Spark RDDs vs DataFrames vs SparkSQL

简介

Spark的 RDD、DataFrame 和 SparkSQL的性能比较。

2方面的比较

1. 单条记录的随机查找

2. aggregation聚合并且sorting后输出

使用以下Spark的三种方式来解决上面的2个问题，对比性能。

1. Using RDD’s

2. Using DataFrames

3. Using SparkSQL

数据源

• 在HDFS中3个文件中存储的9百万不同记录

• 每条记录11个字段

• 总大小 1.4 GB

实验环境

• HDP 2.4

• Hadoop version 2.7

• Spark 1.6

• HDP Sandbox

测试结果

• 原始的RDD 比 DataFrames 和 SparkSQL性能要好 

• DataFrames 和 SparkSQL 性能差不多

• 使用DataFrames 和 SparkSQL 比 RDD 操作更直观

• Jobs都是独立运行，没有其他job的干扰

2个操作

1. Random lookup against 1 order ID from 9 Million unique order ID's

2. GROUP all the different products with their total COUNTS and SORT DESCENDING by product name

代码

RDD Random Lookup

#!/usr/bin/env python
 
from time import time
from pyspark import SparkConf, SparkContext
 
conf = (SparkConf()
  .setAppName("rdd_random_lookup")
  .set("spark.executor.instances", "10")
  .set("spark.executor.cores", 2)
  .set("spark.dynamicAllocation.enabled", "false")
  .set("spark.shuffle.service.enabled", "false")
  .set("spark.executor.memory", "500MB"))
sc = SparkContext(conf = conf)
 
t0 = time()
 
path = "/data/customer_orders*"
lines = sc.textFile(path)
 
## filter where the order_id, the second field, is equal to 96922894
print lines.map(lambda line: line.split('|')).filter(lambda line: int(line[1]) == 96922894).collect()
 
tt = str(time() - t0)
print "RDD lookup performed in " + tt + " seconds"

DataFrame Random Lookup

#!/usr/bin/env python
 
from time import time
from pyspark.sql import *
from pyspark import SparkConf, SparkContext
 
conf = (SparkConf()
  .setAppName("data_frame_random_lookup")
  .set("spark.executor.instances", "10")
  .set("spark.executor.cores", 2)
  .set("spark.dynamicAllocation.enabled", "false")
  .set("spark.shuffle.service.enabled", "false")
  .set("spark.executor.memory", "500MB"))
sc = SparkContext(conf = conf)
 
sqlContext = SQLContext(sc)
 
t0 = time()
 
path = "/data/customer_orders*"
lines = sc.textFile(path)
 
## create data frame
orders_df = sqlContext.createDataFrame( \
lines.map(lambda l: l.split("|")) \
.map(lambda p: Row(cust_id=int(p[0]), order_id=int(p[1]), email_hash=p[2], ssn_hash=p[3], product_id=int(p[4]), product_desc=p[5], \
country=p[6], state=p[7], shipping_carrier=p[8], shipping_type=p[9], shipping_class=p[10]  ) ) )
 
## filter where the order_id, the second field, is equal to 96922894
orders_df.where(orders_df['order_id'] == 96922894).show()
 
tt = str(time() - t0)
print "DataFrame performed in " + tt + " seconds"

SparkSQL Random Lookup

#!/usr/bin/env python
 
from time import time
from pyspark.sql import *
from pyspark import SparkConf, SparkContext
 
conf = (SparkConf()
  .setAppName("spark_sql_random_lookup")
  .set("spark.executor.instances", "10")
  .set("spark.executor.cores", 2)
  .set("spark.dynamicAllocation.enabled", "false")
  .set("spark.shuffle.service.enabled", "false")
  .set("spark.executor.memory", "500MB"))
sc = SparkContext(conf = conf)
 
sqlContext = SQLContext(sc)
 
t0 = time()
 
path = "/data/customer_orders*"
lines = sc.textFile(path)
 
## create data frame
orders_df = sqlContext.createDataFrame( \
lines.map(lambda l: l.split("|")) \
.map(lambda p: Row(cust_id=int(p[0]), order_id=int(p[1]), email_hash=p[2], ssn_hash=p[3], product_id=int(p[4]), product_desc=p[5], \
country=p[6], state=p[7], shipping_carrier=p[8], shipping_type=p[9], shipping_class=p[10]  ) ) )
 
## register data frame as a temporary table
orders_df.registerTempTable("orders")
 
## filter where the customer_id, the first field, is equal to 96922894
print sqlContext.sql("SELECT * FROM orders where order_id = 96922894").collect()
 
tt = str(time() - t0)
print "SparkSQL performed in " + tt + " seconds"

RDD with GroupBy, Count, and Sort Descending

#!/usr/bin/env python
 
from time import time
from pyspark import SparkConf, SparkContext
 
conf = (SparkConf()
  .setAppName("rdd_aggregation_and_sort")
  .set("spark.executor.instances", "10")
  .set("spark.executor.cores", 2)
  .set("spark.dynamicAllocation.enabled", "false")
  .set("spark.shuffle.service.enabled", "false")
  .set("spark.executor.memory", "500MB"))
sc = SparkContext(conf = conf)
 
t0 = time()
 
path = "/data/customer_orders*"
lines = sc.textFile(path)
 
counts = lines.map(lambda line: line.split('|')) \
.map(lambda x: (x[5], 1)) \
.reduceByKey(lambda a, b: a + b) \
.map(lambda x:(x[1],x[0])) \
.sortByKey(ascending=False)
 
for x in counts.collect():
  print x[1] + '\t' + str(x[0])
 
tt = str(time() - t0)
print "RDD GroupBy performed in " + tt + " seconds"

DataFrame with GroupBy, Count, and Sort Descending

#!/usr/bin/env python
 
from time import time
from pyspark.sql import *
from pyspark import SparkConf, SparkContext
 
conf = (SparkConf()
  .setAppName("data_frame_aggregation_and_sort")
  .set("spark.executor.instances", "10")
  .set("spark.executor.cores", 2)
  .set("spark.dynamicAllocation.enabled", "false")
  .set("spark.shuffle.service.enabled", "false")
  .set("spark.executor.memory", "500MB"))
sc = SparkContext(conf = conf)
 
sqlContext = SQLContext(sc)
 
t0 = time()
 
path = "/data/customer_orders*"
lines = sc.textFile(path)
 
## create data frame
orders_df = sqlContext.createDataFrame( \
lines.map(lambda l: l.split("|")) \
.map(lambda p: Row(cust_id=int(p[0]), order_id=int(p[1]), email_hash=p[2], ssn_hash=p[3], product_id=int(p[4]), product_desc=p[5], \
country=p[6], state=p[7], shipping_carrier=p[8], shipping_type=p[9], shipping_class=p[10]  ) ) )
 
results = orders_df.groupBy(orders_df['product_desc']).count().sort("count",ascending=False)
 
for x in results.collect():
  print x
 
tt = str(time() - t0)
print "DataFrame performed in " + tt + " seconds"

SparkSQL with GroupBy, Count, and Sort Descending

#!/usr/bin/env python
 
from time import time
from pyspark.sql import *
from pyspark import SparkConf, SparkContext
 
conf = (SparkConf()
  .setAppName("spark_sql_aggregation_and_sort")
  .set("spark.executor.instances", "10")
  .set("spark.executor.cores", 2)
  .set("spark.dynamicAllocation.enabled", "false")
  .set("spark.shuffle.service.enabled", "false")
  .set("spark.executor.memory", "500MB"))
sc = SparkContext(conf = conf)
 
sqlContext = SQLContext(sc)
 
t0 = time()
 
path = "/data/customer_orders*"
lines = sc.textFile(path)
 
## create data frame
orders_df = sqlContext.createDataFrame(lines.map(lambda l: l.split("|")) \
.map(lambda r: Row(product=r[5])))
 
## register data frame as a temporary table
orders_df.registerTempTable("orders")
 
results = sqlContext.sql("SELECT product, count(*) AS total_count FROM orders GROUP BY product ORDER BY total_count DESC")
 
for x in results.collect():
  print x
 
tt = str(time() - t0)
print "SparkSQL performed in " + tt + " seconds"

 

原文：https://community.hortonworks.com/articles/42027/rdd-vs-dataframe-vs-sparksql.html