STREAMING HIVE流过滤 官网例子 注意中间用的py脚本

Simple Example Use Cases

MovieLens User Ratings

First, create a table with tab-delimited text file format:

CREATE TABLE u_data (
  userid INT,
  movieid INT,
  rating INT,
  unixtime STRING)
ROW FORMAT DELIMITED
FIELDS TERMINATED BY '\t'
STORED AS TEXTFILE;

Then, download the data files from MovieLens 100k on the GroupLens datasets page (which also has a README.txt file and index of unzipped files):

wget http://files.grouplens.org/datasets/movielens/ml-100k.zip

or:

curl --remote-name http://files.grouplens.org/datasets/movielens/ml-100k.zip

Note:  If the link to GroupLens datasets does not work, please report it on HIVE-5341 or send a message to the user@hive.apache.org mailing list.

Unzip the data files:

unzip ml-100k.zip

And load u.data into the table that was just created:

LOAD DATA LOCAL INPATH '<path>/u.data'
OVERWRITE INTO TABLE u_data;

Count the number of rows in table u_data:

SELECT COUNT(*) FROM u_data;

Note that for older versions of Hive which don't include HIVE-287, you'll need to use COUNT(1) in place of COUNT(*).

Now we can do some complex data analysis on the table u_data:

Create weekday_mapper.py:

import sys
import datetime

for line in sys.stdin:
  line = line.strip()
  userid, movieid, rating, unixtime = line.split('\t')
  weekday = datetime.datetime.fromtimestamp(float(unixtime)).isoweekday()
  print '\t'.join([userid, movieid, rating, str(weekday)])

https://cwiki.apache.org/confluence/display/Hive/GettingStarted#GettingStarted-DDLOperations

 

Use the mapper script:

CREATE TABLE u_data_new (
  userid INT,
  movieid INT,
  rating INT,
  weekday INT)
ROW FORMAT DELIMITED
FIELDS TERMINATED BY '\t';

add FILE weekday_mapper.py;

INSERT OVERWRITE TABLE u_data_new
SELECT
  TRANSFORM (userid, movieid, rating, unixtime)
  USING 'python weekday_mapper.py'
  AS (userid, movieid, rating, weekday)
FROM u_data;

SELECT weekday, COUNT(*)
FROM u_data_new
GROUP BY weekday;