InfluxDB has been the most widely used time-series database for years. But the time-series database market has significantly matured since InfluxDB’s initial release in 2013, and enterprises now have a wider range of options for purpose-built time-series solutions. With the recent release of InfluxDB 3, many customers are wondering whether InfluxDB is still the best choice for their time-series data needs.
InfluxDB Limitations on Performance and Consistency
In fact, InfluxDB users continue to report struggling with several pain points, most importantly:
- Query latency and throughput: A common complaint is that data ingestion and query performance tends to deteriorate as datasets increase in size and cardinality. New customers who have not yet accumulated large-scale data in their InfluxDB deployments may be initially satisfied with their query latency and throughput — but later realize that the database cannot keep up with the growth of their business. As time-series datasets typically grow over time, InfluxDB’s limited performance at scale often becomes a serious drawback for enterprises.
- Data storage costs: Some users also have difficulty keeping their cost of operation under control with InfluxDB. The platform is resource-intensive compared with similar time-series solutions, requiring customers to make significant investments in server hardware, especially as the scale of their data increases. Inefficient data compression and lack of automated tiered storage options also mean that data storage costs remain high.
- Inconsistent product experience: InfluxDB continues to make big pivots on core functionality, most notably by dropping Flux support in InfluxDB 3. Each iteration of InfluxDB so far has come with a new storage engine and new query language, forcing users to adapt over and over. This is especially difficult for industrial customers that require stable, long-term solutions and smooth upgrades.
TDengine: Fast, Scalable, and Ready for Your Data
TDengine is a time-series database designed specifically for industrial use cases, with customers in fields such as manufacturing, energy, and logistics. Its high-performance, scalable architecture enables real-time ingestion and storage of petabytes of data per day, generated by billions of sensors and data collectors.
TSBS testing demonstrates that TDengine outperforms InfluxDB in data ingestion, query response time, and disk space usage while using fewer server-side resources.
Up to 16x Faster Ingestion
In all five TSBS scenarios, the ingestion performance of TDengine exceeded that of InfluxDB. Compared with InfluxDB, the performance of TDengine ranged from 1.8 times higher in Scenario 3 to 16 times higher in Scenario 5.
Learn more about TDengine’s high performance.
Average 21.2x Faster Queries
Across all TSBS queries, TDengine returned results 21.2 times faster than InfluxDB on average. In the most complex queries, TDengine showed significantly better performance. Its latency for the avgload and breakdown-frequency queries was 426 and 53 times better, respectively, than InfluxDB.
1/2 Disk Space Usage
In the first three TSBS scenarios, InfluxDB and TDengine used approximately the same disk space. However, in Scenario 4 and Scenario 5, InfluxDB required more than 2 times the disk space of TDengine.
Learn more about TDengine’s high compression.
Minimal Server Resources
InfluxDB took longer to ingest the test data and used a far higher amount of CPU resources than TDengine, even reaching 100% usage at times. In comparison, TDengine used a relatively small amount of CPU resources, remaining under 17% usage during ingestion and processing. This shows that TDengine’s unique data model enables not only higher ingestion performance, but also lower resource usage on the server side.
Learn more about TDengine’s distributed design.
With TDengine’s higher performance and lower resource usage, it can be considered a viable alternative to InfluxDB for customers concerned about query latency, throughput, and costs. It is a lightweight but performant and full-featured solution suitable for deployment on edge servers with limited hardware as well as in data centers or cloud environments.
More Than Performance: What Sets TDengine Apart
TDengine provides more than just high performance: it is a comprehensive solution for industrial data that includes stream processing, data subscription, and caching as built-in components. With InfluxDB, these critical features can be implemented only through integration with third-party software such as Kafka, Redis, and Flink, resulting in complex system architecture that is difficult to maintain, especially for industrial enterprises without large IT teams.
Stream Processing
Although InfluxDB provides continuous queries, this feature is intrinsically limited: preprocessing and transformation in scalar functions, session windows, and low-latency use cases like fault detection are all examples of scenarios where continuous query is not up to the task. In an industrial environment, data must be available for analysis in real time.
TDengine’s stream processing engine provides the capability to process data streams in real time as they are written. Once data is written into the stream’s source table, it is automatically processed and pushed to the destination table. This provides a lightweight built-in solution that replaces complex external systems and can provide millisecond-level latency even during high-throughput data ingestion.
Caching
Industrial scenarios demand that the system return the latest data to the application as soon as possible. In fact, historical data is not even accessed during many queries. However, InfluxDB stores historical and real-time data together, leading to unacceptably slow query responses on real-time data.
An InfluxDB-based solution solves this problem by writing new data points into the database and into a caching product, such as Redis, at the same time. While this design works, it necessitates increased system complexity and higher cost of operation. Instead, TDengine provides read caching as a first-class element out of the box.
Data Subscription
While InfluxDB can store time-series data, it can often be difficult to distribute that data in real time. External applications and algorithms need access to data as soon as it arrives at the database, and especially for emerging technologies like AI and ML, simply polling the database is not acceptable; an industrial data platform must push data to these applications.
TDengine provides built-in Kafka-like pub/sub functionality that is easy to use, with topics being defined by SQL statements, and highly flexible. You can control data granularity by specifying supertables, subtables, and even query results as your topics, and data filtering and preprocessing are handled automatically by TDengine. This reduces the amount of data transmitted and simplifies the application development process.
Data Connectors
Additionally, TDengine Enterprise includes zero-code connectors for a wide variety of data sources:
- Industrial data protocols like MQTT, OPC UA, and OPC DA
- Traditional data historians like PI System and Wonderware Historian
- Relational databases like MySQL and PostgreSQL
- Data collectors and event stores like Prometheus and Telegraf
With these connectors, you can easily ingest data directly from disparate data sources, perform ETL and cleansing as needed, and store it in TDengine’s efficient database management system — all through the GUI. You can even continue using Telegraf for data ingestion if preferred, just like InfluxDB.
Reduce Data Processing TCO by 50%
As data systems grow, hardware and cloud resource costs can quickly spiral out of control. Higher performance systems can significantly reduce these costs because they require fewer resources to deliver the same results. Because TDengine ingests data faster, stores data more efficiently, and responds to queries more quickly than InfluxDB, it uses fewer CPU and storage resources and adds less to your bills.
TDengine does not require specialized “university” training, and is much easier to get started with compared with InfluxDB. The three main reasons are that it supports standard SQL, is easy to integrate with third-party tools, and comes with client libraries for various programming languages, including sample code. This minimizes learning costs and ensures that your team can get to work producing business results in the shortest time.
Finally, by including stream processing, caching, data subscription, and data connectors as described above, TDengine simplifies your system architecture and eliminates the need to deploy third-party products just to handle your everyday operations data. Its components are simple, easy to use, and purpose-built to process time-series data. Non-tech companies in particular benefit from this setup as they generally do not have the technical staff to support multiple open-source components.
Learn more about how TDengine can reduce your cost of data operations.
“It’s About Time” to Migrate Your Data
With the release of InfluxDB 3, existing customers are at a crossroads. Moving from InfluxDB 1.8 or 2.x will be a challenge, not just a simple upgrade. In particular, customers that adopted the proprietary Flux language formerly promoted by InfluxData have been severely disappointed by the complete removal of Flux support.
Considering the difficulty of upgrading, this may be the perfect time to consider your options and decide whether a different time-series database could better suit your business goals. TDengine makes the migration process as simple as possible:
- Zero-code InfluxDB connector: You can use our built-in InfluxDB connector to replicate data from your InfluxDB databases into TDengine, ensuring that all historical data is preserved after migration. See InfluxDB in our official documentation to learn more.
- Telegraf support: You can continue to use Telegraf for data collection if you prefer not to make changes to the other elements of your data stack. Just update your Telegraf configuration to send data to TDengine and you’re ready to go. See Telegraf in our official documentation to learn more.
- InfluxDB line protocol support: TDengine can ingest data sent over InfluxDB’s line protocol, meaning that any devices sending data to InfluxDB can be migrated to TDengine just by changing the IP address of the target database. With TDengine’s schemaless ingestion, all necessary data structures like supertables and subtables are created automatically based on the data that you ingest.
Although TDengine supports only SQL as its query language, Flux users who would need to rewrite all queries and applications for InfluxDB 3 anyway may prepare themselves better for the future by doing so in standard SQL, the most common and widely supported language among all types of database management system.
And while TDengine does not provide preconfigured Grafana dashboards or templates for industrial use cases, we do provide native support for Grafana and other tools so that you can create custom visualizations and reports for your business.
What Our Customers Say
- Renewable Energy: Nevados, a leading provider of solar trackers, chose TDengine over InfluxDB for its superior query performance and standard SQL support. The company now ingests data from their trackers into TDengine via Telegraf and has been impressed by the fast pace of innovation at TDengine as well as the personal level of support offered by TDengine team members to resolve any issues that might occur.
- IIoT: An industrial AI company tested TDengine and InfluxDB to determine which product to select for their IIoT data processing. In their real-world tests, TDengine OSS outperformed InfluxDB OSS 1.8 and 2.7 in most aggregation queries, delivering 3 to 10 times faster performance. Compared to InfluxDB, it reduced response times for complex queries from seconds to milliseconds, significantly improving report performance and enhancing the user experience.
Make the Switch Today
At TDengine, we understand that stable, long-term solutions are essential for any industrial data infrastructure, and customers want a consistent product experience instead of big pivots every few years. Many users have determined that the workload of upgrading legacy InfluxDB projects to InfluxDB 3 is similar to that of simply migrating to another time-series database product altogether. And migrating your time-series data from InfluxDB to TDengine can reduce your total cost of data operations while providing higher performance for business applications.
We realize that changing a core element of your data infrastructure is no easy task, and our customer success team will be with you every step of the way during and after your migration to TDengine. We offer white-glove service to customers of all sizes, and our technical team is available 24/7 year-round in the event that any errors are encountered. Additionally, we are now offering a discount on our paid solutions for customers migrating from InfluxDB — contact us for details.
Originally published at https://tdengine.com on February 7, 2025.
