Tiger Cloud - Annual Commit
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A timeseries for IoT
What do you like best about the product?
The fact that timescaledb is an extension of Postgres and integrates very well with our monitoring stack (OpenCensus) and since it is a SQL base timeseries, most of our developers find is easy to query data.
Hypertable, continuous aggregates provide a great way to speed up our customer-facing queries.
The compression functionality helped us to reduce our cloud cost by more than 50%.
If you are using Managed service or Cloud service, the support is very quick and helpful.
Hypertable, continuous aggregates provide a great way to speed up our customer-facing queries.
The compression functionality helped us to reduce our cloud cost by more than 50%.
If you are using Managed service or Cloud service, the support is very quick and helpful.
What do you dislike about the product?
There is no easy way to backfill historical data after compressing chunks, this will require a lot of custom code from our application and you must be careful when decompressing and updating aggregate to not impact the performance.
in general updating compressed chunks (Hypertable or Aggregates) is a bit painful and wish there is an easy way to update them without decompression.
in general updating compressed chunks (Hypertable or Aggregates) is a bit painful and wish there is an easy way to update them without decompression.
What problems is the product solving and how is that benefiting you?
We are injecting/storing a lot of sensors data in timescale (it is our primary timeseries database that serves all our services), Previously we were using OpenTSDB and the lack of updates, Go library and the management made it very difficult to work with so we decided to move away.
One of the main points that made us choose Timescale was the Hypertable feature, Continuous Aggregates, and compression. with this alone we are able to have a very performing timeseries that is able to inject a lot of sensor data, perform aggregation and manage retention policy very easely.
One of the main points that made us choose Timescale was the Hypertable feature, Continuous Aggregates, and compression. with this alone we are able to have a very performing timeseries that is able to inject a lot of sensor data, perform aggregation and manage retention policy very easely.
Data warehouse for time-series data
What do you like best about the product?
- Timescale is a PostgreSQL extension, so the team was able to leverage all of our previous knowledge of PostgreSQL and standard SQL
- Hypertables and continuous aggregates deliver a massive performance boost for both data ingest and data queries
- Unlike many other time-series databases, which seem to be optimised purely for IoT-like use cases, Timescale was able to handle *mutable* time-series data.
- Active and helpful community (on Slack)
- Hypertables and continuous aggregates deliver a massive performance boost for both data ingest and data queries
- Unlike many other time-series databases, which seem to be optimised purely for IoT-like use cases, Timescale was able to handle *mutable* time-series data.
- Active and helpful community (on Slack)
What do you dislike about the product?
- Managed hosting options (Timescale Cloud and MST) can get expensive, especially as resource requirements grow
- Difficult to retrieve logs & metrics for a specific date range via MST console
- Difficult to retrieve logs & metrics for a specific date range via MST console
What problems is the product solving and how is that benefiting you?
We store massive amounts of marketplace data in Timescale. Previously, on other RDBMS systems, performance for both data ingest and data query become exponentially worse as data volumes increase. With Timescale, we have been able to maintain a high data ingestion rate over time, and leverage capabilities like hypertables and continuous aggregates to deliver decent performance for real-time queries, even over extended time ranges.
TimeSeries IoT Use Case
What do you like best about the product?
We utilize timescale as our data warehouse for IoT device time series data. GREAT platform and quick query time!
What do you dislike about the product?
The apps that are used to manipulate the data. We currently use DBeaver - and it is clunky. Not the easiest to maneuver through.
What problems is the product solving and how is that benefiting you?
We initially used MS SQL Server for our Time Series data - which called complex queries and LONG run time. TimeScale has fixed that for us.
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