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# Profiler — Design & Implementation Plan
## Context
Performance issues were identified during keyboard navigation in the DataGrid (173ms server-side
per command call). The HTMX debug traces (via `htmx_debug.js`) confirmed the bottleneck is
server-side. A persistent, in-application profiling system is needed for continuous analysis
across sessions and future investigations.
---
## Implementation Status
| Phase | Item | Status |
|-------|------|--------|
| **Phase 1 — Core** | `profiler.py` — data model + probe mechanisms | ✅ Done |
| **Phase 1 — Core** | `tests/core/test_profiler.py` — full test suite (7 classes) | ✅ Done |
| **Phase 1 — Core** | Hook `utils.py` — Level A `command_span` | ✅ Done |
| **Phase 1 — Core** | Hook `commands.py` — Level B phases | ⏳ Deferred |
| **Phase 2 — Controls** | `Profiler.py` — global layout (toolbar + list) | 🔄 In progress |
| **Phase 2 — Controls** | `Profiler.py` — detail panel (span tree + pie) | ⏳ Pending |
| **Phase 2 — Controls** | CSS `profiler.css` | 🔄 In progress |
| **Phase 2 — Controls** | `ProfilerPieChart.py` | ⏳ Future |
---
## Design Decisions
### Data Collection Strategy
Two complementary levels:
- **Level A** (route handler): One trace per `/myfasthtml/commands` call. Captures total
server-side duration including lookup, execution, and HTMX swap overhead.
- **Level B** (granular spans): Decomposition of each trace into named phases. Activated
by placing probes in the code.
Both levels are active simultaneously. Level A gives the global picture; Level B gives the
breakdown.
### Probe Mechanisms
Four complementary mechanisms, chosen based on the context:
#### 1. Context manager — partial block instrumentation
```python
with profiler.span("oob_swap"):
# only this block is timed
result = build_oob_elements(...)
```
Metadata can be attached during execution:
```python
with profiler.span("query") as span:
rows = db.query(...)
span.set("row_count", len(rows))
```
#### 2. Decorator — full function instrumentation
```python
@profiler.span("callback")
def execute_callback(self, client_response):
...
```
Function arguments are captured automatically. Metadata can be attached via `current_span()`:
```python
@profiler.span("process")
def process(self, rows):
result = do_work(rows)
profiler.current_span().set("row_count", len(result))
return result
```
#### 3. Cumulative span — loop instrumentation
For loops with many iterations. Aggregates instead of creating one span per iteration.
```python
for row in rows:
with profiler.cumulative_span("process_row"):
process(row)
# or as a decorator
@profiler.cumulative_span("process_row")
def process_row(self, row):
...
```
Exposes: `count`, `total`, `min`, `max`, `avg`. Single entry in the trace tree regardless of
iteration count.
#### 4. `trace_all` — class-level static instrumentation
Wraps all methods of a class at definition time. No runtime overhead beyond the spans themselves.
```python
@profiler.trace_all
class DataGrid(MultipleInstance):
def navigate_cell(self, ...): # auto-spanned
...
# Exclude specific methods
@profiler.trace_all(exclude=["__ft__", "render"])
class DataGrid(MultipleInstance):
...
```
Implementation: uses `inspect` to iterate over methods and wraps each with `@profiler.span()`.
No `sys.settrace()` involved — pure static wrapping.
#### 5. `trace_calls` — sub-call exploration
Traces all function calls made within a single function, recursively. Used for exploration
when the bottleneck location is unknown.
```python
@profiler.trace_calls
def navigate_cell(self, ...):
self._update_selection() # auto-traced as child span
self._compute_visible() # auto-traced as child span
db.query(...) # auto-traced as child span
```
Implementation: uses `sys.setprofile()` scoped to the decorated function's execution only.
Overhead is localized to that function's call stack. This is an exploration tool — use it
to identify hotspots, then replace with explicit probes.
### Span Hierarchy
Hierarchy is determined by code nesting via a `ContextVar` stack (async-safe). No explicit
parent references required.
```python
with profiler.span("execute"): # root
with profiler.span("callback"): # child of execute
result = self.callback(...)
with profiler.span("oob_swap"): # sibling of callback
...
```
When a command calls another command, the second command's spans automatically become children
of the first command's active span.
`profiler.current_span()` provides access to the active span from anywhere in the call stack.
### Storage
- **Scope**: Global (all sessions). Profiling measures server behavior, not per-user state.
- **Structure**: `deque` with a configurable maximum size.
- **Default size**: 500 traces (constant `PROFILER_MAX_TRACES`).
- **Eviction**: Oldest traces are dropped when the buffer is full (FIFO).
- **Persistence**: In-memory only. Lost on server restart.
### Toggle and Clear
- `profiler.enabled` — boolean flag. When `False`, all probe mechanisms are no-ops (zero overhead).
- `profiler.clear()` — empties the trace buffer.
- Both are controllable from the UI control.
### Overhead Measurement
The `ProfilingManager` self-profiles its own `span.__enter__` and `span.__exit__` calls.
Exposes:
- `overhead_per_span_us` — average cost of one span boundary in microseconds
- `total_overhead_ms` — estimated total overhead across all active spans
Visible in the UI to verify the profiler does not bias measurements significantly.
---
## Data Model
```
ProfilingTrace
command_name: str
command_id: str
kwargs: dict
timestamp: datetime
total_duration_ms: float
root_span: ProfilingSpan
ProfilingSpan
name: str
start: float (perf_counter)
duration_ms: float
data: dict (attached via span.set())
children: list[ProfilingSpan | CumulativeSpan]
CumulativeSpan
name: str
count: int
total_ms: float
min_ms: float
max_ms: float
avg_ms: float
```
---
## Code Hooks
### `src/myfasthtml/core/utils.py` — route handler (Level A) ✅
```python
command = CommandsManager.get_command(c_id)
if command:
with profiler.command_span(command.name, c_id, client_response or {}):
return command.execute(client_response)
```
### `src/myfasthtml/core/commands.py` — execution phases (Level B) ⏳ Deferred
Planned breakdown inside `Command.execute()`:
```python
def execute(self, client_response=None):
with profiler.span("before_commands"):
...
with profiler.span("callback"):
result = self.callback(...)
with profiler.span("after_commands"):
...
with profiler.span("oob_swap"):
...
```
Deferred: will be added once the UI control is functional to immediately observe the breakdown.
---
## UI Control Design
### Control name: `Profiler` (SingleInstance)
Single entry point. Replaces the earlier `ProfilerList` name.
**Files:**
- `src/myfasthtml/controls/Profiler.py`
- `src/myfasthtml/assets/core/profiler.css`
### Layout
Split view using `Panel`:
```
┌─────────────────────────────────────────────────────┐
│ [●] [🗑] Overhead/span: 1.2µs Traces: 8/500│ ← toolbar (icon-only)
├──────────────────────┬──────────────────────────────┤
│ Command Duration Time│ NavigateCell — 173.4ms [≡][◕]│
│ ──────────────────────│ ─────────────────────────────│
│ NavigateCell 173ms … │ [Metadata card] │
│ NavigateCell 168ms … │ [kwargs card] │
│ SelectRow 42ms … │ [Span breakdown / Pie chart] │
│ … │ │
└──────────────────────┴──────────────────────────────┘
```
### Toolbar
Icon-only buttons, no `Menu` control (Menu does not support toggle state).
Direct `mk.icon()` calls:
- **Enable/disable**: icon changes between "recording" and "stopped" states based on `profiler.enabled`
- **Clear**: delete icon, always red
- **Refresh**: manual refresh of the trace list (no auto-refresh yet — added in Step 2.1)
Overhead metrics displayed as plain text on the right side of the toolbar.
### Trace list (left panel)
Three columns: command name / duration (color-coded) / timestamp.
Click on a row → update right panel via HTMX.
**Duration color thresholds:**
- Green (`mf-profiler-fast`): < 20 ms
- Orange (`mf-profiler-medium`): 20100 ms
- Red (`mf-profiler-slow`): > 100 ms
### Detail panel (right)
Two view modes, toggled by icons in the detail panel header:
1. **Tree view** (default): Properties-style cards (Metadata, kwargs) + span breakdown with
proportional bars and indentation. Cumulative spans show `×N · min/avg/max` badge.
2. **Pie view**: `ProfilerPieChart` control (future) — distribution of time across spans
at the current zoom level.
The `Properties` control is used as-is for Metadata and kwargs cards.
The span breakdown is custom rendering (not a `Properties` instance).
### Font conventions
- Labels, headings, command names: `--font-sans` (DaisyUI default)
- Values (durations, timestamps, kwargs values): `--font-mono`
- Consistent with `properties.css` (`mf-properties-value` uses `--default-mono-font-family`)
### Visual reference
Mockups available in `examples/`:
- `profiler_mockup.html` — first iteration (monospace font everywhere)
- `profiler_mockup_2.html`**reference** (correct fonts, icon toolbar, tree/pie toggle)
---
## Implementation Plan
### Phase 1 — Core ✅ Complete
1. `ProfilingSpan`, `CumulativeSpan`, `ProfilingTrace` dataclasses
2. `ProfilingManager` with all probe mechanisms
3. `profiler` singleton
4. Hook into `utils.py` (Level A) ✅
5. Hook into `commands.py` (Level B) — deferred
**Tests**: `tests/core/test_profiler.py` — 7 classes, full coverage ✅
### Phase 2 — Controls
#### Step 2.1 — Global layout (current) 🔄
`src/myfasthtml/controls/Profiler.py`:
- `SingleInstance` inheriting
- Toolbar: `mk.icon()` for enable/disable and clear, overhead text
- `Panel` for split layout
- Left: trace list table (command / duration / timestamp), click → select_trace command
- Right: placeholder (empty until Step 2.2)
`src/myfasthtml/assets/core/profiler.css`:
- All `mf-profiler-*` classes
#### Step 2.2 — Detail panel ⏳
Right panel content:
- Metadata and kwargs via `Properties`
- Span tree: custom `_mk_span_tree()` with bars and cumulative badges
- View toggle (tree / pie) in detail header
#### Step 2.3 — Pie chart ⏳ Future
`src/myfasthtml/controls/ProfilerPieChart.py`
---
## Naming Conventions
- Control files: `ProfilerXxx.py`
- CSS classes: `mf-profiler-xxx`
- Logger: `logging.getLogger("Profiler")`
- Constant: `PROFILER_MAX_TRACES = 500` in `src/myfasthtml/core/constants.py`