How can we JOIN two Spark SQL dataframes using a SQL-esque "LIKE" criterion?

Question

We are using the PySpark libraries interfacing with Spark 1.3.1.

We have two dataframes, documents_df := {document_id, document_text} and keywords_df := {keyword}. We would like to JOIN the two dataframes and return a resulting dataframe with {document_id, keyword} pairs, using the criteria that the keyword_df.keyword appears in the document_df.document_text string.

In PostgreSQL, for example, we could achieve this using an ON clause of the form:

document_df.document_text ilike '%' || keyword_df.keyword || '%'

In PySpark however, I cannot get any form of join syntax to work. Has anybody achieved something like this before?

With kind regards,

Will

Answer 1

It is possible in a two different ways but generally speaking not recommended. First lets create a dummy data:

from pyspark.sql import Row

document_row = Row("document_id", "document_text")
keyword_row = Row("keyword") 

documents_df = sc.parallelize([
    document_row(1L, "apache spark is the best"),
    document_row(2L, "erlang rocks"),
    document_row(3L, "but haskell is better")
]).toDF()

keywords_df = sc.parallelize([
    keyword_row("erlang"),
    keyword_row("haskell"),
    keyword_row("spark")
]).toDF()

1. Hive UDFs

   documents_df.registerTempTable("documents")
   keywords_df.registerTempTable("keywords")
   
   query = """SELECT document_id, keyword
       FROM documents JOIN keywords
       ON document_text LIKE CONCAT('%', keyword, '%')"""
   
   like_with_hive_udf = sqlContext.sql(query)
   like_with_hive_udf.show()
   
   ## +-----------+-------+
   ## |document_id|keyword|
   ## +-----------+-------+
   ## |          1|  spark|
   ## |          2| erlang|
   ## |          3|haskell|
   ## +-----------+-------+
   

2. Python UDF

   from pyspark.sql.functions import udf, col 
   from pyspark.sql.types import BooleanType
   
   # Of you can replace `in` with a regular expression
   contains = udf(lambda s, q: q in s, BooleanType())
   
   like_with_python_udf = (documents_df.join(keywords_df)
       .where(contains(col("document_text"), col("keyword")))
       .select(col("document_id"), col("keyword")))
   like_with_python_udf.show()
   
   ## +-----------+-------+
   ## |document_id|keyword|
   ## +-----------+-------+
   ## |          1|  spark|
   ## |          2| erlang|
   ## |          3|haskell|
   ## +-----------+-------+
   

Why not recommended? Because in both cases it requires a Cartesian product:

like_with_hive_udf.explain()

## TungstenProject [document_id#2L,keyword#4]
##  Filter document_text#3 LIKE concat(%,keyword#4,%)
##   CartesianProduct
##    Scan PhysicalRDD[document_id#2L,document_text#3]
##    Scan PhysicalRDD[keyword#4]

like_with_python_udf.explain()

## TungstenProject [document_id#2L,keyword#4]
##  Filter pythonUDF#13
##   !BatchPythonEvaluation PythonUDF#<lambda>(document_text#3,keyword#4), ...
##    CartesianProduct
##     Scan PhysicalRDD[document_id#2L,document_text#3]
##     Scan PhysicalRDD[keyword#4]

There are other ways to achieve a similar effect without a full Cartesian.

1. Join on tokenized document - useful if keywords list is to large to be handled in a memory of a single machine

   from pyspark.ml.feature import Tokenizer
   from pyspark.sql.functions import explode
   
   tokenizer = Tokenizer(inputCol="document_text", outputCol="words")
   
   tokenized = (tokenizer.transform(documents_df)
       .select(col("document_id"), explode(col("words")).alias("token")))
   
   like_with_tokenizer = (tokenized
       .join(keywords_df, col("token") == col("keyword"))
       .drop("token"))
   
   like_with_tokenizer.show()
   
   ## +-----------+-------+
   ## |document_id|keyword|
   ## +-----------+-------+
   ## |          3|haskell|
   ## |          1|  spark|
   ## |          2| erlang|
   ## +-----------+-------+
   

   This requires shuffle but not Cartesian:

   like_with_tokenizer.explain()
   
   ## TungstenProject [document_id#2L,keyword#4]
   ##  SortMergeJoin [token#29], [keyword#4]
   ##   TungstenSort [token#29 ASC], false, 0
   ##    TungstenExchange hashpartitioning(token#29)
   ##     TungstenProject [document_id#2L,token#29]
   ##      !Generate explode(words#27), true, false, [document_id#2L, ...
   ##       ConvertToSafe
   ##        TungstenProject [document_id#2L,UDF(document_text#3) AS words#27]
   ##         Scan PhysicalRDD[document_id#2L,document_text#3]
   ##   TungstenSort [keyword#4 ASC], false, 0
   ##    TungstenExchange hashpartitioning(keyword#4)
   ##     ConvertToUnsafe
   ##      Scan PhysicalRDD[keyword#4]
   

2. Python UDF and broadcast variable - if keywords list is relatively small

   from pyspark.sql.types import ArrayType, StringType
   
   keywords = sc.broadcast(set(
       keywords_df.map(lambda row: row[0]).collect()))
   
   bd_contains = udf(
       lambda s: list(set(s.split()) & keywords.value), 
       ArrayType(StringType()))
   
   
   like_with_bd = (documents_df.select(
       col("document_id"), 
       explode(bd_contains(col("document_text"))).alias("keyword")))
   
   like_with_bd.show()
   
   ## +-----------+-------+
   ## |document_id|keyword|
   ## +-----------+-------+
   ## |          1|  spark|
   ## |          2| erlang|
   ## |          3|haskell|
   ## +-----------+-------+
   

   It requires neither shuffle nor Cartesian but you still have to transfer broadcast variable to each worker node.

   like_with_bd.explain()
   
   ## TungstenProject [document_id#2L,keyword#46]
   ##  !Generate explode(pythonUDF#47), true, false, ...
   ##   ConvertToSafe
   ##    TungstenProject [document_id#2L,pythonUDF#47]
   ##     !BatchPythonEvaluation PythonUDF#<lambda>(document_text#3), ...
   ##      Scan PhysicalRDD[document_id#2L,document_text#3]
   

3. Since Spark 1.6.0 you can mark a small data frame using sql.functions.broadcast to get a similar effect as above without using UDFs and explicit broadcast variables. Reusing tokenized data:

   from pyspark.sql.functions import broadcast
   
   like_with_tokenizer_and_bd = (broadcast(tokenized)
       .join(keywords_df, col("token") == col("keyword"))
       .drop("token"))
   
   like_with_tokenizer.explain()
   
   ## TungstenProject [document_id#3L,keyword#5]
   ##  BroadcastHashJoin [token#10], [keyword#5], BuildLeft
   ##   TungstenProject [document_id#3L,token#10]
   ##    !Generate explode(words#8), true, false, ...
   ##     ConvertToSafe
   ##      TungstenProject [document_id#3L,UDF(document_text#4) AS words#8]
   ##       Scan PhysicalRDD[document_id#3L,document_text#4]
   ##   ConvertToUnsafe
   ##    Scan PhysicalRDD[keyword#5]
   

Related:

• For approximate matching see Efficient string matching in Apache Spark.