def _get_partition_watermarks(self, table, tableRef, partitions):
if "field" in table["timePartitioning"]:
field = table["timePartitioning"]["field"]
else:
field = "_PARTITIONTIME"
low = min(partitions, key=lambda t: t.partition_id)
low_wm = Watermark(
datetime.datetime.fromtimestamp(float(
low.epoch_created)).strftime("%Y-%m-%d %H:%M:%S"),
"bigquery",
tableRef["datasetId"],
tableRef["tableId"],
"{field}={partition_id}".format(field=field,
partition_id=low.partition_id),
part_type="low_watermark",
cluster=tableRef["projectId"],
)
high = max(partitions, key=lambda t: t.partition_id)
high_wm = Watermark(
datetime.datetime.fromtimestamp(float(
high.epoch_created)).strftime("%Y-%m-%d %H:%M:%S"),
"bigquery",
tableRef["datasetId"],
tableRef["tableId"],
"{field}={partition_id}".format(field=field,
partition_id=high.partition_id),
part_type="high_watermark",
cluster=tableRef["projectId"],
)
return low_wm, high_wm
def _get_partition_watermarks(
self, table: Dict[str, Any], tableRef: Dict[str, str],
partitions: List[PartitionInfo]) -> Tuple[Watermark, Watermark]:
if 'field' in table['timePartitioning']:
field = table['timePartitioning']['field']
else:
field = '_PARTITIONTIME'
low = min(partitions, key=lambda t: t.partition_id)
low_wm = Watermark(datetime.datetime.fromtimestamp(
float(low.epoch_created)).strftime('%Y-%m-%d %H:%M:%S'),
'bigquery',
tableRef['datasetId'],
tableRef['tableId'],
'{field}={partition_id}'.format(
field=field, partition_id=low.partition_id),
part_type="low_watermark",
cluster=tableRef['projectId'])
high = max(partitions, key=lambda t: t.partition_id)
high_wm = Watermark(datetime.datetime.fromtimestamp(
float(high.epoch_created)).strftime('%Y-%m-%d %H:%M:%S'),
'bigquery',
tableRef['datasetId'],
tableRef['tableId'],
'{field}={partition_id}'.format(
field=field, partition_id=high.partition_id),
part_type="high_watermark",
cluster=tableRef['projectId'])
return low_wm, high_wm
def test_index_with_data(self) -> None:
extractor = self._get_extractor([self.index_with_data_1])
expected = [
Watermark(
database='elasticsearch',
cluster='cluster_name',
schema='schema_name',
table_name='index_with_data_1',
create_time=datetime.fromtimestamp(1641863003).strftime(
'%Y-%m-%d %H:%M:%S'),
part_name=
f"time={datetime.fromtimestamp(1641863055).strftime('%Y-%m-%d')}",
part_type='low_watermark'),
Watermark(
database='elasticsearch',
cluster='cluster_name',
schema='schema_name',
table_name='index_with_data_1',
create_time=datetime.fromtimestamp(1641863003).strftime(
'%Y-%m-%d %H:%M:%S'),
part_name=
f"time={datetime.fromtimestamp(1641949455).strftime('%Y-%m-%d')}",
part_type='high_watermark')
]
self._extract_and_compare(extractor, expected)
def setUp(self) -> None:
super(TestWatermark, self).setUp()
self.watermark = Watermark(create_time='2017-09-18T00:00:00',
database=DATABASE,
schema=SCHEMA,
table_name=TABLE,
cluster=CLUSTER,
part_type=PART_TYPE,
part_name=NESTED_PART)
self.start_key = f'{DATABASE}://{CLUSTER}.{SCHEMA}/{TABLE}/{PART_TYPE}/'
self.end_key = f'{DATABASE}://{CLUSTER}.{SCHEMA}/{TABLE}'
self.expected_node_result = GraphNode(key=self.start_key,
label='Watermark',
attributes={
'partition_key':
'ds',
'partition_value':
'2017-09-18/feature_id=9',
'create_time':
'2017-09-18T00:00:00'
})
self.expected_serialized_node_results = [{
NODE_KEY:
self.start_key,
NODE_LABEL:
'Watermark',
'partition_key':
'ds',
'partition_value':
'2017-09-18/feature_id=9',
'create_time':
'2017-09-18T00:00:00'
}]
self.expected_relation_result = GraphRelationship(
start_label='Watermark',
end_label='Table',
start_key=self.start_key,
end_key=self.end_key,
type='BELONG_TO_TABLE',
reverse_type='WATERMARK',
attributes={})
self.expected_serialized_relation_results = [{
RELATION_START_KEY:
self.start_key,
RELATION_START_LABEL:
'Watermark',
RELATION_END_KEY:
self.end_key,
RELATION_END_LABEL:
'Table',
RELATION_TYPE:
'BELONG_TO_TABLE',
RELATION_REVERSE_TYPE:
'WATERMARK'
}]
def _retrieve_tables(self, dataset):
# type: () -> Any
sharded_table_watermarks = {}
for page in self._page_table_list_results(dataset):
if 'tables' not in page:
continue
for table in page['tables']:
tableRef = table['tableReference']
if self._is_table_match_regex(tableRef):
table_id = tableRef['tableId']
# BigQuery tables that have 8 digits as last characters are
# considered date range tables and are grouped together in the UI.
# ( e.g. ga_sessions_20190101, ga_sessions_20190102, etc. )
# We use these suffixes to determine high and low watermarks
if self._is_sharded_table(table_id):
suffix = table_id[-BigQueryWatermarkExtractor.DATE_LENGTH:]
prefix = table_id[:-BigQueryWatermarkExtractor.DATE_LENGTH]
if prefix in sharded_table_watermarks:
sharded_table_watermarks[prefix]['low'] = min(sharded_table_watermarks[prefix]['low'], suffix)
sharded_table_watermarks[prefix]['high'] = max(sharded_table_watermarks[prefix]['high'], suffix)
else:
sharded_table_watermarks[prefix] = {'high': suffix, 'low': suffix, 'table': table}
else:
partitions = self._get_partitions(table, tableRef)
if not partitions:
continue
low, high = self._get_partition_watermarks(table, tableRef, partitions)
yield low
yield high
for prefix, td in sharded_table_watermarks.items():
table = td['table']
tableRef = table['tableReference']
yield Watermark(
datetime.datetime.fromtimestamp(float(table['creationTime']) / 1000).strftime('%Y-%m-%d %H:%M:%S'),
'bigquery',
tableRef['datasetId'],
prefix,
'__table__={partition_id}'.format(partition_id=td['low']),
part_type="low_watermark",
cluster=tableRef['projectId']
)
yield Watermark(
datetime.datetime.fromtimestamp(float(table['creationTime']) / 1000).strftime('%Y-%m-%d %H:%M:%S'),
'bigquery',
tableRef['datasetId'],
prefix,
'__table__={partition_id}'.format(partition_id=td['high']),
part_type="high_watermark",
cluster=tableRef['projectId']
)
def setUp(self):
# type: () -> None
super(TestWatermark, self).setUp()
self.watermark = Watermark(create_time='2017-09-18T00:00:00',
database=DATABASE,
schema=SCHEMA,
table_name=TABLE,
cluster=CLUSTER,
part_type=PART_TYPE,
part_name=NESTED_PART)
self.expected_node_result = {
NODE_KEY:
'{database}://{cluster}.{schema}/{table}/{part_type}/'.format(
database=DATABASE.lower(),
cluster=CLUSTER.lower(),
schema=SCHEMA.lower(),
table=TABLE.lower(),
part_type=PART_TYPE.lower()),
NODE_LABEL:
'Watermark',
'partition_key':
'ds',
'partition_value':
'2017-09-18/feature_id=9',
'create_time':
'2017-09-18T00:00:00'
}
self.expected_relation_result = {
RELATION_START_KEY:
'{database}://{cluster}.{schema}/{table}/{part_type}/'.format(
database=DATABASE.lower(),
cluster=CLUSTER.lower(),
schema=SCHEMA.lower(),
table=TABLE.lower(),
part_type=PART_TYPE.lower()),
RELATION_START_LABEL:
'Watermark',
RELATION_END_KEY:
'{database}://{cluster}.{schema}/{table}'.format(
database=DATABASE.lower(),
cluster=CLUSTER.lower(),
schema=SCHEMA.lower(),
table=TABLE.lower()),
RELATION_END_LABEL:
'Table',
RELATION_TYPE:
'BELONG_TO_TABLE',
RELATION_REVERSE_TYPE:
'WATERMARK'
}
def _get_extract_iter(self) -> Iterator[Union[Watermark, None]]:
# Get all the indices
indices: Dict = self._get_indexes()
# Iterate over indices
for index_name, index_metadata in indices.items():
creation_date: Optional[float] = self._get_index_creation_date(index_metadata)
watermark_bounds: Optional[Tuple[float, float]] = self._get_index_watermark_bounds(index_name=index_name)
watermark_min: Optional[float] = None if watermark_bounds is None else watermark_bounds[0]
watermark_max: Optional[float] = None if watermark_bounds is None else watermark_bounds[1]
if creation_date is None or watermark_min is None or watermark_max is None:
continue
creation_date_str: str = datetime.fromtimestamp(creation_date / 1000).strftime('%Y-%m-%d %H:%M:%S')
watermark_min_str: str = datetime.fromtimestamp(watermark_min / 1000).strftime('%Y-%m-%d')
watermark_max_str: str = datetime.fromtimestamp(watermark_max / 1000).strftime('%Y-%m-%d')
yield Watermark(
database=self.database,
cluster=self.cluster,
schema=self.schema,
table_name=index_name,
create_time=creation_date_str,
part_name=f'{self._time_field}={watermark_min_str}',
part_type='low_watermark'
)
yield Watermark(
database=self.database,
cluster=self.cluster,
schema=self.schema,
table_name=index_name,
create_time=creation_date_str,
part_name=f'{self._time_field}={watermark_max_str}',
part_type='high_watermark'
)
class TestWatermark(unittest.TestCase):
def setUp(self) -> None:
super(TestWatermark, self).setUp()
self.watermark = Watermark(create_time='2017-09-18T00:00:00',
database=DATABASE,
schema=SCHEMA,
table_name=TABLE,
cluster=CLUSTER,
part_type=PART_TYPE,
part_name=NESTED_PART)
self.start_key = f'{DATABASE}://{CLUSTER}.{SCHEMA}/{TABLE}/{PART_TYPE}/'
self.end_key = f'{DATABASE}://{CLUSTER}.{SCHEMA}/{TABLE}'
self.expected_node_result = GraphNode(key=self.start_key,
label='Watermark',
attributes={
'partition_key':
'ds',
'partition_value':
'2017-09-18/feature_id=9',
'create_time':
'2017-09-18T00:00:00'
})
self.expected_serialized_node_result = {
NODE_KEY: self.start_key,
NODE_LABEL: 'Watermark',
'partition_key': 'ds',
'partition_value': '2017-09-18/feature_id=9',
'create_time': '2017-09-18T00:00:00'
}
self.expected_relation_result = GraphRelationship(
start_label='Watermark',
end_label='Table',
start_key=self.start_key,
end_key=self.end_key,
type='BELONG_TO_TABLE',
reverse_type='WATERMARK',
attributes={})
self.expected_serialized_relation_result = {
RELATION_START_KEY: self.start_key,
RELATION_START_LABEL: 'Watermark',
RELATION_END_KEY: self.end_key,
RELATION_END_LABEL: 'Table',
RELATION_TYPE: 'BELONG_TO_TABLE',
RELATION_REVERSE_TYPE: 'WATERMARK'
}
def test_get_watermark_model_key(self) -> None:
watermark = self.watermark.get_watermark_model_key()
self.assertEqual(
watermark, f'{DATABASE}://{CLUSTER}.{SCHEMA}/{TABLE}/{PART_TYPE}/')
def test_get_metadata_model_key(self) -> None:
metadata = self.watermark.get_metadata_model_key()
self.assertEqual(metadata, f'{DATABASE}://{CLUSTER}.{SCHEMA}/{TABLE}')
def test_create_nodes(self) -> None:
nodes = self.watermark.create_nodes()
self.assertEquals(len(nodes), 1)
self.assertEquals(nodes[0], self.expected_node_result)
self.assertEqual(neo4_serializer.serialize_node(nodes[0]),
self.expected_serialized_node_result)
def test_create_nodes_neptune(self) -> None:
nodes = self.watermark.create_nodes()
expected_serialized_node_result = {
NEPTUNE_HEADER_ID:
self.start_key,
NEPTUNE_HEADER_LABEL:
'Watermark',
NEPTUNE_LAST_EXTRACTED_AT_RELATIONSHIP_PROPERTY_NAME_BULK_LOADER_FORMAT:
ANY,
NEPTUNE_CREATION_TYPE_NODE_PROPERTY_NAME_BULK_LOADER_FORMAT:
NEPTUNE_CREATION_TYPE_JOB,
'partition_key:String(single)':
'ds',
'partition_value:String(single)':
'2017-09-18/feature_id=9',
'create_time:String(single)':
'2017-09-18T00:00:00'
}
serialized_node = neptune_serializer.convert_node(nodes[0])
self.assertDictEqual(expected_serialized_node_result, serialized_node)
def test_create_relation(self) -> None:
relation = self.watermark.create_relation()
self.assertEquals(len(relation), 1)
self.assertEquals(relation[0], self.expected_relation_result)
self.assertEqual(neo4_serializer.serialize_relationship(relation[0]),
self.expected_serialized_relation_result)
def test_create_relation_neptune(self) -> None:
relation = self.watermark.create_relation()
serialized_relation = neptune_serializer.convert_relationship(
relation[0])
expected = [{
NEPTUNE_HEADER_ID:
"{from_vertex_id}_{to_vertex_id}_{label}".format(
from_vertex_id=self.start_key,
to_vertex_id=self.end_key,
label='BELONG_TO_TABLE'),
NEPTUNE_RELATIONSHIP_HEADER_FROM:
self.start_key,
NEPTUNE_RELATIONSHIP_HEADER_TO:
self.end_key,
NEPTUNE_HEADER_LABEL:
'BELONG_TO_TABLE',
NEPTUNE_LAST_EXTRACTED_AT_RELATIONSHIP_PROPERTY_NAME_BULK_LOADER_FORMAT:
ANY,
NEPTUNE_CREATION_TYPE_RELATIONSHIP_PROPERTY_NAME_BULK_LOADER_FORMAT:
NEPTUNE_CREATION_TYPE_JOB
}, {
NEPTUNE_HEADER_ID:
"{from_vertex_id}_{to_vertex_id}_{label}".format(
from_vertex_id=self.end_key,
to_vertex_id=self.start_key,
label='WATERMARK'),
NEPTUNE_RELATIONSHIP_HEADER_FROM:
self.end_key,
NEPTUNE_RELATIONSHIP_HEADER_TO:
self.start_key,
NEPTUNE_HEADER_LABEL:
'WATERMARK',
NEPTUNE_LAST_EXTRACTED_AT_RELATIONSHIP_PROPERTY_NAME_BULK_LOADER_FORMAT:
ANY,
NEPTUNE_CREATION_TYPE_RELATIONSHIP_PROPERTY_NAME_BULK_LOADER_FORMAT:
NEPTUNE_CREATION_TYPE_JOB
}]
self.assertListEqual(serialized_relation, expected)
def test_create_next_node(self) -> None:
next_node = self.watermark.create_next_node()
self.assertEquals(neo4_serializer.serialize_node(next_node),
self.expected_serialized_node_result)
def test_create_next_relation(self) -> None:
next_relation = self.watermark.create_next_relation()
self.assertEquals(
neo4_serializer.serialize_relationship(next_relation),
self.expected_serialized_relation_result)
class TestWatermark(unittest.TestCase):
def setUp(self):
# type: () -> None
super(TestWatermark, self).setUp()
self.watermark = Watermark(create_time='2017-09-18T00:00:00',
database=DATABASE,
schema_name=SCHEMA,
table_name=TABLE,
cluster=CLUSTER,
part_type=PART_TYPE,
part_name=NESTED_PART)
self.expected_node_result = {
NODE_KEY: '{database}://{cluster}.{schema}/{table}/{part_type}/'
.format(
database=DATABASE.lower(),
cluster=CLUSTER.lower(),
schema=SCHEMA.lower(),
table=TABLE.lower(),
part_type=PART_TYPE.lower()),
NODE_LABEL: 'Watermark',
'partition_key': 'ds',
'partition_value': '2017-09-18/feature_id=9',
'create_time': '2017-09-18T00:00:00'
}
self.expected_relation_result = {
RELATION_START_KEY: '{database}://{cluster}.{schema}/{table}/{part_type}/'
.format(
database=DATABASE.lower(),
cluster=CLUSTER.lower(),
schema=SCHEMA.lower(),
table=TABLE.lower(),
part_type=PART_TYPE.lower()),
RELATION_START_LABEL: 'Watermark',
RELATION_END_KEY: '{database}://{cluster}.{schema}/{table}'
.format(
database=DATABASE.lower(),
cluster=CLUSTER.lower(),
schema=SCHEMA.lower(),
table=TABLE.lower()),
RELATION_END_LABEL: 'Table',
RELATION_TYPE: 'BELONG_TO_TABLE',
RELATION_REVERSE_TYPE: 'WATERMARK'
}
def test_get_watermark_model_key(self):
# type: () -> None
watermark = self.watermark.get_watermark_model_key()
self.assertEquals(
watermark, '{database}://{cluster}.{schema}/{table}/{part_type}/'
.format(database=DATABASE.lower(),
cluster=CLUSTER.lower(),
schema=SCHEMA.lower(),
table=TABLE.lower(),
part_type=PART_TYPE.lower()))
def test_get_metadata_model_key(self):
# type: () -> None
metadata = self.watermark.get_metadata_model_key()
self.assertEquals(metadata, '{database}://{cluster}.{schema}/{table}'
.format(database=DATABASE.lower(),
cluster=CLUSTER.lower(),
schema=SCHEMA.lower(),
table=TABLE.lower()))
def test_create_nodes(self):
# type: () -> None
nodes = self.watermark.create_nodes()
self.assertEquals(len(nodes), 1)
self.assertEquals(nodes[0], self.expected_node_result)
def test_create_relation(self):
# type: () -> None
relation = self.watermark.create_relation()
self.assertEquals(len(relation), 1)
self.assertEquals(relation[0], self.expected_relation_result)
def test_create_next_node(self):
# type: () -> None
next_node = self.watermark.create_next_node()
self.assertEquals(next_node, self.expected_node_result)
def test_create_next_relation(self):
# type: () -> None
next_relation = self.watermark.create_next_relation()
self.assertEquals(next_relation, self.expected_relation_result)
def _retrieve_tables(self, dataset: DatasetRef) -> Iterator[Watermark]:
sharded_table_watermarks: Dict[str, Dict[str, Union[str, Any]]] = {}
cutoff_time_in_epoch = timegm(
time.strptime(self.cutoff_time,
BigQueryWatermarkExtractor.DATE_TIME_FORMAT))
for page in self._page_table_list_results(dataset):
if 'tables' not in page:
continue
for table in page['tables']:
tableRef = table['tableReference']
table_id = tableRef['tableId']
table_creation_time = float(table['creationTime']) / 1000
# only extract watermark metadata for tables created before the cut-off time
if table_creation_time < cutoff_time_in_epoch:
# BigQuery tables that have numeric suffix starts with a date are
# considered date range tables.
# ( e.g. ga_sessions_20190101, ga_sessions_20190102, etc. )
# We use these dates in the suffixes to determine high and low watermarks
if self._is_sharded_table(table_id):
suffix = self._get_sharded_table_suffix(table_id)
prefix = table_id[:-len(suffix)]
date = suffix[:BaseBigQueryExtractor.DATE_LENGTH]
if prefix in sharded_table_watermarks:
sharded_table_watermarks[prefix]['low'] = min(
sharded_table_watermarks[prefix]['low'], date)
sharded_table_watermarks[prefix]['high'] = max(
sharded_table_watermarks[prefix]['high'], date)
else:
sharded_table_watermarks[prefix] = {
'high': date,
'low': date,
'table': table
}
else:
partitions = self._get_partitions(table, tableRef)
if not partitions:
continue
low, high = self._get_partition_watermarks(
table, tableRef, partitions)
yield low
yield high
for prefix, td in sharded_table_watermarks.items():
table = td['table']
tableRef = table['tableReference']
yield Watermark(datetime.datetime.fromtimestamp(
float(table['creationTime']) /
1000).strftime('%Y-%m-%d %H:%M:%S'),
'bigquery',
tableRef['datasetId'],
prefix,
f'__table__={td["low"]}',
part_type="low_watermark",
cluster=tableRef['projectId'])
yield Watermark(datetime.datetime.fromtimestamp(
float(table['creationTime']) /
1000).strftime('%Y-%m-%d %H:%M:%S'),
'bigquery',
tableRef['datasetId'],
prefix,
f'__table__={td["high"]}',
part_type="high_watermark",
cluster=tableRef['projectId'])
class TestWatermark(unittest.TestCase):
def setUp(self) -> None:
super(TestWatermark, self).setUp()
self.watermark = Watermark(create_time='2017-09-18T00:00:00',
database=DATABASE,
schema=SCHEMA,
table_name=TABLE,
cluster=CLUSTER,
part_type=PART_TYPE,
part_name=NESTED_PART)
start_key = '{database}://{cluster}.{schema}/{table}/{part_type}/'.format(
database=DATABASE,
cluster=CLUSTER,
schema=SCHEMA,
table=TABLE,
part_type=PART_TYPE)
end_key = '{database}://{cluster}.{schema}/{table}'.format(
database=DATABASE, cluster=CLUSTER, schema=SCHEMA, table=TABLE)
self.expected_node_result = GraphNode(key=start_key,
label='Watermark',
attributes={
'partition_key':
'ds',
'partition_value':
'2017-09-18/feature_id=9',
'create_time':
'2017-09-18T00:00:00'
})
self.expected_serialized_node_result = {
NODE_KEY: start_key,
NODE_LABEL: 'Watermark',
'partition_key': 'ds',
'partition_value': '2017-09-18/feature_id=9',
'create_time': '2017-09-18T00:00:00'
}
self.expected_relation_result = GraphRelationship(
start_label='Watermark',
end_label='Table',
start_key=start_key,
end_key=end_key,
type='BELONG_TO_TABLE',
reverse_type='WATERMARK',
attributes={})
self.expected_serialized_relation_result = {
RELATION_START_KEY: start_key,
RELATION_START_LABEL: 'Watermark',
RELATION_END_KEY: end_key,
RELATION_END_LABEL: 'Table',
RELATION_TYPE: 'BELONG_TO_TABLE',
RELATION_REVERSE_TYPE: 'WATERMARK'
}
def test_get_watermark_model_key(self) -> None:
watermark = self.watermark.get_watermark_model_key()
self.assertEqual(
watermark,
'{database}://{cluster}.{schema}/{table}/{part_type}/'.format(
database=DATABASE,
cluster=CLUSTER,
schema=SCHEMA,
table=TABLE,
part_type=PART_TYPE))
def test_get_metadata_model_key(self) -> None:
metadata = self.watermark.get_metadata_model_key()
self.assertEqual(
metadata,
'{database}://{cluster}.{schema}/{table}'.format(database=DATABASE,
cluster=CLUSTER,
schema=SCHEMA,
table=TABLE))
def test_create_nodes(self) -> None:
nodes = self.watermark.create_nodes()
self.assertEquals(len(nodes), 1)
self.assertEquals(nodes[0], self.expected_node_result)
self.assertEqual(neo4_serializer.serialize_node(nodes[0]),
self.expected_serialized_node_result)
def test_create_relation(self) -> None:
relation = self.watermark.create_relation()
self.assertEquals(len(relation), 1)
self.assertEquals(relation[0], self.expected_relation_result)
self.assertEqual(neo4_serializer.serialize_relationship(relation[0]),
self.expected_serialized_relation_result)
def test_create_next_node(self) -> None:
next_node = self.watermark.create_next_node()
self.assertEquals(neo4_serializer.serialize_node(next_node),
self.expected_serialized_node_result)
def test_create_next_relation(self) -> None:
next_relation = self.watermark.create_next_relation()
self.assertEquals(
neo4_serializer.serialize_relationship(next_relation),
self.expected_serialized_relation_result)
def create_table_watermarks(
self, table: ScrapedTableMetadata
) -> Optional[List[Tuple[Watermark, Watermark]]]: # noqa c901
"""
Creates the watermark objects that reflect the highest and lowest values in the partition columns
"""
def _is_show_partitions_supported(t: ScrapedTableMetadata) -> bool:
try:
self.spark.sql(f'show partitions {t.schema}.{t.table}')
return True
except Exception as e:
# pyspark.sql.utils.AnalysisException: SHOW PARTITIONS is not allowed on a table that is not partitioned
LOGGER.warning(e)
return False
def _fetch_minmax(table: ScrapedTableMetadata,
partition_column: str) -> Tuple[str, str]:
LOGGER.info(
f'Fetching partition info for {partition_column} in {table.schema}.{table.table}'
)
min_water = ""
max_water = ""
try:
if is_show_partitions_supported:
LOGGER.info('Using SHOW PARTITION')
min_water = str(
self.spark.sql(
f'show partitions {table.schema}.{table.table}').
orderBy(partition_column,
ascending=True).first()[partition_column])
max_water = str(
self.spark.sql(
f'show partitions {table.schema}.{table.table}').
orderBy(partition_column,
ascending=False).first()[partition_column])
else:
LOGGER.info('Using DESCRIBE EXTENDED')
part_info = (self.spark.sql(
f'describe extended {table.schema}.{table.table} {partition_column}'
).collect())
minmax = {}
for mm in list(
filter(lambda x: x['info_name'] in ['min', 'max'],
part_info)):
minmax[mm['info_name']] = mm['info_value']
min_water = minmax['min']
max_water = minmax['max']
except Exception as e:
LOGGER.warning(f'Failed fetching partition watermarks: {e}')
return max_water, min_water
if not table.table_detail:
LOGGER.info(f'No table details found in {table}, skipping')
return None
if 'partitionColumns' not in table.table_detail or len(
table.table_detail['partitionColumns']) < 1:
LOGGER.info(f'No partitions found in {table}, skipping')
return None
is_show_partitions_supported: bool = _is_show_partitions_supported(
table)
if not is_show_partitions_supported:
LOGGER.info('Analyzing table, this can take a while...')
partition_columns = ','.join(
table.table_detail['partitionColumns'])
self.spark.sql(
f"analyze table {table.schema}.{table.table} compute statistics for columns {partition_columns}"
)
# It makes little sense to get watermarks from a string value, with no concept of high and low.
# Just imagine a dataset with a partition by country...
valid_types = ['int', 'float', 'date', 'datetime']
if table.columns:
_table_columns = table.columns
else:
_table_columns = []
columns_with_valid_type = list(
map(
lambda l: l.name,
filter(lambda l: str(l.data_type).lower() in valid_types,
_table_columns)))
r = []
for partition_column in table.table_detail['partitionColumns']:
if partition_column not in columns_with_valid_type:
continue
last, first = _fetch_minmax(table, partition_column)
low = Watermark(create_time=table.table_detail['createdAt'],
database=self._db,
schema=table.schema,
table_name=table.table,
part_name=f'{partition_column}={first}',
part_type='low_watermark',
cluster=self._cluster)
high = Watermark(create_time=table.table_detail['createdAt'],
database=self._db,
schema=table.schema,
table_name=table.table,
part_name=f'{partition_column}={last}',
part_type='high_watermark',
cluster=self._cluster)
r.append((high, low))
return r
class TestWatermark(unittest.TestCase):
def setUp(self) -> None:
super(TestWatermark, self).setUp()
self.watermark = Watermark(
create_time='2017-09-18T00:00:00',
database=DATABASE,
schema=SCHEMA,
table_name=TABLE,
cluster=CLUSTER,
part_type=PART_TYPE,
part_name=NESTED_PART
)
self.start_key = f'{DATABASE}://{CLUSTER}.{SCHEMA}/{TABLE}/{PART_TYPE}/'
self.end_key = f'{DATABASE}://{CLUSTER}.{SCHEMA}/{TABLE}'
self.expected_node_result = GraphNode(
key=self.start_key,
label='Watermark',
attributes={
'partition_key': 'ds',
'partition_value': '2017-09-18/feature_id=9',
'create_time': '2017-09-18T00:00:00'
}
)
self.expected_serialized_node_results = [{
NODE_KEY: self.start_key,
NODE_LABEL: 'Watermark',
'partition_key': 'ds',
'partition_value': '2017-09-18/feature_id=9',
'create_time': '2017-09-18T00:00:00'
}]
self.expected_relation_result = GraphRelationship(
start_label='Watermark',
end_label='Table',
start_key=self.start_key,
end_key=self.end_key,
type='BELONG_TO_TABLE',
reverse_type='WATERMARK',
attributes={}
)
self.expected_serialized_relation_results = [{
RELATION_START_KEY: self.start_key,
RELATION_START_LABEL: 'Watermark',
RELATION_END_KEY: self.end_key,
RELATION_END_LABEL: 'Table',
RELATION_TYPE: 'BELONG_TO_TABLE',
RELATION_REVERSE_TYPE: 'WATERMARK'
}]
def test_get_watermark_model_key(self) -> None:
watermark = self.watermark.get_watermark_model_key()
self.assertEqual(watermark, f'{DATABASE}://{CLUSTER}.{SCHEMA}/{TABLE}/{PART_TYPE}/')
def test_get_metadata_model_key(self) -> None:
metadata = self.watermark.get_metadata_model_key()
self.assertEqual(metadata, f'{DATABASE}://{CLUSTER}.{SCHEMA}/{TABLE}')
def test_create_nodes(self) -> None:
actual = []
node = self.watermark.create_next_node()
while node:
serialized_node = neo4_serializer.serialize_node(node)
actual.append(serialized_node)
node = self.watermark.create_next_node()
self.assertEqual(actual, self.expected_serialized_node_results)
def test_create_nodes_neptune(self) -> None:
expected_serialized_node_results = [{
NEPTUNE_HEADER_ID: 'Watermark:' + self.start_key,
METADATA_KEY_PROPERTY_NAME: 'Watermark:' + self.start_key,
NEPTUNE_HEADER_LABEL: 'Watermark',
NEPTUNE_LAST_EXTRACTED_AT_RELATIONSHIP_PROPERTY_NAME_BULK_LOADER_FORMAT: ANY,
NEPTUNE_CREATION_TYPE_NODE_PROPERTY_NAME_BULK_LOADER_FORMAT: NEPTUNE_CREATION_TYPE_JOB,
'partition_key:String(single)': 'ds',
'partition_value:String(single)': '2017-09-18/feature_id=9',
'create_time:String(single)': '2017-09-18T00:00:00'
}]
actual = []
node = self.watermark.create_next_node()
while node:
serialized_node = neptune_serializer.convert_node(node)
actual.append(serialized_node)
node = self.watermark.create_next_node()
self.assertEqual(expected_serialized_node_results, actual)
def test_create_relation(self) -> None:
actual = []
relation = self.watermark.create_next_relation()
while relation:
serialized_relation = neo4_serializer.serialize_relationship(relation)
actual.append(serialized_relation)
relation = self.watermark.create_next_relation()
self.assertEqual(actual, self.expected_serialized_relation_results)
def test_create_relation_neptune(self) -> None:
actual = []
relation = self.watermark.create_next_relation()
while relation:
serialized_relation = neptune_serializer.convert_relationship(relation)
actual.append(serialized_relation)
relation = self.watermark.create_next_relation()
expected = [
[
{
NEPTUNE_HEADER_ID: "{label}:{from_vertex_id}_{to_vertex_id}".format(
from_vertex_id="Watermark:" + self.start_key,
to_vertex_id="Table:" + self.end_key,
label='BELONG_TO_TABLE'
),
METADATA_KEY_PROPERTY_NAME: "{label}:{from_vertex_id}_{to_vertex_id}".format(
from_vertex_id="Watermark:" + self.start_key,
to_vertex_id="Table:" + self.end_key,
label='BELONG_TO_TABLE'
),
NEPTUNE_RELATIONSHIP_HEADER_FROM: "Watermark:" + self.start_key,
NEPTUNE_RELATIONSHIP_HEADER_TO: "Table:" + self.end_key,
NEPTUNE_HEADER_LABEL: 'BELONG_TO_TABLE',
NEPTUNE_LAST_EXTRACTED_AT_RELATIONSHIP_PROPERTY_NAME_BULK_LOADER_FORMAT: ANY,
NEPTUNE_CREATION_TYPE_RELATIONSHIP_PROPERTY_NAME_BULK_LOADER_FORMAT: NEPTUNE_CREATION_TYPE_JOB
},
{
NEPTUNE_HEADER_ID: "{label}:{from_vertex_id}_{to_vertex_id}".format(
from_vertex_id="Table:" + self.end_key,
to_vertex_id="Watermark:" + self.start_key,
label='WATERMARK'
),
METADATA_KEY_PROPERTY_NAME: "{label}:{from_vertex_id}_{to_vertex_id}".format(
from_vertex_id="Table:" + self.end_key,
to_vertex_id="Watermark:" + self.start_key,
label='WATERMARK'
),
NEPTUNE_RELATIONSHIP_HEADER_FROM: "Table:" + self.end_key,
NEPTUNE_RELATIONSHIP_HEADER_TO: "Watermark:" + self.start_key,
NEPTUNE_HEADER_LABEL: 'WATERMARK',
NEPTUNE_LAST_EXTRACTED_AT_RELATIONSHIP_PROPERTY_NAME_BULK_LOADER_FORMAT: ANY,
NEPTUNE_CREATION_TYPE_RELATIONSHIP_PROPERTY_NAME_BULK_LOADER_FORMAT: NEPTUNE_CREATION_TYPE_JOB
}
]
]
self.assertListEqual(actual, expected)
def test_create_records(self) -> None:
expected = [{
'rk': self.start_key,
'partition_key': 'ds',
'partition_value': '2017-09-18/feature_id=9',
'create_time': '2017-09-18T00:00:00',
'table_rk': self.end_key
}]
actual = []
record = self.watermark.create_next_record()
while record:
serialized_record = mysql_serializer.serialize_record(record)
actual.append(serialized_record)
record = self.watermark.create_next_record()
self.assertEqual(actual, expected)
