5e0b6567cd
New bag implementation improves a lot how bonobo works, even if this is highly backward incompatible (sorry, that's needed, and better sooner than later). * New implementation uses the same approach as python's namedtuple, by dynamically creating the python type's code. This has drawbacks, as it feels like not the right way, but also a lot of benefits that cannot be achieved using a regular approach, especially the constructor parameter order, hardcoded. * Memory usage is now much more efficient. The "keys" memory space will be used only once per "io type", being spent in the underlying type definition instead of in the actual instances. * Transformations now needs to use tuples as output, which will be bound to its "output type". The output type can be infered from the tuple length, or explicitely set by the user using either `context.set_output_type(...)` or `context.set_output_fields(...)` (to build a bag type from a list of field names). Jupyter/Graphviz integration is more tight, allowing to easily display graphs in a notebook, or displaying the live transformation status in an html table instead of a simple <div>. For now, context processors were hacked to stay working as before but the current API is not satisfactory, and should be replaced. This new big change being unreasonable without some time to work on it properly, it is postponed for next versions (0.7, 0.8, ...). Maybe the best idea is to have some kind of "local services", that would use the same dependency injection mechanism as the execution-wide services. Services are now passed by keywoerd arguments only, to avoid confusion with data-arguments.
153 lines
3.9 KiB
Python
153 lines
3.9 KiB
Python
from collections import namedtuple
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from unittest import TestCase
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import pytest
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from bonobo import CsvReader, CsvWriter
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from bonobo.constants import EMPTY
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from bonobo.util.testing import FilesystemTester, BufferingNodeExecutionContext, WriterTest, ConfigurableNodeTest, ReaderTest
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csv_tester = FilesystemTester('csv')
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csv_tester.input_data = 'a,b,c\na foo,b foo,c foo\na bar,b bar,c bar'
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defaults = {'lineterminator': '\n'}
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incontext = ConfigurableNodeTest.incontext
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def test_read_csv_from_file_kwargs(tmpdir):
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fs, filename, services = csv_tester.get_services_for_reader(tmpdir)
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with BufferingNodeExecutionContext(CsvReader(filename, **defaults), services=services) as context:
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context.write_sync(EMPTY)
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assert context.get_buffer_args_as_dicts() == [{
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'a': 'a foo',
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'b': 'b foo',
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'c': 'c foo',
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}, {
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'a': 'a bar',
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'b': 'b bar',
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'c': 'c bar',
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}]
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###
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# CSV Readers / Writers
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###
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class Csv:
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extension = 'csv'
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ReaderNodeType = CsvReader
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WriterNodeType = CsvWriter
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L1, L2, L3, L4 = ('a', 'hey'), ('b', 'bee'), ('c', 'see'), ('d', 'dee')
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LL = ('i', 'have', 'more', 'values')
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class CsvReaderTest(Csv, ReaderTest, TestCase):
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input_data = '\n'.join((
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'id,name',
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'1,John Doe',
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'2,Jane Doe',
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',DPR',
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'42,Elon Musk',
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))
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def check_output(self, context, *, prepend=None):
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out = context.get_buffer()
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assert out == (prepend or list()) + [
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('1', 'John Doe'),
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('2', 'Jane Doe'),
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('', 'DPR'),
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('42', 'Elon Musk'),
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]
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@incontext()
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def test_nofields(self, context):
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context.write_sync(EMPTY)
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context.stop()
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self.check_output(context)
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assert context.get_output_fields() == ('id', 'name')
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@incontext(output_type=tuple)
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def test_output_type(self, context):
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context.write_sync(EMPTY)
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context.stop()
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self.check_output(context, prepend=[('id', 'name')])
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@incontext(
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output_fields=(
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'x',
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'y',
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), skip=1
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)
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def test_output_fields(self, context):
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context.write_sync(EMPTY)
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context.stop()
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self.check_output(context)
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assert context.get_output_fields() == ('x', 'y')
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class CsvWriterTest(Csv, WriterTest, TestCase):
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@incontext()
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def test_fields(self, context):
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context.set_input_fields(['foo', 'bar'])
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context.write_sync(('a', 'b'), ('c', 'd'))
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context.stop()
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assert self.readlines() == (
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'foo,bar',
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'a,b',
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'c,d',
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)
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@incontext()
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def test_fields_from_type(self, context):
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context.set_input_type(namedtuple('Point', 'x y'))
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context.write_sync((1, 2), (3, 4))
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context.stop()
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assert self.readlines() == ('x,y', '1,2', '3,4')
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@incontext()
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def test_nofields_multiple_args(self, context):
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# multiple args are iterated onto and flattened in output
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context.write_sync((L1, L2), (L3, L4))
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context.stop()
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assert self.readlines() == (
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'a,hey',
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'b,bee',
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'c,see',
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'd,dee',
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)
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@incontext()
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def test_nofields_multiple_args_length_mismatch(self, context):
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# if length of input vary, then we get a TypeError (unrecoverable)
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with pytest.raises(TypeError):
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context.write_sync((L1, L2), (L3, ))
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@incontext()
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def test_nofields_single_arg(self, context):
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# single args are just dumped, shapes can vary.
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context.write_sync((L1, ), (LL, ), (L3, ))
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context.stop()
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assert self.readlines() == (
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'a,hey',
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'i,have,more,values',
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'c,see',
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)
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@incontext()
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def test_nofields_empty_args(self, context):
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# empty calls are ignored
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context.write_sync(EMPTY, EMPTY, EMPTY)
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context.stop()
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assert self.readlines() == ()
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