fastavro - Man Page

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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, named_schemas, 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', '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. This will only work for avro files that contain the normal avro schema header like those create from writer(). This function is not intended to be used with binary data created from schemaless_writer() since that does not include the avro header.

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', '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"}),
        ]
    }
]

Using the record hint to specify which branch of a union to take

In addition to the tuple notation for specifing the name of a record, you can also include a special -type attribute (note that this attribute is -type, not type) on a record to do the same thing. So the example above which looked like this:

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

Would now look like this:

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

Unlike the tuple notation which can be used with any avro type in a union, this -type hint can only be used with records. However, this can be useful if you want to make a single record dictionary that can be used both in and out of unions.

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, named_schemas=None, *, 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
  • named_schemas (dict) – Dictionary of named schemas to their schema definition
  • expand (bool) – 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)

Sometimes you might have two schemas where one schema references another. For the sake of example, let’s assume you have a Parent schema that references a Child schema`. If you were to try to parse the parent schema on its own, you would get an exception because the child schema isn’t defined. To accomodate this, we can use the named_schemas argument to pass a shared dictionary when parsing both of the schemas. The dictionary will get populated with the necessary schema references to make parsing possible. For example:

from fastavro import parse_schema

named_schemas = {}
parsed_child = parse_schema(child_schema, named_schemas)
parsed_parent = parse_schema(parent_schema, named_schemas)
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"}
            ]
        }
    }]
}
load_schema(schema_path, *, named_schemas=None, _write_hint=True, _injected_schemas=None)

Returns a schema loaded from the file at schema_path.

Will recursively load referenced schemas assuming they can be found in files in the same directory and named with the convention <full_name>.avsc.

Parameters
  • schema (str) – Path to schema file to load
  • named_schemas (dict) – Dictionary of named schemas to their schema definition
  • _write_hint (bool) – Internal API argument specifying whether or not the __fastavro_parsed marker should be added to the schema
  • _injected_schemas (set) – Internal API argument. Set of names that have been injected

Consider the following example…

Parent.avsc:

{
    "type": "record",
    "name": "Parent",
    "namespace": "namespace",
    "fields": [
        {
            "name": "child",
            "type": "Child"
        }
    ]
}

namespace.Child.avsc:

{
    "type": "record",
    "namespace": "namespace",
    "name": "Child",
    "fields": []
}

Code:

from fastavro.schema import load_schema

parsed_schema = load_schema("Parent.avsc")
load_schema_ordered(ordered_schemas, *, _write_hint=True)

Returns a schema loaded from a list of schemas.

The list of schemas should be ordered such that any dependencies are listed before any other schemas that use those dependencies. For example, if schema A depends on schema B and schema B depends on schema C, then the list of schemas should be [C, B, A].

Parameters
  • ordered_schemas (list) – List of paths to schemas
  • _write_hint (bool) – Internal API argument specifying whether or not the __fastavro_parsed marker should be added to the schema

Consider the following example…

Parent.avsc:

{
    "type": "record",
    "name": "Parent",
    "namespace": "namespace",
    "fields": [
        {
            "name": "child",
            "type": "Child"
        }
    ]
}

namespace.Child.avsc:

{
    "type": "record",
    "namespace": "namespace",
    "name": "Child",
    "fields": []
}

Code:

from fastavro.schema import load_schema_ordered

parsed_schema = load_schema_ordered(
    ["path/to/namespace.Child.avsc", "path/to/Parent.avsc"]
)
to_parsing_canonical_form(schema)

Returns a string represening the parsing canonical form of the schema.

For more details on the parsing canonical form, see here: https://avro.apache.org/docs/current/spec.html#Parsing+Canonical+Form+for+Schemas

fingerprint(parsing_canonical_form, algorithm)

Returns a string represening a fingerprint/hash of the parsing canonical form of a schema.

For more details on the fingerprint, see here: https://avro.apache.org/docs/current/spec.html#schema_fingerprints

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.utils

generate_one(schema: Union[str, List, Dict]) -> Any

Returns a single instance of arbitrary data that conforms to the schema.

Parameters

schema (dict, list, string) – Schema

Example:

from fastavro import schemaless_writer
from fastavro.utils import generate_one

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('weather.avro', 'wb') as out:
    schemaless_writer(out, schema, generate_one(schema))
generate_many(schema: Union[str, List, Dict], count: int) -> Iterator[Any]

A generator that yields arbitrary data that conforms to the schema. It will yield a number of data structures equal to what is given in the count

Parameters
  • schema (dict, list, string) – Schema
  • count (int) – Number of objects to generate

Example:

from fastavro import writer
from fastavro.utils import generate_data

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('weather.avro', 'wb') as out:
    writer(out, schema, generate_data(schema, 5))
anonymize_schema(schema: Union[str, List, Dict]) -> Union[str, List, Dict]

Returns an anonymized schema

Parameters

schema (dict, list, string) – Schema

Example:

from fastavro.utils import anonymize_schema

anonymized_schema = anonymize_schema(original_schema)

Logical Types

Fastavro supports the following official logical types:

  • decimal
  • uuid
  • date
  • time-millis
  • time-micros
  • timestamp-millis
  • timestamp-micros
  • local-timestamp-millis
  • local-timestamp-micros

Fastavro is missing support for the following official logical types:

  • duration

How to specify logical types in your schemas

The docs say that when you want to make something a logical type, you just need to add a logicalType key. Unfortunately, this means that a common confusion is that people will take a pre-existing schema like this:

schema = {
    "type": "record",
    "name": "root",
    "fields": [
        {
            "name": "id",
            "type": "string",
        },
    ]
}

And then add the uuid logical type like this:

schema = {
    "type": "record",
    "name": "root",
    "fields": [
        {
            "name": "id",
            "type": "string",
            "logicalType": "uuid",  # This is the wrong place to add this key
        },
    ]
}

However, that adds the logicalType key to the field schema which is not correct. Instead, we want to group it with the string like so:

schema = {
    "type": "record",
    "name": "root",
    "fields": [
        {
            "name": "id",
            "type": {
                "type": "string",
                "logicalType": "uuid",  # This is the correct place to add this key
            },
        },
    ]
}

Custom Logical Types

The Avro specification defines a handful of logical types that most implementations support. For example, one of the definied logical types is a microsecond precision timestamp. The specification states that this value will get encoded as an avro long type.

For the sake of an example, let’s say you want to create a new logical type for a microsecond precision timestamp that uses a string as the underlying avro type.

To do this, there are a few functions that need to be defined. First, we need an encoder function that will encode a datetime object as a string. The encoder function is called with two arguments: the data and the schema. So we could define this like so:

def encode_datetime_as_string(data, schema):
    return datetime.isoformat(data)

# or

def encode_datetime_as_string(data, *args):
    return datetime.isoformat(data)

Then, we need a decoder function that will transform the string back into a datetime object. The decoder function is called with three arguments: the data, the writer schema, and the reader schema. So we could define this like so:

def decode_string_as_datetime(data, writer_schema, reader_schema):
    return datetime.fromisoformat(data)

# or

def decode_string_as_datetime(data, *args):
    return datetime.fromisoformat(data)

Finally, we need to tell fastavro to use these functions. The schema for this custom logical type will use the type string and can use whatever name you would like as the logicalType. In this example, let’s suppose we call the logicalType datetime2. To have the library actually use the custom logical type, we use the name of <avro_type>-<logical_type>, so in this example that name would be string-datetime2 and then we add those functions like so:

fastavro.write.LOGICAL_WRITERS["string-datetime2"] = encode_datetime_as_string
fastavro.read.LOGICAL_READERS["string-datetime2"] = decode_string_as_datetime

And you are done. Now if the library comes across a schema with a logical type of datetime2 and an avro type of string, it will use the custom functions. For a complete example, see here:

import io
from datetime import datetime

import fastavro
from fastavro import writer, reader


def encode_datetime_as_string(data, *args):
    return datetime.isoformat(data)

def decode_string_as_datetime(data, *args):
    return datetime.fromisoformat(data)

fastavro.write.LOGICAL_WRITERS["string-datetime2"] = encode_datetime_as_string
fastavro.read.LOGICAL_READERS["string-datetime2"] = decode_string_as_datetime


writer_schema = fastavro.parse_schema({
    "type": "record",
    "name": "root",
    "fields": [
        {
            "name": "some_date",
            "type": [
                "null",
                {
                    "type": "string",
                    "logicalType": "datetime2",
                },
            ],
        },
    ]
})

records = [
    {"some_date": datetime.now()}
]

bio = io.BytesIO()

writer(bio, writer_schema, records)

bio.seek(0)

for record in reader(bio):
    print(record)

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

Jun 04, 2021 1.4.1