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QuestDB for streaming high volume and dynamic sensor data
What do you like best about the product?
The simple deployment and upgrade processes. The ability to manage dynamic data with varying variables/sensors per row.
What do you dislike about the product?
The lack of a compressed format for exporting or for returning large result sets. Given the recent developments with added parquet support I guess this situation might change soon.
What problems is the product solving and how is that benefiting you?
Persisting and analysing/querying large sets of sensor data from various PLC machines.
At a client we have a setup with various PLC machines with different sensors attached. We sample data of some 100-300 variables at rates of 500Hz to 1kHz.
Initially we tried other solutions with bad performance due to the varying datasets. Other solutions struggled with performance when the datamodel got updated (added or removed sensors).
With QuestDB we managed to easily deploy a setup where ingesting this type of data was no problem. We went with small edge-deployed servers dealing with single PLC and a two larger QuestDB instances where one was used to aggregate data for all PLC's to facilitate any near-realtime queries of status of all PLC's and the other larger instance was used to persist data-sets that was classified as valuable to keep for long term analysis.
Having the http/rest-api allowing ingestion and queries simplifies integration with other systems of ours without the need to link to specific database libraries.
The built-in UI that houses query capabilities among others allow us to run quick tests and concepts and in recent releases this also hints at bad server configurations and how to fix some bad server configuration to get the best QuestDB experience.
As a developer I have really enjoyed the open and welcoming attitude by the QuestDB developers. They have actively listened and implemented a lot of end-user suggestions.
At a client we have a setup with various PLC machines with different sensors attached. We sample data of some 100-300 variables at rates of 500Hz to 1kHz.
Initially we tried other solutions with bad performance due to the varying datasets. Other solutions struggled with performance when the datamodel got updated (added or removed sensors).
With QuestDB we managed to easily deploy a setup where ingesting this type of data was no problem. We went with small edge-deployed servers dealing with single PLC and a two larger QuestDB instances where one was used to aggregate data for all PLC's to facilitate any near-realtime queries of status of all PLC's and the other larger instance was used to persist data-sets that was classified as valuable to keep for long term analysis.
Having the http/rest-api allowing ingestion and queries simplifies integration with other systems of ours without the need to link to specific database libraries.
The built-in UI that houses query capabilities among others allow us to run quick tests and concepts and in recent releases this also hints at bad server configurations and how to fix some bad server configuration to get the best QuestDB experience.
As a developer I have really enjoyed the open and welcoming attitude by the QuestDB developers. They have actively listened and implemented a lot of end-user suggestions.
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A WIDE range of uses for QuestDB
What do you like best about the product?
QuestDB and it's development team have enabled us to shape our product to our liking. We are working with very large datasets 6000+ columns x 150 rows per second and questdb has enabled us to ingest that quickly. This one feature cannot be understated as we found ourselves limited by an SQL restriction of 4096 columns in other databases. In our year of using QuestDB, the development team has made tremendous progress. In a recent release, a commit was made "5x speed up of WAL Apply for small transaction and add SQL" that opened up a whole new world of speed for us. If you tried it before and it wasn't quite there yet, I would reccomend trying it again, as this single update really allow us to be near livestreaming of all this data. We love that QuestDB uses a SQL backend, as that enables us to quickly bring new team members up to speed. Another huge benefit has been QuestDBs developer connected slack channel. When things have gone wrong, the developers at QuestDB have been quick to step in and provide feedback (generally on what we are doing wrong! haha). Occasionally we have had real bugs, and have seen a direct improvements in the next release cycle. Another benefit we've found has been the variety of languages QuestDB supports. We were quick to integrate with a pandas ingestion libaray and have been off to the races since.
What do you dislike about the product?
There are drawbacks to a newer product like QuestDB. For us it has been mostly minor things as we are building a product from scratch. Not being in production has its benefits. Mostly for us, is it took a decent amount of effort to figure out what was going wrong when it did go wrong. It takes some time to grok that while a failed ingestion via pandas doesn't directly tell you what you did wrong, the logs WILL tell you. Or when you broke your table from having Linux having too low of a max open files amount, we were initially unsure why ingestion just suddenly stopped. I have since seen most of these addressed in the Documentation, as more guinea pigs like myself have bumped into them, so that is great!
What problems is the product solving and how is that benefiting you?
QuestDB is allowing us to run analysis and daily KPIs on data we stream from our equipment.
QuestDB: Performant Time Series suitable for Quant Trading
What do you like best about the product?
As a quant researcher that need to monitor and diagnose details from high frequency sampling of market and trading data, QuestDB has proved to be reliable, performant and easy to use. Specifically I like
1. Helpful time series features that neatly fit the use cases in high frequency trading. E.g. the symbol data type, the sample by, asof join, the window function(could be more complete) etc.
2. High efficiency so that don't need to spend much on the hardware. A couple of hundred million rows with wide columns on a 4-core box serve my needs pretty well
3. Some user-friendly features that makes it easy to get data into the DB. Specifically I like the the flexible table schema, which caters to the need for constantly evolving data columns. The web UI is nice (though could be more polished). The InfluxDB line protocol supports very simple python code to upload pandas dataframes.
1. Helpful time series features that neatly fit the use cases in high frequency trading. E.g. the symbol data type, the sample by, asof join, the window function(could be more complete) etc.
2. High efficiency so that don't need to spend much on the hardware. A couple of hundred million rows with wide columns on a 4-core box serve my needs pretty well
3. Some user-friendly features that makes it easy to get data into the DB. Specifically I like the the flexible table schema, which caters to the need for constantly evolving data columns. The web UI is nice (though could be more polished). The InfluxDB line protocol supports very simple python code to upload pandas dataframes.
What do you dislike about the product?
It took me quite some time to figure out some initial performance issues. It turns out that I need to do quite some adjustments to the configurations and linux kernel settings. QuestDB website has some sections dedicated to performance tuning. Maybe the doc would present some typically setup configs that cover most common use cases. After reading through the docs and made the changes, the DB sprang to full charge.
Another issue I bumped into was how to recover the table after the linux box crashed. I couldn't figure out to why the inserts no longer works after the box is rebooted. In the end I joined the slack channel to ask the question. Thankfully Nicolas showed me the command to resume WAL, which eventually saved my table. So basically I need to read the doc to understand the WAL.
I think my efforts so far has paid off well. The doc could add more examples to highlight some tricky but still quite common cases.
Another issue I bumped into was how to recover the table after the linux box crashed. I couldn't figure out to why the inserts no longer works after the box is rebooted. In the end I joined the slack channel to ask the question. Thankfully Nicolas showed me the command to resume WAL, which eventually saved my table. So basically I need to read the doc to understand the WAL.
I think my efforts so far has paid off well. The doc could add more examples to highlight some tricky but still quite common cases.
What problems is the product solving and how is that benefiting you?
realtime monitoring and diagonsis
Awesome timeseries database : feature-rich with great DX, great doc and support, and it's so fast !
What do you like best about the product?
- We have been using QuestDB Cloud continuously for months to store weather data from sensors, and the price/ performance/DX ratio is hard to beat. Compared to well-known time series databases, QuestDB Cloud is by far the best tool for our needs.
- Thanks to the open source Docker image, we were able to add QuestDB to our local development environment in a matter of minutes.
- It's easy to use, it's so fast and it just works!
- The developer experience is wonderful: everyone in the team has a great time working with it, and QuestDB appears in the "what went well" in every sprint review.
- Really clever extension of the SQL language to make working with time series data much easier.
- It's flexible: we can pause the instance, and we can easily increase/decrease instance resources as needed from our account, which helps control the cost of dev/staging instances.
- The UI is responsive and well thought out, and the monitoring tool is very useful.
- Documentation is clear and detailed.
- The support is fantastic: very responsive and really helpful, with detailed explanations each time that have helped us a lot! And unlike other managed solutions, support is included in the price.
- New features are released regularly and the roadmap shows exciting things to come. For example, cold storage support for old data is coming soon, with the ability to query this old data. This feature will be a big plus for us.
- Thanks to the open source Docker image, we were able to add QuestDB to our local development environment in a matter of minutes.
- It's easy to use, it's so fast and it just works!
- The developer experience is wonderful: everyone in the team has a great time working with it, and QuestDB appears in the "what went well" in every sprint review.
- Really clever extension of the SQL language to make working with time series data much easier.
- It's flexible: we can pause the instance, and we can easily increase/decrease instance resources as needed from our account, which helps control the cost of dev/staging instances.
- The UI is responsive and well thought out, and the monitoring tool is very useful.
- Documentation is clear and detailed.
- The support is fantastic: very responsive and really helpful, with detailed explanations each time that have helped us a lot! And unlike other managed solutions, support is included in the price.
- New features are released regularly and the roadmap shows exciting things to come. For example, cold storage support for old data is coming soon, with the ability to query this old data. This feature will be a big plus for us.
What do you dislike about the product?
Some features are still missing (like row deletion or full join), but we're waiting for these features to come in the future.
What problems is the product solving and how is that benefiting you?
We need to be able to read and write a lot of data at the same time, with great performance.
A great developer experience is also important. The team loves working with it and is more productive thanks to a well-designed product.
We needed a managed database that would allow us to focus on development rather than database administration.
QuestDB meets all our needs perfectly.
And the support team has helped us a lot and saved us a lot of time.
A great developer experience is also important. The team loves working with it and is more productive thanks to a well-designed product.
We needed a managed database that would allow us to focus on development rather than database administration.
QuestDB meets all our needs perfectly.
And the support team has helped us a lot and saved us a lot of time.
Harnessing QuestDB for Renewable Energy Telemetry
What do you like best about the product?
QuestDB shines as a high-performance, time-series database that seamlessly integrates with our renewable energy telemetry platform. Its exceptional ingestion speed, capable of processing millions of rows per second, aligns perfectly with the high-velocity data from our sensors. The SQL and time-series extensions offer a developer-friendly experience, making it straightforward to create complex queries for real-time analytics. Additionally, its compatibility with Grafana enables us to effortlessly visualize data trends, historical records, and sensor data, providing invaluable insights into our operations. The open-source nature of QuestDB, coupled with its enterprise-grade features like role-based access control and multiple availability zones, offers both flexibility and reliability, ensuring our data is not only accessible but also secure.
What do you dislike about the product?
While QuestDB offers numerous advantages, its documentation and learning curve can pose challenges for new users unfamiliar with time-series databases. Navigating through its advanced features and optimizing queries for maximum performance may require a bit more initial effort and exploration. Additionally, although QuestDB's community and support are growing, the ecosystem is not as vast as some other databases.
What problems is the product solving and how is that benefiting you?
QuestDB is solving the problem of efficiently managing and querying large volumes of time-series data. Its high-performance design benefits users by enabling fast data ingestion and real-time analytics. This is particularly useful in our renewable energy telemetry platform, where the ability to quickly process and visualize data from sensors helps in monitoring and optimizing energy production, detecting anomalies, and making data-driven decisions. The speed and efficiency of QuestDB significantly enhance our operational capabilities and contribute to more effective management of renewable energy resources.
One of the best Open Source projects with top level support from Team and community!
What do you like best about the product?
We have been working with the QuestDb Team for a couple of years now and the level of support the Team provides is outstanding. The features that were critical for us got implemented and the quality has always been at top level.
Balance between performance and features is always a challenge and we have not yet experienced loss of performance even though the list of features have grown considerably.
Balance between performance and features is always a challenge and we have not yet experienced loss of performance even though the list of features have grown considerably.
What do you dislike about the product?
Would like to have all features in the open source version.
What problems is the product solving and how is that benefiting you?
Fast access to time series data
Versatile and performant time-series column store with approachable maintainers
What do you like best about the product?
- Easy to explore and get started using the demo and open-source version
- Highly performant, with concurrent writes over different flavours of data API.
- Column-store storage model, aiding prototyping and flexibility to modify the schema of large datasets.
- Provides a relational model and query language which simplifies migration of existing domain models.
- CSV import/export, useful for integration with existing database infrastructure.
- Good set of key primitives for maintenance and data lifecycle management.
- Roadmap brings key improvements around replication, data sharding, and aggregation functions.
- Support for some OLTP use cases, with UPDATE and PUT-style de-duplication.
- Approachable maintainers with a growing and supportive community.
- Supports a variety of third-party integrations, helping to integrate the database as a storage layer within larger systems.
- Overall, a fast and flexible database that can support a variety of use cases.
- Highly performant, with concurrent writes over different flavours of data API.
- Column-store storage model, aiding prototyping and flexibility to modify the schema of large datasets.
- Provides a relational model and query language which simplifies migration of existing domain models.
- CSV import/export, useful for integration with existing database infrastructure.
- Good set of key primitives for maintenance and data lifecycle management.
- Roadmap brings key improvements around replication, data sharding, and aggregation functions.
- Support for some OLTP use cases, with UPDATE and PUT-style de-duplication.
- Approachable maintainers with a growing and supportive community.
- Supports a variety of third-party integrations, helping to integrate the database as a storage layer within larger systems.
- Overall, a fast and flexible database that can support a variety of use cases.
What do you dislike about the product?
- Error handling could improve (particularly for ILP)
- Some inconsistency in how well some tools integrate with the database over Postgres database connectors.
- Limited support for data sharding/partitioning
The issues above are on the roadmap for improvement and would increase the review score.
- Some inconsistency in how well some tools integrate with the database over Postgres database connectors.
- Limited support for data sharding/partitioning
The issues above are on the roadmap for improvement and would increase the review score.
What problems is the product solving and how is that benefiting you?
Ingress, egress and lifecycle management of bulk time-series data.
Quest DB is one of the easiest TimeSeries Database ever created
What do you like best about the product?
QuestDB is so easy that a one person can handle a database with millions of rows.
Nice Dashboard
Very very fast (50million rows in less than 100ms)
Easy to manage with docker and kamal.
Easy to learn
Easy to install and implement
My Daily usage routing is to check QuestDB
The support team from Slack is fantastic
Nice Dashboard
Very very fast (50million rows in less than 100ms)
Easy to manage with docker and kamal.
Easy to learn
Easy to install and implement
My Daily usage routing is to check QuestDB
The support team from Slack is fantastic
What do you dislike about the product?
Window function
Percentiles and Distributions
Compression and database size could be better, but I guess that's coming soon.
Backups could be stored directly in S3 or any compatible alternative.
Percentiles and Distributions
Compression and database size could be better, but I guess that's coming soon.
Backups could be stored directly in S3 or any compatible alternative.
What problems is the product solving and how is that benefiting you?
Perfect Database for Real User Performance Monitoring
Game changer in Time Series Databases
What do you like best about the product?
At my organization, we needed to build a real time analytics system for OTT streams and for this we needed a solid Time Series Database. Initally we were using elastic search but once we needed to scale up we consistently faced latency issues. So we needed a TimeSeries database which could handled load of billions rows every day. We decided to give Timescale DB a try and there also we faced multiple issues regarding latency. Then we discovered QuestDB and it was a game changer for us. After initial analysis we found that QuestDB is more performant than Timescale and Elasticsearch so we decided to give it a chance. Our use case was to ingest 3 million rows per second and QuestDB was able to handle this load easily. We have been Using QuestDB since 2022 now and we never looked back for another database.
What do you dislike about the product?
We have been using QuestDB for last two years and haven't found any issues so far.
What problems is the product solving and how is that benefiting you?
We are an OTT company so we need to collect cdn logs and analyze them to find most streamed contents. These logs come according to timestamp so we needed a time series database. Intially we were ingesting 5k rows per second and Elasticsearch was sufficient for this use case but as scale grew we needed a more performant database. For this we done analysis on TimeScale and QuestDB. With QuestDB we were able to ingest 3 Million rows per second with minimal delay. With timescale while we were facing latency on simple group by queries (upto 30 seconds), Quest DB was able to perform same queries under 500 milliseconds. So to summarize we needed a both read and write performant DB and QuestDB was able to achieve both.
Compelling offering in a competitive landscape with impressive support
What do you like best about the product?
- stellar query performance
- comprehensive documentation
- easy to setup and use
- best in class support
- compares favourably to KDB, once they have missing features, I can see it being actively replacing KDB
- comprehensive documentation
- easy to setup and use
- best in class support
- compares favourably to KDB, once they have missing features, I can see it being actively replacing KDB
What do you dislike about the product?
- I have struggled with write performance. its not easy to see why.
- its a very competitive landscape and I have used several databases, such as KDB. lack of features such as, lack of support for "DELETE WHERE" can put users away
- its a very competitive landscape and I have used several databases, such as KDB. lack of features such as, lack of support for "DELETE WHERE" can put users away
What problems is the product solving and how is that benefiting you?
storing and querying large amounts of data with ease
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