# -*- coding: utf-8 -*-
from __future__ import absolute_import, division, print_function
import sys
import math
from collections import OrderedDict
from datetime import datetime, date, time
from decimal import Decimal
from petl.compat import izip_longest, text_type, string_types, PY3
from petl.io.sources import read_source_from_arg, write_source_from_arg
from petl.transform.headers import skip, setheader
from petl.util.base import Table, dicts, fieldnames, iterpeek, wrap
# region API
[docs]def fromavro(source, limit=None, skips=0, **avro_args):
"""Extract a table from the records of a avro file.
The `source` argument (string or file-like or fastavro.reader) can either
be the path of the file, a file-like input stream or a instance from
fastavro.reader.
The `limit` and `skip` arguments can be used to limit the range of rows
to extract.
The `sample` argument (int, optional) defines how many rows are inspected
for discovering the field types and building a schema for the avro file
when the `schema` argument is not passed.
The rows fields read from file can have scalar values like int, string,
float, datetime, date and decimal but can also have compound types like
enum, :ref:`array <array_schema>`, map, union and record.
The fields types can also have recursive structures defined
in :ref:`complex schemas <complex_schema>`.
Also types with :ref:`logical types <logical_schema>` types are read and
translated to coresponding python types: long timestamp-millis and
long timestamp-micros: datetime.datetime, int date: datetime.date,
bytes decimal and fixed decimal: Decimal, int time-millis and
long time-micros: datetime.time.
Example usage for reading files::
>>> # set up a Avro file to demonstrate with
...
>>> schema1 = {
... 'doc': 'Some people records.',
... 'name': 'People',
... 'namespace': 'test',
... 'type': 'record',
... 'fields': [
... {'name': 'name', 'type': 'string'},
... {'name': 'friends', 'type': 'int'},
... {'name': 'age', 'type': 'int'},
... ]
... }
...
>>> records1 = [
... {'name': 'Bob', 'friends': 42, 'age': 33},
... {'name': 'Jim', 'friends': 13, 'age': 69},
... {'name': 'Joe', 'friends': 86, 'age': 17},
... {'name': 'Ted', 'friends': 23, 'age': 51}
... ]
...
>>> import fastavro
>>> parsed_schema1 = fastavro.parse_schema(schema1)
>>> with open('example.file1.avro', 'wb') as f1:
... fastavro.writer(f1, parsed_schema1, records1)
...
>>> # now demonstrate the use of fromavro()
>>> import petl as etl
>>> tbl1 = etl.fromavro('example.file1.avro')
>>> tbl1
+-------+---------+-----+
| name | friends | age |
+=======+=========+=====+
| 'Bob' | 42 | 33 |
+-------+---------+-----+
| 'Jim' | 13 | 69 |
+-------+---------+-----+
| 'Joe' | 86 | 17 |
+-------+---------+-----+
| 'Ted' | 23 | 51 |
+-------+---------+-----+
.. versionadded:: 1.4.0
"""
source2 = read_source_from_arg(source)
return AvroView(source=source2,
limit=limit,
skips=skips,
**avro_args)
[docs]def toavro(table, target, schema=None, sample=9,
codec='deflate', compression_level=None, **avro_args):
"""
Write the table into a new avro file according to schema passed.
This method assume that each column has values with the same type
for all rows of the source `table`.
`Apache Avro`_ is a data
serialization framework. It is used in data serialization (especially in
Hadoop ecosystem), for dataexchange for databases (Redshift) and RPC
protocols (like in Kafka). It has libraries to support many languages and
generally is faster and safer than text formats like Json, XML or CSV.
The `target` argument is the file path for creating the avro file.
Note that if a file already exists at the given location, it will be
overwritten.
The `schema` argument (dict) defines the rows field structure of the file.
Check fastavro `documentation`_ and Avro schema `reference`_ for details.
The `sample` argument (int, optional) defines how many rows are inspected
for discovering the field types and building a schema for the avro file
when the `schema` argument is not passed.
The `codec` argument (string, optional) sets the compression codec used to
shrink data in the file. It can be 'null', 'deflate' (default), 'bzip2' or
'snappy', 'zstandard', 'lz4', 'xz' (if installed)
The `compression_level` argument (int, optional) sets the level of
compression to use with the specified codec (if the codec supports it)
Additionally there are support for passing extra options in the
argument `**avro_args` that are fowarded directly to fastavro. Check the
fastavro `documentation`_ for reference.
The avro file format preserves type information, i.e., reading and writing
is round-trippable for tables with non-string data values. However the
conversion from Python value types to avro fields is not perfect. Use the
`schema` argument to define proper type to the conversion.
The following avro types are supported by the schema: null, boolean,
string, int, long, float, double, bytes, fixed, enum,
:ref:`array <array_schema>`, map, union, record, and recursive types
defined in :ref:`complex schemas <complex_schema>`.
Also :ref:`logical types <logical_schema>` are supported and translated to
coresponding python types: long timestamp-millis, long timestamp-micros, int date,
bytes decimal, fixed decimal, string uuid, int time-millis, long time-micros.
Example usage for writing files::
>>> # set up a Avro file to demonstrate with
>>> table2 = [['name', 'friends', 'age'],
... ['Bob', 42, 33],
... ['Jim', 13, 69],
... ['Joe', 86, 17],
... ['Ted', 23, 51]]
...
>>> schema2 = {
... 'doc': 'Some people records.',
... 'name': 'People',
... 'namespace': 'test',
... 'type': 'record',
... 'fields': [
... {'name': 'name', 'type': 'string'},
... {'name': 'friends', 'type': 'int'},
... {'name': 'age', 'type': 'int'},
... ]
... }
...
>>> # now demonstrate what writing with toavro()
>>> import petl as etl
>>> etl.toavro(table2, 'example.file2.avro', schema=schema2)
...
>>> # this was what was saved above
>>> tbl2 = etl.fromavro('example.file2.avro')
>>> tbl2
+-------+---------+-----+
| name | friends | age |
+=======+=========+=====+
| 'Bob' | 42 | 33 |
+-------+---------+-----+
| 'Jim' | 13 | 69 |
+-------+---------+-----+
| 'Joe' | 86 | 17 |
+-------+---------+-----+
| 'Ted' | 23 | 51 |
+-------+---------+-----+
.. versionadded:: 1.4.0
.. _Apache Avro: https://avro.apache.org/docs/current/spec.html
.. _reference: https://avro.apache.org/docs/current/spec.html#schemas
.. _documentation : https://fastavro.readthedocs.io/en/latest/writer.html
"""
_write_toavro(table,
target=target,
mode='wb',
schema=schema,
sample=sample,
codec=codec,
compression_level=compression_level,
**avro_args)
[docs]def appendavro(table, target, schema=None, sample=9, **avro_args):
"""
Append rows into a avro existing avro file or create a new one.
The `target` argument can be either an existing avro file or the file
path for creating new one.
The `schema` argument is checked against the schema of the existing file.
So it must be the same schema as used by `toavro()` or the schema of the
existing file.
The `sample` argument (int, optional) defines how many rows are inspected
for discovering the field types and building a schema for the avro file
when the `schema` argument is not passed.
Additionally there are support for passing extra options in the
argument `**avro_args` that are fowarded directly to fastavro. Check the
fastavro documentation for reference.
See :meth:`petl.io.avro.toavro` method for more information and examples.
.. versionadded:: 1.4.0
"""
_write_toavro(table,
target=target,
mode='a+b',
schema=schema,
sample=sample,
**avro_args)
# endregion API
# region Implementation
class AvroView(Table):
'''Read rows from avro file with their types and logical types'''
def __init__(self, source, limit, skips, **avro_args):
self.source = source
self.limit = limit
self.skip = skips
self.avro_args = avro_args
self.avro_schema = None
def get_avro_schema(self):
'''gets the schema stored in avro file header'''
return self.avro_schema
def __iter__(self):
with self.source.open('rb') as source_file:
avro_reader = self._open_reader(source_file)
header = self._decode_schema(avro_reader)
yield header
for row in self._read_rows_from(avro_reader, header):
yield row
def _open_reader(self, source_file):
'''This could raise a error when the file is corrupt or is not avro'''
# delay the import of fastavro for not breaking when unused
import fastavro
avro_reader = fastavro.reader(source_file, **self.avro_args)
return avro_reader
def _decode_schema(self, avro_reader):
'''extract the header from schema stored in avro file header'''
self.avro_schema = avro_reader.writer_schema
if self.avro_schema is None:
return None, None
schema_fields = self.avro_schema['fields']
header = tuple(col['name'] for col in schema_fields)
return header
def _read_rows_from(self, avro_reader, header):
count = 0
maximum = self.limit if self.limit is not None else sys.maxsize
for i, record in enumerate(avro_reader):
if i < self.skip:
continue
if count >= maximum:
break
count += 1
row = self._map_row_from(header, record)
yield row
def _map_row_from(self, header, record):
'''
fastavro auto converts logical types defined in avro schema to
correspoding python types. E.g:
- avro type: long logicalType: timestamp-millis -> python datetime
- avro type: int logicalType: date -> python date
- avro type: bytes logicalType: decimal -> python Decimal
'''
if header is None or PY3:
r = tuple(record.values())
else:
# fastavro on python2 does not respect dict order
r = tuple(record.get(col) for col in header)
return r
def _write_toavro(table, target, mode, schema, sample,
codec='deflate', compression_level=None, **avro_args):
if table is None:
return
# build a schema when not defined by user
if not schema:
schema, table2 = _build_schema_from_values(table, sample)
else:
table2 = _fix_missing_headers(table, schema)
# fastavro expects a iterator of dicts
rows = dicts(table2) if PY3 else _ordered_dict_iterator(table2)
target2 = write_source_from_arg(target, mode=mode)
with target2.open(mode) as target_file:
# delay the import of fastavro for not breaking when unused
from fastavro import parse_schema
from fastavro.write import Writer
parsed_schema = parse_schema(schema)
writer = Writer(fo=target_file,
schema=parsed_schema,
codec=codec,
compression_level=compression_level,
**avro_args)
num = 1
for record in rows:
try:
writer.write(record)
num = num + 1
except ValueError as verr:
vmsg = _get_error_details(target, num, verr, record, schema)
_raise_error(ValueError, vmsg)
except TypeError as terr:
tmsg = _get_error_details(target, num, terr, record, schema)
_raise_error(TypeError, tmsg)
# finish writing
writer.flush()
# endregion Implementation
# region Helpers
def _build_schema_from_values(table, sample):
# table2: try not advance iterators
samples, table2 = iterpeek(table, sample + 1)
props = fieldnames(samples)
peek = skip(samples, 1)
schema_fields = _build_schema_fields_from_values(peek, props)
schema_source = _build_schema_with(schema_fields)
return schema_source, table2
def _build_schema_with(schema_fields):
schema = {
'type': 'record',
'name': 'output',
'namespace': 'avro',
'doc': 'generated by petl',
'fields': schema_fields,
}
return schema
def _build_schema_fields_from_values(peek, props):
# store the previous for calculate max precision and max scale
previous = OrderedDict()
# set a default when value is None in the first row but allow override after
fill_missing = True
fields = OrderedDict()
# iterate on sample rows for dealing with columns with None values
for row in peek:
_update_field_defs_from(props, row, fields, previous, fill_missing)
fill_missing = False
schema_fields = [item for item in fields.values()]
return schema_fields
def _update_field_defs_from(props, row, fields, previous, fill_missing):
for prop, val in izip_longest(props, row):
if prop is None:
break
dprev = previous.get(prop + '_prec')
fprev = previous.get(prop + '_prop')
fcurr = None
if isinstance(val, dict):
# get the fields from a recursive definition of record inside this field
tdef, dcurr, fcurr = _get_definition_from_record(prop, val, fprev, dprev, fill_missing)
else:
# get the field definition for building the schema
tdef, dcurr = _get_definition_from_type_of(prop, val, dprev)
if tdef is not None:
fields[prop] = {'name': prop, 'type': ['null', tdef]}
elif fill_missing:
fields[prop] = {'name': prop, 'type': ['null', 'string']}
if dcurr is not None:
previous[prop + '_prec'] = dcurr
if fcurr is not None:
previous[prop + '_prop'] = fcurr
def _get_definition_from_type_of(prop, val, prev):
# TODO: get type for enum, map and other python types
tdef = None
curr = None
if isinstance(val, datetime):
tdef = {'type': 'long', 'logicalType': 'timestamp-millis'}
elif isinstance(val, time):
tdef = {'type': 'int', 'logicalType': 'time-millis'}
elif isinstance(val, date):
tdef = {'type': 'int', 'logicalType': 'date'}
elif isinstance(val, Decimal):
curr, precision, scale = _get_precision_from_decimal(curr, val, prev)
tdef = {'type': 'bytes', 'logicalType': 'decimal',
'precision': precision, 'scale': scale, }
elif isinstance(val, bytes):
tdef = 'bytes'
elif isinstance(val, list):
tdef, curr = _get_definition_from_array(prop, val, prev)
elif isinstance(val, bool):
tdef = 'boolean'
elif isinstance(val, float):
tdef = 'double'
elif isinstance(val, int):
tdef = 'long'
elif val is not None:
tdef = 'string'
else:
return None, None
return tdef, curr
def _get_definition_from_array(prop, val, prev):
afield = None
for item in iter(val):
if item is None:
continue
field2, curr2 = _get_definition_from_type_of(prop, item, prev)
if field2 is not None:
afield = field2
if curr2 is not None:
prev = curr2
bfield = 'string' if afield is None else afield
tdef = {'type': 'array', 'items': bfield}
return tdef, prev
def _get_definition_from_record(prop, val, fprev, dprev, fill_missing):
if fprev is None:
fprev = OrderedDict()
if dprev is None:
dprev = OrderedDict()
props = list(val.keys())
row = list(val.values())
_update_field_defs_from(props, row, fprev, dprev, fill_missing)
schema_fields = [item for item in fprev.values()]
tdef = {
'type': 'record',
'name': prop + '_record',
'namespace': 'avro',
'fields': schema_fields,
}
return tdef, dprev, fprev
def _get_precision_from_decimal(curr, val, prev):
if val is None:
prec = scale = 0
else:
prec, scale, _, _ = precision_and_scale(val)
if prev is not None:
# get the greatests precision and scale of the sample
prec0, scale0 = prev.get('precision'), prev.get('scale')
prec, scale = max(prec, prec0), max(scale, scale0)
prec = max(prec, 8)
curr = {'precision': prec, 'scale': scale, }
return curr, prec, scale
def precision_and_scale(numeric_value):
sign, digits, exp = numeric_value.as_tuple()
number = 0
for digit in digits:
number = (number * 10) + digit
# delta = exp + scale
delta = 1
number = 10 ** delta * number
inumber = int(number)
bits_req = inumber.bit_length() + 1
bytes_req = (bits_req + 8) // 8
if sign:
inumber = - inumber
prec = int(math.ceil(math.log10(abs(inumber))))
scale = abs(exp)
return prec, scale, bytes_req, inumber
def _fix_missing_headers(table, schema):
'''add missing columns headers from schema'''
if schema is None or 'fields' not in schema:
return table
# table2: try not advance iterators
sample, table2 = iterpeek(table, 2)
cols = fieldnames(sample)
headers = _get_schema_header_names(schema)
if len(cols) >= len(headers):
return table2
table3 = setheader(table2, headers)
return table3
def _get_error_details(target, num, err, record, schema):
'''show last row when failed writing for throubleshooting'''
headers = _get_schema_header_names(schema)
if isinstance(record, dict):
table = [headers, list(record.values())]
else:
table = [headers, record]
example = wrap(table).look()
dest = " output: %s" % target if isinstance(target, string_types) else ''
printed = "failed writing on row #%d: %s\n%s\n schema: %s\n%s"
details = printed % (num, err, dest, schema, example)
return details
def _get_schema_header_names(schema):
fields = schema.get('fields')
if fields is None:
return []
header = [field.get('name') for field in fields]
return header
def _raise_error(ErrorType, new_message):
"""Works like raise Excetion(msg) from prev_exp in python3."""
exinf = sys.exc_info()
tracebk = exinf[2]
try:
if PY3:
raise ErrorType(new_message).with_traceback(tracebk)
# Python2 compatibility workaround
exec('raise ErrorType, new_message, tracebk')
finally:
exinf = None
tracebk = None # noqa: F841
def _ordered_dict_iterator(table):
it = iter(table)
hdr = next(it)
flds = [text_type(f) for f in hdr]
for row in it:
items = list()
for i, f in enumerate(flds):
try:
v = row[i]
except IndexError:
v = None
items.append((f, v))
yield OrderedDict(items)
Table.toavro = toavro
Table.appendavro = appendavro
# endregion