This topic describes the features, benefits, and scenarios of HBase Ganos.

Important ApsaraDB for HBase Enhanced Edition is upgraded to Lindorm. We recommend that you use the Lindorm Ganos service for new business requirements. For more information about the Lindorm Ganos service, see Overview of Lindorm Ganos service.

What is HBase Ganos?

HBase Ganos is a spatio-temporal engine that is developed by Alibaba Cloud. You can use HBase Ganos to manage spatial geometry data, spatio-temporal trajectory data, thematic raster data, and remote sensing image data. HBase Ganos is compatible with open source GeoMesa and GeoServer ecosystems. HBase Ganos provides efficient built-in algorithms, such as algorithms for spatio-temporal indexing, geometric algorithms for calculating topological spaces, and algorithms for processing remote sensing image data. You can use HBase Ganos together with the powerful distributed storage capability of ApsaraDB for HBase and the Spark engine. HBase Ganos is suitable for multiple scenarios. You can use HBase Ganos to store, query, analyze, and mine large amounts of data, including spatial data, spatio-temporal data, and remote sensing image data.


HBase Ganos V2.0
  • You can use HBase Ganos V2.0 to process spatio-temporal geometry data.
  • You can use HBase Ganos V2.0 to express and model spatial geometry data and spatio-temporal trajectory data.
  • You can use HBase Ganos V2.0 to manage spatio-temporal geometry data. For example, you can use HBase Ganos V2.0 to create, write, index, query, or delete data.
  • You can use HBase Ganos V2.0 with the Spark engine to perform data analysis. HBase Ganos V2.0 is compatible with GeoSQL that is defined by Open Geospatial Consortium (OGC).
  • You can develop HBase Ganos V2.0 based on an SDK or by calling RESTfulAPI operations.
  • HBase Ganos V2.0 is compatible with HBase V1.x.
HBase Ganos V2.5
  • HBase Ganos V2.5 is compatible with all spatio-temporal geometry data of HBase Ganos V2.0 and supports spatio-temporal raster data.
  • HBase Ganos V2.5 allows you to use extract, transform, load (ETL) tools to fast import raster data, such as chunking data, creating pyramids, and performing time-dimensional indexing. HBase Ganos V2.5 also provides the native Spark engine.
  • HBase Ganos V2.5 provides a RESTful API for querying spatio-temporal data in a raster database.
  • HBase Ganos V2.5 supports services such as WMS and WMTS and provides native GeoServer plug-ins.
  • HBase Ganos V2.5 is compatible with HBase of versions 2.x.


  • IoT in transportation

    HBase Ganos allows you to write trajectory data in high concurrency mode, store historical trajectory data, query order trajectory, and specify a time range to query spatio-temporal data. In transportation management scenarios, you can use HBase Ganos to schedule transport capacities, organize carpooling, forecast supply and demand, and analyze heatmaps.

  • Sensor networks and real-time geographic information system (GIS)

    A wide range of sensors are required if you need to monitor lifecycle metrics in multiple industries, including environmental protection, meteorology, water conservation, navigation, and aerospace. In these scenarios, you must collect geographic data about the topology, weather, pollution, water levels, and events.

  • Rasterized GIS and aerospace remote sensing

    HBase Ganos provides a general-purpose model for raster data management. It allows you to effectively store, query, analyze, and process a large amount of remote sensing image data and raster data in the GIS. You can use ETL tools to reproject, splice, slice, and import remote sensing images. You can publish services such as OGC WMS and WMTS. You can also analyze and process a large number of raster images based on the high-performance distributed computing engine of Spark.

  • IoT

    IoT devices generate time series data and spatial data. For example, in Internet of Vehicles (IoV) scenarios, a large number of connected vehicles generate trajectory data that contains time and space information. In this scenario, you can use HBase Ganos to monitor vehicle data in real time. For example, you can track vehicle trajectories and identify whether vehicles deviate or enter restricted areas. HBase Ganos also allows you to query spatial data in real time. You can query the trajectory of a vehicle during a period of time. You can also query the vehicles that enter an area during a specific period of time. HBase Ganos can work together with frameworks such as Spark for big data analysis. This allows you to perform spatio-temporal queries and analyze heatmaps.

  • Intelligent logistics and takeaway delivery

    In logistics and takeaway delivery scenarios, users need to track delivery vehicles and riders. You can use HBase Ganos to estimate the delivery time. In this scenario, a cloud system for data analysis must support high concurrency for fast write operations. The system must also plan vehicle routes in real time and monitor vehicle deviations.


  • The public cloud-native architecture uses a NoSQL database architecture and provides an out-of-the-box solution for processing big data. HBase Ganos provides an engine for you to process spatial data, spatio-temporal data, and remote sensing image data.
  • You can use HBase Ganos to store petabytes of data. HBase Ganos supports highly concurrent write operations and handles tens of billions of data entries within a few seconds.
  • HBase Ganos supports separate storage of cold data and hot data and uses efficient compression algorithms. You can use HBase Ganos to store a large amount of data at low costs.
  • You can use HBase Ganos together with Spark. This helps you build large spatial data repositories and platforms that are used to analyze spatial big data.
  • HBase Ganos complies with the OGC standards. This facilitates integration and interoperation between systems.
  • HBase Ganos provides reliable and stable services due to professional O&M and full management based on ApsaraDB for HBase.

Scenarios and architecture

Big data platform for vessel tracking

Scenario: The automatic identification system (AIS) collects hundreds of millions of trajectory data records on a daily basis. The system tracks vessels in real time. HBase Ganos allows you to perform spatio-temporal queries and time series queries on trajectory data within seconds. HBase Ganos provides the following features:
  • Use geofencing to determine whether you are entering or exiting a geographic region in real time.
  • Specify a region and a time range to replay the trajectories of vessels.
  • Specify a time range to replay the trajectories of vessels.
  • Build a data warehouse to store AIS data that is cleansed and processed on Spark.
  • HBase Ganos stores the data to be queried. After the data is stored, you can query the data from a frontend application. For example, you can query geo-fencing data or replay the trajectories of vessels.
  • In this architecture, data queries can be performed within milliseconds and data cleansing can be performed within minutes.

Location tracking

Scenario: A location tracking platform collects the trajectory data of vehicles in real time. You can use the platform to collect the trajectory data of more than 400,000 vehicles and write up to 30,000 trajectory points per second. By using the platform, you can navigate vehicles in real time, perform geo-fence queries, and calculate the similarity between trajectories.

  • HBase Ganos stores all historical trajectory data.
  • If a large amount of historical data is stored, the system automatically dumps cold data to Object Storage Service (OSS). This reduces costs by more than 70 percent.
  • You can query tens of billions of trajectory data records within sub-seconds. You can query trajectories by time range or by space.
  • HBase Ganos can work together with Spark. This way, you can calculate the similarity between trajectories and analyze origin-destination (OD) data.

Remote sensing image data management and intelligent service platform

Scenario: Store, query, and analyze remote sensing image data and enable AI for remote sensing.

Pain points:
  • There is a large amount of data of remote sensing images and the amount of data grows fast. The scaling process for storage resources is inflexible. This increases costs and makes management complex.
  • If you want to fast visualize data, you can divide static data into slices. However, this conventional method samples a small amount of data and cannot meet the requirements for AI-based data analysis.
  • It is difficult for you to perform integrated queries and analysis from multiple spatial data sources.
  • OSS is used to store raw data and separate cold data and hot data. This reduces storage costs.
  • SQL databases and NoSQL databases are used together to store spatio-temporal sequence images, without limits on the storage capacity. This separates storage and computing, and provides high scalability.
  • A cloud native architecture stores and organizes data in chunks. This retains the pixel data of images and is suitable for data analysis and computing.
  • You can use HBase Ganos together with Spark to help accelerate ETL. This also allows you to analyze and process spatial data from multiple sources.