What is our primary use case?
We are developing a trading agent that uses multiple machine learning models to adapt to the crypto market in real time.
InfluxDB is used to collect data on crypto coin prices from exchanges like Binance and Bybit. Our use case requires low latency and the ability to query data effectively. We use
InfluxDB on a
DigitalOcean infrastructure in a containerized environment with
Docker.
What is most valuable?
The most important feature for us is low latency, which is crucial in building a high-performance engine for day trading. InfluxDB can handle around ten thousand messages per second, which is essential for our requirements. The solution's ability to store time series data is also significant in our crypto trading use case where time series data about prices is critical.
What needs improvement?
One area for improvement is the querying language. InfluxDB deprecated FluxQL, which was intuitive since developers are already familiar with standard querying. Though we can adapt to the
Flux language, I would like to see more development in this area and am unsure why FluxQL was deprecated.
For how long have I used the solution?
We have been using InfluxDB for the last eight months.
What was my experience with deployment of the solution?
We did not encounter any issues with the deployment. Using
Kubernetes allowed us to easily set up InfluxDB in a containerized environment. Although
DigitalOcean does not offer a managed database service, deploying our own container was straightforward and aligned with our continuous integration processes.
What do I think about the stability of the solution?
We have not experienced any stability issues with InfluxDB so far, and it has been acceptable for our needs.
What do I think about the scalability of the solution?
Scalability has not been an issue because we have only used one instance of InfluxDB. It is primarily used for real-time data acquisition rather than for extensive scaling.
How are customer service and support?
We have not needed to contact technical support. All resources required were available through documentation, enabling us to resolve any issues on our own.
How would you rate customer service and support?
Which solution did I use previously and why did I switch?
Previously, we used CassandraDB and
ScyllaDB, a fork of CassandraDB. While these were performant, they did not store data in the time series format essential for our needs. Once we discovered that there were databases like InfluxDB designed for time series data, we decided to try it.
How was the initial setup?
The initial setup was straightforward, as we used
Kubernetes to deploy InfluxDB. Although DigitalOcean does not offer a managed database service for InfluxDB, setting up our own container was an easy process.
What about the implementation team?
One person was responsible for the entire deployment of InfluxDB in our organization.
Which other solutions did I evaluate?
I have experience with CassandraDB and
ScyllaDB as alternatives.
What other advice do I have?
My advice for new users would be to ensure you are choosing the right engine for your domain. For InfluxDB, it performs well for low latency inputs and high-performance real-time data. While I would rate InfluxDB a ten on a scale of one to ten, users should be thoughtful about matching the engine to their specific needs.
Which deployment model are you using for this solution?
Public Cloud
If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?
Other
*Disclosure: PeerSpot contacted the reviewer to collect the review and to validate authenticity. The reviewer was referred by the vendor, but the review is not subject to editing or approval by the vendor.
