Time series databases are one of the fasting growing segments of the database market, spreading across industries and use cases. Common requirements include ingesting high volumes of structured data; answering complex, performant queries for both recent and historical time intervals; and performing specialized time-centric analysis and data management.
Today, many developers working with time series data turn to polyglot solutions: a NoSQL database to store their time series data (for scale) and a relational database for associated metadata and key business data. Yet this leads to engineering complexity, operational challenges, and even referential integrity concerns.
Join Matvey Arye to learn how to avoid these operational problems by reengineering Postgres to serve as a general data platform, using TimescaleDB—an open source time series databases, implemented as a Postgres plug-in, that improves insert rates by 20x over vanilla Postgres and enables much faster queries, all while offering full SQL (including JOINs). TimescaleDB achieves this by storing data on an individual server in a manner more common to distributed systems: heavily partitioning (sharding) data into chunks to ensure that hot chunks corresponding to recent time records are maintained in memory.
Matvey offers an overview of two newly released features of TimescaleDB—automated adaptation of time-partitioning intervals and continuous aggregations in near real time—and discusses how these capabilities ease time series data management. Along the way, he also shares real-world use cases, including TimescaleDB’s use with other technologies such as Kafka.
Matvey Arye is a senior software engineer at TimescaleDB, where he works on performance, scalability, and query power. Mat has been working on data infrastructure in both academia and industry. He attended Stuyvesant and the Cooper Union and holds a PhD from Princeton. In his free time, Mat enjoys theater, travel, hiking, and skiing.
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