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GenomicsDB | Highly performant data storage in C for importing

 by   GenomicsDB C++ Version: v1.4.3 License: Non-SPDX

 by   GenomicsDB C++ Version: v1.4.3 License: Non-SPDX

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kandi X-RAY | GenomicsDB Summary

GenomicsDB is a C++ library typically used in Big Data, Spark applications. GenomicsDB has no bugs, it has no vulnerabilities and it has low support. However GenomicsDB has a Non-SPDX License. You can download it from GitHub.
GenomicsDB, originally from Intel Health and Lifesciences, is built on top of a fork of htslib and a tile-based array storage system for importing, querying and transforming variant data. Variant data is sparse by nature (sparse relative to the whole genome) and using sparse array data stores is a perfect fit for storing such data. GenomicsDB is a highly performant scalable data storage written in C++ for importing, querying and transforming genomic variant data. GenomicsDB is packaged into gatk4 and benefits qualitatively from a large user base. The GenomicsDB documentation for users is hosted as a Github wiki: https://github.com/GenomicsDB/GenomicsDB/wiki.
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kandi-support Support

  • GenomicsDB has a low active ecosystem.
  • It has 61 star(s) with 15 fork(s). There are 8 watchers for this library.
  • There were 1 major release(s) in the last 12 months.
  • There are 15 open issues and 13 have been closed. On average issues are closed in 139 days. There are 7 open pull requests and 0 closed requests.
  • It has a neutral sentiment in the developer community.
  • The latest version of GenomicsDB is v1.4.3
GenomicsDB Support
Best in #C++
Average in #C++
GenomicsDB Support
Best in #C++
Average in #C++

quality kandi Quality

  • GenomicsDB has no bugs reported.
GenomicsDB Quality
Best in #C++
Average in #C++
GenomicsDB Quality
Best in #C++
Average in #C++

securitySecurity

  • GenomicsDB has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
GenomicsDB Security
Best in #C++
Average in #C++
GenomicsDB Security
Best in #C++
Average in #C++

license License

  • GenomicsDB 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.
GenomicsDB License
Best in #C++
Average in #C++
GenomicsDB License
Best in #C++
Average in #C++

buildReuse

  • GenomicsDB releases are available to install and integrate.
  • Installation instructions are not available. Examples and code snippets are available.
GenomicsDB Reuse
Best in #C++
Average in #C++
GenomicsDB Reuse
Best in #C++
Average in #C++
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GenomicsDB Key Features

Supported platforms : Linux and MacOS.

Supported filesystems : POSIX, HDFS, EMRFS(S3), GCS and Azure Blob.

JVM/Spark wrappers that allow for streaming VariantContext buffers to/from the C++ layer among other functions. GenomicsDB jars with native libraries and only zlib dependencies are regularly published on Maven Central.

Native tools for incremental ingestion of variants in the form of VCF/BCF/CSV into GenomicsDB for performance.

MPI and Spark support for parallel querying of GenomicsDB.

GenomicsDB Examples and Code Snippets

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Checklist before creating Pull Request

copy iconCopydownload iconDownload
Use spaces instead of tabs.
Use 2 spaces for indenting.
Add brackets even for one line blocks e.g. 
        if (x>0)
           do_foo();
 should ideally be 
       if (x>0) {
         do_foo();
       }
Pad header e.g.
        if(x>0) should be if (x>0)
        while(x>0) should be while (x>0)
One half indent for class modifiers.

See all related Code Snippets

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 GenomicsDB

You can download it from GitHub.

Support

GenomicsDB is open source and all participation is welcome. GenomicsDB is released under the MIT License and all external contributors are expected to grant an MIT License for their contributions.

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