QuestDB Reviews & Product Details

Performance is extremely high, and they’ve recently added statistics and window based functions which make calculating trends on the fly all in SQL very easy to achieve. Their support team is very responsive and knowledgeable, and an absolute pleasure to work with. Quest has been very stable as well. Review collected by and hosted on G2.com.

Only limitations currently are such that for parallel ingestion runs you have to scale vertically rather than horizontally, but that’s an extremely minor qualm considering how great the overall experience is. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

We have been using QuestDB for last two years and haven't found any issues so far. Review collected by and hosted on G2.com.

The simple deployment and upgrade processes. The ability to manage dynamic data with varying variables/sensors per row. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

QuestDB fits our needs. We handle and analyze large datasets of cell growth data with measurements over time, often as CSV files. This time-series-based database fits our use case very well. By importing it into an SQL database, developers can quickly implement a convenient and known way to get statistics from selections made in the front end.

We look forward to integrating streaming data from measurements into the database at a later point, but we are not there yet. We know that it is possible in this setup.

Also, the Slack channel is friendly, and the direct contact with developers is excellent. Review collected by and hosted on G2.com.

I have nothing big to complain about, but if I have to complain about something, there was one non-backwards-compatible upgrade. It was fixed on our side but unexpected from the usually very stable product development. Review collected by and hosted on G2.com.

Byond QuestDB being *incredibly fast*, it's also a joy to use. And more importantly, it's not a pain to use. Other DBs require more ongoing maintenance, troubleshooting, etc. QuestDB truly is a "set it & forget it", trustworthy DB that shines in production workloads.

The inbuilt SQL extensions and curated dialect make writing QuestDB SQL fast, fun, and powerful. The inbult REST support, in addition to the InfluxDB Line Protocol & PG Wire connection methods allow the user to select the best tool for the job.

Being open source, the deployment flexibility is fantastic. The QuestDB team regularly meets with us to ensure we're satisfied, and they regularly ship impactful features.

QuestDB is my definitive go-to for all timeseries needs! Review collected by and hosted on G2.com.

The QuestDB web console UI is helpful for adhoc exploration, but does not currently have a means of organizing / saving queries, so it gets messy fast. Additionally, some BI solutions (like PowerBI) or downstream applications can't connect to QuestDB's flavor of PG Wire, so we've had to go through the REST API in these cases. Fortunately, the QuestDB team is on it and anticipates delivering a fix to that soon. Review collected by and hosted on G2.com.

I had invested a lot of time prototyping a project with another time-series database and was finding a lot of pain points. I decided to give QuestDB a go, and the transition process was really easy and most of the issues I had with the other DB simply went away. Additionally, I gained valuable time when my application starts and has to read current state from the DB... QuestDB delivered my data in half the time the other DB did. I also like the SQL syntax - it's just one less new thing I need to pick up when starting with QuestDB. The free/open source version is great for my purposes. Review collected by and hosted on G2.com.

Documentation is pretty good, but I would like to see some more around best practices, particularly with how to design a schema that is optimal for my needs. Review collected by and hosted on G2.com.

So, we had this real-time analytics use case and we're searching all across for the right time series database with a lightening performance. We landed up to an ad on Google mentioning QuestDB as a better alternative.

We went through their deep dive blog comparing both the DB archs and we understood the gains are on the upside.

Upon testing, it proved to be an excellent choice. Started using it extensively and found it to deliver on its promises.

Another thing we liked is their open Slack community always ready to help with any issues we might have. A big PLUS in my eyes. Review collected by and hosted on G2.com.

Since this is a newer offering compared to the other DBs, it has its pain points. Like random "table suspended" issues with health checks not updating properly requiring manual restarts, and other minor hiccups.

They do take feedback consistently improving their DB and clients like recently they upgraded from TCP to TCP over HTTP on all their clients to rectify our above mentioned issue.

Overall, I would say although it's a newer alternative, it's evolving rapidly, and I would suggest anyone looking for a time series DB, do give this a try. Review collected by and hosted on G2.com.

We recently evaluated several options for storing high frequency historical market data. Our initial choice was TimescaleDB because we use Postgres however it was simply not fast enough for our use case. Eventually we found QuestDB and though it was missing a few 'nice to have' features, it more than delivered on speed and ease of use.

Their support team has also been great with helping us out whenever we need it, certainly one of the best support experiences from any of the services we have enterprise contracts with. Review collected by and hosted on G2.com.

It's not quite as feature rich as other options, specifically we are very much looking forward to materialised view support! Review collected by and hosted on G2.com.

Our project have been using Solr as a statistics data storage for years. It was not a right decision because Solr is not good solution for realtime data processing. The best approach in our case is a time series database. Using such DB in embedded mode was another important requirement. There are a lot of competitors in this market, but QuestDB was the only one who offers embedded mode. So we gave a chance to QuestDB and never regreted.

It`s great to see the rapid evolution of QuestDB. The developers team constantly introduces new features, improvements and bug fixes.

Also want to note an excellent support. The ability to ask a question directly to developers and get answer in Slack channel is a really cool. Review collected by and hosted on G2.com.

Actually the embedded mode chapter looks to be the smallest part of documentation. It would be nice to add some best practice advices and code examples. Review collected by and hosted on G2.com.

- Very responsive and co-operative support from developers/community.

- Helpful features already up and running from various versions, many more to come in the future!

- Clear roadmap that clarifies what we can expect and when.

- Loads of tools to intergrate with. Review collected by and hosted on G2.com.

- Current data storage feature consumes more storage, however the upcoming parquet file format shall reduce it. Review collected by and hosted on G2.com.