Filtering Spark DataFrame on new column - apache-spark

Question

Context: I have a dataset too large to fit in memory I am training a Keras RNN on. I am using PySpark on an AWS EMR Cluster to train the model in batches that are small enough to be stored in memory. I was not able to implement the model as distributed using elephas and I suspect this is related to my model being stateful. I'm not entirely sure though.
The dataframe has a row for every user and days elapsed from the day of install from 0 to 29. After querying the database I do a number of operations on the dataframe:
query = """WITH max_days_elapsed AS (
SELECT user_id,
max(days_elapsed) as max_de
FROM table
GROUP BY user_id
)
SELECT table.*
FROM table
LEFT OUTER JOIN max_days_elapsed USING (user_id)
WHERE max_de = 1
AND days_elapsed < 1"""
df = read_from_db(query) #this is just a custom function to query our database
#Create features vector column
assembler = VectorAssembler(inputCols=features_list, outputCol="features")
df_vectorized = assembler.transform(df)
#Split users into train and test and assign batch number
udf_randint = udf(lambda x: np.random.randint(0, x), IntegerType())
training_users, testing_users = df_vectorized.select("user_id").distinct().randomSplit([0.8,0.2],123)
training_users = training_users.withColumn("batch_number", udf_randint(lit(N_BATCHES)))
#Create and sort train and test dataframes
train = df_vectorized.join(training_users, ["user_id"], "inner").select(["user_id", "days_elapsed","batch_number","features", "kpi1", "kpi2", "kpi3"])
train = train.sort(["user_id", "days_elapsed"])
test = df_vectorized.join(testing_users, ["user_id"], "inner").select(["user_id","days_elapsed","features", "kpi1", "kpi2", "kpi3"])
test = test.sort(["user_id", "days_elapsed"])
The problem I am having is that I cannot seem to be able to filter on batch_number without caching train. I can filter on any of the columns that are in the original dataset in our database, but not on any column I have generated in pyspark after querying the database:
This: train.filter(train["days_elapsed"] == 0).select("days_elapsed").distinct.show() returns only 0.
But, all of these return all of the batch numbers between 0 and 9 without any filtering:
train.filter(train["batch_number"] == 0).select("batch_number").distinct().show()
train.filter(train.batch_number == 0).select("batch_number").distinct().show()
train.filter("batch_number = 0").select("batch_number").distinct().show()
train.filter(col("batch_number") == 0).select("batch_number").distinct().show()
This also does not work:
train.createOrReplaceTempView("train_table")
batch_df = spark.sql("SELECT * FROM train_table WHERE batch_number = 1")
batch_df.select("batch_number").distinct().show()
All of these work if I do train.cache() first. Is that absolutely necessary or is there a way to do this without caching?

Answer 1

Spark >= 2.3 (? - depending on a progress of SPARK-22629)
It should be possible to disable certain optimization using asNondeterministic method.
Spark < 2.3
Don't use UDF to generate random numbers. First of all, to quote the docs:
The user-defined functions must be deterministic. Due to optimization, duplicate invocations may be eliminated or the function may even be invoked more times than it is present in the query.
Even if it wasn't for UDF, there are Spark subtleties, which make it almost impossible to implement this right, when processing single records.
Spark already provides rand:
Generates a random column with independent and identically distributed (i.i.d.) samples from U[0.0, 1.0].
and randn
Generates a column with independent and identically distributed (i.i.d.) samples from the standard normal distribution.
which can be used to build more complex generator functions.
Note:
There can be some other issues with your code but this makes it unacceptable from the beginning (Random numbers generation in PySpark, pyspark. Transformer that generates a random number generates always the same number).