# Trace Processor (C++)

_The Trace Processor is a C++ library
([/src/trace_processor](/src/trace_processor)) that ingests traces encoded in a
wide variety of formats and exposes an SQL interface for querying trace events
contained in a consistent set of tables. It also has other features including
computation of summary metrics, annotating the trace with user-friendly
descriptions and deriving new events from the contents of the trace._

![Trace processor block diagram](/docs/images/trace-processor.png)

## Quickstart

The [quickstart](/docs/quickstart/trace-analysis.md) provides a quick overview
on how to run SQL queries against traces using trace processor.

## Introduction

Events in a trace are optimized for fast, low-overhead recording. Therefore
traces need significant data processing to extract meaningful information from
them. This is compounded by the number of legacy formats which are still in use and
need to be supported in trace analysis tools.

The trace processor abstracts this complexity by parsing traces, extracting the
data inside, and exposing it in a set of database tables which can be queried
with SQL.

Features of the trace processor include:

* Execution of SQL queries on a custom, in-memory, columnar database backed by
  the SQLite query engine.
* Metrics subsystem which allows computation of summarized view of the trace
  (e.g. CPU or memory usage of a process, time taken for app startup etc.).
* Annotating events in the trace with user-friendly descriptions, providing
  context and explanation of events to newer users.
* Creation of new events derived from the contents of the trace.

The formats supported by trace processor include:

* Perfetto native protobuf format
* Linux ftrace
* Android systrace
* Chrome JSON (including JSON embedding Android systrace text)
* Fuchsia binary format
* [Ninja](https://ninja-build.org/) logs (the build system)

The trace processor is embedded in a wide variety of trace analysis tools, including:

* [trace_processor](/docs/analysis/trace-processor.md), a standalone binary
   providing a shell interface (and the reference embedder).
* [Perfetto UI](https://ui.perfetto.dev), in the form of a WebAssembly module.
* [Android GPU Inspector](https://gpuinspector.dev/).
* [Android Studio](https://developer.android.com/studio/).

## Concepts

The trace processor has some foundational terminology and concepts which are
used in the rest of documentation.

### Events

In the most general sense, a trace is simply a collection of timestamped
"events". Events can have associated metadata and context which allows them to
be interpreted and analyzed.

Events form the foundation of trace processor and are one of two types: slices
and counters.

#### Slices

![Examples of slices](/docs/images/slices.png)

A slice refers to an interval of time with some data describing what was
happening in that interval. Some example of slices include:

* Scheduling slices for each CPU
* Atrace slices on Android
* Userspace slices from Chrome

#### Counters

![Examples of counters](/docs/images/counters.png)

A counter is a continuous value which varies over time. Some examples of
counters include:

* CPU frequency for each CPU core
* RSS memory events - both from the kernel and polled from /proc/stats
* atrace counter events from Android
* Chrome counter events

### Tracks

A track is a named partition of events of the same type and the same associated
context. For example:

* Scheduling slices have one track for each CPU
* Sync userspace slice have one track for each thread which emitted an event
* Async userspace slices have one track for each “cookie” linking a set of async
  events

The most intuitive way to think of a track is to imagine how they would be drawn
in a UI; if all the events are in a single row, they belong to the same track.
For example, all the scheduling events for CPU 5 are on the same track:

![CPU slices track](/docs/images/cpu-slice-track.png)

Tracks can be split into various types based on the type of event they contain
and the context they are associated with. Examples include:

* Global tracks are not associated to any context and contain slices
* Thread tracks are associated to a single thread and contain slices
* Counter tracks are not associated to any context and contain counters
* CPU counter tracks are associated to a single CPU and contain counters

### Thread and process identifiers

The handling of threads and processes needs special care when considered in the
context of tracing; identifiers for threads and processes (e.g. `pid`/`tgid` and
`tid` in Android/macOS/Linux) can be reused by the operating system over the
course of a trace. This means they cannot be relied upon as a unique identifier
when querying tables in trace processor.

To solve this problem, the trace processor uses `utid` (_unique_ tid) for
threads and `upid` (_unique_ pid) for processes. All references to threads and
processes (e.g. in CPU scheduling data, thread tracks) uses `utid` and `upid`
instead of the system identifiers.

## Writing Queries

### Context using tracks

A common question when querying tables in trace processor is: "how do I obtain
the process or thread for a slice?". Phrased more generally, the question is
"how do I get the context for an event?".

In trace processor, any context associated with all events on a track is found
on the associated `track` tables.

For example, to obtain the `utid` of any thread which emitted a `measure` slice

```sql
SELECT utid
FROM slice
JOIN thread_track ON thread_track.id = slice.track_id
WHERE slice.name = 'measure'
```

Similarly, to obtain the `upid`s of any process which has a `mem.swap` counter
greater than 1000

```sql
SELECT upid
FROM counter
JOIN process_counter_track ON process_counter_track.id = counter.track_id
WHERE process_counter_track.name = 'mem.swap' AND value > 1000
```

### Thread and process tables

While obtaining `utid`s and `upid`s are a step in the right direction, generally
users want the original `tid`, `pid`, and process/thread names.

The `thread` and `process` tables map `utid`s and `upid`s to threads and
processes respectively. For example, to lookup the thread with `utid` 10

```sql
SELECT tid, name
FROM thread
WHERE utid = 10
```

The `thread` and `process` tables can also be joined with the associated track
tables directly to jump directly from the slice or counter to the information
about processes and threads.

For example, to get a list of all the threads which emitted a `measure` slice

```sql
SELECT thread.name AS thread_name
FROM slice
JOIN thread_track ON slice.track_id = thread_track.id
JOIN thread USING(utid)
WHERE slice.name = 'measure'
GROUP BY thread_name
```

## Helper functions
Helper functions are functions built into C++ which reduce the amount of
boilerplate which needs to be written in SQL.

### Extract args
`EXTRACT_ARG` is a helper function which retreives a property of an
event (e.g. slice or counter) from the `args` table.

It takes an `arg_set_id` and `key` as input and returns the value looked
up in the `args` table.

For example, to retrieve the `prev_comm` field for `sched_switch` events in
the `ftrace_event` table.
```sql
SELECT EXTRACT_ARG(arg_set_id, 'prev_comm')
FROM ftrace_event
WHERE name = 'sched_switch'
```

Behind the scenes, the above query would desugar to the following:
```sql
SELECT
  (
    SELECT string_value
    FROM args
    WHERE key = 'prev_comm' AND args.arg_set_id = raw.arg_set_id
  )
FROM ftrace_event
WHERE name = 'sched_switch'
```

NOTE: while convinient, `EXTRACT_ARG` can inefficient compared to a `JOIN`
when working with very large tables; a function call is required for every
row which will be slower than the batch filters/sorts used by `JOIN`.

## Operator tables
SQL queries are usually sufficient to retrieve data from trace processor.
Sometimes though, certain constructs can be difficult to express pure SQL.

In these situations, trace processor has special "operator tables" which solve
a particular problem in C++ but expose an SQL interface for queries to take
advantage of.

### Span join
Span join is a custom operator table which computes the intersection of
spans of time from two tables or views. A span in this concept is a row in a
table/view which contains a "ts" (timestamp) and "dur" (duration) columns.

A column (called the *partition*) can optionally be specified which divides the
rows from each table into partitions before computing the intersection.

![Span join block diagram](/docs/images/span-join.png)

```sql
-- Get all the scheduling slices
CREATE VIEW sp_sched AS
SELECT ts, dur, cpu, utid
FROM sched;

-- Get all the cpu frequency slices
CREATE VIEW sp_frequency AS
SELECT
  ts,
  lead(ts) OVER (PARTITION BY track_id ORDER BY ts) - ts as dur,
  cpu,
  value as freq
FROM counter
JOIN cpu_counter_track ON counter.track_id = cpu_counter_track.id
WHERE cpu_counter_track.name = 'cpufreq';

-- Create the span joined table which combines cpu frequency with
-- scheduling slices.
CREATE VIRTUAL TABLE sched_with_frequency
USING SPAN_JOIN(sp_sched PARTITIONED cpu, sp_frequency PARTITIONED cpu);

-- This span joined table can be queried as normal and has the columns from both
-- tables.
SELECT ts, dur, cpu, utid, freq
FROM sched_with_frequency;
```

NOTE: A partition can be specified on neither, either or both tables. If
specified on both, the same column name has to be specified on each table.

WARNING: An important restriction on span joined tables is that spans from
the same table in the same partition *cannot* overlap. For performance
reasons, span join does not attempt to detect and error out in this situation;
instead, incorrect rows will silently be produced.

WARNING: Partitions mush be integers. Importantly, string partitions are *not*
supported; note that strings *can* be converted to integers by
applying the `HASH` function to the string column.

Left and outer span joins are also supported; both function analogously to
the left and outer joins from SQL.
```sql
-- Left table partitioned + right table unpartitioned.
CREATE VIRTUAL TABLE left_join
USING SPAN_LEFT_JOIN(table_a PARTITIONED a, table_b);

-- Both tables unpartitioned.
CREATE VIRTUAL TABLE outer_join
USING SPAN_OUTER_JOIN(table_x, table_y);
```

NOTE: there is a subtlety if the partitioned table is empty and is
either a) part of an outer join b) on the right side of a left join.
In this case, *no* slices will be emitted even if the other table is
non-empty. This approach was decided as being the most natural
after considering how span joins are used in practice.

### Ancestor slice
ancestor_slice is a custom operator table that takes a
[slice table's id column](/docs/analysis/sql-tables.autogen#slice) and computes
all slices on the same track that are direct parents above that id (i.e. given
a slice id it will return as rows all slices that can be found by following
the parent_id column to the top slice (depth = 0)).

The returned format is the same as the
[slice table](/docs/analysis/sql-tables.autogen#slice)

For example, the following finds the top level slice given a bunch of slices of
interest.

```sql
CREATE VIEW interesting_slices AS
SELECT id, ts, dur, track_id
FROM slice WHERE name LIKE "%interesting slice name%";

SELECT
  *
FROM
  interesting_slices LEFT JOIN
  ancestor_slice(interesting_slices.id) AS ancestor ON ancestor.depth = 0
```

### Ancestor slice by stack
ancestor_slice_by_stack is a custom operator table that takes a
[slice table's stack_id column](/docs/analysis/sql-tables.autogen#slice) and
finds all slice ids with that stack_id, then, for each id it computes
all the ancestor slices similarly to
[ancestor_slice](/docs/analysis/trace-processor#ancestor-slice).

The returned format is the same as the
[slice table](/docs/analysis/sql-tables.autogen#slice)

For example, the following finds the top level slice of all slices with the
given name.

```sql
CREATE VIEW interesting_stack_ids AS
SELECT stack_id
FROM slice WHERE name LIKE "%interesting slice name%";

SELECT
  *
FROM
  interesting_stack_ids LEFT JOIN
  ancestor_slice_by_stack(interesting_stack_ids.stack_id) AS ancestor
  ON ancestor.depth = 0
```

### Descendant slice
descendant_slice is a custom operator table that takes a
[slice table's id column](/docs/analysis/sql-tables.autogen#slice) and
computes all slices on the same track that are nested under that id (i.e.
all slices that are on the same track at the same time frame with a depth
greater than the given slice's depth.

The returned format is the same as the
[slice table](/docs/analysis/sql-tables.autogen#slice)

For example, the following finds the number of slices under each slice of
interest.

```sql
CREATE VIEW interesting_slices AS
SELECT id, ts, dur, track_id
FROM slice WHERE name LIKE "%interesting slice name%";

SELECT
  *
  (
    SELECT
      COUNT(*) AS total_descendants
    FROM descendant_slice(interesting_slice.id)
  )
FROM interesting_slices
```

### Descendant slice by stack
descendant_slice_by_stack is a custom operator table that takes a
[slice table's stack_id column](/docs/analysis/sql-tables.autogen#slice) and
finds all slice ids with that stack_id, then, for each id it computes
all the descendant slices similarly to
[descendant_slice](/docs/analysis/trace-processor#descendant-slice).

The returned format is the same as the
[slice table](/docs/analysis/sql-tables.autogen#slice)

For example, the following finds the next level descendant of all slices with
the given name.

```sql
CREATE VIEW interesting_stacks AS
SELECT stack_id, depth
FROM slice WHERE name LIKE "%interesting slice name%";

SELECT
  *
FROM
  interesting_stacks LEFT JOIN
  descendant_slice_by_stack(interesting_stacks.stack_id) AS descendant
  ON descendant.depth = interesting_stacks.depth + 1
```

### Connected/Following/Preceding flows

DIRECTLY_CONNECTED_FLOW, FOLLOWING_FLOW and PRECEDING_FLOW are custom operator
tables that take a
[slice table's id column](/docs/analysis/sql-tables.autogen#slice) and collect
all entries of [flow table](/docs/analysis/sql-tables.autogen#flow), that are
directly or indirectly connected to the given starting slice.

`DIRECTLY_CONNECTED_FLOW(start_slice_id)` - contains all entries of
[flow table](/docs/analysis/sql-tables.autogen#flow) that are present in any
chain of kind: `flow[0] -> flow[1] -> ... -> flow[n]`, where
`flow[i].slice_out = flow[i+1].slice_in` and `flow[0].slice_out = start_slice_id
OR start_slice_id = flow[n].slice_in`.

NOTE: Unlike the following/preceding flow functions, this function will not
include flows connected to ancestors or descendants while searching for flows
from a slice. It only includes the slices in the directly connected chain.

`FOLLOWING_FLOW(start_slice_id)` - contains all flows which can be reached from
a given slice via recursively following from flow's outgoing slice to its
incoming one and from a reached slice to its child. The return table contains
all entries of [flow table](/docs/analysis/sql-tables.autogen#flow) that are
present in any chain of kind: `flow[0] -> flow[1] -> ... -> flow[n]`, where
`flow[i+1].slice_out IN DESCENDANT_SLICE(flow[i].slice_in) OR
flow[i+1].slice_out = flow[i].slice_in` and `flow[0].slice_out IN
DESCENDANT_SLICE(start_slice_id) OR flow[0].slice_out = start_slice_id`.

`PRECEDING_FLOW(start_slice_id)` - contains all flows which can be reached from
a given slice via recursively following from flow's incoming slice to its
outgoing one and from a reached slice to its parent. The return table contains
all entries of [flow table](/docs/analysis/sql-tables.autogen#flow) that are
present in any chain of kind: `flow[n] -> flow[n-1] -> ... -> flow[0]`, where
`flow[i].slice_in IN ANCESTOR_SLICE(flow[i+1].slice_out) OR flow[i].slice_in =
flow[i+1].slice_out` and `flow[0].slice_in IN ANCESTOR_SLICE(start_slice_id) OR
flow[0].slice_in = start_slice_id`.

```sql
--number of following flows for each slice
SELECT (SELECT COUNT(*) FROM FOLLOWING_FLOW(slice_id)) as following FROM slice;
```

## Metrics

TIP: To see how to add to add a new metric to trace processor, see the checklist
[here](/docs/contributing/common-tasks.md#new-metric).

The metrics subsystem is a significant part of trace processor and thus is
documented on its own [page](/docs/analysis/metrics.md).

## Python API
The trace processor's C++ library is also exposed through Python. This
is documented on a [separate page](/docs/analysis/trace-processor-python.md).

## Testing

Trace processor is mainly tested in two ways:
1. Unit tests of low-level building blocks
2. "Diff" tests which parse traces and check the output of queries

### Unit tests
Unit testing trace processor is the same as in other parts of Perfetto and
other C++ projects. However, unlike the rest of Perfetto, unit testing is
relatively light in trace processor.

We have discovered over time that unit tests are generally too brittle
when dealing with code which parses traces leading to painful, mechanical
changes being needed when refactorings happen.

Because of this, we choose to focus on diff tests for most areas (e.g.
parsing events, testing schema of tables, testing metrics etc.) and only
use unit testing for the low-level building blocks on which the rest of
trace processor is built.

### Diff tests
Diff tests are essentially integration tests for trace processor and the
main way trace processor is tested.

Each diff test takes as input a) a trace file b) a query file *or* a metric
name. It runs `trace_processor_shell` to parse the trace and then executes
the query/metric. The result is then compared to a 'golden' file and any
difference is highlighted.

All diff tests are organized under [test/trace_processor](/test/trace_processor)
in `tests{_category name}.py` files as methods of a class in each file
and are run by the script
[`tools/diff_test_trace_processor.py`](/tools/diff_test_trace_processor.py).
To add a new test its enough to add a new method starting with `test_` in suitable
python tests file.

Methods can't take arguments and have to return `DiffTestBlueprint`:
```python
class DiffTestBlueprint:
  trace: Union[Path, Json, Systrace, TextProto]
  query: Union[str, Path, Metric]
  out: Union[Path, Json, Csv, TextProto]
```
*Trace* and *Out*: For every type apart from `Path`, contents of the object will be treated as
file contents so it has to follow the same rules.

*Query*: For metric tests it is enough to provide the metric name. For query tests there
can be a raw SQL statement, for example `"SELECT * FROM SLICE"` or path to an `.sql` file.



NOTE: `trace_processor_shell` and associated proto descriptors needs to be
built before running `tools/diff_test_trace_processor.py`. The easiest way
to do this is to run `tools/ninja -C <out directory>` both initially and on
every change to trace processor code or builtin metrics.

#### Choosing where to add diff tests
`diff_tests/` folder contains four directories corresponding to different
areas of trace processor.
1. __stdlib__: Tests focusing on testing Perfetto Standard Library, both
   prelude and the regular modules. The subdirectories in this folder
   should generally correspond to directories in `perfetto_sql/stdlib`.
2. __parser__: Tests focusing on ensuring that different trace files are
   parsed correctly and the corresponding built-in tables are populated.
3. __metrics__: Tests focusing on testing metrics located in
   `trace_processor/metrics/sql`. This organisation is mostly historical
   and code (and corresponding tests) is expected to move to `stdlib` over time.
4. __syntax__: Tests focusing on testing the core syntax of PerfettoSQL
   (i.e. `CREATE PERFETTO TABLE` or `CREATE PERFETTO FUNCTION`).

__Scenario__: A new stdlib module `foo/bar.sql` is being added.

_Answer_: Add the test to the `stdlib/foo/bar_tests.py` file.

__Scenario__: A new event is being parsed, the focus of the test is to ensure
the event is being parsed correctly.

_Answer_: Add the test in one of the `parser` subdirectories. Prefer adding a
test to an existing related directory (i.e. `sched`, `power`) if one exists.

__Scenario__: A new metric is being added and the focus of the test is to
ensure the metric is being correctly computed.

_Answer_: Add the test in one of the `metrics` subdirectories. Prefer adding a
test to an existing related directory if one exists. Also consider adding the
code in question to stdlib.

__Scenario__: A new dynamic table is being added and the focus of the test is to
ensure the dynamic table is being correctly computed...

_Answer_: Add the test to the `stdlib/dynamic_tables` folder

__Scenario__: The interals of trace processor are being modified and the test
is to ensure the trace processor is correctly filtering/sorting important
built-in tables.

_Answer_: Add the test to the `parser/core_tables` folder.