技术mix呢个人页面-阿里云开发者社区

高可用Hadoop平台－运行MapReduce程序

1.概述　　最近有同学反应，如何在配置了HA的Hadoop平台运行MapReduce程序呢？对于刚步入Hadoop行业的同学，这个疑问却是会存在，其实仔细想想，如果你之前的语言功底不错的，应该会想到自动重连，自动重连也可以帮我我们解决运行MapReduce程序的问题。然后，今天我赘述的是利用Hadoop的Java API 来实现。 2.介绍　　下面直接附上代码，代码中我都有注释。 2.1Java操作HDFS HA的API 　　代码如下： /** * */ package cn.hdfs.mr.example; import java.io.IOException; import org.apache.hadoop.conf.Configuration; import org.apache.hadoop.fs.FileStatus; import org.apache.hadoop.fs.FileSystem; import org.apache.hadoop.fs.Path; /** * @author dengjie * @date 2015年3月24日 * @description TODO */ public class DFS { public static void main(String[] args) { Configuration conf = new Configuration(); conf.set("fs.defaultFS", "hdfs://cluster1");//指定hdfs的nameservice为cluster1,是NameNode的URI conf.set("dfs.nameservices", "cluster1");//指定hdfs的nameservice为cluster1 conf.set("dfs.ha.namenodes.cluster1", "nna,nns");//cluster1下面有两个NameNode，分别是nna，nns conf.set("dfs.namenode.rpc-address.cluster1.nna", "10.211.55.26:9000");//nna的RPC通信地址 conf.set("dfs.namenode.rpc-address.cluster1.nns", "10.211.55.27:9000");//nns的RPC通信地址 conf.set("dfs.client.failover.proxy.provider.cluster1", "org.apache.hadoop.hdfs.server.namenode.ha.ConfiguredFailoverProxyProvider");//配置失败自动切换实现方式 FileSystem fs = null; try { fs = FileSystem.get(conf);//获取文件对象 FileStatus[] list = fs.listStatus(new Path("/"));//文件状态集合 for (FileStatus file : list) { System.out.println(file.getPath().getName());//打印目录名 } } catch (IOException e) { e.printStackTrace(); } finally { try { if (fs != null) { fs.close(); } } catch (IOException e) { e.printStackTrace(); } } } } 　　接下来，附上 Java 运行 MapReduce 程序的 API 代码。 2.2Java 运行 MapReduce 程序的 API 　　以 WordCount 为例子，代码如下： package cn.jpush.hdfs.mr.example; import java.io.IOException; import java.util.Random; import java.util.StringTokenizer; import org.apache.hadoop.conf.Configuration; import org.apache.hadoop.fs.Path; import org.apache.hadoop.io.IntWritable; import org.apache.hadoop.io.Text; import org.apache.hadoop.mapreduce.Job; import org.apache.hadoop.mapreduce.Mapper; import org.apache.hadoop.mapreduce.Reducer; import org.apache.hadoop.mapreduce.lib.input.FileInputFormat; import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat; import org.slf4j.Logger; import org.slf4j.LoggerFactory; import cn.jpush.hdfs.utils.ConfigUtils; /** * * @author dengjie * @date 2014年11月29日 * @description Wordcount的例子是一个比较经典的mapreduce例子，可以叫做Hadoop版的hello world。 * 它将文件中的单词分割取出，然后shuffle，sort（map过程），接着进入到汇总统计 * （reduce过程），最后写道hdfs中。基本流程就是这样。 */ public class WordCount { private static Logger log = LoggerFactory.getLogger(WordCount.class); public static class TokenizerMapper extends Mapper<Object, Text, Text, IntWritable> { private final static IntWritable one = new IntWritable(1); private Text word = new Text(); /* * 源文件：a b b * * map之后： * * a 1 * * b 1 * * b 1 */ public void map(Object key, Text value, Context context) throws IOException, InterruptedException { StringTokenizer itr = new StringTokenizer(value.toString());// 整行读取 while (itr.hasMoreTokens()) { word.set(itr.nextToken());// 按空格分割单词 context.write(word, one);// 每次统计出来的单词+1 } } } /* * reduce之前： * * a 1 * * b 1 * * b 1 * * reduce之后: * * a 1 * * b 2 */ public static class IntSumReducer extends Reducer<Text, IntWritable, Text, IntWritable> { private IntWritable result = new IntWritable(); public void reduce(Text key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException { int sum = 0; for (IntWritable val : values) { sum += val.get(); } result.set(sum); context.write(key, result); } } @SuppressWarnings("deprecation") public static void main(String[] args) throws Exception { Configuration conf = new Configuration(); conf.set("fs.defaultFS", "hdfs://cluster1"); conf.set("dfs.nameservices", "cluster1"); conf.set("dfs.ha.namenodes.cluster1", "nna,nns"); conf.set("dfs.namenode.rpc-address.cluster1.nna", "10.211.55.26:9000"); conf.set("dfs.namenode.rpc-address.cluster1.nns", "10.211.55.27:9000"); conf.set("dfs.client.failover.proxy.provider.cluster1", "org.apache.hadoop.hdfs.server.namenode.ha.ConfiguredFailoverProxyProvider"); long random1 = new Random().nextLong();// 重定下输出目录 log.info("random1 -> " + random1); Job job1 = new Job(conf, "word count"); job1.setJarByClass(WordCount.class); job1.setMapperClass(TokenizerMapper.class);// 指定Map计算的类 job1.setCombinerClass(IntSumReducer.class);// 合并的类 job1.setReducerClass(IntSumReducer.class);// Reduce的类 job1.setOutputKeyClass(Text.class);// 输出Key类型 job1.setOutputValueClass(IntWritable.class);// 输出值类型 FileInputFormat.addInputPath(job1, new Path("/home/hdfs/test/hello.txt"));// 指定输入路径 FileOutputFormat.setOutputPath(job1, new Path(String.format(ConfigUtils.HDFS.WORDCOUNT_OUT, random1)));// 指定输出路径 System.exit(job1.waitForCompletion(true) ? 0 : 1);// 执行完MR任务后退出应用 } } 3.运行结果　　下面附上部分运行 Log 日志，如下所示： [Job.main] - Running job: job_local551164419_0001 2015-03-24 11:52:09 INFO [LocalJobRunner.Thread-12] - OutputCommitter set in config null 2015-03-24 11:52:09 INFO [LocalJobRunner.Thread-12] - OutputCommitter is org.apache.hadoop.mapreduce.lib.output.FileOutputCommitter 2015-03-24 11:52:10 INFO [LocalJobRunner.Thread-12] - Waiting for map tasks 2015-03-24 11:52:10 INFO [LocalJobRunner.LocalJobRunner Map Task Executor #0] - Starting task: attempt_local551164419_0001_m_000000_0 2015-03-24 11:52:10 INFO [ProcfsBasedProcessTree.LocalJobRunner Map Task Executor #0] - ProcfsBasedProcessTree currently is supported only on Linux. 2015-03-24 11:52:10 INFO [Task.LocalJobRunner Map Task Executor #0] - Using ResourceCalculatorProcessTree : null 2015-03-24 11:52:10 INFO [MapTask.LocalJobRunner Map Task Executor #0] - Processing split: hdfs://cluster1/home/hdfs/test/hello.txt:0+24 2015-03-24 11:52:10 INFO [MapTask.LocalJobRunner Map Task Executor #0] - Map output collector class = org.apache.hadoop.mapred.MapTask$MapOutputBuffer 2015-03-24 11:52:10 INFO [MapTask.LocalJobRunner Map Task Executor #0] - (EQUATOR) 0 kvi 26214396(104857584) 2015-03-24 11:52:10 INFO [MapTask.LocalJobRunner Map Task Executor #0] - mapreduce.task.io.sort.mb: 100 2015-03-24 11:52:10 INFO [MapTask.LocalJobRunner Map Task Executor #0] - soft limit at 83886080 2015-03-24 11:52:10 INFO [MapTask.LocalJobRunner Map Task Executor #0] - bufstart = 0; bufvoid = 104857600 2015-03-24 11:52:10 INFO [MapTask.LocalJobRunner Map Task Executor #0] - kvstart = 26214396; length = 6553600 2015-03-24 11:52:10 INFO [LocalJobRunner.LocalJobRunner Map Task Executor #0] - 2015-03-24 11:52:10 INFO [MapTask.LocalJobRunner Map Task Executor #0] - Starting flush of map output 2015-03-24 11:52:10 INFO [MapTask.LocalJobRunner Map Task Executor #0] - Spilling map output 2015-03-24 11:52:10 INFO [MapTask.LocalJobRunner Map Task Executor #0] - bufstart = 0; bufend = 72; bufvoid = 104857600 2015-03-24 11:52:10 INFO [MapTask.LocalJobRunner Map Task Executor #0] - kvstart = 26214396(104857584); kvend = 26214352(104857408); length = 45/6553600 2015-03-24 11:52:10 INFO [MapTask.LocalJobRunner Map Task Executor #0] - Finished spill 0 2015-03-24 11:52:10 INFO [Task.LocalJobRunner Map Task Executor #0] - Task:attempt_local551164419_0001_m_000000_0 is done. And is in the process of committing 2015-03-24 11:52:10 INFO [LocalJobRunner.LocalJobRunner Map Task Executor #0] - map 2015-03-24 11:52:10 INFO [Task.LocalJobRunner Map Task Executor #0] - Task 'attempt_local551164419_0001_m_000000_0' done. 2015-03-24 11:52:10 INFO [LocalJobRunner.LocalJobRunner Map Task Executor #0] - Finishing task: attempt_local551164419_0001_m_000000_0 2015-03-24 11:52:10 INFO [LocalJobRunner.Thread-12] - map task executor complete. 2015-03-24 11:52:10 INFO [LocalJobRunner.Thread-12] - Waiting for reduce tasks 2015-03-24 11:52:10 INFO [LocalJobRunner.pool-6-thread-1] - Starting task: attempt_local551164419_0001_r_000000_0 2015-03-24 11:52:10 INFO [ProcfsBasedProcessTree.pool-6-thread-1] - ProcfsBasedProcessTree currently is supported only on Linux. 2015-03-24 11:52:10 INFO [Task.pool-6-thread-1] - Using ResourceCalculatorProcessTree : null 2015-03-24 11:52:10 INFO [ReduceTask.pool-6-thread-1] - Using ShuffleConsumerPlugin: org.apache.hadoop.mapreduce.task.reduce.Shuffle@1197414 2015-03-24 11:52:10 INFO [MergeManagerImpl.pool-6-thread-1] - MergerManager: memoryLimit=1503238528, maxSingleShuffleLimit=375809632, mergeThreshold=992137472, ioSortFactor=10, memToMemMergeOutputsThreshold=10 2015-03-24 11:52:10 INFO [EventFetcher.EventFetcher for fetching Map Completion Events] - attempt_local551164419_0001_r_000000_0 Thread started: EventFetcher for fetching Map Completion Events 2015-03-24 11:52:10 INFO [LocalFetcher.localfetcher#1] - localfetcher#1 about to shuffle output of map attempt_local551164419_0001_m_000000_0 decomp: 50 len: 54 to MEMORY 2015-03-24 11:52:10 INFO [InMemoryMapOutput.localfetcher#1] - Read 50 bytes from map-output for attempt_local551164419_0001_m_000000_0 2015-03-24 11:52:10 INFO [MergeManagerImpl.localfetcher#1] - closeInMemoryFile -> map-output of size: 50, inMemoryMapOutputs.size() -> 1, commitMemory -> 0, usedMemory ->50 2015-03-24 11:52:10 INFO [EventFetcher.EventFetcher for fetching Map Completion Events] - EventFetcher is interrupted.. Returning 2015-03-24 11:52:10 INFO [LocalJobRunner.pool-6-thread-1] - 1 / 1 copied. 2015-03-24 11:52:10 INFO [MergeManagerImpl.pool-6-thread-1] - finalMerge called with 1 in-memory map-outputs and 0 on-disk map-outputs 2015-03-24 11:52:10 INFO [Merger.pool-6-thread-1] - Merging 1 sorted segments 2015-03-24 11:52:10 INFO [Merger.pool-6-thread-1] - Down to the last merge-pass, with 1 segments left of total size: 46 bytes 2015-03-24 11:52:10 INFO [MergeManagerImpl.pool-6-thread-1] - Merged 1 segments, 50 bytes to disk to satisfy reduce memory limit 2015-03-24 11:52:10 INFO [MergeManagerImpl.pool-6-thread-1] - Merging 1 files, 54 bytes from disk 2015-03-24 11:52:10 INFO [MergeManagerImpl.pool-6-thread-1] - Merging 0 segments, 0 bytes from memory into reduce 2015-03-24 11:52:10 INFO [Merger.pool-6-thread-1] - Merging 1 sorted segments 2015-03-24 11:52:10 INFO [Merger.pool-6-thread-1] - Down to the last merge-pass, with 1 segments left of total size: 46 bytes 2015-03-24 11:52:10 INFO [LocalJobRunner.pool-6-thread-1] - 1 / 1 copied. 2015-03-24 11:52:10 INFO [deprecation.pool-6-thread-1] - mapred.skip.on is deprecated. Instead, use mapreduce.job.skiprecords 2015-03-24 11:52:10 INFO [Task.pool-6-thread-1] - Task:attempt_local551164419_0001_r_000000_0 is done. And is in the process of committing 2015-03-24 11:52:10 INFO [LocalJobRunner.pool-6-thread-1] - 1 / 1 copied. 2015-03-24 11:52:10 INFO [Task.pool-6-thread-1] - Task attempt_local551164419_0001_r_000000_0 is allowed to commit now 2015-03-24 11:52:10 INFO [FileOutputCommitter.pool-6-thread-1] - Saved output of task 'attempt_local551164419_0001_r_000000_0' to hdfs://cluster1/output/result/-3636988299559297154/_temporary/0/task_local551164419_0001_r_000000 2015-03-24 11:52:10 INFO [LocalJobRunner.pool-6-thread-1] - reduce > reduce 2015-03-24 11:52:10 INFO [Task.pool-6-thread-1] - Task 'attempt_local551164419_0001_r_000000_0' done. 2015-03-24 11:52:10 INFO [LocalJobRunner.pool-6-thread-1] - Finishing task: attempt_local551164419_0001_r_000000_0 2015-03-24 11:52:10 INFO [LocalJobRunner.Thread-12] - reduce task executor complete. 2015-03-24 11:52:10 INFO [Job.main] - Job job_local551164419_0001 running in uber mode : false 2015-03-24 11:52:10 INFO [Job.main] - map 100% reduce 100% 2015-03-24 11:52:10 INFO [Job.main] - Job job_local551164419_0001 completed successfully 2015-03-24 11:52:10 INFO [Job.main] - Counters: 35 File System Counters FILE: Number of bytes read=462 FILE: Number of bytes written=466172 FILE: Number of read operations=0 FILE: Number of large read operations=0 FILE: Number of write operations=0 HDFS: Number of bytes read=48 HDFS: Number of bytes written=24 HDFS: Number of read operations=13 HDFS: Number of large read operations=0 HDFS: Number of write operations=4 Map-Reduce Framework Map input records=2 Map output records=12 Map output bytes=72 Map output materialized bytes=54 Input split bytes=105 Combine input records=12 Combine output records=6 Reduce input groups=6 Reduce shuffle bytes=54 Reduce input records=6 Reduce output records=6 Spilled Records=12 Shuffled Maps =1 Failed Shuffles=0 Merged Map outputs=1 GC time elapsed (ms)=13 Total committed heap usage (bytes)=514850816 Shuffle Errors BAD_ID=0 CONNECTION=0 IO_ERROR=0 WRONG_LENGTH=0 WRONG_MAP=0 WRONG_REDUCE=0 File Input Format Counters Bytes Read=24 File Output Format Counters Bytes Written=24 　　原文件如下所示： a a c v d d a d d s s x 　　Reduce 结果图，如下所示： 4.总结　　我们可以按以下步骤进行验证代码的可用性：保证 NNA（ active 状态）和 NNS（ standby 状态）。注意，DN 节点都是正常运行的。然后，我们运行 WordCount 程序，看能否统计出结果。若安上述步骤下来，可以统计；我们接着往下执行。若不行，请排查错误，然后继续。然后，我们 kill 掉 NNA 节点的 NameNode 进程，此时，NNS 的状态会由 standby 转变为 active 接着我们在支持 WordCount 程序，看能否统计结果；若是能统计结果，表示代码可用。　　以上就是整个验证的流程。 5.结束语　　这篇文章就分享到这里，如果在验证的过程当中有什么问题，可以加群进行讨论或发送邮件给我，我会尽我所能为您解答，与君共勉！联系方式：邮箱：smartloli.org@gmail.com Twitter：https://twitter.com/smartloli QQ群（Hadoop - 交流社区1）：424769183 温馨提示：请大家加群的时候写上加群理由（姓名＋公司/学校），方便管理员审核，谢谢！热爱生活，享受编程，与君共勉！本文转自哥不是小萝莉博客园博客，原文链接：http://www.cnblogs.com/smartloli/，如需转载请自行联系原作者

iOS开发之详解剪贴板

概述：在iOS中，可以使用剪贴板实现应用程序之中以及应用程序之间实现数据的共享。比如你可以从iPhone QQ复制一个url，然后粘贴到safari浏览器中查看这个链接的内容。一、在iOS中下面三个控件，自身就有复制-粘贴的功能： 1、UITextView 2、UITextField 3、UIWebView 二、UIKit framework提供了几个类和协议方便我们在自己的应用程序中实现剪贴板的功能。 1、UIPasteboard：我们可以向其中写入数据，也可以读取数据 2、UIMenuController：显示一个快捷菜单，用来复制、剪贴、粘贴选择的项。 3、UIResponder中的 canPerformAction:withSender:用于控制哪些命令显示在快捷菜单中。 4、当快捷菜单上的命令点击的时候，UIResponderStandardEditActions将会被调用。三、下面这些项能被放置到剪贴板中 1、UIPasteboardTypeListString — 字符串数组, 包含kUTTypeUTF8PlainText 2、UIPasteboardTypeListURL — URL数组，包含kUTTypeURL 3、UIPasteboardTypeListImage — 图形数组, 包含kUTTypePNG 和kUTTypeJPEG 4、UIPasteboardTypeListColor — 颜色数组四、剪贴板的类型分为两种：系统级：使用UIPasteboardNameGeneral和UIPasteboardNameFind创建，系统级的剪贴板，当应用程序关闭，或者卸载时，数据都不会丢失。应用程序级：通过设置，可以让数据在应用程序关闭之后仍然保存在剪贴板中，但是应用程序卸载之后数据就会失去。我们可用通过pasteboardWithName:create：来创建。了解这些之后，下面通过一系列的例子来说明如何在应用程序中使用剪贴板。例子： 1、复制剪贴文本。下面通过一个例子，可以在tableview上显示一个快捷菜单，上面只有复制按钮，复制tableview上的数据之后，然后粘贴到title上。定义一个单元格类CopyTableViewCell，在这个类的上显示快捷菜单，实现复制功能。 @interface CopyTableViewCell : UITableViewCell {iddelegate; } @property (nonatomic, retain) iddelegate;@end 实现CopyTableViewCell ： #import"CopyTableViewCell.h"@implementation CopyTableViewCell@synthesizedelegate;- (id)initWithStyle:(UITableViewCellStyle)style reuseIdentifier:(NSString *)reuseIdentifier {if ((self = [super initWithStyle:style reuseIdentifier:reuseIdentifier])) { }return self; }- (void)setSelected:(BOOL)selected animated:(BOOL)animated { [super setSelected:selected animated:animated]; }- (void)setHighlighted:(BOOL)highlighted animated:(BOOL)animated { [[self delegate] performSelector:@selector(showMenu:) withObject:self afterDelay:0.9f]; [super setHighlighted:highlighted animated:animated]; }- (BOOL)canBecomeFirstResponder {return YES; }- (BOOL)canPerformAction:(SEL)action withSender:(id)sender{if (action == @selector(cut:)){return NO; } elseif(action == @selector(copy:)){return YES; } elseif(action == @selector(paste:)){return NO; } elseif(action == @selector(select:)){return NO; } elseif(action == @selector(selectAll:)){return NO; }else {return [super canPerformAction:action withSender:sender]; } }- (void)copy:(id)sender { UIPasteboard *pasteboard = [UIPasteboard generalPasteboard]; [pasteboard setString:[[self textLabel]text]]; }- (void)dealloc { [super dealloc]; }@end 定义CopyPasteTextController，实现粘贴功能。 @interface CopyPasteTextController : UIViewController<UITableViewDelegate> {//用来标识是否显示快捷菜单 BOOL menuVisible; UITableView *tableView; } @property (nonatomic, getter=isMenuVisible) BOOL menuVisible; @property (nonatomic, retain) IBOutlet UITableView *tableView;@end 实现CopyPasteTextController ： #import"CopyPasteTextController.h"#import"CopyTableViewCell.h"@implementation CopyPasteTextController@synthesize menuVisible,tableView;- (void)viewDidLoad { [super viewDidLoad]; [self setTitle:@"文字复制粘贴"];//点击这个按钮将剪贴板的内容粘贴到title上 UIBarButtonItem *addButton = [[[UIBarButtonItem alloc] initWithBarButtonSystemItem:UIBarButtonSystemItemRefresh target:self action:@selector(readFromPasteboard:)] autorelease]; [[self navigationItem] setRightBarButtonItem:addButton]; }// Customize the number of sections in the table view.- (NSInteger)numberOfSectionsInTableView:(UITableView *)tableView {return1; }- (NSInteger)tableView:(UITableView *)tableView numberOfRowsInSection:(NSInteger)section {return9; }// Customize the appearance of table view cells.- (UITableViewCell *)tableView:(UITableView *)tableView cellForRowAtIndexPath:(NSIndexPath *)indexPath {static NSString *CellIdentifier =@"Cell"; CopyTableViewCell *cell = (CopyTableViewCell *)[tableView dequeueReusableCellWithIdentifier:CellIdentifier];if (cell == nil) { cell = [[[CopyTableViewCell alloc] initWithStyle:UITableViewCellStyleDefault reuseIdentifier:CellIdentifier] autorelease]; [cell setDelegate:self]; }// Configure the cell. NSString *text = [NSString stringWithFormat:@"Row %d", [indexPath row]]; [[cell textLabel] setText:text];return cell; }- (void)tableView:(UITableView *)tableView didSelectRowAtIndexPath:(NSIndexPath *)indexPath {if([self isMenuVisible]) {return; } [[[self tableView] cellForRowAtIndexPath:indexPath] setSelected:YES animated:YES]; }//显示菜单- (void)showMenu:(id)cell {if ([cell isHighlighted]) { [cell becomeFirstResponder]; UIMenuController * menu = [UIMenuController sharedMenuController]; [menu setTargetRect: [cell frame] inView: [self view]]; [menu setMenuVisible: YES animated: YES]; } }- (void)readFromPasteboard:(id)sender { [self setTitle:[NSString stringWithFormat:@"Pasteboard = %@", [[UIPasteboard generalPasteboard] string]]]; }- (void)didReceiveMemoryWarning {// Releases the view if it doesn't have a superview. [super didReceiveMemoryWarning];// Relinquish ownership any cached data, images, etc that aren't in use.}- (void)viewDidUnload { [super viewDidUnload]; [self.tableView release];// Relinquish ownership of anything that can be recreated in viewDidLoad or on demand.// For example: self.myOutlet = nil;} 效果：复制一行数据：点击右上角的按钮粘贴，将数据显示在title上： 2、图片复制粘贴下面通过一个例子，将图片复制和剪贴到另外一个UIImageView中间。 1、在界面上放置两个uiimageview，一个是图片的数据源，一个是将图片粘贴到的地方。CopyPasteImageViewController 代码如下： @interface CopyPasteImageViewController : UIViewController { UIImageView *imageView; UIImageView *pasteView; UIImageView *selectedView; } @property (nonatomic, retain) IBOutlet UIImageView *imageView; @property (nonatomic, retain) IBOutlet UIImageView *pasteView; @property (nonatomic, retain) UIImageView *selectedView;- (void)placeImageOnPasteboard:(id)view;@end 2、当触摸图片的时候我们显示快捷菜单： - (void)touchesBegan:(NSSet*)touches withEvent:(UIEvent*)event { NSSet *copyTouches = [event touchesForView:imageView]; NSSet *pasteTouches = [event touchesForView:pasteView]; [self becomeFirstResponder];if ([copyTouches count] >0) { [self performSelector:@selector(showMenu:) withObject:imageView afterDelay:0.9f]; }elseif([pasteTouches count] >0) { [self performSelector:@selector(showMenu:) withObject:pasteView afterDelay:0.9f]; } [super touchesBegan:touches withEvent:event]; }- (void)showMenu:(id)view { [self setSelectedView:view]; UIMenuController * menu = [UIMenuController sharedMenuController]; [menu setTargetRect: CGRectMake(5, 10, 1, 1) inView: view]; [menu setMenuVisible: YES animated: YES]; } 这里的快捷菜单，显示三个菜单项：剪贴、粘贴、复制： - (BOOL)canPerformAction:(SEL)action withSender:(id)sender{if (action == @selector(cut:)) {return ([self selectedView] == imageView) ? YES : NO; } elseif (action == @selector(copy:)) {return ([self selectedView] == imageView) ? YES : NO; } elseif (action == @selector(paste:)) {return ([self selectedView] == pasteView) ? YES : NO; } elseif (action == @selector(select:)) {return NO; } elseif (action == @selector(selectAll:)) {return NO; } else {return [super canPerformAction:action withSender:sender]; } }- (void)cut:(id)sender { [self copy:sender]; [imageView setHidden:YES]; }- (void)copy:(id)sender { [self placeImageOnPasteboard:[self imageView]]; }- (void)paste:(id)sender { UIPasteboard *appPasteBoard = [UIPasteboard pasteboardWithName:@"CopyPasteImage" create:YES]; NSData *data =[appPasteBoard dataForPasteboardType:@"com.marizack.CopyPasteImage.imageView"]; pasteView.image = [UIImage imageWithData:data]; } 效果： 1、点击图片，显示菜单按钮。 2、点击复制，将数据复制到剪贴板上： 3、点击粘贴，将数据粘贴到uiimageview上。总结：本文详解了iOS系统应用程序中如何使用剪贴板。本文转自麒麟博客园博客，原文链接：http://www.cnblogs.com/zhuqil/archive/2011/08/04/2127883.html，如需转载请自行联系原作者

技术mix呢个人页面-阿里云开发者社区

个人介绍

擅长的技术

iOS开发之自定义View

iOS开发之UIWebView

Jenkins持久化集成使用

高可用Hadoop平台－运行MapReduce程序

iOS开发之详解剪贴板

安装scapy遇到的问题

python在windows平台的pip安装package时的编译问题

mac osx 系统 brew install hadoop 安装指南

mac OSX 上 brew install hive

Celery的实践指南

理解 python metaclass使用技巧与应用场景分析

hive中grouping sets的使用

python字符串格式化方法 format函数的使用

hive中同列多行数据组合的方法以及array to string要点(行转列)

matplotlib 与 seaborn 中出现中文乱码的解决方法

新浪微博开放平台开发步骤简介（适合新手看）

博客园博客PDF生成器

四种方法把string[]转换为int[]

WinForm(C#)倒计时（年月日时分秒）

一种网页倒计时的实现

博客备份系统之一：PDF，Word，TXT文件操作类

jquery处理textarea中的手动换行

WCF配置文件结构

解析入口参数为实体的表达式树

EF4.1中诡异的GUID为空问题