python - How to filter based on array value in PySpark?

Question

My Schema:

|-- Canonical_URL: string (nullable = true)
 |-- Certifications: array (nullable = true)
 |    |-- element: struct (containsNull = true)
 |    |    |-- Certification_Authority: string (nullable = true)
 |    |    |-- End: string (nullable = true)
 |    |    |-- License: string (nullable = true)
 |    |    |-- Start: string (nullable = true)
 |    |    |-- Title: string (nullable = true)
 |-- CompanyId: string (nullable = true)
 |-- Country: string (nullable = true)
|-- vendorTags: array (nullable = true)
 |    |-- element: struct (containsNull = true)
 |    |    |-- score: double (nullable = true)
 |    |    |-- vendor: string (nullable = true)

I tried the below query to select nested fields from vendorTags

df3 = sqlContext.sql("select vendorTags.vendor from globalcontacts")

How can I query the nested fields in where clause like below in PySpark

df3 = sqlContext.sql("select vendorTags.vendor from globalcontacts where vendorTags.vendor = 'alpha'")

or

df3 = sqlContext.sql("select vendorTags.vendor from globalcontacts where vendorTags.score > 123.123456")

something like this..

I tried the above queries only to get the below error

df3 = sqlContext.sql("select vendorTags.vendor from globalcontacts where vendorTags.vendor = 'alpha'")

16/03/15 13:16:02 INFO ParseDriver: Parsing command: select vendorTags.vendor from globalcontacts where vendorTags.vendor = 'alpha'
16/03/15 13:16:03 INFO ParseDriver: Parse Completed
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/usr/lib/spark/python/pyspark/sql/context.py", line 583, in sql
    return DataFrame(self._ssql_ctx.sql(sqlQuery), self)
  File "/usr/lib/spark/python/lib/py4j-0.9-src.zip/py4j/java_gateway.py", line 813, in __call__
  File "/usr/lib/spark/python/pyspark/sql/utils.py", line 51, in deco
    raise AnalysisException(s.split(': ', 1)[1], stackTrace)
pyspark.sql.utils.AnalysisException: u"cannot resolve '(vendorTags.vendor = cast(alpha as double))' due to data type mismatch: differing types in '(vendorTags.vendor = cast(alpha as double))' (array<string> and double).; line 1 pos 71"

Answer 1

For equality based queries you can use array_contains:

df = sc.parallelize([(1, [1, 2, 3]), (2, [4, 5, 6])]).toDF(["k", "v"])
df.createOrReplaceTempView("df")

# With SQL
sqlContext.sql("SELECT * FROM df WHERE array_contains(v, 1)")

# With DSL
from pyspark.sql.functions import array_contains
df.where(array_contains("v", 1))

If you want to use more complex predicates you'll have to either explode or use an UDF, for example something like this:

from pyspark.sql.types import BooleanType
from pyspark.sql.functions import udf 

def exists(f):
    return udf(lambda xs: any(f(x) for x in xs), BooleanType())

df.where(exists(lambda x: x > 3)("v"))

In Spark 2.4. or later it is also possible to use higher order functions

from pyspark.sql.functions import expr

df.where(expr("""aggregate(
    transform(v, x -> x > 3),
    false, 
    (x, y) -> x or y
)"""))

or

df.where(expr("""
    exists(v, x -> x > 3)
"""))

Python wrappers should be available in 3.1 (SPARK-30681).