def test_exists_raises_error(self, mocker):
delta_ds = DeltaTableDataSet(filepath="")
mocker.patch.object(delta_ds,
"_get_spark",
side_effect=AnalysisException(
"Other Exception", []))
with pytest.raises(DataSetError, match="Other Exception"):
delta_ds.exists()
def test_exists_raises_error(self, mocker):
# exists should raise all errors except for
# AnalysisExceptions clearly indicating a missing file
spark_data_set = SparkDataSet(filepath="")
mocker.patch.object(
spark_data_set,
"_get_spark",
side_effect=AnalysisException("Other Exception", []),
)
with pytest.raises(DataSetError, match="Other Exception"):
spark_data_set.exists()
def _convert_delta_exception(e: "JavaObject") -> Optional[CapturedException]:
"""
Convert Delta's Scala concurrent exceptions to the corresponding Python exceptions.
"""
s: str = e.toString()
c: "JavaObject" = e.getCause()
jvm: "JVMView" = SparkContext._jvm # type: ignore[attr-defined]
gw = SparkContext._gateway # type: ignore[attr-defined]
stacktrace = jvm.org.apache.spark.util.Utils.exceptionString(e)
# Temporary workaround until Delta Lake is upgraded to Spark 3.3
# Below three exception handling cases are copied from
# https://github.com/apache/spark/blob/master/python/pyspark/sql/utils.py#L156
if is_instance_of(gw, e,
"org.apache.spark.sql.catalyst.parser.ParseException"):
return ParseException(s.split(': ', 1)[1], stacktrace, c)
# Order matters. ParseException inherits AnalysisException.
if is_instance_of(gw, e, "org.apache.spark.sql.AnalysisException"):
return AnalysisException(s.split(': ', 1)[1], stacktrace, c)
if is_instance_of(gw, e, "java.lang.IllegalArgumentException"):
return IllegalArgumentException(s.split(': ', 1)[1], stacktrace, c)
if s.startswith(
'io.delta.exceptions.DeltaConcurrentModificationException: '):
return DeltaConcurrentModificationException(
s.split(': ', 1)[1], stacktrace, c)
if s.startswith('io.delta.exceptions.ConcurrentWriteException: '):
return ConcurrentWriteException(s.split(': ', 1)[1], stacktrace, c)
if s.startswith('io.delta.exceptions.MetadataChangedException: '):
return MetadataChangedException(s.split(': ', 1)[1], stacktrace, c)
if s.startswith('io.delta.exceptions.ProtocolChangedException: '):
return ProtocolChangedException(s.split(': ', 1)[1], stacktrace, c)
if s.startswith('io.delta.exceptions.ConcurrentAppendException: '):
return ConcurrentAppendException(s.split(': ', 1)[1], stacktrace, c)
if s.startswith('io.delta.exceptions.ConcurrentDeleteReadException: '):
return ConcurrentDeleteReadException(
s.split(': ', 1)[1], stacktrace, c)
if s.startswith('io.delta.exceptions.ConcurrentDeleteDeleteException: '):
return ConcurrentDeleteDeleteException(
s.split(': ', 1)[1], stacktrace, c)
if s.startswith('io.delta.exceptions.ConcurrentTransactionException: '):
return ConcurrentTransactionException(
s.split(': ', 1)[1], stacktrace, c)
return None
def get_data_frame_count_type_of_topic(data_frame: DataFrame) -> pb.DataFrame:
"""
From all the data, it takes the columns TopicID and Question and for each topic, count the number of+
different SubTopic/Question
:param data_frame: generate with pyspark, and contain all the data from the csv file
:return: data frame of panda package
"""
try:
data_frame = data_frame \
.select("TopicID", "Question") \
.distinct() \
.groupBy("TopicID") \
.count() \
.sort("TopicID")
except Py4JError:
raise AnalysisException('One columns is incorrect')
print("The following table represent the number of the type of each topic")
data_frame.show()
data_frame_pandas = data_frame.toPandas()
return data_frame_pandas
def read_csv_with_data_frame(file_csv: str) -> DataFrame:
"""
Read CSV with as data frame with spark
:param file_csv: file name of csv
:return: all the data of the file as data frame
"""
spark_session = SparkSession \
.builder \
.getOrCreate()
logger = spark_session._jvm.org.apache.log4j
logger.LogManager.getLogger("org").setLevel(logger.Level.WARN)
try:
data_frame = spark_session\
.read\
.format("csv") \
.options(header='true', inferschema='true')\
.load(file_csv)
except Py4JError:
raise AnalysisException('There is no csv file in:' + str(os.path))
return data_frame
def faulty_get_spark():
raise AnalysisException("Other Exception", [])
