stroblmeandClaude Fable 5
895bd89b2b
A dashboard is its own document rather than widgets placed in a flow. Node-RED's dashboard tab is 260 nodes, about forty of them pure layout, which is exactly what the small-graph principle exists to avoid — and since the graph is already wired by message name, a widget can bind to a name without belonging to any flow. Stored beside the flows in the same repository, sharing their write lock and commit, under a directory the flow listing ignores. No draft/publish split: nothing executes a dashboard, so edit mode is its own staging area. Two things it needs from the engine. A message catalog spanning every flow, because a wall panel shows the heating next to the solar and the flow-scoped API is the wrong shape for that. And a way to put a value in without owning a node — a slider is a real value that happened to come from a person — which runs whatever consumes it and applies the same type check a node's output gets. Only a message some flow declares can be published to; flows own the namespace. Charts also need more past than the 120 points a sparkline wanted, so a chart widget declares its depth and the engine keeps that message's series that deep. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_011LF61rxW1FG5YCD2J9YqjY
606 lines
19 KiB
Python
606 lines
19 KiB
Python
"""
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State management module with pluggable backends.
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This module provides state storage backends for pipeline execution,
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supporting both in-memory storage and Redis for distributed execution.
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"""
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from __future__ import annotations
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import json
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from abc import ABC, abstractmethod
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from collections import deque
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from collections.abc import Iterator
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from contextlib import contextmanager
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from threading import RLock
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from typing import Any, cast
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import redis
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# A sparkline only means something for numbers, so the history keeps the values
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# it can plot and nothing else. 120 points fill a panel-wide chart while leaving
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# Redis a cache rather than a time-series database.
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HISTORY_LIMIT = 120
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def as_number(value: Any) -> float | None:
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"""The plottable form of a value, or None if it is not a number."""
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# bool is an int subclass; a flag is not a measurement.
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if isinstance(value, bool) or not isinstance(value, (int, float)):
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return None
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return float(value)
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class StateBackend(ABC):
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"""
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Abstract base class for state storage backends.
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Provides a dict-like interface for storing and retrieving state values
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with support for distributed locking.
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"""
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@abstractmethod
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def get(self, key: str, default: Any = None) -> Any:
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"""
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Get a value by key.
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:param key: The key to retrieve.
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:type key: str
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:param default: Default value if key not found.
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:type default: Any
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:returns: The stored value or default.
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:rtype: Any
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"""
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...
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@abstractmethod
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def set(self, key: str, value: Any) -> None:
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"""
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Set a value by key.
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:param key: The key to set.
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:type key: str
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:param value: The value to store.
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:type value: Any
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"""
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...
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@abstractmethod
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def exists(self, key: str) -> bool:
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"""
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Check if a key exists.
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:param key: The key to check.
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:type key: str
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:returns: True if key exists.
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:rtype: bool
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"""
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...
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@abstractmethod
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def delete(self, key: str) -> None:
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"""Forget one key and any history kept for it."""
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...
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@abstractmethod
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def clear(self) -> None:
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"""Clear all keys in the state."""
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...
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@abstractmethod
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def keys(self) -> list[str]:
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"""
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Get all keys.
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:returns: List of all keys.
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:rtype: list[str]
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"""
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...
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@abstractmethod
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def update(self, mapping: dict[str, Any]) -> None:
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"""
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Update multiple keys atomically.
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:param mapping: Dict of key-value pairs to set.
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:type mapping: dict[str, Any]
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"""
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...
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@abstractmethod
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@contextmanager
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def lock(self, timeout: float = 10.0) -> Iterator[None]:
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"""
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Acquire a lock for thread-safe operations.
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:param timeout: Lock timeout in seconds.
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:type timeout: float
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:yields: Lock context.
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"""
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...
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# -------------------------------------------------------------------------
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# Atomic operations for synchronous node support
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# -------------------------------------------------------------------------
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@abstractmethod
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def increment(self, key: str) -> int:
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"""
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Atomically increment a counter and return the new value.
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:param key: The key to increment.
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:type key: str
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:returns: The new value after incrementing.
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:rtype: int
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"""
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...
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@abstractmethod
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def get_multi(self, keys: list[str]) -> dict[str, Any]:
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"""
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Get multiple values atomically.
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:param keys: List of keys to retrieve.
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:type keys: list[str]
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:returns: Dict mapping keys to values (missing keys have None).
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:rtype: dict[str, Any]
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"""
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...
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@abstractmethod
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def compare_and_swap_multi(
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self,
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expected: dict[str, Any],
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updates: dict[str, Any],
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) -> bool:
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"""
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Atomically check expected values and apply updates if all match.
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This is the key primitive for race-condition-free synchronous node execution.
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If any expected value doesn't match the current value, no updates are applied.
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:param expected: Dict of keys to their expected values.
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:type expected: dict[str, Any]
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:param updates: Dict of keys to their new values (applied only if expected matches).
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:type updates: dict[str, Any]
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:returns: True if the swap succeeded, False if expected values didn't match.
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:rtype: bool
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"""
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...
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# -------------------------------------------------------------------------
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# Message history
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# -------------------------------------------------------------------------
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@abstractmethod
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def append_history(
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self, values: dict[str, Any], ts: float, limits: dict[str, int] | None = None
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) -> None:
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"""
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Record freshly published values in each message's capped history.
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Non-numeric payloads are skipped — the history exists to be plotted.
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:param values: Message names mapped to the value just published.
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:type values: dict[str, Any]
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:param ts: When they were published.
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:param limits: How many points to keep per message, where a chart asks
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for more than the default.
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:type ts: float
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"""
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...
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@abstractmethod
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def history(self, key: str) -> list[tuple[float, float]]:
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"""
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The recorded ``(timestamp, value)`` pairs of one message, oldest first.
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:param key: The message name.
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:type key: str
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:returns: At most ``HISTORY_LIMIT`` points; empty if nothing was recorded.
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:rtype: list[tuple[float, float]]
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"""
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...
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def __contains__(self, key: str) -> bool:
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return self.exists(key)
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def __getitem__(self, key: str) -> Any:
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if not self.exists(key):
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raise KeyError(key)
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return self.get(key)
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def __setitem__(self, key: str, value: Any) -> None:
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self.set(key, value)
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def __iter__(self) -> Iterator[str]:
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return iter(self.keys())
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def __len__(self) -> int:
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return len(self.keys())
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class MemoryState(StateBackend):
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"""
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In-memory state backend using a thread-safe dict.
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Suitable for single-process execution and testing.
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:example:
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>>> state = MemoryState()
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>>> state["key"] = "value"
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>>> state["key"]
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'value'
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"""
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__slots__ = ("_data", "_history", "_lock")
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def __init__(self) -> None:
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self._data: dict[str, Any] = {}
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self._history: dict[str, deque[tuple[float, float]]] = {}
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self._lock = RLock() # Reentrant lock for nested access
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def get(self, key: str, default: Any = None) -> Any:
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with self._lock:
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return self._data.get(key, default)
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def set(self, key: str, value: Any) -> None:
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with self._lock:
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self._data[key] = value
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def exists(self, key: str) -> bool:
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with self._lock:
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return key in self._data
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def delete(self, key: str) -> None:
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with self._lock:
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self._data.pop(key, None)
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self._history.pop(key, None)
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def clear(self) -> None:
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with self._lock:
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self._data.clear()
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self._history.clear()
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def keys(self) -> list[str]:
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with self._lock:
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return list(self._data.keys())
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def update(self, mapping: dict[str, Any]) -> None:
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with self._lock:
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self._data.update(mapping)
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@contextmanager
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def lock(self, timeout: float = 10.0) -> Iterator[None]:
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"""Acquire the internal lock."""
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acquired = self._lock.acquire(timeout=timeout)
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if not acquired:
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raise TimeoutError("Failed to acquire lock")
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try:
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yield
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finally:
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self._lock.release()
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def increment(self, key: str) -> int:
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"""Atomically increment a counter and return the new value."""
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with self._lock:
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value: int = self._data.get(key, 0)
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new_value = value + 1
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self._data[key] = new_value
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return new_value
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def get_multi(self, keys: list[str]) -> dict[str, Any]:
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"""Get multiple values atomically."""
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with self._lock:
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return {k: self._data.get(k) for k in keys}
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def compare_and_swap_multi(
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self,
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expected: dict[str, Any],
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updates: dict[str, Any],
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) -> bool:
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"""Atomically check expected values and apply updates if all match."""
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with self._lock:
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# Check all expected values
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for key, exp_value in expected.items():
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actual = self._data.get(key)
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if actual != exp_value:
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return False
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# All matched, apply updates
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self._data.update(updates)
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return True
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def append_history(
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self, values: dict[str, Any], ts: float, limits: dict[str, int] | None = None
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) -> None:
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"""Append the numeric values to their message's bounded series."""
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with self._lock:
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for key, value in values.items():
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number = as_number(value)
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if number is None:
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continue
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cap = (limits or {}).get(key, HISTORY_LIMIT)
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series = self._history.get(key)
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if series is None or series.maxlen != cap:
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# A widget asking for a deeper series re-caps it in place.
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series = deque(series or (), maxlen=cap)
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self._history[key] = series
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series.append((ts, number))
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def history(self, key: str) -> list[tuple[float, float]]:
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"""The recorded points of one message, oldest first."""
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with self._lock:
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return list(self._history.get(key, ()))
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class RedisState(StateBackend):
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"""
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Redis-based state backend for distributed execution.
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Values are stored as JSON — anything a node passes through must be
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serializable. Uses a namespace prefix to isolate pipeline executions.
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:param host: Redis host address.
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:type host: str
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:param port: Redis port number.
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:type port: int
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:param db: Redis database number.
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:type db: int
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:param namespace: Key prefix for isolation.
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:type namespace: str
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:param password: Redis password (optional).
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:type password: str | None
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:param ttl: Time-to-live for keys in seconds (optional).
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:type ttl: int | None
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:example:
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>>> state = RedisState(host="localhost", namespace="my_pipeline")
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>>> state["result"] = {"value": 42}
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>>> state["result"]
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{'value': 42}
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"""
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__slots__ = ("_client", "_namespace", "_ttl", "_lock_name")
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def __init__(
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self,
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host: str = "localhost",
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port: int = 6379,
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db: int = 0,
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namespace: str = "pipeline",
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password: str | None = None,
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ttl: int | None = None,
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) -> None:
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self._client = redis.Redis(
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host=host,
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port=port,
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db=db,
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password=password,
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decode_responses=False,
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)
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self._namespace = namespace
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self._ttl = ttl
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self._lock_name = f"{namespace}:_lock"
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def _key(self, key: str) -> str:
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"""Generate namespaced key."""
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return f"{self._namespace}:{key}"
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def _serialize(self, value: Any) -> bytes:
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"""Serialize value for storage."""
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return json.dumps(value).encode()
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def _deserialize(self, data: bytes | None) -> Any:
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"""Deserialize value from storage."""
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return json.loads(data) if data else None
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# redis-py types every command as a sync/async union; this is the
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# synchronous client, so the results are narrowed where they are consumed.
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def get(self, key: str, default: Any = None) -> Any:
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data = cast(bytes | None, self._client.get(self._key(key)))
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return self._deserialize(data) if data is not None else default
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def set(self, key: str, value: Any) -> None:
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data = self._serialize(value)
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if self._ttl:
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self._client.setex(self._key(key), self._ttl, data)
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else:
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self._client.set(self._key(key), data)
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def exists(self, key: str) -> bool:
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return bool(self._client.exists(self._key(key)))
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def delete(self, key: str) -> None:
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self._client.delete(self._key(key), self._history_key(key))
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def clear(self) -> None:
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"""Clear all keys in the namespace."""
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pattern = f"{self._namespace}:*"
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cursor = 0
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while True:
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cursor, keys = cast(
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tuple[int, list[bytes]],
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self._client.scan(cursor, match=pattern, count=100),
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)
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if keys:
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self._client.delete(*keys)
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if cursor == 0:
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break
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def keys(self) -> list[str]:
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"""Get all keys in the namespace (without prefix)."""
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pattern = f"{self._namespace}:*"
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prefix_len = len(self._namespace) + 1
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result = []
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cursor = 0
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while True:
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cursor, keys = cast(
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tuple[int, list[bytes]],
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self._client.scan(cursor, match=pattern, count=100),
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)
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for k in keys:
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key_str = k.decode("utf-8")
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# Skip the lock key
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if not key_str.endswith(":_lock"):
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result.append(key_str[prefix_len:])
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if cursor == 0:
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break
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return result
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def update(self, mapping: dict[str, Any]) -> None:
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"""Update multiple keys atomically using a pipeline."""
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if not mapping:
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return
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pipe = self._client.pipeline()
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for key, value in mapping.items():
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data = self._serialize(value)
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if self._ttl:
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pipe.setex(self._key(key), self._ttl, data)
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else:
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pipe.set(self._key(key), data)
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pipe.execute()
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@contextmanager
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def lock(self, timeout: float = 10.0) -> Iterator[None]:
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"""
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Acquire a distributed lock using Redis.
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:param timeout: Lock timeout in seconds.
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:type timeout: float
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:yields: Lock context.
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:raises TimeoutError: If lock cannot be acquired.
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"""
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redis_lock = self._client.lock(
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self._lock_name,
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timeout=timeout,
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blocking_timeout=timeout,
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)
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acquired = redis_lock.acquire()
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if not acquired:
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raise TimeoutError("Failed to acquire Redis lock")
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try:
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yield
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finally:
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try:
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redis_lock.release()
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except redis.exceptions.LockError:
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pass # Lock may have expired
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def ping(self) -> bool:
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"""
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Check if Redis connection is available.
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:returns: True if connected.
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:rtype: bool
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"""
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try:
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return bool(self._client.ping())
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except redis.ConnectionError:
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return False
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def increment(self, key: str) -> int:
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"""Atomically increment a counter using Redis INCR."""
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return cast(int, self._client.incr(self._key(key)))
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def get_multi(self, keys: list[str]) -> dict[str, Any]:
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"""Get multiple values atomically using Redis MGET."""
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if not keys:
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return {}
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full_keys = [self._key(k) for k in keys]
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values = cast(list[bytes | None], self._client.mget(full_keys))
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result = {}
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for key, value in zip(keys, values, strict=True):
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result[key] = self._deserialize(value) if value is not None else None
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return result
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def compare_and_swap_multi(
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self,
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expected: dict[str, Any],
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updates: dict[str, Any],
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) -> bool:
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"""
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Atomically check expected values and apply updates using Redis WATCH/MULTI.
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Uses optimistic locking with WATCH to detect concurrent modifications.
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If any watched key changes before EXEC, the transaction is aborted.
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:param expected: Dict of keys to their expected values.
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:type expected: dict[str, Any]
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:param updates: Dict of keys to their new values.
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:type updates: dict[str, Any]
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:returns: True if the swap succeeded, False otherwise.
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:rtype: bool
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"""
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if not expected:
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# No expectations, just set the updates
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self.update(updates)
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return True
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watch_keys = [self._key(k) for k in expected.keys()]
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try:
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# Start watching the keys for changes
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self._client.watch(*watch_keys)
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# Check current values
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current = self.get_multi(list(expected.keys()))
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|
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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 _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, json.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 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 = json.loads(entry)
|
|
points.append((float(ts), float(value)))
|
|
return points
|