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Graph-Algorithm-MapReduce | Parallel Bread first Search on Hadoop

 by   himank Java Version: Current License: No License

 by   himank Java Version: Current License: No License

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kandi X-RAY | Graph-Algorithm-MapReduce Summary

Graph-Algorithm-MapReduce is a Java library typically used in Big Data, Hadoop applications. Graph-Algorithm-MapReduce has no bugs, it has no vulnerabilities and it has low support. However Graph-Algorithm-MapReduce build file is not available. You can download it from GitHub.
Parallel Bread first Search on Hadoop
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Quality
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Security
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kandi-support Support

  • Graph-Algorithm-MapReduce has a low active ecosystem.
  • It has 14 star(s) with 12 fork(s). There are 2 watchers for this library.
  • It had no major release in the last 12 months.
  • Graph-Algorithm-MapReduce has no issues reported. There are no pull requests.
  • It has a neutral sentiment in the developer community.
  • The latest version of Graph-Algorithm-MapReduce is current.
This Library - Support
Best in #Java
Average in #Java
This Library - Support
Best in #Java
Average in #Java

quality kandi Quality

  • Graph-Algorithm-MapReduce has 0 bugs and 0 code smells.
This Library - Quality
Best in #Java
Average in #Java
This Library - Quality
Best in #Java
Average in #Java

securitySecurity

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

license License

  • Graph-Algorithm-MapReduce 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.
This Library - License
Best in #Java
Average in #Java
This Library - License
Best in #Java
Average in #Java

buildReuse

  • Graph-Algorithm-MapReduce releases are not available. You will need to build from source code and install.
  • Graph-Algorithm-MapReduce has no build file. You will be need to create the build yourself to build the component from source.
  • Graph-Algorithm-MapReduce saves you 42 person hours of effort in developing the same functionality from scratch.
  • It has 111 lines of code, 4 functions and 1 files.
  • It has low code complexity. Code complexity directly impacts maintainability of the code.
This Library - Reuse
Best in #Java
Average in #Java
This Library - Reuse
Best in #Java
Average in #Java
Top functions reviewed by kandi - BETA

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

  • Run Dijstra algorithm .
    • Main entry point .

      Get all kandi verified functions for this library.

      Get all kandi verified functions for this library.

      Graph-Algorithm-MapReduce Key Features

      Parallel Bread first Search on Hadoop

      Graph-Algorithm-MapReduce Examples and Code Snippets

      No Code Snippets are available at this moment for Graph-Algorithm-MapReduce.

      See all Code Snippets related to Java

      Community Discussions

      Trending Discussions on Big Data
      • How to group unassociated content
      • Using Spark window with more than one partition when there is no obvious partitioning column
      • What is the best way to store +3 millions records in Firestore?
      • spark-shell throws java.lang.reflect.InvocationTargetException on running
      • For function over multiple rows (i+1)?
      • Filling up shuffle buffer (this may take a while)
      • Designing Twitter Search - How to sort large datasets?
      • Unnest Query optimisation for singular record
      • handling million of rows for lookup operation using python
      • split function does not return any observations with large dataset
      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 Graph-Algorithm-MapReduce

      You can download it from GitHub.
      You can use Graph-Algorithm-MapReduce 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 Graph-Algorithm-MapReduce 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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