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A high quality time series database that is in production within minutes
What do you like best about the product?
I use timescale cloud; it has been trivial to deploy into our production network (as well as our dev and staging networks).
All of the technical details are abstracted away, but you can get access to them if need be (such as server tuning, etc).
The ability to scale out at the click of a button is great, and the web-based metrics and alerting are also really useful from day one.
Performance seems incredibly good, even on the low-cost plans.
However, the most impressive feature has been the support, both with the personal customer service manager and the engineers' responsiveness and thoroughness (when I have needed to ask a technical question). The engineers are happy to answer questions about general design and best practices, as well as helping solve production issues.
All of the technical details are abstracted away, but you can get access to them if need be (such as server tuning, etc).
The ability to scale out at the click of a button is great, and the web-based metrics and alerting are also really useful from day one.
Performance seems incredibly good, even on the low-cost plans.
However, the most impressive feature has been the support, both with the personal customer service manager and the engineers' responsiveness and thoroughness (when I have needed to ask a technical question). The engineers are happy to answer questions about general design and best practices, as well as helping solve production issues.
What do you dislike about the product?
Timescale cloud is somewhat locked down, i.e. no direct superuser access, which can be a bit hard to get used to at first. However, it is workable - there's nothing I haven't been able to achieve so far using the standard cloud setup.
What problems is the product solving and how is that benefiting you?
We need to store high volumes of time series data, compress this data, retain some but not all of it, have it searchable in an efficient way, and also aggregate the raw data into daily/hourly summaries. Timescale does all of that.
Easily extend Timescale to solve your problems.
What do you like best about the product?
As Timescale extends Postgres, managing both my time series and regular relational data in a single warehouse is effortless. In addition, Timescale's performance makes managing and working with that data much faster than other tools I've used. Finally, as it extends Postgres, I can easily extend its capabilities with its C-based user-defined functions.
What do you dislike about the product?
To leverage the user-defined functions, I need to manage my own installation of Timescale and can't leverage one of the managed instances.
What problems is the product solving and how is that benefiting you?
As a data scientist, I spend much of my time performing feature engineering to extract information that will help my models perform better. This often requires me to process large amounts of data with a time component (such as panel data). Before using Timescale, I would store the data in Postgres, extract it to my Python environment, and have memory and performance issues. With Timescale, I have been able to push these calculations into the database generating significant performance improvements.
Postgres but faster
What do you like best about the product?
We’ve been using Timescale for a while now and I have to say, I’m impressed with their platform. They have a great and active community. Anytime I have a question or need help with something, I found someone to help me. The platform also has a lot of learning materials on their site and blog. I appreciate that they invest time and resources in educating their users, and I’ve learned a lot from their resources.
We were already familiar with postgres, so it was a natural fit for our business. The learning curve is very manageable. It has allowed us to keep scaling with minimal effort. All we had to do was add the timescale extension, and we were able to handle much more data with ease. This has been a game changer for our business.
It’s a great platform with a supportive community, excellent scalability, and plenty of learning materials to help you get started
We were already familiar with postgres, so it was a natural fit for our business. The learning curve is very manageable. It has allowed us to keep scaling with minimal effort. All we had to do was add the timescale extension, and we were able to handle much more data with ease. This has been a game changer for our business.
It’s a great platform with a supportive community, excellent scalability, and plenty of learning materials to help you get started
What do you dislike about the product?
The compression feature in Timescale is not well explained, and it is difficult to update data after compression.
The managed hosting service offered by Timescale is expensive, which may not be feasible for small businesses or individuals.
If you are using hypertables in Timescale, you will lose foreign key constraints, which can be a significant limitation for some users.
Choosing Timescale over the more established and reliable option of PostgreSQL is a risky choice. However, if you do decide to go with Timescale, it should be relatively easy to revert back if necessary. Additionally, Timescale has raised a significant amount of funding, so it is likely to be around for a while.
The managed hosting service offered by Timescale is expensive, which may not be feasible for small businesses or individuals.
If you are using hypertables in Timescale, you will lose foreign key constraints, which can be a significant limitation for some users.
Choosing Timescale over the more established and reliable option of PostgreSQL is a risky choice. However, if you do decide to go with Timescale, it should be relatively easy to revert back if necessary. Additionally, Timescale has raised a significant amount of funding, so it is likely to be around for a while.
What problems is the product solving and how is that benefiting you?
We are building an analytics product built specifically for the website building and hosting company Webflow. We are processing millions of events from various websites and turning them into insightful dashboards.
As our company is growing fast, we found that a quick, reliable database is vital for our company to grow and thrive.
Like many other companies, Nocodelytics started with PostgreSQL. In the beginning, it worked. But the size of the database grew very, very fast. Eventually, with millions of rows, our dashboards became sluggish. Queries for customers with a lot of traffic would take several minutes or even time-out.
My first choice was ClickHouse, which seems to have better performance than Timescale for our use case—but keep reading as there's more to it.
Not everything was great about ClickHouse: It does a lot, which can get confusing, and I’d rather stick with PostgreSQL, which I’ve used for years and know works.
The best feature of TimescaleDB: it's all PostgreSQL, always has been. All your tools, all the existing libraries, and your code already work with it. I’m using TimescaleDB because it’s the same as PostgreSQL but magically faster.
As our company is growing fast, we found that a quick, reliable database is vital for our company to grow and thrive.
Like many other companies, Nocodelytics started with PostgreSQL. In the beginning, it worked. But the size of the database grew very, very fast. Eventually, with millions of rows, our dashboards became sluggish. Queries for customers with a lot of traffic would take several minutes or even time-out.
My first choice was ClickHouse, which seems to have better performance than Timescale for our use case—but keep reading as there's more to it.
Not everything was great about ClickHouse: It does a lot, which can get confusing, and I’d rather stick with PostgreSQL, which I’ve used for years and know works.
The best feature of TimescaleDB: it's all PostgreSQL, always has been. All your tools, all the existing libraries, and your code already work with it. I’m using TimescaleDB because it’s the same as PostgreSQL but magically faster.
Timescale vastly improves the efficiency of our operations with time series data
What do you like best about the product?
Compression is an excellent tool for cost-saving while balancing functionality
What do you dislike about the product?
There is an issue with them transiting between two managed products, resulting in a mismatch of feature/location options, but I believe they are quickly resolving this.
What problems is the product solving and how is that benefiting you?
Scaling with IoT data has been greatly enhanced by our use of Timescale. Feature like time buckets and continuous aggregates really expand our ability to offer greater functionality
A migration we like to think back to
What do you like best about the product?
Timescale enabled us to reduce complexity in our codebase by using its built-in functions.
Achieved 50% cost savings while even improving performance.
Great docs; they not only help you to get a PoC running (where documentation typically starts to thin out) but also cover what you need to run in production.
Customer success team really lives up to its name. Got us access to engineers when it was necessary and helped to prioritise some features we needed.
Achieved 50% cost savings while even improving performance.
Great docs; they not only help you to get a PoC running (where documentation typically starts to thin out) but also cover what you need to run in production.
Customer success team really lives up to its name. Got us access to engineers when it was necessary and helped to prioritise some features we needed.
What do you dislike about the product?
Backfilling data into already compressed chunks could be more performant
What problems is the product solving and how is that benefiting you?
Storing a lot of IoT data, running analytics against it, and visualizing raw data on demand. Initally used MS SQL but had to write a lot of code for partitioning and some of the more complex queries. Timescale takes care of that for us now.
Flexible database service and g
What do you like best about the product?
- I have personally only had positive experiences from Timescale's support. They have been helpful and responsive in answering our questions and helping us optimize our instances, for example by setting up compression.
- Flexible and PostgreSQL based.
- Good documentation and open source.
- Flexible and PostgreSQL based.
- Good documentation and open source.
What do you dislike about the product?
They do not offer the same functionality in their Managed and Cloud services. Unfortunately, a portion of the functionalty that would be useful for us is not available in Managed, and Coud is not available in our region. I know they are working on this so this might change in the future!
What problems is the product solving and how is that benefiting you?
High data ingest and performant queries. With Timescale we can be flexible and it's quick to for example set up new aggregations for our use cases.
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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