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HiveUDFs | My Personal Collection of Hive UDFs

 by   petrabarus Java Version: Current License: No License

 by   petrabarus Java Version: Current License: No License

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kandi X-RAY | HiveUDFs Summary

HiveUDFs is a Java library typically used in Big Data, React applications. HiveUDFs has no bugs, it has no vulnerabilities, it has build file available and it has low support. You can download it from GitHub.
My Personal Collection of Hive UDFs.
Support
Support
Quality
Quality
Security
Security
License
License
Reuse
Reuse

kandi-support Support

  • HiveUDFs has a low active ecosystem.
  • It has 32 star(s) with 22 fork(s). There are 5 watchers for this library.
  • It had no major release in the last 12 months.
  • There are 1 open issues and 3 have been closed. On average issues are closed in 9 days. There are no pull requests.
  • It has a neutral sentiment in the developer community.
  • The latest version of HiveUDFs is current.
HiveUDFs Support
Best in #Java
Average in #Java
HiveUDFs Support
Best in #Java
Average in #Java

quality kandi Quality

  • HiveUDFs has 0 bugs and 0 code smells.
HiveUDFs Quality
Best in #Java
Average in #Java
HiveUDFs Quality
Best in #Java
Average in #Java

securitySecurity

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

license License

  • HiveUDFs does not have a standard license declared.
  • Check the repository for any license declaration and review the terms closely.
  • Without a license, all rights are reserved, and you cannot use the library in your applications.
HiveUDFs License
Best in #Java
Average in #Java
HiveUDFs License
Best in #Java
Average in #Java

buildReuse

  • HiveUDFs 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.
HiveUDFs Reuse
Best in #Java
Average in #Java
HiveUDFs Reuse
Best in #Java
Average in #Java
Top functions reviewed by kandi - BETA

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

  • Returns a region name by region code and region code .
    • Returns the timezone for a given country and region .
      • Initialize database structure .
        • Initializes the object inspector .
          • Returns the distance from the specified location
            • Converts the query into a query string .
              • Gets the date as a date .
                • Converts an IP address to a long .
                  • Returns the URL of the page
                    • Gets the name .

                      Get all kandi verified functions for this library.

                      Get all kandi verified functions for this library.

                      HiveUDFs Key Features

                      My Personal Collection of Hive UDFs

                      HiveUDFs Examples and Code Snippets

                      See all related Code Snippets

                      Compiling

                      copy iconCopydownload iconDownload
                      mvn install

                      LongToIP

                      copy iconCopydownload iconDownload
                      ADD JAR HiveUDFs.jar;
                      CREATE TEMPORARY FUNCTION longtoip as 'net.petrabarus.hiveudfs.LongToIP';
                      SELECT longtopip(2130706433) FROM table;

                      IPToLong

                      copy iconCopydownload iconDownload
                      ADD JAR HiveUDFs.jar;
                      CREATE TEMPORARY FUNCTION iptolong as 'net.petrabarus.hiveudfs.IPToLong';
                      SELECT iptolong("127.0.0.1") FROM table;

                      GeoIP

                      copy iconCopydownload iconDownload
                      ADD JAR HiveUDFs.jar;
                      ADD FILE /usr/share/GeoIP/GeoIPCity.dat;
                      CREATE TEMPORARY FUNCTION geoip as 'net.petrabarus.hiveudfs.GeoIP';
                      SELECT GeoIP(cast(ip AS bigint), 'CITY', './GeoIPCity.dat') FROM table;

                      SearchEngineKeyword

                      copy iconCopydownload iconDownload
                      ADD JAR HiveUDFs.jar
                      CREATE TEMPORARY FUNCTION searchenginekeyword as 'net.petrabarus.hiveudfs.SearchEngineKeyword';
                      SELECT searchenginekeyword(url) FROM table;

                      UCWords

                      copy iconCopydownload iconDownload
                      ADD JAR HiveUDFs.jar
                      CREATE TEMPORARY FUNCTION ucwords as 'net.petrabarus.hiveudfs.UCWords';
                      SELECT ucwords(text) FROM table;

                      Making Spark functions accessible from within a bespoke function in mutate

                      copy iconCopydownload iconDownload
                      library(rlang)
                      library(sparklyr) 
                      library(nycflights13)
                      library(dplyr)
                      
                      sc <- spark_connect(master = "local")
                      
                      just_time <- flights %>%
                           select(time_hour) %>%
                           mutate(time_hour = as.character(time_hour))
                           head(100)
                      
                      spark_flights <- copy_to(sc, just_time, "flights")
                      
                      
                      from_unix_to_nice<- function(x) {
                        x <- enexpr(x)
                        expr(from_unixtime(unix_timestamp(!!x), 'YYYY-MM-dd'))
                      }
                      
                      from_unix_to_nice(test)
                      
                      
                      spark_flights %>%
                        mutate(new_field =  !!from_unix_to_nice(time_hour))
                      

                      See all related Code Snippets

                      Community Discussions

                      Trending Discussions on HiveUDFs
                      • Making Spark functions accessible from within a bespoke function in mutate
                      Trending Discussions on HiveUDFs

                      QUESTION

                      Making Spark functions accessible from within a bespoke function in mutate

                      Asked 2017-Sep-21 at 01:17

                      While working with Spark RDD via , I would like to wrap some of the common transformations to pass them more convientntly to mutate syntax.

                      Example

                      For instance, while working with a data with the following timestamps:

                      2000-01-01 00:00:00.0
                      2000-02-02 00:00:00.0
                      

                      I can convert those to a more useful YYYY-MM-dd format using the syntax:

                      mutate(nice_date= from_unixtime(unix_timestamp(bad_timestamp), 'YYYY-MM-dd'))
                      
                      Challenge

                      As I do it frequently, I would like to wrap the from_unixtime(unix_timestamp(bad_timestamp), 'YYYY-MM-dd')) call and use syntax:

                      mutate(nice_date = from_unix_to_nice(bad_date))
                      

                      Conventional approach would suggest writing a function:

                      from_unix_to_nice<- function(x) {
                          from_unixtime(unix_timestamp(x), 'YYYY-MM-dd')
                      }
                      
                      Problem

                      When applied the function fails:

                      > Error: org.apache.spark.sql.AnalysisException: undefined function
                      > from_unix_to_nice; line 2 pos 62  at
                      > org.apache.spark.sql.hive.HiveFunctionRegistry$$anonfun$lookupFunction$2$$anonfun$1.apply(hiveUDFs.scala:69)
                      >   at
                      > org.apache.spark.sql.hive.HiveFunctionRegistry$$anonfun$lookupFunction$2$$anonfun$1.apply(hiveUDFs.scala:69)
                      >   at scala.Option.getOrElse(Option.scala:120)
                      

                      How can I conveniently develop wrappers for common mutate operations so I can pass those to sparklyr pipeline?

                      ANSWER

                      Answered 2017-Sep-21 at 01:17

                      the issue is that the function needs to be passed un-evaluated to the mutate() function. The rlang package can be done to accomplish this, here is an example:

                      library(rlang)
                      library(sparklyr) 
                      library(nycflights13)
                      library(dplyr)
                      
                      sc <- spark_connect(master = "local")
                      
                      just_time <- flights %>%
                           select(time_hour) %>%
                           mutate(time_hour = as.character(time_hour))
                           head(100)
                      
                      spark_flights <- copy_to(sc, just_time, "flights")
                      
                      
                      from_unix_to_nice<- function(x) {
                        x <- enexpr(x)
                        expr(from_unixtime(unix_timestamp(!!x), 'YYYY-MM-dd'))
                      }
                      
                      from_unix_to_nice(test)
                      
                      
                      spark_flights %>%
                        mutate(new_field =  !!from_unix_to_nice(time_hour))
                      

                      The from_unix_to_nice() function now passes: from_unixtime(unix_timestamp(test), "YYYY-MM-dd") to mutate() as if you would have typed that exact syntax.

                      Source https://stackoverflow.com/questions/46318820

                      Community Discussions, Code Snippets contain sources that include Stack Exchange Network

                      Vulnerabilities

                      No vulnerabilities reported

                      Install HiveUDFs

                      You can download it from GitHub.
                      You can use HiveUDFs 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 HiveUDFs 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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