InfluxDB Cloud Serverless

My main use case for InfluxDB  involves gathering metric data from our storage clusters and putting them into Grafana  dashboards, so InfluxDB  is the data source for Grafana .

I collect and display metrics in Grafana such as throughputs, IOPS, latencies, quota consumptions, network and cart errors.

Regarding my main use case, there are no particular challenges; we run our scripts every five minutes, so we gather data from our clusters every five minutes and keep more than six months.

The best features InfluxDB offers include a web UI that I love because sometimes I need to check some details about my metrics, enabling me to easily see simple details on it.

While the API does not stand out to me since we don't use it regularly for InfluxDB, we primarily use it as a data source for Grafana.

InfluxDB has positively impacted my organization by being a part of our solution, which helps us maintain our solution easily.

It helps me maintain my solution easily because it is very reliable, so we didn't face any performance issues or crashes regarding our queries; we can get the results very fast.

I believe InfluxDB can be improved, but I'm not sure how; maybe some people can say more than me, but for myself, it is enough.

I don't have any additional improvements to suggest regarding documentation, UI, or anything else that I wish was different; nothing comes to mind.

I have been using InfluxDB almost for two years.

InfluxDB is stable.

We haven't faced any issues with InfluxDB's scalability; maybe our capacity consumptions are small, or it is simply good enough to handle that much capacity.

I did not previously use a different solution; there was no switch required.

We do not purchase InfluxDB through the AWS Marketplace ; instead, we use the open-source version.

I haven't seen a return on investment; unfortunately, I cannot share relevant metrics such as time saved, fewer employees needed, or money saved.

I'm not sure about the details regarding pricing, setup cost, and licensing.

Before choosing InfluxDB, I only evaluated Prometheus, but it is not the right solution for us because it has a different methodology than InfluxDB, which exactly matches our requirements.

My advice to others looking into using InfluxDB is that if they need any time-series database, InfluxDB is a good solution with its stable, high performance, and scalable capabilities, and I love the web UI, which also allows us to create dashboards without any other third-party tools.

On a scale of one to ten, I rate InfluxDB a ten.

Hybrid Cloud

Amazon Web Services  (AWS )

I have been using InfluxDB  for one year for my project, Greenhouse  Management using Embedded System, to represent the sensor data on a web dashboard.

I have used InfluxDB  for storing the data and representing it on a web dashboard for my greenhouse project, and that is the only thing I have used InfluxDB for.

InfluxDB offers a database similar to an Excel data sheet, where we can select different data in different fields.

The Excel-like feature of InfluxDB will be beneficial for my greenhouse project, in which I have divided data of different sensors in different boxes so that it is easy to locate that box and view data.

I have successfully completed my greenhouse project with the help of InfluxDB to visualize the data in the dashboard, and it is beneficial for me.

Visualizing my data on a web dashboard helps, as it also gives the data of how it is changing with time, and it also stores data for the future for AI.

It's pretty much good regarding my use case, but I want to tell you that the interface of InfluxDB is so complex and should be made easier for non-technical people.

With InfluxDB, I have to use Grafana , which provides good visualization, so I will tell you that mixing Grafana  and InfluxDB would make visualization better in InfluxDB.

Documentation is also required for InfluxDB, as I haven't got the documentation related to this, but there is a video on Udemy that helped me to use InfluxDB effectively.

I have been using InfluxDB for one year for my project, Greenhouse  Management using Embedded System, to represent the sensor data on a web dashboard.

It is very stable, with no reliability or downtime in InfluxDB; it is very good.

I have normally used InfluxDB and not utilized much scalability, so I haven't experienced the scalability of InfluxDB.

I don't know about the customer support of InfluxDB as I haven't needed help; that's why I haven't experienced customer service.

I haven't used any other solution than InfluxDB, as I appreciate InfluxDB very much.

I haven't evaluated any other options, as I appreciate InfluxDB, which is why I have chosen InfluxDB.

InfluxDB reduced my time to show data without any interruption, also reducing the number of people needed to manage the project; it is very good to have InfluxDB in my project.

I rate InfluxDB a seven out of ten.

It's good, which is why I have chosen it more than five, but it is not rated higher because I have to use Grafana with InfluxDB. I think if it gave visualized data in a better way, then it would have been rated more than seven.

If you want a normal, good visualization or database management system, you can go for InfluxDB, as it is very good software that can visualize the data.

Public Cloud

Other

InfluxDB  is the main component in our large enterprise-scale streaming data application for maritime vessels. We collect position data from vessels around the coast once per second, put it on a Kafka stream, and feed those positions into InfluxDB  continuously. This has been working flawlessly since 2018. We have seven years of time-series data for all the vessels that my company operates, roughly 130 to 140 vessels. Every  move they make is being tracked and stored in InfluxDB.

We mainly write and read data from InfluxDB. We perform very simple queries to do time series on a key, which is a unique ID of the vessel. We will select a vessel and select from time to time stamp. That’s what we do. InfluxDB’s core functionality is crucial as it allows us to store our data and execute queries with excellent response times.

It is challenging to get long-running backups while running InfluxDB in a Microsoft Azure  Kubernetes  cluster. Replicating data for on-prem development and testing is difficult. Having a SQL abstraction in InfluxDB could be beneficial, making it more accessible for teams that prefer querying with SQL-style syntax.

In total, I've been using InfluxDB for six years.

InfluxDB is extremely stable. It serves as the backbone of our application, and its stability is crucial. If InfluxDB stops or doesn’t scale, the entire application stops.

Scalability is critical. We’ve scaled on volume with seven years of continuous data without performance degradation. The scalability allows us to track vessel movements per second back to the application's conception in 2017.

We did not use a different solution for these use cases before InfluxDB.

The initial setup can be intimidating for newcomers, and there is a certain threshold needed due to the performance we get. However, once familiar with the setup, it becomes streamlined.

Four people were involved in the deployment process, and one person is now needed for maintenance.

We haven't gauged any measurable benefits; our company is more operational-focused.

We use the open-source version of InfluxDB, so it is free.

We evaluated using PostgreSQL  and a time-series database in Amazon, though I can't recall its name.

InfluxDB works as expected with excellent scalability and stability, which is critical for our application. I rate InfluxDB ten out of ten overall.

Hybrid Cloud

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 .

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.

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.

We have been using InfluxDB for the last eight months.

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.

We have not experienced any stability issues with InfluxDB so far, and it has been acceptable for our needs.

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.

We have not needed to contact technical support. All resources required were available through documentation, enabling us to resolve any issues on our own.

Neutral

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.

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.

One person was responsible for the entire deployment of InfluxDB in our organization.

I have experience with CassandraDB and ScyllaDB  as alternatives.

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.

Public Cloud

Other