Helpful user defined fuctions / table generating functions for Hive
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HiveSwarm: User Defined Functions for Hive

Hive provides a number of useful user defined functions, but there is certainly room for more. HiveSwarm provides a collection of additional useful functions.
HiveSwarm requires CDH4 running MRv1 (has not been tested with YARN)


Assuming you have Hadoop and Hive set up (along with your HADOOP_HOME and HIVE_HOME environment variables set correctly), run the following:

Download and install Maven
git clone git://
cd HiveSwarm
mvn package

You should now have a jar file in your target folder named HiveSwarm.jar.


Each of the following methods assumes you have first run the following in your hive session:

add jar /path/to/HiveSwarm.jar;

After you do that, you can create temporary functions as needed.

max_date(date string, ...)


create temporary function max_date as 'com.livingsocial.hive.udf.MaxDate'

max_date takes any number of date ('2011-01-10') or date time ('2011-01-10 10:01:00') or null arguments. The max date among non-null arguments is returned.

min_date(date string, ...)

Same as max_date, but returns min.

intervals(group column, interval column)


create temporary function intervals as 'com.livingsocial.hive.udtf.Intervals';

intervals takes a group column argument and an interval argument and returns a two column table with the intervals between the rows per group. The interval column can be a numerical or date/datetime (string) column.

smax(column) / smin(column)


create temporary function smin as 'com.livingsocial.hive.udf.SMin';

smin and smax act just like min and max but treat string columns like timestamps.

ilike(colname, pattern)

Same as regular Hive like but is case irrespective (just like MySQL's like). Use is like:

create temporary function ilike as 'com.livingsocial.hive.udf.ILike';
select city_name, count from city_counts where ilike(city_name, "%baltimore%");

first_n(group column, value column, count)

Table generating function that returns up to count rows per group column of the group and value columns.

create temporary function first_n as 'com.livingsocial.hive.udtf.FirstN';
select first_n(person_id, value, 20) as (one, two) from person_values;

This will output the first 20 rows (by person_id) of (person_id, value).


Same as regular Hive unix_timestamp but can handle "yyyy-MM-dd HH:mm:ss" as well as "yyyy-MM-dd". Use is like:

create temporary function unix_liberal_timestamp as 'com.livingsocial.hive.udf.UnixLiberalTimestamp';
select city_name, unix_liberal_timestsamp(created_at) from cities;

index_of(needle, haystack[, startIndex])

Get first index of string needle in string haystack (optionally, starting search from startIndex). Returns -1 if not found.

create temporary function index_of as 'com.livingsocial.hive.udf.IndexOf';
select email from users where index_of('@', email) > -1;

in_array(needle, haystack)

Returns true if needle (primitive) is in haystack (array of primitives) and if needle is not null. Returns false otherwise.

create temporary function in_array as 'com.livingsocial.hive.udf.InArray';
select in_array(user_id, array(1,2,3,4)) from users;


Get day of week (as integer) from date (of format "yyyy-mm-dd"). Sunday is 1, Monday 2, etc.

create temporary function dayofweek as 'com.livingsocial.hive.udf.DayOfWeek';
select dayofweek(to_date(created_at)) from src;

bin_case(long, array(names))

Get representations of bits in a bitfield (it's like the bin UDF and a long case statement - hence, bin_case). If long represents a (big endian) bit field, bin_case will generate a single column table with a row for each positive bit containing the corresponding value in names. For instance, here are some examples:

create temporary function bin_case as 'com.livingsocial.hive.udtf.BinCase';
select bin_case(1, array("foo", "bar", "baz")) as c from source;
> foo
select bin_case(2, array("foo", "bar", "baz")) as c from source;
> bar
select bin_case(3, array("foo", "bar", "baz")) as c from source;
> foo
> bar
select bin_case(4, array("foo", "bar", "baz")) as c from source;
> baz
select bin_case(5, array("foo", "bar", "baz")) as c from source;
> foo
> baz
select bin_case(7, array("foo", "bar", "baz")) as c from source;
> foo
> bar
> baz

aes_decrypt(encrypted_string, key)

AES decrypt the given string (which should be Base32 hex encoded) with the given key.

create temporary function aes_decrypt as 'com.livingsocial.hive.udf.AESDecrypt';
select aes_decrypt(credit_card_number, "textkey") from credit_cards;

This will require downloading this file from Sun and installing to /usr/java/jdk1.6.0_22/jre/lib/security (due to cryptographic export controls).

gps_distance_from(latitude1 double, longitude1 double, latitude2 double, longitude2 double [, Text options])

Calculate the distance between two gps coordinates, return result in miles (default). Options accepts a parameter of 'km' - returns result in km

create temporary function gps_distance_from as 'com.livingsocial.hive.udf.gpsDistanceFrom'
hive -e "select gps_distance_from(38, -97, 37.33181, -122.02955) from test_coordinates"
> 1365.5982379566033
hive -e "select gps_distance_from(38, -97, 37.33181, -122.02955, 'km') from test_coordinates"
> 2197.717330666032

Coordinates are entered as doubles, and a double is returned. If any of the latitude or longitude values are passed in as null, null is returned


Return the index of an element greater than or equal to all of the other elements. In case of equality earlier elements will be preferred.

create temporary function index_of_max_elem as 'com.livingsocial.hive.udf.IndexOfMaxElem';
select index_of_max_elem(array(3,5,9,2)) from some_table;
> 2

user_agent_parser(user_agent string [, options string])

Parses a user agent string into something a little more legible. By default (without the options field entered), returns a json parameter with all parsed data.

Accepts any of the following entered as a string, as user options

os, os_family, os_major, os_minor, ua, ua_family, ua_major, ua_minor, device

os and ua will return json, with _family, _major and _minor returned as well; other options will return a string.

Note: the underlying parser library is somewhat tuned to LivingSocial's interests; It includes some email clients, and reports AOL windows as AOL (as opposed to MSIE). This library builds off of Tobie's ua-parser can be dropped in if needed/desired (

create temporary function user_agent_parser as 'com.livingsocial.hive.udf.UserAgentParser';

select user_agent_parser(user_agent) from some_table;
> {user_agent: {family: "Firefox", major: "12", minor: "0", patch: null}, os: {family: "Windows", major: "7", minor: null, patch: null, patch_minor: null}, device: {family: null}}

select user_agent_parser(user_agent, 'os') from some_table;
> {family: "Windows", major: "7", minor: null, patch: null, patch_minor: null}

select user_agent_parser(user_agent, 'os_family') from some_table;
> "Windows"


Returns the current date in the form 'YYYY-MM-DD'

create temporary function curdate as 'com.livingsocial.hive.udf.Curdate';
select curdate() from some_table;
> 2012-12-26


Returns the current date and time in the form 'YYYY-MM-DD HH:mm:ss'

create temporary function curdatetime as 'com.livingsocial.hive.udf.CurDateTime';
select curdatetime() from some_table;
> 2012-12-26 13:26:25

iso_year_of_week(some_date string)

Returns the year of an ISO week number. Same as unix date's %G. Used in conjunction with week_of_year. Ensures that each week/year combination has 7 days. Accepts input in the form 'YYYY-MM-DD' and 'YYYY-MM-DD HH:mm:ss'.

create temporary function iso_year_of_week as 'com.livingsocial.hive.udf.IsoYearWeek';
select iso_year_of_week('2012-01-01')  from some_table;
> 2011

md5(string_to_md5 string)

Returns an md5 has of the string passed in Fork of datamine's md5 hash function; originally found at

create temporary function md5 as 'com.livingsocial.hive.udf.Md5';
select md5('test data') from some_table;
> eb733a00c0c9d336e65691a37ab54293

p_rank(column1, column2....)

Returns a ranking of each row within a group of rows

Forked from Edward Capriolo's branch - Wanted to fit the function into LivingSocial's Hive UDF implementation. original copyright: "Copyright 2012 m6d Media6degrees"

create temporary function p_rank as 'com.livingsocial.hive.udf.Rank';

    p_rank(category, country) rank
     FROM p_rank_demo
     category,country,sales desc) t1) t2

> movies  gb      Star Wars iv    300     1
> movies  gb      Star Wars iii   200     2
> movies  gb      spiderman       150     3
> movies  gb      Goldfinger      100     4
> movies  us      Star Wars v     300     1
> movies  us      Star Wars iii   200     2
> movies  us      Star Wars iv    150     3
> movies  us      casablanca      100     4

concat_array(delimiter string, array)

Concatenates the elements of the array separated by the delimiter. Note: This duplicates the functionality of the built in concat_ws UDF, but handles any primitive types in the array instead of only strings.

create temporary function concat_array as 'com.livingsocial.hive.udf.ConcatArray';
-- Generate a comma separated list of products in a category
select category, concat_array(',', collect_set(product)) from products group by category;

least(column1, column2.....)

returns the lowest value amongst several columns

nulls are considered to be the lowest value (which fits how the oracle function least() works).

Inspired by NexR's 'greatest' function (

create temporary function least as 'com.livingsocial.hive.udf.GenericUDFLeast';

select least('2013-05-24','2012-05-09','1004-67-83') from test limit 1
> 1004-67-83

select least(0,1,3,4,65) from test limit 1
> 0

least_non_null(column1, column2.....)

returns the lowest value amongst several columns, excluding nulls.

create temporary function least_non_null as 'com.livingsocial.hive.udf.GenericUDFLeastNonNull';

select least('2013-05-24','2012-05-09','1004-67-83',null) from test limit 1
> 1004-67-83

select least(0,1,3,4,65) from test limit 1
> 0

Code Status

Build Status

Bugs / Contact

Any bugs / request can be submited via tickets on Github.