How would you perform basic joins in Spark using python? In R you could use merg() to do this. What is the syntax using python on spark for:
With two tables (RDD) with a single column in each that has a common key.
RDD(1):(key,U)
RDD(2):(key,V)
I think an inner join is something like this:
rdd1.join(rdd2).map(case (key, u, v) => (key, ls ++ rs));
Is that right? I have searched the internet and can't find a good example of joins. Thanks in advance.
It can be done either using PairRDDFunctions
or Spark Data Frames. Since data frame operations benefit from Catalyst Optimizer the second option is worth considering.
Assuming your data looks as follows:
rdd1 = sc.parallelize([("foo", 1), ("bar", 2), ("baz", 3)])
rdd2 = sc.parallelize([("foo", 4), ("bar", 5), ("bar", 6)])
Inner join:
rdd1.join(rdd2)
Left outer join:
rdd1.leftOuterJoin(rdd2)
Cartesian product (doesn't require RDD[(T, U)]
):
rdd1.cartesian(rdd2)
Broadcast join (doesn't require RDD[(T, U)]
):
Finally there is cogroup
which has no direct SQL equivalent but can be useful in some situations:
cogrouped = rdd1.cogroup(rdd2)
cogrouped.mapValues(lambda x: (list(x[0]), list(x[1]))).collect()
## [('foo', ([1], [4])), ('bar', ([2], [5, 6])), ('baz', ([3], []))]
You can use either SQL DSL or execute raw SQL using sqlContext.sql
.
df1 = spark.createDataFrame(rdd1, ('k', 'v1'))
df2 = spark.createDataFrame(rdd2, ('k', 'v2'))
# Register temporary tables to be able to use `sparkSession.sql`
df1.createOrReplaceTempView('df1')
df2.createOrReplaceTempView('df2')
Inner join:
# inner is a default value so it could be omitted
df1.join(df2, df1.k == df2.k, how='inner')
spark.sql('SELECT * FROM df1 JOIN df2 ON df1.k = df2.k')
Left outer join:
df1.join(df2, df1.k == df2.k, how='left_outer')
spark.sql('SELECT * FROM df1 LEFT OUTER JOIN df2 ON df1.k = df2.k')
Cross join (explicit cross join or configuration changes are required in Spark. 2.0 - spark.sql.crossJoin.enabled for Spark 2.x):
df1.crossJoin(df2)
spark.sql('SELECT * FROM df1 CROSS JOIN df2')
df1.join(df2)
sqlContext.sql('SELECT * FROM df JOIN df2')
Since 1.6 (1.5 in Scala) each of these can be combined with broadcast
function:
from pyspark.sql.functions import broadcast
df1.join(broadcast(df2), df1.k == df2.k)
to perform broadcast join. See also Why my BroadcastHashJoin is slower than ShuffledHashJoin in Spark