Migrate from Neo4j to Memgraph

Memgraph is an open-source graph database built for streaming and compatible with Neo4j. It uses Cypher query language and Bolt protocol. This means that you can use the same tools and drivers that you are already using with Neo4j. Due to the ACID compliance, data persistency and replication support in the community version, Memgraph can be used as the main database for your applications, instead of Neo4j.

This tutorial will show you how to migrate data in the CSV format from Neo4j to Memgraph. If you are more of a visual learner, check out the video tutorial:

💡

If you prefer to export your data from Neo4j into the Cypher queries format, then it's necessary to convert those files into Memgraph CYPHERL format (opens in a new tab).

Learn more about the shortest path to import data into Memgraph by reading best practices for import.

Prerequisites

To follow this tutorial, you will need to have the following:

Running Neo4j instance (with your data, or use the sample data provided)
Latest memgraph/memgraph-platform Docker image (opens in a new tab)

Data schema

One of the first steps to consider is how to migrate your data. If you have your data in the form of Cypher queries or CSV or JSON format, you can import these formats into Memgraph. Keep in mind that for importing larger datasets it is recommended to use CSV format or pure Cypher queries (Memgraph's CYPHERL format), since they can be imported into Memgraph natively, faster than JSON format.

This tutorial will go through exporting data from Neo4j into CSV files and importing it into Memgraph using LOAD CSV query and Memgraph's user visual interface Memgraph Lab.

The sample dataset consists of 3 different kinds of nodes (Employee, Order and Product) connected with 3 types of relationships as described by the graph schema below:

To create this graph in your Neo4j instance run the following queries:

LOAD CSV WITH HEADERS FROM 'https://gist.githubusercontent.com/jexp/054bc6baf36604061bf407aa8cd08608/raw/8bdd36dfc88381995e6823ff3f419b5a0cb8ac4f/orders.csv' AS column
MERGE (order:Order {orderID: column.OrderID})
    ON CREATE SET order.shipName = column.ShipName;
 
LOAD CSV WITH HEADERS FROM 'https://gist.githubusercontent.com/jexp/054bc6baf36604061bf407aa8cd08608/raw/8bdd36dfc88381995e6823ff3f419b5a0cb8ac4f/products.csv' AS column
MERGE (product:Product {productID: column.ProductID})
  ON CREATE SET product.productName = column.ProductName, product.unitPrice = toFloat(column.UnitPrice);
 
LOAD CSV WITH HEADERS FROM 'https://gist.githubusercontent.com/jexp/054bc6baf36604061bf407aa8cd08608/raw/8bdd36dfc88381995e6823ff3f419b5a0cb8ac4f/employees.csv' AS column
MERGE (e:Employee {employeeID:column.EmployeeID})
  ON CREATE SET e.firstName = column.FirstName, e.lastName = column.LastName, e.title = column.Title;
 
CREATE INDEX product_id FOR (p:Product) ON (p.productID);
CREATE INDEX product_name FOR (p:Product) ON (p.productName);
CREATE INDEX employee_id FOR (e:Employee) ON (e.employeeID);
CALL db.awaitIndexes();
 
LOAD CSV WITH HEADERS FROM 'https://gist.githubusercontent.com/jexp/054bc6baf36604061bf407aa8cd08608/raw/8bdd36dfc88381995e6823ff3f419b5a0cb8ac4f/orders.csv' AS column
MATCH (order:Order {orderID: column.OrderID})
MATCH (product:Product {productID: column.ProductID})
MERGE (order)-[op:CONTAINS]->(product)
  ON CREATE SET op.unitPrice = toFloat(column.UnitPrice), op.quantity = toFloat(column.Quantity);
 
LOAD CSV WITH HEADERS FROM 'https://gist.githubusercontent.com/jexp/054bc6baf36604061bf407aa8cd08608/raw/8bdd36dfc88381995e6823ff3f419b5a0cb8ac4f/orders.csv' AS column
MATCH (order:Order {orderID: column.OrderID})
MATCH (employee:Employee {employeeID: column.EmployeeID})
MERGE (employee)-[:SOLD]->(order);
 
LOAD CSV WITH HEADERS FROM 'https://gist.githubusercontent.com/jexp/054bc6baf36604061bf407aa8cd08608/raw/8bdd36dfc88381995e6823ff3f419b5a0cb8ac4f/employees.csv' AS column
MATCH (employee:Employee {employeeID: column.EmployeeID})
MATCH (manager:Employee {employeeID: column.ReportsTo})
MERGE (employee)-[:REPORTS_TO]->(manager);

If you are going to use different dataset to migrate, be aware of the differences between Neo4j and Memgraph data types (for example, Memgraph doesn't support DateTime() as there is no temporal type in Memgraph that supports timezones yet, but you can modify data to use localDateTime()).

Exporting data from Neo4j

Download the CSV file shipping.csv (opens in a new tab) containing the data above if you don't want to go through the exporting process.

To get your data out of Neo4j instance, use the Neo4j APOC export functionality. To install APOC, select the project, then in the right-side menu select Plugins -> APOC and press install.

Then enable export by setting the configuration flag apoc.export.file.enabled to true in the apoc.config file located in the config directory. To open the directory, select the active project, click on ... -> Open folder -> Configuration.

Export the data into a CSV file using:

CALL apoc.export.csv.all("shipping.csv", {})

Once exported, the file is located in Neo4j's Import folder. To open it, select the active project, click on ... -> Open folder -> Import.

Importing data into Memgraph

Now that the CSV file containing the needed data has been generated, let's import data into Memgraph.

As the original location of file is quite cumbersome, relocate it somewhere more accessible.

1. Starting Memgraph with Docker

When working with Docker, the file needs to be transferred from your local directory into the Docker container where Memgraph can access it.

This can be done by copying the file into your running instance.

Run Memgraph

The easiest way to run Memgraph is by running installation scripts that use a pre-prepared docker-compose.yml file by executing one of the following commands, depending on your operating system:

For Linux and macOS:
```
curl https://install.memgraph.com | sh
```
For Windows:
```
iwr https://windows.memgraph.com | iex
```
There are other installation options available as well, so make sure you choose the preferred one for your environment.
To copy the file inside the container, open a new terminal to find out the CONTAINER ID with docker ps, then run:
```
docker cp /path_to_local_folder/shipping.csv <container_ID>:/usr/lib/memgraph/shipping.csv
```
If the container ID is bed1e5c9192d and the file is locally located at C:/Data the command would look like this:
```
docker cp C:/Data/shipping.csv bed1:/usr/lib/memgraph/shipping.csv
```
To check if the files are inside the container, first run:
```
docker exec -it CONTAINER_ID bash
```

List the files inside the /usr/lib/memgraph.

C:\Users\Vlasta>docker ps
CONTAINER ID   IMAGE                        COMMAND                  CREATED         STATUS         PORTS                                                                    NAMES
bed1e5c9192d   memgraph/memgraph-platform   "/bin/sh -c '/usr/bi…"   2 minutes ago   Up 2 minutes   0.0.0.0:3000->3000/tcp, 0.0.0.0:7444->7444/tcp, 0.0.0.0:7687->7687/tcp   recursing_blackburn

C:\Users\Vlasta>docker cp C:/Data/shipping.csv bed1:/usr/lib/memgraph/shipping.csv

C:\Users\Vlasta>docker exec -it bed1 bash
root@bed1e5c9192d:/# ls /usr/lib/memgraph
auth_module  memgraph  python_support  query_modules  shipping.csv
root@bed1e5c9192d:/#

2. Gaining speed with indexes and analytical storage mode

Although the dataset imported in this tutorial is quite small, one day you might want to import really big datasets with billions of nodes and relationships and you will require all the extra speed you can get.

To gain speed you can create indexes on the properties used to connect nodes with relationships which are the values in the _id column in the CSV files, and in Memgraph they will be named nodeID.

To create indexes, run:

CREATE INDEX ON :Employee(nodeID);
CREATE INDEX ON :Order(nodeID);
CREATE INDEX ON :Product(nodeID);

You can also change the storage mode from IN_MEMORY_TRANSACTIONAL to IN_MEMORY_ANALYTICAL. This will disable the creation of durability files (snapshots and WAL files) and you will no longer have any ACID guarantees. Other transactions will be able to see the changes of ongoing transactions. Also, transaction will be able to see the changes they are doing. This means that the transactions can be committed in random orders, and the updates to the data, in the end, might not be correct.

But, if you import on one thread, batch of data after a batch of data, there should be absolutely no issues, and you will gain 6 times faster import with 6 times less memory consumption.

After import you can switch back to the IN_MEMORY_TRANSACTIONAL storage mode or continue running analytics queries (only read queries) in the IN_MEMORY_ANALYTICAL mode to continue benefiting from low memory consumption.

To switch between modes, run the following queries on a running instance:

STORAGE MODE IN_MEMORY_ANALYTICAL;
STORAGE MODE IN_MEMORY_TRANSACTIONAL;

To check the current storage mode, run:

SHOW STORAGE INFO;

Change the storage mode to analytical before import.

STORAGE MODE IN_MEMORY_ANALYTICAL;

3. Importing nodes

To import nodes using a LOAD CSV clause let's examine the clause syntax:

LOAD CSV FROM "csv-file-path.csv" ( WITH | NO ) HEADER [IGNORE BAD] [DELIMITER <delimiter-string>] [QUOTE <quote-string>] AS <variable-name>

The file is now located at /usr/lib/memgraph/shipping.csv and it has a header row. There is no need to ignore bad rows, the default deliminator is , and the default quote character ", the same as in the exported CSV file, so no changes are necessary.

The first row of the LOAD CSV clause therefore looks like this:

LOAD CSV FROM "/usr/lib/memgraph/shipping.csv" WITH HEADER AS row

Nodes are always imported before relationships so they will be imported first.

The shipping.csv file contains the following columns important for node creation: _id, labels, employeeID, firstName, lastName, orderID, productID, productName, shipName, title, unitPrice.

The _id property is actually an internal node ID needed to create relationships later on.

Execute queries in Memgraph Lab. Open your browser and go to http://localhost:3000/, Connect now to the instance and go to the Query Execution section.

Employee nodes

Begin with Employee nodes.

After establishing the location and format of the CSV file, filter out the rows that contain the label :Employee:

LOAD CSV FROM "/usr/lib/memgraph/shipping.csv" WITH HEADER AS row
WITH row WHERE row._labels = ':Employee'

Then, create nodes with a certain label and properties. As an example, let's look at the property _id. To add the property to the node, define its name in Memgraph and assigned the value of a specific column in the CSV file.

LOAD CSV FROM "/usr/lib/memgraph/shipping.csv" WITH HEADER AS row
WITH row WHERE row._labels = ':Employee'
CREATE (e:Employee {nodeID: row._id})

So nodeID: row._id part of the query instructs Memgraph to create a property named nodeID and assign it the value paired with key _id. First created node will be assigned the value from the first data row, second node from the second data row, etc.

After matching up the keys and values for all properties, the finished query looks like this:

LOAD CSV FROM "/usr/lib/memgraph/shipping.csv" WITH HEADER AS row
WITH row WHERE row._labels = ':Employee'
CREATE (e:Employee {nodeID: row._id, employeeID: row.employeeID, firstName: row.firstName, lastName: row.lastName, title: row.title});
 
MATCH (e:Employee)
RETURN e;

The second query will show all 9 created nodes.

Copy the query in the Cypher Editor and Run Query.

Order nodes

Relevant properties for the Order nodes are _id, orderID and shipName.

To create Order nodes run the following query:

LOAD CSV FROM "/usr/lib/memgraph/shipping.csv" WITH HEADER AS row
WITH row WHERE row._labels = ':Order'
CREATE (o:Order {nodeID: row._id, orderID: row.orderID, shipName: row.shipName});
 
MATCH (o:Order)
RETURN o;

The second query will show all 830 created nodes:

Product nodes

Relevant properties for the Product nodes are _id, productID, productName and unitPrice.

As the parser parses all the values as strings, and the unitPrice are numbers, they need to be converted to appropriate data type.

To create Product nodes run the following query:

LOAD CSV FROM "/usr/lib/memgraph/shipping.csv" WITH HEADER AS row
WITH row WHERE row._labels = ':Product'
CREATE (p:Product {nodeID: row._id, productID: row.productID, productName: row.productName, unitPrice: ToFloat(row.unitPrice)});
 
MATCH (p:Product)
RETURN p;

The second query will show all 77 created nodes:

4. Graph improvements

At this point it would be nice to improve the look of the nodes visually. At the moment, nodes in the graph are represented with their labels, but it would be more useful if their name attribute was written.

To adjust the look of the graph using Graph Style Language, open the Graph Style Editor. Find the following code block:

@NodeStyle HasProperty(node, "name") {
  label: AsText(Property(node, "name"))
}

It defines that if the node has the property name, its label on the visual graph will be that property.

As none of the imported nodes have the property name, this part of the code needs to be adjusted to use the properties nodes do have.

Replace those three lines of code with the following block and Apply the changes:

@NodeStyle HasProperty(node, "firstName") {
  label: AsText(Property(node, "firstName"))
}

@NodeStyle HasProperty(node, "orderID") {
  label: AsText(Property(node, "orderID"))
}

@NodeStyle HasProperty(node, "productName") {
  label: AsText(Property(node, "productName"))
}

Visual appearance of the graph can be changed in many different ways, so be sure to check the GSS documentation.

5. Importing relationships

Now that all the 916 nodes have been imported, they can be connected with relationships.

The first row of the LOAD CSV remains the same:

LOAD CSV FROM "/usr/lib/memgraph/shipping.csv" WITH HEADER AS row

The shipping.csv file contains the following values important for relationship creation: _type, _start, _end, quantity and unitPrice.

The _type defines relationships type, _start and _end values define which nodes need to be connected based on their ID.

:REPORTS_TO relationships

Begin with :REPORTS_TO relationship.

After establishing the location and format of the CSV file, filter out the rows that contain the type REPORTS_TO:

LOAD CSV FROM "/usr/lib/memgraph/shipping.csv" WITH HEADER AS row
WITH row WHERE row._type = 'REPORTS_TO'

Now instruct Memgraph to find the nodes with certain IDs in order to create a relationship between them. As node IDs are unique we can just define that any node with a certain ID is a starting point, and another node with a certain ID is the end point of the relationship type REPORTS_TO.

The LOAD CSV query creates 8 :REPORTS_TO relationships:

LOAD CSV FROM "/usr/lib/memgraph/shipping.csv" WITH HEADER AS row
WITH row WHERE row._type = 'REPORTS_TO'
MATCH (n {nodeID: row._start}), (n2 {nodeID: row._end})
CREATE (n)-[:REPORTS_TO]->(n2);
 
MATCH p=()-[:REPORTS_TO]->()
RETURN p;

The second query returns all the nodes connected with the REPORTS_TO type of relationship.

:SOLD relationships

The LOAD CSV query creates 830 :SOLD relationships:

LOAD CSV FROM "/usr/lib/memgraph/shipping.csv" WITH HEADER AS row
WITH row WHERE row._type = 'SOLD'
MATCH (n {nodeID: row._start}), (n2 {nodeID: row._end})
CREATE (n)-[:SOLD]->(n2);
 
MATCH p=()-[:SOLD]->()
RETURN p;

The second query returns all the nodes connected with the SOLD type of relationship.

:CONTAINS relationships

This relationship type has properties about the quantity of products one order contains.

As the parser parses all the values as strings, and the value of this relationship property are numbers, they need to be converted to appropriate type.

The LOAD CSV query creates 2155 CONTAINS relationships:

LOAD CSV FROM "/usr/lib/memgraph/shipping.csv" WITH HEADER AS row
WITH row WHERE row._type = 'CONTAINS'
MATCH (n {nodeID: row._start}), (n2 {nodeID: row._end})
CREATE (n)-[:CONTAINS {quantity: ToInteger(row.quantity)}]->(n2);
 
MATCH p=()-[:CONTAINS]->()
RETURN p;

The second query returns all the nodes connected with the CONTAINS type of relationship.

After import

Once all the 916 nodes and 2993 relationships have been imported decide whether you want to switch back to the transactional storage mode or not. Remember that the analytical storage mode you are using right now has no ACID compliance.

To switch back to the analytical storage mode, run:

STORAGE MODE IN_MEMORY_TRANSACTIONAL;

To check the switch was successful, run:

SHOW STORAGE INFO;

You can query the database using the Cypher query language, use various graph algorithms and modules from our open-source repository MAGE to solve graph analytics problems, create awesome customized visual displays of your nodes and relationships with Graph Style Script and above all - enjoy your new graph database!

Memgraph's office hours

Schedule a 30 min session with one of our engineers to discuss how Memgraph fits with your architecture. Our engineers are highly experienced in helping companies of all sizes to integrate and get the most out of Memgraph in their projects. Talk to us about data modeling, optimizing queries, defining infrastructure requirements or migrating from your existing graph database. No nonsense or sales pitch, just tech.

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