Hive Funnel Analysis UDFs
Funnel analysis is a method for tracking user conversion rates across actions. This enables detection of actions causing high user fallout.
These Hive UDFs enables funnel analysis to be performed simply and easily on any Hive table.
Table of Contents
Requirements
Maven is required to build the funnel UDFs.
How to build
There is a provided Makefile
with all the build targets.
Build JAR
make jar
This creates a funnel.jar
in the target/
directory.
Register JAR with Hive
To use the funnel UDFs, you need to register it with Hive.
With temporary functions:
ADD JAR funnel.jar;
CREATE TEMPORARY FUNCTION funnel AS 'com.yahoo.hive.udf.funnel.Funnel';
CREATE TEMPORARY FUNCTION funnel_merge AS 'com.yahoo.hive.udf.funnel.Merge';
CREATE TEMPORARY FUNCTION funnel_conversion AS 'com.yahoo.hive.udf.funnel.Conversion';
CREATE TEMPORARY FUNCTION funnel_fallout AS 'com.yahoo.hive.udf.funnel.Fallout';
With permenant functions you need to put the JAR on HDFS, and it will be registered with a database (you have to replace DATABASE
and PATH_TO_JAR
with your values):
CREATE FUNCTION DATABASE.funnel AS 'com.yahoo.hive.udf.funnel.Funnel' USING JAR 'hdfs:///PATH_TO_JAR/funnel.jar';
CREATE FUNCTION DATABASE.funnel_merge AS 'com.yahoo.hive.udf.funnel.Merge' USING JAR 'hdfs:///PATH_TO_JAR/funnel.jar';
CREATE FUNCTION DATABASE.funnel_conversion AS 'com.yahoo.hive.udf.funnel.Conversion' USING JAR 'hdfs:///PATH_TO_JAR/funnel.jar';
CREATE FUNCTION DATABASE.funnel_fallout AS 'com.yahoo.hive.udf.funnel.Fallout' USING JAR 'hdfs:///PATH_TO_JAR/funnel.jar';
How to use
There are fout funnel UDFs provided: funnel
,
funnel_merge
, funnel_conversion
,
funnel_fallout
.
The funnel
UDF outputs an array of longs showing conversion rates
across the provided funnel steps.
The funnel_merge
UDF merges multiple arrays of longs by
adding them together.
The funnel_conversion
UDF takes a raw count funnel result and
converts it to the conversion rate.
The funnel_fallout
UDF takes a raw count funnel result and
converts it to the fallout rate.
There is no need to sort the data on timestamp, the UDF will take care of it. If there is a collision in the timestamps, it then sorts on the action column.
funnel
funnel(action_column, timestamp_column, array(funnel_1_a, funnel_1_b), array(funnel_2), ...)
- Builds a funnel report applied to the
action_column
, sorted by thetimestamp_column
. - The funnel steps are arrays of the same type as the
action
column. This allows for multiple matches to move to the next funnel.- For example, funnel_1 could be
array('register_button', 'facebook_invite_register')
. The funnel will match the first occurence of either of these actions and proceed to the next funnel. - Or, funnel_1 could just be
array('register_button')
.
- For example, funnel_1 could be
- You can have an arbitrary number of funnels.
- The
timestamp_column
can be of any comparable type (Strings, Integers, Dates, etc).
funnel_merge
funnel_merge(funnel_column)
- Merges funnels. Use with funnel UDF.
funnel_conversion
funnel_conversion(funnel_column)
- Converts the result of a
funnel_merge
to a conversion rate. Use with funnel and funnel_merge UDF. - For example, a result from
funnel_merge
could look like[245, 110, 54, 13]
. This is result is in raw counts. If we pass this throughfunnel_conversion
then it would look like[1.0, 0.44, 0.49, 0.24]
.
funnel_fallout
funnel_fallout(funnel_column)
- Converts the result of a
funnel_merge
to a fallout rate. Use with funnel and funnel_merge UDF. - For example, a result from
funnel_merge
could look like[245, 110, 54, 13]
. This is result is in raw counts. If we pass this throughfunnel_fallout
then it would look like[0.0, 0.55, 0.50, 0.75]
.
Examples
Assume a table user_data
:
action | timestamp | user_id | gender |
---|---|---|---|
signup_page | 100 | 1 | f |
confirm_button | 200 | 1 | f |
submit_button | 300 | 1 | f |
signup_page | 200 | 2 | m |
submit_button | 400 | 2 | m |
signup_page | 100 | 3 | f |
confirm_button | 200 | 3 | f |
decline | 200 | 3 | f |
... | ... | ... | ... |
Simple funnel
SELECT funnel_merge(funnel)
FROM (SELECT funnel(action, timestamp, array('signup_page', 'email_signup'),
array('confirm_button'),
array('submit_button')) AS funnel
FROM user_data
GROUP BY user_id) t1;
Result: [3, 2, 1]
Simple funnel with conversion rate
SELECT funnel_conversion(funnel_merge(funnel))
FROM (SELECT funnel(action, timestamp, array('signup_page'),
array('confirm_button'),
array('submit_button')) AS funnel
FROM user_data
GROUP BY user_id) t1;
Result: [1.0, 0.66, 0.5]
Funnel with multiple groups
SELECT gender, funnel_merge(funnel)
FROM (SELECT gender,
funnel(action, timestamp, array('signup_page'),
array('confirm_button'),
array('submit_button')) AS funnel
FROM table
GROUP BY user_id, gender) t1
GROUP BY gender;
Result: m: [1, 0, 0], f: [2, 2, 1]
Multiple parallel funnels
SELECT funnel_merge(funnel1), funnel_merge(funnel2)
FROM (SELECT funnel(action, timestamp, array('signup_page'),
array('confirm_button'),
array('submit_button')) AS funnel1
funnel(action, timestamp, array('signup_page'),
array('decline')) AS funnel2
FROM table
GROUP BY user_id) t1;
Result: [3, 2, 1] [3, 1]
Contributors
Josh Walters, [email protected]