- android - RelativeLayout 背景可绘制重叠内容
- android - 如何链接 cpufeatures lib 以获取 native android 库?
- java - OnItemClickListener 不起作用,但 OnLongItemClickListener 在自定义 ListView 中起作用
- java - Android 文件转字符串
我有很大的不。对于小文件,我想使用 CombineFileInputFormat 来合并文件,以便每个文件数据都作为我的 MR 作业中的单个记录。我关注了http://yaseminavcular.blogspot.in/2011/03/many-small-input-files.html并尝试将其转换为新的 api
我面临两个问题:
a) 我只是用 2 个小文件测试它,仍然有 2 个映射器被触发。我预计 1
b) 每行都作为单个记录出现,我希望整个文件作为单个记录。
这可能很痛苦,但请查看下面的代码。我还是hadoop的小白
驱动类
public class MRDriver extends Configured implements Tool {
@Override
public int run(String[] args) throws Exception {
FileSystem fs = new Path(".").getFileSystem(getConf());
fs.printStatistics();
Job job = new Job(getConf());
job.setJobName("Enron MR");
job.setMapperClass(EnronMailReadMapper.class);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(IntWritable.class);
job.setNumReduceTasks(0);
job.setJarByClass(EnronMailReadMapper.class);
RawCombineFileInputFormat.addInputPath(job, new Path(args[0]));
job.setOutputFormatClass(TextOutputFormat.class);
TextOutputFormat.setOutputPath(job, new Path(args[1]));
return job.waitForCompletion(true) ? 0 :1;
}
public static void main(String[] args) throws Exception {
int exitCode = ToolRunner.run(new MRDriver(), args);
System.exit(exitCode);
}
下面的类主要是 LineRecordReader 的复制粘贴,修改了 initialize() 和 nextKeyValue() 函数
public class SingleFileRecordReader extends RecordReader<LongWritable, Text> {
private static final Log LOG = LogFactory.getLog(SingleFileRecordReader.class);
private long start;
private long pos;
private long end;
private LineReader in;
private int maxLineLength;
private LongWritable key = null;
private Text value = null;
public void initialize(InputSplit genericSplit,
TaskAttemptContext context) throws IOException {
FileSplit split = (FileSplit) genericSplit;
Configuration job = context.getConfiguration();
this.maxLineLength = job.getInt("mapred.linerecordreader.maxlength",
Integer.MAX_VALUE);
start = split.getStart();
end = start + split.getLength();
final Path file = split.getPath();
// open the file and seek to the start of the split
FileSystem fs = file.getFileSystem(job);
FSDataInputStream fileIn = fs.open(split.getPath());
fileIn.seek(start);
in = new LineReader(fileIn, job);
// If this is not the first split, we always throw away first record
// because we always (except the last split) read one extra line in
// next() method.
if (start != 0) {
start += in.readLine(new Text(), 0, maxBytesToConsume(start));
}
this.pos = start;
}
private int maxBytesToConsume(long pos) {
return (int) Math.min(Integer.MAX_VALUE, end - pos);
}
private long getFilePosition() throws IOException {
long retVal= pos;
return retVal;
}
public boolean nextKeyValue() throws IOException {
if (key == null) {
key = new LongWritable();
}
key.set(pos);
if (value == null) {
value = new Text();
}
int newSize = 0;
StringBuffer totalValue = new StringBuffer();
// We always read one extra line, which lies outside the upper
// split limit i.e. (end - 1)
while (getFilePosition() <= end) {
newSize = in.readLine(value, maxLineLength,
Math.max(maxBytesToConsume(pos), maxLineLength));
if (newSize == 0) {
break;
}
totalValue.append(value.toString()+"\n");
pos += newSize;
if (newSize < maxLineLength) {
break;
}
// line too long. try again
LOG.info("Skipped line of size " + newSize + " at pos " +
(pos - newSize));
}
if (newSize == 0) {
key = null;
value = null;
return false;
} else {
value = new Text(totalValue.toString());
return true;
}
}
@Override
public LongWritable getCurrentKey() {
return key;
}
@Override
public Text getCurrentValue() {
return value;
}
/**
* Get the progress within the split
*/
public float getProgress() throws IOException {
if (start == end) {
return 0.0f;
} else {
return Math.min(1.0f,
(getFilePosition() - start) / (float)(end - start));
}
}
public synchronized void close() throws IOException {
try {
if (in != null) {
in.close();
}
} finally {
}
}
其他文件
public class RawCombineFileInputFormat extends CombineFileInputFormat <LongWritable,Text>{
@Override
public RecordReader<LongWritable, Text> createRecordReader(
InputSplit split, TaskAttemptContext context) throws IOException {
return new CombineFileRecordReader< LongWritable, Text >((CombineFileSplit) split, context, MultiFileRecordReader.class);
}
和
public class MultiFileRecordReader extends RecordReader < LongWritable, Text > {
private CombineFileSplit split;
private TaskAttemptContext context;
private int index;
private RecordReader< LongWritable, Text > rr;
public MultiFileRecordReader(CombineFileSplit split, TaskAttemptContext context, Integer index) {
this.split = split;
this.context = context;
this.index = index;
this.rr = new SingleFileRecordReader();
}
@Override
public void initialize(InputSplit split, TaskAttemptContext context)
throws IOException, InterruptedException {
this.split = (CombineFileSplit) split;
this.context = context;
if (null == rr) {
rr = new SingleFileRecordReader();
}
FileSplit fileSplit = new FileSplit(this.split.getPath(index),
this.split.getOffset(index),
this.split.getLength(index),
this.split.getLocations());
this.rr.initialize(fileSplit, this.context);
}
@Override
public boolean nextKeyValue() throws IOException, InterruptedException {
// TODO Auto-generated method stub
return this.rr.nextKeyValue();
}
@Override
public LongWritable getCurrentKey() throws IOException, InterruptedException {
// TODO Auto-generated method stub
return this.rr.getCurrentKey();
}
@Override
public Text getCurrentValue() throws IOException, InterruptedException {
// TODO Auto-generated method stub
return this.rr.getCurrentValue();
}
@Override
public float getProgress() throws IOException, InterruptedException {
// TODO Auto-generated method stub
return this.rr.getProgress();
}
@Override
public void close() throws IOException {
if (rr != null) {
rr.close();
rr = null;
}
}
最佳答案
查看此输入格式。这是一种用于在单个映射任务中读取多个文件的输入格式。传递给映射器的每条记录都将读取一个(未拆分的)文件。 WholeFileRecordReader 负责将一个文件内容作为一个值发送。返回的键是 NullWritable,值是每个文件的整体内容。现在您可以使用它并运行您的 mapreduce 作业并查看实际运行了多少映射器并检查您获得的输出是否正确。
记录是从 WholeFileRecordReader 构建的。
public class WholeFileInputFormat extends CombineFileInputFormat<NullWritable, Text>{
@Override
protected boolean isSplitable(JobContext context, Path file) {
return false;
}
/**
* Creates a CombineFileRecordReader to read each file assigned to this InputSplit.
* Note, that unlike ordinary InputSplits, split must be a CombineFileSplit, and therefore
* is expected to specify multiple files.
*
* @param split The InputSplit to read. Throws an IllegalArgumentException if this is
* not a CombineFileSplit.
* @param context The context for this task.
* @return a CombineFileRecordReader to process each file in split.
* It will read each file with a WholeFileRecordReader.
* @throws IOException if there is an error.
*/
@Override
public RecordReader<NullWritable, Text> createRecordReader(
InputSplit split, TaskAttemptContext context) throws IOException {
if (!(split instanceof CombineFileSplit)) {
throw new IllegalArgumentException("split must be a CombineFileSplit");
}
return new CombineFileRecordReader<NullWritable, Text>((CombineFileSplit) split, context, WholeFileRecordReader.class);
}
}
在上面你可以使用 WholeFileRecordReader,如下所示:-
public class WholeFileRecordReader extends RecordReader<NullWritable, Text> {
private static final Logger LOG = Logger.getLogger(WholeFileRecordReader.class);
/** The path to the file to read. */
private final Path mFileToRead;
/** The length of this file. */
private final long mFileLength;
/** The Configuration. */
private final Configuration mConf;
/** Whether this FileSplit has been processed. */
private boolean mProcessed;
/** Single Text to store the file name of the current file. */
// private final Text mFileName;
/** Single Text to store the value of this file (the value) when it is read. */
private final Text mFileText;
/**
* Implementation detail: This constructor is built to be called via
* reflection from within CombineFileRecordReader.
*
* @param fileSplit The CombineFileSplit that this will read from.
* @param context The context for this task.
* @param pathToProcess The path index from the CombineFileSplit to process in this record.
*/
public WholeFileRecordReader(CombineFileSplit fileSplit, TaskAttemptContext context,
Integer pathToProcess) {
mProcessed = false;
mFileToRead = fileSplit.getPath(pathToProcess);
mFileLength = fileSplit.getLength(pathToProcess);
mConf = context.getConfiguration();
assert 0 == fileSplit.getOffset(pathToProcess);
if (LOG.isDebugEnabled()) {
LOG.debug("FileToRead is: " + mFileToRead.toString());
LOG.debug("Processing path " + pathToProcess + " out of " + fileSplit.getNumPaths());
try {
FileSystem fs = FileSystem.get(mConf);
assert fs.getFileStatus(mFileToRead).getLen() == mFileLength;
} catch (IOException ioe) {
// oh well, I was just testing.
}
}
// mFileName = new Text();
mFileText = new Text();
}
/** {@inheritDoc} */
@Override
public void close() throws IOException {
mFileText.clear();
}
/**
* Returns the absolute path to the current file.
*
* @return The absolute path to the current file.
* @throws IOException never.
* @throws InterruptedException never.
*/
@Override
public NullWritable getCurrentKey() throws IOException, InterruptedException {
return NullWritable.get();
}
/**
* <p>Returns the current value. If the file has been read with a call to NextKeyValue(),
* this returns the contents of the file as a BytesWritable. Otherwise, it returns an
* empty BytesWritable.</p>
*
* <p>Throws an IllegalStateException if initialize() is not called first.</p>
*
* @return A BytesWritable containing the contents of the file to read.
* @throws IOException never.
* @throws InterruptedException never.
*/
@Override
public Text getCurrentValue() throws IOException, InterruptedException {
return mFileText;
}
/**
* Returns whether the file has been processed or not. Since only one record
* will be generated for a file, progress will be 0.0 if it has not been processed,
* and 1.0 if it has.
*
* @return 0.0 if the file has not been processed. 1.0 if it has.
* @throws IOException never.
* @throws InterruptedException never.
*/
@Override
public float getProgress() throws IOException, InterruptedException {
return (mProcessed) ? (float) 1.0 : (float) 0.0;
}
/**
* All of the internal state is already set on instantiation. This is a no-op.
*
* @param split The InputSplit to read. Unused.
* @param context The context for this task. Unused.
* @throws IOException never.
* @throws InterruptedException never.
*/
@Override
public void initialize(InputSplit split, TaskAttemptContext context)
throws IOException, InterruptedException {
// no-op.
}
/**
* <p>If the file has not already been read, this reads it into memory, so that a call
* to getCurrentValue() will return the entire contents of this file as Text,
* and getCurrentKey() will return the qualified path to this file as Text. Then, returns
* true. If it has already been read, then returns false without updating any internal state.</p>
*
* @return Whether the file was read or not.
* @throws IOException if there is an error reading the file.
* @throws InterruptedException if there is an error.
*/
@Override
public boolean nextKeyValue() throws IOException, InterruptedException {
if (!mProcessed) {
if (mFileLength > (long) Integer.MAX_VALUE) {
throw new IOException("File is longer than Integer.MAX_VALUE.");
}
byte[] contents = new byte[(int) mFileLength];
FileSystem fs = mFileToRead.getFileSystem(mConf);
FSDataInputStream in = null;
try {
// Set the contents of this file.
in = fs.open(mFileToRead);
IOUtils.readFully(in, contents, 0, contents.length);
mFileText.set(contents, 0, contents.length);
} finally {
IOUtils.closeStream(in);
}
mProcessed = true;
return true;
}
return false;
}
}
以下是您的驱动程序代码:-
public int run(String[] arg) throws Exception {
Configuration conf=getConf();
FileSystem fs = FileSystem.get(conf);
//estimate reducers
Job job = new Job(conf);
job.setJarByClass(WholeFileDriver.class);
job.setJobName("WholeFile");
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(Text.class);
job.setInputFormatClass(WholeFileInputFormat.class);
job.setMapperClass(WholeFileMapper.class);
job.setNumReduceTasks(0);
FileInputFormat.addInputPath(job, new Path(arg[0]));
Path output=new Path(arg[1]);
try {
fs.delete(output, true);
} catch (IOException e) {
LOG.warn("Failed to delete temporary path", e);
}
FileOutputFormat.setOutputPath(job, output);
boolean ret=job.waitForCompletion(true);
if(!ret){
throw new Exception("Job Failed");
}
关于java - 在hadoop中将文件作为单个记录读取,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/17875277/
我们有数据(此时未分配)要转换/聚合/透视到 wazoo。 我在 www 上看了看,我问的所有答案都指向 hadoop 可扩展、运行便宜(没有 SQL 服务器机器和许可证)、快速(如果你有足够的数据)
这很明显,我们都同意我们可以将 HDFS + YARN + MapReduce 称为 Hadoop。但是,Hadoop 生态系统中的其他不同组合和其他产品会怎样? 例如,HDFS + YARN + S
如果 es-hadoop 只是连接到 HDFS 的 Hadoop 连接器,它如何支持 Hadoop 分析? 最佳答案 我假设您指的是 this project .在这种情况下,ES Hadoop 项目
看完this和 this论文,我决定我想在 MapReduce 上为大型数据集实现分布式体积渲染设置作为我的本科论文工作。 Hadoop 是一个合理的选择吗? Java 不会扼杀一些性能提升或使与 C
我一直在尝试查找有关如何通过命令行提交 hadoop 作业的信息。 我知道命令 - hadoop jar jar-file 主类输入输出 还有另一个命令,我正在尝试查找有关它的信息,但未能找到 - h
Hadoop 服务器在 Kubernetes 中。而Hadoop客户端位于外网。所以我尝试使用 kubernetes-service 来使用 Hadoop 服务器。但是 hadoop fs -put
有没有人遇到奇怪的环境问题,在调用 hadoop 命令时被迫使用 SU 而不是 SUDO? sudo su -c 'hadoop fs -ls /' hdfs Found 4 itemsdrwxr-x
在更改 mapred-site.xml 中的属性后,我给出了一个 tar.bz2 文件、.gz 和 tar.gz 文件作为输入。以上似乎都没有奏效。我假设这里发生的是 hadoop 作为输入读取的记录
如何在 Hadoop Pipes 中获取正在 hadoop 映射器 中执行的输入文件 名称? 我可以很容易地在基于 java 的 map reducer 中获取文件名,比如 FileSplit fil
我想使用 MapReduce 方法分析连续的数据流(通过 HTTP 访问),因此我一直在研究 Apache Hadoop。不幸的是,Hadoop 似乎期望以固定大小的输入文件开始作业,而不是能够在新数
名称节点可以执行任务吗?默认情况下,任务在集群的数据节点上执行。 最佳答案 假设您正在询问MapReduce ... 使用YARN,MapReduce任务在应用程序主数据库中执行,而不是在nameno
我有一个关系A包含 (zip-code). 我还有另一个关系B包含 (name:gender:zip-code) (x:m:1234) (y:f:1234) (z:m:1245) (s:f:1235)
我是hadoop地区的新手。您能帮我负责(k2,list[v2,v2,v2...])形式的输出(意味着将键及其所有关联值组合在一起)的责任是吗? 谢谢。 最佳答案 这是Hadoop的MapReduce
因此,我一直在尝试编写一个hadoop程序,该程序将输入作为一个包含许多文件的文件,并且我希望hadoop程序的输出仅是输入文件的一行。但是我还没有做到这一点。我也不想去 reducer 课。如果有人
我使用的输入文本文件的内容是 1 "Come 1 "Defects," 1 "I 1 "Information 1 "J" 2 "Plain 5 "Project 1
谁能告诉我以下grep命令的作用: $ bin/hadoop jar hadoop-*-examples.jar grep input output 'dfs[a-z.]+' 最佳答案 http:/
我不了解mapreducer的基本功能,mapreducer是否有助于将文件放入HDFS 或mapreducer仅有助于分析HDFS中现有文件中的内容 我对hadoop非常陌生,任何人都可以指导我理解
CopyFromLocal将从本地文件系统上载数据。 不要放会从任何文件上传数据,例如。本地FS,亚马逊S3 或仅来自本地fs ??? 最佳答案 请找到两个命令的用法。 put ======= Usa
我开始研究hadoop mapreduce。 我是Java和hadoop的初学者,并且了解hadoop mapreduce的编码,但是有兴趣了解它在云中的内部工作方式。 您能否分享一些很好的链接来说明
我一直在寻找Hadoop mapreduce类的类路径。我正在使用Hortonworks 2.2.4版沙箱。我需要这样的类路径来运行我的javac编译器: javac -cp (CLASS_PATH)
我是一名优秀的程序员,十分优秀!