Apache Avro

Apache Avro is a data serialization format. We can store data as .avro files on disk. Avro files are typically used with Spark but Spark is completely independent of Avro. Avro is a row-based format that is suitable for evolving data schemas. One benefit of using Avro is that schema and metadata travels with the data. If you have an .avro file, you have the schema of the data as well. The Apache Avro Specification provides easy-to-read yet detailed information.

# Python 3 with `avro-python3` package available
import copy
import json
import avro
from avro.datafile import DataFileWriter, DataFileReader
from avro.io import DatumWriter, DatumReader

# Note that we combined namespace and name to get "full name"
schema = {
    'name': 'avro.example.User',
    'type': 'record',
    'fields': [
        {'name': 'name', 'type': 'string'},
        {'name': 'age', 'type': 'int'}
    ]
}

# Parse the schema so we can use it to write the data
schema_parsed = avro.schema.Parse(json.dumps(schema))

# Write data to an avro file
with open('users.avro', 'wb') as f:
    writer = DataFileWriter(f, DatumWriter(), schema_parsed)
    writer.append({'name': 'Pierre-Simon Laplace', 'age': 77})
    writer.append({'name': 'John von Neumann', 'age': 53})
    writer.close()

# Read data from an avro file
with open('users.avro', 'rb') as f:
    reader = DataFileReader(f, DatumReader())
    metadata = copy.deepcopy(reader.meta)
    schema_from_file = json.loads(metadata['avro.schema'])
    users = [user for user in reader]
    reader.close()

print(f'Schema that we specified:\n {schema}')
print(f'Schema that we parsed:\n {schema_parsed}')
print(f'Schema from users.avro file:\n {schema_from_file}')
print(f'Users:\n {users}')

# Schema that we specified:
#  {'name': 'avro.example.User', 'type': 'record',
#   'fields': [{'name': 'name', 'type': 'string'}, {'name': 'age', 'type': 'int'}]}
# Schema that we parsed:
#  {"type": "record", "name": "User", "namespace": "avro.example",
#   "fields": [{"type": "string", "name": "name"}, {"type": "int", "name": "age"}]}
# Schema from users.avro file:
#  {'type': 'record', 'name': 'User', 'namespace': 'avro.example',
#   'fields': [{'type': 'string', 'name': 'name'}, {'type': 'int', 'name': 'age'}]}
# Users:
#  [{'name': 'Pierre-Simon Laplace', 'age': 77}, {'name': 'John von Neumann', 'age': 53}]

Third-party Avro Packages

While avro-python3 is the official Avro package, it appears to be very slow. This is because it is written in pure python. In comparison, fastavrouses C extensions (with regular CPython) making it much faster. Another benefit of using fastavro is that you can install it the same way in both Python 2 and Python 3. fastavro API is also the same2 for both Python 2 and 3.

Handling Avro files in PythonPerfectly Random