A typical industrial scenario encounters thousands of devices with millions of sensors, consistently generating billions of data points. It poses new requirements of time series data management, not well addressed in existing solutions, including (1) device-defined ever-evolving schema, (2) mostly periodical data collection, (3) strongly correlated series, (4) variously delayed data arrival, and (5) highly concurrent data ingestion. In this paper, we present a time series database management system, Apache IoTDB. It consists of (i) a time series native file format, TsFile, with specially designed data encoding, and (ii) an IoTDB engine for efficiently handling delayed data arrivals and processing queries. We introduce a native distributed solution with distributed queries optimized by parallel operators. We also explore efficient TsFile synchronization mechanisms, ensuring seamless data integration without the need for ETL processes. The system achieves a throughput of 10 million inserted values per second. Queries such as 1-day data selection of 0.1 million points and 3-year data aggregation over 10 million points can be processed in 100 ms. Comparisons with InfluxDB, TimescaleDB, KairosDB, Parquet and ORC over real world data loads demonstrate the superiority of IoTDB and TsFile.

The proliferation of the Internet of Things (IoT) has led to an exponential increase in time series data, distributed and applied in various contexts, demanding a dedicated storage solution. Based on our observations and analysis of IoT production systems, we have characterized 3 requirements for time series data: (1) a close association with devices and sensors, (2) continually synchronizing between cloud-edge, and (3) requiring the ability for high ingestion and low latency access on big volume data. Despite the growing trend, current time series database systems lack a standardized file format, and existing open file formats do not adequately leverage the unique characteristics of IoT time series data. In this paper, we introduce Apache TsFile, a specialized file format tailored for IoT time series data. TsFile organizes data by devices, creating indexes based on device-related information. Our experiments demonstrate the efficiency of TsFile in achieving high data ingestion rates, minimizing latency, and optimizing data compactness.

A typical industrial scenario encounters thousands of devices with millions of sensors, consistently generating billions of data points. It poses new requirements of time series data management, not well addressed in existing solutions, including (1) device-defined ever-evolving schema, (2) mostly periodical data collection, (3) strongly correlated series, (4) variously delayed data arrival, and (5) highly concurrent data ingestion. In this paper, we present a time series database management system, Apache IoTDB. It consists of (i) a time series native file format, TsFile, with specially designed data encoding, and (ii) an IoTDB engine for efficiently handling delayed data arrivals and processing queries. The system achieves a throughput of 10 million inserted values per second. Queries such as 1-day data selection of 0.1 million points and 3-year data aggregation over 10 million points can be processed in 100 ms. Comparisons with InfluxDB, TimescaleDB, KairosDB, Parquet and ORC over real world data loads demonstrate the superiority of IoTDB and TsFile.

LSM-Tree is widely adopted for storing time-series data in Internet of Things. According to conventional policy (denoted by$\pi_{c}$), when writing, the data will first be buffered in MemTable in memory. When it is full, the data will be written to the disk to form SSTables. Compaction is triggered to sort the data in each layer of the LSM-Tree on the disk. However, the arrival of data can be unordered due to reasons such as transition delay. Apache IoTDB uses in-order and out-of-order MemTables to separately buffer the in-order and out-of-order data to accelerate queries, namely the separation policy (denoted by$\pi_{s}$). However, given a specific space of memory budget to buffer the data, write amplification (WA) of the leveled LSM-Tree will be influenced by$\pi_{s}$. Whether the influence by separation is positive or negative, and how intense WA is influenced, depend on the properties of workloads and the capacity of the in-order and out-of-order MemTables. It is highly demanded to build robust models for estimating the expected amount of data rewritten in each compaction, and predicting the WA under$\pi_{c}$and$\pi_{s}$. Note that as an industrial paper, rather than proposing novel techniques for research problems, we focus on the practice of whether separating or not for lower write amplification. Experiments on synthetic and real-world datasets show that the models for estimating WA are accurate under various delay distributions. In addition, based on the estimation models, we implement an analyzer module in the open-source Apache IoTDB, for choosing the policy with lower WA. We apply the method in the use case of our industrial partner, a service provider of engineering machinery. The use case verifies the effectiveness of deciding whether separation or not by WA estimation.

The amount of time-series data that is generated has exploded due to the growing popularity of Internet of Things (IoT) devices and applications. These applications require efficient management of the time-series data on both the edge and cloud side that support high throughput ingestion, low latency query and advanced time series analysis. In this demonstration, we present Apache IoTDB managing time-series data to enable new classes of IoT applications. IoTDB has both edge and cloud versions, provides an optimized columnar file format for efficient time-series data storage, and time-series database with high ingestion rate, low latency queries and data analysis support. It is specially optimized for time-series oriented operations like aggregations query, down-sampling and sub-sequence similarity search. An edge-to-cloud time-series data management application is chosen to demonstrate how IoTDB handles time-series data in real-time and supports advanced analytics by integrating with Hadoop and Spark. An end-to-end IoT data management solution is shown by integrating IoTDB with PLC4x, Calcite, and Grafana.