app/backend/fluksio/flow/state.py

"""
State management module with pluggable backends.

This module provides state storage backends for pipeline execution,
supporting both in-memory storage and Redis for distributed execution.
"""

from __future__ import annotations

from abc import ABC, abstractmethod
from collections import deque
from collections.abc import Iterable, Iterator
from contextlib import contextmanager
from threading import RLock
from typing import Any, cast

import orjson
import redis

#: A node may return a dict keyed by something other than a string, and the
#: stdlib encoder this replaced turned those keys into strings rather than
#: refusing them. Two differences remain and are both improvements: a
#: non-finite number is stored as ``null`` instead of the bare ``NaN`` that is
#: not JSON at all, and a datetime serialises rather than raising.
_JSON_OPTS = orjson.OPT_NON_STR_KEYS

# A sparkline only means something for a value that can be placed on an axis,
# so the history keeps those and nothing else. 120 points fill a panel-wide
# chart while leaving Redis a cache rather than a time-series database.
#: How long a Redis call may take before it is treated as a failure, and
#: how long connecting may take. A blocking read must ask for less than the
#: first of these.
SOCKET_TIMEOUT_S = 5.0
CONNECT_TIMEOUT_S = 2.0

HISTORY_LIMIT = 120


def as_number(value: Any) -> float | None:
    """The plottable form of a value, or None if it cannot be placed on an axis."""
    # A flag is not a measurement, but when it was on is worth seeing, and 0/1
    # is what a step curve is drawn from. bool is an int subclass, so it would
    # fall through the isinstance below either way.
    if isinstance(value, bool):
        return 1.0 if value else 0.0
    if not isinstance(value, (int, float)):
        return None
    return float(value)


class StateBackend(ABC):
    """
    Abstract base class for state storage backends.

    Provides a dict-like interface for storing and retrieving state values
    with support for distributed locking.
    """

    @abstractmethod
    def get(self, key: str, default: Any = None) -> Any:
        """
        Get a value by key.

        :param key: The key to retrieve.
        :type key: str
        :param default: Default value if key not found.
        :type default: Any
        :returns: The stored value or default.
        :rtype: Any
        """
        ...

    @abstractmethod
    def set(self, key: str, value: Any) -> None:
        """
        Set a value by key.

        :param key: The key to set.
        :type key: str
        :param value: The value to store.
        :type value: Any
        """
        ...

    @abstractmethod
    def exists(self, key: str) -> bool:
        """
        Check if a key exists.

        :param key: The key to check.
        :type key: str
        :returns: True if key exists.
        :rtype: bool
        """
        ...

    @abstractmethod
    def delete(self, key: str) -> None:
        """Forget one key and any history kept for it."""
        ...

    @abstractmethod
    def clear(self) -> None:
        """Clear all keys in the state."""
        ...

    def message_names(self) -> list[str]:
        """The message names in state, without the bookkeeping keys.

        Its own method because the Redis backend can answer it from a set it
        maintains, rather than by scanning a namespace that holds five
        bookkeeping keys for every message.
        """
        return [k for k in self.keys() if not k.startswith("__")]

    def close(self) -> None:  # noqa: B027
        """Release whatever the backend holds outside this process.

        A run builds a state backend of its own and drops it when it
        finishes; on Redis that is a client and its connection pool, which
        nothing was giving back. Concrete rather than abstract: a backend that
        holds nothing has nothing to answer here.
        """

    @abstractmethod
    def keys(self) -> list[str]:
        """
        Get all keys.

        :returns: List of all keys.
        :rtype: list[str]
        """
        ...

    @abstractmethod
    def update(self, mapping: dict[str, Any]) -> None:
        """
        Update multiple keys atomically.

        :param mapping: Dict of key-value pairs to set.
        :type mapping: dict[str, Any]
        """
        ...

    @abstractmethod
    @contextmanager
    def lock(self, timeout: float = 10.0) -> Iterator[None]:
        """
        Acquire a lock for thread-safe operations.

        :param timeout: Lock timeout in seconds.
        :type timeout: float
        :yields: Lock context.
        """
        ...

    # -------------------------------------------------------------------------
    # Atomic operations for synchronous node support
    # -------------------------------------------------------------------------

    @abstractmethod
    def increment(self, key: str) -> int:
        """
        Atomically increment a counter and return the new value.

        :param key: The key to increment.
        :type key: str
        :returns: The new value after incrementing.
        :rtype: int
        """
        ...

    @abstractmethod
    def get_multi(self, keys: list[str]) -> dict[str, Any]:
        """
        Get multiple values atomically.

        :param keys: List of keys to retrieve.
        :type keys: list[str]
        :returns: Dict mapping keys to values (missing keys have None).
        :rtype: dict[str, Any]
        """
        ...

    @abstractmethod
    def get_present(self, keys: list[str]) -> dict[str, Any]:
        """The values of those keys that exist, in one round trip.

        Unlike :meth:`get_multi`, a key that is absent is left out rather than
        mapped to None — which is what reading a node's inputs needs, since a
        message holding ``null`` is a message the node has, and one that was
        never published is not.
        """
        ...

    @abstractmethod
    def increment_multi(self, keys: list[str]) -> None:
        """Bump several counters at once.

        The new values are not returned: the only reader compares them against
        what it saw last time and reads them back itself.
        """
        ...

    @abstractmethod
    def compare_and_swap_multi(
        self,
        expected: dict[str, Any],
        updates: dict[str, Any],
    ) -> bool:
        """
        Atomically check expected values and apply updates if all match.

        This is the key primitive for race-condition-free synchronous node execution.
        If any expected value doesn't match the current value, no updates are applied.

        :param expected: Dict of keys to their expected values.
        :type expected: dict[str, Any]
        :param updates: Dict of keys to their new values (applied only if expected matches).
        :type updates: dict[str, Any]
        :returns: True if the swap succeeded, False if expected values didn't match.
        :rtype: bool
        """
        ...

    # -------------------------------------------------------------------------
    # Message history
    # -------------------------------------------------------------------------

    @abstractmethod
    def append_history(
        self, values: dict[str, Any], ts: float, limits: dict[str, int] | None = None
    ) -> None:
        """
        Record freshly published values in each message's capped history.

        Non-numeric payloads are skipped — the history exists to be plotted.

        :param values: Message names mapped to the value just published.
        :type values: dict[str, Any]
        :param ts: When they were published.
        :param limits: How many points to keep per message, where a chart asks
            for more than the default.
        :type ts: float
        """
        ...

    def record(
        self,
        values: dict[str, Any],
        stamps: dict[str, float],
        counters: list[str],
        ts: float,
        limits: dict[str, int] | None = None,
        drop: list[str] | None = None,
    ) -> None:
        """Everything a published value owes state, in one round trip.

        The value itself, the timestamp beside it, its series and its version
        counter used to be four calls and four round trips; a backend that can
        batch them does so here. This default is those four calls, so a
        backend that cannot batch gains nothing and breaks nothing.

        :param values: Message names mapped to the value just published.
        :param stamps: The timestamp key of each of those messages.
        :param counters: The version keys to bump.
        :param ts: When they were published.
        :param limits: How many points to keep per message.
        :param drop: Keys to forget in the same trip — a released rate-limit
            hold, which is written and cleared on this same path.
        """
        for key in drop or ():
            self.delete(key)
        self.update({**values, **stamps})
        self.append_history(values, ts, limits)
        self.increment_multi(counters)

    @abstractmethod
    def history(self, key: str) -> list[tuple[float, float]]:
        """
        The recorded ``(timestamp, value)`` pairs of one message, oldest first.

        :param key: The message name.
        :type key: str
        :returns: At most ``HISTORY_LIMIT`` points; empty if nothing was recorded.
        :rtype: list[tuple[float, float]]
        """
        ...

    def __contains__(self, key: str) -> bool:
        return self.exists(key)

    def __getitem__(self, key: str) -> Any:
        if not self.exists(key):
            raise KeyError(key)
        return self.get(key)

    def __setitem__(self, key: str, value: Any) -> None:
        self.set(key, value)

    def __iter__(self) -> Iterator[str]:
        return iter(self.keys())

    def __len__(self) -> int:
        return len(self.keys())


class MemoryState(StateBackend):
    """
    In-memory state backend using a thread-safe dict.

    Suitable for single-process execution and testing.

    :example:
        >>> state = MemoryState()
        >>> state["key"] = "value"
        >>> state["key"]
        'value'
    """

    __slots__ = ("_data", "_history", "_lock")

    def __init__(self) -> None:
        self._data: dict[str, Any] = {}
        self._history: dict[str, deque[tuple[float, float]]] = {}
        self._lock = RLock()  # Reentrant lock for nested access

    def get(self, key: str, default: Any = None) -> Any:
        with self._lock:
            return self._data.get(key, default)

    def set(self, key: str, value: Any) -> None:
        with self._lock:
            self._data[key] = value

    def exists(self, key: str) -> bool:
        with self._lock:
            return key in self._data

    def delete(self, key: str) -> None:
        with self._lock:
            self._data.pop(key, None)
            self._history.pop(key, None)

    def clear(self) -> None:
        with self._lock:
            self._data.clear()
            self._history.clear()

    def keys(self) -> list[str]:
        with self._lock:
            return list(self._data.keys())

    def update(self, mapping: dict[str, Any]) -> None:
        with self._lock:
            self._data.update(mapping)

    @contextmanager
    def lock(self, timeout: float = 10.0) -> Iterator[None]:
        """Acquire the internal lock."""
        acquired = self._lock.acquire(timeout=timeout)
        if not acquired:
            raise TimeoutError("Failed to acquire lock")
        try:
            yield
        finally:
            self._lock.release()

    def increment(self, key: str) -> int:
        """Atomically increment a counter and return the new value."""
        with self._lock:
            value: int = self._data.get(key, 0)
            new_value = value + 1
            self._data[key] = new_value
            return new_value

    def get_multi(self, keys: list[str]) -> dict[str, Any]:
        """Get multiple values atomically."""
        with self._lock:
            return {k: self._data.get(k) for k in keys}

    def get_present(self, keys: list[str]) -> dict[str, Any]:
        """The values of those keys that are there."""
        with self._lock:
            return {k: self._data[k] for k in keys if k in self._data}

    def increment_multi(self, keys: list[str]) -> None:
        with self._lock:
            for key in keys:
                self._data[key] = self._data.get(key, 0) + 1

    def compare_and_swap_multi(
        self,
        expected: dict[str, Any],
        updates: dict[str, Any],
    ) -> bool:
        """Atomically check expected values and apply updates if all match."""
        with self._lock:
            # Check all expected values
            for key, exp_value in expected.items():
                actual = self._data.get(key)
                if actual != exp_value:
                    return False

            # All matched, apply updates
            self._data.update(updates)
            return True

    def append_history(
        self, values: dict[str, Any], ts: float, limits: dict[str, int] | None = None
    ) -> None:
        """Append the numeric values to their message's bounded series."""
        with self._lock:
            for key, value in values.items():
                number = as_number(value)
                if number is None:
                    continue
                cap = (limits or {}).get(key, HISTORY_LIMIT)
                series = self._history.get(key)
                if series is None or series.maxlen != cap:
                    # A widget asking for a deeper series re-caps it in place.
                    series = deque(series or (), maxlen=cap)
                    self._history[key] = series
                series.append((ts, number))

    def record(
        self,
        values: dict[str, Any],
        stamps: dict[str, float],
        counters: list[str],
        ts: float,
        limits: dict[str, int] | None = None,
        drop: list[str] | None = None,
    ) -> None:
        """The four writes under one acquisition of the one lock."""
        with self._lock:
            for key in drop or ():
                self._data.pop(key, None)
                self._history.pop(key, None)
            self._data.update(values)
            self._data.update(stamps)
            for key, value in values.items():
                number = as_number(value)
                if number is None:
                    continue
                cap = (limits or {}).get(key, HISTORY_LIMIT)
                series = self._history.get(key)
                if series is None or series.maxlen != cap:
                    series = deque(series or (), maxlen=cap)
                    self._history[key] = series
                series.append((ts, number))
            for key in counters:
                self._data[key] = self._data.get(key, 0) + 1

    def history(self, key: str) -> list[tuple[float, float]]:
        """The recorded points of one message, oldest first."""
        with self._lock:
            return list(self._history.get(key, ()))


class RedisState(StateBackend):
    """
    Redis-based state backend for distributed execution.

    Values are stored as JSON — anything a node passes through must be
    serializable. Uses a namespace prefix to isolate pipeline executions.

    :param host: Redis host address.
    :type host: str
    :param port: Redis port number.
    :type port: int
    :param db: Redis database number.
    :type db: int
    :param namespace: Key prefix for isolation.
    :type namespace: str
    :param password: Redis password (optional).
    :type password: str | None
    :param ttl: Time-to-live for keys in seconds (optional).
    :type ttl: int | None

    :example:
        >>> state = RedisState(host="localhost", namespace="my_pipeline")
        >>> state["result"] = {"value": 42}
        >>> state["result"]
        {'value': 42}
    """

    __slots__ = ("_client", "_namespace", "_ttl", "_lock_name")

    def __init__(
        self,
        host: str = "localhost",
        port: int = 6379,
        db: int = 0,
        namespace: str = "pipeline",
        password: str | None = None,
        ttl: int | None = None,
    ) -> None:
        self._client = redis.Redis(
            host=host,
            port=port,
            db=db,
            password=password,
            decode_responses=False,
            # Without these a Redis that stops answering without closing the
            # connection — a partition, a firewall dropping state — hangs the
            # caller until the kernel gives up, minutes later. That takes the
            # health endpoint whose job is to notice with it.
            socket_timeout=SOCKET_TIMEOUT_S,
            socket_connect_timeout=CONNECT_TIMEOUT_S,
            health_check_interval=30,
        )
        self._namespace = namespace
        self._ttl = ttl
        self._lock_name = f"{namespace}:_lock"

    def _key(self, key: str) -> str:
        """Generate namespaced key."""
        return f"{self._namespace}:{key}"

    def _serialize(self, value: Any) -> bytes:
        """Serialize value for storage."""
        return orjson.dumps(value, option=_JSON_OPTS)

    def _deserialize(self, data: bytes | None) -> Any:
        """Deserialize value from storage."""
        return orjson.loads(data) if data else None

    # redis-py types every command as a sync/async union; this is the
    # synchronous client, so the results are narrowed where they are consumed.
    def get(self, key: str, default: Any = None) -> Any:
        data = cast(bytes | None, self._client.get(self._key(key)))
        return self._deserialize(data) if data is not None else default

    def _names_key(self) -> str:
        return self._key("__names__")

    def message_names(self) -> list[str]:
        """The message names, from the set kept beside them.

        `keys()` is a SCAN of the whole namespace, which holds a version, a
        timestamp, a history list and a last-seen marker for every message —
        so reading the message names cost several times as many round trips
        as there are messages. Every websocket connect asks for this.
        """
        members = cast(set[bytes], self._client.smembers(self._names_key()))
        return [m.decode("utf-8") for m in members]

    def _note(self, pipe: Any, keys: Iterable[str]) -> None:
        """Record the message names among `keys`, in the caller's pipeline."""
        names = [k for k in keys if not k.startswith("__")]
        if names:
            pipe.sadd(self._names_key(), *names)

    def set(self, key: str, value: Any) -> None:
        data = self._serialize(value)
        pipe = self._client.pipeline()
        if self._ttl:
            pipe.setex(self._key(key), self._ttl, data)
        else:
            pipe.set(self._key(key), data)
        self._note(pipe, [key])
        pipe.execute()

    def exists(self, key: str) -> bool:
        return bool(self._client.exists(self._key(key)))

    def delete(self, key: str) -> None:
        pipe = self._client.pipeline()
        pipe.delete(self._key(key), self._history_key(key))
        pipe.srem(self._names_key(), key)
        pipe.execute()

    def clear(self) -> None:
        """Clear all keys in the namespace."""
        pattern = f"{self._namespace}:*"
        cursor = 0
        while True:
            cursor, keys = cast(
                tuple[int, list[bytes]],
                self._client.scan(cursor, match=pattern, count=100),
            )
            if keys:
                self._client.delete(*keys)
            if cursor == 0:
                break

    def keys(self) -> list[str]:
        """Get all keys in the namespace (without prefix)."""
        pattern = f"{self._namespace}:*"
        prefix_len = len(self._namespace) + 1
        result = []
        cursor = 0
        while True:
            cursor, keys = cast(
                tuple[int, list[bytes]],
                self._client.scan(cursor, match=pattern, count=100),
            )
            for k in keys:
                key_str = k.decode("utf-8")
                # Skip the lock key
                if not key_str.endswith(":_lock"):
                    result.append(key_str[prefix_len:])
            if cursor == 0:
                break
        return result

    def update(self, mapping: dict[str, Any]) -> None:
        """Update multiple keys atomically using a pipeline."""
        if not mapping:
            return
        pipe = self._client.pipeline()
        for key, value in mapping.items():
            data = self._serialize(value)
            if self._ttl:
                pipe.setex(self._key(key), self._ttl, data)
            else:
                pipe.set(self._key(key), data)
        self._note(pipe, mapping)
        pipe.execute()

    @contextmanager
    def lock(self, timeout: float = 10.0) -> Iterator[None]:
        """
        Acquire a distributed lock using Redis.

        :param timeout: Lock timeout in seconds.
        :type timeout: float
        :yields: Lock context.
        :raises TimeoutError: If lock cannot be acquired.
        """
        redis_lock = self._client.lock(
            self._lock_name,
            timeout=timeout,
            blocking_timeout=timeout,
        )
        acquired = redis_lock.acquire()
        if not acquired:
            raise TimeoutError("Failed to acquire Redis lock")
        try:
            yield
        finally:
            try:
                redis_lock.release()
            except redis.exceptions.LockError:
                pass  # Lock may have expired

    def ping(self) -> bool:
        """
        Check if Redis connection is available.

        :returns: True if connected.
        :rtype: bool
        """
        try:
            return bool(self._client.ping())
        except redis.ConnectionError:
            return False

    def increment(self, key: str) -> int:
        """Atomically increment a counter using Redis INCR."""
        return cast(int, self._client.incr(self._key(key)))

    def increment_multi(self, keys: list[str]) -> None:
        """Bump every counter in one round trip rather than one INCR each."""
        if not keys:
            return
        pipe = self._client.pipeline()
        for key in keys:
            pipe.incr(self._key(key))
        pipe.execute()

    def get_multi(self, keys: list[str]) -> dict[str, Any]:
        """Get multiple values atomically using Redis MGET."""
        if not keys:
            return {}

        full_keys = [self._key(k) for k in keys]
        values = cast(list[bytes | None], self._client.mget(full_keys))

        result = {}
        for key, value in zip(keys, values, strict=True):
            result[key] = self._deserialize(value) if value is not None else None

        return result

    def get_present(self, keys: list[str]) -> dict[str, Any]:
        """The keys that are there, from one MGET.

        A missing key comes back as ``None`` from Redis and a stored ``null``
        comes back as the four bytes; only the raw reply tells the two apart,
        which is why this is not ``get_multi`` with the Nones filtered out.
        """
        if not keys:
            return {}
        values = cast(
            list[bytes | None], self._client.mget([self._key(k) for k in keys])
        )
        return {
            key: self._deserialize(raw)
            for key, raw in zip(keys, values, strict=True)
            if raw is not None
        }

    def __getitem__(self, key: str) -> Any:
        # One GET rather than the base class's EXISTS-then-GET: a missing key
        # and a stored `null` differ in the raw reply, so nothing has to ask
        # twice.
        raw = cast(bytes | None, self._client.get(self._key(key)))
        if raw is None:
            raise KeyError(key)
        return self._deserialize(raw)

    def compare_and_swap_multi(
        self,
        expected: dict[str, Any],
        updates: dict[str, Any],
    ) -> bool:
        """
        Atomically check expected values and apply updates using Redis WATCH/MULTI.

        Uses optimistic locking with WATCH to detect concurrent modifications.
        If any watched key changes before EXEC, the transaction is aborted.

        :param expected: Dict of keys to their expected values.
        :type expected: dict[str, Any]
        :param updates: Dict of keys to their new values.
        :type updates: dict[str, Any]
        :returns: True if the swap succeeded, False otherwise.
        :rtype: bool
        """
        if not expected:
            # No expectations, just set the updates
            self.update(updates)
            return True

        watch_keys = [self._key(k) for k in expected.keys()]

        try:
            # Start watching the keys for changes
            self._client.watch(*watch_keys)

            # Check current values
            current = self.get_multi(list(expected.keys()))

            for key, exp_value in expected.items():
                if current.get(key) != exp_value:
                    self._client.unwatch()
                    return False

            # All values match - execute the transaction
            pipe = self._client.pipeline(True)  # True = use MULTI/EXEC

            for key, value in updates.items():
                data = self._serialize(value)
                if self._ttl:
                    pipe.setex(self._key(key), self._ttl, data)
                else:
                    pipe.set(self._key(key), data)

            pipe.execute()
            return True

        except redis.WatchError:
            # Another client modified one of the watched keys
            return False

    def close(self) -> None:
        """Give the connection pool back."""
        self._client.close()

    def _history_key(self, key: str) -> str:
        return self._key(f"__history__:{key}")

    def append_history(
        self, values: dict[str, Any], ts: float, limits: dict[str, int] | None = None
    ) -> None:
        """Push the numeric values onto their capped list, in one round-trip."""
        pipe = self._client.pipeline()
        queued = False
        for key, value in values.items():
            number = as_number(value)
            if number is None:
                continue
            history_key = self._history_key(key)
            cap = (limits or {}).get(key, HISTORY_LIMIT)
            pipe.lpush(history_key, orjson.dumps([ts, number]))
            pipe.ltrim(history_key, 0, cap - 1)
            if self._ttl:
                pipe.expire(history_key, self._ttl)
            queued = True
        if queued:
            pipe.execute()

    def record(
        self,
        values: dict[str, Any],
        stamps: dict[str, float],
        counters: list[str],
        ts: float,
        limits: dict[str, int] | None = None,
        drop: list[str] | None = None,
    ) -> None:
        """Value, timestamp, series and version counter in one round trip.

        These were four pipelines — four round trips — for one published
        value, and a value crossing an edge pays them twice. Redis executes a
        pipeline as one transaction, so batching them changes nothing about
        what lands together and removes three of the four trips.
        """
        pipe = self._client.pipeline()
        for key in drop or ():
            pipe.delete(self._key(key), self._history_key(key))
        for key, value in {**values, **stamps}.items():
            data = self._serialize(value)
            if self._ttl:
                pipe.setex(self._key(key), self._ttl, data)
            else:
                pipe.set(self._key(key), data)
        for key, value in values.items():
            number = as_number(value)
            if number is None:
                continue
            history_key = self._history_key(key)
            cap = (limits or {}).get(key, HISTORY_LIMIT)
            pipe.lpush(history_key, orjson.dumps([ts, number]))
            pipe.ltrim(history_key, 0, cap - 1)
            if self._ttl:
                pipe.expire(history_key, self._ttl)
        for key in counters:
            pipe.incr(self._key(key))
        self._note(pipe, values)
        pipe.execute()

    def history(self, key: str) -> list[tuple[float, float]]:
        """The recorded points of one message, oldest first."""
        entries = cast(list[bytes], self._client.lrange(self._history_key(key), 0, -1))
        # LPUSH puts the newest first, a chart reads the other way round.
        points: list[tuple[float, float]] = []
        for entry in reversed(entries):
            ts, value = orjson.loads(entry)
            points.append((float(ts), float(value)))
        return points