def sync_to_db(cls, name, cluster):
"""Fetches metadata for that datasource and merges the Dashed db"""
print("Syncing Druid datasource [{}]".format(name))
session = get_session()
datasource = session.query(cls).filter_by(datasource_name=name).first()
if not datasource:
datasource = cls(datasource_name=name)
session.add(datasource)
flash("Adding new datasource [{}]".format(name), "success")
else:
flash("Refreshing datasource [{}]".format(name), "info")
datasource.cluster = cluster
cols = datasource.latest_metadata()
if not cols:
return
for col in cols:
col_obj = (
session
.query(DruidColumn)
.filter_by(datasource_name=name, column_name=col)
.first()
)
datatype = cols[col]['type']
if not col_obj:
col_obj = DruidColumn(datasource_name=name, column_name=col)
session.add(col_obj)
if datatype == "STRING":
col_obj.groupby = True
col_obj.filterable = True
if col_obj:
col_obj.type = cols[col]['type']
col_obj.datasource = datasource
col_obj.generate_metrics()
def sync_to_db(cls, name, cluster):
"""Fetches metadata for that datasource and merges the Dashed db"""
print("Syncing Druid datasource [{}]".format(name))
session = get_session()
datasource = session.query(cls).filter_by(datasource_name=name).first()
if not datasource:
datasource = cls(datasource_name=name)
session.add(datasource)
flash("Adding new datasource [{}]".format(name), "success")
else:
flash("Refreshing datasource [{}]".format(name), "info")
datasource.cluster = cluster
cols = datasource.latest_metadata()
if not cols:
return
for col in cols:
col_obj = (session.query(DruidColumn).filter_by(
datasource_name=name, column_name=col).first())
datatype = cols[col]['type']
if not col_obj:
col_obj = DruidColumn(datasource_name=name, column_name=col)
session.add(col_obj)
if datatype == "STRING":
col_obj.groupby = True
col_obj.filterable = True
if col_obj:
col_obj.type = cols[col]['type']
col_obj.datasource = datasource
col_obj.generate_metrics()
def generate_metrics(self):
"""Generate metrics based on the column metadata"""
M = DruidMetric # noqa
metrics = []
metrics.append(DruidMetric(
metric_name='count',
verbose_name='COUNT(*)',
metric_type='count',
json=json.dumps({'type': 'count', 'name': 'count'})
))
# Somehow we need to reassign this for UDAFs
if self.type in ('DOUBLE', 'FLOAT'):
corrected_type = 'DOUBLE'
else:
corrected_type = self.type
if self.sum and self.isnum:
mt = corrected_type.lower() + 'Sum'
name = 'sum__' + self.column_name
metrics.append(DruidMetric(
metric_name=name,
metric_type='sum',
verbose_name='SUM({})'.format(self.column_name),
json=json.dumps({
'type': mt, 'name': name, 'fieldName': self.column_name})
))
if self.min and self.isnum:
mt = corrected_type.lower() + 'Min'
name = 'min__' + self.column_name
metrics.append(DruidMetric(
metric_name=name,
metric_type='min',
verbose_name='MIN({})'.format(self.column_name),
json=json.dumps({
'type': mt, 'name': name, 'fieldName': self.column_name})
))
if self.max and self.isnum:
mt = corrected_type.lower() + 'Max'
name = 'max__' + self.column_name
metrics.append(DruidMetric(
metric_name=name,
metric_type='max',
verbose_name='MAX({})'.format(self.column_name),
json=json.dumps({
'type': mt, 'name': name, 'fieldName': self.column_name})
))
if self.count_distinct:
mt = 'count_distinct'
name = 'count_distinct__' + self.column_name
metrics.append(DruidMetric(
metric_name=name,
verbose_name='COUNT(DISTINCT {})'.format(self.column_name),
metric_type='count_distinct',
json=json.dumps({
'type': 'cardinality',
'name': name,
'fieldNames': [self.column_name]})
))
session = get_session()
for metric in metrics:
m = (
session.query(M)
.filter(M.metric_name == metric.metric_name)
.filter(M.datasource_name == self.datasource_name)
.filter(DruidCluster.cluster_name == self.datasource.cluster_name)
.first()
)
metric.datasource_name = self.datasource_name
if not m:
session.add(metric)
session.commit()
def generate_metrics(self):
"""Generate metrics based on the column metadata"""
M = DruidMetric
metrics = []
metrics.append(
DruidMetric(metric_name='count',
verbose_name='COUNT(*)',
metric_type='count',
json=json.dumps({
'type': 'count',
'name': 'count'
})))
# Somehow we need to reassign this for UDAFs
if self.type in ('DOUBLE', 'FLOAT'):
corrected_type = 'DOUBLE'
else:
corrected_type = self.type
if self.sum and self.isnum:
mt = corrected_type.lower() + 'Sum'
name = 'sum__' + self.column_name
metrics.append(
DruidMetric(metric_name=name,
metric_type='sum',
verbose_name='SUM({})'.format(self.column_name),
json=json.dumps({
'type': mt,
'name': name,
'fieldName': self.column_name
})))
if self.min and self.isnum:
mt = corrected_type.lower() + 'Min'
name = 'min__' + self.column_name
metrics.append(
DruidMetric(metric_name=name,
metric_type='min',
verbose_name='MIN({})'.format(self.column_name),
json=json.dumps({
'type': mt,
'name': name,
'fieldName': self.column_name
})))
if self.max and self.isnum:
mt = corrected_type.lower() + 'Max'
name = 'max__' + self.column_name
metrics.append(
DruidMetric(metric_name=name,
metric_type='max',
verbose_name='MAX({})'.format(self.column_name),
json=json.dumps({
'type': mt,
'name': name,
'fieldName': self.column_name
})))
if self.count_distinct:
mt = 'count_distinct'
name = 'count_distinct__' + self.column_name
metrics.append(
DruidMetric(metric_name=name,
verbose_name='COUNT(DISTINCT {})'.format(
self.column_name),
metric_type='count_distinct',
json=json.dumps({
'type': 'cardinality',
'name': name,
'fieldNames': [self.column_name]
})))
session = get_session()
for metric in metrics:
m = (session.query(M).filter(
M.metric_name == metric.metric_name).filter(
M.datasource_name == self.datasource_name).filter(
DruidCluster.cluster_name ==
self.datasource.cluster_name).first())
metric.datasource_name = self.datasource_name
if not m:
session.add(metric)
session.commit()