# A simple hive demo. If you do not have a table to load from look run MakeHiveTable.py
from pyspark import SparkContext
from pyspark.sql import HiveContext
import json
import sys
if __name__ == "__main__":
if len(sys.argv) != 3:
print "Error usage: LoadHive [sparkmaster] [inputtable]"
sys.exit(-1)
master = sys.argv[1]
inputTable = sys.argv[2]
sc = SparkContext(master, "LoadHive")
hiveCtx = HiveContext(sc)
# Query hive
input = hiveCtx.hql("FROM " + inputTable + " SELECT key, value")
data = input.map(lambda x: x['key'] * x['key'])
result = data.collect()
for element in result:
print "Got data " + str(element)
sc.stop()
print "Done!"
#!/usr/bin/env python # -*- coding: utf-8 -*- ######################################################################### # File Name: loadHive.py # Author: lpqiu # mail: [email protected] # Created Time: 2014年11月30日 星期日 21时31分06秒 ######################################################################### from pyspark import SparkConf,SparkContext,StoreLevel from pyspark.sql import HiveContext, SQLContext conf = SparkConf().setMaster("local").setAppName("My App") sc = SparkContext(conf) hiveCtx = HiveContext(sc) rows = hiveCtx.hql("SELECT key,value FROM src") keys = rows.map(lambda row: row["key"]) # Parquet load example sqlCtx = SQLContext(sc) rows = sqlCtx.ParquetFile("people.parquet") names = rows.map(lambda row:row["name"])
# Createas a hive table and loads an input file into it
# For input you can use examples/src/main/resources/kv1.txt from the spark distribution
from pyspark import SparkContext
from pyspark.sql import HiveContext
import json
import sys
if __name__ == "__main__":
if len(sys.argv) != 4:
print "Error usage: LoadHive [sparkmaster] [inputFile] [inputtable]"
sys.exit(-1)
master = sys.argv[1]
inputFile = sys.argv[2]
inputTable = sys.argv[3]
sc = SparkContext(master, "LoadHive")
hiveCtx = HiveContext(sc)
# Load some data into hive
hiveCtx.hql("CREATE TABLE IF NOT EXISTS " + inputTable +
" (key INT, value STRING)")
hiveCtx.hql("LOAD DATA LOCAL INPATH '" + inputFile + "' INTO TABLE " +
inputTable)
# A simple demo for working with SparkSQL and Tweets
from pyspark import SparkContext, SparkConf
from pyspark.sql import HiveContext, Row, IntegerType
import json
import sys
if __name__ == "__main__":
inputFile = sys.argv[1]
conf = SparkConf().setAppName("SparkSQLTwitter")
sc = SparkContext()
hiveCtx = HiveContext(sc)
print "Loading tweets from " + inputFile
input = hiveCtx.jsonFile(inputFile)
input.registerTempTable("tweets")
topTweets = hiveCtx.hql("SELECT text, retweetCount FROM tweets ORDER BY retweetCount LIMIT 10")
print topTweets.collect()
topTweetText = topTweets.map(lambda row : row.text)
print topTweetText.collect()
# Make a happy person row
happyPeopleRDD = sc.parallelize([Row(name="holden", favouriteBeverage="coffee")])
happyPeopleSchemaRDD = hiveCtx.inferSchema(happyPeopleRDD)
happyPeopleSchemaRDD.registerTempTable("happy_people")
# Make a UDF to tell us how long some text is
hiveCtx.registerFunction("strLenPython", lambda x: len(x), IntegerType())
lengthSchemaRDD = hiveCtx.hql("SELECT strLenPython('text') FROM tweets LIMIT 10")
print lengthSchemaRDD.collect()
sc.stop()
# Createas a hive table and loads an input file into it
# For input you can use examples/src/main/resources/kv1.txt from the spark
# distribution
from pyspark import SparkContext
from pyspark.sql import HiveContext
import json
import sys
if __name__ == "__main__":
if len(sys.argv) != 4:
print "Error usage: LoadHive [sparkmaster] [inputFile] [inputtable]"
sys.exit(-1)
master = sys.argv[1]
inputFile = sys.argv[2]
inputTable = sys.argv[3]
sc = SparkContext(master, "LoadHive")
hiveCtx = HiveContext(sc)
# Load some data into hive
hiveCtx.hql(
"CREATE TABLE IF NOT EXISTS " +
inputTable +
" (key INT, value STRING)")
hiveCtx.hql(
"LOAD DATA LOCAL INPATH '" + inputFile + "' INTO TABLE " + inputTable)