A list of user defined functions are collected here for Hive and Spark SQL.

Note:

• UDAF class is deprecated will move to generic interface.

1. Build the JAR:

   mvn package
   

2. Start Hive CLI with the JAR in your classpath:

   export HADOOP_CLASSPATH=target/df-hiveudf-1.0-SNAPSHOT.jar ; hive
   

3. Execute:

   create temporary function str_lower as 'com.datafibers.hiveudf.udf.StringLower';
   

4. Execute:

   select str_lower("UPPER CASE LETTERS");
   

1. In shell commandline, create a permanent function:

   hdfs dfs -mkdir /apps/hive/functions
   hdfs dfs -chown hive:hive /apps/hive/functions
   cp df-hiveudf-1.0-SNAPSHOT.jar /tmp
   hdfs dfs -put -f /tmp/df-hiveudf-1.0-SNAPSHOT.jar /apps/hive/functions

2. In beeline, create/register/check the function

   DROP FUNCTION IF EXISTS str_lower;
   DROP FUNCTION IF EXISTS arraycontains;
   CREATE FUNCTION str_lower AS 'com.datafibers.hiveudf.udf.StringLower' USING JAR 'hdfs:////apps/hive/functions/df-hiveudf-1.0-SNAPSHOT.jar';
   CREATE FUNCTION arraycontains AS 'com.datafibers.hiveudf.gudf.ArrayContains' USING JAR 'hdfs:////apps/hive/functions/df-hiveudf-1.0-SNAPSHOT.jar';
   DESC FUNCTION arraycontains;
   DESC FUNCTION EXTENDED arraycontains;

3. After that, you can use the functions like hive build-in functions.

   select str_lower(name), work_place from employee where arraycontains(work_place, 'Toronto');

4. Check query explain

   explain select str_lower(name), work_place from employee where arraycontains(work_place, 'Toronto');

For streaming function by python, follow steps below.

hdfs dfs -mkdir /apps/hive/scripts
hdfs dfs -put hive_udf_addfile.py /apps/hive/scripts
hive -f hive_udf_addfile.sql