So the actual problem here is relatively simple. Each subprocess in Python inherits its state from its parent: len(set(sc.parallelize(range(4), 4).map(lambda _: random.getstate()).collect())) # 1 Since parent state has no reason to change in this particular scenario and workers have a limited lifespan, state of every child will be exactly the same on each run.
If you built a spark application, you need to use spark-submit to run the application The code can be written either in python/scala The mode can be either local/cluster If you just want to test/run few individual commands, you can use the shell provided by spark pyspark (for spark in python) spark-shell (for spark in … Read more
EDIT This creates a dataframe with one row containing an array of consecutive dates: from pyspark.sql.functions import sequence, to_date, explode, col spark.sql(“SELECT sequence(to_date(‘2018-01-01’), to_date(‘2018-03-01’), interval 1 month) as date”) +——————————————+ | date | +——————————————+ | [“2018-01-01″,”2018-02-01″,”2018-03-01″] | +——————————————+ You can use the explode function to “pivot” this array into rows: spark.sql(“SELECT sequence(to_date(‘2018-01-01’), to_date(‘2018-03-01’), interval 1 … Read more
You can use also use a function from sql package. It will generate a unique id, however it will not be sequential as it depends on the number of partitions. I believe it is available in Spark 1.5 + from pyspark.sql.functions import monotonicallyIncreasingId # This will return a new DF with all the columns + … Read more
Using spark to read in a CSV file to pandas is quite a roundabout method for achieving the end goal of reading a CSV file into memory. It seems like you might be misunderstanding the use cases of the technologies in play here. Spark is for distributed computing (though it can be used locally). It’s … Read more
It is not possible to create multiple top level columns from a single UDF call but you can create a new struct. It requires an UDF with specified returnType: from pyspark.sql.functions import udf from pyspark.sql.types import StructType, StructField, FloatType schema = StructType([ StructField(“foo”, FloatType(), False), StructField(“bar”, FloatType(), False) ]) def udf_test(n): return (n / 2, … Read more
Mariusz answer didn’t really help me. So if you like me found this because it’s the only result on google and you’re new to pyspark (and spark in general), here’s what worked for me. In my case I was getting that error because I was trying to execute pyspark code before the pyspark environment had … Read more
I had the same problem as you, and then I made the following changes: set PYSPARK_PYTHON as environment variable to point to python.exe in Edit Configurations of Pycharm, here is my example: PYSPARK_PYTHON = D:\Anaconda3\python.exe SPARK_HOME = D:\spark-1.6.3-bin-hadoop2.6 PYTHONUNBUFFERED = 1
Unseen label is a generic message which doesn’t correspond to a specific column. Most likely problem is with a following stage: StringIndexer(inputCol=”lang”, outputCol=”lang_idx”) with pl-PL present in train(“lang”) and not present in test(“lang”). You can correct it using setHandleInvalid with skip: from pyspark.ml.feature import StringIndexer train = sc.parallelize([(1, “foo”), (2, “bar”)]).toDF([“k”, “v”]) test = sc.parallelize([(3, … Read more
It is possible although it is a bit involved. We can use Py4j callback mechanism to pass message from a SparkListener. First lets create a Scala package with all required classes. Directory structure: . ├── build.sbt └── src └── main └── scala └── net └── zero323 └── spark └── examples └── listener ├── Listener.scala ├── … Read more