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fastavro — fastavro Documentation

The current Python avro package is dog slow.

On a test case of about 10K records, it takes about 14sec to iterate over all of them. In comparison the JAVA avro SDK does it in about 1.9sec.

fastavro is an alternative implementation that is much faster. It iterates over the same 10K records in 2.9sec, and if you use it with PyPy it’ll do it in 1.5sec (to be fair, the JAVA benchmark is doing some extra JSON encoding/decoding).

If the optional C extension (generated by Cython) is available, then fastavro will be even faster. For the same 10K records it’ll run in about 1.7sec.

Supported Features

Missing Features

Example

from fastavro import writer, reader, parse_schema

schema = {
    'doc': 'A weather reading.',
    'name': 'Weather',
    'namespace': 'test',
    'type': 'record',
    'fields': [
        {'name': 'station', 'type': 'string'},
        {'name': 'time', 'type': 'long'},
        {'name': 'temp', 'type': 'int'},
    ],
}
parsed_schema = parse_schema(schema)

# 'records' can be an iterable (including generator)
records = [
    {u'station': u'011990-99999', u'temp': 0, u'time': 1433269388},
    {u'station': u'011990-99999', u'temp': 22, u'time': 1433270389},
    {u'station': u'011990-99999', u'temp': -11, u'time': 1433273379},
    {u'station': u'012650-99999', u'temp': 111, u'time': 1433275478},
]

# Writing
with open('weather.avro', 'wb') as out:
    writer(out, parsed_schema, records)

# Reading
with open('weather.avro', 'rb') as fo:
    for record in reader(fo):
        print(record)

Documentation

fastavro.read

class reader(fo, reader_schema=None, return_record_name=False)

Iterator over records in an avro file.

Parameters
  • fo (file-like) – Input stream
  • reader_schema (dict, optional) – Reader schema
  • return_record_name (bool, optional) – If true, when reading a union of records, the result will be a tuple where the first value is the name of the record and the second value is the record itself

Example:

from fastavro import reader
with open('some-file.avro', 'rb') as fo:
    avro_reader = reader(fo)
    for record in avro_reader:
        process_record(record)

The fo argument is a file-like object so another common example usage would use an io.BytesIO object like so:

from io import BytesIO
from fastavro import writer, reader

fo = BytesIO()
writer(fo, schema, records)
fo.seek(0)
for record in reader(fo):
    process_record(record)
metadata

Key-value pairs in the header metadata

codec

The codec used when writing

writer_schema

The schema used when writing

reader_schema

The schema used when reading (if provided)

class block_reader(fo, reader_schema=None, return_record_name=False)

Iterator over Block in an avro file.

Parameters
  • fo (file-like) – Input stream
  • reader_schema (dict, optional) – Reader schema
  • return_record_name (bool, optional) – If true, when reading a union of records, the result will be a tuple where the first value is the name of the record and the second value is the record itself

Example:

from fastavro import block_reader
with open('some-file.avro', 'rb') as fo:
    avro_reader = block_reader(fo)
    for block in avro_reader:
        process_block(block)
metadata

Key-value pairs in the header metadata

codec

The codec used when writing

writer_schema

The schema used when writing

reader_schema

The schema used when reading (if provided)

class Block(bytes_, num_records, codec, reader_schema, writer_schema, offset, size, return_record_name=False)

An avro block. Will yield records when iterated over

num_records

Number of records in the block

writer_schema

The schema used when writing

reader_schema

The schema used when reading (if provided)

offset

Offset of the block from the begining of the avro file

size

Size of the block in bytes

schemaless_reader(fo, writer_schema, reader_schema=None, return_record_name=False)

Reads a single record writen using the schemaless_writer()

Parameters
  • fo (file-like) – Input stream
  • writer_schema (dict) – Schema used when calling schemaless_writer
  • reader_schema (dict, optional) – If the schema has changed since being written then the new schema can be given to allow for schema migration
  • return_record_name (bool, optional) – If true, when reading a union of records, the result will be a tuple where the first value is the name of the record and the second value is the record itself

Example:

parsed_schema = fastavro.parse_schema(schema)
with open('file.avro', 'rb') as fp:
    record = fastavro.schemaless_reader(fp, parsed_schema)

Note: The schemaless_reader can only read a single record.

is_avro(path_or_buffer)

Return True if path (or buffer) points to an Avro file.

Parameters

path_or_buffer (path to file or file-like object) – Path to file

fastavro.write

writer(fo, schema, records, codec='null', sync_interval=16000, metadata=None, validator=None, sync_marker=None, codec_compression_level=None)

Write records to fo (stream) according to schema

Parameters
  • fo (file-like) – Output stream
  • schema (dict) – Writer schema
  • records (iterable) – Records to write. This is commonly a list of the dictionary representation of the records, but it can be any iterable
  • codec (string, optional) – Compression codec, can be ‘null’, ‘deflate’ or ‘snappy’ (if installed)
  • sync_interval (int, optional) – Size of sync interval
  • metadata (dict, optional) – Header metadata
  • validator (None, True or a function) – Validator function. If None (the default) - no validation. If True then then fastavro.validation.validate will be used. If it’s a function, it should have the same signature as fastavro.writer.validate and raise an exeption on error.
  • sync_marker (bytes, optional) – A byte string used as the avro sync marker. If not provided, a random byte string will be used.
  • codec_compression_level (int, optional) – Compression level to use with the specified codec (if the codec supports it)

Example:

from fastavro import writer, parse_schema

schema = {
    'doc': 'A weather reading.',
    'name': 'Weather',
    'namespace': 'test',
    'type': 'record',
    'fields': [
        {'name': 'station', 'type': 'string'},
        {'name': 'time', 'type': 'long'},
        {'name': 'temp', 'type': 'int'},
    ],
}
parsed_schema = parse_schema(schema)

records = [
    {u'station': u'011990-99999', u'temp': 0, u'time': 1433269388},
    {u'station': u'011990-99999', u'temp': 22, u'time': 1433270389},
    {u'station': u'011990-99999', u'temp': -11, u'time': 1433273379},
    {u'station': u'012650-99999', u'temp': 111, u'time': 1433275478},
]

with open('weather.avro', 'wb') as out:
    writer(out, parsed_schema, records)

The fo argument is a file-like object so another common example usage would use an io.BytesIO object like so:

from io import BytesIO
from fastavro import writer

fo = BytesIO()
writer(fo, schema, records)

Given an existing avro file, it’s possible to append to it by re-opening the file in a+b mode. If the file is only opened in ab mode, we aren’t able to read some of the existing header information and an error will be raised. For example:

# Write initial records
with open('weather.avro', 'wb') as out:
    writer(out, parsed_schema, records)

# Write some more records
with open('weather.avro', 'a+b') as out:
    writer(out, parsed_schema, more_records)
schemaless_writer(fo, schema, record)

Write a single record without the schema or header information

Parameters
  • fo (file-like) – Output file
  • schema (dict) – Schema
  • record (dict) – Record to write

Example:

parsed_schema = fastavro.parse_schema(schema)
with open('file.avro', 'rb') as fp:
    fastavro.schemaless_writer(fp, parsed_schema, record)

Note: The schemaless_writer can only write a single record.

Using the tuple notation to specify which branch of a union to take

Since this library uses plain dictionaries to reprensent a record, it is possible for that dictionary to fit the definition of two different records.

For example, given a dictionary like this:

{"name": "My Name"}

It would be valid against both of these records:

child_schema = {
    "name": "Child",
    "type": "record",
    "fields": [
        {"name": "name", "type": "string"},
        {"name": "favorite_color", "type": ["null", "string"]},
    ]
}

pet_schema = {
    "name": "Pet",
    "type": "record",
    "fields": [
        {"name": "name", "type": "string"},
        {"name": "favorite_toy", "type": ["null", "string"]},
    ]
}

This becomes a problem when a schema contains a union of these two similar records as it is not clear which record the dictionary represents. For example, if you used the previous dictionary with the following schema, it wouldn’t be clear if the record should be serialized as a Child or a

`

Pet:

household_schema = {
    "name": "Household",
    "type": "record",
    "fields": [
        {"name": "address", "type": "string"},
        {
            "name": "family_members",
            "type": {
                "type": "array", "items": [
                    {
                        "name": "Child",
                        "type": "record",
                        "fields": [
                            {"name": "name", "type": "string"},
                            {"name": "favorite_color", "type": ["null", "string"]},
                        ]
                    }, {
                        "name": "Pet",
                        "type": "record",
                        "fields": [
                            {"name": "name", "type": "string"},
                            {"name": "favorite_toy", "type": ["null", "string"]},
                        ]
                    }
                ]
            }
        },
    ]
}

To resolve this, you can use a tuple notation where the first value of the tuple is the fully namespaced record name and the second value is the dictionary. For example:

records = [
    {
        "address": "123 Drive Street",
        "family_members": [
            ("Child", {"name": "Son"}),
            ("Child", {"name": "Daughter"}),
            ("Pet", {"name": "Dog"}),
        ]
    }
]

fastavro.json_read

json_reader(fo, schema)

Iterator over records in an avro json file.

Parameters
  • fo (file-like) – Input stream
  • reader_schema (dict) – Reader schema

Example:

from fastavro import json_reader

schema = {
    'doc': 'A weather reading.',
    'name': 'Weather',
    'namespace': 'test',
    'type': 'record',
    'fields': [
        {'name': 'station', 'type': 'string'},
        {'name': 'time', 'type': 'long'},
        {'name': 'temp', 'type': 'int'},
    ]
}

with open('some-file', 'r') as fo:
    avro_reader = json_reader(fo, schema)
    for record in avro_reader:
        print(record)

fastavro.json_write

json_writer(fo, schema, records)

Write records to fo (stream) according to schema

Parameters
  • fo (file-like) – Output stream
  • schema (dict) – Writer schema
  • records (iterable) – Records to write. This is commonly a list of the dictionary representation of the records, but it can be any iterable

Example:

from fastavro import json_writer, parse_schema

schema = {
    'doc': 'A weather reading.',
    'name': 'Weather',
    'namespace': 'test',
    'type': 'record',
    'fields': [
        {'name': 'station', 'type': 'string'},
        {'name': 'time', 'type': 'long'},
        {'name': 'temp', 'type': 'int'},
    ],
}
parsed_schema = parse_schema(schema)

records = [
    {u'station': u'011990-99999', u'temp': 0, u'time': 1433269388},
    {u'station': u'011990-99999', u'temp': 22, u'time': 1433270389},
    {u'station': u'011990-99999', u'temp': -11, u'time': 1433273379},
    {u'station': u'012650-99999', u'temp': 111, u'time': 1433275478},
]

with open('some-file', 'w') as out:
    json_writer(out, parsed_schema, records)

fastavro.schema

parse_schema(schema, expand=False, _write_hint=True, _force=False)

Returns a parsed avro schema

It is not necessary to call parse_schema but doing so and saving the parsed schema for use later will make future operations faster as the schema will not need to be reparsed.

Parameters
  • schema (dict) – Input schema
  • expand (bool) –

    NOTE: This option should be considered a keyword only argument and may get enforced as such when Python 2 support is dropped.

    If true, named schemas will be fully expanded to their true schemas rather than being represented as just the name. This format should be considered an output only and not passed in to other reader/writer functions as it does not conform to the avro specification and will likely cause an exception

  • _write_hint (bool) – Internal API argument specifying whether or not the __fastavro_parsed marker should be added to the schema
  • _force (bool) – Internal API argument. If True, the schema will always be parsed even if it has been parsed and has the __fastavro_parsed marker

Example:

from fastavro import parse_schema
from fastavro import writer

parsed_schema = parse_schema(original_schema)
with open('weather.avro', 'wb') as out:
    writer(out, parsed_schema, records)
fullname(schema)

Returns the fullname of a schema

Parameters

schema (dict) – Input schema

Example:

from fastavro.schema import fullname

schema = {
    'doc': 'A weather reading.',
    'name': 'Weather',
    'namespace': 'test',
    'type': 'record',
    'fields': [
        {'name': 'station', 'type': 'string'},
        {'name': 'time', 'type': 'long'},
        {'name': 'temp', 'type': 'int'},
    ],
}

fname = fullname(schema)
assert fname == "test.Weather"
expand_schema(schema)

Returns a schema where all named types are expanded to their real schema

NOTE: The output of this function produces a schema that can include multiple definitions of the same named type (as per design) which are not valid per the avro specification. Therefore, the output of this should not be passed to the normal writer/reader functions as it will likely result in an error.

Parameters

schema (dict) – Input schema

Example:

from fastavro.schema import expand_schema

original_schema = {
    "name": "MasterSchema",
    "namespace": "com.namespace.master",
    "type": "record",
    "fields": [{
        "name": "field_1",
        "type": {
            "name": "Dependency",
            "namespace": "com.namespace.dependencies",
            "type": "record",
            "fields": [
                {"name": "sub_field_1", "type": "string"}
            ]
        }
    }, {
        "name": "field_2",
        "type": "com.namespace.dependencies.Dependency"
    }]
}

expanded_schema = expand_schema(original_schema)

assert expanded_schema == {
    "name": "com.namespace.master.MasterSchema",
    "type": "record",
    "fields": [{
        "name": "field_1",
        "type": {
            "name": "com.namespace.dependencies.Dependency",
            "type": "record",
            "fields": [
                {"name": "sub_field_1", "type": "string"}
            ]
        }
    }, {
        "name": "field_2",
        "type": {
            "name": "com.namespace.dependencies.Dependency",
            "type": "record",
            "fields": [
                {"name": "sub_field_1", "type": "string"}
            ]
        }
    }]
}

fastavro.validation

validate(datum, schema, field=None, raise_errors=True)

Determine if a python datum is an instance of a schema.

Parameters
  • datum (Any) – Data being validated
  • schema (dict) – Schema
  • field (str, optional) – Record field being validated
  • raise_errors (bool, optional) – If true, errors are raised for invalid data. If false, a simple True (valid) or False (invalid) result is returned

Example:

from fastavro.validation import validate
schema = {...}
record = {...}
validate(record, schema)
validate_many(records, schema, raise_errors=True)

Validate a list of data!

Parameters
  • records (iterable) – List of records to validate
  • schema (dict) – Schema
  • raise_errors (bool, optional) – If true, errors are raised for invalid data. If false, a simple True (valid) or False (invalid) result is returned

Example:

from fastavro.validation import validate_many
schema = {...}
records = [{...}, {...}, ...]
validate_many(records, schema)

fastavro command line script

A command line script is installed with the library that can be used to dump the contents of avro file(s) to the standard output.

Usage:

usage: fastavro [-h] [--schema] [--codecs] [--version] [-p] [file [file ...]]

iter over avro file, emit records as JSON

positional arguments:
  file          file(s) to parse

optional arguments:
  -h, --help    show this help message and exit
  --schema      dump schema instead of records
  --codecs      print supported codecs
  --version     show program's version number and exit
  -p, --pretty  pretty print json

Examples

Read an avro file:

$ fastavro weather.avro

{"temp": 0, "station": "011990-99999", "time": -619524000000}
{"temp": 22, "station": "011990-99999", "time": -619506000000}
{"temp": -11, "station": "011990-99999", "time": -619484400000}
{"temp": 111, "station": "012650-99999", "time": -655531200000}
{"temp": 78, "station": "012650-99999", "time": -655509600000}

Show the schema:

$ fastavro --schema weather.avro

{
 "type": "record",
 "namespace": "test",
 "doc": "A weather reading.",
 "fields": [
  {
   "type": "string",
   "name": "station"
  },
  {
   "type": "long",
   "name": "time"
  },
  {
   "type": "int",
   "name": "temp"
  }
 ],
 "name": "Weather"
}
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Author

Miki Tebeka

Info

Jan 27, 2021 0.23.3