Try Out Multi-Model Functionality with the SAP HANA Database Explorer and Database Objects App

The following steps will create a knowledge graph that provides information on additional hotel amenities, explore the created knowledge graph using the database objects app, and then will perform a query on the knowledge graph.

Before you can create a knowledge graph, please ensure your HANA Instance is version 2025.2 or above, and your instance has triple store activated. Here are the steps to doing this:

1. Go to Manage Configuration on your SAP HANA Cloud Database.

   

2. Under the General Tab, change the version of your instance to 2025.2 or above

   

3. Under the Advanced Settings Tab, check off the Triple Store option if it’s not already on.

   

   The knowledge graph feature is not available for free tier instances.

   To learn more about knowledge graphs see Connecting the Facts: SAP HANA Cloud’s Knowledge Graph Engine for Business Context and Choosing Between Knowledge Graphs and Property Graphs in SAP HANA Cloud and Why Both Matter.

4. Execute the following in the SQL Console. This query creates a KG with several hotels and amenities such as an indoor pool, hot tub, fitness center, etc.

   SQL Copy

   --CALL SPARQL_EXECUTE('DROP GRAPH <kg_hotels>', '', ?, ?);
   CALL SPARQL_EXECUTE('
       INSERT DATA { GRAPH <kg_hotels> { 
           <http://example.org/hotels/Delta> a <Hotel>;
           <name> "Delta";
           <city> "Waterloo";
           <yearBuilt> 2012 .
   
           <http://example.org/hotels/hotelamenities/IndoorPool1> a <IndoorPool>;
           <included_at> <http://example.org/hotels/Delta>;
           <length> 25;
           <width> 10.
   
           <http://example.org/hotels/hotelamenities/Hottub1> a <Hottub>;
           <included_at> <http://example.org/hotels/Delta>;
           <length> 6;
           <width> 6.
   
           <http://example.org/hotels/hotelamenities/FitnessCenter1> a <FitnessCenter>;
           <included_at> <http://example.org/hotels/Delta>.
   
           <http://example.org/hotels/hotelamenities/Keurig> a <CoffeeMaker>;
           <included_at> <http://example.org/hotels/Delta>. 
   
           <http://example.org/hotels/Sunshine> a <Hotel>;
           <name> "Sunshine";
           <city> "Clearwater";
           <yearBuilt> 1975 .
   
           <http://example.org/hotels/hotelamenities/Restaurant1> a <Restaurant>;
           <included_at> <http://example.org/hotels/Sunshine>;
           <seating> 95.
   
           <http://example.org/hotels/hotelamenities/FitnessCenter2> a <FitnessCenter>;
           <included_at> <http://example.org/hotels/Sunshine>.
   
           <http://example.org/hotels/hotelamenities/Laundry1> a <DryCleaning>;
           <included_at> <http://example.org/hotels/Sunshine>. 
   
           <http://example.org/hotels/Congress> a <Hotel>;
           <name> "Congress";
           <city> "Seattle";
           <yearBuilt> 2012 .
   
           <http://example.org/hotels/hotelamenities/IndoorPool2> a <IndoorPool>;
           <included_at> <http://example.org/hotels/Congress>;
           <length> 30;
           <width> 15.
   
           <http://example.org/hotels/hotelamenities/Restaurant2> a <Restaurant>;
           <included_at> <http://example.org/hotels/Congress>;
           <seating> 122.
   
           <http://example.org/hotels/hotelamenities/FitnessCenter3> a <FitnessCenter>;
           <included_at> <http://example.org/hotels/Congress>.
   
           <http://example.org/hotels/hotelamenities/Spa1> a <Spa>;
           <included_at> <http://example.org/hotels/Congress>. 
   
           <http://example.org/hotels/OceanStar> a <Hotel>;
           <name> "Ocean Star";
           <city> "Atlantic City";
           <yearBuilt> 2012 .
   
           <http://example.org/hotels/hotelamenities/IndoorPool3> a <IndoorPool>;
           <included_at> <http://example.org/hotels/OceanStar>;
           <length> 50;
           <width> 30.
   
           <http://example.org/hotels/hotelamenities/Restaurant2> a <Restaurant>;
           <included_at> <http://example.org/hotels/OceanStar>;
           <seating> 205.
   
           <http://example.org/hotels/hotelamenities/Cleaning1> a <CleaningServices>;
           <included_at> <http://example.org/hotels/OceanStar>.
   
           <http://example.org/hotels/hotelamenities/Spa2> a <Spa>;
           <included_at> <http://example.org/hotels/OceanStar>. 
   
           <http://example.org/hotels/LongIsland> a <Hotel>;
           <name> "LongIsland";
           <city> "Long Island";
           <yearBuilt> 2021 .
   
           <http://example.org/hotels/hotelamenities/Entertainment1> a <Entertainment>;
           <included_at> <http://example.org/hotels/LongIsland>.
   
           <http://example.org/hotels/hotelamenities/Restaurant3> a <Restaurant>;
           <included_at> <http://example.org/hotels/LongIsland>;
           <seating> 122.
   
           <http://example.org/hotels/hotelamenities/Cleaning2> a <CleaningServices>;
           <included_at> <http://example.org/hotels/LongIsland>.
   
           <http://example.org/hotels/hotelamenities/Spa3> a <Spa>;
           <included_at> <http://example.org/hotels/LongIsland>. 
   
           <http://example.org/hotels/hotelamenities/Hottub2> a <Hottub>;
           <included_at> <http://example.org/hotels/LongIsland>. 
       } 
       }
       ', '', ?, ?
   );

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   Additional examples can be found at SAP HANA Cloud, SAP HANA Database SPARQL Reference Guide

5. To visualize and examine the knowledge graph database objects, you must first enable the feature in your preferences. Click on your account

   

   Go to the Database Objects tab

   

   Enable RDF Named Graphs

   

   You will now have the RDF Named Graphs view available. Click on it to view the graphs you have created

   

   The DEFAULT graph is where content goes when it doesn’t have a name. For more information, review the DEFAULT_GRAPHS and Named Graphs help page.

   Click on the kg_hotels graph, and open the graph ontology to see the nodes.

   

6. Run a query to find hotels that have an indoor pool.

   SQL Copy

   SELECT *
   FROM SPARQL_TABLE('
   PREFIX ex: <http://example.org/hotels/>
   SELECT ?hotel ?hotelName ?city ?yearBuilt ?poolLength ?poolWidth
   FROM <kg_hotels>
   WHERE {
       ?hotel a <Hotel> ;
   <name> ?hotelName ;
   <city> ?city ;
   <yearBuilt> ?yearBuilt .
       ?pool a <IndoorPool> ;
   <included_at> ?hotel ;
   <length> ?poolLength ;
   <width> ?poolWidth .
   }
   ');

   Further examples of creating SPARQL statements can be found at SPARQL SELECT Queries Using SPARQL_TABLE.

The following steps will create a property graph workspace that can display the distance between hotels in a state.

In SAP HANA Cloud, a property graph is made up of a set of vertices and a set of edges. Vertices are stored in vertex tables, while edges are stored in edge tables. Vertex and edge tables are collectively denoted as graph tables.

1. Create a vertex table that represents distances between hotels by executing the following in the SQL console.

   SQL Copy

   CREATE COLUMN TABLE DISTANCES(
     DKEY INTEGER UNIQUE NOT NULL,
     HSOURCE INTEGER NOT NULL
       REFERENCES HOTEL(HNO),
     HTARGET INTEGER NOT NULL
       REFERENCES HOTEL(HNO),
     DIST_KM DOUBLE
   );

2. Populate the vertex table with distances between hotels that are in the same state.

   SQL Copy

   --Washington
   INSERT INTO DISTANCES VALUES (1, 10, 11, 11.8);
   INSERT INTO DISTANCES VALUES (2, 11, 10, 11.8);
   
   --New York
   INSERT INTO DISTANCES VALUES (3, 12, 13, 217.3);
   INSERT INTO DISTANCES VALUES (4, 13, 12, 217.3);
   INSERT INTO DISTANCES VALUES (5, 12, 14, 71.9);
   INSERT INTO DISTANCES VALUES (6, 14, 12, 71.9);
   INSERT INTO DISTANCES VALUES (7, 12, 15, 71.5);
   INSERT INTO DISTANCES VALUES (8, 15, 12, 71.5);
   INSERT INTO DISTANCES VALUES (9, 13, 14, 212.2);
   INSERT INTO DISTANCES VALUES (10, 14, 13, 212.2);
   INSERT INTO DISTANCES VALUES (11, 13, 15, 212.1);
   INSERT INTO DISTANCES VALUES (12, 15, 13, 212.1);
   INSERT INTO DISTANCES VALUES (13, 14, 15, 0.4);
   INSERT INTO DISTANCES VALUES (14, 15, 14, 0.4);
   
   --Illinois
   INSERT INTO DISTANCES VALUES (15, 16, 17, 23.5);
   INSERT INTO DISTANCES VALUES (16, 17, 16, 23.5);
   
   --Florida
   INSERT INTO DISTANCES VALUES (17, 18, 19, 219.8);
   INSERT INTO DISTANCES VALUES (18, 19, 18, 219.8);
   INSERT INTO DISTANCES VALUES (19, 18, 20, 323.4);
   INSERT INTO DISTANCES VALUES (20, 20, 18, 323.4);
   INSERT INTO DISTANCES VALUES (21, 19, 20, 333.8);
   INSERT INTO DISTANCES VALUES (22, 20, 19, 333.8);
   
   --California
   INSERT INTO DISTANCES VALUES (23, 21, 22, 149.5);
   INSERT INTO DISTANCES VALUES (24, 22, 21, 149.5);
   INSERT INTO DISTANCES VALUES (25, 21, 23, 35.7);
   INSERT INTO DISTANCES VALUES (26, 23, 21, 35.7);
   INSERT INTO DISTANCES VALUES (27, 22, 23, 163.2);
   INSERT INTO DISTANCES VALUES (28, 23, 22, 163.2);

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3. Create a graph workspace.

   SQL Copy

   CREATE GRAPH WORKSPACE DISTANCEGRAPH
   EDGE TABLE DISTANCES
       SOURCE COLUMN HSOURCE
       TARGET COLUMN HTARGET
       KEY COLUMN DKEY
   VERTEX TABLE HOTEL
       KEY COLUMN HNO;

4. Navigate to graph workspaces, select the previously created graph workspace, and open it to view its properties.

   

For additional information, see SAP HANA Cloud, SAP HANA Database Graph Reference.

1. Open the property graph viewer.

   

   The property graph viewer will open in a new tab as shown below.

   

2. Set the vertex and edge names using the property graph viewer settings.

   Set the vertex label to NAME.

   

   Set the edge label to DIST_KM.

   

3. Optionally, adjust a few of the property graph vertices to accommodate viewing by dragging and dropping property graph vertices.

   

4. Apply a filter to vertices where STATE is NY. After specifying the filter, press the Add button and then apply it by pressing the Apply button.

   

   Apply a filter to edges where DIST_KM is less than 100.

   

5. Highlight the Long Island vertex using the property graph viewer settings. You may do so by selecting a color.

   

Additional property graph examples include the Greek Mythology Graph Example and Open Flights. The company graph example does not currently display in the SAP HANA database explorer property graph viewer as it does not currently support the display of homogeneous graphs. Property graph workspaces may also be viewed using the SAP HANA plug-in for Cytoscape.

The shortest path algorithm can be used to provide the optimal route between two vertices. The nearest neighbor algorithm can be used to show only the vertices that are connected to a specified vertex.

The following steps will walk through using the shortest path algorithm to determine the optimal route from Airport Hotel in Rosemont, IL to Regency Hotel in Seattle, WA.

1. Execute the following in SQL to add a few connections between hotels in different states.

   SQL Copy

   --Midtown New York to Lake Michigan Chicago
   INSERT INTO DISTANCES VALUES (29,14,16,1227);
   INSERT INTO DISTANCES VALUES (30,16,14,1227);
   
   --Long Island New York to Lake Michigan Chicago
   INSERT INTO DISTANCES VALUES (31,12,16,1357);
   INSERT INTO DISTANCES VALUES (32,16,12,1357);
   
   --Long Island New York to Beach Florida
   INSERT INTO DISTANCES VALUES (33,12,19,1738);
   INSERT INTO DISTANCES VALUES (34,19,12,1738);
   
   --Congress Seattle to Star California
   INSERT INTO DISTANCES VALUES (35,10,23,1817);
   INSERT INTO DISTANCES VALUES (36,23,10,1817);
   
   --Indian Horse California to Beach Florida
   INSERT INTO DISTANCES VALUES (37,22,19,3861);
   INSERT INTO DISTANCES VALUES (38,19,22,3861);
   
   --Atlantic Florida to Long Beach California
   INSERT INTO DISTANCES VALUES (39,20,21,4348);
   INSERT INTO DISTANCES VALUES (40,21,20,4348);

2. After removing the previously applied filters, navigate to the property graph viewer, and select the algorithms tab. Update the Algorithm field to “Shortest Path”, specify the values shown below, and click Apply.

   

SAP HANA provides the ability to store and query JSON data. This can be useful if the schema of the data is often changed or if you wish to join data in SQL queries that comes from both SQL tables and JSON data.

The following steps will demonstrate how to create a JSON collection that can be used to collect notes about customers staying at a hotel.

The creation of a JSON collection is not supported in the SAP HANA Cloud free tier.

1. Enable the JSON document store.

   For an SAP HANA Cloud database, in the creation wizard, or for an existing instance, the Manage Configuration dialog, enable the document store.

   

   For an on-premise server, add the document service as described at Enable the SAP HANA JSON Document Store.

2. Create a collection named GUEST_NOTES.

   SQL Copy

   CREATE COLLECTION GUEST_NOTES;

3. Insert some data.

   SQL Copy

   INSERT INTO GUEST_NOTES VALUES ('{"FIRST_NAME": "Jenny", "LAST_NAME": "Porter", "REQUEST": "requested a courtesy call at 7:00 am"}');
   INSERT INTO GUEST_NOTES VALUES ('{"FIRST_NAME": "Jenny", "LAST_NAME": "Porter", "REQUEST": "requested an extra blanket"}');
   INSERT INTO GUEST_NOTES VALUES ('{"title": "Mr.", "FIRST_NAME": "Peter", "LAST_NAME": "Brown", "REQUEST": "requested an earl grey at 2:00"}');

   Notice that the structure of the Guest Notes does not need to be defined in advance.

4. (Optional) You can use the Support for JSON Schema feature to define rules for your documents to adhere to. The example below restricts the “title” property to only accept strings.

   SQL Copy

   CREATE COLLECTION "GUEST_NOTES_WITH_SCHEMA" JSON SCHEMA '{
       "$schema": "http://json-schema.org/draft-07/schema#",
           "properties": {
               "title": {
                   "type": "string"
               }
           }
       }';
   
   INSERT INTO "GUEST_NOTES_WITH_SCHEMA" VALUES ('{"title": 1, "FIRST_NAME": "Peter", "LAST_NAME": "Brown", "REQUEST": "requested an earl grey at 2:00"}'); -- throws error 

   

5. The JSON data can be returned as a JSON document, in a tabular result, or can be joined with data from a table.

   SQL Copy

   SELECT * FROM GUEST_NOTES;  --returns JSON
   
   SELECT FIRST_NAME, LAST_NAME, REQUEST FROM GUEST_NOTES; --returns tabular result
   
   WITH myJSON AS (SELECT GUEST_NOTES FROM GUEST_NOTES)
       SELECT '[' || STRING_AGG(TO_NVARCHAR(GUEST_NOTES), ',') || ']' FROM myJSON;  --returns all the results as one JSON document
   
   WITH GN_VIEW AS (SELECT FIRST_NAME, LAST_NAME, REQUEST FROM GUEST_NOTES) --joins a collection with a table
       SELECT DISTINCT GN_VIEW.REQUEST, C.FIRSTNAME, GN_VIEW.LAST_NAME, C.ADDRESS
       FROM GN_VIEW INNER JOIN CUSTOMER AS C ON GN_VIEW.LAST_NAME = C.NAME;

   

   For additional details see the SELECT Statement in the JSON Document Store guide.

6. The properties of the JSON collection can also be viewed.

   

   Click on the glasses icon to view the JSON text.

   

Further examples can be found in the The Small JSON Document Store Cookbook.

JSON collections can be imported using the import data wizard but are required to be in a specified format as mentioned at Import and Export. An alternative method of performing an import is shown in this step.

1. Find a JSON collection to be imported. The example below uses the Parks dataset from City of Waterloo Open Data downloaded as a GeoJSON file.

2. Create a Python application and update the host variable below.

   Shell Copy

   notepad loadJSON.py

   Python Copy

   from hana_ml import docstore
   from hana_ml.dataframe import ConnectionContext
   
   import json
   
   host = 'xxxxx-ee5a-4782-bc7e-297099099b59.hana.prod-ca10.hanacloud.ondemand.com'
   port = 443
   user = 'USER1'
   passwd = 'Password1'
   
   try :
       conn = ConnectionContext(address=host, port=port, user=user, password=passwd)
   except ConnectionContext.Error as er:
       print('Connect failed, exiting')
       print(er)
       exit()
   
   #If no errors, print connected
   print('connected')
   
   with open("ParksInWaterloo.geojson") as json_file:
       data = json.load(json_file)
   
   docstore.create_collection_from_elements(conn, 
       collection_name="WATERLOO_PARKS", elements=[data], drop_exist_coll=True, schema="HOTELS")

3. Add the required packages.

   Shell Copy

   pip install hana_ml
   pip install shapely

4. Run the application.

   Shell Copy

   python loadJSON.py 

   

5. Query the imported collection.

   SQL Copy

   SELECT CARDINALITY("features")  FROM "HOTELS"."WATERLOO_PARKS";
   SELECT "features" FROM "HOTELS"."WATERLOO_PARKS";
   SELECT "features" FROM "HOTELS"."WATERLOO_PARKS" UNNEST "features" as "f";
   SELECT "f"."properties"."NAME", "f"."properties"."TENNIS_COURT" FROM "HOTELS"."WATERLOO_PARKS" UNNEST "features" as "f" ORDER BY "f"."properties"."TENNIS_COURT" DESC;

   

Additional details can be found at hana_ml.docstore package.

This step will import an ESRI shapefile or optionally a GeoJSON file containing points of interest in the city of Waterloo Ontario. The ESRI shapefile import will result in a table while the JSON import will result in a JSON Collection. In the following step, a search will be performed to return the closest points of interest to the Delta hotel located in Waterloo.

1. At the ARCGIS Hub, search for Points of Interest Waterloo. Scroll through the results and choose the selection below.

   

2. Choose to download the data as a shapefile. The final sub-steps provide some details on how a GeoJSON file can be imported.

   

3. To import a shapefile, start the import data wizard.

   

   Choose Import ESRI Shapefiles and select the Points_of_Interest.zip file.

   

   If importing an ESRI Shapefile from a cloud storage provider, the file must be unzipped, and the object name would be folder_name/shapefle_name_minus_the_shp_extension.

4. Choose to import the ESRI shapefile into the schema HOTELS.

   Within the downloaded ESRI shapefile, there is a file named Points_of_Interest.prj. This file mentions the spatial reference system used by this ESRI shapefile. Specify WGS 84 as the spatial reference system.

   

   By default, the database server adds the following spatial reference systems to a new database. Additionally, the ST_SPATIAL_REFERENCE_SYSTEMS System View can be queried for available spatial reference systems.

5. Rename the imported table as it was created using mixed case.

   SQL Copy

   RENAME TABLE "Points_of_Interest" TO POI_WATERLOO;

6. View the table. Notice that the points of interest locations are stored in a column of type ST_GEOMETRY.

   

7. Select Open Data to view the raw data, and select View Spatial Data on the SHAPE column of a point of interest. You may choose to select one or multiple points of interest.

   

   The selected location(s) is shown on a map. You can view more details about the point of interest by clicking on a map marker.

   

   The Leaflet map is not shown in on-premise installs of the SAP HANA database explorer.

8. Perform the below query.

   SQL Copy

   SELECT FACILITY, SHAPE.ST_AsWKT(), SHAPE FROM POI_WATERLOO;

   Notice that the location data can be formatted in a more readable format using the methods ST_AsWKT or ST_AsEWT which in addition shows the SRID.

   

   Additional details on spatial reference systems can be found at SAP HANA Spatial Reference for SAP HANA Cloud.

9. Optionally follow the rest of the sub-steps to import a GeoJSON file as a JSON Collection.

   After downloading the file, rename the file to have a .json file extension, instead of .geojson.

10. Open the file in n Microsoft VS Code.

    Remove the extra metadata at the beginning and end so that the file only contains a JSON document on each line.

    Remove the commas at the end of each line. Press Ctrl F and use a regular expression search and replace to search for \},\n and replace it with }\n.

    

    To see the required formatting for importing JSON documents, see SAP HANA Database JSON Document Store Guide.

11. Upload the file to a cloud storage provider.

    See Export and Import Data and Schema with SAP HANA Database Explorer for more information.

12. Start the import data wizard.

    

    Choose Import Data and provide the credentials to the storage bucket containing the JSON file.

13. Choose to import the JSON file into the schema HOTELS and name the collection POI_WATERLOO_JSON.

    

    Alternatively, a text-based import of the JSON file may be used:

    SQL Copy

    CREATE COLLECTION POI_WATERLOO_JSON;
    IMPORT FROM JSON FILE 's3-us-east-1://dansawsbucket1/Points_of_Interest.json' INTO POI_WATERLOO_JSON WITH FAIL ON INVALID DATA CREDENTIAL 'AWS';

    After the import completes and the catalog is refreshed, a new JSON collection will appear.

    

    Click on the glasses icon to open the JSON document viewer.

    

14. The following SQL queries show a few examples of querying the imported JSON data including the last query which can be shown on the map viewer.

    SQL Copy

    SELECT * FROM POI_WATERLOO_JSON;  --JSON data format. Can use the built-in viewer.
    
    SELECT "properties".FACILITY as name, "properties".TYPE as type, "geometry" as geometry FROM POI_WATERLOO_JSON;  --Can return data in column format.
    
    SELECT "properties".FACILITY as name, "properties".TYPE as type, JSON_VALUE("geometry", '$.coordinates[0]') as long, JSON_VALUE("geometry", '$.coordinates[1]') as lat FROM POI_WATERLOO_JSON; --Can return longitude and latitude
    
    SELECT "properties".FACILITY as name, "properties".TYPE as type, ST_GeomFromGeoJSON("geometry", 4326) as shape FROM POI_WATERLOO_JSON; --Can now be shown in the map viewer

    Additional details can be found at JSON_VALUE Function and ST_GeomFromGeoJSON.

1. The following statement shows the list of points of interest within 3 kilometers of the Delta hotel.

   SQL Copy

   SELECT
       P.FACILITY,
       P.TYPE,
       P.ADDRESS,
       ROUND(H.LOCATION.ST_Distance(P.SHAPE, 'kilometer'), 2) as DISTANCE,
       P.SHAPE
   FROM HOTEL H, POI_WATERLOO P
   WHERE
       H.HNO=26 /*Delta*/ AND
       TO_BOOLEAN(H.LOCATION.ST_WithinDistance(NEW ST_Point(SHAPE.ST_AsWKT(), 4326), 3, 'kilometer')) = TRUE
   ORDER BY DISTANCE ASC;

   Each point can be viewed as shown below.

   

   

   Alternatively, all the points can be shown together.

   SQL Copy

   SELECT
       ST_UnionAggr(P.SHAPE)
   FROM HOTEL H, POI_WATERLOO P
   WHERE
       H.HNO=26 /*Delta*/ AND
       TO_BOOLEAN(H.LOCATION.ST_WithinDistance(NEW ST_Point(SHAPE.ST_AsWKT(), 4326), 3, 'kilometer')) = TRUE;

   

   The map may not display if the byte limit is exceeded. The byte limit can be changed in the SQL console settings.

   For additional details, see ST_Point Type, ST_Distance, ST_WithinDistance, and ST_UnionAggr Method.

   To view all access methods for spatial data, see SAP HANA Spatial Reference for SAP HANA Cloud - Accessing and Manipulating Spatial Data.

   The latitude and longitude for a location in Google Maps can be obtained for a given address via the marker’s context menu.

   

   It should also be noted that when a used in a ST_POINT, x is longitude and y is latitude so the above point would be represented as NEW ST_Point('POINT (-94.71832 32.50459)', 4326).

Congratulations! You have explored a few of the multi-model features in SAP HANA and are now familiar with graph workspaces, JSON collections, and spatial data when using the SAP HANA database explorer.