What is Helixir?

Helixir is a Rustlings-style tutorial for HelixDB. It is a collection of exercises that will help you learn how to create a simple hierarchical HelixDB database. After completing the tutorial, you will be able to translate the concepts in this guide to your own use cases.

Getting Started

Installing Helixir
Install Helixir using the following command:

cargo install helixir

Or you can check out the Helixir repository. Installing HelixDB
Install the HelixDB CLI using the following command:

curl -sSL https://install.helix-db.com | bash

Add Helix to your PATH

echo 'export PATH="$HOME/.local/bin:$PATH"' >> "$HOME/.zshrc"
source ~/.zshrc

Install the HelixDB container using the following command:

helix install

Setup a new project Setup a new project using the following command:

helix init --path helixdb-cfg

Great, now you might have noticed that a helixdb-cfg folder appeared. In there you will start writing the schema for this tutorial.

Defining Nodes

We will be using HelixDB to model the relationships between continents, countries, and cities as a graph. First, we have to define what kind of entities/nodes will be in our graph. From the schema, we can see that we have 3 types of nodes: continents, countries, and cities. Node Definitions

The Continent node will have a name property (String)
The Country node will have: name (String), currency (String), population (U64), and gdp (F64)
The City node will have: name (String), description (String), and zip_codes (array of strings)

Instructions:

Create a Continent , Country , and City node with their respective properties in schema.hx.

Answer

schema.hx

N::Continent {
    name: String
}

N::Country {
    name: String,
    currency: String,
    population: U64,
    gdp: F64
}

N::City {
    name: String,
    description: String,
    zip_codes: [String]
}

Defining Relationships

Now that we know what type of nodes are in our schema, we will define the relationships between those nodes. For this example, there is a hierarchical pattern where a city is in a country and a country is in a continent. Instructions:

Create a Continent_to_Country and Country_to_City edge connecting their respective nodes with no properties in schema.hx.

Answer

Meta Relationships

In addition to the structural relationships between the nodes, you can also define relationships based on metadata. For example, a country must have a capital city. Instructions:

Create a Country_to_Capital edge connecting Country to City in schema.hx .

Answer

Defining Vectors

Vectors in HelixDB allow us to create vector-based searches for semantic similarity. A vector is an array of floating-point numbers that represents the semantic meaning of data. In this case, we’ll create a vector for city descriptions to enable semantic search capabilities. Instructions:

Create a CityDescription vector with vector property that takes an array of F64.

Answer

Basic Node Creation

Now that we have our schema, we need to write queries to insert the data. The best way to go about this given the structure of our data is to go from top (broad) to bottom (narrow) of the hierarchy. First, we will start with a basic query to create a continent. Usually, creation queries almost always include all the properties of the node in the arguments. In this case, we only need to know the continent’s name. Then we will use AddN to add a Continent node with property name. For best practices, make sure to return the continent that was added in your query. Instructions:

Write a query to create a Continent node in queries.hx.

Query Parameters:

createContinent
- name: String

Answer

Relational Node Creation

Most of the nodes in our schema are related to other nodes, which means that we have to also create edges between them. However, we can optimize this process by creating both the node and the edge connecting it to other existing nodes in one query. In this exercise, we will create a Country node and connect it to its corresponding Continent node. First we will first create a new Country node using AddN. Then we will get the Continent node via the node’s ID so that we can create a Continent_to_Country edge going from the created Continent to Country node using AddE. We will also do the same thing for creating a City node. Instructions:

Write a query to create a Country node and connect it to its respective Continent node by continent ID.
Write a query to create a City node and connect it to its respective Country node by country ID.

Query Parameters:

createCountry
- continent_id: ID
- name: String
- currency: String
- population: I64
- gdp: F64
createCity
- country_id: ID
- name: String
- description: String

Answer

queries.hx

QUERY createCountry (continent_id: ID, name: String, currency: String, population: I64, gdp: F64) =>
    country <- AddN<Country>({name: name, currency: currency, population: population, gdp: gdp})
    continent <- N<Continent>(continent_id)
    continent_country <- AddE<Continent_to_Country>()::From(continent)::To(country)
    RETURN country

QUERY createCity (country_id: ID, name: String, description: String) =>
    city <- AddN<City>({name: name, description: description})
    country <- N<Country>(country_id)
    country_city <- AddE<Country_to_City>()::From(country)::To(city)
    RETURN city

Creating Meta Relationships

In order to add meta relationships into our graph, we will connect nodes together with the edges that define the meta relationships. For this example, we will create a Country_to_Capital edge from a Country node to a City node. Instructions:

Write a query to set a City node as the capital city of a Country node using their IDs.

Query Parameters:

setCapital
- country_id: ID
- city_id: ID

Answer

queries.hx

QUERY setCapital (country_id: ID, city_id: ID) =>
    country <- N<Country>(country_id)
    city <- N<City>(city_id)
    country_capital <- AddE<Country_to_Capital>()::From(country)::To(city)
    RETURN country_capital

Creating Vector Embeddings

Vector embeddings allow us to perform similarity-based searches on our data. For city descriptions, this means we can find cities with similar characteristics even if they don’t share exact properties. We will create a vector embedding for each city’s description. Instructions: Write a query to create a CityDescription vector and connect it to its respective City node by city ID. Query Parameters:

embedDescription
- city_id: ID
- vector: [F64]

Answer

queries.hx

QUERY embedDescription (city_id: ID, vector: [F64]) =>
    embedding <- AddV<CityDescription>(vector)
    city <- N<City>(city_id)
    city_embedding <- AddE<City_to_Embedding>()::From(city)::To(embedding)
    RETURN embedding

Get Nodes by ID

Now that we know how to create nodes and their relationships, we need to be able to retrieve nodes from our graph. The simplest way is to retrieve nodes when we know their ID. Instructions:

Write 3 queries to get Continent , Country , and City by node ID.

Query Parameters:

getContinent
- continent_id: ID
getCountry
- country_id: ID
getCity
- city_id: ID

Answer

queries.hx

QUERY getContinent (continent_id: ID) =>
    continent <- N<Continent>(continent_id)
    RETURN continent

QUERY getCountry (country_id: ID) =>
    country <- N<Country>(country_id)
    RETURN country

QUERY getCity (city_id: ID) =>
    city <- N<City>(city_id)
    RETURN city

Get All Nodes of Type

In addition to retrieving nodes by ID, we often want to retrieve all nodes of a certain type. Since we have a hierarchical structure, we will also want to get all countries within a continent and all cities within a country. Instructions:

Write 3 queries to get all Continent, Country, and City nodes.
Write 2 queries to get all Country and City nodes by their parent IDs.

Query Parameters:

getAllContinents
getAllCountries
getAllCities
getCountriesInContinent
- continent_id: ID
getCitiesInCountry
- country_id: ID

Answer

queries.hx

QUERY getAllContinents () =>
    continents <- N<Continent>
    RETURN continents

QUERY getAllCountries () =>
    countries <- N<Country>
    RETURN countries

QUERY getAllCities () =>
    cities <- N<City>
    RETURN cities

QUERY getCountriesInContinent (continent_id: ID) =>
    continent <- N<Continent>(continent_id)
    countries <- continent::Out<Continent_to_Country>
    RETURN countries

QUERY getCitiesInCountry (country_id: ID) =>
    country <- N<Country>(country_id)
    cities <- country::Out<Country_to_City>
    RETURN cities

Get Nodes by Meta Relationship

Similar to getting nodes by their hierarchical relationships, we can also get nodes via their meta relationships. For this example, we will retrieve the capital city of a country. We’ll do this by traversing the Country_to_Capital edge from a Country node to find its capital City node. Instructions:

Write a query to get a country’s capital City node by the country’s ID.

Query Parameters:

getCapital
- country_id: ID

Answer

Get Node Properties

Sometimes we don’t need the full node, just a few specific properties. For example, we can display only the names and populations of countries without pulling in the entire node. In this case, we can use property selection syntax to retrieve just the fields we care about. This allows for more efficient querying and cleaner data handling when building visualizations or summaries. Instructions:

Write a query to get each country’s name and population.

Query Parameters:

getCountryNames

Answer

Get Nodes by Property

In addition to retrieving nodes by their ID or relationship, we often need to find nodes based on their properties. This allows for more flexible querying of our graph database. We will write queries to retrieve nodes by specific properties they contain. Instructions:

Write 3 queries that get the Continent, Country, and City nodes by their names.

Query Parameters:

getContinentByName
- continent_name: String
getCountryByName
- country_name: String
getCityByName
- city_name: String

Answer

queries.hx

QUERY getContinentByName (continent_name: String) =>
    continent <- N<Continent>::WHERE(_::{name}::EQ(continent_name))
    RETURN continent

QUERY getCountryByName (country_name: String) =>
    country <- N<Country>::WHERE(_::{name}::EQ(country_name))
    RETURN country
    
QUERY getCityByName (city_name: String) =>
    city <- N<City>::WHERE(_::{name}::EQ(city_name))
    RETURN city

Get Nodes by Property Cont.

Building on property-based queries, you can also filter nodes using comparison operators. This allows you to find nodes that meet specific criteria rather than exact matches. You’ll practice with different comparison operators to filter countries by various attributes. Instructions:

Write a query to get Country nodes by their currency.
Write a query to get Country nodes with population less than max_population.
Write a query to get Country nodes with GDP greater than or equal to min_gdp .

Query Parameters:

getCountriesByCurrency
- currency: String
getCountriesByPopulation
- max_population: I64
getCountriesByGdp
- min_gdp: F64

Answer

queries.hx

QUERY getCountriesByCurrency (currency: String) =>
    countries <- N<Country>::WHERE(_::{currency}::EQ(currency))
    RETURN countries

QUERY getCountriesByPopulation (max_population: I64) =>
    countries <- N<Country>::WHERE(_::{population}::LT(max_population))
    RETURN countries

QUERY getCountriesByGdp (min_gdp: F64) =>
    countries <- N<Country>::WHERE(_::{gdp}::GTE(min_gdp))
    RETURN countries

Get Nodes by Many Properties

Now that we’ve seen how to get nodes by individual properties, we can also combine multiple conditions to perform more advanced filtering. For this example, we’ll write queries that retrieve Country nodes based on a combination of property values. This includes filtering countries with a population greater than a minimum and a GDP less than or equal to a maximum, as well as retrieving countries that either use a specific currency or have a population below a certain threshold. These types of queries allow us to refine our searches and extract more targeted subsets of data from our graph. Instructions:

Write a query to find Country nodes with both population greater than min_population and GDP less than or equal to max_gdp.
Write a query to find Country nodes with either a specific currency or a population less than or equal to max_population.

Query Parameters:

getCountriesByPopGdp
- min_population: I64
- max_gdp: F64
getCitiesByCurrPop
- currency: String
- max_population: I64

Answer

queries.hx

QUERY getCountriesByPopGdp (min_population: I64, max_gdp: F64) =>
    countries <- N<Country>::WHERE(
            AND(
                _::{population}::GT(min_population),
                _::{gdp}::LTE(max_gdp)
            )
        )
    RETURN countries

QUERY getCountriesByCurrPop (currency: String, max_population: I64) =>
    countries <- N<Country>::WHERE(
            OR(
                _::{currency}::EQ(currency),
                _::{population}::LTE(max_population)
            )
        )
    RETURN countries

Get Nodes by Meta Relationships

In addition to traversing structural relationships, we can also query nodes based on meta relationships. For example, we can retrieve all Country nodes that have a capital city assigned. This involves checking for the existence of an outgoing Country_to_Capital edge from each Country node. Meta relationship queries like this are useful for identifying nodes with specific contextual connections beyond hierarchical structures. Instructions: Write a query to get Country nodes that have capital cities. Query Parameters:

getCountriesWithCapitals

Answer

Get Range of Nodes

When working with large datasets, it’s often useful to limit the number of results returned from a query. The RANGE operator allows you to implement pagination and control result set size efficiently. This is particularly important for performance when dealing with queries that might return many nodes. The RANGE operator takes two parameters: the starting index (0-based) and the number of items to return. Instructions:

Write a query to get the first k (I64) City nodes in a continent given the continent’s name.

Query Parameters:

getContinentCities
- continent_name: String
- k: I64

Answer

queries.hx

QUERY getContinentCities (continent_name: String, k: I64) =>
    continent <- N<Continent>::WHERE(_::{name}::EQ(continent_name))
    countries <- continent::Out<Continent_to_Country>
    cities <- countries::Out<Country_to_City>::RANGE(0, k)
    RETURN cities

Get Count of Nodes

In some cases, we want to gather basic statistics about our graph. For example, we can count the number of capital cities by checking how many City nodes have an incoming Country_to_Capital edge. Using the COUNT operation, we can quickly compute aggregate statistics like this to better understand the structure and distribution of data across our graph. Instructions:

Write a query to get the number of capital cities.

Query Parameters:

countCapitals

Answer

Get Nodes with Anonymous Traversals

Sometimes we want to filter nodes based other node’s properties. For example, we can get all countries that have more than a certain number of cities. To do this, we’ll count the number of outgoing Country_to_City edges from each Country node and filter by num_cities. This pattern of anonymous traversal is useful when we care about the structure or degree of connectivity in the graph, rather than the specific linked nodes themselves. Instructions:

Write a query to get Country nodes that has more cities than num_cities .

Query Parameters:

getCountryByCityCnt
- num_cities: I64

Answer

Semantic Search Vectors

Semantic search allows us to go beyond exact matches by comparing the meaning of data. For example, we can find cities with similar descriptions using vector embeddings. By searching against CityDescription vectors, we can retrieve the top-k most semantically similar City nodes to a given input vector. This is especially useful when we want to find cities that share common characteristics or themes, even if their properties don’t match exactly.

For Helixir, we will be using fake embeddings to test the semantic search functionality. In real applications, you would use proper embeddings from providers like OpenAI, Gemini, etc.

Instructions:

Write a query to semantically search a vector against CityDescription vectors and returning the top k City nodes.

Query Parameters:

searchDescriptions
- vector: [F64]
- k: I64

Answer

Updating Nodes

Updating nodes allows us to modify the properties of existing entities in our graph without needing to recreate them. To update a node, we use the UPDATE operation followed by the fields we want to modify. For example, we can update a country’s currency by its ID, or simultaneously update both its population and GDP. Keeping node data up-to-date ensures our graph remains accurate and relevant for queries, visualizations, and downstream analytics. Instructions:

Write a query to update a country’s currency by a country’s ID.
Write a query to update a country’s population and gdp by a country’s ID.

Query Parameters:

updateCurrency
- country_id: ID
- currency: String
updatePopGdp
- country_id: ID
- population: I64
- gdp: F64

Answer

queries.hx

QUERY updateCurrency (country_id: ID, currency: String) =>
    country <- N<Country>(country_id)::UPDATE({currency: currency})
    RETURN country
    
QUERY updatePopGdp (country_id: ID, population: I64, gdp: F64) =>
    country <- N<Country>(country_id)::UPDATE({population: population, gdp: gdp})
    RETURN country

Deleting Nodes

Deleting nodes is useful when we want to clean up outdated or incorrect data from our graph. However, this can get tricky in a graph database because not only do we have to drop the node but also the relationships connected to that node. Additionally, the order in which we drop them is very important. For example, if a city is no longer relevant or a country needs to be removed entirely, we can drop the node and its relationship to the country as a city and also potentially as a capital city. In cases where the node is linked through specific edges, like a capital city connection, it’s important to remove those edges first to maintain the graph structure and allowing us to drop the other edges later. This ensures that dependent edges don’t linger in the system, avoiding potential inconsistencies during traversal or analytics. Instructions:

Write a query to delete a City node given its ID.
Write a query to delete a capital City node given its country’s ID.
Write a query to delete a Country node given its ID.

Query Parameters:

deleteCity
- city_id: ID
deleteCapital
- country_id: ID
deleteCountry
- country_id: ID

Answer

queries.hx

QUERY deleteCity (city_id: ID) =>
		DROP N<City>(city_id)
		RETURN "success"
		
QUERY deleteCapital (country_id: ID) =>
		DROP N<Country<(country_id)::Out<Country_to_Capital>
		RETURN "success"

QUERY deleteCountry (country_id: ID) =>
		DROP N<Country>(country_id)::OutE<Country_to_City>
		DROP N<Country>(country_id)
		RETURN "success"

Updating Meta Relationships

Sometimes you need to update the meta relationships between nodes rather than creating new ones. For example, you might want to change which city serves as a country’s capital. This involves removing the existing capital relationship and creating a new one with a different city. When updating meta relationships, it’s important to properly manage the edge connections to maintain graph consistency. Instructions:

Write a query to update the capital City node of a Coutry node given the country’s ID and the new capital city’s ID.

Query Parameters:

updateCapital
- country_id: ID
- city_id: ID

Answer

queries.hx

QUERY updateCapital (country_id: ID, city_id: ID) =>
    DROP N<Country>(country_id)::OutE<Country_to_Capital>
    country <- N<Country>(country_id)
    city <- N<City>(city_id)
    capital <- AddE<Country_to_Capital>::From(country)::To(city)
    RETURN city

Updating Embeddings

When working with vector embeddings, you often need to update both the node properties and their associated vector embeddings. For example, when a city’s description changes, you need to update the description property and also update the corresponding vector embedding to reflect the new semantic meaning. This ensures that semantic searches remain accurate and relevant. Instructions:

Write a query to update the description of a City node given its ID and also update the CityDescription vector embedding given a new vector.

Query Parameters:

updateDescription
- city_id: ID
- description: String
- vector: [F64]

Answer

queries.hx

QUERY updateDescription (city_id: ID, description: String, vector: [F64]) =>
    DROP N<City>(city_id)::OutE<City_to_Embedding>
    city <- N<City>(city_id)::UPDATE({description: description})
    description_embedding <- AddV<CityDescription>(vector)
    RETURN city

Congratulations!

You’ve completed the Helixir guide!

In this guide, we covered the basics of querying a graph database using Helixir. We learned how to create nodes, relationships, and properties. We also learned how to filter, update, and delete nodes and their relationships. This is just the beginning of what you can do with Helixir. There are many more features and capabilities that you can explore. If you have any questions or feedback, please feel free to reach out to us!

Next Steps

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Discover everything HelixDB has to offer with our cutting-edge features.

Guides

Use Cases

​What is Helixir?

​Getting Started

​Defining Nodes

​Defining Relationships

​Meta Relationships

​Defining Vectors

​Basic Node Creation

​Relational Node Creation

​Creating Meta Relationships

​Creating Vector Embeddings

​Get Nodes by ID

​Get All Nodes of Type

​Get Nodes by Meta Relationship

​Get Node Properties

​Get Nodes by Property

​Get Nodes by Property Cont.

​Get Nodes by Many Properties

​Get Nodes by Meta Relationships

​Get Range of Nodes

​Get Count of Nodes

​Get Nodes with Anonymous Traversals

​Semantic Search Vectors

​Updating Nodes

​Deleting Nodes

​Updating Meta Relationships

​Updating Embeddings

​Congratulations!

​You’ve completed the Helixir guide!

​Next Steps

What is Helixir?

Getting Started

Defining Nodes

Defining Relationships

Meta Relationships

Defining Vectors

Basic Node Creation

Relational Node Creation

Creating Meta Relationships

Creating Vector Embeddings

Get Nodes by ID

Get All Nodes of Type

Get Nodes by Meta Relationship

Get Node Properties

Get Nodes by Property

Get Nodes by Property Cont.

Get Nodes by Many Properties

Get Nodes by Meta Relationships

Get Range of Nodes

Get Count of Nodes

Get Nodes with Anonymous Traversals

Semantic Search Vectors

Updating Nodes

Deleting Nodes

Updating Meta Relationships

Updating Embeddings

Congratulations!

You’ve completed the Helixir guide!

Next Steps