com.linkedin.drelephant.mapreduce.heuristics.MapperMemoryHeuristicTest.java Source code

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Here is the source code for com.linkedin.drelephant.mapreduce.heuristics.MapperMemoryHeuristicTest.java

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/*
 * Copyright 2016 LinkedIn Corp.
 *
 * Licensed under the Apache License, Version 2.0 (the "License"); you may not
 * use this file except in compliance with the License. You may obtain a copy of
 * the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
 * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
 * License for the specific language governing permissions and limitations under
 * the License.
 */

package com.linkedin.drelephant.mapreduce.heuristics;

import com.linkedin.drelephant.analysis.ApplicationType;
import com.linkedin.drelephant.analysis.Heuristic;
import com.linkedin.drelephant.analysis.HeuristicResult;
import com.linkedin.drelephant.analysis.Severity;
import com.linkedin.drelephant.mapreduce.data.MapReduceCounterData;
import com.linkedin.drelephant.mapreduce.data.MapReduceApplicationData;
import com.linkedin.drelephant.mapreduce.data.MapReduceTaskData;

import com.linkedin.drelephant.configurations.heuristic.HeuristicConfigurationData;
import java.io.IOException;
import java.util.HashMap;
import java.util.Map;
import java.util.Properties;

import org.apache.commons.io.FileUtils;

import junit.framework.TestCase;

public class MapperMemoryHeuristicTest extends TestCase {

    private static Map<String, String> paramsMap = new HashMap<String, String>();
    private static Heuristic _heuristic = new MapperMemoryHeuristic(new HeuristicConfigurationData("test_heuristic",
            "test_class", "test_view", new ApplicationType("test_apptype"), paramsMap));

    private int NUMTASKS = 100;

    public void testLargeContainerSizeCritical() throws IOException {
        assertEquals(Severity.CRITICAL, analyzeJob(2048, 8192));
    }

    public void testLargeContainerSizeSevere() throws IOException {
        assertEquals(Severity.SEVERE, analyzeJob(3072, 8192));
    }

    public void testLargeContainerSizeModerate() throws IOException {
        assertEquals(Severity.MODERATE, analyzeJob(4096, 8192));
    }

    public void testLargeContainerSizeNone() throws IOException {
        assertEquals(Severity.NONE, analyzeJob(6144, 8192));
    }

    // If the task use default container size, it should not be flagged
    public void testDefaultContainerNone() throws IOException {
        assertEquals(Severity.NONE, analyzeJob(256, 2048));
    }

    public void testDefaultContainerNoneMore() throws IOException {
        assertEquals(Severity.NONE, analyzeJob(1024, 2048));
    }

    private Severity analyzeJob(long taskAvgMemMB, long containerMemMB) throws IOException {
        MapReduceCounterData jobCounter = new MapReduceCounterData();
        MapReduceTaskData[] mappers = new MapReduceTaskData[NUMTASKS];

        MapReduceCounterData counter = new MapReduceCounterData();
        counter.set(MapReduceCounterData.CounterName.PHYSICAL_MEMORY_BYTES, taskAvgMemMB * FileUtils.ONE_MB);

        Properties p = new Properties();
        p.setProperty(MapperMemoryHeuristic.MAPPER_MEMORY_CONF, Long.toString(containerMemMB));

        int i = 0;
        for (; i < NUMTASKS; i++) {
            mappers[i] = new MapReduceTaskData(counter, new long[5]);
        }

        MapReduceApplicationData data = new MapReduceApplicationData().setCounters(jobCounter)
                .setMapperData(mappers);
        data.setJobConf(p);
        HeuristicResult result = _heuristic.apply(data);
        return result.getSeverity();
    }
}