Data type mismatch while transforming data in spark dataset

I created a parquet-structure from a csv file using spark:

Dataset<Row> df = park.read().format("com.databricks.spark.csv").option("inferSchema", "true")
            .option("header", "true").load("sample.csv");
df.write().parquet("sample.parquet");

I’m reading the parquet-structure and I’m trying to transform the data in a dataset:

Dataset<org.apache.spark.sql.Row> df = spark.read().parquet("sample.parquet");
df.createOrReplaceTempView("tmpview");
Dataset<Row> namesDF = spark.sql("SELECT *, md5(station_id) as hashkey FROM tmpview");

Unfortunately I get a data type mismatch error. Do I have to explicitly assign data types?

17/04/12 09:21:52 INFO SparkSqlParser: Parsing command: SELECT *, md5(station_id) as hashkey FROM tmpview Exception in thread “main” org.apache.spark.sql.AnalysisException: cannot resolve ‘md5(tmpview.station_id)’ due to data type mismatch: argument 1 requires binary type, however, ‘tmpview.station_id‘ is of int type.; line 1 pos 10; ‘Project [station_id#0, bikes_available#1, docks_available#2, time#3, md5(station_id#0) AS hashkey#16] +- SubqueryAlias tmpview, tmpview +- Relation[station_id#0,bikes_available#1,docks_available#2,time#3] parquet

Answer

Yes, as per Spark documentation, md5 function works only on binary (text/string) columns so you need to cast station_id into string before applying md5. In Spark SQL, you can chain both md5 and cast together, e.g.:

Dataset<Row> namesDF = spark.sql("SELECT *, md5(cast(station_id as string)) as hashkey FROM tmpview");

Or you can create a new column in dataframe and apply md5 on it, e.g.:

val newDf = df.withColumn("station_id_str", df.col("station_id").cast(StringType))
newDf.createOrReplaceTempView("tmpview");
Dataset<Row> namesDF = spark.sql("SELECT *, md5(station_id_str) as hashkey FROM tmpview");