Which will return the following rows (comparing the two hashing methods) based on the same input data. We will use what we can get for free in Spark which is the Murmur3. When you want to create strong hash codes you can rely on different hashing techniques from Cyclic Redundancy Checks (CRC), to the efficient Murmur Hash (v3). There are many ways to generate a hash, and the application of hashing can be used from bucketing, to graph traversal. This uses the Murmur3 Hashing algorithm, and explicit binary transformations before feeding into the base64 encoder. Returns the hex string result of SHA-2 family of hash functions (SHA-224, SHA-256, SHA-384, and SHA-512). We can look at a stronger technique for hashing. The end result of the full columnar expression is as follows. show +-+ | concat| +-+ |happy-smile| |angry-frown| +-+ Posting here as answer in case any one else runs to the same problem: import as F df2 df.withColumn ('hash', F.hash (col ('Col1'))) df2.show () Solves my problem. df.withColumn("concat", concat_ws("-",$"name",$"emotion")). It turns our there is a 'hash' function in that does the job that I needed. This is broken down as the following flow. The results of this transformation yield us a new column that is the result of base64 encoding the concatenated string values from the columns name and emotion. Hashing Stringsīase64 Encoded String Values val hashed = df.withColumn( "hash", base64( concat_ws("-", $"name", $"emotion") ) ) Next we can add a base64 encoder column to the DataFrame simply by using the withColumn function and passing in the Spark SQL Functions we want to use. The following PySpark snippet resolves the skew problem presented in our scenario using Broadcast Hash Join. The contents of which are shown using df.show(). Python3 import pyspark from pyspark.sql import SparkSession from pyspark. createDataFrame( ( Seq( Row("happy","smile", 1),Row("angry", "frown", 2)) ), schema )Īt this point you should have a very simple DataFrame that you can now apply the Spark SQL Functions to. Example 1: The following example is to see how to apply a single condition on Dataframe using the where () method. add(StructField("uuid", IntegerType, true)) val df = spark. add(StructField("emotion", StringType, true)). add(StructField("name", StringType, true)). *(:paste, then paste the code, and then cmd+D to process the code) Import the Libraries and Implicits import ._ import .functions._ import .types._ import spark.implicits._ Create a DataFrame val schema = new StructType(). With the spark-shell up and running, you can follow the next step just by running :paste in the shell to paste multiline.
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