# DataStax Graph Fluent API

The fluent API adds graph features to the core driver:

* A TinkerPop GraphTraversalSource builder to execute traversals on a DSE cluster
* The ability to execution traversal queries explicitly using execute_graph
* GraphSON serializers for all DSE Graph types.
* DSE Search predicates

The Graph fluent API depends on Apache TinkerPop and is not installed by default. Make sure
you have the Graph requirements are properly [installed](installation.md#installation-datastax-graph).

You might be interested in reading the [DSE Graph Getting Started documentation](graph.md) to
understand the basics of creating a graph and its schema.

## Graph Traversal Queries

The driver provides `Session.execute_graph()`, which allows users to execute traversal
query strings. Here is a simple example:

```default
session.execute_graph("g.addV('genre').property('genreId', 1).property('name', 'Action').next();")
```

Since graph queries can be very complex, working with strings is not very convenient and is
hard to maintain. This fluent API allows you to build Gremlin traversals and write your graph
queries directly in Python. These native traversal queries can be executed explicitly, with
a Session object, or implicitly:

```default
g = DseGraph.traversal_source(session=dse_session)
g.addV('genre').property('genreId', 1).property('name', 'Action').next()

# implicit execution caused by iterating over results
for v in g.V().has('genre', 'name', 'Drama').in('belongsTo').valueMap():
    print(v)
```

These [Python types](graph.md#graph-types) are also supported transparently:

```default
g.addV('person').property('name', 'Mike').property('birthday', datetime(1984, 3, 11)). \
    property('house_yard', Polygon(((30, 10), (40, 40), (20, 40), (10, 20), (30, 10)))
```

More readings about Gremlin:

* [DataStax Drivers Fluent API](https://www.datastax.com/dev/blog/datastax-drivers-fluent-apis-for-dse-graph-are-out)
* [gremlin-python documentation](http://tinkerpop.apache.org/docs/current/reference/#gremlin-python)

## Configuring a Traversal Execution Profile

The fluent api takes advantage of *configuration profiles* to allow
different execution configurations for the various query handlers. Graph traversal
execution requires a custom execution profile to enable Gremlin-bytecode as
query language. Here is how to accomplish this configuration:

```python
from cassandra.cluster import Cluster, EXEC_PROFILE_GRAPH_DEFAULT
from cassandra.datastax.graph.fluent import DseGraph

ep = DseGraph.create_execution_profile('graph_name')
cluster = Cluster(execution_profiles={EXEC_PROFILE_GRAPH_DEFAULT: ep})
session = cluster.connect()

g = DseGraph.traversal_source(session)  # Build the GraphTraversalSource
print g.V().toList()  # Traverse the Graph
```

Note that the execution profile created with `DseGraph.create_execution_profile` cannot
be used for any groovy string queries.

If you want to change execution property defaults, please see the [Execution Profile documentation](execution_profiles.md)
for a more generalized discussion of the API. Graph traversal queries use the same execution profile defined for DSE graph. If you
need to change the default properties, please refer to the [DSE Graph query documentation page](graph.md)

## Explicit Graph Traversal Execution with a DSE Session

Traversal queries can be executed explicitly using session.execute_graph or session.execute_graph_async. These functions
return results as DSE graph types. If you are familiar with DSE queries or need async execution, you might prefer that way.
Below is an example of explicit execution. For this example, assume the schema has been generated as above:

```python
from cassandra.datastax.graph.fluent import DseGraph
from pprint import pprint

# create a tinkerpop graphson2 ExecutionProfile
ep = DseGraph.create_execution_profile('graph_name')
cluster = Cluster(execution_profiles={EXEC_PROFILE_GRAPH_DEFAULT: ep})
session = cluster.connect()

g = DseGraph.traversal_source(session=session)
addV_query = DseGraph.query_from_traversal(
    g.addV('genre').property('genreId', 1).property('name', 'Action')
)
v_query = DseGraph.query_from_traversal(g.V())

for result in session.execute_graph(addV_query):
    pprint(result.value)
for result in session.execute_graph(v_query):
    pprint(result.value)
```

## Implicit Graph Traversal Execution with TinkerPop

Using the `DseGraph` class, you can build a GraphTraversalSource
that will execute queries on a DSE session without explicitly passing anything to
that session. We call this *implicit execution* because the Session is not
explicitly involved. Everything is managed internally by TinkerPop while
traversing the graph and the results are TinkerPop types as well.

### Synchronous Example

```python
# Build the GraphTraversalSource
g = DseGraph.traversal_source(session)
# implicitly execute the query by traversing the TraversalSource
g.addV('genre').property('genreId', 1).property('name', 'Action').next()

# blocks until the query is completed and return the results
results = g.V().toList()
pprint(results)
```

### Asynchronous Exemple

You can execute a graph traversal query asynchronously by using .promise(). It returns a
python [Future](https://docs.python.org/3/library/concurrent.futures.html#concurrent.futures.Future).

```python
# Build the GraphTraversalSource
g = DseGraph.traversal_source(session)
# implicitly execute the query by traversing the TraversalSource
g.addV('genre').property('genreId', 1).property('name', 'Action').next()  # not async

# get a future and wait
future = g.V().promise()
results = list(future.result())
pprint(results)

# or set a callback
def cb(f):
    results = list(f.result())
    pprint(results)
future = g.V().promise()
future.add_done_callback(cb)
# do other stuff...
```

## Specify the Execution Profile explicitly

If you don’t want to change the default graph execution profile (EXEC_PROFILE_GRAPH_DEFAULT), you can register a new
one as usual and use it explicitly. Here is an example:

```python
from cassandra.cluster import Cluster
from cassandra.datastax.graph.fluent import DseGraph

cluster = Cluster()
ep = DseGraph.create_execution_profile('graph_name')
cluster.add_execution_profile('graph_traversal', ep)
session = cluster.connect()

g = DseGraph.traversal_source()
query = DseGraph.query_from_traversal(g.V())
session.execute_graph(query, execution_profile='graph_traversal')
```

You can also create multiple GraphTraversalSources and use them with
the same execution profile (for different graphs):

```python
g_movies = DseGraph.traversal_source(session, graph_name='movies', ep)
g_series = DseGraph.traversal_source(session, graph_name='series', ep)

print g_movies.V().toList()  # Traverse the movies Graph
print g_series.V().toList()  # Traverse the series Graph
```

## Batch Queries

DSE Graph supports batch queries using a `TraversalBatch` object
instantiated with `DseGraph.batch`. A `TraversalBatch` allows
you to execute multiple graph traversals in a single atomic transaction. A
traversal batch is executed with `Session.execute_graph()` or using
`TraversalBatch.execute` if bounded to a DSE session.

Either way you choose to execute the traversal batch, you need to configure
the execution profile accordingly. Here is a example:

```default
from cassandra.cluster import Cluster
from cassandra.datastax.graph.fluent import DseGraph

ep = DseGraph.create_execution_profile('graph_name')
cluster = Cluster(execution_profiles={'graphson2': ep})
session = cluster.connect()

g = DseGraph.traversal_source()
```

To execute the batch using `Session.execute_graph()`, you need to convert
the batch to a GraphStatement:

```default
batch = DseGraph.batch()

batch.add(
    g.addV('genre').property('genreId', 1).property('name', 'Action'))
batch.add(
    g.addV('genre').property('genreId', 2).property('name', 'Drama'))  # Don't use `.next()` with a batch

graph_statement = batch.as_graph_statement()
graph_statement.is_idempotent = True  # configure any Statement parameters if needed...
session.execute_graph(graph_statement, execution_profile='graphson2')
```

To execute the batch using `TraversalBatch.execute`, you need to bound the batch to a DSE session:

```default
batch = DseGraph.batch(session, 'graphson2')  # bound the session and execution profile

batch.add(
    g.addV('genre').property('genreId', 1).property('name', 'Action'))
batch.add(
    g.addV('genre').property('genreId', 2).property('name', 'Drama'))  # Don't use `.next()` with a batch

batch.execute()
```

## DSL (Domain Specific Languages)

DSL are very useful to write better domain-specific APIs and avoiding
code duplication. Let’s say we have a graph of People and we produce
a lot of statistics based on age. All graph traversal queries of our
application would look like:

```default
g.V().hasLabel("people").has("age", P.gt(21))...
```

which is not really verbose and quite annoying to repeat in a code base. Let’s create a DSL:

```default
from gremlin_python.process.graph_traversal import GraphTraversal, GraphTraversalSource

class MyAppTraversal(GraphTraversal):

  def younger_than(self, age):
      return self.has("age", P.lt(age))

  def older_than(self, age):
      return self.has("age", P.gt(age))


class MyAppTraversalSource(GraphTraversalSource):

  def __init__(self, *args, **kwargs):
      super(MyAppTraversalSource, self).__init__(*args, **kwargs)
      self.graph_traversal = MyAppTraversal

  def people(self):
      return self.get_graph_traversal().V().hasLabel("people")
```

Now, we can use our DSL that is a lot cleaner:

```default
from cassandra.datastax.graph.fluent import DseGraph

# ...
g = DseGraph.traversal_source(session=session, traversal_class=MyAppTraversalsource)

g.people().younger_than(21)...
g.people().older_than(30)...
```

To see a more complete example of DSL, see the [Python killrvideo DSL app](https://github.com/datastax/graph-examples/tree/master/killrvideo/dsl/python)

## Search

DSE Graph can use search indexes that take advantage of DSE Search functionality for
efficient traversal queries. Here are the list of additional search predicates:

Text tokenization:

* `token`
* `token_prefix`
* `token_regex`
* `token_fuzzy`

Text match:

* `prefix`
* `regex`
* `fuzzy`
* `phrase`

Geo:

* `inside`

### Create search indexes

For text tokenization:

```python
s.execute_graph("schema.vertexLabel('my_vertex_label').index('search').search().by('text_field').asText().add()")
```

For text match:

```python
s.execute_graph("schema.vertexLabel('my_vertex_label').index('search').search().by('text_field').asString().add()")
```

For geospatial:

You can create a geospatial index on Point and LineString fields.

```python
s.execute_graph("schema.vertexLabel('my_vertex_label').index('search').search().by('point_field').add()")
```

### Using search indexes

Token:

```python
from cassandra.datastax.graph.fluent.predicates import Search
# ...

g = DseGraph.traversal_source()
query = DseGraph.query_from_traversal(
    g.V().has('my_vertex_label','text_field', Search.token_regex('Hello.+World')).values('text_field'))
session.execute_graph(query)
```

Text:

```python
from cassandra.datastax.graph.fluent.predicates import Search
# ...

g = DseGraph.traversal_source()
query = DseGraph.query_from_traversal(
    g.V().has('my_vertex_label','text_field', Search.prefix('Hello')).values('text_field'))
session.execute_graph(query)
```

Geospatial:

```python
from cassandra.datastax.graph.fluent.predicates import Geo
from cassandra.util import Distance
# ...

g = DseGraph.traversal_source()
query = DseGraph.query_from_traversal(
    g.V().has('my_vertex_label','point_field', Geo.inside(Distance(46, 71, 100)).values('point_field'))
session.execute_graph(query)
```

For more details, please refer to the official [DSE Search Indexes Documentation](https://docs.datastax.com/en/dse/6.7/dse-admin/datastax_enterprise/search/searchReference.html)