Spark 1.3 doesn’t support broadcast joins using DataFrame. In Spark >= 1.5.0 you can use broadcast function to apply broadcast joins: from pyspark.sql.functions import broadcast data1.join(broadcast(data2), data1.id == data2.id) For older versions the only option is to convert to RDD and apply the same logic as in other languages. Roughly something like this: from pyspark.sql … Read more
You can bring the previous day column by using lag function, and add additional column that does actual day-to-day return from the two columns, but you may have to tell spark how to partition your data and/or order it to do lag, something like this: from pyspark.sql.window import Window import pyspark.sql.functions as func from pyspark.sql.functions … Read more
explode and split are SQL functions. Both operate on SQL Column. split takes a Java regular expression as a second argument. If you want to separate data on arbitrary whitespace you’ll need something like this: df = sqlContext.createDataFrame( [(‘cat \n\n elephant rat \n rat cat’, )], [‘word’] ) df.select(explode(split(col(“word”), “\s+”)).alias(“word”)).show() ## +——–+ ## | word| … Read more
There is no package called spark.implicits. With spark here it refers to SparkSession. If you are inside the REPL the session is already defined as spark so you can just type: import spark.implicits._ If you have defined your own SparkSession somewhere in your code, then adjust it accordingly: val mySpark = SparkSession .builder() .appName(“Spark SQL … Read more
Observation Spark SQL can optimize join only if join condition is based on the equality operator. This means we can consider equijoins and non-equijoins separately. Equijoin Equijoin can be implemented in a type safe manner by mapping both Datasets to (key, value) tuples, performing join based on keys, and reshaping the result: import org.apache.spark.sql.Encoder import … Read more
Because: Apache Spark is a complex framework designed to distribute processing across hundreds of nodes, while ensuring correctness and fault tolerance. Each of these properties has significant cost. Because purely in-memory in-core processing (Pandas) is orders of magnitude faster than disk and network (even local) I/O (Spark). Because parallelism (and distributed processing) add significant overhead, … Read more
There are two versions of orderBy, one that works with strings and one that works with Column objects (API). Your code is using the first version, which does not allow for changing the sort order. You need to switch to the column version and then call the desc method, e.g., myCol.desc. Now, we get into … Read more
Spark >= 2.2 Spark 2.2 introduced wholeFile multiLine option which can be used to load JSON (not JSONL) files: spark.read .option(“multiLine”, true).option(“mode”, “PERMISSIVE”) .json(“/path/to/user.json”) See: SPARK-18352 – Parse normal, multi-line JSON files (not just JSON Lines). SPARK-20980 – Rename the option wholeFile to multiLine for JSON and CSV. Spark < 2.2 Well, using JSONL formated … Read more
where documentation: Filters rows using the given condition. This is an alias for filter. filter is simply the standard Scala (and FP in general) name for such a function, and where is for people who prefer SQL.
AFAIk you need to call withColumn twice (once for each new column). But if your udf is computationally expensive, you can avoid to call it twice with storing the “complex” result in a temporary column and then “unpacking” the result e.g. using the apply method of column (which gives access to the array element). Note … Read more