def test_materialize_dataset_hadoop_config(tmpdir_factory):
"""Test that using materialize_dataset does not alter the hadoop_config"""
path = tmpdir_factory.mktemp('data').strpath
tmp_url = "file://" + path
# This test does not properly check if parquet.enable.summary-metadata is restored properly with pyspark < 2.4
spark = SparkSession.builder.getOrCreate()
hadoop_config = spark.sparkContext._jsc.hadoopConfiguration()
parquet_metadata_level = "COMMON_ONLY"
parquet_row_group_check = 100
# Set the parquet summary files and row group size check min
hadoop_config.set('parquet.summary.metadata.level', parquet_metadata_level)
hadoop_config.setInt('parquet.row-group.size.row.check.min',
parquet_row_group_check)
assert hadoop_config.get('parquet.summary.metadata.level') == str(
parquet_metadata_level)
assert hadoop_config.get('parquet.row-group.size.row.check.min') == str(
parquet_row_group_check)
create_test_dataset(tmp_url, range(10), spark=spark)
assert not os.path.exists(os.path.join(path, "_metadata"))
# Check that they are back to the original values after writing the dataset
hadoop_config = spark.sparkContext._jsc.hadoopConfiguration()
assert hadoop_config.get('parquet.summary.metadata.level') == str(
parquet_metadata_level)
assert hadoop_config.get('parquet.row-group.size.row.check.min') == str(
parquet_row_group_check)
# Other options should return to being unset
assert hadoop_config.get('parquet.block.size') is None
assert hadoop_config.get('parquet.block.size.row.check.min') is None
spark.stop()
def test_ngram_delta_small_threshold_tf():
"""Test to verify that a small threshold work in ngrams."""
with temporary_directory() as tmp_dir:
tmp_url = 'file://{}'.format(tmp_dir)
ids = range(0, 99, 5)
create_test_dataset(tmp_url, ids)
fields = {
0: [
TestSchema.id, TestSchema.id2, TestSchema.image_png,
TestSchema.matrix
],
1: [TestSchema.id, TestSchema.id2, TestSchema.sensor_name],
}
ngram = NGram(fields=fields,
delta_threshold=1,
timestamp_field=TestSchema.id)
reader = Reader(
schema_fields=ngram,
dataset_url=tmp_url,
reader_pool=DummyPool(),
)
with tf.Session() as sess:
with pytest.raises(OutOfRangeError):
sess.run(tf_tensors(reader))
reader.stop()
reader.join()
def test_materialize_dataset_hadoop_config(synthetic_dataset):
"""Test that using materialize_dataset does not alter the hadoop_config"""
spark = SparkSession.builder.getOrCreate()
hadoop_config = spark.sparkContext._jsc.hadoopConfiguration()
parquet_summary_metadata = False
parquet_row_group_check = 100
# Set the parquet summary giles and row group size check min
hadoop_config.setBoolean('parquet.enable.summary-metadata',
parquet_summary_metadata)
hadoop_config.setInt('parquet.row-group.size.row.check.min',
parquet_row_group_check)
assert hadoop_config.get('parquet.enable.summary-metadata') == str(
parquet_summary_metadata).lower()
assert hadoop_config.get('parquet.row-group.size.row.check.min') == str(
parquet_row_group_check)
destination = synthetic_dataset.path + '_moved'
create_test_dataset('file://{}'.format(destination),
range(10),
spark=spark)
# Check that they are back to the original values after writing the dataset
hadoop_config = spark.sparkContext._jsc.hadoopConfiguration()
assert hadoop_config.get('parquet.enable.summary-metadata') == str(
parquet_summary_metadata).lower()
assert hadoop_config.get('parquet.row-group.size.row.check.min') == str(
parquet_row_group_check)
# Other options should return to being unset
assert hadoop_config.get('parquet.block.size') is None
assert hadoop_config.get('parquet.block.size.row.check.min') is None
spark.stop()
rmtree(destination)
def generate_dataset_for_legacy_test():
"""Generates a test dataset and stores it into petastorm/tests/data/legacy/x.x.x folder. The version number
is acquired automatically from petastorm.__version__"""
dataset_name = petastorm.__version__
path = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'data',
'legacy', dataset_name)
url = 'file://' + path
create_test_dataset(url, range(100))
def test_materialize_with_summary_metadata(tmpdir_factory):
"""Verify _summary_metadata appears, when requested"""
path = tmpdir_factory.mktemp('data').strpath
tmp_url = "file://" + path
spark = SparkSession.builder.getOrCreate()
create_test_dataset(tmp_url,
range(10),
spark=spark,
use_summary_metadata=True)
assert os.path.exists(os.path.join(path, "_metadata"))
spark.stop()
def setUpClass(cls):
"""Initializes dataset once per test. All tests in this class will use the same fake dataset."""
# Write a fake dataset to this location
cls._dataset_dir = mkdtemp('test_metadata_read')
cls._dataset_url = 'file://{}'.format(cls._dataset_dir)
cls._dataset_dicts = create_test_dataset(cls._dataset_url,
range(ROWS_COUNT))
def dataset_num_files_1(tmpdir_factory):
path = tmpdir_factory.mktemp("data").strpath
url = 'file://' + path
data = create_test_dataset(url, range(99), num_files=1)
dataset = SyntheticDataset(url=url, path=path, data=data)
return dataset
def dataset_0_3_8_10_11_20_23(tmpdir_factory):
path = tmpdir_factory.mktemp("data").strpath
url = 'file://' + path
ids = [0, 3, 8, 10, 11, 20, 23]
data = create_test_dataset(url, ids, num_files=1)
dataset = SyntheticDataset(url=url, path=path, data=data)
return dataset
def make_test_metadata(path):
"""
Use test_common to make a dataset for the TestSchema.
:param path: path to store the test dataset
:return: resulting dataset as a dictionary
"""
assert path, 'Please supply a nonempty path to store test dataset.'
return create_test_dataset('file://{}'.format(path), range(ROWS_COUNT))
def setUpClass(cls):
"""Initializes dataset once per test. All tests in this class will use the same fake dataset."""
# Write a fake dataset to this location
cls._dataset_dir = mkdtemp('end_to_end_petastorm')
cls._dataset_url = 'file://{}'.format(cls._dataset_dir)
ROWS_COUNT = 1000
cls._dataset_dicts = create_test_dataset(cls._dataset_url, range(ROWS_COUNT))
# Remove crc files due to https://issues.apache.org/jira/browse/HADOOP-7199
for crc_file in glob.glob(cls._dataset_dir + '/.*.crc'):
os.remove(crc_file)
def test_ngram_delta_small_threshold():
"""Test to verify that a small threshold work in ngrams."""
with temporary_directory() as tmp_dir:
tmp_url = 'file://{}'.format(tmp_dir)
ids = range(0, 99, 5)
create_test_dataset(tmp_url, ids)
fields = {
0: [
TestSchema.id, TestSchema.id2, TestSchema.image_png,
TestSchema.matrix
],
1: [TestSchema.id, TestSchema.id2, TestSchema.sensor_name],
}
ngram = NGram(fields=fields,
delta_threshold=1,
timestamp_field=TestSchema.id)
with Reader(schema_fields=ngram,
dataset_url=tmp_url,
reader_pool=ThreadPool(10)) as reader:
with pytest.raises(StopIteration):
next(reader)
def _synthetic_dataset_no_cache():
path = tmpdir_factory.mktemp("data").strpath
url = 'file://' + path
data = create_test_dataset(url, range(_ROWS_COUNT))
dataset = SyntheticDataset(url=url, path=path, data=data)
return dataset
def _dataset_generator():
path = tmpdir_factory.mktemp("data").strpath
url = 'file://' + path
ids = range(0, 99, 5)
data = create_test_dataset(url, ids)
return SyntheticDataset(url=url, path=path, data=data)
def _dataset_generator():
path = tmpdir_factory.mktemp("data").strpath
url = 'file://' + path
ids = [0, 3, 8, 10, 11, 20, 23]
data = create_test_dataset(url, ids, num_files=1)
return SyntheticDataset(url=url, path=path, data=data)
def _dataset_generator():
path = tmpdir_factory.mktemp("data").strpath
url = 'file://' + path
data = create_test_dataset(url, range(99), num_files=1)
return SyntheticDataset(url=url, path=path, data=data)
def synthetic_dataset(tmpdir_factory):
path = tmpdir_factory.mktemp('data').strpath
url = 'file://' + path
data = create_test_dataset(url, range(ROWS_COUNT))
return SyntheticDataset(url=url, path=path, data=data)
