GraphQL 101: What is GraphQL?
Most people within the tech world have heard of GraphQL at this point. Adoption is also booming at steady ...
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Welcome to the eighth episode of getting started with Dgraph.
In the previous episode, we learned about building a twitter-like user-search feature using Dgraph’s fuzzy search.
In this episode, we’ll build a graph of tourist locations around San Francisco and help our Zoologist friend, Mary, and her team in their mission to conserve birds using Dgraph’s geolocation capabilities.
You might have used Google to find the restaurants near you or to find the shopping centres within a mile of your current location. Applications like these make use of your geolocation data.
Geolocation has become an integral part of mobile applications, especially with the advent of smartphones in the last decade, the list of applications which revolves around users location to power application features has grown beyond imagination.
Let’s take Uber, for instance, the location data of the driver and passenger is pivotal for the functionality of the application. We’re gathering more GPS data than ever before, being able to store and query the location data efficiently can give you an edge over your competitors.
Real-world data is interconnected; they are not sparse; this is more relevant when it comes to location data. The natural representation of railway networks, maps, routes are graphs.
The good news is that Dgraph, the world’s most advanced graph database, comes with functionalities to efficiently store and perform useful queries on graphs containing location data.
If you want to run queries like find me the hotels near Golden Gate Bridge, or find me all the tourist location around Golden Gate Park, Dgraph has your back.
First, let’s learn how to represent Geolocation data in Dgraph.
You can represent location data in Dgraph using two ways:
Point location contains the geo-coordinate tuple (latitude, longitude) of your location of interest.
The following image has the point location with the latitude and longitude for the Eifel tower in Paris. Point locations are useful for representing a precise location; for instance, your location when booking a cab or your delivery address.
It’s not possible to represent geographical entities which are spread across multiple geo-coordinates just using a point location.
To represent geo entities like a city, a lake, or a national park, you should use a polygonal location.
Here is an example:
The polygonal fence above represents the city of Delhi, India.
This polygonal fence or the geo-fence is formed by connecting multiple straight-line boundaries, and they are collectively represented using an array of location tuples of format [(latitude, longitude), (latitude, longitude), ...].
Each tuple pair (2 tuples and 4 coordinates) represents a straight line boundary of the geo-fence, and a polygonal fence can contain any number of lines.
Let’s start with building a simple San Francisco tourist graph, here’s the graph model.
The above graph has three entities represented by the nodes:
A city node represents the tourist city.
Our dataset only contains the city of San Francisco, and a node in the graph represents it.
A location node, along with the name of the location, it contains the point or polygonal location of the place of interest.
A location type consists of the type of location.
There are four types of location in our dataset: zoo, museum, hotel or a tourist attraction.
The location nodes with geo-coordinates of a hotel also contains their pricing information.
There are different ways to model the same graph.
For instance, the location type could just be a property or a predicate of the location node, rather than being a node of its own.
The queries you want to perform or the relationships you like to explore mostly influence the modelling decisions. The goal of the tutorial is not to arrive at the ideal graph model, but to use a simple dataset to demonstrate the geolocation capabilities of Dgraph.
For the rest of the tutorial, let’s call the node representing a City as a city node, and the node representing a Location as a location node, and the node representing the Location Type as a location type node.
Here’s the relationship between these nodes:
city node is connected to a location node via the has_location edge.location node is connected to its node representing a location type via the has_type edge.Note: Dgraph allows you to associate one or more types for the nodes using its type system feature, for now, we are using nodes without types, we’ll learn about type system for nodes in a future episode. Check this page from the documentation site, if you want to explore type system feature for nodes.
Here is our sample dataset. Open Ratel, go to the mutate tab, paste the mutation, and click Run.
{
"set": [
{
"city": "San Francisco",
"uid": "_:SFO",
"has_location": [
{
"name": "USS Pampanito",
"location": {
"type": "Polygon",
"coordinates": [
[
[
-122.4160088,
37.8096674
],
[
-122.4161147,
37.8097628
],
[
-122.4162064,
37.8098357
],
[
-122.4163467,
37.8099312
],
[
-122.416527,
37.8100471
],
[
-122.4167504,
37.8101792
],
[
-122.4168272,
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],
[
-122.4167719,
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],
[
-122.4165683,
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],
[
-122.4163888,
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],
[
-122.4162492,
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],
[
-122.4161469,
37.8097352
],
[
-122.4160088,
37.8096674
]
]
]
},
"has_type": [
{
"uid": "_:museum",
"loc_type": "Museum"
}
]
},
{
"name": "Alameda Naval Air Museum",
"location": {
"type": "Polygon",
"coordinates": [
[
[
-122.2995054,
37.7813924
],
[
-122.2988538,
37.7813582
],
[
-122.2988421,
37.7814972
],
[
-122.2994937,
37.7815314
],
[
-122.2995054,
37.7813924
]
]
]
},
"street": "Ferry Point Road",
"has_type": [
{
"uid": "_:museum"
}
]
},
{
"name": "Burlingame Museum of PEZ Memorabilia",
"location": {
"type": "Polygon",
"coordinates": [
[
[
-122.3441509,
37.5792003
],
[
-122.3438207,
37.5794257
],
[
-122.3438987,
37.5794587
],
[
-122.3442289,
37.5792333
],
[
-122.3441509,
37.5792003
]
]
]
},
"street": "California Drive",
"has_type": [
{
"uid": "_:museum"
}
]
},
{
"name": "Carriage Inn",
"location": {
"type": "Polygon",
"coordinates": [
[
[
-122.3441509,
37.5792003
],
[
-122.3438207,
37.5794257
],
[
-122.3438987,
37.5794587
],
[
-122.3442289,
37.5792333
],
[
-122.3441509,
37.5792003
]
]
]
},
"street": "7th street",
"price_per_night": 350.00,
"has_type": [
{
"uid": "_:hotel",
"loc_type": "Hotel"
}
]
},
{
"name": "Lombard Motor In",
"location": {
"type": "Polygon",
"coordinates": [
[
[
-122.4260484,
37.8009811
],
[
-122.4260137,
37.8007969
],
[
-122.4259083,
37.80081
],
[
-122.4258724,
37.8008144
],
[
-122.4257962,
37.8008239
],
[
-122.4256354,
37.8008438
],
[
-122.4256729,
37.8010277
],
[
-122.4260484,
37.8009811
]
]
]
},
"street": "Lombard Street",
"price_per_night": 400.00,
"has_type": [
{
"uid": "_:hotel"
}
]
},
{
"name": "Holiday Inn San Francisco Golden Gateway",
"location": {
"type": "Polygon",
"coordinates": [
[
[
-122.4214895,
37.7896108
],
[
-122.4215628,
37.7899798
],
[
-122.4215712,
37.790022
],
[
-122.4215987,
37.7901606
],
[
-122.4221004,
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],
[
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],
[
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],
[
-122.4218207,
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],
[
-122.4216158,
37.7895961
],
[
-122.4214895,
37.7896108
]
]
]
},
"street": "Van Ness Avenue",
"price_per_night": 250.00,
"has_type": [
{
"uid": "_:hotel"
}
]
},
{
"name": "Golden Gate Bridge",
"location": {
"type": "Polygon",
"coordinates": [
[
[
-122.479784,
37.8288329
],
[
-122.4775646,
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],
[
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],
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],
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],
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],
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],
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],
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],
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],
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],
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],
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],
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],
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],
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],
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],
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],
[
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],
[
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],
[
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],
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],
[
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],
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],
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],
[
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],
[
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],
[
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],
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],
[
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],
[
-122.4804046,
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],
[
-122.4805121,
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],
[
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],
[
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],
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],
[
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],
[
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],
[
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],
[
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],
[
-122.4806215,
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],
[
-122.4805254,
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],
[
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],
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],
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],
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],
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],
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],
[
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],
[
-122.479784,
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]
]
]
},
"street": "Golden Gate Bridge",
"has_type": [
{
"uid": "_:attraction",
"loc_type": "Tourist Attraction"
}
]
},
{
"name": "Carriage Inn",
"location": {
"type": "Polygon",
"coordinates": [
[
[
-122.3441509,
37.5792003
],
[
-122.3438207,
37.5794257
],
[
-122.3438987,
37.5794587
],
[
-122.3442289,
37.5792333
],
[
-122.3441509,
37.5792003
]
]
]
},
"street": "7th street",
"has_type": [
{
"uid": "_:attraction"
}
]
},
{
"name": "San Francisco Zoo",
"location": {
"type": "Polygon",
"coordinates": [
[
[
-122.5036126,
37.7308562
],
[
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],
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],
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],
[
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]
]
]
},
"street": "San Francisco Zoo",
"has_type": [
{
"uid": "_:zoo",
"loc_type": "Zoo"
}
]
},
{
"name": "Flamingo Park",
"location": {
"type": "Polygon",
"coordinates": [
[
[
-122.5033039,
37.7334601
],
[
-122.5032811,
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],
[
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],
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],
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]
]
]
},
"street": "San Francisco Zoo",
"has_type": [
{
"uid": "_:zoo"
}
]
},
{
"name": "Peace Lantern",
"location": {
"type": "Point",
"coordinates": [
-122.4705776,
37.7701084
]
},
"street": "Golden Gate Park",
"has_type": [
{
"uid": "_:attraction"
}
]
},
{
"name": "Buddha",
"location": {
"type": "Point",
"coordinates": [
-122.469942,
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]
},
"street": "Golden Gate Park",
"has_type": [
{
"uid": "_:attraction"
}
]
},
{
"name": "Japanese Tea Garden",
"location": {
"type": "Polygon",
"coordinates": [
[
[
-122.4692131,
37.7705116
],
[
-122.4698998,
37.7710069
],
[
-122.4702431,
37.7710137
],
[
-122.4707248,
37.7708919
],
[
-122.4708911,
37.7701541
],
[
-122.4708428,
37.7700354
],
[
-122.4703492,
37.7695011
],
[
-122.4699255,
37.7693989
],
[
-122.4692131,
37.7705116
]
]
]
},
"street": "Golden Gate Park",
"has_type": [
{
"uid": "_:attraction"
}
]
}
]
}
]
}
Note: If this mutation syntax is new to you, refer to the first tutorial to learn the basics of mutations in Dgraph.
Run the query below to fetch the entire graph:
{
entire_graph(func: has(city)) {
city
has_location {
name
has_type {
loc_type
}
}
}
}
Note: Check the second tutorial if you want to learn more about traversal queries like the above one.
Here’s our graph!
Our graph has:
city node.
We just have one node which represents the city of San Francisco.location nodes.
We have a total of 13 locations.location types.
We have four kinds of locations in our dataset: museum, zoo, hotel, and tourist attractions.You can also see that Dgraph has auto-detected the data types of the predicates from the schema tab, and the location predicate has been auto-assigned geo type.
Note: Check out the previous tutorial to know more about data types in Dgraph.
Before we start, please say Hello to Mary, a zoologist who has dedicated her research for the cause of conserving various bird species.
For the rest of the tutorial, let’s help Mary and her team of zoologists in their mission to conserving birds.
Several research projects done by Mary suggested that Flamingos thrive better when there are abundant water bodies for their habitat.
Her team got approval for expanding the water source for the Flamingos in the San Francisco Zoo, and her team is ready for a trip to San Francisco with Mary remotely monitoring the progress of the team.
Her teammates wish to stay close to the Golden Gate Bridge so that they could cycle around the Golden gate, enjoy the breeze, and the sunrise every morning.
Let’s help them find a hotel which is within a reasonable distance from the Golden Gate Bridge, and we’ll do so using Dgraph’s geolocation functions.
Dgraph provides a variety of functions to query geolocation data.
To use them, you have to set the geo index first.
Go to the Schema tab and set the index on the location predicate.
After setting the geo index on the location predicate, you can use Dgraph’s built-in function near to find the hotels near the Golden gate bridge.
Here is the syntax of the near function: near(geo-predicate, [long, lat], distance).
The near function matches and returns all the geo-predicates stored in the database which are within distance meters of geojson coordinate [long, lat] provided by the user.
Let’s search for hotels within 7KM of from a point on the Golden Gate bridge.
Go to the query tab, paste the query below and click Run.
{
find_hotel(func: near(location, [-122.479784,37.82883295],7000) ) {
name
has_type {
loc_type
}
}
}
Wait! The search returns not just the hotels, but also all other locations within 7 Km from the point coordinate on the Golden Gate Bridge.
Let’s use the @filter function to filter for search results containing only the hotels.
You can visit our third tutorial of the series to refresh our previous discussions around using the @filter directive.
{
find_hotel(func: near(location, [-122.479784,37.82883295],7000)) {
name
has_type @filter(eq(loc_type, "hotel")){
loc_type
}
}
}
Oops, we forgot to add an index while using the eq comparator in the filter.
Let’s add a hash index to the loc_type and re-run the query.
Note: Refer to the third tutorial of the series to learn more about hash index and comparator functions in Dgraph.
The search result still contains nodes representing locations which are not hotels. That’s because the root query first finds all the location nodes which are within 7KM from the specified point location, and then it applies the filter while selectively traversing to the location type nodes.
Only the predicates in the location nodes can be filtered at the root level, and you cannot filter the location types without traversing to the location type nodes.
We have the filter to select only the hotels while we traverse the location type nodes.
Can we cascade or bubble up the filter to the root level, so that, we only have hotels in the final result?
Yes you can! You can do by using the @cascade directive.
The @cascade directive helps you cascade or bubble up the filters applied to your inner query traversals to the root level nodes, by doing so, we get only the locations of hotels in our result.
{
find_hotel(func: near(location, [-122.479784,37.82883295],7000)) @cascade {
name
price_per_night
has_type @filter(eq(loc_type,"Hotel")){
loc_type
}
}
}
Voila! You can see in the result that, after adding the @cascade directive in the query, only the locations with type hotel appear in the result.
We have two hotels in the result, and one of them is over their budget of 300$ per night. Let’s add another filter to search for Hotels priced below $300 per night.
The price information of every hotel is stored in the location nodes along with their coordinates, hence the filter on the pricing should be at the root level of the query, not at the level we traverse the location type nodes.
Before you jump onto run the query, don’t forget to add an index on the price_per_night predicate.
{
find_hotel(func: near(location, [-122.479784,37.82883295],7000)) @cascade @filter(le(price_per_night, 300)){
name
price_per_night
has_type @filter(eq(loc_type,"Hotel")){
loc_type
}
}
}
Now we have a hotel well within the budget, and also close to the Golden Gate Bridge!
In this tutorial, we learned about geolocation capabilities in Dgraph, and helped Mary’s team book a hotel near Golden bridge.
In the next episode, we’ll showcase more geolocation functionalities in Dgraph and assist Mary’s team in their quest for conserving Flamingo’s.
See you all in the next episode. Till then, happy Graphing!
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