I want to filter a Pyspark DataFrame with a SQL-like IN clause, as in
sc = SparkContext()
sqlc = SQLContext(sc)
df = sqlc.sql('SELECT * from my_df WHERE field1 IN a')
where a is the tuple (1, 2, 3). I am getting this error:
java.lang.RuntimeException: [1.67] failure: “(” expected but identifier a found
which is basically saying it was expecting something like ‘(1, 2, 3)’ instead of a. The problem is I can’t manually write the values in a as it’s extracted from another job.
How would I filter in this case?
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Answer
String you pass to SQLContext it evaluated in the scope of the SQL environment. It doesn’t capture the closure. If you want to pass a variable you’ll have to do it explicitly using string formatting:
df = sc.parallelize([(1, "foo"), (2, "x"), (3, "bar")]).toDF(("k", "v"))
df.registerTempTable("df")
sqlContext.sql("SELECT * FROM df WHERE v IN {0}".format(("foo", "bar"))).count()
## 2
Obviously this is not something you would use in a “real” SQL environment due to security considerations but it shouldn’t matter here.
In practice DataFrame DSL is a much choice when you want to create dynamic queries:
from pyspark.sql.functions import col
df.where(col("v").isin({"foo", "bar"})).count()
## 2
It is easy to build and compose and handles all details of HiveQL / Spark SQL for you.