我正在尝试从oozie工作流运行mr作业，并使用avro数据文件作为输入和输出。Map器发出文本和intwritable。我正在使用一个新的mrapi-mapreduce。我的工作流定义如下：

<workflow-app xmlns="uri:oozie:workflow:0.5" name="map-reduce-wf">
<global>
   <job-tracker>${jobTracker}</job-tracker>
   <name-node>${nameNode}</name-node>
   <configuration>
        <property>
            <name>mapreduce.job.queuename</name>
            <value>${queueName}</value>
        </property>
    </configuration>
</global>

    <start to="mr-node"/>

    <action name="mr-node">
        <map-reduce>
            <prepare>
                <delete path="${nameNode}/${outputDir}"/>
            </prepare>
            <configuration>
                <!-- BEGIN: SNIPPET TO ADD IN ORDER TO MAKE USE OF NEW HADOOP API -->
                <property>
                  <name>mapred.reducer.new-api</name>
                  <value>true</value>
                </property>
                <property>
                  <name>mapred.mapper.new-api</name>
                  <value>true</value>
                </property>
                <!-- END: SNIPPET -->
                <property>
                    <name>mapreduce.map.class</name>
                    <value>com.ncr.bigdata.mr.avro.AvroPifDriver$PifMapper</value>
                </property>
                <property>
                    <name>mapreduce.reduce.class</name>
                    <value>com.ncr.bigdata.mr.avro.AvroPifDriver$PifReducer</value>
                </property>
                <property>
                    <name>mapred.map.tasks</name>
                    <value>1</value>
                </property>
                <property>
                    <name>mapred.input.dir</name>
                    <value>${nameNode}/${inputDir}</value>
                </property>
                <property>
                    <name>mapred.output.dir</name>
                    <value>${nameNode}/${outputDir}</value>
                </property>                 
                <property>
                    <name>mapred.input.format.class</name>
                    <value>org.apache.avro.mapreduce.AvroKeyInputFormat</value>
                </property>
                <property>
                    <name>avro.schema.input.key</name>
                    <value>{"type":"record","name":"SampleRecord","namespace":"org.co.sample.etl.domain","fields":[{"name":"requiredName","type":"string"},{"name":"optionalName","type":["null","string"]},{"name":"dataItemLong","type":"long"},{"name":"dataItemInt","type":"int"},{"name":"startTime","type":"long"},{"name":"endTime","type":"long"}]}</value>
                </property>

                <property>
                    <name>mapred.output.format.class</name>
                    <value>org.apache.avro.mapreduce.AvroKeyValueOutputFormat</value>
                </property> 
                <property>
                    <name>mapred.output.key.class</name>
                    <value>org.apache.avro.mapred.AvroKey</value>
                </property>   
                <property>
                    <name>mapred.output.value.class</name>
                    <value>org.apache.avro.mapred.AvroValue</value>
                </property> 

                <property>
                    <name>avro.schema.output.key</name>
                    <value>string</value>
                </property> 
                <property>
                    <name>avro.schema.output.value</name>
                    <value>int</value>
                </property> 

            </configuration>
        </map-reduce>
        <ok to="end"/>
        <error to="fail"/>
    </action>
    <kill name="fail">
        <message>Map/Reduce failed, error message[${wf:errorMessage(wf:lastErrorNode())}]</message>
    </kill>
    <end name="end"/>
</workflow-app>

我的Map器如下所示：

import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.NullWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;

 public static class PifMapper extends Mapper<AvroKey<PosData>, NullWritable, Text, IntWritable> {

        @Override
        public void map(AvroKey<PosData> key, NullWritable value, Context context)
                throws IOException, InterruptedException {
        ...
        }
}

我收到以下错误：

140807041959771-oozie-oozi-W@mr-node] Launcher exception: mapred.input.format.class is incompatible with new map API mode.
java.io.IOException: mapred.input.format.class is incompatible with new map API mode.
    at org.apache.hadoop.mapreduce.Job.ensureNotSet(Job.java:1172)
    at org.apache.hadoop.mapreduce.Job.setUseNewAPI(Job.java:1198)
    at org.apache.hadoop.mapreduce.Job.submit(Job.java:1261)
    at org.apache.hadoop.mapred.JobClient$1.run(JobClient.java:562)
    at org.apache.hadoop.mapred.JobClient$1.run(JobClient.java:557)
    at java.security.AccessController.doPrivileged(Native Method)
    at javax.security.auth.Subject.doAs(Subject.java:415)
...

我使用的是hadoop2.2.0（hdp2.0）、oozie4.0.0和avro1.7.4
通过驱动程序类提交的map reduce作业工作正常。org.apache.avro.mapreduce.avrokeyinputformat也应该是新mapreduce的实现。
为了确保没有库冲突，我从ozzie中删除了共享库，所有库都包含在workflow lib dir中。
有什么提示吗？