THIS IS A TEST INSTANCE. ALL YOUR CHANGES WILL BE LOST!!!!
NOTE: This page is deprecated and merged into Hive- GettingStarted.
User Guide
The query language specification is available at HiveQL LanguageManual. Also see, GettingStarted for setup instructions.
Supported Features
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MovieLens User Ratings
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CREATE TABLE u_data (
userid INT,
movieid INT,
rating INT,
unixtime STRING)
ROW FORMAT DELIMITED
FIELDS TERMINATED BY '\t'
STORED AS TEXTFILE;
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Apache Access Log Tables
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add jar ../build/contrib/hive_contrib.jar;
CREATE TABLE apachelog (
host STRING,
identity STRING,
user STRING,
time STRING,
request STRING,
status STRING,
size STRING,
referer STRING,
agent STRING)
ROW FORMAT SERDE 'org.apache.hadoop.hive.contrib.serde2.RegexSerDe'
WITH SERDEPROPERTIES (
"input.regex" = "([^]*) ([^]*) ([^]*) (-|\\[^\\]*\\]) ([^ \"]*|\"[^\"]*\") (-|[0-9]*) (-|[0-9]*)(?: ([^ \"]*|\"[^\"]*\") ([^ \"]*|\"[^\"]*\"))?",
"output.format.string" = "%1$s %2$s %3$s %4$s %5$s %6$s %7$s %8$s %9$s"
)
STORED AS TEXTFILE;
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Control Separated Tables
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CREATE TABLE mylog (
name STRING, language STRING, groups ARRAY<STRING>, entities MAP<INT, STRING>)
ROW FORMAT DELIMITED
FIELDS TERMINATED BY '\001'
COLLECTION ITEMS TERMINATED BY '\002'
MAP KEYS TERMINATED BY '\003'
STORED AS TEXTFILE;
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Download and extract the data:
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wget http://www.grouplens.org/system/files/ml-data.tar+0.gz
tar xvzf ml-data.tar+0.gz
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Load it in:
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LOAD DATA LOCAL INPATH 'ml-data/u.data'
OVERWRITE INTO TABLE u_data;
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MovieLens User Ratings
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SELECT COUNT(1) FROM u_data;
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Create weekday_mapper.py:
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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)])
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Use the mapper script:
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CREATE TABLE u_data_new (
userid INT,
movieid INT,
rating INT,
weekday INT)
ROW FORMAT DELIMITED
FIELDS TERMINATED BY '\t';
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(1)
FROM u_data_new
GROUP BY weekday;
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