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big-data-plugin | Kettle plugin that provides support

 by   pentaho Java Version: Current License: Apache-2.0

 by   pentaho Java Version: Current License: Apache-2.0

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kandi X-RAY | big-data-plugin Summary

big-data-plugin is a Java library typically used in Big Data, Spark applications. big-data-plugin 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, GitLab.
The Pentaho Big Data Plugin Project provides support for an ever-expanding Big Data community within the Pentaho ecosystem. It is a plugin for the Pentaho Kettle engine which can be used within Pentaho Data Integration (Kettle), Pentaho Reporting, and the Pentaho BI Platform.
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Support
Quality
Quality
Security
Security
License
License
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Reuse

kandi-support Support

  • big-data-plugin has a low active ecosystem.
  • It has 195 star(s) with 309 fork(s). There are 107 watchers for this library.
  • It had no major release in the last 12 months.
  • big-data-plugin has no issues reported. There are 27 open pull requests and 0 closed requests.
  • It has a neutral sentiment in the developer community.
  • The latest version of big-data-plugin is current.
big-data-plugin Support
Best in #Java
Average in #Java
big-data-plugin Support
Best in #Java
Average in #Java

quality kandi Quality

  • big-data-plugin has 0 bugs and 0 code smells.
big-data-plugin Quality
Best in #Java
Average in #Java
big-data-plugin Quality
Best in #Java
Average in #Java

securitySecurity

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

license License

  • big-data-plugin is licensed under the Apache-2.0 License. This license is Permissive.
  • Permissive licenses have the least restrictions, and you can use them in most projects.
big-data-plugin License
Best in #Java
Average in #Java
big-data-plugin License
Best in #Java
Average in #Java

buildReuse

  • big-data-plugin 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.
  • big-data-plugin saves you 93392 person hours of effort in developing the same functionality from scratch.
  • It has 102038 lines of code, 6315 functions and 801 files.
  • It has high code complexity. Code complexity directly impacts maintainability of the code.
big-data-plugin Reuse
Best in #Java
Average in #Java
big-data-plugin Reuse
Best in #Java
Average in #Java
Top functions reviewed by kandi - BETA

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

  • Add content tab .
    • Get the table mapping
      • Checks if the key information is available .
        • Reads all fields from an object .
          • Build the setup tab .
            • Add source tab .
              • Converts an HBase row to a tuple table
                • Gets the S3 client .
                  • Builds the setup section .
                    • Get the row metadata .

                      Get all kandi verified functions for this library.

                      Get all kandi verified functions for this library.

                      big-data-plugin Key Features

                      Kettle plugin that provides support for interacting within many "big data" projects including Hadoop, Hive, HBase, Cassandra, MongoDB, and others.

                      Pre-requisites

                      copy iconCopydownload iconDownload
                      Option 1: Copy this file into your <user-home>/.m2 folder and name it "settings.xml".
                      Warning: If you do this, it will become your default settings.xml for all maven builds.
                      
                      Option 2: Copy this file into some other folder--possibly the project folder for the project you want to build and use the maven 's' option to build with this settings.xml file. Example: `mvn -s public-settings.xml install`.
                      
                      The Pentaho profile defaults to pull all artifacts through the Pentaho public repository.
                      If you want to try resolving maven plugin dependencies through the maven central repository instead of the Pentaho public repository, activate the "central" profile like this:
                      
                      `mvn -s -public-settings.xml -P central install`
                      
                      
                      If your fails to resolve the jacoco-maven-plugin version 0.7.7-SNAPSHOT

                      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 big-data-plugin

                      You can download it from GitHub, GitLab.
                      You can use big-data-plugin 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 big-data-plugin 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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