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Antigravity Agent
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id: wiki-2026-0508-bottlenecks
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title: Bottlenecks (Performance & Process)
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category: 10_Wiki/Topics
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status: verified
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canonical_id: self
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aliases: [병목, bottleneck, theory of constraints, TOC, critical path, profiling]
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duplicate_of: none
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source_trust_level: A
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confidence_score: 0.93
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verification_status: applied
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tags: [performance, bottleneck, profiling, theory-of-constraints, optimization, scalability, latency]
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raw_sources: []
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last_reinforced: 2026-05-10
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github_commit: pending
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tech_stack:
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language: any
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framework: profiling tools
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---
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# Bottlenecks
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## 📌 한 줄 통찰
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> **"매 system 의 throat"**. 매 weakest link 의 throughput 의 결정. 매 non-bottleneck 의 improve = 매 시간 낭비. 매 Goldratt's TOC: 매 5 step. 매 modern AI: 매 HBM bandwidth + 매 network 의 bottleneck.
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## 📖 핵심
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### 매 type
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1. **Hardware**: CPU / GPU / RAM / disk / network.
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2. **Software**: algorithm / blocking / lock contention.
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3. **Process**: approval / single point of expertise.
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4. **Data**: schema / indexing / partitioning.
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5. **Cognitive** (team): meeting / context-switch.
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### Theory of Constraints (Goldratt)
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1. **Identify** the bottleneck.
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2. **Exploit** it (use 100%).
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3. **Subordinate** non-bottleneck (don't over-feed).
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4. **Elevate** it (invest to widen).
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5. **Repeat** (new bottleneck emerges).
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### Amdahl's Law (related)
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- 매 90% 의 100× → 매 전체 의 매 10× cap.
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- 매 bottleneck 의 X 의 의미.
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### 매 hardware bottleneck 의 modern (LLM)
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- **HBM bandwidth**: 매 H100 = 매 3 TB/s. 매 LLM inference 의 dominant.
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- **NVLink**: 매 GPU-GPU.
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- **Network** (RDMA, InfiniBand): 매 distributed train.
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- **PCIe**: 매 GPU-CPU.
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- **Storage**: 매 NVMe vs spinning.
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- **Power / cooling**: 매 datacenter limit.
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### 매 software bottleneck
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- **CPU-bound**: 매 compute heavy.
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- **I/O-bound**: 매 disk / network wait.
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- **Memory-bound**: 매 swap / cache miss.
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- **Lock contention**: 매 mutex.
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- **GIL** (Python): 매 single-thread CPU.
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- **N+1 query**: 매 ORM 의 typical.
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### 매 detection
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- **Profiler**: cProfile, perf, async-profiler.
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- **Trace**: distributed tracing (Jaeger).
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- **Metric**: CPU/mem/disk/network util.
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- **APM**: Datadog, NewRelic.
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- **Flame graph**.
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- **Critical path**.
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### 매 process bottleneck
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- 매 approval chain.
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- 매 single expert.
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- 매 environment provisioning.
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- 매 review SLA.
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- 매 meeting cadence.
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→ 매 DORA Lead Time 의 component.
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### 매 data bottleneck
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- 매 single hot row.
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- 매 missing index.
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- 매 cross-shard transaction.
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- 매 schema migration block.
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### 매 distributed bottleneck (modern)
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- 매 leader 의 single (Raft, Paxos).
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- 매 cross-region call.
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- 매 sync replication.
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- 매 connection pool limit.
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## 💻 패턴
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### Profile (Python cProfile)
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```python
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import cProfile, pstats
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def main():
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expensive_call()
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cheap_call()
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cProfile.run('main()', 'out.prof')
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stats = pstats.Stats('out.prof').sort_stats('cumulative')
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stats.print_stats(20)
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```
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### Linux perf (system-level)
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```bash
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# 매 CPU profile
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perf record -F 99 -p $PID -- sleep 10
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perf report
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# 매 flame graph
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perf script | ./stackcollapse-perf.pl | ./flamegraph.pl > flame.svg
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```
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### Async profiler (JVM)
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```bash
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# 매 sample lock contention
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java -jar async-profiler.jar -e lock -d 30 -f lock.html $PID
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# 매 wall clock (I/O bound 도)
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java -jar async-profiler.jar -e wall -d 30 -f wall.html $PID
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```
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### N+1 detect (Django)
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```python
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from django.test.utils import CaptureQueriesContext
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from django.db import connection
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with CaptureQueriesContext(connection) as ctx:
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posts = Post.objects.all()
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for post in posts:
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print(post.author.name) # 매 N+1
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if len(ctx.captured_queries) > 5:
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log(f'N+1 detected: {len(ctx.captured_queries)} queries')
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# 매 fix
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posts = Post.objects.select_related('author') # 매 1 query
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```
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### GPU bottleneck profile (PyTorch)
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```python
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import torch.profiler as prof
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with prof.profile(
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activities=[prof.ProfilerActivity.CPU, prof.ProfilerActivity.CUDA],
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record_shapes=True,
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profile_memory=True,
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) as p:
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model(input)
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print(p.key_averages().table(sort_by='cuda_time_total', row_limit=20))
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# 매 HBM bandwidth bottleneck 의 reveal
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```
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### Lock contention detection
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```python
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import threading
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class LockMonitor:
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def __init__(self, lock):
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self.lock = lock
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self.wait_times = []
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def __enter__(self):
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start = time.time()
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self.lock.acquire()
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self.wait_times.append(time.time() - start)
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def __exit__(self, *args):
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self.lock.release()
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def report(self):
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if not self.wait_times: return
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avg = sum(self.wait_times) / len(self.wait_times)
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if avg > 0.1: log(f'Lock contention: avg wait {avg*1000}ms')
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```
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### Distributed trace (Jaeger)
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```python
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from opentelemetry import trace
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from opentelemetry.sdk.trace import TracerProvider
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from opentelemetry.exporter.jaeger.thrift import JaegerExporter
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trace.set_tracer_provider(TracerProvider())
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tracer = trace.get_tracer(__name__)
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@tracer.start_as_current_span('handle_request')
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def handle(req):
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with tracer.start_as_current_span('db_query') as span:
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span.set_attribute('db.statement', 'SELECT ...')
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result = db.query(...)
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return result
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```
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→ 매 시각적 bottleneck identify.
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### Process bottleneck (workflow analysis)
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```python
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def analyze_workflow(stage_durations):
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"""매 stage 별 의 throughput 의 비교."""
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rates = {stage: 1 / dur for stage, dur in stage_durations.items()}
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bottleneck = min(rates, key=rates.get)
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overall_rate = rates[bottleneck]
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waste = sum(r - overall_rate for r in rates.values() if r > overall_rate)
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return {
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'bottleneck': bottleneck,
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'overall_rate_per_min': overall_rate * 60,
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'capacity_wasted': waste,
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}
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```
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### Critical path (DAG)
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```python
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import networkx as nx
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def critical_path(tasks):
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"""매 longest path through DAG."""
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G = nx.DiGraph()
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for task in tasks:
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G.add_node(task.id, duration=task.duration)
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for dep in task.deps:
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G.add_edge(dep, task.id)
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# 매 longest path
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return nx.dag_longest_path(G, weight='duration')
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```
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## 🤔 결정 기준
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| 증상 | Tool |
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|---|---|
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| Slow request | APM + distributed trace |
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| CPU pegged | Flame graph (perf) |
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| GPU underutilized | Memory bandwidth (PyTorch profiler) |
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| Slow query | EXPLAIN + slow query log |
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| Lock contention | async-profiler -e lock |
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| Long lead time | Process / DORA analysis |
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| Thundering herd | Coordination check |
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**기본값**: 매 measure first. 매 hypothesis-based optimize.
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## 🔗 Graph
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- 부모: [[System-Design]]
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- 변형: [[CPU-Bound]]
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- 응용: [[Theory-of-Constraints]] · [[Amdahl's Law]] · [[Critical-Path]]
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- Tool: [[Profiling]] · [[Flame-Graph]] · [[Distributed Tracing]]
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- Adjacent: [[Optimization]] · [[Scalability]] · [[DORA-Metrics]]
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## 🤖 LLM 활용
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**언제**: 매 performance optimization. 매 capacity planning. 매 incident root cause. 매 process improvement.
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**언제 X**: 매 hypothesis 없 의 optimize.
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## ❌ 안티패턴
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- **Optimize without measure**: 매 wrong place.
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- **Non-bottleneck improve**: 매 시간 waste (TOC).
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- **모든 part 의 평등 invest**: 매 ROI low.
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- **Single profile 의 trust**: 매 representative X.
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- **Process 의 "사람 의 fault"**: 매 system issue 가 대부분.
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- **Premature optimization**: 매 simplicity lose.
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## 🧪 검증 / 중복
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- Verified (Goldratt TOC, Knuth premature optimization, Brendan Gregg Systems Performance).
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- 신뢰도 A.
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- Related: [[Amdahl's Law]] · [[Theory-of-Constraints]] · [[Profiling]] · [[Critical-Path]].
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## 🕓 Changelog
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| 날짜 | 변경 |
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|---|---|
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| 2026-05-08 | Phase 1 |
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| 2026-05-10 | Manual cleanup — type + TOC + 매 profile / N+1 / GPU / trace code |
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