off-disk because often it is the same data that people search. You can define how much memory Postgres uses and this can be easily set up. There are also several advanced caching tools available so if you understand what sort of queries your database receives it is easy to ramp up performance.
  • Extensions: There are many powerful extensions to Postgres which are installed in the same way as plug-ins so there is no need to add other data stores to increase functionality.
  • PostGIS Extension: This extension is used for geospatial data and is a popular addition to Postgres for users wanting to run location queries on the database.

How to Scale Postgres

Before you consider moving to another data store have a look at some of the optimizations offered by Postgres to maximize its performance. These small changes will make a huge difference and may mean you can avoid developing a hugely complex infrastructure.

Avoid Over-Indexing

If you have too many indexes you will crowd the cache, freeing this up is key for performance so avoid unnecessary indexing or excessive use of multi-column indexes. If queries are slow look for missing indexes.

Indexes must be used correctly to function at full capacity.

Consider Partial Indexing

You can save space on the system by indexing selected values only.

Choose the Right Type of Index

This is vital to increase performance. These are the most common index types and where they should be used:

  • B-tree indexes – binary trees. These are the default index and are an efficient way to sort data. Inconsistencies can be an issue on scaled systems so you will need to check these using the amcheck extension.
  • BRIN indexes – Block Range INdex. Used when sorting tables by column.
  • Bloom filter index – great for use where there are multiple column queries on large tables. It uses a mathematical structure that helps minimize the use of space.
  • GIN and GiST indexes – indexes based on text, JSON and arrays.

Sometimes Another Data Store Is Necessary

Postgres does have its limitations and there are circumstances where adding another store is the way forward:

  • Postgres may not be able to read certain types of data.
  • Your situation requires a lot of heavy real-time processing
  • You need an instant full-text search facility

To conclude, adding multiple data stores can cause multiple problems in a system, so if you have already started using Postgres it is worth seeing if you can increase performance by using the lesser-known functions. However, there are use cases where other systems work more efficiently.

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