我们正在使用与Spark 1.3.1连接的PySpark库。
我们有两个数据帧，documents_df := {document_id, document_text}和keywords_df := {keyword}。我们想结合两个数据帧，并使用document_df.document_text字符串中出现keyword_df.keyword的条件返回带有{document_id, keyword}对的结果数据帧。
例如，在PostgreSQL中，我们可以使用以下形式的ON子句来实现此目的:document_df.document_text ilike '%' || keyword_df.keyword || '%'
但是，在PySpark中，我无法使用任何形式的连接语法。有人做过这样的事情吗？
亲切的问候，
将要
最佳答案
可能有两种不同的方式，但通常不建议这样做。首先让我们创建一个虚拟数据:
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()
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|
## +-----------+-------+
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|
## +-----------+-------+
为什么不推荐？因为在两种情况下都需要笛卡尔积:
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]
在没有完整笛卡尔坐标的情况下，还有其他方法可以达到类似的效果。
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|
## +-----------+-------+
这需要洗牌，但不需要笛卡尔:
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]
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|
## +-----------+-------+
它既不需要随机播放，也不需要笛卡尔坐标，但是您仍然必须将广播变量传输到每个工作节点。
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]
sql.functions.broadcast标记一个小的数据框，从而获得与上述类似的效果，而无需使用UDF和显式广播变量。重用标记化数据: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]
相关的: