# Converting arbitrary timestamped data to Perfetto

In this guide, you'll learn how to:

- Convert your own timestamped data into the Perfetto trace format.
- Create custom tracks, slices, and counters.
- Visualize your custom data in the Perfetto UI.

If you have existing logs or timestamped data from your own systems, you don't
need to miss out on Perfetto's powerful visualization and analysis capabilities.
By converting your data into Perfetto's native protobuf-based trace format, you
can create synthetic traces that can be opened in the Perfetto UI and queried
with Trace Processor.

This page provides a guide on how to programmatically generate these synthetic
traces.

## The Basics: Perfetto's Trace Format

A Perfetto trace file (`.pftrace` or `.perfetto-trace`) is a sequence of
[TracePacket](/protos/perfetto/trace/trace_packet.proto) messages, wrapped in a
root [Trace](/protos/perfetto/trace/trace.proto) message. Each `TracePacket` can
contain various types of data.

For generating traces from custom data, the most common and flexible payload to
use within a `TracePacket` is the
[TrackEvent](/protos/perfetto/trace/track_event/track_event.proto). `TrackEvent`
allows you to define:

- **Tracks**: A single sequence of events (slices or counter) over time.
  Corresponds to a single "swim-lane" in the Perfetto UI.
- **Slices**: Events with a name, start timestamp, and duration (e.g., function
  calls, tasks).
- **Counters**: Numeric values that change over time (e.g., memory usage, custom
  metrics).
- **Flows**: Arrows connecting related slices across different tracks.

## Generating Traces Programmatically

The examples in this guide use Python and a helper class from the `perfetto`
Python library to demonstrate how to construct these protobuf messages. However,
the underlying principles and protobuf definitions are language-agnostic. You
can generate Perfetto traces in any programming language that has Protocol
Buffer support.

- **Official Protobuf Libraries:** Google provides official protobuf compilers
  and runtime libraries for languages like
  [Java](https://protobuf.dev/reference/java/generated-code/),
  [C++](https://protobuf.dev/reference/cpp/generated-code/),
  [Python](https://protobuf.dev/reference/python/python-generated/),
  [Go](https://protobuf.dev/reference/go/go-generated/), and
  [more](https://protobuf.dev/reference/).
- **Third-Party Libraries:** Numerous third-party libraries also provide
  protobuf support for a wide range of languages.

Regardless of the language, the core task is to construct `TracePacket` messages
according to the Perfetto
[protobuf schemas](https://source.chromium.org/chromium/chromium/src/+/main:third_party/perfetto/protos/perfetto/trace/)
and serialize them into a binary file.

### Python Script Template

For the Python examples in the following sections, we'll use a script template.
This script handles the basics of creating a trace file and serializing
`TracePacket` messages. You'll fill in the `populate_packets` function with the
specific logic for the type of trace data you want to create.

First, ensure you have the `perfetto` library installed, which provides the
necessary protobuf classes and potentially a builder utility (like the
`TraceProtoBuilder` class you've designed, or an equivalent from the library).

```bash
pip install perfetto
```

Here is the Python script template. Save this as `trace_converter_template.py`
or a similar name. Each subsequent example will show you what code to place
inside the `populate_packets` function.

<details>
<summary><a style="cursor: pointer;"><b>Click to expand/collapse Python code</b></a></summary>

```python
#!/usr/bin/env python3
import uuid

from perfetto.trace_builder.proto_builder import TraceProtoBuilder
from perfetto.protos.perfetto.trace.perfetto_trace_pb2 import TrackEvent, TrackDescriptor, ProcessDescriptor, ThreadDescriptor

def populate_packets(builder: TraceProtoBuilder):
    """
    This function is where you will define and add your TracePackets
    to the trace. The examples in the following sections will provide
    the specific code to insert here.

    Args:
        builder: An instance of TraceProtoBuilder to add packets to.
    """
    # ======== BEGIN YOUR PACKET CREATION CODE HERE ========
    # Example (will be replaced by specific examples later):
    #
    # packet = builder.add_packet()
    # packet.timestamp = 1000
    # packet.track_event.type = TrackEvent.TYPE_SLICE_BEGIN
    # packet.track_event.name = "My Example Event"
    # packet.track_event.track_uuid = 12345
    #
    # packet2 = builder.add_packet()
    # packet2.timestamp = 2000
    # packet2.track_event.type = TrackEvent.TYPE_SLICE_END
    # packet2.track_event.track_uuid = 12345
    #
    # ========  END YOUR PACKET CREATION CODE HERE  ========

    # Remove this 'pass' when you add your code
    pass


def main():
    """
    Initializes the TraceProtoBuilder, calls populate_packets to fill it,
    and then writes the resulting trace to a file.
    """
    builder = TraceProtoBuilder()
    populate_packets(builder)

    output_filename = "my_custom_trace.pftrace"
    with open(output_filename, 'wb') as f:
      f.write(builder.serialize())

    print(f"Trace written to {output_filename}")
    print(f"Open with [https://ui.perfetto.dev](https://ui.perfetto.dev).")

if __name__ == "__main__":
    main()
```

</details>

**To use this template:**

1. Save the code above as a Python file (e.g. `trace_converter_template.py`).
2. For each example in the sections that follow (e.g., "Thread-scoped slices,"
   "Counters"), copy the Python code provided in that section and paste it into
   the `populate_packets` function in your `trace_converter_template.py` file,
   replacing the example placeholder content.
3. Run the script: `python trace_converter_template.py`. This will generate
   `my_custom_trace.pftrace`.

The TraceProtoBuilder class (which is imported from `perfetto` pip package)
helps manage the list of `TracePacket` messages that form the `Trace`. The
`populate_packets` function is where you'll define the content of these packets
based on your specific data.

## Creating Basic Timeline Slices

The most fundamental way to represent an activity in Perfetto is as a "slice." A
slice is simply a named event that has a start time and a duration. Slices live
on "tracks," which are visual timelines in the Perfetto UI. Essentially, slices
are used in any situation where you want to say "a named activity was happening
during this specific interval of time."

Common examples of what slices can represent include:

- The interval of time during which a particular **function was executing**.
- The interval of time spent **waiting for a server to respond** to a network
  request.
- The time it takes for a **resource (like an image, a script, or a data file)
  to load**.
- The duration of a specific phase in an application's lifecycle, like "parsing
  data" or "rendering frame."

To create slices from your custom data, you'll typically:

1.  Define a **track** where your slices will appear. This is done using a
    `TrackDescriptor` packet. For basic custom data, you can create a generic
    track that isn't tied to a specific process or thread.
2.  For each event in your data, emit `TrackEvent` packets to mark the beginning
    and end of the slice.

### Python Example

Let's say you have data representing tasks with a name, start time, and end
time. Here's how you could convert them into Perfetto slices on a custom track.
This first example will show distinct, non-nested slices and a single instant
event.

Copy the following Python code into the `populate_packets(builder)` function in
your `trace_converter_template.py` script.

<details>
<summary><a style="cursor: pointer;"><b>Click to expand/collapse Python code</b></a></summary>

```python
    # Define a unique ID for this sequence of packets (generate once per trace producer)
    TRUSTED_PACKET_SEQUENCE_ID = 1001 # Choose any unique integer

    # Define a unique UUID for your custom track (generate a 64-bit random number)
    CUSTOM_TRACK_UUID = 12345678 # Example UUID

    # 1. Define the Custom Track
    # This packet describes the track on which your events will be displayed.
    # Emit this once at the beginning of your trace.
    packet = builder.add_packet()
    packet.track_descriptor.uuid = CUSTOM_TRACK_UUID
    packet.track_descriptor.name = "My Custom Data Timeline"

    # 2. Emit events for this custom track
    # Example Event 1: "Task A"
    packet = builder.add_packet()
    packet.timestamp = 1000  # Start time in nanoseconds
    packet.track_event.type = TrackEvent.TYPE_SLICE_BEGIN
    packet.track_event.track_uuid = CUSTOM_TRACK_UUID # Associates with the track
    packet.track_event.name = "Task A"
    packet.trusted_packet_sequence_id = TRUSTED_PACKET_SEQUENCE_ID

    packet = builder.add_packet()
    packet.timestamp = 1500  # End time in nanoseconds
    packet.track_event.type = TrackEvent.TYPE_SLICE_END
    packet.track_event.track_uuid = CUSTOM_TRACK_UUID
    packet.trusted_packet_sequence_id = TRUSTED_PACKET_SEQUENCE_ID

    # Example Event 2: "Task B" - a separate, non-nested task occurring later
    packet = builder.add_packet()
    packet.timestamp = 1600  # Start time in nanoseconds
    packet.track_event.type = TrackEvent.TYPE_SLICE_BEGIN
    packet.track_event.track_uuid = CUSTOM_TRACK_UUID
    packet.track_event.name = "Task B"
    packet.trusted_packet_sequence_id = TRUSTED_PACKET_SEQUENCE_ID

    packet = builder.add_packet()
    packet.timestamp = 1800  # End time in nanoseconds
    packet.track_event.type = TrackEvent.TYPE_SLICE_END
    packet.track_event.track_uuid = CUSTOM_TRACK_UUID
    packet.trusted_packet_sequence_id = TRUSTED_PACKET_SEQUENCE_ID

    # Example Event 3: An instantaneous event
    packet = builder.add_packet()
    packet.timestamp = 1900 # Timestamp in nanoseconds
    packet.track_event.type = TrackEvent.TYPE_INSTANT
    packet.track_event.track_uuid = CUSTOM_TRACK_UUID
    packet.track_event.name = "Milestone Y"
    packet.trusted_packet_sequence_id = TRUSTED_PACKET_SEQUENCE_ID
```

</details>

After running the script, opening the generated `my_custom_trace.pftrace` in the
[Perfetto UI](https://ui.perfetto.dev) will display the following output:

![Basic Timeline Slices](/docs/images/converting-basic-slices.png)

## Nested Slices (Hierarchical Activities)

Often, an activity or operation is made up of several sub-activities that must
complete before the main activity can finish. Nested slices are perfect for
representing these hierarchical relationships. The key rule is that child slices
must start after their parent slice begins and finish before their parent slice
ends.

This is very common for:

- **Function execution:** A function call (parent slice) contains calls to other
  functions (child slices).
- **Structured concurrency:** Operations like Kotlin Coroutines, where child
  coroutines are launched within the scope of a parent coroutine and must
  complete before the parent.
- **Phases of a larger operation:** A complex task like "Compiling Module"
  (parent) might have distinct phases like "Lexical Analysis," "Parsing,"
  "Optimization," and "Code Generation" as nested child slices.
- **UI rendering pipelines:** A "RenderFrame" slice might encompass "Measure
  Pass," "Layout Pass," and "Draw Pass" as child slices.
- **Request handling with sub-operations:** A web server handling a
  "ProcessHTTPRequest" (parent) might have nested slices for "ParseHeaders,"
  "AuthenticateUser," "FetchDataFromDB," and "RenderResponse."

The Perfetto UI will visually nest these slices, making the hierarchy clear.

### Python Example

This example demonstrates creating multiple stacks of nested slices on a custom
track. The packets are emitted in timestamp order to correctly represent the
nesting. We'll define a small helper function `add_event` inside
`populate_packets` to reduce boilerplate.

Copy the following Python code into the `populate_packets(builder)` function in
your `trace_converter_template.py` script.

<details>
<summary><a style="cursor: pointer;"><b>Click to expand/collapse Python code</b></a></summary>

```python
    # Define a unique ID for this sequence of packets
    TRUSTED_PACKET_SEQUENCE_ID = 2002 # Using a new ID for this example

    # Define a unique UUID for this example's custom track
    NESTED_SLICE_TRACK_UUID = 987654321 # Example UUID

    # 1. Define the Custom Track for Nested Slices
    # Emit this once at the beginning.
    packet = builder.add_packet()
    packet.track_descriptor.uuid = NESTED_SLICE_TRACK_UUID
    packet.track_descriptor.name = "My Nested Operations Timeline"

    # Helper function to add a TrackEvent packet
    def add_event(ts, event_type, name=None):
        packet = builder.add_packet()
        packet.timestamp = ts
        packet.track_event.type = event_type
        packet.track_event.track_uuid = NESTED_SLICE_TRACK_UUID
        if name:
            packet.track_event.name = name
        packet.trusted_packet_sequence_id = TRUSTED_PACKET_SEQUENCE_ID

    # --- Stack 1: Operation Alpha ---
    add_event(ts=2000, event_type=TrackEvent.TYPE_SLICE_BEGIN, name="Operation Alpha")
    add_event(ts=2050, event_type=TrackEvent.TYPE_SLICE_BEGIN, name="Alpha.LoadConfig")
    add_event(ts=2150, event_type=TrackEvent.TYPE_SLICE_END) # Closes Alpha.LoadConfig
    add_event(ts=2200, event_type=TrackEvent.TYPE_SLICE_BEGIN, name="Alpha.Execute")
    add_event(ts=2250, event_type=TrackEvent.TYPE_SLICE_BEGIN, name="Alpha.Execute.SubX")
    add_event(ts=2350, event_type=TrackEvent.TYPE_SLICE_END) # Closes Alpha.Execute.SubX
    add_event(ts=2400, event_type=TrackEvent.TYPE_SLICE_BEGIN, name="Alpha.Execute.SubY")
    add_event(ts=2500, event_type=TrackEvent.TYPE_SLICE_END) # Closes Alpha.Execute.SubY
    add_event(ts=2800, event_type=TrackEvent.TYPE_SLICE_END) # Closes Alpha.Execute
    add_event(ts=3000, event_type=TrackEvent.TYPE_SLICE_END) # Closes Operation Alpha

    # --- Stack 2: Operation Beta (on the same track) ---
    add_event(ts=3200, event_type=TrackEvent.TYPE_SLICE_BEGIN, name="Operation Beta")
    add_event(ts=3250, event_type=TrackEvent.TYPE_SLICE_BEGIN, name="Beta.Initialize")
    add_event(ts=3350, event_type=TrackEvent.TYPE_SLICE_END) # Closes Beta.Initialize
    add_event(ts=3400, event_type=TrackEvent.TYPE_SLICE_BEGIN, name="Beta.Process")
    add_event(ts=3700, event_type=TrackEvent.TYPE_SLICE_END) # Closes Beta.Process
    add_event(ts=3800, event_type=TrackEvent.TYPE_SLICE_END) # Closes Operation Beta

    # --- An independent slice after all stacks ---
    add_event(ts=4000, event_type=TrackEvent.TYPE_SLICE_BEGIN, name="Cleanup")
    add_event(ts=4100, event_type=TrackEvent.TYPE_SLICE_END) # Closes Cleanup
```

</details>

After running the script, opening the generated `my_custom_trace.pftrace` in the
[Perfetto UI](https://ui.perfetto.dev) will display the following output:

![Nested Slices](/docs/images/converting-nested.png)

## Asynchronous Slices and Overlapping Events

Many systems deal with asynchronous operations where multiple activities can be
in progress simultaneously and their lifetimes can overlap without strict
nesting. Examples include:

- **Network Requests:** A process might issue multiple network requests
  concurrently.
- **Broadcast Receivers (Android):** An application can receive multiple
  broadcast intents. The handling of each can overlap.
- **Wakelocks (Android/Linux):** Multiple components can hold wakelocks
  simultaneously.
- **File I/O Operations:** A program might initiate several asynchronous read or
  write operations to different files.

In these scenarios, you cannot represent all these overlapping events on a
single track if you are using begin/end slice semantics, because
`TYPE_SLICE_END` always closes the most recently opened slice _on that specific
track_.

The Perfetto way to model this is to assign each concurrent, potentially
overlapping operation to its **own unique track (with a unique UUID)**. To
achieve visual grouping in the Perfetto UI for these related asynchronous
operations, you can give the `TrackDescriptor` of each of these individual
operation tracks the **same `name`** (e.g., "Network Connections" or "File
I/O"). The slices themselves on these tracks can have distinct names (e.g., "GET
/api/data", "Read /config.txt").

The Perfetto UI will group or visually merge tracks that have the same name.
This is a convention and can be controlled by the user. For more details, see
the section on controlling merging in the
[synthetic track event reference docs](/docs/reference/synthetic-track-event.md#controlling-track-merging).

### Python Example

Imagine we are tracking active network connections. Each connection is an
independent asynchronous event. We'll give all connection tracks the same name
to encourage the UI to group them. We'll use helper functions to define tracks
and add events.

Copy the following Python code into the `populate_packets(builder)` function in
your `trace_converter_template.py` script:

<details>
<summary><a style="cursor: pointer;"><b>Click to expand/collapse Python code</b></a></summary>

```python
    TRUSTED_PACKET_SEQUENCE_ID = 3003
    # Common name for all individual connection tracks for UI grouping
    ASYNC_TRACK_GROUP_NAME = "HTTP Connections"

    # Helper to define a new track with a unique UUID
    def define_track(group_name):
        track_uuid = uuid.uuid4().int & ((1 << 63) - 1)
        packet = builder.add_packet()
        packet.track_descriptor.uuid = track_uuid
        packet.track_descriptor.name = group_name
        return track_uuid

    # Helper to add a begin or end slice event to a specific track
    def add_slice_event(ts, event_type, event_track_uuid, name=None):
        packet = builder.add_packet()
        packet.timestamp = ts
        packet.track_event.type = event_type
        packet.track_event.track_uuid = event_track_uuid
        if name:
            packet.track_event.name = name
        packet.trusted_packet_sequence_id = TRUSTED_PACKET_SEQUENCE_ID

    # --- Network Connection 1 ---
    conn1_track_uuid = define_track(ASYNC_TRACK_GROUP_NAME)
    add_slice_event(ts=1000, event_type=TrackEvent.TYPE_SLICE_BEGIN, event_track_uuid=conn1_track_uuid, name="GET /data/config")
    add_slice_event(ts=1500, event_type=TrackEvent.TYPE_SLICE_END, event_track_uuid=conn1_track_uuid)

    # --- Network Connection 2 (Overlapping with Connection 1) ---
    conn2_track_uuid = define_track(ASYNC_TRACK_GROUP_NAME)
    add_slice_event(ts=1100, event_type=TrackEvent.TYPE_SLICE_BEGIN, event_track_uuid=conn2_track_uuid, name="POST /submit/form")
    add_slice_event(ts=2000, event_type=TrackEvent.TYPE_SLICE_END, event_track_uuid=conn2_track_uuid)

    # --- Network Connection 3 (Starts after 1 ends, overlaps with 2) ---
    conn3_track_uuid = define_track(ASYNC_TRACK_GROUP_NAME)
    add_slice_event(ts=1600, event_type=TrackEvent.TYPE_SLICE_BEGIN, event_track_uuid=conn3_track_uuid, name="GET /status/check")
    add_slice_event(ts=2200, event_type=TrackEvent.TYPE_SLICE_END, event_track_uuid=conn3_track_uuid)
```

</details>

After running the script, opening the generated `my_custom_trace.pftrace` in the
[Perfetto UI](https://ui.perfetto.dev) will display the following output:

![Asynchronous Slices](/docs/images/converting-async-slices.png)

## Counters (Values Changing Over Time)

Counters are used to represent a numerical value that changes over time. They
are excellent for tracking metrics or states that are not event-based but rather
reflect a continuous or sampled quantity.

Common examples of what counters can represent include:

- **Memory usage:** Total memory consumed by a process, or specific memory
  pools.
- **CPU frequency:** The current operating frequency of a CPU core.
- **Queue sizes:** The number of outstanding requests in a network queue or
  tasks in a work queue.
- **Battery percentage:** The remaining battery charge.
- **Resource limits:** The current value of a resource like file descriptors or
  network bandwidth being utilized.

To create a counter track, you'll:

1.  Define a `TrackDescriptor` for your counter. This track needs a `uuid`, a
    `name`, and importantly, its `counter` field should be populated. This tells
    Perfetto to treat this track as a counter.
2.  Emit `TrackEvent` packets with `type: TYPE_COUNTER`. Each such packet should
    have a `timestamp` and a `counter_value` (which can be an integer or a
    double).

### Python Example

Let's say we want to track the number of outstanding network requests over time.

Copy the following Python code into the `populate_packets(builder)` function in
your `trace_converter_template.py` script.

<details>
<summary><a style="cursor: pointer;"><b>Click to expand/collapse Python code</b></a></summary>

```python
    TRUSTED_PACKET_SEQUENCE_ID = 4004
    # UUID for the counter track
    OUTSTANDING_REQUESTS_TRACK_UUID = uuid.uuid4().int & ((1 << 63) - 1)

    # 1. Define the Counter Track
    packet = builder.add_packet()
    track_desc = packet.track_descriptor
    track_desc.uuid = OUTSTANDING_REQUESTS_TRACK_UUID
    track_desc.name = "Outstanding Network Requests"
    # To mark this as a counter track, set the 'counter' field as existing.
    track_desc.counter.SetInParent()

    # Helper to add a counter event
    def add_counter_event(ts, value):
        packet = builder.add_packet()
        packet.timestamp = ts
        packet.track_event.type = TrackEvent.TYPE_COUNTER
        packet.track_event.track_uuid = OUTSTANDING_REQUESTS_TRACK_UUID
        packet.track_event.counter_value = value
        packet.trusted_packet_sequence_id = TRUSTED_PACKET_SEQUENCE_ID

    # 2. Emit counter values over time
    add_counter_event(ts=1000, value=0)
    add_counter_event(ts=1100, value=1) # One request starts
    add_counter_event(ts=1200, value=2) # Second request starts
    add_counter_event(ts=1300, value=3) # Third request starts
    add_counter_event(ts=1400, value=2) # First request finishes
    add_counter_event(ts=1500, value=2) # No change
    add_counter_event(ts=1600, value=1) # Second request finishes
    add_counter_event(ts=1700, value=0) # Third request finishes
    add_counter_event(ts=1800, value=1) # New request starts
    add_counter_event(ts=1900, value=0) # Last request finishes
```

</details>

After running the script, opening the generated `my_custom_trace.pftrace` in the
[Perfetto UI](https://ui.perfetto.dev) will display the following output:

![Counters](/docs/images/converting-counters.png)

## Flows (Connecting Causally Related Events)

Flows are used to visually connect slices that have an explicit causal or
dependency relationship, especially when these slices occur on different tracks
(like different threads or even different processes). They are crucial for
understanding how an action in one part of a system triggers or enables an
action in another.

Think of flows as drawing an arrow from a "cause" or "dispatch" event to an
"effect" or "handling" event. Common scenarios include:

- A UI thread dispatches a task to a worker thread: a flow connects the dispatch
  slice to the execution slice on the worker.
- A service makes an RPC/IPC call to another service: a flow can link the
  client-side call initiation to the server-side request handling.
- An event is posted to a message queue and later processed: a flow can show the
  link from posting to processing.

In Perfetto's `TrackEvent` model, you establish a flow by:

1.  Assigning one or more unique 64-bit `flow_id`s to the `TrackEvent`s that are
    part of the flow. This ID acts as the link.
2.  Typically, a `flow_id` is added to a `TYPE_SLICE_BEGIN` or `TYPE_SLICE_END`
    event to mark the origin or termination of a causal link from/to that slice.
3.  The same `flow_id` is then added to another `TrackEvent` (often a
    `TYPE_SLICE_BEGIN` on a different track) to show the continuation or
    handling of that causally linked operation.

The Perfetto UI will draw arrows connecting the slices that share a common
`flow_id`, making the dependency chain explicit.

### Python Example

Let's model a simple system where a "Request Handler" track dispatches work to a
"Data Processor" track. We'll use flows to link the request dispatch to its
processing, and then link the processing completion back to the handler
acknowledging completion.

Copy the following Python code into the `populate_packets(builder)` function in
your `trace_converter_template.py` script.

<details>
<summary><a style="cursor: pointer;"><b>Click to expand/collapse Python code</b></a></summary>

```python
    TRUSTED_PACKET_SEQUENCE_ID = 5005

    # --- Define Custom Tracks ---
    REQUEST_HANDLER_TRACK_UUID = uuid.uuid4().int & ((1 << 63) - 1)
    DATA_PROCESSOR_TRACK_UUID = uuid.uuid4().int & ((1 << 63) - 1)

    # Request Handler Track
    packet = builder.add_packet()
    packet.track_descriptor.uuid = REQUEST_HANDLER_TRACK_UUID
    packet.track_descriptor.name = "Request Handler"

    # Data Processor Track
    packet = builder.add_packet()
    packet.track_descriptor.uuid = DATA_PROCESSOR_TRACK_UUID
    packet.track_descriptor.name = "Data Processor"

    # Helper to add a slice event (BEGIN or END)
    def add_slice_event(ts, event_type, event_track_uuid, name=None, flow_ids=None):
        packet = builder.add_packet()
        packet.timestamp = ts
        packet.track_event.type = event_type
        packet.track_event.track_uuid = event_track_uuid
        if name:
            packet.track_event.name = name
        if flow_ids:
            for flow_id in flow_ids:
                packet.track_event.flow_ids.append(flow_id)
        packet.trusted_packet_sequence_id = TRUSTED_PACKET_SEQUENCE_ID

    # --- Define unique flow IDs for the causal links ---
    DISPATCH_TO_PROCESS_FLOW_ID = uuid.uuid4().int & ((1<<63)-1)
    PROCESS_COMPLETION_FLOW_ID = uuid.uuid4().int & ((1<<63)-1)

    # 1. Request Handler: Dispatch data processing (origin of the first flow)
    add_slice_event(ts=1000, event_type=TrackEvent.TYPE_SLICE_BEGIN,
                    event_track_uuid=REQUEST_HANDLER_TRACK_UUID, name="DispatchProcessing",
                    flow_ids=[DISPATCH_TO_PROCESS_FLOW_ID])
    add_slice_event(ts=1050, event_type=TrackEvent.TYPE_SLICE_END,
                    event_track_uuid=REQUEST_HANDLER_TRACK_UUID)

    # 2. Data Processor: Process the data (flow from handler's dispatch)
    # This slice's BEGIN event includes DISPATCH_TO_PROCESS_FLOW_ID, linking it.
    # It also starts the PROCESS_COMPLETION_FLOW_ID from its BEGIN event.
    add_slice_event(ts=1100, event_type=TrackEvent.TYPE_SLICE_BEGIN,
                    event_track_uuid=DATA_PROCESSOR_TRACK_UUID, name="ProcessDataItem",
                    flow_ids=[DISPATCH_TO_PROCESS_FLOW_ID, PROCESS_COMPLETION_FLOW_ID])
    add_slice_event(ts=1300, event_type=TrackEvent.TYPE_SLICE_END,
                    event_track_uuid=DATA_PROCESSOR_TRACK_UUID)

    # 3. Request Handler: Acknowledge completion (PROCESS_COMPLETION_FLOW_ID terminates here)
    add_slice_event(ts=1350, event_type=TrackEvent.TYPE_SLICE_BEGIN,
                    event_track_uuid=REQUEST_HANDLER_TRACK_UUID, name="AcknowledgeCompletion",
                    flow_ids=[PROCESS_COMPLETION_FLOW_ID])
    add_slice_event(ts=1400, event_type=TrackEvent.TYPE_SLICE_END,
                    event_track_uuid=REQUEST_HANDLER_TRACK_UUID)
```

</details>

After running the script, opening the generated `my_custom_trace.pftrace` in the
[Perfetto UI](https://ui.perfetto.dev) will display the following output:

![Flows](/docs/images/converting-flows.png)

## Grouping Tracks with Hierarchies

As traces become more complex, you might want to group related tracks together
to create a more organized and understandable visualization. Perfetto allows you
to define a parent-child relationship between tracks using the `parent_uuid`
field in the `TrackDescriptor`.

This is useful when:

- You have a high-level component (parent track) that comprises several
  sub-components (child tracks), and you want to see them grouped in the UI.
- You want to create logical groupings for different types of asynchronous
  events or different sets of counters.
- You are representing a system with inherent hierarchical structures (e.g., a
  machine with multiple GPUs, each GPU having multiple engines).

A parent track can serve two main purposes:

- **Pure Grouping:** The parent track itself might not have any direct events
  (slices or counters) but acts solely as a container to group its child tracks
  in the UI.
- **Summary Track:** The parent track can also have its own slices or counters.
  These could represent an overview or a summary of the activity detailed in its
  child tracks, or an independent set of events related to the parent itself.

The Perfetto UI will typically render these as an expandable tree.

### Python Example

Let's create a hierarchy:

- A "Main System" track, which will also have its own summary slice.
- Two child tracks of "Main System": "Subsystem A" and "Subsystem B".
- "Subsystem A" will further have its own child track, "Detail A.1".
- We'll then place slices on the parent "Main System" track, "Subsystem B", and
  on the deepest child track "Detail A.1".

Copy the following Python code into the `populate_packets(builder)` function in
your `trace_converter_template.py` script.

<details>
<summary><a style="cursor: pointer;"><b>Click to expand/collapse Python code</b></a></summary>

```python
    TRUSTED_PACKET_SEQUENCE_ID = 6006

    # --- Define Track UUIDs ---
    main_system_track_uuid = uuid.uuid4().int & ((1 << 63) - 1)
    subsystem_a_track_uuid = uuid.uuid4().int & ((1 << 63) - 1)
    subsystem_b_track_uuid = uuid.uuid4().int & ((1 << 63) - 1)
    detail_a1_track_uuid = uuid.uuid4().int & ((1 << 63) - 1)

    # Helper to define a TrackDescriptor
    def define_custom_track(track_uuid, name, parent_track_uuid=None):
        packet = builder.add_packet()
        desc = packet.track_descriptor
        desc.uuid = track_uuid
        desc.name = name
        if parent_track_uuid:
            desc.parent_uuid = parent_track_uuid

    # Helper to add a slice event
    def add_slice_event(ts, event_type, event_track_uuid, name=None):
        packet = builder.add_packet()
        packet.timestamp = ts
        packet.track_event.type = event_type
        packet.track_event.track_uuid = event_track_uuid
        if name:
            packet.track_event.name = name
        packet.trusted_packet_sequence_id = TRUSTED_PACKET_SEQUENCE_ID

    # 1. Define the Track Hierarchy
    define_custom_track(main_system_track_uuid, "Main System")
    define_custom_track(subsystem_a_track_uuid, "Subsystem A", parent_track_uuid=main_system_track_uuid)
    define_custom_track(subsystem_b_track_uuid, "Subsystem B", parent_track_uuid=main_system_track_uuid)
    define_custom_track(detail_a1_track_uuid, "Detail A.1", parent_track_uuid=subsystem_a_track_uuid)

    # 2. Emit slices on various tracks in the hierarchy

    # Slice on the parent "Main System" track (summary/overall activity)
    add_slice_event(ts=4800, event_type=TrackEvent.TYPE_SLICE_BEGIN,
                    event_track_uuid=main_system_track_uuid, name="System Initialization Phase")
    add_slice_event(ts=7000, event_type=TrackEvent.TYPE_SLICE_END,
                    event_track_uuid=main_system_track_uuid)

    # Slice on "Detail A.1" (child of "Subsystem A")
    add_slice_event(ts=5000, event_type=TrackEvent.TYPE_SLICE_BEGIN,
                    event_track_uuid=detail_a1_track_uuid, name="Activity in A.1")
    add_slice_event(ts=5500, event_type=TrackEvent.TYPE_SLICE_END,
                    event_track_uuid=detail_a1_track_uuid)

    # Slice on "Subsystem B"
    add_slice_event(ts=6000, event_type=TrackEvent.TYPE_SLICE_BEGIN,
                    event_track_uuid=subsystem_b_track_uuid, name="Work in Subsystem B")
    add_slice_event(ts=6200, event_type=TrackEvent.TYPE_SLICE_END,
                    event_track_uuid=subsystem_b_track_uuid)

    # Another slice on "Detail A.1"
    add_slice_event(ts=5600, event_type=TrackEvent.TYPE_SLICE_BEGIN,
                    event_track_uuid=detail_a1_track_uuid, name="Further Activity in A.1")
    add_slice_event(ts=5800, event_type=TrackEvent.TYPE_SLICE_END,
                    event_track_uuid=detail_a1_track_uuid)
```

</details>

After running the script, opening the generated `my_custom_trace.pftrace` in the
[Perfetto UI](https://ui.perfetto.dev) will display the following output:

![Grouping Tracks with Hierarchies](/docs/images/converting-track-groups.png)

## Track Hierarchies for Waterfall / Trace Views

Another powerful use of track hierarchies is to visualize the breakdown of a
complex operation or request, similar to how "trace views" or "span views" are
displayed in distributed tracing systems. This is useful when an operation
involves sequential or parallel steps, potentially across different logical
components, and you want to see the timing and relationship of these steps in a
waterfall or Gantt-like chart.

In this model:

- A **root track** represents the entire end-to-end request or operation.
- Each **major step, function call, or RPC call** within that operation is
  represented as a **child track** parented under the root track (or under
  another step if it's a sub-sub-step).
- A **slice** on each child track shows the duration of that specific step.
- The `parent_uuid` field creates the hierarchy. The UI will then typically
  render these as an expandable tree, and the start/end times of the slices on
  these hierarchically arranged tracks create the "waterfall" effect.

### Python Example: Service Request Breakdown

Let's imagine a frontend service makes a request that involves calls to two
backend services: an Authentication Service and a Data Service. The Data Service
call can only happen after the Authentication Service call completes.

Copy the following Python code into the `populate_packets(builder)` function in
your `trace_converter_template.py` script.

<details>
<summary><a style="cursor: pointer;"><b>Click to expand/collapse Python code</b></a></summary>

```python
    TRUSTED_PACKET_SEQUENCE_ID = 7007

    # --- Define Track UUIDs ---
    root_request_track_uuid = uuid.uuid4().int & ((1 << 63) - 1)
    auth_service_call_track_uuid = uuid.uuid4().int & ((1 << 63) - 1)
    data_service_call_track_uuid = uuid.uuid4().int & ((1 << 63) - 1)
    # UUID for an internal step within data_service_call
    data_service_internal_step_track_uuid = uuid.uuid4().int & ((1<<63)-1)

    # Helper to define a TrackDescriptor
    def define_custom_track(track_uuid, name, parent_track_uuid=None):
        packet = builder.add_packet()
        desc = packet.track_descriptor
        desc.uuid = track_uuid
        desc.name = name
        if parent_track_uuid:
            desc.parent_uuid = parent_track_uuid

    # Helper to add a slice event
    def add_slice_event(ts, event_type, event_track_uuid, name=None):
        packet = builder.add_packet()
        packet.timestamp = ts
        packet.track_event.type = event_type
        packet.track_event.track_uuid = event_track_uuid
        if name:
            packet.track_event.name = name
        packet.trusted_packet_sequence_id = TRUSTED_PACKET_SEQUENCE_ID

    # 1. Define the Root Track for the overall request
    define_custom_track(root_request_track_uuid, "Frontend Request: /api/user/profile")

    # Add a slice for the total duration of the frontend request on its own track
    add_slice_event(ts=10000, event_type=TrackEvent.TYPE_SLICE_BEGIN,
                    event_track_uuid=root_request_track_uuid, name="Total Request Duration")

    # 2. Define child tracks for each service call (span) as children of the root request
    define_custom_track(auth_service_call_track_uuid, "Call: AuthService.AuthenticateUser",
                        parent_track_uuid=root_request_track_uuid)
    define_custom_track(data_service_call_track_uuid, "Call: DataService.GetUserData",
                        parent_track_uuid=root_request_track_uuid)

    # 3. Emit slices on these service call tracks
    # Auth Service Call
    add_slice_event(ts=10100, event_type=TrackEvent.TYPE_SLICE_BEGIN,
                    event_track_uuid=auth_service_call_track_uuid, name="AuthService.AuthenticateUser")
    add_slice_event(ts=10300, event_type=TrackEvent.TYPE_SLICE_END,
                    event_track_uuid=auth_service_call_track_uuid)

    # Data Service Call (starts after Auth completes)
    add_slice_event(ts=10350, event_type=TrackEvent.TYPE_SLICE_BEGIN,
                    event_track_uuid=data_service_call_track_uuid, name="DataService.GetUserData")

    # Simulate an internal step within DataService.GetUserData, shown on its own sub-track
    # This track will be a child of the "Call: DataService.GetUserData" track.
    define_custom_track(data_service_internal_step_track_uuid, "Internal: QueryDatabase",
                        parent_track_uuid=data_service_call_track_uuid)

    add_slice_event(ts=10400, event_type=TrackEvent.TYPE_SLICE_BEGIN,
                    event_track_uuid=data_service_internal_step_track_uuid, name="QueryDatabase")
    add_slice_event(ts=10550, event_type=TrackEvent.TYPE_SLICE_END,
                    event_track_uuid=data_service_internal_step_track_uuid)

    add_slice_event(ts=10600, event_type=TrackEvent.TYPE_SLICE_END, # End of DataService.GetUserData
                    event_track_uuid=data_service_call_track_uuid)

    # End of the total frontend request
    add_slice_event(ts=10700, event_type=TrackEvent.TYPE_SLICE_END,
                    event_track_uuid=root_request_track_uuid)
```

</details>

After running the script, opening the generated `my_custom_trace.pftrace` in the
[Perfetto UI](https://ui.perfetto.dev) will display the following output:

![Track Hierarchies for Waterfall / Trace Views](/docs/images/converting-waterfall.png)

## Next Steps

You've now seen how to convert various types of custom timestamped data into
Perfetto traces using Python and the `TrackEvent` protobuf. With these
techniques, you can represent simple activities, nested operations, asynchronous
events, counters, flows, and create organized track hierarchies.

Once you have your custom data in the Perfetto trace format (`.pftrace` file),
you can:

- **Explore advanced `TrackEvent` features:** For more detailed control over
  track and event appearance, interning, and other advanced capabilities of the
  `TrackEvent` protobuf, refer to the
  [Writing synthetic traces using TrackEvent protobufs](/docs/reference/synthetic-track-event.md)
  reference page.
- **Visualize your trace:** Open your generated `.pftrace` file in the
  [Perfetto UI](https://ui.perfetto.dev) to explore your data on an interactive
  timeline.
- **Analyze with SQL:** Use the
  [Trace Processor](/docs/analysis/getting-started.md) to query your custom
  trace data. Your custom tracks and events will populate standard tables like
  `slice`, `track`, `counter`, etc.
- **Handle large datasets:** If you are generating very large traces and want to
  avoid high memory usage, learn how to stream data directly to a file in the
  [Advanced Guide's section on streaming](/docs/reference/synthetic-track-event.md#handling-large-traces-with-streaming).