我最近使用spark 1.5.1来处理hadoop数据。但是,我对spark的经验不太适合处理动作操作的缓慢性(例如,.count(),.collect())。我的任务可以描述如下:
我有这样一个Dataframe:
----------------------------
trans item_code item_qty
----------------------------
001 A 2
001 B 3
002 A 4
002 B 6
002 C 10
003 D 1
----------------------------
我需要找到两个项目的关联规则,例如,a中的一个将导致b中的一个半,置信度为0.8。所需的结果Dataframe如下:
----------------------------
item1 item2 conf coef
----------------------------
A B 0.8 1.5
B A 1.0 0.67
A C 0.7 2.5
----------------------------
我的方法是先使用fp-growth生成频繁项目集,然后过滤一个项目的项目集和两个项目的项目集。之后我可以计算出一个项目的置信度,从而得出另一个项目。例如,有(itemset=[a],support=0.4),(itemset=[b],support=0.2),(itemset=[a,b],support=0.2),我可以生成关联规则:(rule=(a->b),confidence=0.5),(rule=(b->a),confidence=1.0)。但是,当我将一项频繁项集广播为dictionary时,.collectasmap的操作非常慢。我试过使用。加入,速度更慢。我甚至需要等上几个小时才能看到rdd.count()。我知道我们应该避免在spark中使用任何动作操作,但有时这是不可避免的。所以我很好奇,当我们面对动作操作时,提高速度的关键是什么。
我的代码在这里:
# !/usr/bin/python
from pyspark import SparkContext,HiveContext
from pyspark.mllib.fpm import FPGrowth
import time
# read raw data from database
def read_data():
sql="""select t.orderno_nosplit,
t.prod_code,
t.item_code,
sum(t.item_qty)
as item_qty
from ioc_fdm.fdm_dwr_ioc_fcs_pk_spu_item_f_chain t
group by t.prod_code, t.orderno_nosplit,t.item_code """
data=sql_context.sql(sql)
return data.cache()
# calculate quantity coefficient of two items
def qty_coef(item1,item2):
sql =""" select t1.item, t1.qty from table t1
where t1.trans in
(select t2.trans from spu_table t2 where t2.item ='%s'
and
(select t3.trans from spu_table t3 where t3.item = '%s' """ % (item1,item2)
df=sql_context.sql(sql)
qty_item1=df.filter(df.item_code==item1).agg({"item_qty":"sum"}).first()[0]
qty_item2=df.filter(df.item_code==item2).agg({"item_qty":"sum"}).first()[0]
coef=float(qty_item2)/qty_item1
return coef
def train(prod):
spu=total_spu.filter(total_spu.prod_code == prod)
print 'data length',spu.count(),time.strftime("%H:%M:%S")
supp=0.1
conf=0.7
sql_context.registerDataFrameAsTable(spu,'spu_table')
sql_context.cacheTable('spu_table')
print 'table register over', time.strftime("%H:%M:%S")
trans_sets=spu.rdd.repartition(32).map(lambda x:(x[0],x[2])).groupByKey().mapvalues(list).values().cache()
print 'trans group over',time.strftime("%H:%M:%S")
model=FPGrowth.train(trans_sets,supp,10)
print 'model train over',time.strftime("%H:%M:%S")
model_f1=model.freqItemsets().filter(lambda x: len(x[0]==1))
model_f2=model.freqItemsets().filter(lambda x: len(x[0]==2))
#register model_f1 as dictionary
model_f1_tuple=model_f1.map(lambda (U,V):(tuple(U)[0],V))
model_f1Map=model_f1_tuple.collectAsMap()
#convert model_f1Map to broadcast
bc_model=sc.broadcast(model_f1Map)
#generate association rules
model_f2_conf=model_f2.map(lambda x:(x[0][0],x[0][1],float(x[1])/bc_model.value[x[0][0]],float(x[1]/bc_model.value[x[0][1]])))
print 'conf calculation over',time.strftime("%H:%M:%S")
model_f2_conf_flt=model_f2_conf.flatMap(lambda x: (x[0],x[1]))
#filter the association rules by confidence threshold
model_f2_conf_flt_ftr=model_f2_conf_flt.filter(lambda x: x[2]>=conf)
#calculate the quantity coefficient for the filtered association rules
#since we cannot use nested sql operations in rdd, I have to collect the rules to list first
asso_list=model_f2_conf_flt_ftr.map(lambda x: list(x)).collect()
print 'coef calculation over',time.strftime("%H:%M:%S")
for row in asso_list:
row.append(qty_coef(row[0],row[1]))
#rewrite the list to dataframe
asso_df=sql_context.createDataFrame(asso_list,['item1','item2','conf','coef'])
sql_context.clearCache()
path = "hdfs:/user/hive/wilber/%s"%(prod)
asso_df.write.mode('overwrite').parquet(path)
if __name__ == '__main__':
sc = SparkContext()
sql_context=HiveContext(sc)
prod_list=sc.textFile('hdfs:/user/hive/wilber/prod_list').collect()
total_spu=read_data()
print 'spu read over',time.strftime("%H:%M:%S")
for prod in list(prod_list):
print 'prod',prod
train(prod)
暂无答案!
目前还没有任何答案,快来回答吧!