I am doing join of 2 data frames and select all columns of left frame for example:
val join_df = first_df.join(second_df, first_df("id") === second_df("id") , "left_outer")
in above I want to do select first_df.* .How can I select all columns of one frame in join ?
With alias:
first_df.alias("fst").join(second_df, Seq("id"), "left_outer").select("fst.*")
We can also do it with leftsemi join. leftsemi join will select the data from left side dataframe from a joined dataframe.
Here we join two dataframes df1 and df2 based on column col1.
df1.join(df2, df1.col("col1").equalTo(df2.col("col1")), "leftsemi")
Suppose you:
Want to use the DataFrame syntax.
Want to select all columns from df1 but only a couple from df2.
This is cumbersome to list out explicitly due to the number of columns in df1.
Then, you might do the following:
val selectColumns = df1.columns.map(df1(_)) ++ Array(df2("field1"), df2("field2"))
df1.join(df2, df1("key") === df2("key")).select(selectColumns:_*)
Just to add one possibility, whithout using alias, I was able to do that in pyspark with
first_df.join(second_df, "id", "left_outer").select( first_df["*"] )
Not sure if applies here, but hope it helps
Related
I tried to do Join two dataframes in spark shell.
One of the dataframe is having 15000 records and another is having 14000 rows.
I tried Left outer join and inner join of these dataframes, but result is having count of 29000 rows.
How is that happening?
The code which i tried is given below.
val joineddf = finaldf.as("df1").join(cosmos.as("df2"), $"df1.BatchKey" === $"df2.BatchKey", "left_outer").select(($"df1.*"),col("df2.BatchKey").as("B2"))
val joineddf = finaldf.as("df1").join(cosmos.as("df2"), $"df1.BatchKey" === $"df2.BatchKey", "inner").select(($"df1.*"),col("df2.BatchKey").as("B2"))
Both above methods are resulted in a dataframe where count is sum of both dataframes.
Even I tried the below method, but still getting same result.
finaldf.createOrReplaceTempView("df1")
cosmos.createOrReplaceTempView("df2")
val test = spark.sql("""SELECT df1.*, df2.* FROM df1 LEFT OUTER JOIN df2 ON trim(df1.BatchKey) == trim(df2.BatchKey)""")
If i try to add more condition for join then the no of count is again increasing.
How to get exact result for a left outer join?
here in the case max count should be 15000
Antony,
Can you try performing the join below :
val joineddf = finaldf.join(cosmos.select("BatchKey"), Seq("BatchKey"), "left_outer")
Here I'm not using any alias.
I have a dataframe with column having values like "COR//xxxxxx-xx-xxxx" or "xxxxxx-xx-xxxx"
I need to compare this column with another column in a different dataframe based on the column value.
If column value have "COR//xxxxx-xx-xxxx", I need to use substring("column", 4, length($"column")
If the column value have "xxxxx-xx-xxxx", I can compare directly without using substring.
For example:
val DF1 = DF2.join(DF3, upper(trim($"column1".substr(4, length($"column1")))) === upper(trim(DF3("column1"))))
I am not sure how to add the condition while joining. Could anyone please let me know how can we achieve this in Spark dataframe?
You can try adding a new column based on the conditions and join on the new column. Something like this.
val data = List("COR//xxxxx-xx-xxxx", "xxxxx-xx-xxxx")
val DF2 = ps.sparkSession.sparkContext.parallelize(data).toDF("column1")
val DF4 = DF2.withColumn("joinCol", when(col("column1").like("%COR%"),
expr("substring(column1, 6, length(column1)-1)")).otherwise(col("column1")) )
DF4.show(false)
The new column will have values like this.
+------------------+-------------+
|column1 |joinCol |
+------------------+-------------+
|COR//xxxxx-xx-xxxx|xxxxx-xx-xxxx|
|xxxxx-xx-xxxx |xxxxx-xx-xxxx|
+------------------+-------------+
You can now join based on the new column added.
val DF1 = DF4.join(DF3, upper(trim(DF4("joinCol"))) === upper(trim(DF3("column1"))))
Hope this helps.
Simply create a new column to use in the join:
DF2.withColumn("column2",
when($"column1" rlike "COR//.*",
$"column1".substr(lit(4), length($"column1")).
otherwise($"column1"))
Then use column2 in the join. It is also possible to add the whole when clause directly in the join but it would look very messy.
Note that to use a constant value in substr you need to use lit. And if you want to remove the whole "COR//" part, use 6 instead of 4.
I have one dataframe (df) with ip addresses and their corresponding long value (ip_int) and now I want to search in an another dataframe (ip2Country) which contains geolocation information to find their corresponding country name. How should I do it in Scala. My code currently didnt work out: Memory limit exceed.
val ip_ints=df.select("ip_int").distinct.collect().flatMap(_.toSeq)
val df_list = ListBuffer[DataFrame]()
for(v <- ip_ints){
var ip_int=v.toString.toLong
df_list +=ip2Country.filter(($"network_start_integer"<=ip_int)&&($"network_last_integer">=ip_int)).select("country_name").withColumn("ip_int", lit(ip_int))
}
var df1 = df_list.reduce(_ union _)
df=df.join(df1,Seq("ip_int"),"left")
Basically I try to iterate through every ip_int value and search them in ip2Country and merge them back with df.
Any help is much appreciated!
A simple join should do the trick for you
df.join(df1, df1("network_start_integer")<=df("ip_int") && df1("network_last_integer")>=df("ip_int"), "left")
.select("ip", "ip_int", "country_name")
If you want to remove the null country_name then you can add filter too
df.join(df1, df1("network_start_integer")<=df("ip_int") && df1("network_last_integer")>=df("ip_int"), "left")
.select("ip", "ip_int", "country_name")
.filter($"country_name".isNotNull)
I hope the answer is helpful
You want to do a non-equi join, which you can implement by cross joining and then filtering, though it is resource heavy to do so. Assuming you are using Spark 2.1:
df.createOrReplaceTempView("ip_int")
df.select("network_start_integer", "network_start_integer", "country_name").createOrReplaceTempView("ip_int_lookup")
// val spark: SparkSession
val result: DataFrame = spark.sql("select a.*, b.country_name from ip_int a, ip_int_lookup b where b.network_start_integer <= a.ip_int and b.network_last_integer >= a.ip_int)
If you want to include null ip_int, you will need to right join df to result.
I feel puzzled here.
df1("network_start_integer")<=df("ip_int") && df1("network_last_integer")>=df("ip_int")
Can we use the
df1("network_start_integer")===df("ip_int"）
here please?
I have a dataframe (df1) which has 50 columns, the first one is a cust_id and the rest are features. I also have another dataframe (df2) which contains only cust_id. I'd like to add one records per customer in df2 to df1 with all the features as 0. But as the two dataframe have two different schema, I cannot do a union. What is the best way to do that?
I use a full outer join but it generates two cust_id columns and I need one. I should somehow merge these two cust_id columns but don't know how.
You can try to achieve something like that by doing a full outer join like the following:
val result = df1.join(df2, Seq("cust_id"), "full_outer")
However, the features are going to be null instead of 0. If you really need them to be zero, one way to do it would be:
val features = df1.columns.toSet - "cust_id" // Remove "cust_id" column
val newDF = features.foldLeft(df2)(
(df, colName) => df.withColumn(colName, lit(0))
)
df1.unionAll(newDF)
I have joined two Dataframes in spark using below code -
Dataframes are: expDataFrame, accountList
val expDetails = expDataFrame.as("fex").join(accountList.as("acctlist"),$"fex.acct_id" === $"acctlist.acct_id", "inner")
Now I am trying to show both acct_id from both dataframe.
I have done below code -
expDetails.select($"fex.acct_id",$"acct_id.acct_id").show
but getting same column name twice as acct_id
I want two unique column name like fex_acct_id, acctlist_acct_id to identify the column from which dataframe.
You simply have to add an alias to the columns using the as or alias methods. This will do the job :
expDetails.select(
$"fex.acct_id".as("fex_acct_id"),
$"acct_id.acct_id".as("acctlist_acct_id")
).show