Ryan Booz

In a world where data continues to grow at an overwhelming rate, knowing how to load and insert data into PostgreSQL as efficiently as possible is a key skill for any software or data engineer. Whether loading data from files, manually generating multi-valued INSERT or UPSERT statements, or using some of the more popular language SDKs, knowing which option is right for each situation can sometimes be difficult to figure out.

This talk will review how to efficiently insert large amounts of data into PostgreSQL using freely available tools and well-crafted SQL. We'll demonstrate the usage of tools like COPY, how to batch data using multi-valued INSERT statements, functions to look for in language SDKs, and even demonstrate how to use array parameters to speed up multi-valued inserts.

By the end of this session, you will know at least four ways to insert bulk data, the impact it will have on your PostgreSQL database, and options for improving your current application code to perform at its best!

Exploring new features in PostgreSQL or reproducing an unusual query plan can be tricky without representative data to utilize. While there are a plethora of sources for sample data and tools to import it, you can end up spending too much time finding representative data to work with. In our day-to-day work at Timescale, we often need to quickly create lots of sample time-series data to demonstrate new features, run a benchmark, or help community members with examples as they learn.

Although using real application data would be ideal, PostgreSQL provides the generate_series() function which makes it easy to create a representative time-series dataset using varying cardinalities and different lengths of time.

In this talk we'll introduce generate_series() and demonstrate how to use it to create realistic-looking time-series data of all shapes and sizes, using custom PostgreSQL user-defined functions. Once we've mastered the basics, we'll dial it up a notch by incorporating PostgreSQL math functions and relational data to create realistic time-series patterns of data for various use cases like sales or website visits.