moad-sweep: 9 initial findings — MOAD-0005 (Hungry Regex) x1, MOAD-0006 (Thundering Herd) x8
MOAD-0005 candidates (ReDoS): - bleach-MOADX-0001: sanitize_css O(2^N) — 71-char input causes 12s hang HIGH MOAD-0006 candidates (Thundering Herd / cache stampede): - hibernate-orm-MOADX-0001: QueryInterpretationCacheStandardImpl HQL plan cache HIGH - elasticsearch-MOADX-0001: EnrichCache "intentionally non-locking" enrich search HIGH - hadoop-MOADX-0001: FederationJCache/FederationCaffeineCache YARN default config HIGH - celery-MOADX-0001: BaseBackend.get_task_meta chord fan-in Redis stampede HIGH - traefik-MOADX-0001: CNAMEFlatten data race + N*30s DNS stampede HIGH - kafka-MOADX-0001: CachedConnectors.lookup classloader scan MEDIUM - spring-MOADX-0001: AbstractFallbackCacheOperationSource @Cacheable cold start MEDIUM - django-MOADX-0001: cached.Loader.get_template template compile MEDIUM CLEAN: Consul (singleflight), Kubernetes (RWMutex), Prometheus (per-scraper), Pulsar (AsyncLoadingCache), Cassandra (LoadingCache), Flink (CAS loop), ZooKeeper (synchronized), Airflow (hand-rolled singleflight)
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# UNDF: (pending)
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# bleach-MOADX-0001: BleachSanitizerFilter.sanitize_css — ReDoS O(2^N) on style attribute
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## MOAD-0005 Candidate — The Hungry Regex
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| Field | Value |
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|-------|-------|
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| ID | bleach-MOADX-0001 |
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| Severity | HIGH |
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| Ecosystem | bleach (Python HTML sanitizer) |
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| File | `bleach/sanitizer.py` |
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| Lines | 553–558 |
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| Pattern | `^([-/:,#%.'"\sa-zA-Z0-9!]\|\w-\w\|'[\s\w]+'\s*\|"[\s\w]+"\|\([\d,%\.\s]+\))*$` |
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| Trigger | `style="a-a-a-a-...-a-@"` — 71 chars causes ~12-second hang |
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| Input vector | HTML `style` attribute value from user-submitted content |
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| Input length limit | none |
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## Defect
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`bleach/sanitizer.py`, function `sanitize_css`, lines 553–558:
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```python
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parts = style.split(';')
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gauntlet = re.compile(
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r"""^([-/:,#%.'"\sa-zA-Z0-9!]|\w-\w|'[\s\w]+'\s*|"[\s\w]+"|\([\d,%\.\s]+\))*$"""
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)
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for part in parts:
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if not gauntlet.match(part):
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return ''
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```
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The pattern `^(A|B|C|D|E)*$` where:
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- `A` = `[-/:,#%.'"\sa-zA-Z0-9!]` — single char (broad set including `-`)
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- `B` = `\w-\w` — three chars: word-hyphen-word
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The alternation `A|B` creates exponential ambiguity: a string like `a-a-a-a-` can
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be parsed as `[a][-][a][-]` (all via `A`) or `[a-a][-][a-]` (via `B` then `A`) or
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countless other combinations. For each prefix position, the regex engine must
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explore both interpretations.
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When the input does NOT match (the trailing `$` fails), Python's NFA backtracks
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through all O(2^N) parse trees, where N is the number of `a-` repetitions.
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**Measurement (Python 3.x, bleach 3.0.0):**
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| Input length | Time |
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|---|---|
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| 41 chars (`a-`×20 + `@`) | 0.011s |
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| 51 chars (`a-`×25 + `@`) | 0.11s |
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| 61 chars (`a-`×30 + `@`) | 0.97s |
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| 71 chars (`a-`×35 + `@`) | 12.8s |
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Growth factor: ~10× per 10 chars = O(2^N).
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**Attack payload:**
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```
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style="a-a-a-a-a-a-a-a-a-a-a-a-a-a-a-a-a-a-a-a-a-a-a-a-a-a-a-a-a-a-a-a-a-a-a-@"
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```
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71-char style attribute causes a ~12-second Python thread hang.
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Triggering characters for the non-matching terminal: `@`, `$`, `^`, `~`, `` ` ``,
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`|`, `\`, `{`, `}`, `<`, `>` — all realistic HTML/CSS chars.
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**Input path:** HTML user content → `bleach.clean()` → `BleachSanitizerFilter.sanitize_token()` →
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`sanitize_css()` → `gauntlet.match(part)`.
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## Fix
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The ambiguity is between `A` matching `-` as a single character and `B` matching
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`\w-\w` as a three-character sequence. Eliminating the single-char `-` from group
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`A` and keeping it only in `B` removes the ambiguity, OR use atomic grouping
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(Python 3.11+ via `re` module possessive `(?>...)`), OR replace with a proper
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CSS tokenizer.
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**Minimal fix — remove `-` from the single-char group:**
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```python
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gauntlet = re.compile(
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r"""^([-/:,#%.'"\sa-zA-Z0-9!]|(?<!\w)-(?!\w)|\w-\w|'[\s\w]+'\s*|"[\s\w]+"|\([\d,%\.\s]+\))*$"""
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)
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```
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**Better fix — switch to a proper CSS tokenizer** (e.g., `tinycss2` or `cssutils`)
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instead of regex-based validation. Bleach already depends on `html5lib`; a small
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CSS tokenizer dependency is acceptable.
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**Best fix — impose input length limit before matching:**
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```python
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MAX_STYLE_LEN = 1000
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for part in parts[:100]: # limit number of semicolon-separated parts
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part = part[:MAX_STYLE_LEN]
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if not gauntlet.match(part):
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return ''
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```
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The second pattern on line 561 (`^\s*([-\w]+\s*:[^:;]*(;\s*|$))*$`) does NOT
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exhibit catastrophic backtracking — its character class `[^:;]*` is bounded by
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the `:` and `;` delimiters.
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## References
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- bleach 3.0.0: `bleach/sanitizer.py` lines 540–575
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- Pattern first introduced: commit history shows this predates 3.0.0
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- Note: bleach has a history of ReDoS issues (CVE-2020-6802 linkifier, CVE-2021-23980 cleaner)
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but the CSS gauntlet pattern has not been separately reported.
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# UNDF: (pending)
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# celery-MOADX-0001: BaseBackend.get_task_meta — stampede O(N) concurrent backend fetches on shared result cache
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## MOAD-0006 Candidate — The Thundering Herd
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| Field | Value |
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|-------|-------|
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| ID | celery-MOADX-0001 |
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| Severity | HIGH |
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| Ecosystem | Celery |
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| Language | Python |
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| File | `celery/backends/base.py` |
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| Lines | 722–745 |
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| Pattern | LRUCache[key] → KeyError → expensive backend fetch → LRUCache[key]=meta (no lock around fetch) |
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| Trigger | N threads simultaneously poll the same task_id before result is cached; `result_backend_thread_safe=True` |
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| Compute cost | Network round-trip to Redis / database / AMQP broker |
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## Defect
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`BaseBackend.get_task_meta` is the result-retrieval path called by every `AsyncResult.get()` call. When the Celery app is configured with `result_backend_thread_safe = True`, the backend instance is stored as an app-level singleton (`_backend_cache`) shared across all threads:
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```python
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# celery/app/base.py:1468-1469
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if backend.thread_safe:
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self._backend_cache = backend # shared singleton
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```
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The shared backend contains `self._cache`, a `kombu.utils.functional.LRUCache` (backed by `threading.RLock`) that caches successful task results:
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```python
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# celery/backends/base.py:722-743
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def get_task_meta(self, task_id, cache=True):
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self._ensure_not_eager()
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if cache:
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try:
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return self._cache[task_id] # <-- RLock, returns if hit
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except KeyError:
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pass
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# NO LOCK AROUND THIS BLOCK:
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meta = self._ensure_retryable(
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self._get_task_meta_for, # <-- network call to Redis/DB
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fallback_exc=BackendGetMetaError,
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fallback_msg="failed to get meta",
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task_id=task_id
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)
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if cache and meta.get('status') == states.SUCCESS:
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self._cache[task_id] = meta # <-- RLock
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return meta
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```
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The `LRUCache.__getitem__` and `__setitem__` each hold `self.mutex` (RLock) individually, but the three-step sequence — check for hit → fetch from backend → write to cache — is not atomic.
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**Stampede scenario:** A task completes. At the moment of completion, N threads are all blocked in `AsyncResult.get()` polling the same `task_id`. The task result is not yet in `_cache`:
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1. All N threads call `get_task_meta(task_id)`.
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2. All N get `KeyError` from `self._cache[task_id]`.
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3. All N call `self._get_task_meta_for(task_id)` — each sends an independent query to Redis/database.
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4. All N receive the same result and write it to `self._cache[task_id]` (redundant).
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**Grower signal:** Works fine at low concurrency. With N=100 threads all waiting on the same long-running task (common in fan-out → fan-in patterns with `group()` + `chord()`), result publication triggers 100 simultaneous Redis GET commands instead of 1.
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The pattern also applies to `get_group_meta` (lines 769–775) which follows an identical structure for group-level result caching.
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## Fix
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**Option A — per-key `threading.Lock` with double-checked locking (preferred):**
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```python
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import threading
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class BaseBackend:
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def __init__(self, ...):
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...
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self._cache = _nulldict() if cmax == -1 else LRUCache(limit=cmax)
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self._pending_meta = {} # key → threading.Event
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self._pending_meta_lock = threading.Lock()
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def get_task_meta(self, task_id, cache=True):
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self._ensure_not_eager()
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if cache:
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try:
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return self._cache[task_id]
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except KeyError:
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pass
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# Singleflight: only one thread fetches, others wait
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with self._pending_meta_lock:
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if task_id in self._pending_meta:
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event = self._pending_meta[task_id]
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is_worker = False
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else:
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event = threading.Event()
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self._pending_meta[task_id] = event
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is_worker = True
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if not is_worker:
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event.wait(timeout=30)
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try:
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return self._cache[task_id]
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except KeyError:
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pass
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# fallthrough: worker failed, retry ourselves
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try:
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meta = self._ensure_retryable(
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self._get_task_meta_for,
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fallback_exc=BackendGetMetaError,
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fallback_msg="failed to get meta",
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task_id=task_id
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)
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if cache and meta.get('status') == states.SUCCESS:
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self._cache[task_id] = meta
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return meta
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finally:
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if is_worker:
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with self._pending_meta_lock:
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self._pending_meta.pop(task_id, None)
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event.set()
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```
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**Option B — accept redundant fetches, add comment (low-cost mitigation):**
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Not recommended. This only documents the defect without fixing it.
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**Option C — use `result_backend_thread_safe = False` (default) to avoid sharing:**
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Avoids the defect by giving each thread its own backend instance, but consumes more memory and connections. Not a fix — a workaround.
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The same pattern applies to `get_group_meta` and should receive the same treatment.
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## Impact
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- **Redis stampede:** N simultaneous GET commands per result-ready task when N threads poll the same task_id. At N=100 threads waiting on a chord callback, result publication triggers 100 Redis round-trips instead of 1.
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- **Database stampede:** With database backends (Django ORM, SQLAlchemy), N concurrent SELECT queries hit the same row.
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- **Fix:** After fix, first thread fetches, all others wait on Event and read from cache. Network round-trips per task result: 1 regardless of fan-in depth.
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# UNDF: (pending)
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# django-MOADX-0001: cached.Loader.get_template — stampede O(N) concurrent template compilations
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## MOAD-0006 Candidate — The Thundering Herd
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| Field | Value |
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|-------|-------|
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| ID | django-MOADX-0001 |
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| Severity | MEDIUM |
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| Ecosystem | Django |
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| Language | Python |
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| File | `django/template/loaders/cached.py` |
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| Lines | 28–65 |
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| Pattern | dict.get → None check → expensive compile → dict[key]=template (no lock) |
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| Trigger | N concurrent WSGI threads all request the same template during startup / cache clear |
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| Compute cost | File system read + template lexing + compilation to AST nodes |
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## Defect
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Django's `cached.Loader` wraps other template loaders and caches compiled `Template` objects in `self.get_template_cache` — a plain Python `dict`. The `Loader` instance is created once per engine alias (`EngineHandler.__getitem__`) and shared across all WSGI worker threads in the process.
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```python
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# django/template/loaders/cached.py:28-65
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def get_template(self, template_name, skip=None):
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key = self.cache_key(template_name, skip)
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cached = self.get_template_cache.get(key) # <-- dict lookup, no lock
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if cached:
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# ... return or raise from cache
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return cached
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# NO LOCK AROUND THIS BLOCK:
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try:
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template = super().get_template(template_name, skip) # <-- file I/O + compile
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except TemplateDoesNotExist as e:
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self.get_template_cache[key] = (
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copy_exception(e) if self.engine.debug else TemplateDoesNotExist
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)
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raise
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else:
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self.get_template_cache[key] = template # <-- dict write
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return template
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```
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`super().get_template()` walks each configured loader, opens the template file from disk, and compiles the template string through Django's lexer and parser into a `Template` object tree. This is orders of magnitude more expensive than a cached dict lookup.
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**CPython GIL consideration:** Individual `dict.get` and `dict[key] = value` operations are atomic under CPython's GIL. However, the three-step sequence (check → file load + compile → write) is not atomic. Threads can and do interleave between the miss check and the subsequent write:
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- Thread A: `cache.get(key)` → `None`
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- Thread B: `cache.get(key)` → `None` (before A has written)
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- Thread A: `super().get_template(...)` → opens file, parses template
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- Thread B: `super().get_template(...)` → opens file, parses template (duplicate work)
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- Thread A: `cache[key] = template`
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- Thread B: `cache[key] = template` (redundant write)
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**Grower signal:** Harmless at low concurrency. Triggers on:
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1. Application startup: all templates cold-cached, first request burst loads all at once.
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2. `loader.reset()` called during configuration reload: cache cleared, all threads stampede on first access.
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3. Templates with `skip` variants generate distinct cache keys, multiplying stampede surface.
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For a Django app with 50 templates and 32 WSGI workers, a `loader.reset()` followed by a traffic burst can produce 1600 redundant file reads and parses (32 workers × 50 templates).
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## Fix
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**Option A — `threading.Lock` per template key (fine-grained):**
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```python
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import threading
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class Loader(BaseLoader):
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def __init__(self, engine, loaders):
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self.get_template_cache = {}
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self._cache_locks = {}
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self._meta_lock = threading.Lock()
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self.loaders = engine.get_template_loaders(loaders)
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super().__init__(engine)
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def _get_or_create_key_lock(self, key):
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with self._meta_lock:
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if key not in self._cache_locks:
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self._cache_locks[key] = threading.Lock()
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return self._cache_locks[key]
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def get_template(self, template_name, skip=None):
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key = self.cache_key(template_name, skip)
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cached = self.get_template_cache.get(key)
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if cached:
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if isinstance(cached, type) and issubclass(cached, TemplateDoesNotExist):
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raise cached(template_name)
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elif isinstance(cached, TemplateDoesNotExist):
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raise copy_exception(cached)
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return cached
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key_lock = self._get_or_create_key_lock(key)
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with key_lock:
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# Double-check after acquiring lock
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cached = self.get_template_cache.get(key)
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if cached:
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if isinstance(cached, type) and issubclass(cached, TemplateDoesNotExist):
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raise cached(template_name)
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elif isinstance(cached, TemplateDoesNotExist):
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raise copy_exception(cached)
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return cached
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try:
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template = super().get_template(template_name, skip)
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except TemplateDoesNotExist as e:
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self.get_template_cache[key] = (
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copy_exception(e) if self.engine.debug else TemplateDoesNotExist
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)
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raise
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else:
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self.get_template_cache[key] = template
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return template
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```
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**Option B — coarse `threading.RLock` (simpler, acceptable given templates are rarely loaded):**
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```python
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self._lock = threading.RLock()
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def get_template(self, template_name, skip=None):
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key = self.cache_key(template_name, skip)
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# Fast path without lock
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cached = self.get_template_cache.get(key)
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if cached:
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...
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with self._lock:
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cached = self.get_template_cache.get(key)
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if cached:
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...
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# compile under lock — acceptable since templates load once per key
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...
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```
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**Option C — `functools.cached_property` pattern or `threading.local` per-thread cache:** Less correct since it defeats the purpose of a shared cache.
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Option A is preferred: per-key locking means different templates compile concurrently, only the same template key serializes.
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## Impact
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- **Redundant work:** N threads × file read + template parse per cache miss burst.
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- **Resource pressure:** Multiple simultaneous file handles to the same template file; parser AST allocations multiplied by N.
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- **Fix:** After fix, first miss for each key compiles once; subsequent requests wait and receive the cached result. Net: 1 compile per unique (template_name, skip) key regardless of concurrency.
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@ -0,0 +1,111 @@
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# UNDF: (pending)
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# elasticsearch-MOADX-0001: EnrichCache.computeIfAbsent — cache stampede on enrich index search
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## MOAD-0006 Candidate — The Thundering Herd
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| Field | Value |
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|-------|-------|
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| ID | elasticsearch-MOADX-0001 |
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| Severity | HIGH |
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| Ecosystem | Elasticsearch (x-pack enrich) |
|
||||
| File | `x-pack/plugin/enrich/src/main/java/org/elasticsearch/xpack/enrich/EnrichCache.java` lines 94–121 |
|
||||
| Cache type | Enrich policy result cache (lookup index search results) |
|
||||
| Pattern | get → null → searchResponseFetcher (Elasticsearch search query) → put (explicitly non-locking per comment) |
|
||||
| Trigger | N concurrent ingest pipeline requests all miss the same enrich cache key → N×Elasticsearch search queries fired simultaneously |
|
||||
| Hot path | Every document ingested through an enrich processor that misses cache |
|
||||
|
||||
## Defect
|
||||
|
||||
`EnrichCache.computeIfAbsent` (method name misleadingly implies atomicity) performs a raw get-then-execute-then-put without any synchronization. The code's own comment acknowledges this:
|
||||
|
||||
```java
|
||||
public void computeIfAbsent(
|
||||
ProjectId projectId,
|
||||
String enrichIndex,
|
||||
Object lookupValue,
|
||||
int maxMatches,
|
||||
Consumer<ActionListener<SearchResponse>> searchResponseFetcher,
|
||||
ActionListener<List<Map<?, ?>>> listener
|
||||
) {
|
||||
// intentionally non-locking for simplicity...it's OK if we re-put the same key/value in the cache during a race condition.
|
||||
long cacheStart = relativeNanoTimeProvider.getAsLong();
|
||||
var cacheKey = new CacheKey(projectId, enrichIndex, lookupValue, maxMatches);
|
||||
List<Map<?, ?>> response = get(cacheKey); // (1) all N threads: miss
|
||||
long cacheRequestTime = relativeNanoTimeProvider.getAsLong() - cacheStart;
|
||||
if (response != null) {
|
||||
hitsTimeInNanos.add(cacheRequestTime);
|
||||
listener.onResponse(response);
|
||||
} else {
|
||||
final long retrieveStart = relativeNanoTimeProvider.getAsLong();
|
||||
searchResponseFetcher.accept(ActionListener.wrap(resp -> { // (2) N×search queries
|
||||
CacheValue cacheValue = toCacheValue(resp);
|
||||
put(cacheKey, cacheValue); // (3) N redundant puts
|
||||
// ...
|
||||
}, listener::onFailure));
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
The comment "it's OK if we re-put the same key/value" is wrong for high-cardinality enrich lookups. It is not OK when:
|
||||
1. **The enrich index is large**: each search hits the Lucene index for that enrich data, consuming heap and I/O
|
||||
2. **The cache key has high cardinality** (e.g., IP address lookup in a geo-enrich policy): TTL expiry under ingestion load causes N threads to all fire searches for the same IP simultaneously
|
||||
3. **The search requires network hops**: in a multi-node cluster the enrich coordinator proxies searches to the shard owner (see `EnrichCoordinatorProxyAction`) — N concurrent searches all go to the same shard
|
||||
|
||||
### Ingest pipeline throughput amplification
|
||||
|
||||
Enrich processors are designed to be on the hot path for bulk indexing. At 10,000 documents/second with a TTL-expired cache entry, the cluster can receive 10,000 simultaneous search queries for the same lookup value before the first one completes and populates the cache.
|
||||
|
||||
## Impact
|
||||
|
||||
- Enrich index shard becomes saturated with redundant identical searches
|
||||
- Ingest throughput drops dramatically during cache cold start or TTL expiry
|
||||
- In worst case (hot key + small TTL), the stampede is continuous: searches pile up before cache is populated, the pipeline backs up, and ES node heap fills with queued search contexts
|
||||
- Kibana's `_enrich/stats` will show abnormally high `misses_time_in_nanos` with ratio misses/hits > 1
|
||||
|
||||
## Fix
|
||||
|
||||
Use a `ConcurrentHashMap` of in-flight `CompletableFuture`s to coalesce concurrent misses for the same key — the same pattern as Pulsar's `BookkeeperSchemaStorage.readSchemaOperations`:
|
||||
|
||||
```java
|
||||
private final ConcurrentHashMap<CacheKey, CompletableFuture<List<Map<?, ?>>>> inflight =
|
||||
new ConcurrentHashMap<>();
|
||||
|
||||
public void computeIfAbsent(
|
||||
ProjectId projectId,
|
||||
String enrichIndex,
|
||||
Object lookupValue,
|
||||
int maxMatches,
|
||||
Consumer<ActionListener<SearchResponse>> searchResponseFetcher,
|
||||
ActionListener<List<Map<?, ?>>> listener
|
||||
) {
|
||||
var cacheKey = new CacheKey(projectId, enrichIndex, lookupValue, maxMatches);
|
||||
List<Map<?, ?>> cached = get(cacheKey);
|
||||
if (cached != null) {
|
||||
listener.onResponse(cached);
|
||||
return;
|
||||
}
|
||||
// Coalesce: only one search fires per key; all other waiters share the future
|
||||
CompletableFuture<List<Map<?, ?>>> future = inflight.computeIfAbsent(cacheKey, k -> {
|
||||
CompletableFuture<List<Map<?, ?>>> f = new CompletableFuture<>();
|
||||
searchResponseFetcher.accept(ActionListener.wrap(resp -> {
|
||||
CacheValue cacheValue = toCacheValue(resp);
|
||||
put(cacheKey, cacheValue);
|
||||
inflight.remove(cacheKey);
|
||||
f.complete(deepCopy(cacheValue.hits, false));
|
||||
}, ex -> {
|
||||
inflight.remove(cacheKey);
|
||||
f.completeExceptionally(ex);
|
||||
}));
|
||||
return f;
|
||||
});
|
||||
future.whenComplete((result, ex) -> {
|
||||
if (ex != null) {
|
||||
listener.onFailure(new RuntimeException(ex));
|
||||
} else {
|
||||
listener.onResponse(result);
|
||||
}
|
||||
});
|
||||
}
|
||||
```
|
||||
|
||||
Alternatively, replace the custom `Cache` with Caffeine `AsyncLoadingCache` which provides this deduplication automatically.
|
||||
|
|
@ -0,0 +1,113 @@
|
|||
# UNDF: (pending)
|
||||
# hadoop-MOADX-0001: FederationJCache/FederationCaffeineCache — cache stampede on YARN Federation state store
|
||||
|
||||
## MOAD-0006 Candidate — The Thundering Herd
|
||||
|
||||
| Field | Value |
|
||||
|-------|-------|
|
||||
| ID | hadoop-MOADX-0001 |
|
||||
| Severity | HIGH |
|
||||
| Ecosystem | Apache Hadoop |
|
||||
| Files | `hadoop-yarn-project/hadoop-yarn/hadoop-yarn-server/hadoop-yarn-server-common/src/main/java/org/apache/hadoop/yarn/server/federation/cache/FederationJCache.java` lines 101–138; `FederationCaffeineCache.java` lines 89–125 |
|
||||
| Cache type | YARN Federation state store (sub-cluster registry, policy configurations, application home sub-cluster) |
|
||||
| Pattern | get → null → stateStore.getXxx() → put (no lock, no CAS) |
|
||||
| Trigger | N concurrent Router requests all miss the same cache key → N×stateStore calls hit ZooKeeper/SQL simultaneously |
|
||||
| Hot path | YARN Router per-request path: every application submission, every allocation, every cluster query goes through `FederationStateStoreFacade.getSubClusters()` or `getApplicationHomeSubCluster()` |
|
||||
|
||||
## Defect
|
||||
|
||||
Both `FederationJCache` and `FederationCaffeineCache` implement the same unsynchronized check-then-act pattern across three methods:
|
||||
|
||||
### `FederationJCache.getSubClusters` (lines 101–110)
|
||||
|
||||
```java
|
||||
public Map<SubClusterId, SubClusterInfo> getSubClusters(boolean filterInactiveSubClusters)
|
||||
throws YarnException {
|
||||
final String cacheKey = buildCacheKey(className, GET_SUBCLUSTERS_CACHEID,
|
||||
Boolean.toString(filterInactiveSubClusters));
|
||||
CacheRequest<String, ?> cacheRequest = cache.get(cacheKey); // (1) all N threads: miss
|
||||
if (cacheRequest == null) {
|
||||
cacheRequest = buildGetSubClustersCacheRequest(className, filterInactiveSubClusters); // (2) N×stateStore.getSubClusters() → ZooKeeper/SQL
|
||||
cache.put(cacheKey, cacheRequest); // (3) N redundant puts
|
||||
}
|
||||
return buildSubClusterInfoMap(cacheRequest);
|
||||
}
|
||||
```
|
||||
|
||||
The same pattern exists verbatim in:
|
||||
- `FederationJCache.getPoliciesConfigurations` (lines 113–123) — calls `stateStore.getPoliciesConfigurations()`
|
||||
- `FederationJCache.getApplicationHomeSubCluster` (lines 125–138) — calls `stateStore.getApplicationHomeSubCluster()`
|
||||
- All three methods duplicated identically in `FederationCaffeineCache.java`
|
||||
|
||||
### The state store calls are network calls
|
||||
|
||||
`buildGetSubClustersCacheRequest` calls `stateStore.getSubClusters(request)` which in production is backed by:
|
||||
- `ZookeeperFederationStateStore` — ZooKeeper read (10–50ms each)
|
||||
- `SQLFederationStateStore` — SQL database query (5–100ms each)
|
||||
|
||||
When TTL expires (default: `yarn.federation.cache-ttl.secs` = 60s) under load, N threads all miss simultaneously and all fire the state store call.
|
||||
|
||||
### Default configuration makes this the production path
|
||||
|
||||
`YarnConfiguration.java`:
|
||||
```java
|
||||
public static final String FEDERATION_CACHE_CLASS_DEFAULT =
|
||||
"org.apache.hadoop.yarn.server.federation.cache.FederationJCache";
|
||||
```
|
||||
|
||||
`FederationJCache` is used unless explicitly overridden. `FederationCaffeineCache` was added as an alternative but carries the exact same defect.
|
||||
|
||||
## Impact
|
||||
|
||||
YARN Router in a large federation (50+ sub-clusters, 10,000+ applications) issues these calls on every resource allocation request. At cache TTL expiry under high load:
|
||||
- N concurrent Router threads all miss `getSubClusters`
|
||||
- N ZooKeeper reads fire simultaneously
|
||||
- ZooKeeper watch limit or connection pool exhausted
|
||||
- Allocation requests time out
|
||||
- Entire allocation path stalls until cache is populated
|
||||
|
||||
## Fix
|
||||
|
||||
### Option 1: Use `computeIfAbsent` with Caffeine LoadingCache (preferred for `FederationCaffeineCache`)
|
||||
|
||||
```java
|
||||
// FederationCaffeineCache — replace manual check-then-act with LoadingCache
|
||||
private LoadingCache<String, CacheRequest> cache;
|
||||
|
||||
// In initCache():
|
||||
this.cache = Caffeine.newBuilder()
|
||||
.maximumSize(cacheEntityNums)
|
||||
.expireAfterWrite(cacheTimeToLive, TimeUnit.SECONDS)
|
||||
.build(key -> loadFromStateStore(key)); // loader called exactly once per key per miss
|
||||
|
||||
// In getSubClusters():
|
||||
public Map<SubClusterId, SubClusterInfo> getSubClusters(boolean filterInactiveSubClusters)
|
||||
throws YarnException {
|
||||
final String cacheKey = buildCacheKey(className, GET_SUBCLUSTERS_CACHEID,
|
||||
Boolean.toString(filterInactiveSubClusters));
|
||||
return buildSubClusterInfoMap(cache.get(cacheKey)); // LoadingCache serializes misses per key
|
||||
}
|
||||
```
|
||||
|
||||
### Option 2: Synchronized block around miss path (for `FederationJCache`)
|
||||
|
||||
```java
|
||||
public Map<SubClusterId, SubClusterInfo> getSubClusters(boolean filterInactiveSubClusters)
|
||||
throws YarnException {
|
||||
final String cacheKey = buildCacheKey(className, GET_SUBCLUSTERS_CACHEID,
|
||||
Boolean.toString(filterInactiveSubClusters));
|
||||
CacheRequest<String, ?> cacheRequest = cache.get(cacheKey);
|
||||
if (cacheRequest == null) {
|
||||
synchronized (this) {
|
||||
cacheRequest = cache.get(cacheKey); // double-checked locking
|
||||
if (cacheRequest == null) {
|
||||
cacheRequest = buildGetSubClustersCacheRequest(className, filterInactiveSubClusters);
|
||||
cache.put(cacheKey, cacheRequest);
|
||||
}
|
||||
}
|
||||
}
|
||||
return buildSubClusterInfoMap(cacheRequest);
|
||||
}
|
||||
```
|
||||
|
||||
Apply the same fix to `getPoliciesConfigurations` and `getApplicationHomeSubCluster` in both `FederationJCache` and `FederationCaffeineCache`.
|
||||
|
|
@ -0,0 +1,156 @@
|
|||
# UNDF: (pending)
|
||||
# hibernate-orm-MOADX-0001: QueryInterpretationCacheStandardImpl — cache stampede O(N) concurrent plan builds on cold start
|
||||
|
||||
## MOAD-0006 Candidate — The Thundering Herd
|
||||
|
||||
| Field | Value |
|
||||
|-------|-------|
|
||||
| ID | hibernate-orm-MOADX-0001 |
|
||||
| Severity | HIGH |
|
||||
| Ecosystem | hibernate-orm |
|
||||
| File | `hibernate-core/src/main/java/org/hibernate/query/internal/QueryInterpretationCacheStandardImpl.java` |
|
||||
| Lines | 81–103 (`resolveSelectQueryPlan`), 115–149 (`resolveHqlInterpretation`) |
|
||||
| Pattern | get → null check → compute → put (no lock, no computeIfAbsent) |
|
||||
| Trigger | N concurrent requests with same uncached query → N concurrent expensive plan builds |
|
||||
| Hot path | Per-query execution path: every `session.createQuery()` / `session.createMutationQuery()` call |
|
||||
| Compute cost | HQL → SQM AST translation + SQL query plan building (type resolution, join analysis, SQL generation strategy) |
|
||||
|
||||
## Defect
|
||||
|
||||
`QueryInterpretationCacheStandardImpl` is Hibernate ORM's query plan cache, used on every HQL/JPQL query execution. Two methods exhibit the classic check-then-act stampede pattern on the underlying `InternalCache` (`BoundedConcurrentHashMap`):
|
||||
|
||||
### `resolveSelectQueryPlan` (lines 81–103)
|
||||
|
||||
```java
|
||||
@Override
|
||||
public <K extends Key, R> SelectQueryPlan<R> resolveSelectQueryPlan(
|
||||
K key,
|
||||
Function<K, SelectQueryPlan<R>> creator) {
|
||||
@SuppressWarnings("unchecked")
|
||||
final var cached = (SelectQueryPlan<R>) queryPlanCache.get( key ); // 1. read
|
||||
if ( cached != null ) {
|
||||
return cached; // 2. hit
|
||||
}
|
||||
|
||||
final var plan = creator.apply( key ); // 3. expensive: SQL plan build
|
||||
queryPlanCache.put( key.prepareForStore(), plan ); // 4. write
|
||||
return plan;
|
||||
}
|
||||
```
|
||||
|
||||
`creator.apply(key)` calls `buildSelectQueryPlan()` → `QuerySplitter.split()` + `buildConcreteQueryPlan()` → `new ConcreteSqmSelectQueryPlan(...)`. This involves SQM polymorphism expansion, entity graph resolution, fetch strategy analysis, and SQL generation strategy computation.
|
||||
|
||||
### `resolveHqlInterpretation` (lines 115–149)
|
||||
|
||||
```java
|
||||
@Override
|
||||
public <R> HqlInterpretation<R> resolveHqlInterpretation(
|
||||
String queryString,
|
||||
Class<R> expectedResultType,
|
||||
HqlTranslator translator) {
|
||||
...
|
||||
final var existing = hqlInterpretationCache.get( cacheKey ); // 1. read
|
||||
if ( existing != null ) {
|
||||
return (HqlInterpretation<R>) existing; // 2. hit
|
||||
}
|
||||
// optional second lookup for query-only key
|
||||
final var hqlInterpretation =
|
||||
createHqlInterpretation( queryString, expectedResultType, translator, statistics ); // 3. expensive
|
||||
hqlInterpretationCache.put( cacheKey, hqlInterpretation ); // 4. write
|
||||
return hqlInterpretation;
|
||||
}
|
||||
```
|
||||
|
||||
`createHqlInterpretation()` calls `translator.translate(queryString, expectedResultType)` — full HQL lexing, parsing, AST construction, semantic analysis (type binding, relationship traversal, parameter extraction).
|
||||
|
||||
### Inconsistency
|
||||
|
||||
`resolveNativeQueryParameters` (line 192) in the *same class* correctly uses `computeIfAbsent`:
|
||||
|
||||
```java
|
||||
return nativeQueryParamCache.computeIfAbsent( queryString, creator );
|
||||
```
|
||||
|
||||
This confirms the fix was known but not applied consistently.
|
||||
|
||||
## Stampede Scenario
|
||||
|
||||
1. Application starts and first `N` concurrent HTTP requests all execute the same JPQL query (e.g. `SELECT u FROM User u WHERE u.id = :id`).
|
||||
2. All N threads call `resolveHqlInterpretation` simultaneously.
|
||||
3. All N threads see a cache miss (step 1) — the cache is empty.
|
||||
4. All N threads enter `createHqlInterpretation` simultaneously.
|
||||
5. All N threads independently: lex and parse the HQL string, build an SQM AST, resolve entity types, extract parameters.
|
||||
6. All N writes race to `put` — result is the same but work was done N times.
|
||||
7. Under moderate load (N=50 threads, ~20 query types), 1000 duplicate plan builds execute before the cache warms.
|
||||
|
||||
Same cascade applies after Hibernate's `queryPlanCacheMaxSize` evicts a plan (default 2048) — a popular query that gets evicted causes a burst of recomputes.
|
||||
|
||||
## Fix
|
||||
|
||||
Replace the get-null-compute-put triple with a single `computeIfAbsent` call, consistent with what `resolveNativeQueryParameters` already does.
|
||||
|
||||
### `resolveSelectQueryPlan`
|
||||
|
||||
```java
|
||||
@Override
|
||||
public <K extends Key, R> SelectQueryPlan<R> resolveSelectQueryPlan(
|
||||
K key,
|
||||
Function<K, SelectQueryPlan<R>> creator) {
|
||||
LOG.tracef( "Resolving cached query plan for [%s]", key );
|
||||
final var statistics = getStatistics();
|
||||
final boolean statisticsEnabled = statistics.isStatisticsEnabled();
|
||||
|
||||
@SuppressWarnings("unchecked")
|
||||
final var plan = (SelectQueryPlan<R>) queryPlanCache.computeIfAbsent(
|
||||
key,
|
||||
k -> {
|
||||
if ( statisticsEnabled ) {
|
||||
statistics.queryPlanCacheMiss( k.getQueryString() );
|
||||
}
|
||||
return creator.apply( (K) k );
|
||||
});
|
||||
|
||||
if ( statisticsEnabled && plan != null ) {
|
||||
// hit was recorded if computeIfAbsent returned without calling the function
|
||||
// (we cannot distinguish hit from miss here without a flag, but misses are counted inside)
|
||||
}
|
||||
return plan;
|
||||
}
|
||||
```
|
||||
|
||||
Or simpler — use the existing `computeIfAbsent` interface on `InternalCache`:
|
||||
|
||||
```java
|
||||
final var plan = (SelectQueryPlan<R>) queryPlanCache.computeIfAbsent(
|
||||
key,
|
||||
k -> creator.apply( (K) k ) );
|
||||
```
|
||||
|
||||
### `resolveHqlInterpretation`
|
||||
|
||||
```java
|
||||
@Override
|
||||
public <R> HqlInterpretation<R> resolveHqlInterpretation(
|
||||
String queryString,
|
||||
Class<R> expectedResultType,
|
||||
HqlTranslator translator) {
|
||||
final Object cacheKey = expectedResultType != null
|
||||
? new HqlInterpretationCacheKey( queryString, expectedResultType )
|
||||
: queryString;
|
||||
|
||||
@SuppressWarnings("unchecked")
|
||||
final var result = (HqlInterpretation<R>) hqlInterpretationCache.computeIfAbsent(
|
||||
cacheKey,
|
||||
k -> createHqlInterpretation( queryString, expectedResultType, translator, getStatistics() ) );
|
||||
return result;
|
||||
}
|
||||
```
|
||||
|
||||
Note: `InternalCache.computeIfAbsent` is already defined in the interface (line in `InternalCache.java`) and implemented in `LegacyInternalCacheImplementation` via `BoundedConcurrentHashMap.computeIfAbsent`. The infrastructure is in place — only the call sites need updating.
|
||||
|
||||
## Severity Rationale
|
||||
|
||||
- **HIGH**: Called on every query execution, not just startup. Cache misses recur after eviction.
|
||||
- Compute cost: HQL parsing + type resolution is CPU-intensive and involves synchronization inside the HQL translator.
|
||||
- At N=100 concurrent requests on a cold start (k8s pod restart, cache eviction), 100 redundant plan builds stall the thread pool simultaneously — classic thundering herd causing cascading latency.
|
||||
- The fix requires changing two call sites to use the already-present `computeIfAbsent` API.
|
||||
|
|
@ -0,0 +1,93 @@
|
|||
# UNDF: (pending)
|
||||
# kafka-MOADX-0001: CachedConnectors.lookup — TOCTOU stampede on connector class loading
|
||||
|
||||
## MOAD-0006 Candidate — The Thundering Herd
|
||||
|
||||
| Field | Value |
|
||||
|-------|-------|
|
||||
| ID | kafka-MOADX-0001 |
|
||||
| Severity | MEDIUM |
|
||||
| Ecosystem | Apache Kafka Connect |
|
||||
| File | `connect/runtime/src/main/java/org/apache/kafka/connect/runtime/CachedConnectors.java` lines 57–73 |
|
||||
| Cache type | Connector class instance cache (connector name + version → Connector object) |
|
||||
| Pattern | containsKey+get (non-atomic) → null check → plugins.newConnector() (classloader scan + instantiation) → put (unsynchronized) |
|
||||
| Trigger | N concurrent connector-start requests for the same connector type all fail the containsKey check → N classloader scans and instantiations |
|
||||
| Hot path | `WorkerConnector.start()` and `Worker.startConnector()` — called once per connector instance startup |
|
||||
|
||||
## Defect
|
||||
|
||||
`CachedConnectors.lookup` performs a check-then-act that is not atomic, allowing multiple threads to all miss the cache for the same (connectorName, version) key simultaneously:
|
||||
|
||||
```java
|
||||
private Connector lookup(String connectorName, VersionRange range) {
|
||||
String version = range == null ? LATEST_VERSION : range.toString();
|
||||
// TOCTOU: containsKey + get is not atomic — another thread can insert between these two calls
|
||||
if (connectors.containsKey(connectorName) && connectors.get(connectorName).containsKey(version)) {
|
||||
return connectors.get(connectorName).get(version); // (1) all N threads: miss (concurrent startup)
|
||||
}
|
||||
|
||||
try {
|
||||
Connector connector = plugins.newConnector(connectorName, range); // (2) N×classloader scan + instantiation
|
||||
connectors.computeIfAbsent(connectorName, k -> new ConcurrentHashMap<>()).put(version, connector); // (3) N puts
|
||||
return connector;
|
||||
} catch (VersionedPluginLoadingException e) {
|
||||
invalidVersions.computeIfAbsent(connectorName, k -> new ConcurrentHashMap<>()).put(version, e);
|
||||
throw e;
|
||||
} catch (Exception e) {
|
||||
invalidConnectors.put(connectorName, e);
|
||||
throw e;
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
### What `plugins.newConnector` does
|
||||
|
||||
`plugins.newConnector(connectorName, range)` calls `pluginLoader(classOrAlias, range, sourceLoader).loadClass(classOrAlias)` which:
|
||||
1. Scans all plugin sources for the connector class (classpath scan if using reflective discovery)
|
||||
2. Creates an isolated classloader (`DelegatingClassLoader`) for the plugin
|
||||
3. Instantiates the connector via reflection
|
||||
|
||||
This is CPU-intensive (classpath scan can take 10–500ms depending on plugin count) and involves `synchronized` blocks inside the JVM class loading machinery. Under concurrent load, multiple threads competing for the same class create contention inside `ClassLoader.loadClass`.
|
||||
|
||||
### Scenario
|
||||
|
||||
A Kafka Connect cluster running in distributed mode adds a new connector with 100 tasks. All 100 task startup requests arrive at the `Worker` simultaneously. Each calls `getConnector(connectorName, null)` → `lookup()`. All 100 threads fail the `containsKey` check and all fire `plugins.newConnector()`. The JVM class loading lock then serializes them, but the outer scan work is duplicated 100×.
|
||||
|
||||
### Note on outer `connectors` map
|
||||
|
||||
`connectors` is a `ConcurrentHashMap<String, Map<String, Connector>>` but the inner map is a freshly created `ConcurrentHashMap` added via `computeIfAbsent`. The race is: thread A checks `containsKey`, thread B also checks `containsKey` (both miss), both call `newConnector`, both try to `computeIfAbsent` the inner map and `put`. Both complete — correctness is preserved but the redundant class loading is wasteful.
|
||||
|
||||
## Impact
|
||||
|
||||
- On startup with many connector tasks: N redundant classloader scans and instantiations
|
||||
- JVM class loading lock contention under the concurrent instantiation attempts
|
||||
- Connector plugins that have expensive `init()` or static initializers run N times
|
||||
- MEDIUM severity because this is not a per-request hot path — it happens at connector start only, not per-message
|
||||
|
||||
## Fix
|
||||
|
||||
Use `computeIfAbsent` directly to make the check-and-insert atomic:
|
||||
|
||||
```java
|
||||
private Connector lookup(String connectorName, VersionRange range) {
|
||||
String version = range == null ? LATEST_VERSION : range.toString();
|
||||
// Atomic: computeIfAbsent ensures loader is called exactly once per (name, version) pair
|
||||
Map<String, Connector> versionMap = connectors.computeIfAbsent(
|
||||
connectorName, k -> new ConcurrentHashMap<>());
|
||||
return versionMap.computeIfAbsent(version, v -> {
|
||||
try {
|
||||
return plugins.newConnector(connectorName, range);
|
||||
} catch (VersionedPluginLoadingException e) {
|
||||
invalidVersions.computeIfAbsent(connectorName, k -> new ConcurrentHashMap<>()).put(version, e);
|
||||
throw e;
|
||||
} catch (Exception e) {
|
||||
invalidConnectors.put(connectorName, e);
|
||||
throw e;
|
||||
}
|
||||
});
|
||||
}
|
||||
```
|
||||
|
||||
Note: `computeIfAbsent` on `ConcurrentHashMap` guarantees the mapping function is called at most once per key per miss (per the Java 8+ specification). This eliminates both the redundant instantiation and the TOCTOU window.
|
||||
|
||||
The error-caching paths (`invalidConnectors`, `invalidVersions`) should similarly be guarded, but errors are less likely to be concurrent.
|
||||
|
|
@ -0,0 +1,138 @@
|
|||
# UNDF: (pending)
|
||||
# spring-MOADX-0001: AbstractFallbackCacheOperationSource — cache stampede O(N) concurrent annotation scans on cold start
|
||||
|
||||
## MOAD-0006 Candidate — The Thundering Herd
|
||||
|
||||
| Field | Value |
|
||||
|-------|-------|
|
||||
| ID | spring-MOADX-0001 |
|
||||
| Severity | MEDIUM |
|
||||
| Ecosystem | spring-framework |
|
||||
| File 1 | `spring-context/src/main/java/org/springframework/cache/interceptor/AbstractFallbackCacheOperationSource.java` |
|
||||
| File 2 | `spring-context-support/src/main/java/org/springframework/cache/jcache/interceptor/AbstractFallbackJCacheOperationSource.java` |
|
||||
| Lines | 95–121 (standard), 67–91 (JCache) |
|
||||
| Pattern | ConcurrentHashMap.get → null check → reflection scan → ConcurrentHashMap.put |
|
||||
| Trigger | N concurrent requests to same `@Cacheable`-annotated method before cache warms → N concurrent annotation reflection scans |
|
||||
| Hot path | Every `@Cacheable` / `@CacheResult` method invocation before the operation metadata is cached |
|
||||
| Compute cost | Java reflection: `AopUtils.getMostSpecificMethod`, method/class annotation traversal, proxy unwrapping |
|
||||
|
||||
## Defect
|
||||
|
||||
`AbstractFallbackCacheOperationSource` is the core metadata resolver for Spring's `@Cacheable`, `@CachePut`, and `@CacheEvict` annotations. It caches the resolved `CacheOperation` collection in a `ConcurrentHashMap`, but uses a non-atomic get-null-compute-put pattern:
|
||||
|
||||
```java
|
||||
// AbstractFallbackCacheOperationSource.java:95–121
|
||||
private @Nullable Collection<CacheOperation> getCacheOperations(
|
||||
Method method, @Nullable Class<?> targetClass, boolean cacheNull) {
|
||||
|
||||
if (ReflectionUtils.isObjectMethod(method)) {
|
||||
return null;
|
||||
}
|
||||
|
||||
Object cacheKey = getCacheKey(method, targetClass);
|
||||
Collection<CacheOperation> cached = this.operationCache.get(cacheKey); // 1. read
|
||||
|
||||
if (cached != null) {
|
||||
return (cached != NULL_CACHING_MARKER ? cached : null); // 2. hit
|
||||
}
|
||||
else {
|
||||
Collection<CacheOperation> cacheOps = computeCacheOperations(method, targetClass); // 3. compute
|
||||
if (cacheOps != null) {
|
||||
this.operationCache.put(cacheKey, cacheOps); // 4. write
|
||||
}
|
||||
else if (cacheNull) {
|
||||
this.operationCache.put(cacheKey, NULL_CACHING_MARKER);
|
||||
}
|
||||
return cacheOps;
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
`computeCacheOperations` resolves the most-specific method (proxy unwrapping), then walks the method and declaring class for cache annotations — up to 4 reflection calls:
|
||||
|
||||
```java
|
||||
private @Nullable Collection<CacheOperation> computeCacheOperations(Method method, @Nullable Class<?> targetClass) {
|
||||
Method specificMethod = AopUtils.getMostSpecificMethod(method, targetClass); // proxy unwrap + class hierarchy
|
||||
Collection<CacheOperation> opDef = findCacheOperations(specificMethod); // method annotation scan
|
||||
if (opDef != null) return opDef;
|
||||
opDef = findCacheOperations(specificMethod.getDeclaringClass()); // class annotation scan
|
||||
if (opDef != null && ClassUtils.isUserLevelMethod(method)) return opDef;
|
||||
if (specificMethod != method) {
|
||||
opDef = findCacheOperations(method); // original method scan
|
||||
if (opDef != null) return opDef;
|
||||
opDef = findCacheOperations(method.getDeclaringClass()); // original class scan
|
||||
if (opDef != null && ClassUtils.isUserLevelMethod(method)) return opDef;
|
||||
}
|
||||
return null;
|
||||
}
|
||||
```
|
||||
|
||||
The identical pattern exists in `AbstractFallbackJCacheOperationSource.getCacheOperation` for `@CacheResult`/`@CachePut`/`@CacheRemove` annotations (lines 67–91).
|
||||
|
||||
## Stampede Scenario
|
||||
|
||||
1. Application receives `N` concurrent requests to the same `@Cacheable`-annotated method (e.g., a REST endpoint called on startup health check or during load test ramp-up).
|
||||
2. The `operationCache` is empty (cold start, or first invocation after context refresh).
|
||||
3. All N threads call `getCacheOperations` simultaneously.
|
||||
4. All N threads get a cache miss (step 1).
|
||||
5. All N threads independently call `computeCacheOperations`: proxy unwrapping, annotation scanning.
|
||||
6. All N threads race to `put` the same result — identical values, redundant work.
|
||||
|
||||
In a large Spring application with many `@Cacheable` beans, concurrent startup traffic forces redundant annotation scans across hundreds of method-class pairs simultaneously. This delays initial response times and causes contention in the reflection infrastructure.
|
||||
|
||||
## Fix
|
||||
|
||||
Replace get-null-compute-put with `computeIfAbsent`. The `ConcurrentHashMap` already supports this atomically:
|
||||
|
||||
### Standard cache annotation source
|
||||
|
||||
```java
|
||||
// AbstractFallbackCacheOperationSource.java
|
||||
private @Nullable Collection<CacheOperation> getCacheOperations(
|
||||
Method method, @Nullable Class<?> targetClass, boolean cacheNull) {
|
||||
|
||||
if (ReflectionUtils.isObjectMethod(method)) {
|
||||
return null;
|
||||
}
|
||||
|
||||
Object cacheKey = getCacheKey(method, targetClass);
|
||||
// computeIfAbsent is atomic: only one thread computes, others wait for the result
|
||||
Object cached = this.operationCache.computeIfAbsent(cacheKey, k -> {
|
||||
Collection<CacheOperation> ops = computeCacheOperations(method, targetClass);
|
||||
return (ops != null) ? ops : (cacheNull ? NULL_CACHING_MARKER : null);
|
||||
});
|
||||
|
||||
if (cached == null) {
|
||||
return null; // cacheNull was false and no operation found
|
||||
}
|
||||
return (cached != NULL_CACHING_MARKER ? (Collection<CacheOperation>) cached : null);
|
||||
}
|
||||
```
|
||||
|
||||
Note: `ConcurrentHashMap.computeIfAbsent` does not accept null return values; the `NULL_CACHING_MARKER` sentinel handles the "no annotation" case correctly.
|
||||
|
||||
### JCache annotation source
|
||||
|
||||
Same fix applies to `AbstractFallbackJCacheOperationSource.getCacheOperation`:
|
||||
|
||||
```java
|
||||
private @Nullable JCacheOperation<?> getCacheOperation(Method method, @Nullable Class<?> targetClass, boolean cacheNull) {
|
||||
if (ReflectionUtils.isObjectMethod(method)) {
|
||||
return null;
|
||||
}
|
||||
MethodClassKey cacheKey = new MethodClassKey(method, targetClass);
|
||||
Object cached = this.operationCache.computeIfAbsent(cacheKey, k -> {
|
||||
JCacheOperation<?> op = computeCacheOperation(method, targetClass);
|
||||
return (op != null) ? op : (cacheNull ? NULL_CACHING_MARKER : null);
|
||||
});
|
||||
if (cached == null) return null;
|
||||
return (cached != NULL_CACHING_MARKER ? (JCacheOperation<?>) cached : null);
|
||||
}
|
||||
```
|
||||
|
||||
## Severity Rationale
|
||||
|
||||
- **MEDIUM**: The compute cost (annotation reflection) is lower than I/O or SQL plan building, but reflection is synchronized on the JVM class object and can serialize under high parallelism.
|
||||
- Triggered on every cold start or after Spring context refresh — affects all `@Cacheable`-heavy applications.
|
||||
- At N=200 concurrent startup requests across 50 annotated methods, 10,000 redundant annotation scans execute before the cache warms. Each scan acquires internal JVM locks.
|
||||
- The fix is a one-line change per method — `computeIfAbsent` is already present on the `ConcurrentHashMap` backing the `operationCache`.
|
||||
|
|
@ -0,0 +1,114 @@
|
|||
# UNDF: (pending)
|
||||
# traefik-MOADX-0001: CNAMEFlatten — cache stampede + data race O(N) concurrent DNS lookups
|
||||
|
||||
## MOAD-0006 Candidate — The Thundering Herd
|
||||
|
||||
| Field | Value |
|
||||
|-------|-------|
|
||||
| ID | traefik-MOADX-0001 |
|
||||
| Severity | HIGH |
|
||||
| Ecosystem | Traefik |
|
||||
| Language | Go |
|
||||
| File | `pkg/middlewares/requestdecorator/hostresolver.go` |
|
||||
| Lines | 37–75 |
|
||||
| Pattern | get → miss → expensive DNS lookup → set (no singleflight, no mutex) |
|
||||
| Trigger | N concurrent goroutines all hit same hostname with cold/expired cache |
|
||||
| Compute cost | Full CNAME-chain DNS resolution via `cnameResolve()` with 30-second timeout per DNS server |
|
||||
|
||||
## Defect
|
||||
|
||||
`CNAMEFlatten` is called by `RequestDecorator.ServeHTTP` on every inbound HTTP request in the entrypoint's handler chain. The `Resolver` struct is a single instance shared across all goroutines for that entrypoint (created once in `NewTCPEntryPoint`, passed to `requestdecorator.New`).
|
||||
|
||||
**Race 1 — lazy cache init data race:**
|
||||
|
||||
```go
|
||||
// pkg/middlewares/requestdecorator/hostresolver.go:38-39
|
||||
func (hr *Resolver) CNAMEFlatten(ctx context.Context, host string) string {
|
||||
if hr.cache == nil {
|
||||
hr.cache = cache.New(30*time.Minute, 5*time.Minute) // DATA RACE
|
||||
}
|
||||
// ...
|
||||
}
|
||||
```
|
||||
|
||||
Multiple goroutines read `hr.cache == nil` simultaneously, then all write `hr.cache = ...`. Go's race detector flags this immediately. The `*cache.Cache` pointer is written without any synchronization.
|
||||
|
||||
**Race 2 — cache stampede on miss:**
|
||||
|
||||
```go
|
||||
// pkg/middlewares/requestdecorator/hostresolver.go:50-68
|
||||
value, found := hr.cache.Get(host) // thread-safe individually
|
||||
if found {
|
||||
return value.(string)
|
||||
}
|
||||
|
||||
// NO LOCK OR SINGLEFLIGHT AROUND THIS BLOCK:
|
||||
for depth := range hr.ResolvDepth {
|
||||
resolv, err := cnameResolve(ctx, request, hr.ResolvConfig) // expensive DNS call
|
||||
// ...
|
||||
}
|
||||
|
||||
hr.cache.Set(host, result, cacheDuration) // thread-safe individually
|
||||
```
|
||||
|
||||
`go-cache`'s `Get` and `Set` are individually mutex-protected, but the check-then-compute-then-set sequence is not atomic. When N goroutines all request the same hostname simultaneously on a cold or expired cache entry, all N pass the `found == false` check and all N execute `cnameResolve()` in parallel — each doing up to `ResolvDepth` DNS round-trips (each with 30-second timeouts).
|
||||
|
||||
**Grower signal:** Works fine at low traffic. Under a traffic spike to a new backend hostname, or when the 30-minute TTL expires on a popular hostname, all concurrent requests trigger DNS lookups simultaneously. With `ResolvDepth=5` (default) and N=1000 concurrent goroutines, this is 5000 concurrent DNS UDP/TCP connections.
|
||||
|
||||
## Fix
|
||||
|
||||
**Option A — `singleflight.Group` (preferred, matches Traefik's existing pattern):**
|
||||
|
||||
```go
|
||||
import "golang.org/x/sync/singleflight"
|
||||
|
||||
type Resolver struct {
|
||||
CnameFlattening bool
|
||||
ResolvConfig string
|
||||
ResolvDepth int
|
||||
cache *cache.Cache
|
||||
cacheOnce sync.Once
|
||||
group singleflight.Group
|
||||
}
|
||||
|
||||
func (hr *Resolver) CNAMEFlatten(ctx context.Context, host string) string {
|
||||
hr.cacheOnce.Do(func() {
|
||||
hr.cache = cache.New(30*time.Minute, 5*time.Minute)
|
||||
})
|
||||
|
||||
if value, found := hr.cache.Get(host); found {
|
||||
return value.(string)
|
||||
}
|
||||
|
||||
result, _, _ := hr.group.Do(host, func() (any, error) {
|
||||
// Only one goroutine resolves; others wait and share the result.
|
||||
res := host
|
||||
req := host
|
||||
cacheDuration := 0 * time.Second
|
||||
for depth := range hr.ResolvDepth {
|
||||
resolv, err := cnameResolve(ctx, req, hr.ResolvConfig)
|
||||
if err != nil || resolv == nil {
|
||||
break
|
||||
}
|
||||
res = resolv.Record
|
||||
if depth == 0 {
|
||||
cacheDuration = resolv.TTL
|
||||
}
|
||||
req = resolv.Record
|
||||
}
|
||||
hr.cache.Set(host, res, cacheDuration)
|
||||
return res, nil
|
||||
})
|
||||
return result.(string)
|
||||
}
|
||||
```
|
||||
|
||||
**Option B — `sync.Once` for init + `sync.Map` as cache:**
|
||||
|
||||
Replace `*cache.Cache` with `sync.Map` and use a per-key `singleflight.Group`. Traefik already uses `singleflight` in `basic_auth.go` (line 118) and `healthcheck.go` — the pattern is established in the codebase.
|
||||
|
||||
## Impact
|
||||
|
||||
- **Data race:** Go runtime will panic under `-race` flag; undefined behavior in production (cache pointer torn write).
|
||||
- **Stampede:** N×ResolvDepth DNS connections on cache miss. At N=1000 goroutines with ResolvDepth=5, a single cache expiry produces 5000 DNS queries from one Traefik instance. DNS resolver exhaustion, connection table blowup, upstream SERVFAIL cascade.
|
||||
- **Fix speedup:** After fix, cache miss costs 1 DNS lookup regardless of concurrent request count.
|
||||
Loading…
Add table
Add a link
Reference in a new issue