Hello I have two Dataframes:
df1 with columns: first_name, last_name, id, location, phone_number.
df2 with columns: last_name, id, location, employer.
I am trying to create a new dataset that displays only the columns in df1 that returns only the rows where the last_name, and id is present in df2. So I decided that a inner join on the two tables. The issue is that join appends the columns from df2 to the end of df1 so my resulting df is much larger than I need. I only care about the columns in df1.
My join was: df1.join(df2, df1.col("last_name").equalTo(df2.col("last_name").and(df1.col("id").equalTo(df2.col("id")), "inner");
The problem with this is I got a new table of: first_name, last_name, id, location, phone_number, employer. Where id and last_name was ambiguous.
Is there any way to keep the same table format of df1 after the join? (Without dropping individual columns, because I am using with a large table with about 30 columns).
You can use the the join method of a Dataframe with the following function signature (from the API docs):
def join(right: Dataset[_], usingColumns: Seq[String]): DataFrame
This will only keep 1 column of the joining columns, removing your ambiguity problem.
After that, you can just select the columns of df1 dynamically, by using df1.columns. In total, it would look something like this:
import spark.implicits._
val df1 = Seq(
("joe", "shmoe", 1, "x", 123),
("jack", "johnson", 2, "y", 456)
).toDF("first_name", "last_name", "id", "location", "phone_number")
df1.show
+----------+---------+---+--------+------------+
|first_name|last_name| id|location|phone_number|
+----------+---------+---+--------+------------+
| joe| shmoe| 1| x| 123|
| jack| johnson| 2| y| 456|
+----------+---------+---+--------+------------+
val df2 = Seq(
("shmoe", 1, "x", "someCoolGuy"),
("otherName", 3, "z", "employer2")
).toDF("last_name", "id", "location", "employer")
df2.show
+---------+---+--------+-----------+
|last_name| id|location| employer|
+---------+---+--------+-----------+
| shmoe| 1| x|someCoolGuy|
|otherName| 3| z| employer2|
+---------+---+--------+-----------+
val output = df1
.join(df2.select("last_name", "id"), Seq("last_name", "id")) // only selecting interesting columns of df2 for the join
.select(df1.columns.head, df1.columns.tail: _*)
output.show
+----------+---------+---+--------+------------+
|first_name|last_name| id|location|phone_number|
+----------+---------+---+--------+------------+
| joe| shmoe| 1| x| 123|
+----------+---------+---+--------+------------+
Hope this helps!
I have a data frame with a string column and I want to create multiple columns out of it.
Here is my input data and pagename is my string column
I want to create multiple columns from it. The format of the string is the same - col1:value1 col2:value2 col3:value3 ... colN:valueN . In the output, I need multiple columns - col1 to colN with values as rows for each column. Here is the output -
How can I do this in spark? Scala or Python both is fine for me. Below code creates the input dataframe -
scala> val df = spark.sql(s"""select 1 as id, "a:100 b:500 c:200" as pagename union select 2 as id, "a:101 b:501 c:201" as pagename """)
df: org.apache.spark.sql.DataFrame = [id: int, pagename: string]
scala> df.show(false)
+---+-----------------+
|id |pagename |
+---+-----------------+
|2 |a:101 b:501 c:201|
|1 |a:100 b:500 c:200|
+---+-----------------+
scala> df.printSchema
root
|-- id: integer (nullable = false)
|-- pagename: string (nullable = false)
Note - The example shows only 3 columns here but in general I have more than 100 columns that I expect to deal with.
You can use str_to_map, explode the resulting map and pivot:
val df2 = df.select(
col("id"),
expr("explode(str_to_map(pagename, ' ', ':'))")
).groupBy("id").pivot("key").agg(first("value"))
df2.show
+---+---+---+---+
| id| a| b| c|
+---+---+---+---+
| 1|100|500|200|
| 2|101|501|201|
+---+---+---+---+
So two options immediately come to mind
Delimiters
You've got some obvious delimiters that you can split on. For this use the split function
from pyspark.sql import functions as F
delimiter = ":"
df = df.withColumn(
"split_column",
F.split(F.col("pagename"), delimiter)
)
# "split_column" is now an array, so we need to pull items out the array
df = df.withColumn(
"a",
F.col("split_column").getItem(0)
)
Not ideal, as you'll still need to do some string manipulation to remove the whitespace and then do the int converter - but this is easily applied to multiple columns.
Regex
As the format is pretty fixed, you can do the same thing with a regex.
import re
regex_pattern = r"a\:() b\:() c\:()"
match_groups = ["a", "b", "c"]
for i in range(re.compile(regex_pattern).groups):
df = df.withColumn(
match_groups[i],
F.regexp_extract(F.col(pagename), regex_pattern, i + 1),
)
CAVEAT: Check that Regex before you try and run anything (as I don't have an editor handy)
I have a SQL table containing 40 columns: ID, Product, Product_ID, Date etc. and would like to iterate over all columns to get distinct values.
Customer table (sample):
ID Product
1 gadget
2 VR
2 AR
3 hi-fi
I have tried using dropDuplicates within a function that loops over all columns but the resultant output is only spitting out one distinct value per column instead of all possible distinct values.
Expected Result:
Column Value
ID 1
ID 2
ID 3
Product gadget
Product VR
Product AR
Product hi-fi
Actual Result:
Column Value
ID 1
Product gadget
The idea is to use collect_set() to fetch distinct elements in a column and then exploding the dataframe.
#All columns which need to be aggregated should be added here in col_list.
col_list = ['ID','Product']
exprs = [collect_set(x) for x in col_list]
Let's start aggregating.
from pyspark.sql.functions import lit , collect_set, explode, array, struct, col, substring, length, expr
df = spark.createDataFrame([(1,'gadget'),(2,'VR'),(2,'AR'),(3,'hi-fi')], schema = ['ID','Product'])
df = df.withColumn('Dummy',lit('Dummy'))
#While exploding later, the datatypes must be the same, so we have to cast ID as a String.
df = df.withColumn('ID',col('ID').cast('string'))
#Creating the list of distinct values.
df = df.groupby("Dummy").agg(*exprs)
df.show(truncate=False)
+-----+---------------+-----------------------+
|Dummy|collect_set(ID)|collect_set(Product) |
+-----+---------------+-----------------------+
|Dummy|[3, 1, 2] |[AR, VR, hi-fi, gadget]|
+-----+---------------+-----------------------+
def to_transpose(df, by):
# Filter dtypes and split into column names and type description
cols, dtypes = zip(*((c, t) for (c, t) in df.dtypes if c not in by))
# Spark SQL supports only homogeneous columns
assert len(set(dtypes)) == 1, "All columns have to be of the same type"
# Create and explode an array of (column_name, column_value) structs
kvs = explode(array([
struct(lit(c).alias("key"), col(c).alias("val")) for c in cols
])).alias("kvs")
return df.select(by + [kvs]).select(by + ["kvs.key", "kvs.val"])
df = to_transpose(df, ['Dummy']).drop('Dummy')
df.show()
+--------------------+--------------------+
| key| val|
+--------------------+--------------------+
| collect_set(ID)| [3, 1, 2]|
|collect_set(Product)|[AR, VR, hi-fi, g...|
+--------------------+--------------------+
df = df.withColumn('val', explode(col('val')))
df = df.withColumnRenamed('key', 'Column').withColumnRenamed('val', 'Value')
df = df.withColumn('Column', expr("substring(Column,13,length(Column)-13)"))
df.show()
+-------+------+
| Column| Value|
+-------+------+
| ID| 3|
| ID| 1|
| ID| 2|
|Product| AR|
|Product| VR|
|Product| hi-fi|
|Product|gadget|
+-------+------+
Note: All the columns which are not strings, should be converted into String like df = df.withColumn('ID',col('ID').cast('string')). Otherwise, you will get error.
I have two dataframes with the following columns:
df1.columns
// Array(ts, id, X1, X2)
and
df2.columns
// Array(ts, id, Y1, Y2)
After I do
val df_combined = df1.join(df2, Seq(ts,id))
I end up with the following columns: Array(ts, id, X1, X2, ts, id, Y1, Y2). I could expect that the common columns would be dropped. Is there something that additional that needs to be done?
The simple answer (from the Databricks FAQ on this matter) is to perform the join where the joined columns are expressed as an array of strings (or one string) instead of a predicate.
Below is an example adapted from the Databricks FAQ but with two join columns in order to answer the original poster's question.
Here is the left dataframe:
val llist = Seq(("bob", "b", "2015-01-13", 4), ("alice", "a", "2015-04-23",10))
val left = llist.toDF("firstname","lastname","date","duration")
left.show()
/*
+---------+--------+----------+--------+
|firstname|lastname| date|duration|
+---------+--------+----------+--------+
| bob| b|2015-01-13| 4|
| alice| a|2015-04-23| 10|
+---------+--------+----------+--------+
*/
Here is the right dataframe:
val right = Seq(("alice", "a", 100),("bob", "b", 23)).toDF("firstname","lastname","upload")
right.show()
/*
+---------+--------+------+
|firstname|lastname|upload|
+---------+--------+------+
| alice| a| 100|
| bob| b| 23|
+---------+--------+------+
*/
Here is an incorrect solution, where the join columns are defined as the predicate left("firstname")===right("firstname") && left("lastname")===right("lastname").
The incorrect result is that the firstname and lastname columns are duplicated in the joined data frame:
left.join(right, left("firstname")===right("firstname") &&
left("lastname")===right("lastname")).show
/*
+---------+--------+----------+--------+---------+--------+------+
|firstname|lastname| date|duration|firstname|lastname|upload|
+---------+--------+----------+--------+---------+--------+------+
| bob| b|2015-01-13| 4| bob| b| 23|
| alice| a|2015-04-23| 10| alice| a| 100|
+---------+--------+----------+--------+---------+--------+------+
*/
The correct solution is to define the join columns as an array of strings Seq("firstname", "lastname"). The output data frame does not have duplicated columns:
left.join(right, Seq("firstname", "lastname")).show
/*
+---------+--------+----------+--------+------+
|firstname|lastname| date|duration|upload|
+---------+--------+----------+--------+------+
| bob| b|2015-01-13| 4| 23|
| alice| a|2015-04-23| 10| 100|
+---------+--------+----------+--------+------+
*/
This is an expected behavior. DataFrame.join method is equivalent to SQL join like this
SELECT * FROM a JOIN b ON joinExprs
If you want to ignore duplicate columns just drop them or select columns of interest afterwards. If you want to disambiguate you can use access these using parent DataFrames:
val a: DataFrame = ???
val b: DataFrame = ???
val joinExprs: Column = ???
a.join(b, joinExprs).select(a("id"), b("foo"))
// drop equivalent
a.alias("a").join(b.alias("b"), joinExprs).drop(b("id")).drop(a("foo"))
or use aliases:
// As for now aliases don't work with drop
a.alias("a").join(b.alias("b"), joinExprs).select($"a.id", $"b.foo")
For equi-joins there exist a special shortcut syntax which takes either a sequence of strings:
val usingColumns: Seq[String] = ???
a.join(b, usingColumns)
or as single string
val usingColumn: String = ???
a.join(b, usingColumn)
which keep only one copy of columns used in a join condition.
I have been stuck with this for a while, and only recently I came up with a solution what is quite easy.
Say a is
scala> val a = Seq(("a", 1), ("b", 2)).toDF("key", "vala")
a: org.apache.spark.sql.DataFrame = [key: string, vala: int]
scala> a.show
+---+----+
|key|vala|
+---+----+
| a| 1|
| b| 2|
+---+----+
and
scala> val b = Seq(("a", 1)).toDF("key", "valb")
b: org.apache.spark.sql.DataFrame = [key: string, valb: int]
scala> b.show
+---+----+
|key|valb|
+---+----+
| a| 1|
+---+----+
and I can do this to select only the value in dataframe a:
scala> a.join(b, a("key") === b("key"), "left").select(a.columns.map(a(_)) : _*).show
+---+----+
|key|vala|
+---+----+
| a| 1|
| b| 2|
+---+----+
You can simply use this
df1.join(df2, Seq("ts","id"),"TYPE-OF-JOIN")
Here TYPE-OF-JOIN can be
left
right
inner
fullouter
For example, I have two dataframes like this:
// df1
word count1
w1 10
w2 15
w3 20
// df2
word count2
w1 100
w2 150
w5 200
If you do fullouter join then the result looks like this
df1.join(df2, Seq("word"),"fullouter").show()
word count1 count2
w1 10 100
w2 15 150
w3 20 null
w5 null 200
try this,
val df_combined = df1.join(df2, df1("ts") === df2("ts") && df1("id") === df2("id")).drop(df2("ts")).drop(df2("id"))
This is a normal behavior from SQL, what I am doing for this:
Drop or Rename source columns
Do the join
Drop renamed column if any
Here I am replacing "fullname" column:
Some code in Java:
this
.sqlContext
.read()
.parquet(String.format("hdfs:///user/blablacar/data/year=%d/month=%d/day=%d", year, month, day))
.drop("fullname")
.registerTempTable("data_original");
this
.sqlContext
.read()
.parquet(String.format("hdfs:///user/blablacar/data_v2/year=%d/month=%d/day=%d", year, month, day))
.registerTempTable("data_v2");
this
.sqlContext
.sql(etlQuery)
.repartition(1)
.write()
.mode(SaveMode.Overwrite)
.parquet(outputPath);
Where the query is:
SELECT
d.*,
concat_ws('_', product_name, product_module, name) AS fullname
FROM
{table_source} d
LEFT OUTER JOIN
{table_updates} u ON u.id = d.id
This is something you can do only with Spark I believe (drop column from list), very very helpful!
Inner Join is default join in spark, Below is simple syntax for it.
leftDF.join(rightDF,"Common Col Nam")
For Other join you can follow the below syntax
leftDF.join(rightDF,Seq("Common Columns comma seperated","join type")
If columns Name are not common then
leftDF.join(rightDF,leftDF.col("x")===rightDF.col("y),"join type")
Best practice is to make column name different in both the DF before joining them and drop accordingly.
df1.columns =[id, age, income]
df2.column=[id, age_group]
df1.join(df2, on=df1.id== df2.id,how='inner').write.saveAsTable('table_name')
will return an error while error for duplicate columns
Try this instead try this:
df2_id_renamed = df2.withColumnRenamed('id','id_2')
df1.join(df2_id_renamed, on=df1.id== df2_id_renamed.id_2,how='inner').drop('id_2')
If anyone is using spark-SQL and wants to achieve the same thing then you can use USING clause in join query.
val spark = SparkSession.builder().master("local[*]").getOrCreate()
spark.sparkContext.setLogLevel("ERROR")
import spark.implicits._
val df1 = List((1, 4, 3), (5, 2, 4), (7, 4, 5)).toDF("c1", "c2", "C3")
val df2 = List((1, 4, 3), (5, 2, 4), (7, 4, 10)).toDF("c1", "c2", "C4")
df1.createOrReplaceTempView("table1")
df2.createOrReplaceTempView("table2")
spark.sql("select * from table1 inner join table2 using (c1, c2)").show(false)
/*
+---+---+---+---+
|c1 |c2 |C3 |C4 |
+---+---+---+---+
|1 |4 |3 |3 |
|5 |2 |4 |4 |
|7 |4 |5 |10 |
+---+---+---+---+
*/
After I've joined multiple tables together, I run them through a simple function to rename columns in the DF if it encounters duplicates. Alternatively, you could drop these duplicate columns too.
Where Names is a table with columns ['Id', 'Name', 'DateId', 'Description'] and Dates is a table with columns ['Id', 'Date', 'Description'], the columns Id and Description will be duplicated after being joined.
Names = sparkSession.sql("SELECT * FROM Names")
Dates = sparkSession.sql("SELECT * FROM Dates")
NamesAndDates = Names.join(Dates, Names.DateId == Dates.Id, "inner")
NamesAndDates = deDupeDfCols(NamesAndDates, '_')
NamesAndDates.saveAsTable("...", format="parquet", mode="overwrite", path="...")
Where deDupeDfCols is defined as:
def deDupeDfCols(df, separator=''):
newcols = []
for col in df.columns:
if col not in newcols:
newcols.append(col)
else:
for i in range(2, 1000):
if (col + separator + str(i)) not in newcols:
newcols.append(col + separator + str(i))
break
return df.toDF(*newcols)
The resulting data frame will contain columns ['Id', 'Name', 'DateId', 'Description', 'Id2', 'Date', 'Description2'].
Apologies this answer is in Python - I'm not familiar with Scala, but this was the question that came up when I Googled this problem and I'm sure Scala code isn't too different.
I have two dataframes (deleting the fields that are not relevant to the question):
df1: org.apache.spark.sql.DataFrame = [rawValue: bigint]
df2: org.apache.spark.sql.DataFrame = [startLong: bigint, endLong: bigint]
I now want to join the two dataframes where:
rawValue(df1) >= startLong(df2) AND <= endLong(df2)
Can anyone recommend an efficient way of doing this? The one option I was thinking of was to flatmap df2 and then do a straight join, but I don't want to do that if there is an efficient way to do the above join.
You can directly use the condition that you have while joining the two dataframes
Let me illustrate with an example. I created two dataframes identical to the ones you've mentioned
val df1 = Seq((2L), (5L), (15L), (9L)).toDF("rawValue")
//df1: org.apache.spark.sql.DataFrame = [rawValue: bigint]
val df2 = Seq((3L, 5L), (10L, 16L), (9L, 9L)).toDF("startLong", "endLong")
//df2: org.apache.spark.sql.DataFrame = [startLong: bigint, endLong: bigint]
I now want to join the two dataframes where rawValue(df1) >= startLong(df2) AND <= endLong(df2)
For that you can use the condition as
df1.join(df2, df1("rawValue") >= df2("startLong") && df1("rawValue") <= df2("endLong")).show(false)
which should give you
+--------+---------+-------+
|rawValue|startLong|endLong|
+--------+---------+-------+
|5 |3 |5 |
|15 |10 |16 |
|9 |9 |9 |
+--------+---------+-------+