August 2021 Summaries

PostgreSQL provides a built-in function called `generate_series()` that can be used to quickly create large datasets for testing and visualization. This function returns sequential numbers or dates between a start parameter and stop parameter, and can be modified to include step parameters to increment values by a certain amount. The function can also be used in conjunction with other SQL functions to add static and dynamic content to each row, and can be joined with multiple sets of data to create a Cartesian product. By understanding how to use `generate_series()` and date math, users can quickly generate large datasets for testing purposes, including time-series data with varying cardinalities and different lengths of time. The function can also be used to create more realistic-looking data by using custom user-defined functions and mathematical functions to add shape and trends to the generated data.

This installment of the "Community Member Spotlight" series highlights Adam Inoue, a software engineer at Messari, who shares his experience with using TimescaleDB to bring transparency to the cryptoeconomy. Messari is a data analytics and research company that provides an organized and contextualized view of the crypto market, enabling professionals to make informed decisions. Adam's team uses TimescaleDB to power their real-time views, handling massive amounts of data from various sources, including on-chain integrations with The Graph. The transition to TimescaleDB improved performance, scalability, and developer experience, allowing the team to easily manage hypertables, compression policies, and continuous aggregates. With its pure PostgreSQL interface and impressive performance characteristics, TimescaleDB has become a crucial part of Messari's infrastructure, enabling them to derive new metrics from multiple existing datasets and scale with ease.

TimescaleDB has released several new features and updates, including the ability to safely downgrade versions, bug fixes, and new SQL functions for simplifying time-series data analysis. Promscale 0.5 also includes performance improvements for faster ingest and querying of data. The company is sharing educational resources such as tutorials, videos, and talks on topics like continuous aggregates, panel discussions, and the future of data in a cloud-native world. Additionally, TimescaleDB tips, reading lists, and community spotlights are being shared, including a discussion on time-weighted averages, high cardinality, and how to power a successful cryptobot with the database. The company is also celebrating PostgreSQL's 25th birthday and is currently hiring across all teams for remote positions.