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hadoop-java-example | simple example of using Hadoop

 by   umermansoor Java Version: Current License: No License

 by   umermansoor Java Version: Current License: No License

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kandi X-RAY | hadoop-java-example Summary

hadoop-java-example is a Java library typically used in Big Data, Docker, Kafka, Spark, Hadoop applications. hadoop-java-example has no bugs, it has no vulnerabilities, it has build file available and it has low support. You can download it from GitHub.
This program demonstrates Hadoop's Map-Reduce concept in Java using a very simple example. The input is raw data files listing earthquakes by region, magnitude and other information. nc,71920701,1,”Saturday, January 12, 2013 19:43:18 UTC”,38.7865,-122.7630,1.5,1.10,27,“Northern California”.
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  • hadoop-java-example has a low active ecosystem.
  • It has 70 star(s) with 47 fork(s). There are 6 watchers for this library.
  • It had no major release in the last 12 months.
  • There are 1 open issues and 0 have been closed. On average issues are closed in 1090 days. There are no pull requests.
  • It has a neutral sentiment in the developer community.
  • The latest version of hadoop-java-example is current.
hadoop-java-example Support
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hadoop-java-example Support
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Average in #Java

quality kandi Quality

  • hadoop-java-example has 0 bugs and 0 code smells.
hadoop-java-example Quality
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Average in #Java
hadoop-java-example Quality
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securitySecurity

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

license License

  • hadoop-java-example does not have a standard license declared.
  • Check the repository for any license declaration and review the terms closely.
  • Without a license, all rights are reserved, and you cannot use the library in your applications.
hadoop-java-example License
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hadoop-java-example License
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buildReuse

  • hadoop-java-example 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.
  • It has 112 lines of code, 6 functions and 5 files.
  • It has low code complexity. Code complexity directly impacts maintainability of the code.
hadoop-java-example Reuse
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hadoop-java-example Reuse
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Top functions reviewed by kandi - BETA

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

  • Main method for testing
    • Maps a number of values to a text .
      • Reduces the value to the specified key .

        Get all kandi verified functions for this library.

        Get all kandi verified functions for this library.

        hadoop-java-example Key Features

        A very simple example of using Hadoop's MapReduce functionality in Java.

        Community Discussions

        Trending Discussions on Big Data
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        • Filling up shuffle buffer (this may take a while)
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        • Unnest Query optimisation for singular record
        • handling million of rows for lookup operation using python
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        Trending Discussions on Big Data

        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 hadoop-java-example

        You can download it from GitHub.
        You can use hadoop-java-example 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 hadoop-java-example 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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