Discovering properties about graphs and traversals

After schema and data are inserted into a graph, it is important to verify that the information is correct. Checking simple information about inserted data is a good way to get started with traversals. The schema().describe() can be used to check how the graph is storing data, and to retrieve schema that can be used to re-create schema, if necessary.

Procedure

  • Use the graph traversal instance g to check if data is loaded by checking the count of vertices:

    g.V().count()
    ==>100

  • Use the graph traversal instance g to check if data is loaded by checking the count of edges:

    g.E().count()
    ==>121

  • Check the properties of one vertex. Find all the information for the vertex with an name value of Julia CHILD.

    g.V().hasLabel('person').has('name', 'Julia CHILD').elementMap()
    ==>{id=dseg:/person/e7cd5752-bc0d-4157-a80f-7523add8dbcd, 
label=person, country=[('USA','1912-08-12','1944-01-01'), 
('Ceylon','1944-01-01','1945-06-01'), ('France','1948-01-01','1950-01-01'), 
('USA','1960-01-01','2004-08-13')], gender=F, name=Julia CHILD, 
nickname=[Jay, Julia], person_id=e7cd5752-bc0d-4157-a80f-7523add8dbcd}

    This query relies on an index for name because person_id is the partition key. If an index is not created, then only the partition key can be used to discover information about any element.

  • Check the properties of a one edge. Find all the information for the edge with a label of reviewed from a particular person to a particular recipe.

    g.E('dseg:/person-reviewed-recipe/6c09f656-5aef-46df-97f9-e7f984c9a3d9/2001').elementMap()
    i==>{id=dseg:/person-reviewed-recipe/6c09f656-5aef-46df-97f9-e7f984c9a3d9/2001, 
label=reviewed, IN={id=dseg:/recipe/2001, label=recipe, recipe_id=2001}, 
OUT={id=dseg:/person/6c09f656-5aef-46df-97f9-e7f984c9a3d9, 
label=person, person_id=6c09f656-5aef-46df-97f9-e7f984c9a3d9}, year=2014-02-01, 
comment=Yummy!, stars=5, time=12:00}

  • Find the id information for vertices:

    g.V().hasLabel('fridge_sensor').id()
    ==>dseg:/fridge_sensor/45/300/66665/1
==>dseg:/fridge_sensor/45/300/66665/2
==>dseg:/fridge_sensor/45/300/66665/3
==>dseg:/fridge_sensor/31/100/55555/1
==>dseg:/fridge_sensor/31/100/55555/2
==>dseg:/fridge_sensor/31/100/55555/3
==>dseg:/fridge_sensor/31/200/55556/1
==>dseg:/fridge_sensor/31/200/55556/2
==>dseg:/fridge_sensor/31/200/55556/3

    Edge id information can be found in a similar fashion.

  • Discover schema information using a describe() step. This traversal step provides a sorted list of the same information as the next alternative below.

    schema.describe()
    ==>schema.type('address').ifNotExists().property('address1', Varchar).property('address2', Varchar).property('city_code', Varchar).property('state_code', Varchar).property('zip_code', Varchar).create()
schema.type('fullname').ifNotExists().property('firstname', Varchar).property('lastname', Varchar).create()
schema.type('location_details').ifNotExists().property('loc_address', frozen(typeOf('address'))).property('telephone', listOf(Varchar)).create()
schema.vertexLabel('book').ifNotExists().partitionBy('book_id', Int).property('book_discount', Varchar).property('isbn', Varchar).property('name', Varchar).property('publish_year', Int).property('category', setOf(Varchar)).create()
schema.vertexLabel('flag').ifNotExists().partitionBy('country_id', Int).clusterBy('country', Varchar, Asc).property('flag', Varchar, Static).create()
schema.vertexLabel('fridge_sensor').ifNotExists().partitionBy('state_id', Int).partitionBy('city_id', Int).partitionBy('zipcode_id', Int).clusterBy('sensor_id', Int, Asc).property('name', Varchar).create()
schema.vertexLabel('home').ifNotExists().partitionBy('home_id', Int).property('name', Varchar).create()
schema.vertexLabel('ingredient').ifNotExists().partitionBy('ingred_id', Int).property('name', Varchar).create()
schema.vertexLabel('location').ifNotExists().partitionBy('loc_id', Varchar).property('geo_point', Point).property('loc_details', frozen(typeOf('location_details'))).property('name', Varchar).create()
schema.vertexLabel('meal').ifNotExists().partitionBy('type', Varchar).partitionBy('meal_id', Int).create()
schema.vertexLabel('meal_item').ifNotExists().partitionBy('item_id', Int).property('calories', Int).property('name', Varchar).property('serv_amt', Varchar).property('macro', listOf(Int)).create()
schema.vertexLabel('person').ifNotExists().partitionBy('person_id', Uuid).property('cal_goal', Int).property('gender', Varchar).property('name', Varchar).property('badge', mapOf(Varchar, Date)).property('country', listOf(tupleOf(Varchar, Date, Date))).property('macro_goal', listOf(Int)).property('nickname', setOf(Varchar)).create()
schema.vertexLabel('recipe').ifNotExists().partitionBy('recipe_id', Int).property('instructions', Varchar).property('name', Varchar).property('notes', Varchar).property('cuisine', setOf(Varchar)).create()
schema.vertexLabel('store').ifNotExists().partitionBy('store_id', Int).property('name', Varchar).create()
schema.edgeLabel('contains').ifNotExists().from('fridge_sensor').to('ingredient').partitionBy(OUT, 'state_id', 'fridge_sensor_state_id').partitionBy(OUT, 'city_id', 'fridge_sensor_city_id').partitionBy(OUT, 'zipcode_id', 'fridge_sensor_zipcode_id').clusterBy(OUT, 'sensor_id', 'fridge_sensor_sensor_id', Asc).clusterBy(IN, 'ingred_id', 'ingredient_ingred_id', Asc).property('expire_date', Date).create()
schema.edgeLabel('is_located_at').ifNotExists().from('fridge_sensor').to('location').partitionBy(OUT, 'state_id', 'fridge_sensor_state_id').partitionBy(OUT, 'city_id', 'fridge_sensor_city_id').partitionBy(OUT, 'zipcode_id', 'fridge_sensor_zipcode_id').clusterBy(OUT, 'sensor_id', 'fridge_sensor_sensor_id', Asc).clusterBy(IN, 'loc_id', 'location_loc_id', Asc).create()
schema.edgeLabel('is_located_at').ifNotExists().from('home').to('location').partitionBy(OUT, 'home_id', 'home_home_id').clusterBy(IN, 'loc_id', 'location_loc_id', Asc).create()
schema.edgeLabel('includes').ifNotExists().from('ingredient').to('recipe').partitionBy(OUT, 'ingred_id', 'ingredient_ingred_id').clusterBy(IN, 'recipe_id', 'recipe_recipe_id', Asc).property('amount', Varchar).create()
schema.edgeLabel('includes').ifNotExists().from('meal').to('meal_item').partitionBy(OUT, 'type', 'meal_type').partitionBy(OUT, 'meal_id', 'meal_meal_id').clusterBy(IN, 'item_id', 'meal_item_item_id', Asc).property('num_serv', Int).create()
schema.edgeLabel('ate').ifNotExists().from('person').to('meal').partitionBy(OUT, 'person_id', 'person_person_id').clusterBy(IN, 'type', 'meal_type', Asc).clusterBy(IN, 'meal_id', 'meal_meal_id', Asc).property('meal_date', Date).create()
schema.edgeLabel('authored').ifNotExists().from('person').to('book').partitionBy(OUT, 'person_id', 'person_person_id').clusterBy(IN, 'book_id', 'book_book_id', Asc).create()
schema.edgeLabel('created').ifNotExists().from('person').to('recipe').partitionBy(OUT, 'person_id', 'person_person_id').clusterBy(IN, 'recipe_id', 'recipe_recipe_id', Asc).property('create_date', Date).create()
schema.edgeLabel('knows').ifNotExists().from('person').to('person').partitionBy(OUT, 'person_id', 'out_person_id').clusterBy(IN, 'person_id', 'in_person_id', Asc).property('since', Date).create()
schema.edgeLabel('reviewed').ifNotExists().from('person').to('recipe').partitionBy(OUT, 'person_id', 'person_person_id').clusterBy(IN, 'recipe_id', 'recipe_recipe_id', Asc).property('comment', Varchar).property('stars', Int).property('time', Time).property('year', Date).create()
schema.edgeLabel('included_in').ifNotExists().from('recipe').to('book').partitionBy(OUT, 'recipe_id', 'recipe_recipe_id').clusterBy(IN, 'book_id', 'book_book_id', Asc).create()
schema.edgeLabel('included_in').ifNotExists().from('recipe').to('meal').partitionBy(OUT, 'recipe_id', 'recipe_recipe_id').clusterBy(IN, 'type', 'meal_type', Asc).clusterBy(IN, 'meal_id', 'meal_meal_id', Asc).property('amount', Varchar).create()
schema.edgeLabel('is_located_at').ifNotExists().from('store').to('location').partitionBy(OUT, 'store_id', 'store_store_id').clusterBy(IN, 'loc_id', 'location_loc_id', Asc).create()
schema.edgeLabel('is_stocked_with').ifNotExists().from('store').to('ingredient').partitionBy(OUT, 'store_id', 'store_store_id').clusterBy(IN, 'ingred_id', 'ingredient_ingred_id', Asc).property('expire_date', Date).create()
schema.vertexLabel('book').searchIndex().ifNotExists().by('book_id').by('name').asString().by('publish_year').create()
schema.vertexLabel('ingredient').materializedView('ingredient_by_name').ifNotExists().partitionBy('name').clusterBy('ingred_id', Asc).create()
schema.vertexLabel('location').materializedView('location_by_name').ifNotExists().partitionBy('name').clusterBy('loc_id', Asc).create()
schema.vertexLabel('location').searchIndex().ifNotExists().by('loc_id').asString().by('geo_point').create()
schema.vertexLabel('meal').materializedView('meal_by_type').ifNotExists().partitionBy('type').clusterBy('meal_id', Asc).create()
schema.vertexLabel('meal_item').materializedView('meal_item_by_name').ifNotExists().partitionBy('name').clusterBy('item_id', Asc).create()
schema.vertexLabel('person').materializedView('person_by_name').ifNotExists().partitionBy('name').clusterBy('person_id', Asc).create()
schema.vertexLabel('person').secondaryIndex('person_2i_by_badge').ifNotExists().by('badge').indexKeys().create()
schema.vertexLabel('person').secondaryIndex('person_2i_by_country').ifNotExists().by('country').indexValues().create()
schema.vertexLabel('person').secondaryIndex('person_2i_by_nickname').ifNotExists().by('nickname').indexValues().create()
schema.vertexLabel('person').searchIndex().ifNotExists().by('person_id').by('country').create()
schema.vertexLabel('recipe').materializedView('recipe_by_name').ifNotExists().partitionBy('name').clusterBy('recipe_id', Asc).create()
schema.vertexLabel('recipe').secondaryIndex('recipe_2i_by_cuisine').ifNotExists().by('cuisine').indexValues().create()
schema.vertexLabel('recipe').searchIndex().ifNotExists().by('recipe_id').by('instructions').create()
schema.edgeLabel('reviewed').from('person').to('recipe').materializedView('person__reviewed__recipe_by_person_person_id_stars').ifNotExists().partitionBy(OUT, 'person_id').partitionBy('stars').clusterBy(IN, 'recipe_id', Asc).create()
schema.edgeLabel('reviewed').from('person').to('recipe').materializedView('person__reviewed__recipe_by_person_person_id_year').ifNotExists().partitionBy(OUT, 'person_id').clusterBy('year', Asc).clusterBy(IN, 'recipe_id', Asc).create()

  • An alternative to discover schema information uses a elementMap() step on the traversal:

    schema.traversal().V().elementMap()

    Partial results:

    i==>{id=0, label=schema, engine=Core}
==>{id=512, label=propertyKey, dataType=Time, name=time, type=Regular}
==>{id=2, label=vertexLabel, name=meal_item}
==>{id=258, label=propertyKey, name=badge}
==>{id=4, label=propertyKey, dataType=Int, name=item_id, type=PartitionKey}
==>{id=261, label=propertyKey, name=cal_goal}
==>{id=517, label=propertyKey, dataType=Date, name=year, type=Regular}
==>{id=264, label=propertyKey, name=country}
==>{id=9, label=propertyKey, dataType=Int, name=calories, type=Regular}
==>{id=522, label=edgeIndex, name=person__reviewed__recipe_by_comment, type=MaterializedView}
==>{id=267, label=propertyKey, name=gender}
==>{id=525, label=propertyKey, name=comment, type=PartitionKey}
==>{id=14, label=propertyKey, dataType=Varchar, name=name, type=Regular}
==>{id=270, label=propertyKey, name=macro_goal}
==>{id=273, label=propertyKey, name=name}
==>{id=529, label=propertyKey, name=person_person_id, type=Clustering, order=Asc}
==>{id=19, label=propertyKey, dataType=Varchar, name=serv_amt, type=Regular}
==>{id=277, label=vertexLabel, name=location}
==>{id=534, label=propertyKey, name=recipe_recipe_id, type=Clustering, order=Asc}
==>{id=279, label=propertyKey, dataType=Varchar, name=loc_id, type=PartitionKey}
==>{id=24, label=propertyKey, dataType=listOf(Int), name=macro, type=Regular}
==>{id=539, label=propertyKey, name=stars, type=Regular}
==>{id=284, label=propertyKey, dataType=Point, name=geo_point, type=Regular}
==>{id=29, label=vertexLabel, name=ingredient}
==>{id=31, label=propertyKey, dataType=Int, name=ingred_id, type=PartitionKey}
==>{id=543, label=propertyKey, name=time, type=Regular}
==>{id=289, label=propertyKey, dataType=frozen(typeOf('location_details')), name=loc_details, type=Regular}
==>{id=547, label=propertyKey, name=year, type=Regular}
==>{id=36, label=propertyKey, dataType=Varchar, name=name, type=Regular}
==>{id=294, label=propertyKey, dataType=Varchar, name=name, type=Regular}
==>{id=552, label=incident}
==>{id=41, label=vertexLabel, name=home}
==>{id=43, label=propertyKey, dataType=Int, name=home_id, type=PartitionKey}
==>{id=299, label=vertexIndex, name=food_location_solr_query_index, type=Search}
==>{id=556, label=edgeLabel, name=included_in}
==>{id=302, label=propertyKey, name=loc_id}
==>{id=558, label=propertyKey, dataType=Varchar, name=amount, type=Regular}
==>{id=48, label=propertyKey, dataType=Varchar, name=name, type=Regular}
==>{id=305, label=propertyKey, name=geo_point}
==>{id=563, label=incident}
==>{id=308, label=propertyKey, name=loc_details}
==>{id=53, label=vertexLabel, name=store}
==>{id=55, label=propertyKey, dataType=Int, name=store_id, type=PartitionKey}
==>{id=567, label=edgeLabel, name=includes}
==>{id=312, label=vertexLabel, name=recipe}
==>{id=569, label=propertyKey, dataType=Varchar, name=amount, type=Regular}
==>{id=314, label=propertyKey, dataType=Int, name=recipe_id, type=PartitionKey}
==>{id=60, label=propertyKey, dataType=Varchar, name=name, type=Regular}
==>{id=574, label=incident}
==>{id=319, label=propertyKey, dataType=Varchar, name=instructions, type=Regular}

Using elementMap() or valueMap() without specifying properties can result in slow query latencies, if a large number of property keys exist for the queried vertex or edge. Specific properties can be specified, such as elementMap('name') or``valueMap('name').

  • Running traversal() will supply information about the number of schema element exist for vertices and edges, as well as the TraversalSource type.

    schema.traversal()
    ==>graphtraversalsource[tinkergraph[vertices:140 edges:140], standard]

  • Get the name of the current graphs:

    system.graphs()
    ==>food_qs
==>food_cql
==>food

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