"""Statistics helper."""
from __future__ import annotations
from collections import defaultdict
from collections.abc import Callable, Iterable
import contextlib
import dataclasses
from datetime import datetime, timedelta
from itertools import chain, groupby
import json
import logging
import os
import re
from statistics import mean
from typing import TYPE_CHECKING, Any, Literal
from sqlalchemy import bindparam, func
from sqlalchemy.exc import SQLAlchemyError, StatementError
from sqlalchemy.ext import baked
from sqlalchemy.orm.scoping import scoped_session
from sqlalchemy.sql.expression import literal_column, true
from homeassistant.const import (
PRESSURE_PA,
TEMP_CELSIUS,
VOLUME_CUBIC_FEET,
VOLUME_CUBIC_METERS,
)
from homeassistant.core import Event, HomeAssistant, callback
from homeassistant.exceptions import HomeAssistantError
from homeassistant.helpers import entity_registry
from homeassistant.helpers.json import JSONEncoder
from homeassistant.helpers.storage import STORAGE_DIR
import homeassistant.util.dt as dt_util
import homeassistant.util.pressure as pressure_util
import homeassistant.util.temperature as temperature_util
from homeassistant.util.unit_system import UnitSystem
import homeassistant.util.volume as volume_util
from .const import DATA_INSTANCE, DOMAIN, MAX_ROWS_TO_PURGE
from .models import (
StatisticData,
StatisticMetaData,
StatisticResult,
Statistics,
StatisticsMeta,
StatisticsRuns,
StatisticsShortTerm,
process_timestamp,
process_timestamp_to_utc_isoformat,
)
from .util import execute, retryable_database_job, session_scope
if TYPE_CHECKING:
from . import Recorder
QUERY_STATISTICS = [
Statistics.metadata_id,
Statistics.start,
Statistics.mean,
Statistics.min,
Statistics.max,
Statistics.last_reset,
Statistics.state,
Statistics.sum,
]
QUERY_STATISTICS_SHORT_TERM = [
StatisticsShortTerm.metadata_id,
StatisticsShortTerm.start,
StatisticsShortTerm.mean,
StatisticsShortTerm.min,
StatisticsShortTerm.max,
StatisticsShortTerm.last_reset,
StatisticsShortTerm.state,
StatisticsShortTerm.sum,
]
QUERY_STATISTICS_SUMMARY_MEAN = [
StatisticsShortTerm.metadata_id,
func.avg(StatisticsShortTerm.mean),
func.min(StatisticsShortTerm.min),
func.max(StatisticsShortTerm.max),
]
QUERY_STATISTICS_SUMMARY_SUM = [
StatisticsShortTerm.metadata_id,
StatisticsShortTerm.start,
StatisticsShortTerm.last_reset,
StatisticsShortTerm.state,
StatisticsShortTerm.sum,
func.row_number()
.over(
partition_by=StatisticsShortTerm.metadata_id,
order_by=StatisticsShortTerm.start.desc(),
)
.label("rownum"),
]
QUERY_STATISTICS_SUMMARY_SUM_LEGACY = [
StatisticsShortTerm.metadata_id,
StatisticsShortTerm.last_reset,
StatisticsShortTerm.state,
StatisticsShortTerm.sum,
]
QUERY_STATISTIC_META = [
StatisticsMeta.id,
StatisticsMeta.statistic_id,
StatisticsMeta.source,
StatisticsMeta.unit_of_measurement,
StatisticsMeta.has_mean,
StatisticsMeta.has_sum,
StatisticsMeta.name,
]
QUERY_STATISTIC_META_ID = [
StatisticsMeta.id,
StatisticsMeta.statistic_id,
]
MAX_DUPLICATES = 1000000
STATISTICS_BAKERY = "recorder_statistics_bakery"
STATISTICS_META_BAKERY = "recorder_statistics_meta_bakery"
STATISTICS_SHORT_TERM_BAKERY = "recorder_statistics_short_term_bakery"
# Convert pressure and temperature statistics from the native unit used for statistics
# to the units configured by the user
UNIT_CONVERSIONS = {
PRESSURE_PA: lambda x, units: pressure_util.convert(
x, PRESSURE_PA, units.pressure_unit
)
if x is not None
else None,
TEMP_CELSIUS: lambda x, units: temperature_util.convert(
x, TEMP_CELSIUS, units.temperature_unit
)
if x is not None
else None,
VOLUME_CUBIC_METERS: lambda x, units: volume_util.convert(
x, VOLUME_CUBIC_METERS, _configured_unit(VOLUME_CUBIC_METERS, units)
)
if x is not None
else None,
}
_LOGGER = logging.getLogger(__name__)
def split_statistic_id(entity_id: str) -> list[str]:
"""Split a state entity ID into domain and object ID."""
return entity_id.split(":", 1)
VALID_STATISTIC_ID = re.compile(r"^(?!.+__)(?!_)[\da-z_]+(?<!_):(?!_)[\da-z_]+(?<!_)$")
def valid_statistic_id(statistic_id: str) -> bool:
"""Test if a statistic ID is a valid format.
Format: <domain>:<statistic> where both are slugs.
"""
return VALID_STATISTIC_ID.match(statistic_id) is not None
@dataclasses.dataclass
class ValidationIssue:
"""Error or warning message."""
type: str
data: dict[str, str | None] | None = None
def as_dict(self) -> dict:
"""Return dictionary version."""
return dataclasses.asdict(self)
def async_setup(hass: HomeAssistant) -> None:
"""Set up the history hooks."""
hass.data[STATISTICS_BAKERY] = baked.bakery()
hass.data[STATISTICS_META_BAKERY] = baked.bakery()
hass.data[STATISTICS_SHORT_TERM_BAKERY] = baked.bakery()
def entity_id_changed(event: Event) -> None:
"""Handle entity_id changed."""
old_entity_id = event.data["old_entity_id"]
entity_id = event.data["entity_id"]
with session_scope(hass=hass) as session:
session.query(StatisticsMeta).filter(
(StatisticsMeta.statistic_id == old_entity_id)
& (StatisticsMeta.source == DOMAIN)
).update({StatisticsMeta.statistic_id: entity_id})
@callback
def entity_registry_changed_filter(event: Event) -> bool:
"""Handle entity_id changed filter."""
if event.data["action"] != "update" or "old_entity_id" not in event.data:
return False
return True
if hass.is_running:
hass.bus.async_listen(
entity_registry.EVENT_ENTITY_REGISTRY_UPDATED,
entity_id_changed,
event_filter=entity_registry_changed_filter,
)
def get_start_time() -> datetime:
"""Return start time."""
now = dt_util.utcnow()
current_period_minutes = now.minute - now.minute % 5
current_period = now.replace(minute=current_period_minutes, second=0, microsecond=0)
last_period = current_period - timedelta(minutes=5)
return last_period
def _update_or_add_metadata(
hass: HomeAssistant,
session: scoped_session,
new_metadata: StatisticMetaData,
) -> int:
"""Get metadata_id for a statistic_id.
If the statistic_id is previously unknown, add it. If it's already known, update
metadata if needed.
Updating metadata source is not possible.
"""
statistic_id = new_metadata["statistic_id"]
old_metadata_dict = get_metadata_with_session(
hass, session, statistic_ids=[statistic_id]
)
if not old_metadata_dict:
meta = StatisticsMeta.from_meta(new_metadata)
session.add(meta)
session.flush() # Flush to get the metadata id assigned
_LOGGER.debug(
"Added new statistics metadata for %s, new_metadata: %s",
statistic_id,
new_metadata,
)
return meta.id # type: ignore[no-any-return]
metadata_id, old_metadata = old_metadata_dict[statistic_id]
if (
old_metadata["has_mean"] != new_metadata["has_mean"]
or old_metadata["has_sum"] != new_metadata["has_sum"]
or old_metadata["unit_of_measurement"] != new_metadata["unit_of_measurement"]
):
session.query(StatisticsMeta).filter_by(statistic_id=statistic_id).update(
{
StatisticsMeta.has_mean: new_metadata["has_mean"],
StatisticsMeta.has_sum: new_metadata["has_sum"],
StatisticsMeta.unit_of_measurement: new_metadata["unit_of_measurement"],
},
synchronize_session=False,
)
_LOGGER.debug(
"Updated statistics metadata for %s, old_metadata: %s, new_metadata: %s",
statistic_id,
old_metadata,
new_metadata,
)
return metadata_id
def _find_duplicates(
session: scoped_session, table: type[Statistics | StatisticsShortTerm]
) -> tuple[list[int], list[dict]]:
"""Find duplicated statistics."""
subquery = (
session.query(
table.start,
table.metadata_id,
literal_column("1").label("is_duplicate"),
)
.group_by(table.metadata_id, table.start)
.having(func.count() > 1)
.subquery()
)
query = (
session.query(table)
.outerjoin(
subquery,
(subquery.c.metadata_id == table.metadata_id)
& (subquery.c.start == table.start),
)
.filter(subquery.c.is_duplicate == 1)
.order_by(table.metadata_id, table.start, table.id.desc())
.limit(MAX_ROWS_TO_PURGE)
)
duplicates = execute(query)
original_as_dict = {}
start = None
metadata_id = None
duplicate_ids: list[int] = []
non_identical_duplicates_as_dict: list[dict] = []
if not duplicates:
return (duplicate_ids, non_identical_duplicates_as_dict)
def columns_to_dict(duplicate: type[Statistics | StatisticsShortTerm]) -> dict:
"""Convert a SQLAlchemy row to dict."""
dict_ = {}
for key in duplicate.__mapper__.c.keys():
dict_[key] = getattr(duplicate, key)
return dict_
def compare_statistic_rows(row1: dict, row2: dict) -> bool:
"""Compare two statistics rows, ignoring id and created."""
ignore_keys = ["id", "created"]
keys1 = set(row1).difference(ignore_keys)
keys2 = set(row2).difference(ignore_keys)
return keys1 == keys2 and all(row1[k] == row2[k] for k in keys1)
for duplicate in duplicates:
if start != duplicate.start or metadata_id != duplicate.metadata_id:
original_as_dict = columns_to_dict(duplicate)
start = duplicate.start
metadata_id = duplicate.metadata_id
continue
duplicate_as_dict = columns_to_dict(duplicate)
duplicate_ids.append(duplicate.id)
if not compare_statistic_rows(original_as_dict, duplicate_as_dict):
non_identical_duplicates_as_dict.append(
{"duplicate": duplicate_as_dict, "original": original_as_dict}
)
return (duplicate_ids, non_identical_duplicates_as_dict)
def _delete_duplicates_from_table(
session: scoped_session, table: type[Statistics | StatisticsShortTerm]
) -> tuple[int, list[dict]]:
"""Identify and delete duplicated statistics from a specified table."""
all_non_identical_duplicates: list[dict] = []
total_deleted_rows = 0
while True:
duplicate_ids, non_identical_duplicates = _find_duplicates(session, table)
if not duplicate_ids:
break
all_non_identical_duplicates.extend(non_identical_duplicates)
deleted_rows = (
session.query(table)
.filter(table.id.in_(duplicate_ids))
.delete(synchronize_session=False)
)
total_deleted_rows += deleted_rows
if total_deleted_rows >= MAX_DUPLICATES:
break
return (total_deleted_rows, all_non_identical_duplicates)
def delete_duplicates(instance: Recorder, session: scoped_session) -> None:
"""Identify and delete duplicated statistics.
A backup will be made of duplicated statistics before it is deleted.
"""
deleted_statistics_rows, non_identical_duplicates = _delete_duplicates_from_table(
session, Statistics
)
if deleted_statistics_rows:
_LOGGER.info("Deleted %s duplicated statistics rows", deleted_statistics_rows)
if non_identical_duplicates:
isotime = dt_util.utcnow().isoformat()
backup_file_name = f"deleted_statistics.{isotime}.json"
backup_path = instance.hass.config.path(STORAGE_DIR, backup_file_name)
os.makedirs(os.path.dirname(backup_path), exist_ok=True)
with open(backup_path, "w", encoding="utf8") as backup_file:
json.dump(
non_identical_duplicates,
backup_file,
indent=4,
sort_keys=True,
cls=JSONEncoder,
)
_LOGGER.warning(
"Deleted %s non identical duplicated %s rows, a backup of the deleted rows "
"has been saved to %s",
len(non_identical_duplicates),
Statistics.__tablename__,
backup_path,
)
if deleted_statistics_rows >= MAX_DUPLICATES:
_LOGGER.warning(
"Found more than %s duplicated statistic rows, please report at "
'https://github.com/home-assistant/core/issues?q=is%%3Aissue+label%%3A"integration%%3A+recorder"+',
MAX_DUPLICATES - 1,
)
deleted_short_term_statistics_rows, _ = _delete_duplicates_from_table(
session, StatisticsShortTerm
)
if deleted_short_term_statistics_rows:
_LOGGER.warning(
"Deleted duplicated short term statistic rows, please report at "
'https://github.com/home-assistant/core/issues?q=is%%3Aissue+label%%3A"integration%%3A+recorder"+'
)
def compile_hourly_statistics(
instance: Recorder, session: scoped_session, start: datetime
) -> None:
"""Compile hourly statistics.
This will summarize 5-minute statistics for one hour:
- average, min max is computed by a database query
- sum is taken from the last 5-minute entry during the hour
"""
start_time = start.replace(minute=0)
end_time = start_time + timedelta(hours=1)
# Compute last hour's average, min, max
summary: dict[str, StatisticData] = {}
baked_query = instance.hass.data[STATISTICS_SHORT_TERM_BAKERY](
lambda session: session.query(*QUERY_STATISTICS_SUMMARY_MEAN)
)
baked_query += lambda q: q.filter(
StatisticsShortTerm.start >= bindparam("start_time")
)
baked_query += lambda q: q.filter(StatisticsShortTerm.start < bindparam("end_time"))
baked_query += lambda q: q.group_by(StatisticsShortTerm.metadata_id)
baked_query += lambda q: q.order_by(StatisticsShortTerm.metadata_id)
stats = execute(
baked_query(session).params(start_time=start_time, end_time=end_time)
)
if stats:
for stat in stats:
metadata_id, _mean, _min, _max = stat
summary[metadata_id] = {
"start": start_time,
"mean": _mean,
"min": _min,
"max": _max,
}
# Get last hour's last sum
if instance._db_supports_row_number: # pylint: disable=[protected-access]
subquery = (
session.query(*QUERY_STATISTICS_SUMMARY_SUM)
.filter(StatisticsShortTerm.start >= bindparam("start_time"))
.filter(StatisticsShortTerm.start < bindparam("end_time"))
.subquery()
)
query = (
session.query(subquery)
.filter(subquery.c.rownum == 1)
.order_by(subquery.c.metadata_id)
)
stats = execute(query.params(start_time=start_time, end_time=end_time))
if stats:
for stat in stats:
metadata_id, start, last_reset, state, _sum, _ = stat
if metadata_id in summary:
summary[metadata_id].update(
{
"last_reset": process_timestamp(last_reset),
"state": state,
"sum": _sum,
}
)
else:
summary[metadata_id] = {
"start": start_time,
"last_reset": process_timestamp(last_reset),
"state": state,
"sum": _sum,
}
else:
baked_query = instance.hass.data[STATISTICS_SHORT_TERM_BAKERY](
lambda session: session.query(*QUERY_STATISTICS_SUMMARY_SUM_LEGACY)
)
baked_query += lambda q: q.filter(
StatisticsShortTerm.start >= bindparam("start_time")
)
baked_query += lambda q: q.filter(
StatisticsShortTerm.start < bindparam("end_time")
)
baked_query += lambda q: q.order_by(
StatisticsShortTerm.metadata_id, StatisticsShortTerm.start.desc()
)
stats = execute(
baked_query(session).params(start_time=start_time, end_time=end_time)
)
if stats:
for metadata_id, group in groupby(stats, lambda stat: stat["metadata_id"]): # type: ignore
(
metadata_id,
last_reset,
state,
_sum,
) = next(group)
if metadata_id in summary:
summary[metadata_id].update(
{
"start": start_time,
"last_reset": process_timestamp(last_reset),
"state": state,
"sum": _sum,
}
)
else:
summary[metadata_id] = {
"start": start_time,
"last_reset": process_timestamp(last_reset),
"state": state,
"sum": _sum,
}
# Insert compiled hourly statistics in the database
for metadata_id, stat in summary.items():
session.add(Statistics.from_stats(metadata_id, stat))
@retryable_database_job("statistics")
def compile_statistics(instance: Recorder, start: datetime) -> bool:
"""Compile 5-minute statistics for all integrations with a recorder platform.
The actual calculation is delegated to the platforms.
"""
start = dt_util.as_utc(start)
end = start + timedelta(minutes=5)
# Return if we already have 5-minute statistics for the requested period
with session_scope(session=instance.get_session()) as session: # type: ignore
if session.query(StatisticsRuns).filter_by(start=start).first():
_LOGGER.debug("Statistics already compiled for %s-%s", start, end)
return True
_LOGGER.debug("Compiling statistics for %s-%s", start, end)
platform_stats: list[StatisticResult] = []
# Collect statistics from all platforms implementing support
for domain, platform in instance.hass.data[DOMAIN].items():
if not hasattr(platform, "compile_statistics"):
continue
platform_stat = platform.compile_statistics(instance.hass, start, end)
_LOGGER.debug(
"Statistics for %s during %s-%s: %s", domain, start, end, platform_stat
)
platform_stats.extend(platform_stat)
# Insert collected statistics in the database
with session_scope(
session=instance.get_session(), # type: ignore
exception_filter=_filter_unique_constraint_integrity_error(instance),
) as session:
for stats in platform_stats:
metadata_id = _update_or_add_metadata(instance.hass, session, stats["meta"])
_insert_statistics(
session,
StatisticsShortTerm,
metadata_id,
stats["stat"],
)
if start.minute == 55:
# A full hour is ready, summarize it
compile_hourly_statistics(instance, session, start)
session.add(StatisticsRuns(start=start))
return True
def _insert_statistics(
session: scoped_session,
table: type[Statistics | StatisticsShortTerm],
metadata_id: int,
statistic: StatisticData,
) -> None:
"""Insert statistics in the database."""
try:
session.add(table.from_stats(metadata_id, statistic))
except SQLAlchemyError:
_LOGGER.exception(
"Unexpected exception when inserting statistics %s:%s ",
metadata_id,
statistic,
)
def _update_statistics(
session: scoped_session,
table: type[Statistics | StatisticsShortTerm],
stat_id: int,
statistic: StatisticData,
) -> None:
"""Insert statistics in the database."""
try:
session.query(table).filter_by(id=stat_id).update(
{
table.mean: statistic.get("mean"),
table.min: statistic.get("min"),
table.max: statistic.get("max"),
table.last_reset: statistic.get("last_reset"),
table.state: statistic.get("state"),
table.sum: statistic.get("sum"),
},
synchronize_session=False,
)
except SQLAlchemyError:
_LOGGER.exception(
"Unexpected exception when updating statistics %s:%s ",
id,
statistic,
)
def get_metadata_with_session(
hass: HomeAssistant,
session: scoped_session,
*,
statistic_ids: list[str] | tuple[str] | None = None,
statistic_type: Literal["mean"] | Literal["sum"] | None = None,
statistic_source: str | None = None,
) -> dict[str, tuple[int, StatisticMetaData]]:
"""Fetch meta data.
Returns a dict of (metadata_id, StatisticMetaData) tuples indexed by statistic_id.
If statistic_ids is given, fetch metadata only for the listed statistics_ids.
If statistic_type is given, fetch metadata only for statistic_ids supporting it.
"""
# Fetch metatadata from the database
baked_query = hass.data[STATISTICS_META_BAKERY](
lambda session: session.query(*QUERY_STATISTIC_META)
)
if statistic_ids is not None:
baked_query += lambda q: q.filter(
StatisticsMeta.statistic_id.in_(bindparam("statistic_ids"))
)
if statistic_source is not None:
baked_query += lambda q: q.filter(
StatisticsMeta.source == bindparam("statistic_source")
)
if statistic_type == "mean":
baked_query += lambda q: q.filter(StatisticsMeta.has_mean == true())
elif statistic_type == "sum":
baked_query += lambda q: q.filter(StatisticsMeta.has_sum == true())
result = execute(
baked_query(session).params(
statistic_ids=statistic_ids, statistic_source=statistic_source
)
)
if not result:
return {}
return {
meta["statistic_id"]: (
meta["id"],
{
"source": meta["source"],
"statistic_id": meta["statistic_id"],
"unit_of_measurement": meta["unit_of_measurement"],
"has_mean": meta["has_mean"],
"has_sum": meta["has_sum"],
"name": meta["name"],
},
)
for meta in result
}
def get_metadata(
hass: HomeAssistant,
*,
statistic_ids: list[str] | tuple[str] | None = None,
statistic_type: Literal["mean"] | Literal["sum"] | None = None,
statistic_source: str | None = None,
) -> dict[str, tuple[int, StatisticMetaData]]:
"""Return metadata for statistic_ids."""
with session_scope(hass=hass) as session:
return get_metadata_with_session(
hass,
session,
statistic_ids=statistic_ids,
statistic_type=statistic_type,
statistic_source=statistic_source,
)
def _configured_unit(unit: str, units: UnitSystem) -> str:
"""Return the pressure and temperature units configured by the user."""
if unit == PRESSURE_PA:
return units.pressure_unit
if unit == TEMP_CELSIUS:
return units.temperature_unit
if unit == VOLUME_CUBIC_METERS:
if units.is_metric:
return VOLUME_CUBIC_METERS
return VOLUME_CUBIC_FEET
return unit
def clear_statistics(instance: Recorder, statistic_ids: list[str]) -> None:
"""Clear statistics for a list of statistic_ids."""
with session_scope(session=instance.get_session()) as session: # type: ignore
session.query(StatisticsMeta).filter(
StatisticsMeta.statistic_id.in_(statistic_ids)
).delete(synchronize_session=False)
def update_statistics_metadata(
instance: Recorder, statistic_id: str, unit_of_measurement: str | None
) -> None:
"""Update statistics metadata for a statistic_id."""
with session_scope(session=instance.get_session()) as session: # type: ignore
session.query(StatisticsMeta).filter(
StatisticsMeta.statistic_id == statistic_id
).update({StatisticsMeta.unit_of_measurement: unit_of_measurement})
def list_statistic_ids(
hass: HomeAssistant,
statistic_type: Literal["mean"] | Literal["sum"] | None = None,
) -> list[dict | None]:
"""Return all statistic_ids and unit of measurement.
Queries the database for existing statistic_ids, as well as integrations with
a recorder platform for statistic_ids which will be added in the next statistics
period.
"""
units = hass.config.units
statistic_ids = {}
# Query the database
with session_scope(hass=hass) as session:
metadata = get_metadata_with_session(
hass, session, statistic_type=statistic_type
)
for _, meta in metadata.values():
if (unit := meta["unit_of_measurement"]) is not None:
# Display unit according to user settings
unit = _configured_unit(unit, units)
meta["unit_of_measurement"] = unit
statistic_ids = {
meta["statistic_id"]: {
"name": meta["name"],
"source": meta["source"],
"unit_of_measurement": meta["unit_of_measurement"],
}
for _, meta in metadata.values()
}
# Query all integrations with a registered recorder platform
for platform in hass.data[DOMAIN].values():
if not hasattr(platform, "list_statistic_ids"):
continue
platform_statistic_ids = platform.list_statistic_ids(hass, statistic_type)
for statistic_id, info in platform_statistic_ids.items():
if (unit := info["unit_of_measurement"]) is not None:
# Display unit according to user settings
unit = _configured_unit(unit, units)
platform_statistic_ids[statistic_id]["unit_of_measurement"] = unit
for key, value in platform_statistic_ids.items():
statistic_ids.setdefault(key, value)
# Return a list of statistic_id + metadata
return [
{
"statistic_id": _id,
"name": info.get("name"),
"source": info["source"],
"unit_of_measurement": info["unit_of_measurement"],
}
for _id, info in statistic_ids.items()
]
def _statistics_during_period_query(
hass: HomeAssistant,
end_time: datetime | None,
statistic_ids: list[str] | None,
bakery: Any,
base_query: Iterable,
table: type[Statistics | StatisticsShortTerm],
) -> Callable:
"""Prepare a database query for statistics during a given period.
This prepares a baked query, so we don't insert the parameters yet.
"""
baked_query = hass.data[bakery](lambda session: session.query(*base_query))
baked_query += lambda q: q.filter(table.start >= bindparam("start_time"))
if end_time is not None:
baked_query += lambda q: q.filter(table.start < bindparam("end_time"))
if statistic_ids is not None:
baked_query += lambda q: q.filter(
table.metadata_id.in_(bindparam("metadata_ids"))
)
baked_query += lambda q: q.order_by(table.metadata_id, table.start)
return baked_query # type: ignore[no-any-return]
def _reduce_statistics(
stats: dict[str, list[dict[str, Any]]],
same_period: Callable[[datetime, datetime], bool],
period_start_end: Callable[[datetime], tuple[datetime, datetime]],
period: timedelta,
) -> dict[str, list[dict[str, Any]]]:
"""Reduce hourly statistics to daily or monthly statistics."""
result: dict[str, list[dict[str, Any]]] = defaultdict(list)
for statistic_id, stat_list in stats.items():
max_values: list[float] = []
mean_values: list[float] = []
min_values: list[float] = []
prev_stat: dict[str, Any] = stat_list[0]
# Loop over the hourly statistics + a fake entry to end the period
for statistic in chain(
stat_list, ({"start": stat_list[-1]["start"] + period},)
):
if not same_period(prev_stat["start"], statistic["start"]):
start, end = period_start_end(prev_stat["start"])
# The previous statistic was the last entry of the period
result[statistic_id].append(
{
"statistic_id": statistic_id,
"start": start.isoformat(),
"end": end.isoformat(),
"mean": mean(mean_values) if mean_values else None,
"min": min(min_values) if min_values else None,
"max": max(max_values) if max_values else None,
"last_reset": prev_stat.get("last_reset"),
"state": prev_stat.get("state"),
"sum": prev_stat["sum"],
}
)
max_values = []
mean_values = []
min_values = []
if statistic.get("max") is not None:
max_values.append(statistic["max"])
if statistic.get("mean") is not None:
mean_values.append(statistic["mean"])
if statistic.get("min") is not None:
min_values.append(statistic["min"])
prev_stat = statistic
return result
def same_day(time1: datetime, time2: datetime) -> bool:
"""Return True if time1 and time2 are in the same date."""
date1 = dt_util.as_local(time1).date()
date2 = dt_util.as_local(time2).date()
return date1 == date2
def day_start_end(time: datetime) -> tuple[datetime, datetime]:
"""Return the start and end of the period (day) time is within."""
start = dt_util.as_utc(
dt_util.as_local(time).replace(hour=0, minute=0, second=0, microsecond=0)
)
end = start + timedelta(days=1)
return (start, end)
def _reduce_statistics_per_day(
stats: dict[str, list[dict[str, Any]]]
) -> dict[str, list[dict[str, Any]]]:
"""Reduce hourly statistics to daily statistics."""
return _reduce_statistics(stats, same_day, day_start_end, timedelta(days=1))
def same_month(time1: datetime, time2: datetime) -> bool:
"""Return True if time1 and time2 are in the same year and month."""
date1 = dt_util.as_local(time1).date()
date2 = dt_util.as_local(time2).date()
return (date1.year, date1.month) == (date2.year, date2.month)
def month_start_end(time: datetime) -> tuple[datetime, datetime]:
"""Return the start and end of the period (month) time is within."""
start_local = dt_util.as_local(time).replace(
day=1, hour=0, minute=0, second=0, microsecond=0
)
start = dt_util.as_utc(start_local)
end_local = (start_local + timedelta(days=31)).replace(day=1)
end = dt_util.as_utc(end_local)
return (start, end)
def _reduce_statistics_per_month(
stats: dict[str, list[dict[str, Any]]],
) -> dict[str, list[dict[str, Any]]]:
"""Reduce hourly statistics to monthly statistics."""
return _reduce_statistics(stats, same_month, month_start_end, timedelta(days=31))
def statistics_during_period(
hass: HomeAssistant,
start_time: datetime,
end_time: datetime | None = None,
statistic_ids: list[str] | None = None,
period: Literal["5minute", "day", "hour", "month"] = "hour",
start_time_as_datetime: bool = False,
) -> dict[str, list[dict[str, Any]]]:
"""Return statistics during UTC period start_time - end_time for the statistic_ids.
If end_time is omitted, returns statistics newer than or equal to start_time.
If statistic_ids is omitted, returns statistics for all statistics ids.
"""
metadata = None
with session_scope(hass=hass) as session:
# Fetch metadata for the given (or all) statistic_ids
metadata = get_metadata_with_session(hass, session, statistic_ids=statistic_ids)
if not metadata:
return {}
metadata_ids = None
if statistic_ids is not None:
metadata_ids = [metadata_id for metadata_id, _ in metadata.values()]
if period == "5minute":
bakery = STATISTICS_SHORT_TERM_BAKERY
base_query = QUERY_STATISTICS_SHORT_TERM
table = StatisticsShortTerm
else:
bakery = STATISTICS_BAKERY
base_query = QUERY_STATISTICS
table = Statistics
baked_query = _statistics_during_period_query(
hass, end_time, statistic_ids, bakery, base_query, table
)
stats = execute(
baked_query(session).params(
start_time=start_time, end_time=end_time, metadata_ids=metadata_ids
)
)
if not stats:
return {}
# Return statistics combined with metadata
if period not in ("day", "month"):
return _sorted_statistics_to_dict(
hass,
session,
stats,
statistic_ids,
metadata,
True,
table,
start_time,
start_time_as_datetime,
)
result = _sorted_statistics_to_dict(
hass, session, stats, statistic_ids, metadata, True, table, start_time, True
)
if period == "day":
return _reduce_statistics_per_day(result)
return _reduce_statistics_per_month(result)
def _get_last_statistics(
hass: HomeAssistant,
number_of_stats: int,
statistic_id: str,
convert_units: bool,
table: type[Statistics | StatisticsShortTerm],
) -> dict[str, list[dict]]:
"""Return the last number_of_stats statistics for a given statistic_id."""
statistic_ids = [statistic_id]
with session_scope(hass=hass) as session:
# Fetch metadata for the given statistic_id
metadata = get_metadata_with_session(hass, session, statistic_ids=statistic_ids)
if not metadata:
return {}
if table == StatisticsShortTerm:
bakery = STATISTICS_SHORT_TERM_BAKERY
base_query = QUERY_STATISTICS_SHORT_TERM
else:
bakery = STATISTICS_BAKERY
base_query = QUERY_STATISTICS
baked_query = hass.data[bakery](lambda session: session.query(*base_query))
baked_query += lambda q: q.filter_by(metadata_id=bindparam("metadata_id"))
metadata_id = metadata[statistic_id][0]
baked_query += lambda q: q.order_by(table.metadata_id, table.start.desc())
baked_query += lambda q: q.limit(bindparam("number_of_stats"))
stats = execute(
baked_query(session).params(
number_of_stats=number_of_stats, metadata_id=metadata_id
)
)
if not stats:
return {}
# Return statistics combined with metadata
return _sorted_statistics_to_dict(
hass,
session,
stats,
statistic_ids,
metadata,
convert_units,
table,
None,
)
def get_last_statistics(
hass: HomeAssistant, number_of_stats: int, statistic_id: str, convert_units: bool
) -> dict[str, list[dict]]:
"""Return the last number_of_stats statistics for a statistic_id."""
return _get_last_statistics(
hass, number_of_stats, statistic_id, convert_units, Statistics
)
def get_last_short_term_statistics(
hass: HomeAssistant, number_of_stats: int, statistic_id: str, convert_units: bool
) -> dict[str, list[dict]]:
"""Return the last number_of_stats short term statistics for a statistic_id."""
return _get_last_statistics(
hass, number_of_stats, statistic_id, convert_units, StatisticsShortTerm
)
def _statistics_at_time(
session: scoped_session,
metadata_ids: set[int],
table: type[Statistics | StatisticsShortTerm],
start_time: datetime,
) -> list | None:
"""Return last known statics, earlier than start_time, for the metadata_ids."""
# Fetch metadata for the given (or all) statistic_ids
if table == StatisticsShortTerm:
base_query = QUERY_STATISTICS_SHORT_TERM
else:
base_query = QUERY_STATISTICS
query = session.query(*base_query)
most_recent_statistic_ids = (
session.query(
func.max(table.id).label("max_id"),
)
.filter(table.start < start_time)
.filter(table.metadata_id.in_(metadata_ids))
)
most_recent_statistic_ids = most_recent_statistic_ids.group_by(table.metadata_id)
most_recent_statistic_ids = most_recent_statistic_ids.subquery()
query = query.join(
most_recent_statistic_ids,
table.id == most_recent_statistic_ids.c.max_id,
)
return execute(query)
def _sorted_statistics_to_dict(
hass: HomeAssistant,
session: scoped_session,
stats: list,
statistic_ids: list[str] | None,
_metadata: dict[str, tuple[int, StatisticMetaData]],
convert_units: bool,
table: type[Statistics | StatisticsShortTerm],
start_time: datetime | None,
start_time_as_datetime: bool = False,
) -> dict[str, list[dict]]:
"""Convert SQL results into JSON friendly data structure."""
result: dict = defaultdict(list)
units = hass.config.units
metadata = dict(_metadata.values())
need_stat_at_start_time = set()
stats_at_start_time = {}
def no_conversion(val: Any, _: Any) -> float | None:
"""Return x."""
return val # type: ignore
# Set all statistic IDs to empty lists in result set to maintain the order
if statistic_ids is not None:
for stat_id in statistic_ids:
result[stat_id] = []
# Identify metadata IDs for which no data was available at the requested start time
for meta_id, group in groupby(stats, lambda stat: stat.metadata_id): # type: ignore
first_start_time = process_timestamp(next(group).start)
if start_time and first_start_time > start_time:
need_stat_at_start_time.add(meta_id)
# Fetch last known statistics for the needed metadata IDs
if need_stat_at_start_time:
assert start_time # Can not be None if need_stat_at_start_time is not empty
tmp = _statistics_at_time(session, need_stat_at_start_time, table, start_time)
if tmp:
for stat in tmp:
stats_at_start_time[stat.metadata_id] = (stat,)
# Append all statistic entries, and optionally do unit conversion
for meta_id, group in groupby(stats, lambda stat: stat.metadata_id): # type: ignore
unit = metadata[meta_id]["unit_of_measurement"]
statistic_id = metadata[meta_id]["statistic_id"]
convert: Callable[[Any, Any], float | None]
if convert_units:
convert = UNIT_CONVERSIONS.get(unit, lambda x, units: x) # type: ignore
else:
convert = no_conversion
ent_results = result[meta_id]
for db_state in chain(stats_at_start_time.get(meta_id, ()), group):
start = process_timestamp(db_state.start)
end = start + table.duration
ent_results.append(
{
"statistic_id": statistic_id,
"start": start if start_time_as_datetime else start.isoformat(),
"end": end.isoformat(),
"mean": convert(db_state.mean, units),
"min": convert(db_state.min, units),
"max": convert(db_state.max, units),
"last_reset": process_timestamp_to_utc_isoformat(
db_state.last_reset
),
"state": convert(db_state.state, units),
"sum": convert(db_state.sum, units),
}
)
# Filter out the empty lists if some states had 0 results.
return {metadata[key]["statistic_id"]: val for key, val in result.items() if val}
def validate_statistics(hass: HomeAssistant) -> dict[str, list[ValidationIssue]]:
"""Validate statistics."""
platform_validation: dict[str, list[ValidationIssue]] = {}
for platform in hass.data[DOMAIN].values():
if not hasattr(platform, "validate_statistics"):
continue
platform_validation.update(platform.validate_statistics(hass))
return platform_validation
def _statistics_exists(
session: scoped_session,
table: type[Statistics | StatisticsShortTerm],
metadata_id: int,
start: datetime,
) -> int | None:
"""Return id if a statistics entry already exists."""
result = (
session.query(table.id)
.filter((table.metadata_id == metadata_id) & (table.start == start))
.first()
)
return result["id"] if result else None
@callback
def async_add_external_statistics(
hass: HomeAssistant,
metadata: StatisticMetaData,
statistics: Iterable[StatisticData],
) -> None:
"""Add hourly statistics from an external source.
This inserts an add_external_statistics job in the recorder's queue.
"""
# The statistic_id has same limitations as an entity_id, but with a ':' as separator
if not valid_statistic_id(metadata["statistic_id"]):
raise HomeAssistantError("Invalid statistic_id")
# The source must not be empty and must be aligned with the statistic_id
domain, _object_id = split_statistic_id(metadata["statistic_id"])
if not metadata["source"] or metadata["source"] != domain:
raise HomeAssistantError("Invalid source")
for statistic in statistics:
start = statistic["start"]
if start.tzinfo is None or start.tzinfo.utcoffset(start) is None:
raise HomeAssistantError("Naive timestamp")
if start.minute != 0 or start.second != 0 or start.microsecond != 0:
raise HomeAssistantError("Invalid timestamp")
statistic["start"] = dt_util.as_utc(start)
# Insert job in recorder's queue
hass.data[DATA_INSTANCE].async_external_statistics(metadata, statistics)
def _filter_unique_constraint_integrity_error(
instance: Recorder,
) -> Callable[[Exception], bool]:
def _filter_unique_constraint_integrity_error(err: Exception) -> bool:
"""Handle unique constraint integrity errors."""
if not isinstance(err, StatementError):
return False
ignore = False
if (
instance.engine.dialect.name == "sqlite"
and "UNIQUE constraint failed" in str(err)
):
ignore = True
if (
instance.engine.dialect.name == "postgresql"
and hasattr(err.orig, "pgcode")
and err.orig.pgcode == "23505"
):
ignore = True
if instance.engine.dialect.name == "mysql" and hasattr(err.orig, "args"):
with contextlib.suppress(TypeError):
if err.orig.args[0] == 1062:
ignore = True
if ignore:
_LOGGER.warning(
"Blocked attempt to insert duplicated statistic rows, please report at "
'https://github.com/home-assistant/core/issues?q=is%%3Aissue+label%%3A"integration%%3A+recorder"+',
exc_info=err,
)
return ignore
return _filter_unique_constraint_integrity_error
@retryable_database_job("statistics")
def add_external_statistics(
instance: Recorder,
metadata: StatisticMetaData,
statistics: Iterable[StatisticData],
) -> bool:
"""Process an add_statistics job."""
with session_scope(
session=instance.get_session(), # type: ignore
exception_filter=_filter_unique_constraint_integrity_error(instance),
) as session:
metadata_id = _update_or_add_metadata(instance.hass, session, metadata)
for stat in statistics:
if stat_id := _statistics_exists(
session, Statistics, metadata_id, stat["start"]
):
_update_statistics(session, Statistics, stat_id, stat)
else:
_insert_statistics(session, Statistics, metadata_id, stat)
return True