SparkSession has a number of createDataFrame methods that create a DataFrame given an RDD. I imagine one of these will work for your context. For example: Creates a DataFrame from an RDD containing Rows using the given schema.
You need to make sure your pandas dataframe columns are appropriate for the type spark is inferring. If your pandas dataframe lists something like: And you’re getting that error try: Now, make sure .astype(str) is actually the type you want those columns to be. Basically, when the underlying Java code tries to infer the type from an … Read more
By the way, if you use PyCharm, you could add PYSPARK_PYTHON and PYSPARK_DRIVER_PYTHON to run/debug configurations per image below
mapValues is only applicable for PairRDDs, meaning RDDs of the form RDD[(A, B)]. In that case, mapValues operates on the value only (the second part of the tuple), while map operates on the entire record (tuple of key and value). In other words, given f: B => C and rdd: RDD[(A, B)], these two are identical (almost – see comment at the bottom): The latter is simply shorter … Read more
RDDs extend the Serialisable interface, so this is not what’s causing your task to fail. Now this doesn’t mean that you can serialise an RDD with Spark and avoid NotSerializableException Spark is a distributed computing engine and its main abstraction is a resilient distributed dataset (RDD), which can be viewed as a distributed collection. Basically, RDD’s elements are … Read more
Where is the sparkContext object defined, is it inside the main function? I too faced the same problem, the mistake which i did was i initiated the sparkContext outside the main function and inside the class. When I initiated it inside the main function, it worked fine.
Here is an example of the difference, as a spark-shell session: First, some data – two lines of text: Now, map transforms an RDD of length N into another RDD of length N. For example, it maps from two lines into two line-lengths: But flatMap (loosely speaking) transforms an RDD of length N into a collection of N collections, then flattens … Read more
Is just the max value .The goal is to calculate OVERHEAD as a percentage of real executor memory, as used by RDDs and DataFrames controls the executor heap size, but JVMs can also use some memory off heap, for example for interned Strings and direct byte buffers. The value of the spark.yarn.executor.memoryOverhead property is added to the … Read more
You need to specify the path of libraries used when compiling your Scala code. This is usually not done manually, but using a build tool such as Maven or sbt. You can find a minimal sbt setup at http://spark.apache.org/docs/1.2.0/quick-start.html#self-contained-applications
You should use & / | operators and be careful about operator precedence (== has lower precedence than bitwise AND and OR):