Table Aggregate

Render a table with aggregated data.

HTML output

Options

title:

Type(s): [string, null], Default: NULL

Caption for the table

partition:

Type(s): [string, string[]], Default: []

Partition the data using these column names - the row expressions will to aggregate the data in each partition

row:

Type(s): array, Default: []

Set of expressions used to evaluate the partitions, the key is the column name, the value is the expression

groups:

Type(s): array, Default: []

Group columns together, e.g. {"groups":{"group_name": {"cols": ["col1", "col2"]}}}

Example

Given the following configuration:

{
    "runner.path": "NothingBench.php",
    "runner.executor": "debug",
    "runner.env_enabled_providers": ["test"],
    "report.generators": {
        "nested": {
            "generator": "component",
            "title": "My Aggregated Table",
            "components": [
                {
                    "component": "table_aggregate",
                    "title": "Table",
                    "row": {
                        "benchmark": "first(partition['benchmark_name'])",
                        "net_time": "sum(partition['result_time_net'])"
                    }
                }
            ]
        }
    }
}

When we run PHPBench with the configured report above:

phpbench run --report=nested

Then it generates the following with the console renderer:

My Aggregated Table
===================

Table
+--------------+----------+
| benchmark    | net_time |
+--------------+----------+
| NothingBench | 10       |
+--------------+----------+

Advanced Columns

More advanced behavior can be accessed by passing an object instead of an expression, specifying a “column processor” type.

expand

You can “expand” columns dynamically by further partitioning the data frame with the expand column processor:

{
    "runner.path": "MultipleSubjects.php",
    "runner.executor": "debug-example",
    "runner.iterations": 5,
    "runner.executors": {
        "debug-example": {
            "executor": "debug",
            "times":[10, 20, 30, 40, 50],
            "spread": [4, 5, 2, 3]
        }
    },
    "runner.env_enabled_providers": ["test"],
    "report.generators": {
        "expanded": {
            "generator": "component",
            "components": [
                {
                    "component": "table_aggregate",
                    "title": "Table with rows per iteration",
                    "partition": "subject_name",
                    "row": {
                        "benchmark": "first(partition['subject_name'])",
                        "by_iteration": {
                            "type": "expand",
                            "partition": ["iteration_index"],
                            "cols": {
                                "Iteration #{{ key }}": "mode(partition['result_time_avg'])"
                            }
                        }
                    }
                }
            ]
        }
    }
}

Above we:

• Use an object as the value of the column definition by_iteration (the name is arbitrary and subsequently not used, it needs only to be unique)

• Specified the type to be expand

• We group (partition) the data by iteration_index)

• Specify a set of columns

Which would produce:

Table with rows per iteration
+---------------+--------------+--------------+--------------+--------------+--------------+
| benchmark     | Iteration #0 | Iteration #1 | Iteration #2 | Iteration #3 | Iteration #4 |
+---------------+--------------+--------------+--------------+--------------+--------------+
| benchSubject1 | 14           | 25           | 32           | 43           | 54           |
| benchSubject2 | 14           | 25           | 32           | 43           | 54           |
| benchSubject3 | 14           | 25           | 32           | 43           | 54           |
+---------------+--------------+--------------+--------------+--------------+--------------+

Note that:

• The partition parameter is used by default, overwriting the partition from the parent scope. You can rename this using the var option.

• The key variable is made available representing the “key” of the partition, you can rename this variable with the key_var option.