Michael Freedman
Cofounder / CTO at TimescaleDB
Michael J. Freedman is the co-founder and CTO of TimescaleDB, an open-source database that scales SQL for time-series data, and a Professor of Computer Science at Princeton University. His research focuses on distributed systems, networking, and security.
Previously, Freedman developed CoralCDN (a decentralized CDN serving millions of daily users) and Ethane (the basis for OpenFlow / software-defined networking). He co-founded Illuminics Systems (acquired by Quova, now part of Neustar) and is a technical advisor to Blockstack.
Honors include: Presidential Early Career Award for Scientists and Engineers (PECASE, given by President Obama), SIGCOMM Test of Time Award, Caspar Bowden Award for Privacy Enhancing Technologies, Sloan Fellowship, NSF CAREER Award, Office of Naval Research Young Investigator Award, DARPA Computer Science Study Group membership, and multiple award publications. Prior to joining Princeton in 2007, he received his Ph.D. in computer science from NYU's Courant Institute, and his bachelors and masters degrees from MIT.
Upcoming presentations
No upcoming presentations...
Past presentations
Advanced compression in TimescaleDB with hybrid row/columnar storage
TimescaleDB native compression combines the best of both worlds: (1) all of the benefits of PostgreSQL, including the insert performance and shallow-and-wide query performance for recent data from a row store, combined with (2) the compression and additional query performance -- to ensure we only read the compressed columns specified in a query -- for deep-and-narrow queries of a columnar store.
Storage systems like databases and file systems have long used compression to reduce their storage footprint. Yet the most effective compression techniques were traditionally limited to column stores, where increased data-type locality provides greater options for advanced capabilities. It has often been assumed that fundamental differences between column-store and row-store architectures lead ...
Building a distributed time-series database on PostgreSQL
In this talk, Michael discusses the five objectives for scaling a database for time-series workloads -- total storage volume, insert rate, query concurrency, query latency, and fault-tolerant replication
Time-series data tends to accumulate very quickly, across devops, IoT, industrial and energy, finance, and other domains. To drive real-time decisions and data science, software developers often seek to wrangle this large volume of data into a variety of database systems.
In this talk, Michael discusses the five objectives for scaling a database for time-series workloads -- total storage vol...
Building a scalable time-series database on PostgreSQL
TimescaleDB, packaged as a PostgresSQL extension
Today everything is instrumented, generating more and more time-series data streams that need to be monitored and analyzed. When it comes to storing this data, many developers often start with some well-trusted system like PostgreSQL, enjoying the convenience of having their data in one place, with time-series data stored alongside relational data and queried together using SQL. But when their ...
oday everything is instrumented, generating more and more time-series data streams that need to be monitored and analyzed. When it comes to storing this data, many developers often start with some well-trusted system like PostgreSQL, enjoying the convenience of having their data in one place, with time-series data stored alongside relational data and queried together using SQL. But when their d...
Time-series data is now everywhere -- IoT, user event streams, system monitoring, finance, adtech, industrial control, transportation, and logistics -- and increasingly used to power core applications. It also creates a number of technical challenges: to ingest high volumes of structured data; to ask complex, performant queries for both recent and historical time intervals; to perform specializ...
Apr 18 2018