Comprehensive Comparison Between TDengine and TimescaleDB

In the realm of time-series databases, TDengine and TimescaleDB are two formidable contenders, each offering unique features and capabilities tailored for managing timestamped data efficiently. In this detailed comparison, we will discuss various aspects of these databases to provide a comprehensive understanding of their functionalities, architectures, features, and use cases.

About TDengine

TDengine is a next generation data historian purpose-built for Industry 4.0 and Industrial IoT. It enables real-time data ingestion, storage, analysis, and distribution of petabytes per day, generated by billions of sensors and data collectors. With TDengine making big data affordable and accessible, digital transformation has never been easier.

About TimescaleDB

TimescaleDB is an open-source time-series database built on top of PostgreSQL. Its design addresses many challenges encountered in managing time-series data, such as scalability, query performance, and data retention policies. First released in 2017, TimescaleDB has become a popular choice for storing and analyzing time-series data, thanks to its compatibility with PostgreSQL, performance optimizations, and flexible data retention policies.

Comprehensive Comparison

TDengine Database Functions

• Efficient Data Writing: Supports SQL writing and modeless writing, seamlessly integrated with various third-party tools for data writing without any code changes.
• Efficient Querying: Supports standard SQL queries and provides a range of time-series specific queries and window functions, with support for User-Defined Functions (UDFs).
• Streaming Computation: Besides continuous queries, TDengine supports event-driven streaming computation, eliminating the need for streaming computation components like Flink or Spark when processing time-series data.
• Data Subscription: Applications can subscribe to data from a table or a group of tables, offering Kafka-like APIs with the ability to specify filter conditions.
• Caching Functionality: Caches the last record of each table, enabling efficient processing of time-series data without the need for Redis.
• Visualization: Seamless integration with various third-party visualization components such as Grafana, Seeq, Google Data Studio, etc.
• Cluster Support: Horizontal scalability achieved by adding nodes to enhance processing capacity, with high availability provided through multiple replicas. Supports deployment via Kubernetes for managing TDengine clusters.
• Management: Monitoring instances running in TDengine, supporting various data import/export methods.
• Tools: Provides an interactive command-line interface (CLI) for cluster management, system status checking, and ad-hoc queries. Offers the taosBenchmark stress testing tool for assessing TDengine performance.
• Connectors in Various Languages: Supports connectors for various languages such as C/C++, Java, Go, Node.js, Rust, Python, C#, etc., with REST interface support.

TimescaleDB Database Functions

• Full SQL Support: TimescaleDB offers comprehensive support for SQL syntax, making it easy to use and extendable in a manner similar to traditional relational databases.
• High-Performance Analytical Capabilities: It boasts powerful analytical capabilities suitable for processing large volumes of time-series data efficiently.
• PostgreSQL Extension: As a plugin for PostgreSQL, TimescaleDB leverages existing PostgreSQL features, facilitating seamless integration into existing infrastructure.
• Automatic Sharding: TimescaleDB automatically shards data based on time and space to achieve efficient storage and querying.
• Robust Write Capability: It can handle writing millions of data points per second, demonstrating strong write performance.
• Parallel Querying of Multiple Servers and Chunks: Supports parallel querying of multiple servers and data blocks simultaneously, enhancing query performance.
• Automatic Time-Based Retention Policies: Automatically retains data based on time, allowing for the automatic purging of older data according to specified retention policies.

TDengine Key Concepts

• Metric: Metrics refer to the physical quantities collected by sensors, devices, or other types of collection points. These quantities, such as current, voltage, temperature, pressure, GPS location, etc., change over time. They can be of various data types including integer, float, boolean, or string.
• Label: Labels represent the static attributes of sensors, devices, or other types of collection points. Unlike metrics, labels do not change over time. Examples of labels include device model, color, location of the device, etc. Labels can have any data type.
• Data Collection Point: Data collection points are hardware or software entities that collect physical quantities according to predefined time periods or triggered events. Each data collection point can collect one or more metrics, all of which are collected at the same moment and share the same timestamp. Complex devices often have multiple data collection points, each with potentially different collection periods, and they operate independently and asynchronously.
• Table: Since metrics are generally structured data, and to reduce the learning curve, TDengine manages data using a traditional relational database model. Users need to create a database first, then create tables, and only then can they insert or query data.
• Super Table: Due to the large number of tables created when each data collection point has its own table, managing them becomes challenging. Moreover, applications often require aggregation operations between collection points, which complicates aggregation operations. To address this issue, TDengine introduces the concept of Super Tables (STables). An STable is a collection of data collection points of a specific type.
• Subtable: When creating a table for a specific data collection point, users can use the definition of the super table as a template and specify the specific label values for that particular collection point to create the table. Tables created using the definition of a super table are called subtables.
• Database: A database is a collection of tables. TDengine allows multiple databases in a single running instance, and each database can be configured with different storage policies.

TimescaleDB Key Concepts

• Hypertables: Hypertable is a virtual table that appears as a single table but is actually composed of multiple underlying tables, known as chunks.
• Chunks: Chunks are the actual storage units of a Hypertable, containing data within a certain time range.
• Continuous Aggregates: Continuous Aggregates are materialized views that compute and store aggregated results of time-series data in real-time.
• Compression: TimescaleDB provides data compression functionality to reduce storage space and costs.
• Retention Policies: Retention Policy is a mechanism for automatically managing the lifecycle of data.
• Time Buckets: Time Buckets are a mechanism for grouping time-series data based on time intervals.
• Multi-Node: TimescaleDB supports multi-node architecture, distributing data and query loads across multiple nodes.
• Background Workers: TimescaleDB uses background workers to handle asynchronous tasks such as data compression and continuous aggregate refreshing.
• Integration with PostgreSQL: TimescaleDB is fully compatible with PostgreSQL and extends its functionality.
• Adaptive Chunking: Adaptive Chunking is a mechanism that automatically adjusts chunk sizes based on data write speed and query patterns.

TDengine Underlying Architecture

TDengine can be deployed locally, in the cloud, or as a hybrid solution, providing flexibility in deployment and management.

The architecture of TDengine primarily consists of the following components:

• Storage Layer: Responsible for storing data, TDengine’s storage layer utilizes a columnar storage structure to improve query performance and compress data size. Data is stored on local disks to ensure data persistence and reliability.
• Compute Layer: The compute layer of TDengine is responsible for executing queries and computational tasks. It includes query processors and computation engines for parsing query statements, performing calculations, and returning results to clients.
• Distributed Architecture: TDengine supports a distributed architecture, allowing data to be sharded and stored across multiple nodes for horizontal scalability and load balancing. Each node can independently handle query requests and execute computational tasks, thereby improving system performance and reliability.
• Metadata Management: TDengine uses metadata to manage data storage and distribution. Metadata includes information about databases, tables, partitions, and the distribution of data across nodes. Metadata management enables TDengine to effectively manage and route data.
• Client Interfaces: TDengine provides various client interfaces including SQL interface, HTTP interface, and client libraries, empowering developers to interact with the database.

TimescaleDB Underlying Architecture

TimescaleDB’s underlying architecture is based on PostgreSQL and extended through plugins. The main components of TimescaleDB include:

• Hypertables: TimescaleDB introduces the concept of Hypertables, a special type of table designed for storing time-series data. Hypertables partition data based on time for efficient storage and querying.
• Chunks: Data within Hypertables is divided into multiple chunks, each containing data for a specific time period. Chunks are automatically created and deleted to adapt to changes in data.
• Continuous Aggregates: This is an optimization feature used to precompute aggregate results to accelerate queries. Continuous aggregates are automatically updated in the background.
• Compression: TimescaleDB supports data compression to reduce storage space utilization.
• Distributed Architecture: TimescaleDB can be deployed across multiple nodes to achieve distributed storage and querying.

In summary, TimescaleDB’s underlying architecture leverages PostgreSQL’s capabilities and is optimized for time-series data, making it a powerful tool for handling large-scale time-series data.

TDengine Main Features

Due to its thorough utilization of time-series data characteristics such as structured nature, lack of transactions, infrequent deletions or updates, and a write-heavy, read-light workload, TDengine stands out among other time-series databases with the following features:

• High Performance: TDengine is the only time-series database that solves the high cardinality problem in time-series data storage. It supports tens of millions of data collection points and excels in data insertion, querying, and compression compared to other time-series databases.
• Simplified Time-Series Data Platform: Built-in features like caching, stream processing, and data subscription provide a straightforward solution for handling time-series data, significantly reducing the complexity of system design and operational costs.
• Cloud-Native: With native distributed design, data sharding and partitioning, storage-compute separation, RAFT protocol, Kubernetes deployment, and comprehensive observability, TDengine is a cloud-native time-series database deployable in public, private, and hybrid clouds.
• Ease of Use: TDengine significantly reduces the cost of management and maintenance for system administrators. For developers, TDengine offers simple interfaces, straightforward solutions, and seamless integration with third-party tools. For data analysts, TDengine provides convenient data access capabilities.
• Analytical Capabilities: Through features like hypertables, storage-compute separation, partitioning, chunking, precomputing, and others, TDengine efficiently explores, formats, and accesses data.
• Core Open Source: The core code of TDengine, including cluster functionality, is publicly available under open-source licenses. With over 528.7k running instances globally and 22.9k GitHub stars (data as of 2024.5.10), it boasts an active developer community.

TimescaleDB Main Features

• Hypertable: Hypertable is a PostgreSQL table that automatically partitions data by time. It functions similarly to conventional PostgreSQL tables but includes additional features to facilitate the management of time-series data. You can handle time-series data by creating hypertables, thus improving performance and query efficiency.
• Continuous Aggregates: TimescaleDB supports aggregate operations within continuous time windows, enabling more efficient processing of large volumes of time-series data.
• Compression: TimescaleDB utilizes columnar storage formats, enabling more effective data compression and reducing I/O operations. This is crucial for storing and querying large volumes of time-series data.

TDengine Application Scenarios

• Internet of Things (IoT): With the increasing volume of data in the IoT domain, traditional big data solutions and relational database-centric solutions are becoming inadequate. TDengine is a suitable choice for real-time data storage, querying, and analysis in IoT platforms.
• Industrial Internet: In the field of industrial big data, a large amount of sensor data with timestamps is generated during production, testing, and operation phases. TDengine is well-suited for managing time-series data in various industries such as manufacturing, power, chemicals, engineering, and smart manufacturing, given its characteristics of write-heavy, read-light workload and large data volume.
• Connected Vehicles: By analyzing vehicle message streams, real-time vehicle network quality monitoring, vehicle component health monitoring, user driving behavior monitoring, vehicle system security analysis, compliance monitoring, and other businesses can be implemented. Choosing the right time-series database can prevent bottlenecks in data storage for vehicle message platforms.
• Power and Energy: With the development of the power IoT, the data generated in various links such as generation, transmission, transformation, distribution, and consumption is increasing, posing serious challenges to traditional solutions centered around relational databases. TDengine is an urgent choice for storage, querying, and analysis of power and energy data under large data volume.
• IT Operations and Maintenance (O&M): With the increasing number of servers, IoT devices, and the installation of various new sensors, traditional O&M methods have become increasingly laborious, severely limiting business development. Therefore, a platform based on massive time-series data is urgently needed to support complex O&M tasks.
• Finance: TDengine provides asset management, real-time monitoring, performance analysis, risk analysis, sentiment control, stock backtesting, signal simulation, report output, and other application research services based on financial market data. Given its ability to handle large volumes of real-time data with fixed data formats and long retention periods, TDengine is essential for storing and computing financial market data.

TimescaleDB Application Scenarios

TimescaleDB is a PostgreSQL-based time-series database suitable for various scenarios involving large-scale time-series data. Some key application scenarios include:

• Internet of Things (IoT) Systems: Utilize TimescaleDB for storing and analyzing sensor data from IoT devices, leveraging its time-series optimizations for real-time monitoring and reporting.
• FinTech Applications: Integrate TimescaleDB into FinTech solutions to process high-frequency trading data, ensuring robust performance during peak periods.
• Geospatial Analysis: Store geolocation data, such as taxi trajectories, vessel positions, etc., in TimescaleDB for time-series analysis.
• Monitoring and Alerting Systems: Use TimescaleDB to store and query monitoring data, event logs, and alarm information for rapid response and analysis.
• Sensor Data Collection: TimescaleDB is suitable for storing and analyzing various sensor data types, including weather, environmental, and industrial equipment data.