The Storage Model and Data Partitioning/Sharding in TDengine

Storage Model

The data stored by TDengine include collected time-series data, metadata related to libraries and tables, tag data, etc. These data are specifically divided into three parts:

  • Time-series data: stored in vnode and composed of data, head and last files. The amount of data is large and query amount depends on the application scenario. Out-of-order writing is allowed, but delete operation is not supported for the time being, and update operation is only allowed when update parameter is set to 1. By adopting the model with one table for each collection point, the data of a given time period is continuously stored, and the writing against one single table is a simple add operation. Multiple records can be read at one time, thus ensuring the insert and query operation of a single collection point with best performance.
  • Tag data: meta files stored in vnode support four standard operations of add, delete, modify and check. The amount of data is not large. If there are N tables, there are N records, so all can be stored in memory. If there are many tag filtering operations, queries will be very frequent and TDengine supports multi-core and multi-threaded concurrent queries. As long as the computing resources are sufficient, even in face of millions of tables, the filtering results will return in milliseconds.
  • Metadata: stored in mnode, including system node, user, DB, Table Schema and other information. Four standard operations of add, delete, modify and query are supported. The amount of these data are not large and can be stored in memory, moreover the query amount is not large because of the client cache. Therefore, TDengine uses centralized storage management, however, there will be no performance bottleneck.
  • Greatly reduce the redundancy of tag data storage: general NoSQL database or time-series database adopts K-V storage, in which Key includes timestamp, device ID and various tags. Each record carries these duplicates, so wasting storage space. Moreover, if the application needs to add, modify or delete tags on historical data, it has to traverse the data and rewrite again, which is extremely expensive to operate.
  • Realize extremely efficient aggregation query between multiple tables: when doing aggregation query between multiple tables, it firstly finds out the tag filtered tables, and then find out the corresponding data blocks of these tables to greatly reduce the data sets to be scanned, thus greatly improving the query efficiency. Moreover, tag data is managed and maintained in a full-memory structure, and tag data queries in tens of millions can return in milliseconds.

Data Sharding

For large-scale data management, to achieve scale-out, it is generally necessary to adopt the a Partitioning strategy as Sharding. TDengine implements data sharding via vnode, and time-series data partitioning via one data file for each time range.

Data Partitioning

In addition to vnode sharding, TDengine partitions the time-series data by time range. Each data file contains only one time range of time-series data, and the length of the time range is determined by DB’s configuration parameter “days”. This method of partitioning by time rang is also convenient to efficiently implement the data retention strategy. As long as the data file exceeds the specified number of days (system configuration parameter ‘keep’), it will be automatically deleted. Moreover, different time ranges can be stored in different paths and storage media, so as to facilitate the cold/hot management of big data and realize tiered-storage.

Load Balancing

Each dnode regularly reports its status (including hard disk space, memory size, CPU, network, number of virtual nodes, etc.) to the mnode (virtual management node) for declaring the status of the entire cluster. Based on the overall state, when an mnode finds an overloaded dnode, it will migrate one or more vnodes to other dnodes. In the process, external services keep running and the data insertion, query and calculation operations are not affected.

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TDengine

TDengine

Open-source, cloud-native time-series database optimized for IoT. See more at https://tdengine.com or https://github.com/taosdata/TDengine