kandi background
Explore Kits

Elasticsearch-HBase-River | import river similar to the elasticsearch mysql

 by   mallocator Java Version: Current License: Apache-2.0

 by   mallocator Java Version: Current License: Apache-2.0

Download this library from

kandi X-RAY | Elasticsearch-HBase-River Summary

Elasticsearch-HBase-River is a Java library typically used in Big Data, MongoDB, Spring Boot, Spring, Spark, JavaFX applications. Elasticsearch-HBase-River 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.
An import river similar to the elasticsearch mysql river. If you’re looking for an alternative sollution that uses the core hbase libraries and uses hbase replication for moving data, you can find one here: https://github.com/posix4e/Elasticsearch-HBase-River.
Support
Support
Quality
Quality
Security
Security
License
License
Reuse
Reuse

kandi-support Support

  • Elasticsearch-HBase-River has a low active ecosystem.
  • It has 37 star(s) with 36 fork(s). There are 10 watchers for this library.
  • It had no major release in the last 12 months.
  • There are 0 open issues and 3 have been closed. On average issues are closed in 41 days. There are no pull requests.
  • It has a neutral sentiment in the developer community.
  • The latest version of Elasticsearch-HBase-River is current.
This Library - Support
Best in #Java
Average in #Java
This Library - Support
Best in #Java
Average in #Java

quality kandi Quality

  • Elasticsearch-HBase-River 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

  • Elasticsearch-HBase-River has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
  • Elasticsearch-HBase-River 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

  • Elasticsearch-HBase-River 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.
This Library - License
Best in #Java
Average in #Java
This Library - License
Best in #Java
Average in #Java

buildReuse

  • Elasticsearch-HBase-River 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, examples and code snippets are available.
  • Elasticsearch-HBase-River saves you 481 person hours of effort in developing the same functionality from scratch.
  • It has 1133 lines of code, 53 functions and 11 files.
  • It has medium 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 Elasticsearch-HBase-River and discovered the below as its top functions. This is intended to give you an instant insight into Elasticsearch-HBase-River implemented functionality, and help decide if they suit your requirements.

  • Starts the HBase stream .
    • Parses a bulk of rows .
      • Parse data from HBase table .
        • Waits for the index to reach .
          • Invokes the handler method .
            • private static helper
              • Return the description .
                • The name of theriver - hbase - hbase - hbase - hbase - hbase - hbase - hbase - hBase .

                  Get all kandi verified functions for this library.

                  Get all kandi verified functions for this library.

                  Elasticsearch-HBase-River Key Features

                  An import river similar to the elasticsearch mysql river

                  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 Elasticsearch-HBase-River

                  Just copy the .zip file on the elasticsearch server should be using the plugin and run the "plugin" script coming with elasticsearch in the bin folder.

                  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 .

                  DOWNLOAD this Library from

                  Find, review, and download reusable Libraries, Code Snippets, Cloud APIs from
                  over 430 million Knowledge Items
                  Find more libraries
                  Reuse Solution Kits and Libraries Curated by Popular Use Cases
                  Explore Kits

                  Save this library and start creating your kit

                  Share this Page

                  share link
                  Consider Popular Java Libraries
                  Try Top Libraries by mallocator
                  Compare Java Libraries with Highest Support
                  Compare Java Libraries with Highest Quality
                  Compare Java Libraries with Highest Security
                  Compare Java Libraries with Permissive License
                  Compare Java Libraries with Highest Reuse
                  Find, review, and download reusable Libraries, Code Snippets, Cloud APIs from
                  over 430 million Knowledge Items
                  Find more libraries
                  Reuse Solution Kits and Libraries Curated by Popular Use Cases
                  Explore Kits

                  Save this library and start creating your kit

                  • © 2022 Open Weaver Inc.