问题
Hi I am relatively new to apache spark. I wanted to understand the difference between RDD,dataframe and datasets.
For example, I am pulling data from s3 bucket.
df=spark.read.parquet("s3://output/unattributedunattributed*")
In this case when I am loading data from s3, what would be RDD? Also since RDD is immutable , I can change value for df so df couldn't be rdd.
Appreciate if someone can explain the difference between RDD,dataframe and datasets.
回答1:
df=spark.read.parquet("s3://output/unattributedunattributed*")
With this statement, you are creating a data frame.
To create RDD use
df=spark.textFile("s3://output/unattributedunattributed*")
RDD stands for Resilient Distributed Datasets. It is Read-only partition collection of records. RDD is the fundamental data structure of Spark. It allows a programmer to perform in-memory computations
In Dataframe, data organized into named columns. For example a table in a relational database. It is an immutable distributed collection of data. DataFrame in Spark allows developers to impose a structure onto a distributed collection of data, allowing higher-level abstraction.
- If you want to apply a map or filter to the whole dataset, use RDD
- If you want to work on an individual column or want to perform operations/calculations on a column then use Dataframe.
for example, if you want to replace 'A' in whole data with 'B' then RDD is useful.
rdd = rdd.map(lambda x: x.replace('A','B')
if you want to update the data type of the column, then use Dataframe.
dff = dff.withColumn("LastmodifiedTime_timestamp", col('LastmodifiedTime_time').cast('timestamp')
RDD can be converted into Dataframe and vice versa.
来源：https://stackoverflow.com/questions/57566876/whats-the-difference-between-rdd-and-dataframe-in-spark