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- scala - What is RDD in spark - Stack Overflow
RDD in relation to Spark Spark is simply an implementation of RDD RDD in relation to Hadoop The power of Hadoop reside in the fact that it let users write parallel computations without having to worry about work distribution and fault tolerance However, Hadoop is inefficient for the applications that reuse intermediate results
- Whats the difference between RDD and Dataframe in Spark?
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')
- What is the difference between spark checkpoint and persist to a disk
Another important difference is that if you persist cache an RDD, and later dependent RDD-s need to be calculated, then the persisted cached RDD content is used automatically by Spark to speed up things But if you just checkpoint the same RDD, it won't be utilized when calculating dependent RDD-s I wonder when a checkpointed RDD is used by
- python - Pyspark JSON object or file to RDD - Stack Overflow
I am trying to create an RDD which I then hope to perform operation such as map and flatmap I was advised to get the json in a jsonlines format but despite using pip to install jsonlines, I am unable to import the package in the PySpark notebook Below is what I have tried for reading in the json
- Removing duplicates from rows based on specific columns in an RDD Spark . . .
Now, you have a key-value RDD that is keyed by columns 1,3 and 4 The next step would be either a reduceByKey or groupByKey and filter This would eliminate duplicates r = m reduceByKey(lambda x,y: (x))
- Difference between DataFrame, Dataset, and RDD in Spark
Spark RDD (resilient distributed dataset): RDD is the core data abstraction API and is available since very first release of Spark (Spark 1 0) It is a lower-level API for manipulating distributed collection of data The RDD APIs exposes some extremely useful methods which can be used to get very tight control over underlying physical data
- Difference and use-cases of RDD and Pair RDD - Stack Overflow
Basically, RDD in spark is designed as each dataset in RDD is divided into logical partitions Further, we can say here each partition may be computed on different nodes of the cluster Moreover, Spark RDDs contain user-defined classes
- Why cant we create an RDD using Spark session
As RDD was main API, it was created and manipulated using context API’s For every other API,we needed to use different contexts For streaming, we needed StreamingContext, for SQL sqlContext and for hive HiveContext But as DataSet and Dataframe API’s are becoming new standard API’s Spark need an entry point build for them
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