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qminer | Analytic platform

 by   qminer C++ Version: v9.4.0 License: Non-SPDX

 by   qminer C++ Version: v9.4.0 License: Non-SPDX

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kandi X-RAY | qminer Summary

qminer is a C++ library typically used in Big Data, Spark applications. qminer has no bugs, it has no vulnerabilities and it has low support. However qminer has a Non-SPDX License. You can download it from GitHub.
QMiner is an analytics platform for large-scale real-time streams containing structured and unstructured data. It is designed for scaling to millions of data points on high-end commodity hardware, providing efficient storage, retrieval and analytics mechanisms with real-time response.
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kandi-support Support

  • qminer has a low active ecosystem.
  • It has 215 star(s) with 58 fork(s). There are 23 watchers for this library.
  • There were 2 major release(s) in the last 12 months.
  • There are 36 open issues and 91 have been closed. On average issues are closed in 235 days. There are 1 open pull requests and 0 closed requests.
  • It has a neutral sentiment in the developer community.
  • The latest version of qminer is v9.4.0
qminer Support
Best in #C++
Average in #C++
qminer Support
Best in #C++
Average in #C++

quality kandi Quality

  • qminer has 0 bugs and 0 code smells.
qminer Quality
Best in #C++
Average in #C++
qminer Quality
Best in #C++
Average in #C++

securitySecurity

  • qminer has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
  • qminer code analysis shows 0 unresolved vulnerabilities.
  • There are 0 security hotspots that need review.
qminer Security
Best in #C++
Average in #C++
qminer Security
Best in #C++
Average in #C++

license License

  • qminer has a Non-SPDX License.
  • Non-SPDX licenses can be open source with a non SPDX compliant license, or non open source licenses, and you need to review them closely before use.
qminer License
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Average in #C++
qminer License
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buildReuse

  • qminer releases are available to install and integrate.
  • Installation instructions, examples and code snippets are available.
qminer Reuse
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qminer Reuse
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qminer Key Features

Analytic platform for real-time large-scale streams containing structured and unstructured data.

qminer Examples and Code Snippets

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Install

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npm install qminer

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Community Discussions

Trending Discussions on Big Data
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  • 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

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