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Filtering Data

One of the key parts of querying data is being able to filter out bits you may not want. This page will cover some of the common ways you can filter the returned data from an Operation to get the data you want.

In Gaffer the main way you filter data is by applying whats known as a View to a returned set of elements. A View, as the name suggests, allows you to view the data in a different way; this can be either via a filter, aggregation, transformation or just general manipulation of the results.

Use cases with a View usually fall into one of the following catagories:

  • Filtering - General filtering on elements based on predicates. Filtering can be applied pre-aggregation, post aggregation and post transformation.

  • Aggregation - This is to control how similar elements are aggregated together. You can provide a subset of the schema groupBy properties and override existing aggregation functions.

  • Transformation - Transformations can be applied by providing Functions to transform properties and vertex values. This is a powerful feature, you can override the existing values or you can transform and save the new value into a new transient property.

  • Property Removal - The relevant properties you want to be returned can be controlled. You can use either properties or excludeProperties to define the list of properties to be included or excluded.

Filtering in Practice

We will demonstrate general filtering on a query. Take the following graph as a basic example, where we have a Person node with ID John that has a few Created edges with a weight property on them.

graph LR
    A(["Person

        ID: John"])
    --
    "Created
     weight: 0.2"
    -->
    B(["Software

        ID: 1"])
    A
    --
    "Created
     weight: 0.6"
    -->
    C(["Software

        ID: 2"])

Lets say the weight property represents how much a Person contributed to creating something and that we wanted to only find the most significant contributions. To do this we can apply a filter to act as a threshold to only get edges with a weight more than a specific value.

First we use a simple query to get the node with ID John and any edges associated with it. Then we can apply a filter to include only edges where the weight property is over a certain value.

In this scenario it is analogous to asking, "Get all the Created edges on node John that have a weight greater than 0.4".

// Define the View to use
final View viewWithFilters = new View.Builder()
    .edge("Created", new ViewElementDefinition.Builder()
            .preAggregationFilter(new ElementFilter.Builder()
                    .select("weight")
                    .execute(new IsMoreThan(0.4))
                    .build())
            .build())
    .build();

// Create the operation to execute
final GetElements operation = new GetElements.Builder()
    .input(new EntitySeed("John"))
    .view(viewWithFilters)
    .build();

graph.execute(operation, user);

{
    "class": "GetElements",
    "input": [
        {
            "class": "EntitySeed",
            "vertex": "John"
        }
    ],
    "view": {
        "edges": {
            "Created": {
                "preAggregationFilterFunctions": [
                    {
                        "selection": [
                            "weight"
                        ],
                        "predicate": {
                            "class": "IsMoreThan",
                            "orEqualTo": false,
                            "value": {
                                "Float": 0.4
                            }
                        }
                    }
                ]
            }
        }
    }
}

elements = gc.execute_operation(
    operation = g.GetElements(
        input = [g.EntitySeed(vertex = "John")]
        view = g.View(
            edges = [
                g.ElementDefinition(
                    group = 'Created',
                    pre_aggregation_filter_functions = [
                        g.PredicateContext(
                            selection = ['weight'],
                            predicate = g.IsMoreThan(
                                value = g.float_(0.4),
                                or_equal_to = False
                            )
                        )
                    ]
                )
            ]
        )
    )
)

Results:

Edge[source="John",destination=2,directed=true,matchedVertex=SOURCE,group=Created,properties=Properties[weight=<java.lang.Float>0.6]]

[
    {
        "class": "uk.gov.gchq.gaffer.data.element.Edge",
        "group": "Created",
        "source": "John",
        "destination": "2",
        "directed": true,
        "matchedVertex": "SOURCE",
        "properties": {
            "weight": 0.6
        }
    }
]

To form relevant filters and queries it is usually required that you know the graph schema in use. The schema determines what properties and elements you can reference in your queries and the general structure of the data in the graph. For an introduction and background on Gaffer schemas please see the guide.

Tip

As you can see filtering is based around predicates which are similar to if else statements in traditional programming. For a full list of available predicates refer to the reference documentation.

Filtering Properties

If you are only interested in specific properties then it is more efficient to tell Gaffer to only return those properties. This can be easily achieved by applying the properties or excludeProperties field to a View.

If we take a similar example to before but instead add a couple of properties to both the Person and Created elements to give a graph like the following:

graph LR
    A(["Person

        ID: John
        age: 34"])
    --
    "Created
     weight: 0.2
     hours: 100"
    -->
    B(["Software

        ID: 1"])
    A
    --
    "Created
     weight: 0.6
     hours: 800"
    -->
    C(["Software

        ID: 2"])

Now as before we can run a query on this graph to get the elements relevant to the Person node. However, this time we will filter so that only specific properties are returned.

Here we are asking to only include the hours property from the Created edges in the output, and specifically excluding the age property from any returned Person entities.

// Define the View to use
final View viewWithFilters = new View.Builder()
    .edge("Created", new ViewElementDefinition.Builder()
            .properties("hours")
            .build())
    .entities("Person", new ViewElementDefinition.Builder()
            .excludeProperties("age")
            .build())
    .build();

// Create the operation to execute
final GetElements operation = new GetElements.Builder()
    .input(new EntitySeed("John"))
    .view(viewWithFilters)
    .build();

graph.execute(operation, user);

{
    "class": "GetElements",
    "input": [
        {
            "class": "EntitySeed",
            "vertex": "John"
        }
    ],
    "view": {
        "edges": {
            "Created": {
                "properties" : [ "hours" ]
            }
        },
        "entities" : {
            "Person" : {
                "excludeProperties" : [ "age" ]
            }
        }
    }
}

elements = gc.execute_operation(
    operation = g.GetElements(
        input = [g.EntitySeed(vertex = "John")]
        view = g.View(
            edges = [
                g.ElementDefinition(
                    group = 'Created',
                    properties = [ "hours" ]
                )
            ]
            entities = [
                g.ElementDefinition(
                    group = 'Person',
                    exclude_properties = [ "age" ]
                )
            ]
        )
    )
)

Results:

Edge[source="John",destination=2,directed=true,matchedVertex=SOURCE,group=Created,properties=Properties[hours=<java.lang.Integer>800]]
Edge[source="John",destination=1,directed=true,matchedVertex=SOURCE,group=Created,properties=Properties[hours=<java.lang.Integer>100]]
Entity[vertex="John",group=Person,properties=Properties[]]

[
    {
        "class": "uk.gov.gchq.gaffer.data.element.Edge",
        "group": "Created",
        "source": "John",
        "destination": "2",
        "directed": true,
        "matchedVertex": "SOURCE",
        "properties": {
            "hours": 800
        }
    },
    {
        "class": "uk.gov.gchq.gaffer.data.element.Edge",
        "group": "Created",
        "source": "John",
        "destination": "1",
        "directed": true,
        "matchedVertex": "SOURCE",
        "properties": {
            "hours": 100
        }
    },
    {
        "class": "uk.gov.gchq.gaffer.data.element.Entity",
        "group": "Person",
        "vertex": "John",
        "properties": {}
    }
]

Filtering by Element Type

You can use Views to filter the element types returned from your query. So, if you only wanted edges returned you can use allEdges(true) in a View or if you only wanted entities then use allEntities(true).

This essentially works by getting all the Schema groups for the edges or entities and adding them to the view, e.g. entity(getSchema().getEntityGroups()).

Filter which will return only entities from the query.

final GetAllElements operation = new GetAllElements()
        .view(new View.Builder()
            .allEntities(true)
            .build())
        .build();

{
  "class" : "GetAllElements",
  "view": {
    "allEntities": true
  }
}

g.GetAllElements(
    view=g.View(
        all_entities=True
    )
)

Results:

Entity[vertex="John",group=Person,properties=Properties[age=<java.lang.Integer>34]]
Entity[vertex="1",group=Software,properties=Properties[]]
Entity[vertex="2",group=Software,properties=Properties[]]

[
    {
        "class": "uk.gov.gchq.gaffer.data.element.Entity",
        "group": "Person",
        "vertex": "John",
        "properties": {}
    },
    {
        "class": "uk.gov.gchq.gaffer.data.element.Entity",
        "group": "Software",
        "vertex": "1",
        "properties": {}
    },
    {
        "class": "uk.gov.gchq.gaffer.data.element.Entity",
        "group": "Software",
        "vertex": "2",
        "properties": {}
    }
]

Filter which will return only the edges from the query.

final GetAllElements operation = new GetAllElements()
        .view(new View.Builder()
            .allEdges(true)
            .build())
        .build();

{
  "class" : "GetAllElements",
  "view": {
    "allEdges": true
  }
}

g.GetAllElements(
    view=g.View(
        all_edges=True
    )
)

Results:

    Edge[source="John",destination=2,directed=true,matchedVertex=SOURCE,group=Created,properties=Properties[weight=<java.lang.Float>0.6,hours=<java.lang.Integer>800,minutes=<java.lang.Integer>48000]]
    Edge[source="John",destination=1,directed=true,matchedVertex=SOURCE,group=Created,properties=Properties[weight=<java.lang.Float>0.2,hours=<java.lang.Integer>100,minutes=<java.lang.Integer>6000]]

[
    {
    "class": "uk.gov.gchq.gaffer.data.element.Edge",
    "group": "Created",
    "source": "John",
    "destination": "2",
    "directed": true,
    "matchedVertex": "SOURCE",
    "properties": {
        "weight": 0.2,
        "hours": 800,
        "minutes": 48000
    }
    },
    {
        "class": "uk.gov.gchq.gaffer.data.element.Edge",
        "group": "Created",
        "source": "John",
        "destination": "1",
        "directed": true,
        "matchedVertex": "SOURCE",
        "properties": {
            "weight": 0.6,
            "hours": 100,
            "minutes": 6000
        }
    }
]

Warning

Users should not use this filter in conjunction with a GetAdjacentIds operation as this already only returns entities and results may not be returned correctly.

Global View Definitions

If you wish to use the same filter on multiple groups, you could use a global View definition. These include:

  • globalElements - these are applied to all edges and entities.
  • globalEdges - these are applied to all edges.
  • globalEntities - these are applied to all entities.

Global aggregations and transformations work in a similar way to filtering. Properties can be included/excluded globally using filtering. These examples show including global properties in the filter, excluded global properties work in a similar fashion.

If we only wanted to return the weight property for all elements this can be done as follows:

// Define the View to use
final View viewWithGlobalFilter = new View.Builder()
    .globalElements(new GlobalViewElementDefinition.Builder()
        .properties("weight")
        .build())
    .build();

{
    "view": {
        "globalElements": [ {
            "properties": ["weight"]
        }]
    }
}

view_with_global_filter = g.View(
    global_elements=[
        g.GlobalElementDefinition(
            properties=["weight"]
        ),
    ],
    all_edges=False,
    all_entities=False
)

Global View definitions can also be used to filter all elements for specific properties.

If we want all elements with a weight more than 0.4.

// Define the View to use
final View viewWithGlobalFilter = new View.Builder()
    .globalElements(new GlobalViewElementDefinition.Builder()
        .postAggregationFilter(new ElementsFilter.Builder()
            .select("weight")
            .execute(new IsMoreThan(0.4))
            .build())
        .build())
    .build();

{
    "view": {
        "globalElements" : [ {
            "postAggregationFilterFunctions" : [ {
                "selection" : [ "weight" ],
                "predicate" : {
                    "class" : "IsMoreThan",
                    "orEqualTo" : false,
                    "value" : {
                        "Float" : 0.4
                    }
                }
            } ]
        } ]
    }
}

view_with_global_filter = g.View(
    global_elements=[
        g.GlobalElementDefinition(
            post_aggregation_filter_functions=[
                g.PredicateContext(
                    selection=["weight"],
                    predicate=g.IsMoreThan(
                        value={'java.lang.Float': 0.4},
                        or_equal_to=False
                    )
                )
            ]
        )
    ],
    all_edges=False,
    all_entities=False
)

In addition to global definitions, you can add specific view definitions to different element groups as you would usually. The global definitions are then merged with each of the element specific filters using an AND operator.

This example applies a global filter to both Commit edges and Person entities to get all those with a weight property as well as all Person entities with an age property.

// Define the View to use
final View globalAndSpecificFilter = new View.Builder()
        .globalElements(new GlobalViewElementDefinition.Builder()
            .properties("weight")
            .build())
        .edge("Commit")
        .entity("Person", new ViewElementDefinition.Builder()
            .properties("age")
            .build())
    .build();

{
    "view": {
        "edges" : {
            "Commit" : { }
        },
        "entities" : {
            "Person" : {
                "properties" : [ "age" ]
            }
        },
        "globalElements" : [ {
            "properties" : [ "weight" ]
        } ]
    }
}

global_and_specific_filter = g.View(
    entities=[
        g.ElementDefinition(
            group="Person",
            properties=["age"]
        )
    ],
    edges=[
        g.ElementDefinition(
            group="Commit"
        )
    ],
    global_elements=[
        g.GlobalElementDefinition(
            properties=["weight"]
        )
    ],
    all_edges=False,
    all_entities=False
)

Transformation

It is possible to apply a transformation to the output of a query which then gives you the opportunity to manipulate the results into a more useful output.

When a transform is applied the new results are saved into what is known as a transient property. A transient property is just a property that is not persisted; simply created at query time by a transform function.

To use a transform you must use a transform function, this is a Java class that extends the java.util.Function class to take some input and give a new output. Commonly you would want to write your own transform function class as it can be quite specific to your graph data and what analytics you want to get out of it. However, the Koryphe module (included by default with Gaffer) provides some default functions you can make use of.

As an example of transformation we will use the same graph from the previous section to transform the hours property into minutes and save the returned information into a new minutes transient property.

Here you can see we select the inputs for the function as the "hours" property we then use the MultiplyBy Koryphe function to transform a property and project the result into a transient property named "minutes".

final GetElements getEdgesWithMinutes = new GetElements.Builder()
    .input(new EntitySeed("John"))
    .view(new View.Builder()
            .edge("Created", new ViewElementDefinition.Builder()
                    .transientProperty("minutes", Integer.class)
                    .transformer(new MultiplyBy(60))
                    .build())
            .build())
    .build();

graph.execute(getEdgesWithMinutes, user);

{
    "class": "GetElements",
    "input": [
        {
            "class": "EntitySeed",
            "vertex": "John"
        }
    ],
    "view": {
        "edges": {
            "Created": {
                "transientProperties" : {
                    "minutes" : "java.lang.Integer"
                },
                "transformFunctions" : [
                    {
                        "selection" : [ "hours" ],
                        "function" : {
                            "class" : "MultiplyBy",
                            "by" : 60
                        },
                        "projection" : [ "minutes" ]
                    }
                ]
            }

        }
    }
}

elements = gc.execute_operation(
    operation = g.GetElements(
        input = [g.EntitySeed(vertex = "John")]
        view = g.View(
            edges = [
                g.ElementDefinition(
                    group = 'Created',
                    transient_properties = {'minutes': 'java.lang.Integer'},
                    transform_functions = [
                        g.FunctionContext(
                            selection = [ "hours" ],
                            function = g.MultiplyBy(by = 60),
                            projection = [ "minutes" ]
                        )
                    ]
                )
            ]
        )
    )
)

Results:

Edge[source="John",destination=2,directed=true,matchedVertex=SOURCE,group=Created,properties=Properties[weight=<java.lang.Float>0.6,hours=<java.lang.Integer>800,minutes=<java.lang.Integer>48000]]
Edge[source="John",destination=1,directed=true,matchedVertex=SOURCE,group=Created,properties=Properties[weight=<java.lang.Float>0.2,hours=<java.lang.Integer>100,minutes=<java.lang.Integer>6000]]
Entity[vertex="John",group=Person,properties=Properties[age=<java.lang.Integer>34]]

[
    {
        "class": "uk.gov.gchq.gaffer.data.element.Edge",
        "group": "Created",
        "source": "John",
        "destination": "2",
        "directed": true,
        "matchedVertex": "SOURCE",
        "properties": {
            "weight": 0.2,
            "hours": 800,
            "minutes": 48000
        }
    },
    {
        "class": "uk.gov.gchq.gaffer.data.element.Edge",
        "group": "Created",
        "source": "John",
        "destination": "1",
        "directed": true,
        "matchedVertex": "SOURCE",
        "properties": {
            "weight": 0.6,
            "hours": 100,
            "minutes": 6000
        }
    },
    {
        "class": "uk.gov.gchq.gaffer.data.element.Entity",
        "group": "Person",
        "vertex": "John",
        "properties": {
            "age": 34
        }
    }
]

The selection in a transform is similar to the way we select properties and identifiers in a filter and as demonstrated you can select (and also project) any property but also any of these unique identifiers:

  • VERTEX - This is the vertex on an Entity.
  • SOURCE - This is the source vertex on an Edge.
  • DESTINATION - This is the destination vertex on an Edge.
  • DIRECTED - This is the directed field on an Edge.
  • MATCHED_VERTEX - This is the vertex that was matched in the query, either the SOURCE or the DESTINATION.
  • ADJACENT_MATCHED_VERTEX - This is the adjacent vertex that was matched in the query, either the SOURCE or the DESTINATION. For example, if your seed matches the source of the edge this would resolve to the DESTINATION value.

Query-time Aggregation

Gaffer allows grouping results together based on their properties to form a new result, this is known as aggregation. Aggregation can be applied at both data ingest or query-time, this guide will focus on the latter but an overview of both techniques is available in the gaffer basics guide.

Generally to apply aggregation at query-time you must override what is known as the groupBy property to create your own aggregator in the query. To demonstrate this we will use the following example graph.

A simple graph representing commits by a Person to a Repository with each Commit being an edge with properties for the lines added and removed.

graph LR
    A(["Person

        ID: John"])
    --
    "Commit
     added: 10
     removed: 3"
    -->
    B(["Repository

        ID: 1"])
    A
    --
    "Commit
     added: 3
     removed: 5"
    -->
    B

We will use aggregation to group the properties of the Commit edges to get a total for all the added and removed properties.

Usually a result would contain all the edges on the Person node but instead we have applied aggregation so the result will contain an element with a Sum of all the added and removed properties.

final GetElements getEdgesAggregated = new GetElements.Builder()
    .input(new EntitySeed("John"))
    .view(new View.Builder()
            .edge("Commit", new ViewElementDefinition.Builder()
                    .groupBy()
                    .aggregator(new ElementAggregator.Builder()
                        .select(["added", "removed"])
                        .execute(new Sum())
                        .build()))
            .build())
    .build();

graph.execute(getEdgesAggregated, user);

{
    "class": "GetElements",
    "input": [
        {
            "class": "EntitySeed",
            "vertex": "John"
        }
    ],
    "view": {
        "edges": {
            "Commit": {
                "groupBy" : [ ],
                "aggregator" : {
                    "operators" : [
                        {
                            "selection" : [ "added", "removed" ],
                            "binaryOperator" : {
                                "class" : "Sum"
                            }
                        }
                    ]
                }
            }

        }
    }
}

elements = gc.execute_operation(
    operation = g.GetElements(
        input = [g.EntitySeed(vertex = "John")],
        view = g.View(
            edges = [
                g.ElementDefinition(
                    group = 'Commit',
                    group_by = [],
                    aggregate_functions = [
                        g.BinaryOperatorContext(
                            selection=[ "added", "removed" ],
                            binary_operator = g.Sum()
                        )
                    ]
                )
            ]
        )
    )
)

Results:

Edge[source="John",destination=1,directed=true,matchedVertex=SOURCE,group=Commit,properties=Properties[added=<java.lang.Integer>13,removed=<java.lang.Integer>8]]
Entity[vertex="John",group=Person,properties=Properties[]]

[
    {
        "class": "uk.gov.gchq.gaffer.data.element.Edge",
        "group": "Commit",
        "source": "John",
        "destination": "1",
        "directed": true,
        "matchedVertex": "SOURCE",
        "properties": {
            "added": 13,
            "removed": 8
        }
    },
    {
        "class": "uk.gov.gchq.gaffer.data.element.Entity",
        "group": "Person",
        "vertex": "John",
        "properties": {}
    }
]

Summarisation

Elements in Gaffer are often stored in daily time buckets; this allows users to query a summary of the elements that occurred on a particular day. For example, a count of the commits John made to the repository on the first of the month. The daily time buckets are controlled by using the groupBy properties.

You can get edges with the same source and destination aggregrated together, regardless of any dates. This is achieved by overriding the groupBy field with an empty array which tells Gaffer not to group by any properties and to instead summarise all elements, e.g. all John committed to repo edges.

So, we can add a timestamp to John's commit data: 

    Commit, John, 1, 2024-05-01 10:00:00
    Commit, John, 1, 2024-05-01 11:00:00
    Commit, John, 1, 2024-05-04 09:30:00
    Commit, John, 1, 2024-05-10 10:00:00
    Commit, John, 1, 2024-05-10 16:50:00
Then an empty groupBy tells Gaffer to not group by any properties and to just summarise all John's commits, regardless of date.

final GetElements getEdgesSummarised = new GetElements.Builder()
    .input(new EntitySeed("John"))
    .view(new View.Builder()
            .edge("Commit", new ViewElementDefinition.Builder()
                    .groupBy()
                    .build())
            .build())
    .build();

{
    "class": "GetElements",
    "input": [
        {
            "class": "EntitySeed",
            "vertex": "John"
        }
    ],
    "view": {
        "edges": {
            "Commit": {
                "groupBy" : [],
            }

        }
    }
}

elements = gc.execute_operation(
    operation = g.GetElements(
        input = [g.EntitySeed(vertex = "John")],
        view = g.View(
            edges = [
                g.ElementDefinition(
                    group = 'Commit',
                    group_by = []
                )
            ]
        )
    )
)

Result:

Edge[source="John",destination=1,directed=true,matchedVertex=SOURCE,group=Commit,properties=Properties[date=<java.util.Date>Wed May 01 00:00:00 UTC 2024, count=<java.lang.Long>5]]

If you apply some pre-aggregation filtering, you can also select a time window to aggregate over.

Gets all Commit edges that fall within a certain time window and filters out other edges.

final GetElements getEdgeSummarisedInTimeWindow = new GetElements.Builder()
    .input(new EntitySeed("John"))
    .view(new View.Builder()
            .edge("Commit", new ViewElementDefinition.Builder()
                 .preAggregationFilter(new ElementFilter.Builder()
                        .select("date")
                        .execute(new InDateRange.Builder()
                            .start("2024/05/01")
                            .end("2024/05/03")
                            .startInclusive(true)
                            .endInclusive(false)
                        .build()))
                    .groupBy()
                    .build())
            .build())
    .build();

{
    "class": "GetElements",
    "input": [
        {
            "class": "EntitySeed",
            "vertex": "John"
        }
    ],
    "view": {
        "edges": {
            "Commit": {
                "groupBy" : [ ],
                "preAggregationFilterFunctions": [{
                    "selection" : ["date"],
                    "predicate" : {
                        "class" : "InDateRange",
                        "start" : "2024/05/01",
                        "end" : "2024/05/03",
                        "startInclusive": true,
                        "endInclusive": false
                    }
                }]
            }

        }
    }
}

elements = gc.execute_operation(
    operation = g.GetElements(
        input = [g.EntitySeed(vertex = "John")],
        view = g.View(
            edges = [
                g.ElementDefinition(
                    group = 'Commit',
                    group_by = [],
                    pre_aggregation_filter_functions=[
                        g.PredicateContext(
                            predicate=g.Or(
                                predicates=[
                                    g.InDateRange(
                                        start="2024/05/01",
                                        end="2024/05/03",
                                        start_inclusive=True,
                                        end_inclusive=False
                                    )
                                ]
                            )
                        )
                    ]
                )
            ]
        )
    )
)

Result:

Edge[source="John",destination=1,directed=true,matchedVertex=SOURCE,group=Commit,properties=Properties[date=<java.util.Date>Wed May 01 00:00:00 UTC 2024, count=<java.lang.Long>2]]

There is also a more advanced feature of query-time aggregation which allows you to override the logic of how Gaffer aggregates properties. For example, by default the count property is aggregated with Sum, as above, but at query time you can force the count to be aggregated using another operator, e.g. the Min operator. This would allow you to find things such as the minimum daily count. This feature does not affect any of the persisted values and any ingest aggregation that has already occurred will not be modified.

This asks at query time what was the minimum added by John on one of his commits between two dates. This is done by adding an extra 'aggregator' to the operation View.

final GetAllElements edgesSummarisedInTimeWindowWithMinCountOperation = new GetAllElements.Builder()
    .view(new View.Builder()
        .edge("Commit", new ViewElementDefinition.Builder()
            .preAggregationFilter(new ElementFilter.Builder()
                .execute(new InDateRange.Builder()
                    .start("2024/05/01")
                    .end("2024/05/05")
                    .startInclusive(true)
                    .endInclusive(false)
                .build()))
            .groupBy()
            .aggregator(new ElementAggregator.Builder()
                    .select("added")
                    .execute(new Min())
                    .build())
            .build())
    .build())
.build();

{
    "class" : "GetAllElements",
    "view" : {
        "edges" : {
            "Commit" : {
                "preAggregationFilterFunctions" : [ {
                    "selection" : [ "date" ],
                    "predicate" : {
                        "class" : "InDateRange",
                        "start" : "2024/05/01",
                        "end" : "2024/05/05",
                        "startInclusive": true,
                        "endInclusive": false
                    }
                }],
                "groupBy" : [ ],
                "aggregator" : {
                    "operators" : [ {
                        "selection" : [ "added" ],
                        "binaryOperator" : {
                        "class" : "uk.gov.gchq.koryphe.impl.binaryoperator.Min"
                        }
                    } ]
                }
            }
        }
    }
}

 elements = gc.execute_operation(
    operation = g.GetElements(
        input = [g.EntitySeed(vertex = "John")],
        view = g.View(
            edges = [
                g.ElementDefinition(
                    group = 'Commit',
                    group_by = [],
                    pre_aggregation_filter_functions=[
                        g.PredicateContext(
                            predicate=g.Or(
                                predicates=[
                                    g.InDateRange(
                                        start="2024/05/01",
                                        end="2024/05/05",
                                        start_inclusive=True,
                                        end_inclusive=False
                                    )
                                ]
                            )
                        )
                    ],
                    aggregate_functions = [
                        g.BinaryOperatorContext(
                            selection=["added"],
                            binary_operator = g.Min()
                        )
                    ]
                )
            ]
        )
    )
)

More commonly, you will want to set the groupBy to empty for all groups in your schema. This can be done using the globalElements section in the view:

final GetElements getEdgesSummarised = new GetElements.Builder()
    .input(new EntitySeed("John"))
    .view(new View.Builder()
            .globalElements(new GlobalViewElementDefinition.Builder()
                    .groupBy()
                    .build())
            .build())
    .build();

{
    "class": "GetElements",
    "input": [
        {
            "class": "EntitySeed",
            "vertex": "John"
        }
    ],
    "view": {
        "globalElements": {
            "groupBy" : []
        }
    }
}

elements = gc.execute_operation(
    operation = g.GetElements(
        input = [g.EntitySeed(vertex = "John")],
        view = g.View(
            global_elements = g.GlobalViewElementDefinition(
                group_by = []
            )
        )
    )
)

Tip

As with some of the other examples we again use a class from the Koryphe module to help with the aggregation, please see the reference material for a full list and examples.