This should be pretty straightforward, but I'm having an issue with the following code:
val test = spark.read
.option("header", "true")
.option("delimiter", ",")
.csv("sample.csv")
test.select("Type").show()
test.select("Provider Id").show()
test is a dataframe like so:
Type
Provider Id
A
asd
A
bsd
A
csd
B
rrr
Exception in thread "main" org.apache.spark.sql.AnalysisException:
cannot resolve '`Provider Id`' given input columns: [Type, Provider Id];;
'Project ['Provider Id]
It selected and shows the Type column just fine but couldn't get it to work for the Provider Id. I wondered if it were because the column name had a space, so I tried using backticks, removing and replacing the space, but nothing seemed to work. Also, it ran fine when I'm using Spark libraries 3.x but doesn't work when I'm using Spark 2.1.x (meanwhile I need to use 2.1.x)
Additional: I tried changing the CSV column order from Type - Provider Id to Provider Id then Type. The error was the opposite, Provider Id shows but for Type it's throwing an exception now.
Any suggestions?
test.printSchema()
You can use the result from printSchema() to see how exactly spark read your column in, then use that in your code.
Related
Using Scala here:
Val df = spark.read.format("jdbc").
option("url", "<host url>").
option("dbtable", "UPPERCASE_SCHEMA.table_name").
option("user", "postgres").
option("password", "<password>").
option("numPartitions", 50).
option("fetchsize", 20).
load()
The database I'm using the above code to call from has many schemas and they are all in uppercase letters (UPPERCASE_SCHEMA).
No matter how I try to denote that the schema is in all caps, Spark converts it to lowercase which fails to initialize with the actual DB.
I've tried making it a variable and explicitly denoting it is all uppercase, etc. in multiple languages, but no luck.
Would anyone know a workaround?
When I went into the actual DB (Postgres) and temporarily changed the schema to all lowercase, it worked absolutely fine.
Try to set spark.sql.caseSensitive to true (false by default)
spark.conf.set('spark.sql.caseSensitive', true)
You can see in the source code its definition:
https://github.com/apache/spark/blob/master/sql/catalyst/src/main/scala/org/apache/spark/sql/internal/SQLConf.scala#L833
In addition, you can see in the JDBCWriteSuite how it affects the JDBC connector:
https://github.com/apache/spark/blob/ee95ec35b4f711fada4b62bc27281252850bb475/sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCWriteSuite.scala
I have a requirement to check match percentage of 2columns from a table.
For example:
Sample data:
ColA
ColB
AAB
Aab
AACC
Aacc
WER
Wer
from difflib import SequenceMatcher
def similar(a, b):
return SequenceMatcher( None,a, b).ratio()
spark.udf.register('similar',similar)
Output:
similar('AAB','Aab')
Out[16]: 0.3333333333333333
I am able to achieve the requirement by using sequenceMatcher lib but the issue is I am not able to use that function inside spark sql and facing below error. Is there any other way we can achieve the same??
df=spark.sql(f"""SELECT ColA,ColB,Similar(ColA,ColB) FROM test""")
display(df)
Error:
PythonException: 'AttributeError: 'SequenceMatcher' object has no attribute 'matching_blocks'', from , line 4. Full traceback below:
• SequenceMatcher accepts strings along with a junk value criteria.
• If any of these input strings are None the error occurs. Empty strings work.
Make sure None inputs are replaced by empty strings.
Hi I am trying to add one column in my spark dataframe and calculating value based on existing dataframe column. I am writing below code.
val df1=spark.sql("select id,dt1,salary frm dbdt1.tabledt1")
val df2=df1.withColumn("new_date",WHEN (month(to_date(from_unixtime(unix_timestamp(dt1), 'dd-MM- yyyy')))
IN (01,02,03)) THEN
CONCAT(CONCAT(year(to_date(from_unixtime(unix_timestamp(dt1), 'dd-MM- yyyy')))-1,'-'),
substr(year(to_date(from_unixtime(unix_timestamp(dt1), 'dd-MM-yyyy'))),3,4))
.otherwise(CONCAT(CONCAT(year(to_date(from_unixtime(unix_timestamp(dt1), 'dd-MM- yyyy'))),'-')
,SUBSTR(year(to_date(from_unixtime(unix_timestamp(dt1), 'dd-MM-yyyy')))+1,3,4))))
But it always showing issue error: unclosed character literal. Can someone plase guide me how should i add this new column or modify the existing code.
Incorrect syntax in many places. First I suggest you look at a few spark sql examples online and also the org.apache.spark.sql.functions API documentation because your use of WHEN, CONCAT, IN are all incorrect.
Scala strings are enclosed by double quotes, you appear to be using SQL string syntax.
'dd-MM-yyyy' should be "dd-MM-yyyy"
To reference a column dt1 on DataFrame df1 you can use one of the following:
df1("dt1")
col("dt1") // if you import org.apache.spark.sql.functions.col
$"dt1" // if you import spark.implicits._ locally
For example:
from_unixtime(unix_timestamp(col("dt1")), 'dd-MM- yyyy')
I am processing a large XML file using the Spark XML Library (HyukjinKwon:spark-xml:0.1.1-s_2.11). The XML processing fails with an analysis exception for a couple of records. I would like to keep processing the file ignoring these records.
I have the below code for processing the xml and I tried the option of 'DROPMALFORMED' but didn't help.
df = (spark.read.format("xml")
.option("rootTag","Articles")
.option("rowTag", "Article")
.option("inferSchema", "true")
.option("mode", "DROPMALFORMED")
.load("/mnt/RawAdl2/problemfile.xml"))
AnalysisException: "cannot resolve '['Affiliation']' due to data type mismatch: argument 2 requires integral type, however, ''Affiliation'' is of string type.;
I would like to drop the malformed records and continue with the processing of the file. Is there any other option I could try? Appreciate the inputs!
EDIT: Looking at the source code link the Malformed option is supported by the library. As I am not well versed with Scala, I am not really sure whether I am using the correct syntax for this option. Please advise.
After going through the source code, I tried this below code but no luck
.option("mode", "DROP_MALFORMED_MODE")
Try setting the badRecords path:
.option("badRecordsPath", "/tmp/badRecordsPath")
https://docs.databricks.com/spark/latest/spark-sql/handling-bad-records.html
Spark version 1.60, Scala version 2.10.5.
I have a spark-sql dataframe df like this,
+-------------------------------------------------+
|addess | attributes |
+-------------------------------------------------+
|1314 44 Avenue | Tours, Mechanics, Shopping |
|115 25th Ave | Restaurant, Mechanics, Brewery|
+-------------------------------------------------+
From this dataframe, I would like values as below,
Tours, Mechanics, Shopping, Brewery
If I do this,
df.select(df("attributes")).collect().foreach(println)
I get,
[Tours, Mechanics, Shopping]
[Restaurant, Mechanics, Brewery]
I thought I could use flatMapinstead found this, so, tried to put this into a variable using,
val allValues = df.withColumn(df("attributes"), explode("attributes"))
but I am getting an error:
error: type mismatch;
found:org.apache.spark.sql.column
required:string
I was thinking if I can get an output using explode I can use distinct to get the unique values after flattening them.
How can I get the desired output?
I strongly recommend you to use spark 2.x version. In Cloudera, when you issue "spark-shell", it launches 1.6.x version.. however, if you issue "spark2-shell", you get the 2.x shell. Check with your admin
But if you need with Spark 1.6 and rdd solution, try this.
import spark.implicits._
import scala.collection.mutable._
val df = Seq(("1314 44 Avenue",Array("Tours", "Mechanics", "Shopping")),
("115 25th Ave",Array("Restaurant", "Mechanics", "Brewery"))).toDF("address","attributes")
df.rdd.flatMap( x => x.getAs[mutable.WrappedArray[String]]("attributes") ).distinct().collect.foreach(println)
Results:
Brewery
Shopping
Mechanics
Restaurant
Tours
If the "attribute" column is not an array, but comma separated string, then use the below one which gives you same results
val df = Seq(("1314 44 Avenue","Tours,Mechanics,Shopping"),
("115 25th Ave","Restaurant,Mechanics,Brewery")).toDF("address","attributes")
df.rdd.flatMap( x => x.getAs[String]("attributes").split(",") ).distinct().collect.foreach(println)
The problem is that withColumn expects a String in its first argument (which is the name of the added column), but you're passing it a Column here df.withColumn(df("attributes").
You only need to pass "attributes" as a String.
Additionally, you need to pass a Column to the explode function, but you're passing a String - to make it a column you can use df("columName") or the Scala shorthand $ syntax, $"columnName".
Hope this example can help you.
import org.apache.spark.sql.functions._
val allValues = df.select(explode($"attributes").as("attributes")).distinct
Note that this will only preserve the attributes Column, since you want the distinct elements on that one.