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hbaseread.py
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hbaseread.py
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# -*- coding: utf-8 -*-
from __future__ import print_function
import json
from jieba import analyse
from pyspark import SparkContext
import sys
reload(sys)
sys.setdefaultencoding('utf-8')
"""
author:Kira(Chenghao Guo)
根据时间筛选top k
Create test data in HBase first:
hbase(main):016:0> create 'test', 'f1'
0 row(s) in 1.0430 seconds
hbase(main):017:0> put 'test', 'row1', 'f1:a', 'value1'
0 row(s) in 0.0130 seconds
hbase(main):018:0> put 'test', 'row1', 'f1:b', 'value2'
0 row(s) in 0.0030 seconds
hbase(main):019:0> put 'test', 'row2', 'f1', 'value3'
0 row(s) in 0.0050 seconds
hbase(main):020:0> put 'test', 'row3', 'f1', 'value4'
0 row(s) in 0.0110 seconds
hbase(main):021:0> scan 'test'
ROW COLUMN+CELL
row1 column=f1:a, timestamp=1401883411986, value=value1
row1 column=f1:b, timestamp=1401883415212, value=value2
row2 column=f1:, timestamp=1401883417858, value=value3
row3 column=f1:, timestamp=1401883420805, value=value4
4 row(s) in 0.0240 seconds
"""
def wordcut(v):
try:
x=eval("'%s'"%v['value'])
except Exception,ex:
x='invalid'
# seglist=jieba.cut(x)
seglist=analyse.extract_tags(x,10)
myvalue='|'.join(seglist)
return myvalue
def content_analyse(v):
try:
x=eval("'%s'"%v['value'])
except Exception,ex:
x='invalid'
# seglist=jieba.cut(x)
seglist=analyse.extract_tags(x,10)
myvalue='|'.join(seglist)
return myvalue
def inverted(v):
url=v[0]
return ((word,url) for word in v[1].split('|'))
def ridoff(ids):
news_ids=list(set(ids))
# news_ids.sort(ids.index)
return news_ids
def hbaseput(sc,host,table,args): #单独插入性能比较差，并行插入
'''
./bin/spark-submit --driver-class-path /path/to/example/jar \
/path/to/examples/hbase_outputformat.py <args>
Assumes you have created <table> with column family <family> in HBase
running on <host> already
'''
conf = {"hbase.zookeeper.quorum": host,
"hbase.mapred.outputtable": table,
"mapreduce.outputformat.class": "org.apache.hadoop.hbase.mapreduce.TableOutputFormat",
"mapreduce.job.output.key.class": "org.apache.hadoop.hbase.io.ImmutableBytesWritable",
"mapreduce.job.output.value.class": "org.apache.hadoop.io.Writable"}
keyConv = "org.apache.spark.examples.pythonconverters.StringToImmutableBytesWritableConverter"
valueConv = "org.apache.spark.examples.pythonconverters.StringListToPutConverter"
sc.parallelize([args]).map(lambda x: (x[0], x)).saveAsNewAPIHadoopDataset(
conf=conf,
keyConverter=keyConv,
valueConverter=valueConv)
if __name__ == "__main__":
if len(sys.argv) != 3:
print("""
Usage: hbase_inputformat <host> <table>
Run with example jar:
./bin/spark-submit --driver-class-path /path/to/example/jar \
/path/to/examples/hbase_inputformat.py <host> <table> [<znode>]
Assumes you have some data in HBase already, running on <host>, in <table>
optionally, you can specify parent znode for your hbase cluster - <znode>
""", file=sys.stderr)
exit(-1)
host = sys.argv[1]
table = sys.argv[2]
# outputdir=sys.argv[3]
sc = SparkContext(appName="HBaseInputFormat")
# sc.addJar('/home/scidb/spark-1.5.2/lib/spark-examples-1.5.2-hadoop2.6.0.jar')
conf = {"hbase.zookeeper.quorum": host, "hbase.mapreduce.inputtable": table}
if len(sys.argv) > 3:
conf = {"hbase.zookeeper.quorum": host, "zookeeper.znode.parent": sys.argv[3],
"hbase.mapreduce.inputtable": table}
keyConv = "org.apache.spark.examples.pythonconverters.ImmutableBytesWritableToStringConverter"
valueConv = "org.apache.spark.examples.pythonconverters.HBaseResultToStringConverter"
hbase_rdd = sc.newAPIHadoopRDD("org.apache.hadoop.hbase.mapreduce.TableInputFormat","org.apache.hadoop.hbase.io.ImmutableBytesWritable","org.apache.hadoop.hbase.client.Result",keyConverter=keyConv,valueConverter=valueConv,conf=conf)
hbase_rdd = hbase_rdd.flatMapValues(lambda v: v.split("\n")).mapValues(json.loads)
hbase_rdd_title=hbase_rdd.filter(lambda keyValue: keyValue[1]['qualifier']=='title' and keyValue[1]['value']!=None)
hbase_rdd_title=hbase_rdd_title.mapValues(wordcut) #分析title中所有的关键词，title的权重更加重一些
hbase_rdd_content=hbase_rdd.filter(lambda keyValue: keyValue[1]['qualifier']=='content' and keyValue[1]['value']!=None)
hbase_rdd_content=hbase_rdd_content.mapValues(content_analyse) #按照tf-idf分析去除不相干的关键词以及得到top k的词
# tags=jieba.analyse.extract_tags(content,top_num)
'''
|著名|导演|郭宝昌|最新|执导|的|中国|首部|历史|谋略|情节剧|《|谋圣|鬼谷子|》|正在|浙江省|象山|影视城|热拍|。|郭宝昌|出|“|宅门|”|后|首次|玩|“|谋略|”|，|让|这部|剧|深受|观众|期待|，|他|表示|《|谋圣|鬼谷子|》|要|打|造成|中国|版|《|权力|的|游戏|》|。|
'''
hbase_rdd_new=hbase_rdd_title.union(hbase_rdd_content)
#hbase_rdd_title=hbase_rdd_title.flatMap(inverted)
hbase_rdd_new=hbase_rdd_new.flatMap(inverted).groupByKey()
#list(set(myList)) 对list去重，一行里面包括多个url并rank
hbase_rdd_new=hbase_rdd_new.filter(lambda keyValue:len(keyValue[0])>1 and len(keyValue[0])<=4) #过滤太短的关键词
#rank策略 content基于tfidf后
# hbase_rdd_new=hbase_rdd_new.mapValues(lambda v: list(set(v))).mapValues(lambda v: "|".join(v))
hbase_rdd_new=hbase_rdd_new.mapValues(ridoff).mapValues(lambda v: "|".join(v))
# sc.union(rdd1, rdd2)
# output = hbase_rdd_new.collect()
# for (k, v) in output:
# for url in v:
# if len(k)>1:
# hbaseput(sc,'ubuntu1','test3',[k,'f','index',url])
# print(k+':'+",".join(v)) #记得删除重复的url
# if v['qualifier']=='content':
# print(eval("'%s'"%v['value']))
# wordRDD=tc.flatMap(lambda x:jieba.cut(x))
# wordFreRDD=wordRDD.map(lambda x:(x,1))
# counts=wordFreRDD.reduceByKey(add)
# tags=jieba.analyse.extract_tags(content,top_num)
#hbase_outputformat <host> test row1 f q1 value1
host='ubuntu1'
table='newsindex'
confout = {"hbase.zookeeper.quorum": host,
"hbase.mapred.outputtable": table,
"mapreduce.outputformat.class": "org.apache.hadoop.hbase.mapreduce.TableOutputFormat",
"mapreduce.job.output.key.class": "org.apache.hadoop.hbase.io.ImmutableBytesWritable",
"mapreduce.job.output.value.class": "org.apache.hadoop.io.Writable"}
keyConvout = "org.apache.spark.examples.pythonconverters.StringToImmutableBytesWritableConverter"
valueConvout = "org.apache.spark.examples.pythonconverters.StringListToPutConverter"
#rowid的设计是唯一的，但内容不唯一
hbase_rdd_new.map(lambda x: [x[0],'f','index',x[1]]).map(lambda x: (x[0], x)).saveAsNewAPIHadoopDataset(
conf=confout,
keyConverter=keyConvout,
valueConverter=valueConvout)
sc.stop()
#处理value的内容
'''
result = pairs.filter(lambda keyValue: len(keyValue[1]) < 20)
nums = sc.parallelize([1, 2, 3, 4])
squared = nums.map(lambda x: x * x).collect()
for num in squared:
print "%i " % (num)
pairs = lines.map(lambda x: (x.split(" ")[0], x))
'''
#print((k, v))
'''
(u'http://www.chinanews.com/yl/2015/12-13/7668707.shtml', {u'qualifier': u'title', u'timestamp': u'1449980290800',
u'value': u'\\xE9\\xA6\\x99\\xE6\\xB8\\xAF\\xE6\\xBC\\x94\\xE5\\x91\\x98\\xE6\\x9E\\x97\\xE5\\xAD\\x90\\xE8\\x81\\xAA\\xE5\\xBD\\x93\\xE7\\x88\\xB8\\xE7\\x88\\xB8 \\xE8\\xA2\\xAB\\xE5\\x84\\xBF\\xE5\\xAD\\x90\\xE8\\x84\\x9A\\xE8\\xB8\\xA2\\xE6\\x84\\x9F\\xE5\\x8A\\xA8\\xE5\\x88\\xB0\\xE5\\x93\\xAD(\\xE5\\x9B\\xBE)', u'columnFamily': u'f', u'type': u'Put', u'row': u'http://www.chinanews.com/yl/2015/12-13/7668707.shtml'})
u'row1', {u'qualifier': u'a', u'timestamp': u'1450598363113', u'value': u'value1', u'columnFamily': u'f1', u'type': u'Put', u'row': u'row1'}
(u'row1', {u'qualifier': u'b', u'timestamp': u'1450598369239', u'value': u'value2', u'columnFamily': u'f1', u'type': u'Put', u'row': u'row1'})
(u'row2', {u'qualifier': u'', u'timestamp': u'1450598376945', u'value': u'value3', u'columnFamily': u'f1', u'type': u'Put', u'row': u'row2'})
(u'row3', {u'qualifier': u'', u'timestamp': u'1450598382736', u'value': u'value4', u'columnFamily': u'f1', u'type': u'Put', u'row': u'row3'})
'''