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 has been tested with mrv1 on cdh4 and YARN on cdh5

Installation

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 http://maven.apache.org/download.cgi
git clone git://github.com/livingsocial/HiveSwarm.git
cd HiveSwarm
mvn package

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

Usage

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, ...)

Run:

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)

Run:

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)

Run:

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).

unix_liberal_timestamp(datetimestring)

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;

dayofweek(date)

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(encrypted_data, "textkey") from secure_storage;

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

index_of_max_elem(array)

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 http://github.com/p5k6/ua-parser. Tobie's ua-parser can be dropped in if needed/desired (http://github.com/tobie/ua-parser)

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"

curdate()

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

curdatetime()

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_hash string)

Returns an md5 hash of the string passed in Fork of datamine's md5 hash function; originally found at https://gist.github.com/1050002

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

sha1(string_to_hash string)

Returns the sha1 hash of the string passed in

create temporary function sha1 as 'com.livingsocial.hive.udf.Sha1';
select sha1('test data') from some_table;
> f48dd853820860816c75d54d0f584dc863327a7c

ls_hash(something_to_hash string, [some_salt string, [debug string]]

Returns a deterministic 'random' number based on the sha1 has of the passed in string and salt. This is intended to be used in place of many rand() uses. It has the benefit of being repeatable, consistent, and easily implementable by any system. An id for a row is required as the first input. An optional string salt can be passed in as the second argument. A third string can be passed in and the output will change to a string output showing internal debugging information.

This implementation can be used in other systems so the same samples can be shared by only sharing the logic and the salt. The pseudo-code logic for this is:

to_hash = something_to_hash + som