What is Postgres known for?

Relational data storage with SQL language.

What are the limitations of Postgres?

Handling Big Data exceeding 50 gigabytes.

Data indexed in time order, like stock prices.

Extension of Postgres for time series data.

Through continuous aggregations and data refresh policies.

Postgres is a widely used database for relational data, but it struggles with Big Data exceeding 50 gigabytes due to limitations.
Timescale DB, an extension of Postgres, specializes in time series data like stock prices, offering speed and scalability by partitioning data into hyper tables and providing continuous aggregations for significant performance enhancements.

What is Postgres known for?
Relational data storage with SQL language.
What are the limitations of Postgres?
Handling Big Data exceeding 50 gigabytes.
What is time series data?
Data indexed in time order, like stock prices.
What is Timescale DB?
Extension of Postgres for time series data.
How can Timescale DB optimize query speeds?
Through continuous aggregations and data refresh policies.

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Postgres is a favored database, especially for relational data, with SQL being the preferred language.
Despite its popularity, Postgres has limitations, particularly with handling Big Data, typically data exceeding 50 gigabytes.
Big Data often involves time series data, which is data indexed in time order, like stock prices or temperature readings.
To optimize Postgres for time series data, consider partitioning data across logically grouped tables.
An alternative to Postgres for time series data is Timescale DB, an open-source extension of Postgres known for speed and scalability.
To use Timescale DB, start by obtaining a large time series data set, like the New York City Taxi data set.
Set up Timescale DB by either self-hosting, using Docker, or opting for Timescale's managed service, which offers a 30-day free trial.
Create tables for the data, including a hyper table for time series data, which is a partitioned table managed by Timescale.
Use the Migrate tool to run database migrations and load the data into Timescale DB, which may take several hours due to the data size.
Timescale DB offers continuous aggregations, a feature that efficiently computes and updates data, providing significant performance improvements over traditional methods like materialized views.

14:30

Creating a refresh policy for continuous aggregation involves refreshing data in the database from one year to one month ago every hour, achieved by running a make Command for database migration and initial materialized view calculation, followed by checking and querying the continuous aggregate.
The continuous aggregate offers faster query speeds, more granular data analysis on a daily level, and the ability to refresh data selectively, showcasing the benefits of Timescale DB in significantly reducing query times from 12.5 minutes to 10 milliseconds within a PostgreSQL interface.