InfluxDB Reviews

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.

InfluxDB is a database where you can insert data. However, it would be best if you had different components for alerting, data sending, and visualization. You need to install tools to collect data from servers. It must be installed on Windows or Linux servers. During installation, ensure that the configuration file is correct to prevent issues. Once data is collected, it can be sent to InfluxDB. For visualization, you can use open-source tools like Grafana.

InfluxDB is generally stable, but we've encountered issues with the configuration file in our ticket stack. For instance, a mistake in one of the metrics out of a hundred KPIs can disrupt data collection for all KPIs. This happens because the agent stops working if there's an issue with any configuration part. To address this, it is essential to ensure that all configurations are part of the agent's EXE file when provided. This makes it easier to package the agent for server installation and ensures all KPIs are available from the server. Additionally, the agent cannot encrypt and decrypt passwords for authentication, which can be problematic when monitoring URLs or requiring authentication tokens. This requires additional scripting and can prolong service restart times.

Initially, we used Prometheus and Grafana for alerts. But, it wasn't feasible to handle logs and other types of databases. We then switched to InfluxDB due to its user interface, which allowed us to customize alerts, data visualization, and filtering for monitoring purposes. 

The user interface is well-designed and easy to use. It provides a clear overview of the data, making it simple to understand the information at hand.

Our usage of Influx is very specific. We use it for our casino stack and applications. We use it for monitoring, logging, and metrics and trend analysis for player wagering. It is for casino data. We correlate all that data and generate reports for data warehousing. So, we use it massively in our stack for the casino for a lot of things. 

We haven't yet migrated to Influx 2.0. We are still using Influx 1.8 because we have multiple workloads in production. It is going to take some time, and we need to ensure that the latest one is stable and the framework also brings a lot of different languages.

We use it on-premise and in the cloud. We have both. It is a private cloud on AWS.

In our case, it started with a necessity to fill the gap that we had in monitoring. We had very reactive monitoring without trend analysis and without some advanced features. We were able to implement them by using a time series database. We are able to have all the data from applications, logs, and systems, and we can use a simple query language to correlate all the data and make things happen, especially with monitoring. We could more proactively monitor our systems and our players' trends.

We can integrate it with everything. It has an API. We can put any data there. We are developing apps. We are creating scripts. We are doing lots of things. There are different ways to configure it. As a database, it just has to ensure that data is there, and it is properly stored with the tags, fields, measurement names, and time stamps. The focus is that the data that you put in there is perfectly stored. 

I use the solution to store and manage data from various sensors in a production environment. I have developed a system where data from these sensors is communicated through an OPC UA receiver and stored in InfluxDB. It handles serial data from sensors effectively and integrates well with Grafana for visualization.

The platform operates very quickly. It is easy to configure, connect, and query and integrates seamlessly with Grafana.

I'm using InfluxDB to store documents related to the database and to populate data through their funnel.

The most valuable feature of the solution is we can use InfluxDB to integrate with and plug into any other tools.

I use InfluxDB for querying based on time ranges and window operations.

InfluxDB's best feature is that it's a cloud offering. Other good features include its time-series DB, fast time-bulk queries, and window operations.