package com.google.common.collect;

import static java.util.Arrays.sort;

import com.google.caliper.BeforeExperiment;
import com.google.caliper.Benchmark;
import com.google.caliper.Param;
import java.util.Arrays;
import java.util.Comparator;
import java.util.Random;

/**
 * A benchmark to determine the overhead of sorting with {@link Ordering#from(Comparator)}, or with

package com.google.common.collect;

import static com.google.common.base.Preconditions.checkArgument;
import static java.util.Collections.sort;

import com.google.caliper.BeforeExperiment;
import com.google.caliper.Benchmark;
import com.google.caliper.Param;
import java.util.ArrayList;
import java.util.Collections;
import java.util.LinkedHashSet;
import java.util.List;
import java.util.Random;
import java.util.Set;

import java.nio.file.Paths;
import java.util.List;
import java.util.concurrent.TimeUnit;
import java.util.stream.Collectors;

import org.apache.maven.api.model.InputSource;
import org.openjdk.jmh.annotations.Benchmark;
import org.openjdk.jmh.annotations.BenchmarkMode;
import org.openjdk.jmh.annotations.Level;
import org.openjdk.jmh.annotations.Measurement;
import org.openjdk.jmh.annotations.Mode;

package com.google.common.collect;

import static com.google.common.base.Preconditions.checkArgument;
import static java.util.Collections.sort;

import com.google.caliper.BeforeExperiment;
import com.google.caliper.Benchmark;
import com.google.caliper.Param;
import java.util.ArrayList;
import java.util.Collections;
import java.util.LinkedHashSet;
import java.util.List;
import java.util.Random;
import java.util.Set;

# CPU WHEEL
test:linux_cpu_wheel_test_filters --test_tag_filters=-no_oss,-oss_excluded,-oss_serial,-gpu,-tpu,-benchmark-test,-v1only,-no_oss_py38,-no_oss_py39,-no_oss_py310
test:linux_cpu_wheel_test_filters --build_tag_filters=-no_oss,-oss_excluded,-oss_serial,-gpu,-tpu,-benchmark-test,-v1only,-no_oss_py38,-no_oss_py39,-no_oss_py310
test:linux_cpu_wheel_test_filters --test_lang_filters=py --test_size_filters=small,medium

    checkElementIndex(index, size);
    // requireNonNull is safe because we guarantee that the first `size` elements are non-null.
    return (E) requireNonNull(array[index]);
  }

  // TODO(lowasser): benchmark optimizations for equals() and see if they're worthwhile

  // redeclare to help optimizers with b/310253115
  @SuppressWarnings("RedundantOverride")
  @Override
  @J2ktIncompatible // serialization

 *
 * @author Jared Levy
 * @author Louis Wasserman
 * @since 2.0 (implements {@code NavigableSet} since 12.0)
 */
// TODO(benyu): benchmark and optimize all creation paths, which are a mess now
@GwtCompatible(serializable = true, emulated = true)
@SuppressWarnings("serial") // we're overriding default serialization
@ElementTypesAreNonnullByDefault

 *
 * @author Jared Levy
 * @author Louis Wasserman
 * @since 2.0 (implements {@code NavigableSet} since 12.0)
 */
// TODO(benyu): benchmark and optimize all creation paths, which are a mess now
@GwtCompatible(serializable = true, emulated = true)
@SuppressWarnings("serial") // we're overriding default serialization
@ElementTypesAreNonnullByDefault

# 벤치마크

독립적인 TechEmpower 벤치마크에 따르면 **FastAPI** 애플리케이션이 Uvicorn을 사용하여 <a href="https://www.techempower.com/benchmarks/#section=test&runid=7464e520-0dc2-473d-bd34-dbdfd7e85911&hw=ph&test=query&l=zijzen-7" class="external-link" target="_blank">가장 빠른 Python 프레임워크 중 하나</a>로 실행되며, Starlette와 Uvicorn 자체(내부적으로 FastAPI가 사용하는 도구)보다 조금 아래에 위치합니다.

그러나 벤치마크와 비교를 확인할 때 다음 사항을 염두에 두어야 합니다.

## 벤치마크와 속도

벤치마크를 확인할 때, 일반적으로 여러 가지 유형의 도구가 동등한 것으로 비교되는 것을 볼 수 있습니다.

### Migration