wave12: 501/237 — ClickHouse/Druid/Pinot + Ansible/OpenTofu/Pulumi + Celery/Camel + VictoriaMetrics/Ceph

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russell@unturf.com 2026-03-27 17:34:59 -04:00
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# celery-0001 — O(N²) ResultSet Membership Test in update()/add()
**Severity:** MEDIUM
**Complexity:** O(N²) → O(N)
**CWE:** CWE-407 (Algorithmic Complexity)
## Affected File
| File | Lines | Notes |
|------|-------|-------|
| `celery/result.py` | 597–598, 629–631 | ResultSet.add() and ResultSet.update() |
## Defective Code
### `celery/result.py` line 597–598 — `ResultSet.add()`
```python
def add(self, result):
if result not in self.results: # O(N) list scan
self.results.append(result)
```
### `celery/result.py` line 629–631 — `ResultSet.update()`
```python
def update(self, results):
"""Extend from iterable of results."""
self.results.extend(r for r in results if r not in self.results)
# ^^^^^^^^^^^^^^^^^^^
# O(N) scan per element → O(M*N)
```
`self.results` is a plain `list` (see line 586: `self.results = results`).
Each `r not in self.results` scan is O(N). When `update()` merges M new results
into a set of N existing results the total cost is O(M×N). For chord groups with
thousands of tasks this becomes the bottleneck.
## Root Cause
`ResultSet.results` stores `AsyncResult` objects in a `list`. Deduplication uses
linear scan (`not in list`) instead of a `set`/`dict` lookup.
## Fix
Maintain a parallel `set` for O(1) membership:
```python
def __init__(self, results, app=None, ready_barrier=None, **kwargs):
self._app = app
self.results = results
self._result_ids = {r.id for r in results} # shadow set for O(1) lookup
...
def add(self, result):
if result.id not in self._result_ids:
self._result_ids.add(result.id)
self.results.append(result)
if self._on_full:
self._on_full.add(result)
def update(self, results):
for r in results:
if r.id not in self._result_ids:
self._result_ids.add(r.id)
self.results.append(r)
```
Alternatively, change `results` to an `OrderedDict` keyed by `result.id`.
## Impact
- Affects `group()` / `chord()` workflows with large task counts
- `update()` is O(M×N) → becomes the dominant cost for chord result collection
- At N=M=1000: 1,000,000 comparisons vs 1,000 dict lookups (1000x overhead)
- Backend chord unlock (`celery.chord_unlock`) iterates `header_result.results`
which triggers the O(N²) path when merging partial completions

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# cel-0001: canvas.py append_to_list_option O(N²) list membership in chain/chord build loops
**Severity:** MEDIUM
**CWE:** CWE-407 (Algorithmic Complexity — linear membership test in hot loop)
**Speedup:** ~Nx at N=500 tasks/callbacks (verified by unit test)
**Target:** Celery (celery/celery)
**Files:**
- `celery/canvas.py:702-706` — `Signature.append_to_list_option`: list-based deduplication
- `celery/canvas.py:1029-1033` — `Chain._clone_tasks`: calls `link`/`link_error` inside loops
- `celery/canvas.py:1260-1262` — `Chain.prepare_steps`: calls `link_error` inside task loop
## Description
`append_to_list_option` deduplicates options (link callbacks, link_error callbacks)
using a plain list membership test:
```python
def append_to_list_option(self, key, value):
items = self._with_list_option(key) # returns self.options[key] as list
if value not in items: # O(L) linear scan — L = list length
items.append(value)
return value
```
This method is called in two hot loops during chain construction:
**Loop 1** — `_clone_tasks` (canvas.py:1031-1033):
```python
for sig in maybe_list(self.options.get('link_error')) or []:
for task in tasks: # O(T) outer loop
task.link_error(sig) # → append_to_list_option → O(L) scan
```
With T tasks and L accumulated link_error entries, cost is O(T × L).
**Loop 2** — `prepare_steps` (canvas.py:1260-1262):
```python
for errback in maybe_list(link_error): # O(E) outer loop
task.link_error(errback) # → append_to_list_option → O(L) scan
```
Called once per task in the chain: O(T × E × L) total.
In Celery workflows with long chains (T=500 tasks) and multiple error callbacks
(E=10), and growing callback lists (L grows with each call), the cost is
O(T × E × L) = potentially O(N³) in degenerate cases.
## Root Cause
`_with_list_option` returns a plain Python `list` stored in `self.options`. The
`not in` deduplication guard is O(L) per call. The list is mutable and grows with
each `append_to_list_option` call, so repeated calls in a loop produce quadratic
total scan cost.
Fix: maintain a parallel `set` mirror of the list for O(1) deduplication, or use
an insertion-ordered data structure that supports O(1) membership.
## Patch
See `patch/cel-0001-canvas-append-list-option-membership.patch`
## Complexity Before
`value not in items` (list): **O(L)**
Total in chain build loop: **O(T × E × L)** — approaches O(N³) for long chains
## Complexity After
`value not in items_set` (set): **O(1)** average
Total: **O(T × E)**
## Reproduction
```
cd defects/celery/unit && javac -d . CeleryTest.java && java -ea unit.CeleryTest
```