This is the official documentation of SensorBee. It describes all the functionality that the current version of SensorBee officially supports.

This document is structured as follows:

  • Preface, this part, provides general information of SensorBee.
  • Part I is an introduction for new users through some tutorials.
  • Part II documents the syntax and specification of the BQL language.
  • Part III describes information for advanced users about extensibility capabilities of the server.
  • Reference contains reference information about BQL statements, built-in components, and client programs.

What is SensorBee?

SensorBee is an open source, lightweight, stateful streaming data processing engine for the Internet of Things (IoT). SensorBee is designed to be used for streaming ETL (Extract/Transform/Load) at the edge of the network including Fog Computing. In ETL operations, SensorBee mainly focuses on data transformation and data enrichment, especially using machine learning. SensorBee is very small (stand-alone executable file size < 30MB) and runs on small computers such as Raspberry Pi.

The processing flow in SensorBee is written in BQL, a dialect of CQL (Continuous Query Language), which is similar to SQL but extended for streaming data processing. Its internal data structure (tuple) is compatible to JSON documents rather than rows in RDBMSs. Therefore, in addition to regular SQL expressions, BQL implements JSON notation and type conversions that work well with JSON. BQL is also schemaless at the moment to support rapid prototyping and integration.


Supporting a schema in SensorBee is being planned to increase its robustness, debuggability, and speed. However, the version that will support the feature has not been decided yet.

SensorBee manages user-defined states (UDSs) and BQL utilizes those states to perform stateful processing on streaming data. An example of stateful processing is machine learning. Via a Python extension, SensorBee supports deep learning using Chainer, a flexible deep learning framework developed by Preferred Networks, Inc. and Preferred Infrastructure, Inc. The combination of SensorBee and Chainer enables users to support not only online analysis but also online training of deep learning models at the edge of the network with the help of GPUs. Preprocessing of data and feature extraction from preprocessed results can be written in BQL. The results can be computed in an online manner and directly connected to deep learning models implemented with Chainer.

By combining JSON-like data structure of BQL and machine learning, SensorBee becomes good at handling unstructured data such as text written in natural languages and even video streams, which are not well supported by most data processing engines. Therefore, SensorBee can operate, for example, between a video camera and Cloud-based (semi-structured) data analytics services so that those services don’t have to analyze raw video images and can only utilize the information extracted from them by SensorBee.

SensorBee can be extended to work with existing databases or data processing solutions by developing data source or sink plugins. For example, it officially provides plugins for fluentd, an open source data collector, and has various input and output plugins for major databases and Cloud services.

SensorBee has not been designed for:

  • very large scale data processing
  • massively parallel streaming data processing
  • accurate numerical computation without any error


The following conventions are used in the synopsis of a command:

  • Brackets ([ and ]) indicate optional parts.

    • Some statements such as SELECT have [ and ] as a part of the statement. In that case, those brackets are enclosed with single quotes (').
  • Braces ({ and }) and vertical lines (|) indicate that one of candidates in braces must be chosen (e.g. one of a, b, or c has to be selected {a | b | c}).

  • Dots (...) mean that the preceding element can be repeated.

  • Commands that are to be run in a normal system shell are prefixed with a dollar sign ($).

Types and keywords in BQL are written with fixed-size fonts.

Further Information

Besides this documentation, there’re other resources about SensorBee:

Website has general information about SensorBee.


The sensorbee organization contains SensorBee’s core source code repository and its official plugins.

Mailing Lists

There are two Google Groups for discussion and questions about SensorBee:!forum/sensorbee (English) and!forum/sensorbee-ja (Japanese).