def session_length_stats(self):
session_lens = Action.query.join(Action, Session.actions)\
.filter(Session.analysis_id == self.id)\
.with_entities(Session.id,
func.count(Action.id).label('length'))\
.group_by(Session.id)\
.subquery()
return db.session.query(
func.avg(session_lens.c.length).label('avg'),
func.stddev_pop(session_lens.c.length).label('std')).first()
def get_slow_pages(path=None):
l = label("average", func.avg(ViewLog.load_time))
c = label("count", func.count(ViewLog.id))
return (
DBSession.query(
ViewLog.path,
l,
c,
label("cumulative_time", func.sum(ViewLog.load_time)),
label("stddev", func.stddev_pop(ViewLog.load_time)),
label("maximum", func.max(ViewLog.load_time)),
label("minimum", func.min(ViewLog.load_time)),
)
.filter(ViewLog.path == path if path != None else True)
.having(c > 2)
.group_by(ViewLog.path)
.order_by(l.desc())
)
from ceilometer.storage import models as api_models
from ceilometer.storage.sqlalchemy import models
from ceilometer.storage.sqlalchemy import utils as sql_utils
from ceilometer import utils
LOG = log.getLogger(__name__)
STANDARD_AGGREGATES = dict(avg=func.avg(models.Sample.volume).label('avg'),
sum=func.sum(models.Sample.volume).label('sum'),
min=func.min(models.Sample.volume).label('min'),
max=func.max(models.Sample.volume).label('max'),
count=func.count(
models.Sample.volume).label('count'))
UNPARAMETERIZED_AGGREGATES = dict(
stddev=func.stddev_pop(models.Sample.volume).label('stddev'))
PARAMETERIZED_AGGREGATES = dict(
validate=dict(
cardinality=lambda p: p in ['resource_id', 'user_id', 'project_id']),
compute=dict(cardinality=lambda p: func.count(
distinct(getattr(models.Resource, p))).label('cardinality/%s' % p)))
AVAILABLE_CAPABILITIES = {
'meters': {
'query': {
'simple': True,
'metadata': True
}
},
'resources': {
from ceilometer.storage.sqlalchemy import utils as sql_utils
from ceilometer import utils
LOG = log.getLogger(__name__)
STANDARD_AGGREGATES = dict(
avg=func.avg(models.Sample.volume).label('avg'),
sum=func.sum(models.Sample.volume).label('sum'),
min=func.min(models.Sample.volume).label('min'),
max=func.max(models.Sample.volume).label('max'),
count=func.count(models.Sample.volume).label('count')
)
UNPARAMETERIZED_AGGREGATES = dict(
stddev=func.stddev_pop(models.Sample.volume).label('stddev')
)
PARAMETERIZED_AGGREGATES = dict(
validate=dict(
cardinality=lambda p: p in ['resource_id', 'user_id', 'project_id']
),
compute=dict(
cardinality=lambda p: func.count(
distinct(getattr(models.Resource, p))
).label('cardinality/%s' % p)
)
)
AVAILABLE_CAPABILITIES = {
'meters': {'query': {'simple': True,
gene_expression_data = ome.query(
Gene.id.label('gene_id'),
Gene.name.label('gene_name'),
Gene.locus_id.label('locus_id'),
Strain.id.label('strain_id'),
Strain.name.label('strain'),
InVivoEnvironment.id.label('environment_id'),
InVivoEnvironment.carbon_source.label('carbon_source'),
InVivoEnvironment.nitrogen_source.label('nitrogen_source'),
InVivoEnvironment.electron_acceptor.label('electron_acceptor'),
DataSet.type.label('dataset_type'),
DataSet.group_name.label('group_name'),
func.max(DataSet.id).label('max_dataset_id'),
func.avg(GenomeData.value).label('value'),
func.stddev_pop(GenomeData.value).label('stddev')).\
join(GenomeData, DataSet, Strain, InVivoEnvironment).\
group_by(Gene.id, DataSet.group_name, Strain.id, DataSet.type, InVivoEnvironment.id,
Gene.locus_id, Gene.name, Strain.name, InVivoEnvironment.carbon_source,
InVivoEnvironment.nitrogen_source,
InVivoEnvironment.electron_acceptor).order_by(DataSet.type, InVivoEnvironment.id).subquery()
class GeneExpressionData(Base):
__table__ = gene_expression_data
__mapper_args__ = {
'primary_key':[gene_expression_data.c.gene_id, gene_expression_data.c.max_dataset_id]
}
gene_expression_data = ome.query(
Gene.id.label('gene_id'),
Gene.name.label('gene_name'),
Gene.locus_id.label('locus_id'),
Strain.id.label('strain_id'),
Strain.name.label('strain'),
InVivoEnvironment.id.label('environment_id'),
InVivoEnvironment.carbon_source.label('carbon_source'),
InVivoEnvironment.nitrogen_source.label('nitrogen_source'),
InVivoEnvironment.electron_acceptor.label('electron_acceptor'),
Dataset.type.label('dataset_type'),
Dataset.group_name.label('group_name'),
func.max(Dataset.id).label('max_dataset_id'),
func.avg(GenomeData.value).label('value'),
func.stddev_pop(GenomeData.value).label('stddev')).\
join(GenomeData, Dataset, Strain, InVivoEnvironment).\
group_by(Gene.id, Dataset.group_name, Strain.id, Dataset.type, InVivoEnvironment.id,
Gene.locus_id, Gene.name, Strain.name, InVivoEnvironment.carbon_source,
InVivoEnvironment.nitrogen_source,
InVivoEnvironment.electron_acceptor).order_by(Dataset.type, InVivoEnvironment.id).subquery()
class GeneExpressionData(Base):
__table__ = gene_expression_data
__mapper_args__ = {
'primary_key': [
gene_expression_data.c.gene_id,
gene_expression_data.c.max_dataset_id
]
from ceilometer.storage.sqlalchemy import models
from ceilometer.storage.sqlalchemy import utils as sql_utils
from ceilometer import utils
LOG = log.getLogger(__name__)
STANDARD_AGGREGATES = dict(
avg=func.avg(models.Sample.volume).label("avg"),
sum=func.sum(models.Sample.volume).label("sum"),
min=func.min(models.Sample.volume).label("min"),
max=func.max(models.Sample.volume).label("max"),
count=func.count(models.Sample.volume).label("count"),
)
UNPARAMETERIZED_AGGREGATES = dict(stddev=func.stddev_pop(models.Sample.volume).label("stddev"))
PARAMETERIZED_AGGREGATES = dict(
validate=dict(cardinality=lambda p: p in ["resource_id", "user_id", "project_id"]),
compute=dict(cardinality=lambda p: func.count(distinct(getattr(models.Resource, p))).label("cardinality/%s" % p)),
)
AVAILABLE_CAPABILITIES = {
"meters": {"query": {"simple": True, "metadata": True}},
"resources": {"query": {"simple": True, "metadata": True}},
"samples": {"query": {"simple": True, "metadata": True, "complex": True}},
"statistics": {
"groupby": True,
"query": {"simple": True, "metadata": True},
"aggregation": {
"standard": True,