With the rapid development of the Internet of Things (IoT), connected cars, and other industries, enterprises are experiencing explosive growth in data collection. This surge in data poses significant challenges for IT infrastructure and databases. Traditional single-node databases are increasingly struggling to handle high concurrency data ingestion and complex queries. As a result, databases need to be designed with horizontal scalability to ensure efficient performance as data volumes continue to expand.
InfluxDB initially offered clustering capabilities in its open-source version, allowing the community to leverage these features for distributed data management. This made InfluxDB a popular choice for businesses looking for scalable time-series data solutions without the high costs of proprietary systems. However, in a controversial move, InfluxDB later restricted these clustering features to its paid offering InfluxDB Enterprise (now called InfluxDB Clustered), forcing organizations using the open-source version to either compromise on functionality or invest heavily in developing their own proxy systems for data sharding and query aggregation. This decision, which effectively closed off key capabilities to the community, left many businesses frustrated with increased complexity and maintenance costs. In contrast, TDengine has taken a more community-driven approach, openly offering its clustering capabilities since August 2020, setting a new standard for open-source time-series databases.
The Limitations of InfluxDB’s Open-Source Version
The main limitation of InfluxDB OSS is its lack of full support for high availability, fault tolerance, and system scalability—key features that are essential for handling the demands of large-scale, high-concurrency environments. Without access to clustering functionality, small and medium-sized enterprises often face significant challenges in achieving these capabilities, which are critical for ensuring reliable and scalable performance. To compensate, many businesses are forced to make the following compromises:
- Lack of High Availability: Without clustering, businesses must implement custom solutions to ensure redundancy and fault tolerance, increasing system complexity and operational overhead.
- Limited Scalability: To handle growing data volumes, companies are required to invest heavily in proxy systems for data sharding, making it difficult to scale horizontally across multiple nodes efficiently.
- Complex Query Aggregation: Without distributed querying capabilities, businesses rely on proxies to perform aggregation across nodes, which adds significant burden to development teams and hampers overall system efficiency.
TDengine’s Clustering Capabilities: Built for Horizontal Scalability from the Start
To meet the ever-increasing demands of data processing, TDengine was designed from the ground up with horizontal scalability and high availability as core principles. It employs a distributed architecture, assuming that both hardware and software systems are unreliable and that no single machine can provide the computational power or storage capacity needed to process vast amounts of data. This design ensures robust horizontal scalability. Furthermore, TDengine efficiently utilizes computational and storage resources across heterogeneous clusters through node virtualization and load balancing, significantly reducing hardware costs.
TDengine clusters offer the following key features:
- Data Sharding and Horizontal Scalability TDengine uses virtual nodes (vnodes) to partition physical nodes in the cluster. Each vnode stores data from specific collection points, and each data point is stored in exactly one vnode. This design ensures:
- Horizontal scalability during ingestion: Clients write data directly to the appropriate vnode. As the number of nodes increases, the system’s throughput grows.
- Horizontal scalability during queries: For aggregate queries, initial aggregation happens within each vnode, and clients then perform secondary aggregation. This distributed computation model simplifies query aggregation complexity.
- Data Partitioning
- Improved query efficiency: Queries can quickly access data within the corresponding time partitions.
- Data retention management: Data beyond the retention period can be deleted by removing the corresponding partition files.
- High Availability and Automatic Failover
TDengine’s system design incorporates vnode groups to ensure high availability. Data within a group is synchronized using a leader-follower model: The Leader node handles read and write requests and synchronizes data with multiple Follower nodes. If the Leader node fails, the system automatically elects a new Leader, ensuring uninterrupted data access and smooth failover. This architecture guarantees data consistency, redundancy, and robust fault tolerance.
TDengine clusters can scale to accommodate up to thousands of data nodes. Applications can connect to any node in the cluster, simplifying interactions with the cluster and enhancing both scalability and usability.
TDengine Cluster vs. InfluxDB Cluster
Since open-sourcing its clustering features in 2020, TDengine has broken down the barriers set by InfluxDB, which restricts clustering capabilities to its InfluxDB Clustered. Key advantages of TDengine include:
- Full-featured distributed database support available to users without high licensing fees.
- Open-source nature, allowing both the community and enterprises to explore and customize TDengine’s cluster architecture for development and optimization.
In contrast to InfluxDB, which faces performance and scalability issues when handling complex queries, TDengine’s open-source clustering supports both high-throughput data ingestion and complex query aggregation. This makes TDengine particularly well-suited for use cases that require processing vast amounts of real-time data and performing complex historical data analysis, such as in IoT, industrial monitoring, and smart city applications.
Conclusion
In time-series data management, TDengine’s clustering capabilities provide enterprises with robust horizontal scalability and high availability. Unlike InfluxDB, which limits clustering features to its closed-source InfluxDB Clustered, TDengine’s open-source clustering removes licensing barriers and allows users to address the challenges of rapid data growth at a lower cost. TDengine’s innovative sharding, partitioning, and vnode technology make it a leader in both efficient data management and complex query performance. For enterprises seeking scalability, ease of maintenance, and cost-effectiveness, TDengine is the more attractive option.
