feat: add profiling framework with command echoing

Created a comprehensive profiling framework for dtail commands (dcat, dgrep, dmap)
to analyze CPU usage and memory allocations. The framework now prints all executed
commands to stdout for full transparency.

Key features:
- Integrated Go profiling (CPU, memory, allocations) into all three commands
- Created profile.sh bash script for analyzing pprof profiles
- Added multiple Makefile targets for different profiling scenarios
- Automated profiling scripts with command echoing
- Support for different data sizes (quick, normal, full)
- Special handling for dmap MapReduce format

All profiling commands are now echoed to stdout before execution, making it
easy to understand what the framework is doing and reproduce commands manually.

🤖 Generated with [Claude Code](https://claude.ai/code)

Co-Authored-By: Claude <noreply@anthropic.com>

1 files changed, 372 insertions, 0 deletions

diff --git a/benchmarks/PROFILING.md b/benchmarks/PROFILING.md
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+# DTail Profiling Framework
+
+This document describes the profiling framework for dtail commands (dcat, dgrep, dmap) to analyze CPU usage and memory allocations.
+
+## Overview
+
+The profiling framework provides:
+- CPU profiling to identify performance bottlenecks
+- Memory profiling to track allocations and detect leaks
+- Integration with existing benchmarks
+- Analysis tools for profile interpretation
+
+## Quick Start
+
+### 1. Build the Tools
+
+```bash
+make build  # Builds all tools including dprofile
+```
+
+### 2. Run Commands with Profiling
+
+Each command now supports profiling flags:
+
+```bash
+# Profile dcat
+./dcat -profile -profiledir profiles -plain -cfg none /path/to/file.log
+
+# Profile dgrep with specific profiling types
+./dgrep -cpuprofile -memprofile -profiledir profiles -regex "error" /path/to/file.log
+
+# Profile dmap
+./dmap -profile -query "select count(*) from data.csv"
+```
+
+### 3. Analyze Profiles
+
+Use the included `profile.sh` script for quick analysis:
+
+```bash
+# Analyze CPU profile
+./profiling/profile.sh profiles/dcat_cpu_20240101_120000.prof
+
+# Show top 20 functions
+./profiling/profile.sh -top 20 profiles/dgrep_mem_20240101_120000.prof
+
+# Sort by cumulative time/allocations
+./profiling/profile.sh -cum profiles/dmap_cpu_20240101_120000.prof
+
+# List all profiles
+./profiling/profile.sh -list profiles/
+
+# Open web browser with flame graph
+./profiling/profile.sh -web profiles/dcat_cpu_*.prof
+```
+
+## Profiling Options
+
+### Command-line Flags
+
+All dtail commands support these profiling flags:
+
+- `-cpuprofile`: Enable CPU profiling only
+- `-memprofile`: Enable memory profiling only
+- `-profile`: Enable both CPU and memory profiling
+- `-profiledir <dir>`: Directory to store profiles (default: "profiles")
+
+### Profile Types
+
+1. **CPU Profile** (`*_cpu_*.prof`)
+   - Samples CPU usage during execution
+   - Identifies hot functions and code paths
+   - Useful for optimizing computational bottlenecks
+
+2. **Memory Profile** (`*_mem_*.prof`)
+   - Captures heap allocations at end of execution
+   - Shows memory usage by function
+   - Helps identify memory leaks
+
+3. **Allocation Profile** (`*_alloc_*.prof`)
+   - Tracks all allocations during execution
+   - More detailed than memory profile
+   - Useful for reducing allocation pressure
+
+## Using with Benchmarks
+
+### Automated Profiling Script
+
+Run the included profiling script:
+
+```bash
+cd benchmarks
+./profile_benchmarks.sh
+```
+
+This script:
+- Generates test data of various sizes
+- Profiles dcat and dgrep with different workloads
+- Stores profiles in the `profiles` directory
+- Provides analysis commands
+
+For dmap profiling (requires MapReduce format):
+```bash
+cd benchmarks
+./profile_dmap.sh
+```
+
+### Using Make Targets
+
+```bash
+# Quick profiling with immediate results
+make profile-quick
+
+# Profile individual commands
+make profile-dcat
+make profile-dgrep
+make profile-dmap  # Uses MapReduce format
+
+# Full automated profiling
+make profile-auto
+```
+
+### Benchmark Integration
+
+Run profiling-enabled benchmarks:
+
+```bash
+cd benchmarks
+go test -bench="WithProfiling" -benchtime=1x
+```
+
+### Custom Profile Runner
+
+Use the profile runner in your benchmarks:
+
+```go
+import "github.com/mimecast/dtail/benchmarks"
+
+func BenchmarkMyFeature(b *testing.B) {
+    benchmarks.ProfileBenchmark(b, "MyFeature", "dcat", 
+        "--plain", "--cfg", "none", "testfile.log")
+}
+```
+
+## Profile Analysis
+
+### Using go tool pprof
+
+For interactive analysis:
+
+```bash
+# Interactive mode
+go tool pprof profiles/dcat_cpu_*.prof
+
+# Common pprof commands:
+# top       - Show top functions
+# list func - Show source code for function
+# web       - Generate SVG graph
+# peek func - Show callers/callees of function
+```
+
+Generate visualizations:
+
+```bash
+# Flame graph (requires graphviz)
+go tool pprof -http=:8080 profiles/dcat_cpu_*.prof
+
+# Generate SVG
+go tool pprof -svg profiles/dgrep_mem_*.prof > profile.svg
+
+# Generate text report
+go tool pprof -text profiles/dmap_alloc_*.prof > report.txt
+```
+
+### Using profile.sh
+
+The `profile.sh` script provides quick summaries:
+
+```bash
+# List all profiles
+./profiling/profile.sh -list profiles/
+
+# Analyze specific profile
+./profiling/profile.sh profiles/dcat_cpu_20240101_120000.prof
+
+# Get help
+./profiling/profile.sh -help
+```
+
+## Optimization Workflow
+
+1. **Baseline Performance**
+   ```bash
+   # Run benchmarks without profiling
+   cd benchmarks
+   go test -bench="BenchmarkDCat" -benchtime=10s
+   ```
+
+2. **Profile Execution**
+   ```bash
+   # Run with profiling
+   ./dcat -profile -profiledir profiles large_file.log
+   ```
+
+3. **Identify Bottlenecks**
+   ```bash
+   # Analyze CPU profile
+   ./dprofile -profile profiles/dcat_cpu_*.prof -top 10
+   
+   # Check memory allocations
+   go tool pprof -alloc_space profiles/dcat_alloc_*.prof
+   ```
+
+4. **Optimize Code**
+   - Focus on functions with high Flat% (direct CPU usage)
+   - Reduce allocations in hot paths
+   - Consider buffering and pooling
+
+5. **Verify Improvements**
+   ```bash
+   # Re-run benchmarks after optimization
+   go test -bench="BenchmarkDCat" -benchtime=10s
+   ```
+
+## Common Performance Issues
+
+### CPU Bottlenecks
+
+Look for:
+- Regex compilation in loops
+- Excessive string operations
+- Inefficient algorithms (O(n²) or worse)
+- Unnecessary type conversions
+
+Example optimization:
+```go
+// Before: Regex compiled every time
+for _, line := range lines {
+    if regexp.MustCompile(pattern).MatchString(line) {
+        // ...
+    }
+}
+
+// After: Compile once
+re := regexp.MustCompile(pattern)
+for _, line := range lines {
+    if re.MatchString(line) {
+        // ...
+    }
+}
+```
+
+### Memory Issues
+
+Common patterns:
+- String concatenation in loops
+- Large temporary slices
+- Unclosed resources
+- Excessive goroutines
+
+Example optimization:
+```go
+// Before: Many allocations
+result := ""
+for _, s := range strings {
+    result += s + "\n"
+}
+
+// After: Single allocation
+var buf strings.Builder
+buf.Grow(estimatedSize)
+for _, s := range strings {
+    buf.WriteString(s)
+    buf.WriteByte('\n')
+}
+result := buf.String()
+```
+
+## Tips and Best Practices
+
+1. **Profile Real Workloads**
+   - Use production-like data sizes
+   - Test with actual file formats
+   - Include network operations if relevant
+
+2. **Compare Profiles**
+   ```bash
+   # Compare before/after optimization
+   go tool pprof -diff_base=before.prof after.prof
+   ```
+
+3. **Focus on Hot Paths**
+   - Optimize functions with >5% CPU usage first
+   - Small improvements in hot paths have big impact
+
+4. **Memory Profiling**
+   - Use `-alloc_space` for total allocations
+   - Use `-inuse_space` for current heap usage
+   - Check for growing heap over time
+
+5. **Benchmark Regularly**
+   - Add profiling to CI/CD pipeline
+   - Track performance over releases
+   - Set performance regression alerts
+
+## Troubleshooting
+
+### No profiles generated
+- Check write permissions for profile directory
+- Ensure command completes successfully
+- Verify profiling flags are correct
+
+### Empty or small profiles
+- Run command with larger workload
+- Increase execution time
+- Check if command exits too quickly
+
+### Analysis tools fail
+- Ensure profile format is valid
+- Check Go version compatibility
+- Verify graphviz is installed for visualizations
+
+## Advanced Usage
+
+### Custom Profiling Points
+
+Add profiling snapshots in code:
+
+```go
+import "github.com/mimecast/dtail/internal/profiling"
+
+func processLargeFile() {
+    profiler := profiling.GetProfiler() // Assumes global profiler
+    
+    // Take memory snapshot before processing
+    profiler.Snapshot("before_processing")
+    
+    // ... process file ...
+    
+    // Take snapshot after
+    profiler.Snapshot("after_processing")
+}
+```
+
+### Continuous Profiling
+
+For long-running operations:
+
+```go
+// Start periodic metrics logging
+ticker := time.NewTicker(30 * time.Second)
+go func() {
+    for range ticker.C {
+        profiler.LogMetrics("periodic")
+    }
+}()
+defer ticker.Stop()
+```
+
+## Contributing
+
+When adding new features:
+1. Include benchmark tests
+2. Run profiling before submitting PR
+3. Document any performance implications
+4. Add profiling examples for new commands
+
+## References
+
+- [Go Profiling Documentation](https://go.dev/blog/pprof)
+- [pprof Tool Guide](https://github.com/google/pprof)
+- [Go Performance Tips](https://go.dev/wiki/Performance)
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