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azure-tables-hadoop | Hadoop input format and a Hive storage handler

 by   mooso Java Version: Current License: Apache-2.0

 by   mooso Java Version: Current License: Apache-2.0

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kandi X-RAY | azure-tables-hadoop Summary

azure-tables-hadoop is a Java library typically used in Big Data, Spark, Amazon S3, Hadoop applications. azure-tables-hadoop 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.
A Hadoop input format and a Hive storage handler so that you can access data stored in Windows Azure Storage tables from within a Hadoop (or HdInsight) cluster.
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kandi-support Support

  • azure-tables-hadoop has a low active ecosystem.
  • It has 26 star(s) with 14 fork(s). There are 9 watchers for this library.
  • It had no major release in the last 12 months.
  • There are 3 open issues and 1 have been closed. There are no pull requests.
  • It has a neutral sentiment in the developer community.
  • The latest version of azure-tables-hadoop is current.
azure-tables-hadoop Support
Best in #Java
Average in #Java
azure-tables-hadoop Support
Best in #Java
Average in #Java

quality kandi Quality

  • azure-tables-hadoop has 0 bugs and 0 code smells.
azure-tables-hadoop Quality
Best in #Java
Average in #Java
azure-tables-hadoop Quality
Best in #Java
Average in #Java

securitySecurity

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

license License

  • azure-tables-hadoop 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.
azure-tables-hadoop License
Best in #Java
Average in #Java
azure-tables-hadoop License
Best in #Java
Average in #Java

buildReuse

  • azure-tables-hadoop 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.
azure-tables-hadoop Reuse
Best in #Java
Average in #Java
azure-tables-hadoop Reuse
Best in #Java
Average in #Java
Top functions reviewed by kandi - BETA

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

  • Gets the data from a DynamicTableEntity .
    • The main entry point .
      • Creates a table client .
        • Initialize Hive .
          • Gets the partition keys at the given table .
            • Gets the input splits for a table .
              • Gets all partition keys .
                • Move the next record .
                  • Deserialize fields from the given input stream .
                    • Transfers the property from the table .

                      Get all kandi verified functions for this library.

                      Get all kandi verified functions for this library.

                      azure-tables-hadoop Key Features

                      A Hadoop input format and a Hive storage handler so that you can access data stored in Windows Azure Storage tables from within a Hadoop (or HdInsight) cluster.

                      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 azure-tables-hadoop

                      You can download it from GitHub.
                      You can use azure-tables-hadoop 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 azure-tables-hadoop 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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