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Version: 2.0.1

Import Avro data

If you want to import your data in Memgraph using Apache Avro serialization, you need to know the Avro schema of your data. This is necessary for deserializing the data. Each schema contains a single schema definition, so there should be a separate schema for each data representation you want to import into Memgraph.

Datatype mapping#

Avro data types will be flexibly mapped to the target schema; that is, Avro and openCypher types do not need to match exactly. Use the table below for data type mappings:

Avro Data TypeCypher Casting Function


Let's assume we have the following schemas coming out of their respective topics avroStreamProfile, avroStreamCompany, avroStreamWorksAt:

profile_schema = """ {    "namespace": "example.avro",    "name": "Person",    "type": "record",    "fields": [        {"name": "name", "type": "string"},        {"name": "age", "type": "int"},        {"name": "email", "type": "string"},        {"name": "address", "type": "string"}    ]}"""
company_schema = """{    "namespace": "example.avro",    "name": "Company",    "type": "record",    "fields": [        {"name": "name", "type": "string"},        {"name": "address", "type": "string"}    ]} """
works_at_schema = """ {    "namespace": "example.avro",    "name": "Works_At",    "type": "record",    "fields": [        {"name": "name", "type": "string"},        {"name": "company", "type": "string"}    ]}"""

We can use the schemas to build the following graph:


Data received by the Memgraph consumer is a byte array and needs to be deserialized. The following method will help you deserialize your data with the help of Confluent Kafka:

from confluent_kafka.schema_registry import SchemaRegistryClientfrom confluent_kafka.schema_registry.avro import AvroDeserializer
def process_record_confluent(record: bytes, src: SchemaRegistryClient, schema: str):    deserializer = AvroDeserializer(schema_str=schema, schema_registry_client=src)    return deserializer(record, None) # returns dict

Transformation modules#

Before consuming data from a stream, we need to implement transformation modules that will produce queries. In order to create a transformation module, you need to:

  1. Create a Python module
  2. Save it into the Memgraph's query-modules directory (default: /usr/lib/memgraph/query_modules)
  3. Load it into Memgraph either on startup (automatically) or by running the CALL mg.load_all query

Example for the profile_transformation module:

@mgp.transformationdef profile_transformation(messages: mgp.Messages) -> mgp.Record(query = str, parameters=mgp.Nullable[mgp.Map]):    result_queries = []
    for i in range(messages.total_messages()):        message_avro = messages.message_at(i)        msg_value = message_avro.payload()        message = process_record_confluent(msg_value, src= SchemaRegistryClient({'url': 'http://localhost:8081'}), schema=profile_schema)        result_queries.append(mgp.Record (                query=f'CREATE (p: Person {{ name: "{message["name"]}", age: ToInteger({message["age"]}), address: "{message["address"]}", email:"{message["email"]}" }});' ,                parameters=None            ))
    return result_queries

Creating streams#

To import data into Memgraph, we need to create a stream for each topic and apply our transformation module on incoming data:

CREATE STREAM avroStreamProfile TOPICS avro-stream-profile TRANSFORM avro_transform.profile_transformation;CREATE STREAM avroStreamCompany TOPICS avro-stream-company TRANSFORM avro_transform.company_transformation;CREATE STREAM avroStreamWorksAt TOPICS avro-stream-worksat TRANSFORM avro_transform.works_at_transformation;

To start the streams, execute the following query:


Run the following query to check if all the streams were started correctly:


You can also check the node counter in Memgraph Lab (Overview tab) to see if new nodes and relationships are arriving.

Next steps#

Check out the example-streaming-app on GitHub to see how Memgraph can be connected to a Kafka stream.