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Hi all - Very excited to announce the news, and are here in case there are any questions. Thanks!

by akulkarni 9y ago

vasilipupkin 9y ago
How are you different than influxdb?
- mfreed 9y ago
  Influx is a NoSQL, non-relational database, written from scratch, that optimizes for properties like higher compression and column scans for specific label sets. While it supports something SQL-ish, the query language is fairly limited, and I think they are moving further away to a more declarative, non-SQL language. On the other hand, TimescaleDB has figured out how to re-engineer PostgreSQL to make it scale for time-series data. In doing so, it: - Supports full SQL, including complex WHERE predicates, secondary indexes, non-time based aggregates, sub-queries, window functions, CTEs, etc. - Allows you to store regular relational tables alongside your time-series data and perform arbitrary JOINS. - Supports both full schema-tized and JSON/JSONB data models. - Inherits the full ecosystem of Postgres (visualization like Tableau or Grafana, ORMs like Ruby and Django, connectors like Kafka, etc.). If it speaks to Postgres, it speaks to TimescaleDB. - Also works with other Postgres extensions, including things like postgis for geo-spatial analysis. - Enjoys the reliability of Postgres, including streaming replication for HA, clustered read replicas, point-in-time recovery, incremental backups, etc. For more details: http://docs.timescale.com/ http://docs.timescale.com/
zenlot 9y ago
Will you support clustering in open source version?
- RobAtticus 9y ago
  Our open source version already supports some forms of scaling/clustering today: e.g., read-only replicas for hot standbys and increasing query throughput. Timescale also supports hundreds of thousands of row inserts (millions of metrics) per second on a single node, and allows users to elastically add disks to scale up single-node capacity to ~100TB. For scaling beyond those insert and storage limits, we're currently working on full, scale-out clustering. We'll have more to say about that soon.
  - mrkurt 9y ago
    I _really_ love that you all have built something that works really well when scaled vertically, and not kneecapped the whole thing in search of horizontal scale chasing the 1%-ers.
chuhnk 9y ago
Curious to know who is currently using timescaleDB? I can't find anything on the website. Congrats on the funding.
- stalller 9y ago
  Thanks for asking. For some of the larger companies, takes a little bit of time to work with their marketing / legal teams :) We do list some of them within the blog post: - STE Energy, to back operational dashboards used to monitor 47 power plants in Italy, Albania, Colombia, Perú, Honduras, Costa Rica, and Chile, replacing Redis - InTraffic, which monitors all of the roughly 5,000 remote controllable railroad switches of the Dutch railways, for use with Tableau as a Business Intelligence solution - One of the largest American telecom companies, for storing server health data - A top financial services firm, to replace Oracle for powering interactive analytics of market data - A public lighting and semiconductor company, for deployment in a small-footprint IoT edge device collecting and analyzing sensor readings

Hi all - Very excited to announce the news, and are here in case there are any questions. Thanks!

vasilipupkin 9y ago

How are you different than influxdb?

mfreed 9y ago
Influx is a NoSQL, non-relational database, written from scratch, that optimizes for properties like higher compression and column scans for specific label sets. While it supports something SQL-ish, the query language is fairly limited, and I think they are moving further away to a more declarative, non-SQL language. On the other hand, TimescaleDB has figured out how to re-engineer PostgreSQL to make it scale for time-series data. In doing so, it: - Supports full SQL, including complex WHERE predicates, secondary indexes, non-time based aggregates, sub-queries, window functions, CTEs, etc. - Allows you to store regular relational tables alongside your time-series data and perform arbitrary JOINS. - Supports both full schema-tized and JSON/JSONB data models. - Inherits the full ecosystem of Postgres (visualization like Tableau or Grafana, ORMs like Ruby and Django, connectors like Kafka, etc.). If it speaks to Postgres, it speaks to TimescaleDB. - Also works with other Postgres extensions, including things like postgis for geo-spatial analysis. - Enjoys the reliability of Postgres, including streaming replication for HA, clustered read replicas, point-in-time recovery, incremental backups, etc. For more details: http://docs.timescale.com/ http://docs.timescale.com/

zenlot 9y ago

Will you support clustering in open source version?

RobAtticus 9y ago
Our open source version already supports some forms of scaling/clustering today: e.g., read-only replicas for hot standbys and increasing query throughput. Timescale also supports hundreds of thousands of row inserts (millions of metrics) per second on a single node, and allows users to elastically add disks to scale up single-node capacity to ~100TB. For scaling beyond those insert and storage limits, we're currently working on full, scale-out clustering. We'll have more to say about that soon.
- mrkurt 9y ago
  I _really_ love that you all have built something that works really well when scaled vertically, and not kneecapped the whole thing in search of horizontal scale chasing the 1%-ers.

chuhnk 9y ago

Curious to know who is currently using timescaleDB? I can't find anything on the website. Congrats on the funding.

stalller 9y ago
Thanks for asking. For some of the larger companies, takes a little bit of time to work with their marketing / legal teams :) We do list some of them within the blog post: - STE Energy, to back operational dashboards used to monitor 47 power plants in Italy, Albania, Colombia, Perú, Honduras, Costa Rica, and Chile, replacing Redis - InTraffic, which monitors all of the roughly 5,000 remote controllable railroad switches of the Dutch railways, for use with Tableau as a Business Intelligence solution - One of the largest American telecom companies, for storing server health data - A top financial services firm, to replace Oracle for powering interactive analytics of market data - A public lighting and semiconductor company, for deployment in a small-footprint IoT edge device collecting and analyzing sensor readings