def test_fdb_result_data_empty_pandas_fetch_all():
raw_response = {
'rowsetBase64': '',
'rowtype': [{"name": "c1"}, {"name": "c2"}],
'chunks': ['f1', 'f2', 'f3']
}
random.seed(datetime.datetime.now())
column_meta = [
{"logicalType": "FIXED", "precision": "38", "scale": "0"},
{"logicalType": "FIXED", "precision": "38", "scale": "0"}
]
def int64_generator():
return random.randint(-9223372036854775, 9223372036854775)
chunk_count = 3
expected_chunk_result = chunk_count * [None]
arrow_stream = chunk_count * [None]
batch_row_count = 10
batch_count = 9
for i in range(chunk_count):
arrow_stream[i], expected_chunk_result[i] = generate_data([pyarrow.int64(), pyarrow.int64()],
column_meta,
int64_generator,
batch_count,
batch_row_count)
con = MockConnection()
cur = MockCursor(con)
res = ArrowResult(raw_response,
cur,
use_dict_result=False,
_chunk_downloader=MockDownloader(arrow_stream))
cur._query_result_format = 'arrow'
cur._result = res
df = res._fetch_pandas_all()
# assert row count
assert df.shape[0] == batch_row_count * batch_count * chunk_count
# assert column count
assert df.shape[1] == 2
for i in range(2):
col = df.iloc[:, i]
for idx, val in col.items():
expected_chunk, expected_row_index_in_chunk = divmod(idx, batch_row_count * batch_count)
expected_batch_index, expected_row_index_in_batch = divmod(expected_row_index_in_chunk, batch_row_count)
expected_val = \
expected_chunk_result[expected_chunk][expected_batch_index][i][expected_row_index_in_batch]
if math.isnan(val):
assert expected_val is None
else:
assert abs(expected_val - val) < EPSILON
def test_fdb_result_data_empty_row_fetch():
raw_response = {
'rowsetBase64': '',
'rowtype': [{"name": "c1"}, {"name": "c2"}],
'chunks': ['f1', 'f2', 'f3']
}
random.seed(datetime.datetime.now())
column_meta = [
{"logicalType": "FIXED", "precision": "38", "scale": "0"},
{"logicalType": "FIXED", "precision": "38", "scale": "0"}
]
def int64_generator():
return random.randint(-9223372036854775808, 9223372036854775807)
chunk_count = 3
expected_chunk_result = chunk_count * [None]
arrow_stream = chunk_count * [None]
batch_row_count = 10
batch_count = 9
for i in range(chunk_count):
arrow_stream[i], expected_chunk_result[i] = generate_data([pyarrow.int64(), pyarrow.int64()],
column_meta,
int64_generator,
batch_count,
batch_row_count)
con = MockConnection()
cur = MockCursor(con)
res = ArrowResult(raw_response,
cur,
use_dict_result=False,
_chunk_downloader=MockDownloader(arrow_stream))
cur._query_result_format = 'arrow'
cur._result = res
count = 0
while True:
try:
data = res.__next__()
expected_chunk, expected_row_index_in_chunk = divmod(count, batch_row_count * batch_count)
expected_batch_index, expected_row_index_in_batch = divmod(expected_row_index_in_chunk, batch_row_count)
assert data[0] == \
expected_chunk_result[expected_chunk][expected_batch_index][0][expected_row_index_in_batch]
assert data[1] == \
expected_chunk_result[expected_chunk][expected_batch_index][1][expected_row_index_in_batch]
count += 1
except StopIteration:
break
assert count == chunk_count * batch_row_count * batch_count
