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LY002: Log Loop

Prevent unconditional logging in hot loops

Or: "The rule that will save your ops team from a meltdown"

Problem

Unconditional logging inside loops is like opening a fire hose in your log aggregation system. Everything gets wet. Nothing is readable. Everyone is angry.

# ❌ This logs 10,000 times. For 10,000 users. Every. Single. One.
for user in users:
    logger.info(f"Processing user {user.id}")
    process_user(user)

If users has 10,000 items, you've just created 10,000 log entries. In production, this is a disaster: - Your disk fills up in hours - Your log aggregation system throws a fit and starts dropping logs - Critical errors are buried under an avalanche of spam - Your ops team stops answering your Slack messages (they're all updating their résumés)

Solution

Use conditional logging or summarize before/after the loop:

# ✅ Log every 100th iteration (the sampler)
for i, user in enumerate(users):
    if i % 100 == 0:
        logger.info(f"Progress: {i}/{len(users)}")
    process_user(user)

# ✅ Or summarize before and after (the executive summary approach)
logger.info(f"Processing {len(users)} users")
for user in users:
    process_user(user)  # No logging here. Just work.
logger.info(f"Processed {len(users)} users successfully")

That's it. You're done. Your ops team can breathe again.

Impact

Before (the disaster scenario): - 10,000 iterations = 10,000 log entries per run - ~150ms of logging overhead (eating your performance) - CloudWatch costs: ~$5/GB for ingestion - Ops team: furious

After (the hero scenario): - 10,000 iterations = 100 log entries per run (1% of the spam) - ~1.5ms of logging overhead (negligible) - 99% reduction in log volume = 99% cost reduction - Ops team: sends you cookies

Configuration

log_loop:
  levels: [info, debug]  # Check info and debug logs
  severity: fail         # Block CI if violations found

Options

levels: Which log levels to check

  • [info, debug] - Info and debug (recommended)
  • [any] - All levels including error/warning
  • [debug] - Only debug logs

severity: Violation handling

  • fail - Exit with error (recommended for CI)
  • warn - Print warning only
  • info - Informational

Examples

✅ Pass (The Responsible Logging)

# Conditional logging - the gold standard
for i, item in enumerate(items):
    if i % 100 == 0:  # Only log every 100th item
        logger.info(f"Progress: {i}/{len(items)}")
    process(item)

# Logging in exception handler - exceptions deserve documentation
for item in items:
    try:
        process(item)
    except Exception:
        logger.error("Failed processing", exc_info=True)  # Exceptions are rare, log them all

# Logging in if-statement - gated by a condition
for user in users:
    if user.is_admin:  # Only log when something interesting happens
        logger.info(f"Processing admin: {user.id}")
    process(user)

# Logging before/after loop - the summary approach
logger.info(f"Starting batch of {len(items)} items")
for item in items:
    process(item)  # No logging here. Silent work.
logger.info("Batch complete")  # Just report the results

❌ Fail (The Spam Machine)

# Unconditional logging in for-loop - the classic mistake
for user in users:
    logger.info(f"Processing {user}")  # Screaming into the void, 10,000 times
    process(user)

# Unconditional logging in while-loop - burns forever
while queue.has_items():
    item = queue.get()
    logger.debug(f"Got item: {item}")  # Queue has 50,000 items. 50,000 log entries.
    process(item)

# Unconditional logging in nested loop - multiplies like rabbits
for batch in batches:
    for item in batch:
        logger.info(f"Item: {item}")  # 100 batches × 100 items = 10,000 logs
        process(item)

Detection Logic

loly detects unconditional logging by checking if a log call is:

  1. Inside a loop (for or while)
  2. Not gated by a condition (no if, except, etc.)

Conditional logging gets a free pass: 

for item in items:
    if should_log(item):  # ✅ Protected by a condition. You're safe.
        logger.info(f"Special item: {item}")

When to Use

Avoid unconditional logging in: - Data processing loops (ETL, batch jobs) - could be millions of items - Message queue consumers - queues are infinite - API request handlers with iteration - lists can grow to astronomical sizes

Acceptable to log every iteration: - Small, bounded loops (< 10 iterations) - only if you're absolutely sure - Critical audit trails (legal requirements override performance) - Loops that execute rarely (maybe once a day)

Performance Data

From production systems where this rule saved the day:

E-commerce checkout processing: - Before: 1,200 logs/sec, 45% CPU spent on logging (no processing!) - After: 12 logs/sec, < 1% CPU on logging (finally doing actual work) - Result: 40% faster checkout, more money in the bank, everyone happy

Data pipeline: - Before: 500GB logs/day, $150/day in CloudWatch costs (per day!) - After: 5GB logs/day, $1.50/day in CloudWatch costs - Result: 99% cost reduction. Finance team stops calling. You're a hero.

Real-World Example

From our battle-test analysis of real-world codebases (aka "the carnage"):

Airflow codebase - Loop logging in DAG serialization: 

# airflow/serialization/serde.py:375
for item in items:
    logger.debug(f"Serializing {item}")  # ❌ For 1000+ tasks = 1000+ debug logs per serialization
    serialize(item)

For DAGs with 1000+ tasks, this generates 1000+ debug entries. If you serialize 10 times, that's 10,000 logs. In a large Airflow deployment, this is a disaster.

Alternative Patterns

When you absolutely must log inside a loop, use one of these approaches:

Pattern 1: Sampling 

import random

for item in items:
    if random.random() < 0.01:  # Log ~1% of iterations
        logger.info(f"Sample: {item}")
    process(item)

Pattern 2: Time-based 

import time

last_log = 0
for item in items:
    now = time.time()
    if now - last_log > 5:  # Log every 5 seconds, not every iteration
        logger.info(f"Processed {item}")
        last_log = now
    process(item)

Pattern 3: Aggregation (the sensible approach) 

processed = 0
errors = 0

for item in items:
    try:
        process(item)
        processed += 1
    except Exception:
        errors += 1

logger.info(f"Batch complete: {processed} processed, {errors} errors")
# One log entry for the whole operation. Your ops team will love you.

← Back to Policies - You've made it this far. Might as well read LY001 too.