Identifying Performance Bottlenecks

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#!/bin/bash
# =====================================================
# UNDERSTANDING MEMORY USAGE ON LINUX
# =====================================================
 
# Process memory breakdown
cat /proc/<pid>/status | grep -E "Vm|Rss"
 
# VmPeak: Maximum virtual memory used
# VmSize: Current virtual memory size
# VmRSS:  Resident Set Size - actual RAM used
# VmData: Heap size
# VmStk:  Stack size
 
# More detailed: pmap
pmap -x <pid> | head -50
 
# Example output:
# Address           Kbytes     RSS   Dirty Mode  Mapping
# 0000000000400000    2048    1024       0 r-x-- java
# 00000000006f4000     128     128     128 rw--- java  (data segment)
# 00007f0000000000  262144  200000  200000 rw---   [heap]
# 
# The [heap] line shows your dynamic memory
 
 
# =====================================================
# JVM-SPECIFIC MEMORY ANALYSIS
# =====================================================
 
# JVM native memory breakdown
jcmd <pid> VM.native_memory summary
 
# Output shows:
# - Java Heap: where objects live
# - Class: loaded class metadata
# - Thread: per-thread stack memory
# - Code: JIT compiled code
# - GC: garbage collector data structures
# - Internal: JVM internal data
# - Symbol: interned strings
 
 
# =====================================================
# KEY METRICS TO MONITOR
# =====================================================
 
# 1. RSS (Resident Set Size): Actual RAM used
#    - If approaching system memory: risk of OOM
 
# 2. Heap usage vs heap size: 
#    - High usage (>80%): frequent GC, risk of OOM
#    - Low usage (<30%): heap oversized, wasting RAM
 
# 3. GC time percentage:
#    - >5% is concerning
#    - >10% is a serious problem
 
# 4. Memory growth over time:
#    - Constant growth without plateau = memory leak

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#!/bin/bash
# =====================================================
# JVM GC ANALYSIS
# =====================================================
 
# Enable GC logging (Java 11+)
java -Xlog:gc*:file=gc.log:time,uptimemillis:filecount=5,filesize=100m -jar app.jar
 
# Key patterns to look for in GC logs:
# 
# [0.234s] GC(0) Pause Young (Normal) (G1 Evacuation Pause) 23M->8M(256M) 4.234ms
#          ^^^^^^                      ^^^^^^^^^^^^^^^^^^    ^^^^^^^^^^  ^^^^^^^^
#          GC ID                       Reason                Heap change Pause time
#
# Pause time > 100ms: Investigate
# Frequent GC (every few seconds): Allocation rate too high
# Heap not shrinking after GC: Possible leak or right-sized
 
# Analyze GC logs with tools:
# - GCViewer: visual analysis
# - GCEasy: online analysis
# - JClarity Censum: detailed reports
 
 
# =====================================================
# DETECTING GC IMPACT ON LATENCY
# =====================================================
 
# Pattern: Request latency spikes correlate with GC pauses
#
# Application logs:      [10:45:32.100] Request completed in 5ms
#                        [10:45:32.200] Request completed in 312ms  <- spike
#                        [10:45:32.500] Request completed in 4ms
#
# GC logs:               [10:45:32.100] GC(42) Pause Full 280.5ms
#
# Correlation: The 312ms request included a 280ms GC pause
 
# JVM: Enable pause-time logging
# -XX:+PrintGCTimeStamps -XX:+PrintGCDateStamps
 
# Application: Log with nanosecond timestamps
# Compare slow request timestamps with GC pause timestamps
 
 
# =====================================================
# REAL-TIME GC MONITORING
# =====================================================
 
# jstat: GC statistics
jstat -gc <pid> 1000  # Every 1 second
 
# Columns to watch:
# EC - Eden capacity
# EU - Eden used
# OC - Old gen capacity
# OU - Old gen used
# GCT - Total GC time
# FGCT - Full GC time
 
# If FGCT grows continuously: Full GCs happening (bad)
# If EU oscillates near EC: Young gen might be undersized
 
 
# =====================================================
# GC TUNING FOR LOW LATENCY
# =====================================================
 
# Switch to ZGC (Java 15+)
java -XX:+UseZGC -Xmx8g -jar app.jar
 
# ZGC characteristics:
# - Pause times < 10ms regardless of heap size
# - Concurrent marking, relocation, and reference processing
# - Slight throughput overhead (~5-15%)
 
# Or Shenandoah (OpenJDK)
java -XX:+UseShenandoahGC -Xmx8g -jar app.jar

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# =====================================================
# COMMON MEMORY LEAK PATTERNS
# =====================================================
 
# PATTERN 1: Unbounded cache without eviction
class LeakingCache:
    def __init__(self):
        self._cache = {}  # Grows forever!
    
    def get(self, key, compute_fn):
        if key not in self._cache:
            self._cache[key] = compute_fn()  # Never evicted
        return self._cache[key]
 
# FIX: Use LRU cache with size limit
from functools import lru_cache
 
@lru_cache(maxsize=1000)  # Max 1000 entries
def compute_cached(key):
    return expensive_computation(key)
 
 
# PATTERN 2: Global registries that aren't cleared
class EventBus:
    _listeners = []  # Class-level list - never cleared!
    
    @classmethod
    def register(cls, listener):
        cls._listeners.append(listener)  # Listener never removed
    
    # Missing: unregister method
 
# FIX: Weak references for optional retention
import weakref
 
class SafeEventBus:
    _listeners = []
    
    @classmethod
    def register(cls, listener):
        # Weak reference - GC can collect if no other refs
        cls._listeners.append(weakref.ref(listener))
    
    @classmethod
    def emit(cls, event):
        # Clean up dead references during emit
        cls._listeners = [l for l in cls._listeners if l() is not None]
        for listener_ref in cls._listeners:
            listener = listener_ref()
            if listener:
                listener.handle(event)
 
 
# PATTERN 3: Closure capturing large context
def create_handlers_bad(large_objects):
    handlers = []
    for obj in large_objects:
        # Each lambda captures entire 'obj' - prevents GC
        handlers.append(lambda: print(obj.id))
    return handlers
 
# FIX: Capture only needed data
def create_handlers_good(large_objects):
    handlers = []
    for obj in large_objects:
        obj_id = obj.id  # Extract only needed value
        handlers.append(lambda id=obj_id: print(id))
    return handlers
 
 
# =====================================================
# DETECTING MEMORY LEAKS IN PYTHON
# =====================================================
 
import tracemalloc
import gc
 
# Enable tracing
tracemalloc.start()
 
# ... run application ...
 
# Take snapshots and compare
snapshot1 = tracemalloc.take_snapshot()
 
# ... more application work ...
 
snapshot2 = tracemalloc.take_snapshot()
 
# Compare snapshots - shows what grew
top_stats = snapshot2.compare_to(snapshot1, 'lineno')
print("[ Top memory increases ]")
for stat in top_stats[:10]:
    print(stat)
 
# Force GC and check unreachable objects
gc.collect()
print(f"Unreachable objects: {len(gc.garbage)}")

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#!/bin/bash
# =====================================================
# JAVA/JVM MEMORY LEAK DETECTION
# =====================================================
 
# Step 1: Take heap dump when memory is high
jmap -dump:format=b,file=heap.hprof <pid>
 
# Or enable automatic dump on OOM
java -XX:+HeapDumpOnOutOfMemoryError -XX:HeapDumpPath=/path/to/dumps -jar app.jar
 
 
# Step 2: Analyze heap dump
# Tools: Eclipse MAT (Memory Analyzer Tool), VisualVM, JProfiler
 
# In Eclipse MAT:
# 1. Open heap dump
# 2. Run "Leak Suspects" report - automated detection
# 3. Check "Dominator Tree" - who retains most memory
# 4. Look for unexpected object counts
#
# Common findings:
# - HashMap with 10 million entries (should be 1000)
# - String[] taking 2GB (log accumulator?)
# - Many instances of same class (object pool not releasing)
 
 
# =====================================================
# LIVE OBJECT MONITORING
# =====================================================
 
# Class histogram - live object counts by class
jcmd <pid> GC.class_histogram | head -30
 
# Output:
#  num     #instances         #bytes  class name
# -----------------------------------------------
#    1:       4234567      169382680  java.lang.String
#    2:       1234567       39506144  java.util.HashMap$Node
#    3:        567890       27258720  [C  (char arrays)
 
# If #instances keeps growing: potential leak in that class
 
 
# =====================================================
# COMPARING HEAP DUMPS OVER TIME
# =====================================================
 
# Take dumps at different times during memory growth
jmap -dump:format=b,file=heap_t1.hprof <pid>
# Wait...
jmap -dump:format=b,file=heap_t2.hprof <pid>
 
# Compare in MAT:
# File -> Compare Heap Dumps
# Shows objects that grew between snapshots
# Focus investigation on growing classes

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# =====================================================
# KUBERNETES MEMORY CONFIGURATION
# =====================================================
 
# Common mistake: Set JVM heap = container limit
# Result: JVM uses native memory beyond heap, OOMKilled
 
# WRONG:
apiVersion: v1
kind: Pod
spec:
  containers:
  - name: app
    resources:
      limits:
        memory: "4Gi"  # Container limit
    env:
    - name: JAVA_OPTS
      value: "-Xmx4g"  # Heap = container limit = OOMKill!
 
 
# CORRECT: Leave headroom for non-heap memory
apiVersion: v1
kind: Pod
spec:
  containers:
  - name: app
    resources:
      requests:
        memory: "4Gi"   # Guaranteed memory
      limits:
        memory: "4Gi"   # Max memory (set equal for predictability)
    env:
    - name: JAVA_OPTS
      # Heap = 75% of container, leaves room for:
      # - Metaspace (~100-300MB)
      # - Native memory (thread stacks, JNI)
      # - Code cache
      # - GC overhead
      value: "-Xmx3g -Xms3g -XX:MaxMetaspaceSize=256m"
 
 
# =====================================================
# MEMORY SIZING CHECKLIST
# =====================================================
 
# For a JVM application:
# 1. Container memory = Heap + Metaspace + Native + Buffer
#    Example: 3GB heap + 256MB meta + 512MB native + 256MB buffer = 4GB
#
# 2. Set -Xms = -Xmx for predictable behavior
#    Avoids heap resizing overhead
#
# 3. Monitor these metrics:
#    - jvm_memory_used_bytes{area="heap"}
#    - jvm_memory_used_bytes{area="nonheap"}
#    - container_memory_working_set_bytes
#    - container_memory_rss
#
# 4. Alert if:
#    - Heap usage > 80% sustained
#    - Container memory approaching limit
#    - OOMKill events
 
 
# =====================================================
# NON-JVM APPLICATIONS
# =====================================================
 
# Python/Ruby/Node: No fixed heap, memory grows as needed
# Key: Monitor RSS and set memory limits to prevent runaway
 
# Python example (gunicorn):
# - Each worker uses ~100-500MB baseline
# - 8 workers = 800MB - 4GB
# - Set container limit with headroom
 
# Node.js:
# - Default heap limit ~1.5GB (varies by version)
# - Override with --max-old-space-size=4096 (MB)
# - Container should be larger than max heap

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# =====================================================
# CACHE SIZING STRATEGY
# =====================================================
 
# Key question: What's your target hit rate?
# 
# Zipf's Law: Most accesses hit a small set of items
# - 20% of items often account for 80%+ of accesses
# - Caching just the "hot" items provides most benefit
 
# Example: 1 million unique items, want 90% hit rate
# If access follows Zipf distribution:
# - Caching top 10% (100K items) might give 70% hit rate
# - Caching top 30% (300K items) might give 90% hit rate
 
from functools import lru_cache
import sys
 
# Measure cache entry size
sample_entry = {"id": 12345, "name": "Sample Product", "price": 99.99}
entry_size = sys.getsizeof(sample_entry)  # Approximate
 
# Calculate max entries for target memory
target_memory_mb = 512
max_entries = (target_memory_mb * 1024 * 1024) // entry_size
print(f"Max cache entries for {target_memory_mb}MB: {max_entries}")
 
 
# =====================================================
# MONITORING CACHE EFFECTIVENESS
# =====================================================
 
class MonitoredCache:
    def __init__(self, maxsize: int):
        self._cache = {}
        self._maxsize = maxsize
        self._hits = 0
        self._misses = 0
    
    def get(self, key):
        if key in self._cache:
            self._hits += 1
            return self._cache[key]
        self._misses += 1
        return None
    
    def put(self, key, value):
        if len(self._cache) >= self._maxsize:
            # Evict oldest (simplified - use proper LRU)
            oldest = next(iter(self._cache))
            del self._cache[oldest]
        self._cache[key] = value
    
    @property
    def hit_rate(self):
        total = self._hits + self._misses
        return self._hits / total if total > 0 else 0
    
    @property
    def size(self):
        return len(self._cache)
 
# Regular monitoring:
# - If hit_rate < 50%: cache too small or wrong data
# - If hit_rate < 20% and cache full: reconsider caching strategy
# - If hit_rate > 95% with room left: might be oversized
 
 
# =====================================================
# CACHE SIZING FORMULA
# =====================================================
 
def calculate_cache_size(
    unique_items: int,
    target_hit_rate: float,
    access_pattern: str = "zipf"  # or "uniform"
) -> int:
    """
    Estimate cache size needed for target hit rate.
    Assumes Zipf distribution by default (typical for web).
    """
    if access_pattern == "uniform":
        # Uniform access: need to cache hit_rate % of items
        return int(unique_items * target_hit_rate)
    else:
        # Zipf distribution: power law relationship
        # This is an approximation - real data varies
        zipf_exponent = 1.0
        # For 90% hit rate, typically need ~30% of items
        # For 80% hit rate, typically need ~15% of items
        # For 99% hit rate, might need ~50-70% of items
        size_multiplier = target_hit_rate ** (1 / zipf_exponent)
        return int(unique_items * size_multiplier * 0.5)
 
# Example:
# 1 million products, 90% hit rate target
needed_size = calculate_cache_size(1_000_000, 0.9)
# Result: ~300,000 entries (rough estimate)

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#!/bin/bash
# =====================================================
# STEP 1: CURRENT MEMORY STATE
# =====================================================
 
# System-wide memory
free -h
# total        used        free      shared  buff/cache   available
# Mem:          16Gi        8Gi       2Gi       100Mi        5Gi        7Gi
# 
# Key: available (not free) - includes reclaimable cache
 
# Process-specific
ps aux --sort=-%mem | head -10  # Top memory consumers
 
# Detailed process memory
cat /proc/<pid>/status | grep -E "VmRSS|VmSize|VmPeak"
 
 
# =====================================================
# STEP 2: MEMORY OVER TIME
# =====================================================
 
# Watch memory growth
watch -n 1 'ps -p <pid> -o rss,vsz,pmem'
 
# Or plot over time (requires logging)
while true; do
    echo "$(date +%s),$(ps -p <pid> -o rss=)"
    sleep 60
done > memory_log.csv
 
 
# =====================================================
# STEP 3: CHECK FOR OOM HISTORY
# =====================================================
 
# Kernel OOM killer logs
dmesg | grep -i "oom|killed"
 
# Kubernetes OOMKilled events
kubectl describe pod <pod-name> | grep OOM
 
 
# =====================================================
# STEP 4: GC ANALYSIS (IF APPLICABLE)
# =====================================================
 
# JVM: GC activity
jstat -gc <pid> 1000 5  # 5 samples, 1 second apart
 
# Check for:
# - FGC (Full GC) count increasing
# - FGCT (Full GC Time) growing
# - OU (Old gen Used) near OC (Old gen Capacity)
 
 
# =====================================================
# STEP 5: HEAP DUMP FOR DETAILED ANALYSIS
# =====================================================
 
# If memory is high and you need to understand why:
jmap -dump:format=b,file=heap.hprof <pid>
 
# Or for Python:
# guppy3 / tracemalloc / memory_profiler
 
# Analyze with appropriate tool for your stack

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# =====================================================
# STREAMING INSTEAD OF LOADING
# =====================================================
 
# ANTI-PATTERN: Load entire file into memory
def process_log_bad(filepath):
    with open(filepath) as f:
        lines = f.readlines()  # Loads entire file!
    
    for line in lines:
        process(line)
    
    # 10GB log file = 10GB+ memory usage
 
# PATTERN: Stream line by line
def process_log_good(filepath):
    with open(filepath) as f:
        for line in f:  # Streams, constant memory
            process(line)
    
    # 10GB log file = ~1MB memory usage
 
 
# =====================================================
# OBJECT POOLING
# =====================================================
 
from queue import Queue
from contextlib import contextmanager
 
class ObjectPool:
    """Reuse expensive objects instead of creating new ones."""
    
    def __init__(self, factory, size=10):
        self._factory = factory
        self._pool = Queue()
        for _ in range(size):
            self._pool.put(factory())
    
    @contextmanager
    def acquire(self):
        obj = self._pool.get()
        try:
            yield obj
        finally:
            self._pool.put(obj)  # Return to pool
 
# Usage:
buffer_pool = ObjectPool(lambda: bytearray(1024 * 1024), size=10)
 
def process_data(data):
    with buffer_pool.acquire() as buffer:
        # Reuse buffer instead of allocating new each time
        buffer[:len(data)] = data
        process(buffer)
 
 
# =====================================================
# GENERATORS FOR LAZY EVALUATION
# =====================================================
 
# ANTI-PATTERN: Materialize entire result
def get_all_users_bad():
    users = []
    for row in db.query("SELECT * FROM users"):
        users.append(User(**row))
    return users  # 1 million users = 1 million objects in memory
 
# PATTERN: Generator - one at a time
def get_all_users_good():
    for row in db.query("SELECT * FROM users"):
        yield User(**row)  # Yields one at a time
 
# Consumer still uses same code:
for user in get_all_users_good():
    process(user)
# But only one User object in memory at a time
 
 
# =====================================================
# PRIMITIVE COLLECTIONS (Java-specific, but concept applies)
# =====================================================
 
# Java: ArrayList<Integer> vs IntArrayList (primitive)
# ArrayList<Integer>: Each int wrapped in Integer object (~16 bytes overhead)
# IntArrayList (e.g., Eclipse Collections): Raw ints (~4 bytes each)
# 
# 1 million integers:
# - ArrayList<Integer>: ~20MB
# - IntArrayList: ~4MB
 
# Python equivalent: array module vs list
import array
 
numbers_list = [1.0] * 1_000_000        # ~8MB per float + object overhead
numbers_array = array.array('d', [1.0] * 1_000_000)  # ~8MB total
 
# NumPy arrays even more efficient:
import numpy as np
numbers_numpy = np.ones(1_000_000, dtype=np.float64)  # ~8MB, highly optimized