Do you want to use PostgreSQL Analytics? Have you looked all over the internet to find a solution for it? If yes, then this blog will answer all your queries. With many cloud-based data warehouse services becoming popular, using a traditional relational database for your analytics is not a simple decision. In such a case, PostgreSQL is a great choice. Postgres provides an extensive set of querying capabilities, Foreign Data Wrapper, etc. In this post, you will explore the viability of using PostgreSQL analytics and the caveats that come with it.
Let’s see how this blog is structured for you:
- Introduction to PostgreSQL
- Key Features of PostgreSQL
- PostgreSQL Data Types and Structures
- Benefits of Using PostgreSQL Analytics
- Limitations of Using PostgreSQL Analytics
Let’s start your learning about PostgreSQL analytics.
Introduction to PostgreSQL
PostgreSQL is a popular, free and open-source relational database that can be used for different types of workloads. Postgres provides querying abilities and windowing functions.
Its versatility makes it possible to be used as a transactional database as well as a data warehouse for analytics. With its origins dating back to 1986 and the strong community support that comes with it, Postgres boasts of high reliability, extensibility, and data integrity.
Postgres also supports a unique object-relational model, which is generally not found in counterparts like MySQL and MariaDB. This feature allows users to define custom data types if the already rich data types are not enough for the use case.
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Key Features of PostgreSQL
Other than the standard features of a relational database that includes typical data types, transactional integrity, and querying layer, Postgres possesses some unique features that make it different from other databases in comparison. Let us look into these features as listed below:
- Conformance to SQL Standard: Postgres derives its strength from a comprehensive querying layer that conforms to the internationally accepted SQL standard as much as possible. It supports 170 of the 179 mandatory features of the SQL standard that are continuously being changed to support the full feature set.
- Object Relational Model: Another feature of Postgres is its object-relational model that allows users to define custom data types and functions to deal with the custom-defined data types. It means that you will never want data type support when you work with complex data structures.
- Unique Indexing Mechanisms: Other than the traditional indexing mechanism, Postgres has support for expression index and partial index. The expression index lets you generate indexes on derived values that are calculated based on column data. Partial indexes help users build indexes on a subset of data based on conditional expressions. Another unique feature is the ability of Postgres to create full-text indexes based on JSON data that is stored in binary format. Postgres GIN and BRIN indexing models deserve a specific mention here.
- Common Table Expressions or WITH Clause: Postgres allows you to define temporary tables that exist only for the lifetime of a query. It helps developers to neatly organize their queries instead of having nested SELECT statements that are a nightmare to maintain. It is beneficial in the case of running analytical use cases where queries can become complex.
- Foreign Data Wrapper: This feature enables users to create data sources in Postgres based on external data sources like MySQL. With this functionality, Postgres becomes an ideal candidate for a master querying database, if your architecture contains many external sources.
- Geospatial Database: Postgres provides unique features for handling geospatial data. Other than supporting Geospatial data types and queries, it even offers a separate extension called PostGIS, if your use case is primarily Geospatial in nature.
- Disaster Recovery and Replication: Postgres supports both physical and logical replication. Physical replication deals with multiple instances of the same database that are accessed using a Load balancer. Logical replication is the mechanism of replication using a unique replication identity or primary key. It is offered based on a publish-subscribe model and plays a critical role in change data capture.
- Materialized Views: Materialized views are Postgres features that allow users to create a snapshot of tables in an alternate form suitable for specific queries. They are different from normal tables as the normal views are never stored physically, and they are built dynamically at the time of queries. Materialized views store the alternate form of table physically, thereby they have the ability to return results quickly.
- Window Functions: Window functions are functions that enable data processing over several rows based on clauses like OVER, GROUP BY, etc. The rich window function set that support functions like RANK, NTILE, etc. deserve special mention because of their use, specifically in analytical queries.
PostgreSQL Data Types and Structures
Other than the typical Numeric, String, Integer, Boolean, and Date/Time data types, Postgres also supports some unique data types that are not found in traditional databases. Let us spend some time looking into these unique types:
- Arrays: Arrays help users to store an array of items in a column. Combined with the ability to create custom data types, it makes Postgres a powerful mechanism to play around with lists. Databases like MYSQL don’t support this data type.
- Geometric Data types: It includes data types like Point, Circle, Box, Polygon, etc. that are very useful when dealing with geospatial data.
- JSON and JSONB: Even though the JSON data type is supported by many databases these days, what makes Postgres special is the JSONB data type that stores JSON in binary format in a more space-efficient manner and allows full-text searching.
- Range Type: This data type enables storing a range of values in a single column and query based on the range. It is useful when you are dealing with time ranges and constraints based on them.
- Network Address Data Type: It is another unique data type that is not generally found in other relational databases. As the name suggests, it allows storing network addresses and queries based on them.
- Composite Data Types: It allows for combining different data types and creating newer custom data types. Such custom data types and the ability to query over them is extremely useful in dealing with complex structures.
Benefits of Using PostgreSQL Analytics
Now that you have learned about the key features and the data types of Postgres, in this section, you will learn about the key benefits of PostgreSQL Analytics:
- The must-have feature of any analytics database is a querying layer of the fast querying performance. With good support for complex data types, windows functions, custom functions add custom data types, Postgres is behind no other database when it comes to this.
- With its origin dating back to 1986, it comes with great community support. If you get stuck in anything related to Postgres, you can be assured of finding solutions with a quick search, since in most cases, somebody would have already faced that problem and solved it.
- There are many third-party extensions available to solve the shortcomings of Postgres. In the unfortunate event, if you find a feature missing, Postgres’s custom extension support and dynamic loading will provide you with a way out.
- Postgres’s ability to integrate with external data sources using Foreign Data Wrapper is a great help while using it as an analytics database where data comes in from all sorts of sources.
- Its ability to support textual data types, like JSON, XML, etc. and searching abilities over them makes it an ideal candidate while dealing with use cases that involving a mix of structured and unstructured data.
- Most software-as-a-service counterparts, like Redshift, have adopted Postgres Standard as their querying standard because of its popularity and conformance to SQL standard. Not only does it tell you about its versatility, but it also assures you that in case you want to move towards a SaaS database, you can do it without much changes to your queries.
- Postgres comes with great security features and access control mechanisms. It comes with columns and row-level security. Multi-factor authentication is also supported.
Limitations of Using PostgreSQL Analytics
Even with all those benefits, Postgres is not without its faults and shortcomings. Some of the limitations of using PostgreSQL Analytics are as follows:
- Postgres is not efficient when it comes to Big Data. It is true when your data goes to TBs, and you have queries that process a big part of that data size.
- The horizontal scaling abilities of Postgres is limited. It means that if you want to scale your database, you may have to deal with complex procedures and downtime.
- Since it is free and open-source, it does not come with specific paid enterprise support. It may be a deal-breaker for some organizations who are used to databases like Oracle and SQLServer, that comes with the backing of enterprises. Some companies offer support packages for Postgres, but not at the level that can be provided by organizations like Microsoft.
- Postgres has the reputation for having complex procedures for upgrades that involves downtime. Recent versions of Postgres have solved this problem to an extent, but the fact remains, the upgrade path is not as smooth as other competitors like MySQL.
As evident from the prose above, Postgres makes a strong case for it to be used as your primary analytics database. Unless you have TBs of data and requires a database with horizontal scaling, there are few reasons why someone might get tempted to look over Postgres. In case you decide to go with Postgres, you will need an ETL framework to move your data from different sources to Postgres.
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