Published at Mar 6, 2024
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Written by Carlota Soto
InfluxDB carved a significant niche in the time-series database world, earning the trust of developers and companies throughout the years. But, as the industry landscape evolves and broadens, many are exploring InfluxDB alternatives. In this blog post, we discuss five popular alternatives to InfluxDB.
InfluxDB, built by InfluxData, has been a cornerstone in time-series databases. Since its inception as an open-source project, InfluxDB has served the time-series data needs of many industries, including IoT, network monitoring, finance, and others.
InfluxDB utilizes a columnar storage and a schemaless model, with an underlying schema created automatically. Data is organized into measurements containing a timestamp, tagset (metadata), and fieldset (data values). Fields support float, int, string, and boolean data types and can't be modified without rewriting data. Tagsets, always strings, are indexed, while fieldsets are not. Plus, tagsets cannot be updated.
On its first version (InfluxDB 1.x), InfluxDB started by facilitating the fast retrieval and manipulation of time-series data via a SQL-like syntax. With InfluxDB 2.x, Flux was introduced as InfluxDB’s new query language, inviting more complex, multi-faceted time-based queries and data transformation. With the launch of InfluxDB 3.x, InfluxData is returning to InfluxQL, rebuilding its database to improve its performance for high-cardinality datasets, compatibility, and storage efficiency.
One of the main use cases for InfluxDB is the storage of monitoring and observability metrics. Let’s cover two open-source InfluxDB alternatives for this use case (often used together): Prometheus and Grafana.
Prometheus is an open-source monitoring and alerting toolkit designed for reliability and scalability. It was originally developed by SoundCloud and is now a standalone project maintained independently of any company. Prometheus has become a widely adopted standard for monitoring, supporting multiple programming languages and data storage backends.
Prometheus is particularly popular in Kubernetes and other cloud-native ecosystems since its characteristics are especially fitting for monitoring microservices architectures:
Prometheus employs a multi-dimensional data model, where time-series data is identified by metric name and key-value pairs.
It uses PromQL as a query language, specifically tailored to querying Prometheus metrics, offering a range of operators and functions for analyzing such metrics.
Prometheus operates on a “pull” model. It “scrapes” metrics from instrumented applications, either directly or via intermediary push gateway for short-lived jobs, supporting various service discovery mechanisms to discover target services to scrape dynamically.
Prometheus is a robust solution for altering, with features including silencing, inhibition, aggregation. It sends notifications via email, PagerDuty, and Slack, and allows the definition of alerting and recording rules to change the way data is processed and alerts are generated.
Prometheus is a widely used tool, providing a healthy ecosystem of client libraries for the easy instrumentation of services along with community-contributed exporters to bridge services that don’t natively support Prometheus.
Prometheus can act as a time-series database since it stores and manages time-series data—but it is not designed as a long-term storage solution. In a broader context, Prometheus is more of a monitoring and alerting toolkit than a traditional database. Prometheus stores data locally, and its stronger features go beyond data storage (including data collection, querying, visualization, and alerting). It has to be used along with other storage backends for the long-term storage of metrics.
Prometheus is a solution built for the following use cases:
Real-time monitoring. Prometheus excels at providing real-time monitoring of systems, applications, and infrastructure. It is adept at collecting metrics related to CPU usage, memory, disk space, and network activity metrics. It’s also highly efficient for monitoring application health and performance, tracking metrics like error rates, request counts, and response times.
Cloud-native environments. Prometheus is widely adopted in Kubernetes environments for its ease of integration and robust monitoring capabilities, offering insights into cluster health and application performance. Its pull data model is useful in this environment of short-lived microservices.
Alerting. Prometheus supports real-time alerting, allowing teams to respond promptly to issues affecting system or application performance. The Alert Manager handles alerts sent by client applications, and it’s especially useful for managing notifications and silencing during maintenance windows.
There’s a fundamental difference between InfluxDB and Prometheus: while influxDB is a traditional time-series database, Prometheus is primarily a monitoring and alerting tool, not conceived as a long-term data store.
Taking into account this fundamental difference in the character of both tools, let’s summarize a few of their main design differences:
Data collection. InfluxDB relies on a traditional “push” model, where data is sent to the database by the monitored systems or applications. Instead, Prometheus scrapes metrics from monitored targets at specified intervals.
Data model. InfluxDB has a flexible schema and supports tags, making it suitable for various types of time-series data. Instead, Prometheus uses a multi-dimensional data model tailored specifically to the metrics it collects and that identifies the data by metric names and key-value pairs.
Query language. InfluxDB uses InfluxQL and Flux, while Prometheus uses PromQL. All three are proprietary languages with different characteristics. Of all three, InfluxQL is the closest to SQL.
Alerting capabilities. InfluxDB can support alerting but relies more on external tools or scripts to manage and send alerts. As we were saying previously, alerting is one of the strongest functionalities in Prometheus.
As it always happens in the world of databases (and technology in general), the comparative advantages of using Prometheus instead of InfluxDB will depend on the use case. As mentioned, Prometheus is a solution specialized in the monitoring use case, especially within the context of cloud-native applications running in Kubernetes. Prometheus is a very strong solution for this particular use case, offering several advantages vs. InfluxDB:
Efficient metrics collection and management for cloud-native environments. Prometheus’ pull model is highly efficient and manageable in dynamic environments, offering autonomy and adaptability in metrics collection from various sources.
Fast querying and advanced alerting built-in. PromQL is a powerful query language for the monitoring use case: it is fast, and it allows for quite complex and granular analytic queries. This, combined with Prometheus altering features, makes it a very performant (and practical) tool for monitoring.
Broad community and ecosystem support. Due to its popularity, Prometheus has become the de facto standard for observability and monitoring in cloud-native applications. Its open-source nature fosters an active, global community that continuously contributes to improving the product, with a wealth of resources available, regular feature updates, and a vibrant ecosystem of integrations and exporters.
The specialized nature of Prometheus makes it hard to beat for the monitoring use case—but of course, this also implies that Prometheus is a limited solution in nature. Comparatively, InfluxDB is a more flexible time-series database that can be used in many other contexts.
If your use case is not purely server and application monitoring, and especially if you’re not running a cloud-native application in Kubernetes, you might find that using a time-series database like InfluxDB is better for you for the following reasons:
Long-term data storage. As mentioned earlier, and unlike InfluxDB, Prometheus is not primarily designed for long-term data storage. You will need to integrate Prometheus with other storage backends if you want to keep your metrics for a longer period of time. This might not be a big deal for server monitoring, but you probably want to keep your data around for other time-series data use cases.
Versatility and flexibility. In scenarios requiring varied applications of time-series data, e.g., for storing time-series data that are different from server metrics, InfluxDB proves to be more adaptable.
Traditional data collection (push model). Prometheus’ pull model works very well in microservices environments but doesn’t fit all use cases. InfluxDB will work best in environments where data needs to be written actively into the database.
Now, let’s cover Grafana, a solution somewhat related to Prometheus (they’re often used together as InfluxDB alternatives for monitoring).
Grafana is an open-source platform that enables users to create, explore, and share dashboards that provide real-time insights into their data. It offers a wide range of visualization options, including graphs, tables, heat maps, etc. You can customize and interact with these visualizations to gain detailed insights into your data.
Due to the performant nature of its dashboards, Grafana is most useful in contexts where real-time data visualization and analysis are key—like monitoring and observability. In this context, it is often paired with Prometheus, although it is worth mentioning that Grafana supports a myriad of data sources, including PostgreSQL, Elasticsearch, MySQL, and even InfluxDB.
Grafana is not a direct substitute for InfluxDB, as they serve different primary purposes: InfluxDB is a time-series database, while Grafana is a data visualization tool. However, Grafana is often considered an indirect alternative to InfluxDB:
InfluxDB, particularly via Influx Cloud or via the Chronograf component in the TICK stack, offers data visualization features. Grafana provides more advanced, flexible, and customizable visualization options, making it a preferred choice for many users aiming to explore and visualize their time-series data.
Grafana’s customizable dashboards and extensive library of pre-built panels and dashboard templates offer more visualization options compared to the built-in options in InfluxDB.
Grafana can integrate with a multitude of data sources, including InfluxDB, Prometheus, and PostgreSQL, among others. This flexibility allows users to pull data from various sources into a unified dashboard, which is impossible with Influx Cloud.
Although InfluxDB also has alerting capabilities via InfluxDB Cloud or Kapacitor, the Grafana-Prometheus duo offers a more powerful and user-friendly interface to visualize, manage, and assess alerts, making the alerting process more intuitive and manageable.
Paired together, Prometheus and Grafana act as a platform for monitoring: Prometheus handles the metrics collection and storage, while Grafana provides the visualizations offering real-time views into the health and performance of the systems and applications. Grafana supports PromQL, making it easy to integrate with Prometheus: users can write and execute complex queries to filter and visualize the metrics collected by Prometheus, with Grafana rendering the results in a user-friendly and interactive format.
Both solutions also complement each other for alerting. While Prometheus handles alert rules and notifications, Grafana aids in visualizing alerts, making it easy to oversee and manage alerting rules and current alerts directly from the dashboards. Grafana’s alerting interface allows users to configure and tweak alerts graphically, making alert management more intuitive.
Ultimately, the nature of your use case and specific needs will determine if Prometheus and Grafana are the right InfluxDB alternatives for you. Generally, we advise you to do the following:
If your time-series needs are limited to monitoring and observability use cases, and you don’t necessarily care about storing your observability metrics in-database for a long time, Prometheus is probably the most robust solution.
If you host your applications in a cloud-native environment, especially Kubernetes, the combination of Prometheus and Grafana is hard to beat. Prometheus’ automatic service discovery will prove extremely helpful, and its alerting capabilities—paired with Grafana’s dynamic dashboards—will give you all you need to set up continuous and accurate monitoring.
In the previous section, we discussed metrics monitoring extensively as it's a common use case for InfluxDB. But there are many more time-series data use cases involving IoT and sensor data, fleet location data, energy metrics, tick data, crypto, events, weather data, etc. For these time-series use cases, you’ll be looking for a time-series database that gives you enough query and ingest performance and where you can store large volumes of data long-term in an effective way.
Let’s discuss three database alternatives to InfluxDB for broader time series use cases like IoT.
MongoDB is a popular NoSQL database known for its flexibility and scalability. It uses a document-oriented data model, storing data in JSON-like BSON (Binary JSON) format, allowing diverse and complex data structures to be stored and manipulated efficiently.
At its start, MongoDB became widely popular as a document store for quickly prototyping and easily scaling web apps. Today, MongoDB has grown into a versatile solution able to handle many different use cases, including time-series data.
In some ways, using MongoDB for time-series data instead of InfluxDB might offer some advantages: for example, MongoDB is a robust solution with a strong and stable feature set. It is also a database ready to scale and handle large volumes of data. Its flexible schema makes it easy to start and adapt to evolving data structures. But generally speaking, using MongoDB as an alternative to InfluxDB for a time-series use case is not the best idea:
While InfluxDB is a specialized database for time-series data, MongoDB is a much broader tool. Switching InfluxDB for MongoDB will be like switching a scalpel for a Swiss Army knife. Yes, MongoDB can be used as a time-series database, but you’ll be forcing MongoDB to behave this way—time-series data applications are usually quite different from the original design case for MongoDB (building web applications).
Even if MongoDB can be easy to set up due to its flexible “schemaless” nature, running it efficiently in production is another thing. It is not simple to optimize MongoDB’s performance for time series and handle its scalability in a production environment: you’ll need to gain a good understanding of its architecture, indexing, and sharding mechanisms, which implies a learning curve.
MongoDB’s performance for time-series workloads is not the best, at least in our experience. MongoDB’s document structure doesn’t fit the append-only nature of time-series workloads, and its JSON syntax gets quite verbose when writing common queries for time-series analysis.
MongoDB is not designed with time-series data management as the core use case and, therefore, doesn’t have built-in functionality tailored to time-series data. You will miss some built-in functionality InfluxDB supports, like continuous queries and retention policies.
Lastly, MongoDB can get quite expensive to run.
ClickHouse is a columnar database designed for OLAP (Online Analytical Processing) tasks, providing real-time query processing and data analytics capabilities. It was developed in 2016 by Yandex to process and analyze large volumes of data in real time for Yandex Metrica, one of the world's largest web analytics platforms. It has since evolved into an open-source database management system.
If you have a classic OLAP use case (e.g.. immutable data, reduced number of clients), Clickhouse can offer you some significant advantages vs InfluxDB:
Clickhouse offers high performance for analytics due to its columnar storage and vectorized query execution.
ClickHouse is also a scalable solution that can handle large data volumes.
Different from MongoDB, ClickHouse utilizes a SQL-based query language, making it easier to transition if you’re using InfluxQL. ClickHouse supports a rich set of useful SQL functionalities for analytics, like real-time queries, joins, and subqueries.
This said it is important to notice that ClickHouse’s use case is specific to OLAP. If your application has an OLTP (OnLine Transaction Processing) character or demands more flexibility, ClickHouse may not be the best solution. While ClickHouse displays excellent performance and scalability for OLAP use cases, it lacks some important features that can be key for transactional workloads. For example, it lacks efficient data modifications, and its sparse indexes make it less efficient for point queries, retrieving single rows by their keys.
In conclusion, ClickHouse can be a good InfluxDB alternative, but only if your time-series use case is clearly OLAP—for example, analytics processing, reporting on large volumes of immutable data, or other scenarios where data is written once and read many times for insights, and where real-time data modification and deletion are not critical requirements.
TimescaleDB is a time-series database built as a PostgreSQL extension. It adds a boost in performance to PostgreSQL, effectively transforming it into a time-series database by giving it the performance and scalability required by time-series data use cases. TimescaleDB achieves this by incorporating extra functionality on top of PostgreSQL, including automatic time-based partitioning, columnar compression, advanced query capabilities, data retention features, and function libraries for time series.
TimescaleDB’s advantage relies on its PostgreSQL foundation (and, yes, we may be slightly biased here). Timescale shines for use cases already using PostgreSQL or PostgreSQL-compatible tools within their stack and that benefit from the frequent joining of time series alongside relational data.
Users can store their time-series data and regular PostgreSQL tables in one place, making it easy to integrate Timescale with other tools due to the broad PostgreSQL compatibility. Timescale uses standard SQL as the query language.
Compared to the other two InfluxB alternatives, TimescaleDB acts as a middle term between the jack-of-all-trades database that is MongoDB, and the specialized OLAP database that is ClickHouse. Its PostgreSQL foundation enables great flexibility, making it much more appropriate for OLTP time-series use cases than ClickHouse, while its focus on time-series data gives it a more specialized character over MongoDB.
In essence, TimescaleDB can be considered a good alternative to InfluxDB due to the following reasons:
SQL as the query language. Leveraging the familiar and powerful SQL query language, Timescale provides more robust, flexible, and extensive query capabilities, together with broad compatibility with other tools.
Proven and stable technology. Timescale runs on the solid foundation of PostgreSQL, ensuring high data integrity, consistency, and reliability.
Join operations. With its PostgreSQL foundation, Timescale supports complex join operations between time series and relational data, a feature less innate to InfluxDB. You can use the same PostgreSQL database to store all data—time series and otherwise.
Efficient data compression. Timescale offers superior columnar compression tailored for time-series data, ensuring efficient storage management and reduced storage costs compared to InfluxDB.
Extensibility and ecosystem. Timescale benefits from PostgreSQL’s rich ecosystem of tools, extensions, and community support, offering users a wider range of options for customization, integration, and management.
The advantages of Timescale vs. MongoDB and InfluxDB are primarily rooted in the PostgreSQL foundation of TimescaleDB. PostgreSQL (and standard SQL) equips Timescale with a robust, reliable, and proven database architecture. Due to PostgreSQL's mature and well-tested ecosystem, users can benefit from enhanced data integrity, consistency, and compatibility. In contrast to MongoDB's NoSQL structure and InfluxDB's domain-specific query languages, Timescale’s SQL support offers familiar, flexible, and extensive querying. Timescale enables users to perform complex join operations effortlessly, coupled with efficient data compression and an extensive range of tools and extensions.
To be more specific, we can highlight the following advantages of TimescaleDB as an InfluxDB alternative vs MongoDB:
Performance. When it comes to querying time-series data, TimescaleDB exhibits better performance than MongoDB: in our testing, Timescale showed 200 % to 5,400 % faster queries and 170 % to 260 % faster writes.
Time-series features. Like InfluxDB, Timescale offers tailored features specifically for working with time series that will make your life easier—these include time_bucket, continuous aggregates, or data retention policies, among others.
Storage efficiency. MongoDB becomes inefficient space-wise due to the BSON storage format and the lack of specialized compression algorithms for time-series data. On the contrary, Timescale has developed specialized solutions to reduce the storage footprint of large time series datasets like columnar compression and data tiering.
Query language: TimescaleDB supports SQL, a widely-used and powerful query language if compared with InfluxDB’s InfluxQL or Flux. SQL provides more flexible querying and analytical capabilities than MongoDB’s query language, which is more suited for working with time series.
Data relationships. TimescaleDB supports complex join operations, enabling users to easily combine time-series data with other relational data for comprehensive analytics, a more complex operation both in MongoDB and InfluxDB.
Timescale will be a better choice than ClickHouse depending on your time-series use case's character and your priorities. If we’re talking about a straightforward OLAP use case (e.g., reporting over large volumes of immutable data), ClickHouse is a very performant solution. As a user, you would need to evaluate how much you value performance over flexibility or robustness because ClickHouse lacks in those two departments.
If your time-series use case has some OLTP character, or if you’re already using PostgreSQL within your stack, Timescale will most likely be a better InfluxDB alternative due to the following benefits:
Versatility. Timescale offers a great compromise in terms of performance for both OLTP and OLAP time-series workloads, thanks to the versatility of its PostgreSQL foundation. ClickHouse is primarily geared towards OLAP, thus being very restrictive for transactional operations (e.g., data cannot be directly modified on a table).
Full SQL support. ClickHouse’s query language supports many standard SQL features, but not all. One example is JOINs, which are generally discouraged in ClickHouse. Joining data from multiple tables containing time series and relational data is not easy to implement in InfluxDB—but they’re seamless in Timescale.
Robusticity and reliability. TimescaleDB is ACID-compliant, ensuring strict data integrity, consistency, and reliability standards, an area where ClickHouse has limitations. For example, ClickHouse’s architecture lacks consistency in backups.
Broad compatibility. It’s easy to add Timescale to an existing stack due to the broad compatibility and ecosystem of PostgreSQL, which can be a considerable advantage if compared to adopting newer, more specialized solutions like ClickHouse.
In this article, we’ve explored alternatives to InfluxDB: Prometheus, Grafana, MongoDB, ClickHouse, and, of course, TimescaleDB. Each solution has its strengths, depending on the needs of every use case and your priorities as a developer.
On this last note, we want to emphasize that there’s never a one-side-fits-all database. So, make sure you consider your own needs when making your decision! The right database choice is not just a technical decision but a strategic one: you’ll be stuck with your new database for a long time. Be sure to align your present needs with your future aspirations, making a choice that will support your team and applications for years to come.
As the developers of Timescale, we believe our product is an excellent InfluxDB alternative for many time-series use cases, offering an unmatched balance between performance and usability for your time-series data. Plus, PostgreSQL is the most loved database among developers today—don’t you want to find out why that is?
If we’ve piqued your curiosity, try the Timescale platform for free. It takes only a few seconds to create an account, no credit card required. You can also check out Outflux, our migration tool, or reach out for expert advice if you have any questions about Timescale. We can help you migrate from InfluxDB—we have helped many others successfully!