jeans/core
jeans/core
1
0
Fork 0
mirror of https://github.com/home-assistant/core.git synced 2025-07-24 05:37:44 +00:00
Code Issues Packages Projects Releases Wiki Activity

Cleanup some duplicate code in recorder statistics (#90549)

Browse Source 
* Cleanup some duplicate code in recorder statistics

* more cleanup

* reduce

* reduce
This commit is contained in:
J. Nick Koston 2023-04-01 15:40:14 -10:00 committed by GitHub
parent 5fc103947f
commit 84292d4797
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
2 changed files with 60 additions and 72 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

106
homeassistant/components/recorder/statistics.py
View File

@ -1034,18 +1034,19 @@ def _reduce_statistics_per_month(
def _generate_statistics_during_period_stmt(
columns: Select,
start_time: datetime,
end_time: datetime | None,
metadata_ids: list[int] | None,
table: type[StatisticsBase],
types: set[Literal["last_reset", "max", "mean", "min", "state", "sum"]],
) -> StatementLambdaElement:
"""Prepare a database query for statistics during a given period.
This prepares a lambda_stmt query, so we don't insert the parameters yet.
"""
start_time_ts = start_time.timestamp()
stmt = lambda_stmt(lambda: columns.filter(table.start_ts >= start_time_ts))
stmt = _generate_select_columns_for_types_stmt(table, types)
stmt += lambda q: q.filter(table.start_ts >= start_time_ts)
if end_time is not None:
end_time_ts = end_time.timestamp()
stmt += lambda q: q.filter(table.start_ts < end_time_ts)
@ -1491,6 +1492,33 @@ def statistic_during_period(
return {key: convert(value) if convert else value for key, value in result.items()}
_type_column_mapping = {
"last_reset": "last_reset_ts",
"max": "max",
"mean": "mean",
"min": "min",
"state": "state",
"sum": "sum",
}
def _generate_select_columns_for_types_stmt(
table: type[StatisticsBase],
types: set[Literal["last_reset", "max", "mean", "min", "state", "sum"]],
) -> StatementLambdaElement:
columns = select(table.metadata_id, table.start_ts)
track_on: list[str | None] = [
table.__tablename__, # type: ignore[attr-defined]
]
for key, column in _type_column_mapping.items():
if key in types:
columns = columns.add_columns(getattr(table, column))
track_on.append(column)
else:
track_on.append(None)
return lambda_stmt(lambda: columns, track_on=track_on)
def _statistics_during_period_with_session(
hass: HomeAssistant,
session: Session,
@ -1525,21 +1553,8 @@ def _statistics_during_period_with_session(
table: type[Statistics | StatisticsShortTerm] = (
Statistics if period != "5minute" else StatisticsShortTerm
)
columns = select(table.metadata_id, table.start_ts) # type: ignore[call-overload]
if "last_reset" in types:
columns = columns.add_columns(table.last_reset_ts)
if "max" in types:
columns = columns.add_columns(table.max)
if "mean" in types:
columns = columns.add_columns(table.mean)
if "min" in types:
columns = columns.add_columns(table.min)
if "state" in types:
columns = columns.add_columns(table.state)
if "sum" in types:
columns = columns.add_columns(table.sum)
stmt = _generate_statistics_during_period_stmt(
columns, start_time, end_time, metadata_ids, table
start_time, end_time, metadata_ids, table, types
)
stats = cast(Sequence[Row], execute_stmt_lambda_element(session, stmt))
@ -1771,34 +1786,34 @@ def get_latest_short_term_statistics(
def _generate_statistics_at_time_stmt(
columns: Select,
table: type[StatisticsBase],
metadata_ids: set[int],
start_time_ts: float,
types: set[Literal["last_reset", "max", "mean", "min", "state", "sum"]],
) -> StatementLambdaElement:
"""Create the statement for finding the statistics for a given time."""
return lambda_stmt(
lambda: columns.join(
(
most_recent_statistic_ids := (
select(
# https://github.com/sqlalchemy/sqlalchemy/issues/9189
# pylint: disable-next=not-callable
func.max(table.start_ts).label("max_start_ts"),
table.metadata_id.label("max_metadata_id"),
)
.filter(table.start_ts < start_time_ts)
.filter(table.metadata_id.in_(metadata_ids))
.group_by(table.metadata_id)
.subquery()
stmt = _generate_select_columns_for_types_stmt(table, types)
stmt += lambda q: q.join(
(
most_recent_statistic_ids := (
select(
# https://github.com/sqlalchemy/sqlalchemy/issues/9189
# pylint: disable-next=not-callable
func.max(table.start_ts).label("max_start_ts"),
table.metadata_id.label("max_metadata_id"),
)
),
and_(
table.start_ts == most_recent_statistic_ids.c.max_start_ts,
table.metadata_id == most_recent_statistic_ids.c.max_metadata_id,
),
)
.filter(table.start_ts < start_time_ts)
.filter(table.metadata_id.in_(metadata_ids))
.group_by(table.metadata_id)
.subquery()
)
),
and_(
table.start_ts == most_recent_statistic_ids.c.max_start_ts,
table.metadata_id == most_recent_statistic_ids.c.max_metadata_id,
),
)
return stmt
def _statistics_at_time(
@ -1809,23 +1824,8 @@ def _statistics_at_time(
types: set[Literal["last_reset", "max", "mean", "min", "state", "sum"]],
) -> Sequence[Row] | None:
"""Return last known statistics, earlier than start_time, for the metadata_ids."""
columns = select(table.metadata_id, table.start_ts)
if "last_reset" in types:
columns = columns.add_columns(table.last_reset_ts)
if "max" in types:
columns = columns.add_columns(table.max)
if "mean" in types:
columns = columns.add_columns(table.mean)
if "min" in types:
columns = columns.add_columns(table.min)
if "state" in types:
columns = columns.add_columns(table.state)
if "sum" in types:
columns = columns.add_columns(table.sum)
start_time_ts = start_time.timestamp()
stmt = _generate_statistics_at_time_stmt(
columns, table, metadata_ids, start_time_ts
)
stmt = _generate_statistics_at_time_stmt(table, metadata_ids, start_time_ts, types)
return cast(Sequence[Row], execute_stmt_lambda_element(session, stmt))

26
tests/components/recorder/test_statistics.py
View File

@ -1244,28 +1244,21 @@ def test_monthly_statistics(
def test_cache_key_for_generate_statistics_during_period_stmt() -> None:
"""Test cache key for _generate_statistics_during_period_stmt."""
columns = select(StatisticsShortTerm.metadata_id, StatisticsShortTerm.start_ts)
stmt = _generate_statistics_during_period_stmt(
columns, dt_util.utcnow(), dt_util.utcnow(), [0], StatisticsShortTerm
dt_util.utcnow(), dt_util.utcnow(), [0], StatisticsShortTerm, set()
)
cache_key_1 = stmt._generate_cache_key()
stmt2 = _generate_statistics_during_period_stmt(
columns, dt_util.utcnow(), dt_util.utcnow(), [0], StatisticsShortTerm
dt_util.utcnow(), dt_util.utcnow(), [0], StatisticsShortTerm, set()
)
cache_key_2 = stmt2._generate_cache_key()
assert cache_key_1 == cache_key_2
columns2 = select(
StatisticsShortTerm.metadata_id,
StatisticsShortTerm.start_ts,
StatisticsShortTerm.sum,
StatisticsShortTerm.mean,
)
stmt3 = _generate_statistics_during_period_stmt(
columns2,
dt_util.utcnow(),
dt_util.utcnow(),
[0],
StatisticsShortTerm,
{"sum", "mean"},
)
cache_key_3 = stmt3._generate_cache_key()
assert cache_key_1 != cache_key_3
@ -1321,18 +1314,13 @@ def test_cache_key_for_generate_max_mean_min_statistic_in_sub_period_stmt() -> N
def test_cache_key_for_generate_statistics_at_time_stmt() -> None:
"""Test cache key for _generate_statistics_at_time_stmt."""
columns = select(StatisticsShortTerm.metadata_id, StatisticsShortTerm.start_ts)
stmt = _generate_statistics_at_time_stmt(columns, StatisticsShortTerm, {0}, 0.0)
stmt = _generate_statistics_at_time_stmt(StatisticsShortTerm, {0}, 0.0, set())
cache_key_1 = stmt._generate_cache_key()
stmt2 = _generate_statistics_at_time_stmt(columns, StatisticsShortTerm, {0}, 0.0)
stmt2 = _generate_statistics_at_time_stmt(StatisticsShortTerm, {0}, 0.0, set())
cache_key_2 = stmt2._generate_cache_key()
assert cache_key_1 == cache_key_2
columns2 = select(
StatisticsShortTerm.metadata_id,
StatisticsShortTerm.start_ts,
StatisticsShortTerm.sum,
StatisticsShortTerm.mean,
stmt3 = _generate_statistics_at_time_stmt(
StatisticsShortTerm, {0}, 0.0, {"sum", "mean"}
)
stmt3 = _generate_statistics_at_time_stmt(columns2, StatisticsShortTerm, {0}, 0.0)
cache_key_3 = stmt3._generate_cache_key()
assert cache_key_1 != cache_key_3