Difference Between PostgreSQL vs RedShift
The following article provides an outline for PostgreSQL vs RedShift. An open-source RDBMS system with SQL support and extensions that use a variant of SQL called T-SQL to enable more advanced procedures in their queries is called PostgreSQL. This helps to store and scale any workloads, be it complex or simple in its system. It can store data for any type of application. A data warehouse product by Amazon which has less execution time with the help of parallel processing and compression is called RedShift. RedShift is compatible with PostgreSQL and we can run T-SQL queries in RedShift to get advanced queries. This is mostly used for analytics and reporting.
Head to Head Comparison Between PostgreSQL vs RedShift (Infographics)
Below are the top 5 differences between PostgreSQL vs RedShift:
Key Difference Between PostgreSQL vs RedShift
Let us discuss some of the major key differences between PostgreSQL vs RedShift:
- RedShift is more used in analytics and hence the column database helps in processing the data faster. Index keys are not used in RedShift which is replaced by SORT and DIST keys. Foreign keys or any other constraints are not present and hence it will take time to sort out the values in RedShift database. Cluster is used here so that we can manage billions of records in a single short. PostgreSQL is suitable for simple queries and less data. The data is stored in nodes and there are no clusters here. We have indexes, a foreign key concept in PostgreSQL. Performance for analytics is best in RedShift than PostgreSQL.
- SQL is used in both RedShift and PostgreSQL but the application of SQL commands differ. We can do the distributions and sort algorithms in RedShift with the help of CREATE TABLE but inheritance and partitioning are not supported in RedShift. We can do insert and update the table but it does not allow us to create new tables along with the insert command. With Create command, we can create tables along with sorting, inheritance and partitioning in PostgreSQL. We can insert and delete tables along with ‘WITH’ clause in PostgreSQL.
- Different distribution styles are followed in RedShift so that the data is inserted faster into the database. We have key distribution in RedShift where the data is crosschecked with the key values in the column and placed in the same columns. The values are compared with other columns and if there are matching values, the columns are placed together. This helps users to locate the data easily. No distribution styles or patterns are followed in PostgreSQL where we must locate similar data-carrying columns with queries.
- The use of VACUUM is different in both databases. PostgreSQL helps to get back the space in the database with the VACUUM command whereas RedShift sorts all rows as well along with reclaiming space in the database.
- A leader node is present in the database cluster so that it manages the data insertion and management into the database. Work is distributed among different worker nodes and the data is managed well so that it can be queried whenever needed. We do not have any leader node or worker nodes in PostgreSQL as it works with a single node database.
Comparison Table of PostgreSQL vs RedShift
Let’s discuss the top comparison between PostgreSQL vs RedShift:
| PostgreSQL | RedShift |
| Data is stored and managed in rows that helps in creating tables directly. This helps to build queries around the rows inserted and also we can manage the tables in the way the data got inserted into the tables. | Data is inserted in the form of columns. This helps to read data faster and return the queries more efficiently than PostgreSQL. Storage efficiency is increased here as compression of data happens in the column level since each column carries similar data. |
| Scaling is not easy in PostgreSQL as the compute nodes are not present in this database. Scaling can be done only by creating a new server for the data or copying the entire data into a separate database. Vertical scaling is done with PostgreSQL which is costly. | Scaling is easy in RedShift as AWS helps to manage node configuration and scale horizontally with parallel processing. This expansion of nodes helps to create more clusters. Horizontal scaling will not require more servers as it is done with the help of compute nodes so that the scaling can be done at a low cost. |
| PostgreSQL is simple and easy to use. We can insert data and write queries in T-SQL which will give us the results. But this is suited when the data is less. If we have more data, the speed is reduced and it is not possible to scale the database easily. This is good for beginners as it is free of cost. | RedShift is not freely offered and it is used along with Amazon S3 storage. But the querying is faster and the queries are similar to PostgreSQL. For any amount of data, querying can be done within minutes and this characteristic makes users select RedShift. |
| PostgreSQL supports arrays, bits, range, JSON, numeric and geometric types, XML, timestamp data and many other forms. If it is not big data, it is good to stick with PostgreSQL as it has different functions and triggers along with sequences. | RedShift does not support all the functions as PostgreSQL and particularly no support for timestamp data. These are some disadvantages we should foresee when we go for scaling and performance of the database. For bigdata, RedShift is a good choice. |
| A single database is connected to one CPU and hence the data should be processed one after the other. We cannot expect the database to function faster with a single CPU where scaling is not an option. We do not have clusters and only nodes are present. | Massive parallel processing is done in RedShift which helps to process data simultaneously and this helps in completing the queries at a faster pace than expected. Different nodes are employed to carry on the process in the database with the cluster configuration. |
Conclusion
When the amount of data is in terabytes and if there is no increase in data in near future, we can go with PostgreSQL. If it is bigdata and analytical queries are running always, RedShift is a good choice. Also, if we have a database already with AWS, PostgreSQL will not be good as it will cost more than the expected amount.
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