When it comes to data storage, there are few alternatives that can compete with the venerable Prometheus, such as InfluxDB. But what if you need more than just collected data? What if you need real-time insights into your systems? Another powerful platform for real-time data analytics and storage is InfluxDB. Let’s compare how they fare with one another.
Prometheus is a memory-efficient, quick, and simple infrastructure monitoring system. InfluxDB, on the other hand, is a distributed time-series database used to gather information from various system nodes.
In this article, we are going to compare Prometheus and InfluxDB. Both systems have their strengths and weaknesses, but they are both effective monitoring tools. If you are looking for a system that can monitor your database servers, then Prometheus is a good option. If you are looking for a system that can monitor your entire infrastructure, then InfluxDB is a better choice.
What exactly is Prometheus?
Prometheus is a time-series database and monitoring tool that is open source. Prometheus gives its users sophisticated query languages, storage, and visualization tools. It also includes several client libraries for easy interaction. Prometheus can also work with various systems (for example, Docker, StatsD, MySQL, Consul, etc.)
Prometheus can be great for monitoring as long as the environment does not exceed 1000 nodes. Prometheus + Grafana = best ecosystem
What is InfluxDB?
By InfluxData, Inc., a database management system called InfluxDB was created. InfluxDB is open-source and cost-free to use. The InfluxDB Enterprise version is installed on a server inside a corporate network and comes with maintenance contracts and unique access controls for business customers. A web-based user interface for data ingestion and visualization is also included in the new InfluxDB 2.0 version, which operates as a cloud service that is fully customizable.
When it comes to storing monitoring metrics, InfluxDB excels (e.g. performance data). If you need to store different sorts of data, InfluxDB is not the best option (like plain text, data relations, etc.)
Let’s see how these differ from one another
|Prometheus is a system that operates on the principle of pull. The metrics are published by an application at a certain endpoint and Prometheus retrieves them on a regular basis.
|The system InfluxDB is based on is a push-based system. It requires an application to push data into InfluxDB on a regular basis.
|Prometheus and InfluxDB both follow the key/value datastores. However, these are executed very differently on the two systems. Each metric in Prometheus is kept in its own file and is stored in indices that use LevelDB. Metrics recording and monitoring based on those are the major uses of Prometheus.
|Both the indices and the metric values are stored in monolithic databases by InfluxDB. Compared to Prometheus, InfluxDB often uses more disc space. The best database for event logging is InfluxDB. So we have a choice based on the requirements.
|Extensibility and Plug-ins
|Prometheus’ key benefit is its widespread community support, which stems from its CNCF-accredited project status. Many apps, particularly cloud-native applications, already support Prometheus.
|While InfluxDB has a lot of integrations, it doesn’t possess as many as Prometheus.
|Prometheus was designed for monitoring, specifically distributed, cloud-native monitoring. It shines in this category, with several beneficial integrations with current products.
|While InfluxDB can support monitoring, it is not as well known as Prometheus for this purpose. As a result, you may have to develop your own integrations. If you want to do more than a mere monitoring tool, InfluxDB is a fantastic solution for storing time-series data, such as data from sensor networks or data used in real-time analytics.
|Prometheus uses PromQL, a language that is much easier and has no connection to conventional SQL syntax. Let’s say we want a number for CPU load that is greater than 0.5. In that case, we can simply enter CPU load>0.5 in the Prometheus command prompt.
|For its querying purposes, InfluxDB uses a standard SQL syntax known as InfluxQL. For instance, we could write select * from tbl where CPU load>0.5 in the Prometheus cell. This seems simple to an associate with a background in SQL, but Prometheus is also not a challenging experience.
|Prometheus is an open-source project with a huge community of users that can rapidly resolve your queries. Having a big network of support is an added benefit since there is a high probability that the challenges one is having might previously have been encountered by someone in the community.
|InfluxDB, despite its popularity, needs to improve on community support in comparison to Prometheus.
|When the load rises, the monitoring Prometheus servers require scaling as well.This is due to the fact that the Prometheus server is independent. Thus, the Prometheus server works great for the simpler load.
|Since the commercial section of Influx DB is distributed, there will be many interconnected nodes. As a result, as the server scales up, we don’t have to worry about scaling nodes. Thus, Influxdb nodes might be considered redundant while handling complicated loads.
InfluxDB performs exceptionally well at storing monitoring metrics (e.g., performance data). Compared to Prometheus, InfluxDB utilizes more disc space and has a monolithic data storage strategy. It performs well for recording occurrences.
As a result, you can consider the factors discussed in this article while choosing between Prometheus and InfluxDB as monitoring systems for time series data, depending on your business case. When it regards monitoring services for time series data, both platforms are extremely well-liked by enterprises. Some claim that PromQL is new and InfluxQL is similar to SQL and will thus be better, but the reality is different. PromQL is considerably more user-friendly for querying, so go for it. Prometheus has a lot more functionality and integrations, so you can choose it. InfluxDB is a better option if you’re looking for something specifically for IoT, sensors, and other analytics.