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BigFileUploadJava | Using Apache HttpComponents

 by   clxy Java Version: Current License: MIT

 by   clxy Java Version: Current License: MIT

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kandi X-RAY | BigFileUploadJava Summary

BigFileUploadJava is a Java library typically used in Big Data, Gradle, Hadoop applications. BigFileUploadJava has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can download it from GitHub.
Upload big file by java. Using Apache HttpComponents. Can work on Android too.
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kandi-support Support

  • BigFileUploadJava has a low active ecosystem.
  • It has 81 star(s) with 44 fork(s). There are 19 watchers for this library.
  • It had no major release in the last 12 months.
  • There are 0 open issues and 2 have been closed. On average issues are closed in 163 days. There are no pull requests.
  • It has a neutral sentiment in the developer community.
  • The latest version of BigFileUploadJava is current.
BigFileUploadJava Support
Best in #Java
Average in #Java
BigFileUploadJava Support
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Average in #Java

quality kandi Quality

  • BigFileUploadJava has 0 bugs and 0 code smells.
BigFileUploadJava Quality
Best in #Java
Average in #Java
BigFileUploadJava Quality
Best in #Java
Average in #Java

securitySecurity

  • BigFileUploadJava has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
  • BigFileUploadJava code analysis shows 0 unresolved vulnerabilities.
  • There are 0 security hotspots that need review.
BigFileUploadJava Security
Best in #Java
Average in #Java
BigFileUploadJava Security
Best in #Java
Average in #Java

license License

  • BigFileUploadJava is licensed under the MIT License. This license is Permissive.
  • Permissive licenses have the least restrictions, and you can use them in most projects.
BigFileUploadJava License
Best in #Java
Average in #Java
BigFileUploadJava License
Best in #Java
Average in #Java

buildReuse

  • BigFileUploadJava releases are not available. You will need to build from source code and install.
  • Build file is available. You can build the component from source.
  • Installation instructions are not available. Examples and code snippets are available.
  • BigFileUploadJava saves you 168 person hours of effort in developing the same functionality from scratch.
  • It has 417 lines of code, 36 functions and 8 files.
  • It has low code complexity. Code complexity directly impacts maintainability of the code.
BigFileUploadJava Reuse
Best in #Java
Average in #Java
BigFileUploadJava Reuse
Best in #Java
Average in #Java
Top functions reviewed by kandi - BETA

kandi has reviewed BigFileUploadJava and discovered the below as its top functions. This is intended to give you an instant insight into BigFileUploadJava implemented functionality, and help decide if they suit your requirements.

  • Perform the upload operation .
    • Performs a POST .
      • Creates a HTTP client .
        • Helper method to wait for future execution .
          • Send a notification to the client .
            • Retry indexes .
              • Uploads the file .
                • Gets the byte array content .
                  • Gets the attribute name .

                    Get all kandi verified functions for this library.

                    Get all kandi verified functions for this library.

                    BigFileUploadJava Key Features

                    读取文件到小的文件块。考虑到I/O竞争只用单线程;考虑到内存消耗采取分批读取。

                    将读取的文件块上传。通常多个线程会好些,但是太多又不行,默认用5线程上传。

                    所有文件块全部上传后,通知服务器合并。

                    如果有部分文件块处理失败,可以重试失败部分。

                    收到文件块后直接保存。

                    收到通知后合并所有文件块。

                    使用生产/消费者模式。

                    读取和上传的通信使用BlockingQueue。

                    目前上传用Apache HttpComponents。可以有其他实现。

                    可以用在Android上。请参考[BigFileUploadAndroid](https://github.com/clxy/BigFileUploadAndroid)。

                    BigFileUploadJava Examples and Code Snippets

                    See all related Code Snippets

                    default

                    copy iconCopydownload iconDownload
                    Upload big file using java.
                    
                    ### Process Flow
                    Basically, read a big file into small parts and upload. When all file parts upload is complete, combine at server.
                    
                    #### Client
                    
                    1. Read the big file into several small parts. Considering I/O contention, use just one thread; Considering memory usage, read file part by part into fixed size queue.
                    2. Upload every readed file part. Usually multiple threads would be better, but can't too much, default threads count is 5.
                    3. After all parts uploaded, notify server to combine.
                    4. Can retry the specific parts only if failed to process.
                    
                    #### Server
                    
                    1. Save recieved file parts.
                    2. Combine all parts by notification.
                    
                    #### Others
                    
                    - Producer/Consumer pattern.
                    - Communicate read and upload processes by BlockingQueue.
                    - Uploading is using Apache HttpComponents currently. There can be other implementations.
                    - Can be used in the android. Please refer to [BigFileUploadAndroid](https://github.com/clxy/BigFileUploadAndroid)。
                    
                    
                    ### Usage
                    
                    #### Configuration
                    Please refer to [cn.clxy.upload.Config](https://github.com/clxy/BigFileUploadJava/blob/master/src/main/java/cn/clxy/upload/Config.java).
                    
                    Please note that the maximum memory usage might be:
                    ```PART_SIZE * (MAX_UPLOAD + MAX_READ - 1)```
                    
                    #### Upload
                    	UploadFileService service = new UploadFileService(yourFileName);
                    	service.upload();
                    
                    #### Retry failed parts
                    	UploadFileService service = new UploadFileService(yourFileName);
                    	service.retry(1, 2);
                    
                    ### Server
                    Because it is via HTTP, so it can be in any language, such as Java, PHP, Python, etc.
                    Here is a java example.
                    
                    ```Java
                    ...
                    try (FileOutputStream dest = new FileOutputStream(destFile, true)) {
                    
                    	FileChannel dc = dest.getChannel();// the final big file.
                    	for (long i = 0; i < count; i++) {
                    		File partFile = new File(destFileName + "." + i);// every small parts.
                    		if (!partFile.exists()) {
                    			break;
                    		}
                    		try (FileInputStream part = new FileInputStream(partFile)) {
                    			FileChannel pc = part.getChannel();
                    			pc.transferTo(0, pc.size(), dc);// combine.
                    		}
                    		partFile.delete();
                    	}
                    	statusCode = OK;// set ok at last.
                    } catch (Exception e) {
                    	log.error("combine failed.", e);
                    }
                    ```
                    
                    * * ** * ** * ** * ** * ** * ** * ** * ** * *
                    
                    
                    概要<a id="chinese" name="chinese"></a>

                    See all related Code Snippets

                    Community Discussions

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                    QUESTION

                    How to group unassociated content

                    Asked 2022-Apr-15 at 12:43

                    I have a hive table that records user behavior

                    like this

                    userid behavior timestamp url
                    1 view 1650022601 url1
                    1 click 1650022602 url2
                    1 click 1650022614 url3
                    1 view 1650022617 url4
                    1 click 1650022622 url5
                    1 view 1650022626 url7
                    2 view 1650022628 url8
                    2 view 1650022631 url9

                    About 400GB is added to the table every day.

                    I want to order by timestamp asc, then one 'view' is in a group between another 'view' like this table, the first 3 lines belong to a same group , then subtract the timestamps, like 1650022614 - 1650022601 as the view time.

                    How to do this?

                    i try lag and lead function, or scala like this

                            val pairRDD: RDD[(Int, String)] = record.map(x => {
                                if (StringUtil.isDateString(x.split("\\s+")(0))) {
                                    partition = partition + 1
                                    (partition, x)
                                } else {
                                    (partition, x)
                                }
                            })
                    

                    or java like this

                            LongAccumulator part = spark.sparkContext().longAccumulator("part");
                    
                            JavaPairRDD<Long, Row> pairRDD = spark.sql(sql).coalesce(1).javaRDD().mapToPair((PairFunction<Row, Long, Row>) row -> {
                                if (row.getAs("event") == "pageview") {
                                    part.add(1L);
                                }
                            return new Tuple2<>(part.value(), row);
                            });
                    

                    but when a dataset is very large, this code just stupid.

                    save me plz

                    ANSWER

                    Answered 2022-Apr-15 at 12:43

                    If you use dataframe, you can build partition by using window that sum a column whose value is 1 when you change partition and 0 if you don't change partition.

                    You can transform a RDD to a dataframe with sparkSession.createDataframe() method as explained in this answer

                    Back to your problem. In you case, you change partition every time column behavior is equal to "view". So we can start with this condition:

                    import org.apache.spark.sql.functions.col
                    
                    val df1 = df.withColumn("is_view", (col("behavior") === "view").cast("integer"))
                    

                    You get the following dataframe:

                    +------+--------+----------+----+-------+
                    |userid|behavior|timestamp |url |is_view|
                    +------+--------+----------+----+-------+
                    |1     |view    |1650022601|url1|1      |
                    |1     |click   |1650022602|url2|0      |
                    |1     |click   |1650022614|url3|0      |
                    |1     |view    |1650022617|url4|1      |
                    |1     |click   |1650022622|url5|0      |
                    |1     |view    |1650022626|url7|1      |
                    |2     |view    |1650022628|url8|1      |
                    |2     |view    |1650022631|url9|1      |
                    +------+--------+----------+----+-------+
                    

                    Then you use a window ordered by timestamp to sum over the is_view column:

                    import org.apache.spark.sql.expressions.Window
                    import org.apache.spark.sql.functions.sum
                    
                    val df2 = df1.withColumn("partition", sum("is_view").over(Window.partitionBy("userid").orderBy("timestamp")))
                    

                    Which get you the following dataframe:

                    +------+--------+----------+----+-------+---------+
                    |userid|behavior|timestamp |url |is_view|partition|
                    +------+--------+----------+----+-------+---------+
                    |1     |view    |1650022601|url1|1      |1        |
                    |1     |click   |1650022602|url2|0      |1        |
                    |1     |click   |1650022614|url3|0      |1        |
                    |1     |view    |1650022617|url4|1      |2        |
                    |1     |click   |1650022622|url5|0      |2        |
                    |1     |view    |1650022626|url7|1      |3        |
                    |2     |view    |1650022628|url8|1      |1        |
                    |2     |view    |1650022631|url9|1      |2        |
                    +------+--------+----------+----+-------+---------+
                    

                    Then, you just have to aggregate per userid and partition:

                    import org.apache.spark.sql.functions.{max, min}
                    
                    val result = df2.groupBy("userid", "partition")
                      .agg((max("timestamp") - min("timestamp")).as("duration"))
                    

                    And you get the following results:

                    +------+---------+--------+
                    |userid|partition|duration|
                    +------+---------+--------+
                    |1     |1        |13      |
                    |1     |2        |5       |
                    |1     |3        |0       |
                    |2     |1        |0       |
                    |2     |2        |0       |
                    +------+---------+--------+
                    

                    The complete scala code:

                    import org.apache.spark.sql.expressions.Window
                    import org.apache.spark.sql.functions.{col, max, min, sum}
                    
                    val result = df
                      .withColumn("is_view", (col("behavior") === "view").cast("integer"))
                      .withColumn("partition", sum("is_view").over(Window.partitionBy("userid").orderBy("timestamp")))
                      .groupBy("userid", "partition")
                      .agg((max("timestamp") - min("timestamp")).as("duration"))
                    

                    Source https://stackoverflow.com/questions/71883786

                    Community Discussions, Code Snippets contain sources that include Stack Exchange Network

                    Vulnerabilities

                    No vulnerabilities reported

                    Install BigFileUploadJava

                    You can download it from GitHub.
                    You can use BigFileUploadJava like any standard Java library. Please include the the jar files in your classpath. You can also use any IDE and you can run and debug the BigFileUploadJava component as you would do with any other Java program. Best practice is to use a build tool that supports dependency management such as Maven or Gradle. For Maven installation, please refer maven.apache.org. For Gradle installation, please refer gradle.org .

                    Support

                    For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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