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import argparse
import json
import numpy as np
import os
import pandas as pd
from dataclasses import asdict, dataclass
from pathlib import Path
from scipy.stats import ttest_rel
from tabulate import tabulate
from typing import Final
def flatten_data(json_data: dict) -> dict:
"""
Extracts and flattens metrics from JSON data.
This is needed because the JSON data can be nested.
For example, the JSON data entry might look like this:
"gc":{"cycles":13,"heapSize":5404549120,"totalBytes":9545876464}
Flattened:
"gc.cycles": 13
"gc.heapSize": 5404549120
...
See https://github.com/NixOS/nix/blob/187520ce88c47e2859064704f9320a2d6c97e56e/src/libexpr/eval.cc#L2846
for the ultimate source of this data.
Args:
json_data (dict): JSON data containing metrics.
Returns:
dict: Flattened metrics with keys as metric names.
"""
flat_metrics = {}
for key, value in json_data.items():
# This key is duplicated as `time.cpu`; we keep that copy.
if key == "cpuTime":
continue
if isinstance(value, (int, float)):
flat_metrics[key] = value
elif isinstance(value, dict):
for subkey, subvalue in value.items():
assert isinstance(subvalue, (int, float)), subvalue
flat_metrics[f"{key}.{subkey}"] = subvalue
else:
assert isinstance(value, (float, int, dict)), (
f"Value `{value}` has unexpected type"
)
return flat_metrics
def load_all_metrics(path: Path) -> dict:
"""
Loads all stats JSON files in the specified file or directory and extracts metrics.
These stats JSON files are created by Nix when the `NIX_SHOW_STATS` environment variable is set.
If the provided path is a directory, it must have the structure $path/$system/$stats,
where $path is the provided path, $system is some system from `lib.systems.doubles.*`,
and $stats is a stats JSON file.
If the provided path is a file, it is a stats JSON file.
Args:
path (Path): Directory containing JSON files or a stats JSON file.
Returns:
dict: Dictionary with filenames as keys and extracted metrics as values.
"""
metrics = {}
if path.is_dir():
for system_dir in path.iterdir():
assert system_dir.is_dir()
for chunk_output in system_dir.iterdir():
with chunk_output.open() as f:
data = json.load(f)
metrics[f"{system_dir.name}/${chunk_output.name}"] = flatten_data(data)
else:
with path.open() as f:
metrics[path.name] = flatten_data(json.load(f))
return metrics
def metric_table_name(name: str, explain: bool) -> str:
"""
Returns the name of the metric, plus a footnote to explain it if needed.
"""
return f"{name}[^{name}]" if explain else name
METRIC_EXPLANATION_FOOTNOTE: Final[str] = """
[^time.cpu]: Number of seconds of CPU time accounted by the OS to the Nix evaluator process. On UNIX systems, this comes from [`getrusage(RUSAGE_SELF)`](https://man7.org/linux/man-pages/man2/getrusage.2.html).
[^time.gc]: Number of seconds of CPU time accounted by the Boehm garbage collector to performing GC.
[^time.gcFraction]: What fraction of the total CPU time is accounted towards performing GC.
[^gc.cycles]: Number of times garbage collection has been performed.
[^gc.heapSize]: Size in bytes of the garbage collector heap.
[^gc.totalBytes]: Size in bytes of all allocations in the garbage collector.
[^envs.bytes]: Size in bytes of all `Env` objects allocated by the Nix evaluator. These are almost exclusively created by [`nix-env`](https://nix.dev/manual/nix/stable/command-ref/nix-env.html).
[^list.bytes]: Size in bytes of all [lists](https://nix.dev/manual/nix/stable/language/syntax.html#list-literal) allocated by the Nix evaluator.
[^sets.bytes]: Size in bytes of all [attrsets](https://nix.dev/manual/nix/stable/language/syntax.html#list-literal) allocated by the Nix evaluator.
[^symbols.bytes]: Size in bytes of all items in the Nix evaluator symbol table.
[^values.bytes]: Size in bytes of all values allocated by the Nix evaluator.
[^envs.number]: The count of all `Env` objects allocated.
[^nrAvoided]: The number of thunks avoided being created.
[^nrExprs]: The number of expression objects ever created.
[^nrFunctionCalls]: The number of function calls ever made.
[^nrLookups]: The number of lookups into an attrset ever made.
[^nrOpUpdateValuesCopied]: The number of attrset values copied in the process of merging attrsets.
[^nrOpUpdates]: The number of attrsets merge operations (`//`) performed.
[^nrPrimOpCalls]: The number of function calls to primops (Nix builtins) ever made.
[^nrThunks]: The number of [thunks](https://nix.dev/manual/nix/latest/language/evaluation.html#laziness) ever made. A thunk is a delayed computation, represented by an expression reference and a closure.
[^sets.number]: The number of attrsets ever made.
[^symbols.number]: The number of symbols ever added to the symbol table.
[^values.number]: The number of values ever made.
[^envs.elements]: The number of values contained within an `Env` object.
[^list.concats]: The number of list concatenation operations (`++`) performed.
[^list.elements]: The number of values contained within a list.
[^sets.elements]: The number of values contained within an attrset.
[^sizes.Attr]: Size in bytes of the `Attr` type.
[^sizes.Bindings]: Size in bytes of the `Bindings` type.
[^sizes.Env]: Size in bytes of the `Env` type.
[^sizes.Value]: Size in bytes of the `Value` type.
"""
@dataclass(frozen=True)
class PairwiseTestResults:
updated: pd.DataFrame
equivalent: pd.DataFrame
@staticmethod
def tabulate(table, headers) -> str:
return tabulate(
table, headers, tablefmt="github", floatfmt=".4f", missingval="-"
)
def updated_to_markdown(self, explain: bool) -> str:
assert not self.updated.empty
# Header (get column names and format them)
return self.tabulate(
headers=[str(column) for column in self.updated.columns],
table=[
[
# The metric acts as its own footnote name
metric_table_name(row["metric"], explain),
# Check for no change and NaN in p_value/t_stat
*[
None if np.isnan(val) or np.allclose(val, 0) else val
for val in row[1:]
],
]
for _, row in self.updated.iterrows()
],
)
def equivalent_to_markdown(self, explain: bool) -> str:
assert not self.equivalent.empty
return self.tabulate(
headers=[str(column) for column in self.equivalent.columns],
table=[
[
# The metric acts as its own footnote name
metric_table_name(row["metric"], explain),
row["value"],
]
for _, row in self.equivalent.iterrows()
],
)
def to_markdown(self, explain: bool) -> str:
result = ""
if not self.equivalent.empty:
result += "## Unchanged values\n\n"
result += self.equivalent_to_markdown(explain)
if not self.updated.empty:
result += ("\n\n" if result else "") + "## Updated values\n\n"
result += self.updated_to_markdown(explain)
if explain:
result += METRIC_EXPLANATION_FOOTNOTE
return result
@dataclass(frozen=True)
class Equivalent:
metric: str
value: float
@dataclass(frozen=True)
class Comparison:
metric: str
mean_before: float
mean_after: float
mean_diff: float
mean_pct_change: float
@dataclass(frozen=True)
class ComparisonWithPValue(Comparison):
p_value: float
t_stat: float
def metric_sort_key(name: str) -> str:
if name in ("time.cpu", "time.gc", "time.gcFraction"):
return (1, name)
elif name.startswith("gc"):
return (2, name)
elif name.endswith(("bytes", "Bytes")):
return (3, name)
elif name.startswith("nr") or name.endswith("number"):
return (4, name)
else:
return (5, name)
def perform_pairwise_tests(
before_metrics: dict, after_metrics: dict
) -> PairwiseTestResults:
common_files = sorted(set(before_metrics) & set(after_metrics))
all_keys = sorted(
{
metric_keys
for file_metrics in before_metrics.values()
for metric_keys in file_metrics.keys()
},
key=metric_sort_key,
)
updated = []
equivalent = []
for key in all_keys:
before_vals = []
after_vals = []
for fname in common_files:
if key in before_metrics[fname] and key in after_metrics[fname]:
before_vals.append(before_metrics[fname][key])
after_vals.append(after_metrics[fname][key])
if len(before_vals) == 0:
continue
before_arr = np.array(before_vals)
after_arr = np.array(after_vals)
diff = after_arr - before_arr
# If there's no difference, add it all to the equivalent output.
if np.allclose(diff, 0):
equivalent.append(Equivalent(metric=key, value=before_vals[0]))
else:
pct_change = 100 * diff / before_arr
result = Comparison(
metric=key,
mean_before=np.mean(before_arr),
mean_after=np.mean(after_arr),
mean_diff=np.mean(diff),
mean_pct_change=np.mean(pct_change),
)
# If there are enough values to perform a t-test, do so.
if len(before_vals) > 1:
t_stat, p_val = ttest_rel(after_arr, before_arr)
result = ComparisonWithPValue(
**asdict(result), p_value=p_val, t_stat=t_stat
)
updated.append(result)
return PairwiseTestResults(
updated=pd.DataFrame(map(asdict, updated)),
equivalent=pd.DataFrame(map(asdict, equivalent)),
)
def main():
parser = argparse.ArgumentParser(
description="Performance comparison of Nix evaluation statistics"
)
parser.add_argument(
"--explain", action="store_true", help="Explain the evaluation statistics"
)
parser.add_argument(
"before", help="File or directory containing baseline (data before)"
)
parser.add_argument(
"after", help="File or directory containing comparison (data after)"
)
options = parser.parse_args()
before_stats = Path(options.before)
after_stats = Path(options.after)
before_metrics = load_all_metrics(before_stats)
after_metrics = load_all_metrics(after_stats)
pairwise_test_results = perform_pairwise_tests(before_metrics, after_metrics)
markdown_table = pairwise_test_results.to_markdown(explain=options.explain)
print(markdown_table)
if __name__ == "__main__":
main()
|
