My requirement is to write only Header CSV record using Spark Scala DataFrame. Can any one help me on this.
val OHead1 = "/xxxxx/xxxx/xxxx/xxx/OHead1/"
val sc = sparkFile.sparkContext
val outDF = csvDF.select("col_01", "col_02", "col_03").schema
sc.parallelize(Seq(outDF.fieldNames.mkString("\t"))).coalesce(1).saveAsTextFile(s"$OHead1")
The above one is working and able to create header in the CSV with tab delimiter. Since I am using spark session I am creating sparkContext in the second line. outDF is my dataframe created before these statements.
Two things are outstanding, can you one of you help me.
1. The above working code is not overriding the files, so every time I need to delete the files manually. I could not find override option, can you help me.
2. Since I am doing a select statement and schema, will it be consider as action and start another lineage for this statement. If it is true then this would degrade the performance.
If you need to output only header you can use this code:
df.schema.fieldNames.reduce(_ + "," + _)
It will create line of CSV with names of columns
I tested and the solution below did not affect any performance.
val OHead1 = "/xxxxx/xxxx/xxxx/xxx/OHead1/"
val sc = sparkFile.sparkContext
val outDF = csvDF.select("col_01", "col_02", "col_03").schema
sc.parallelize(Seq(outDF.fieldNames.mkString("\t"))).coalesce(1).saveAsTextFile(s"$OHead1")
I got a solution to handle this situation. Define the columns in the configuration file and write those columns in an file. Here is the snipet.
val Header = prop.getProperty("OUT_HEADER_COLUMNS").replaceAll("\"","").replaceAll(",","\t")
scala.tools.nsc.io.File(s"$HeadOPath").writeAll(s"$Header")
Related
I am having one external table on on gs bucket and to do some compaction logic, I want to determine the full path on which the table is created.
val tableName="stock_ticks_cow_part"
val primaryKey="key"
val versionPartition="version"
val datePartition="dt"
val datePartitionCol=new org.apache.spark.sql.ColumnName(datePartition)
import spark.implicits._
val compactionTable = spark.table(tableName).withColumnRenamed(versionPartition, "compaction_version").withColumnRenamed(datePartition, "date_key")
compactionTable. <code for determining the path>
Let me know if anyone knows how to determine the table path in scala.
I think you can use .inputFiles to
Returns a best-effort snapshot of the files that compose this Dataset
Be aware that this returns an Array[String], so you should loop through it to get all information you're looking for.
So actually just call
compactionTable.inputFiles
and look at each element of the Array
Here is the correct answer:
import org.apache.spark.sql.catalyst.TableIdentifier
lazy val tblMetadata = catalog.getTableMetadata(new TableIdentifier(tableName,Some(schema)))
lazy val s3location: String = tblMetadata.location.getPath
You can use SQL commands SHOW CREATE TABLE <tablename> or DESCRIBE FORMATTED <tablename>. Both should return the location of the external table, but they need some logic to extract this path...
See also How to get the value of the location for a Hive table using a Spark object?
Use the DESCRIBE FORMATTED SQL command and collect the path back to the driver.
In Scala:
val location = spark.sql("DESCRIBE FORMATTED table_name").filter("col_name = 'Location'").select("data_type").head().getString(0)
The same in Python:
location = spark.sql("DESCRIBE FORMATTED table_name").filter("col_name = 'Location'").select("data_type").head()[0]
I'm reading from a path say /json//myfiles_.json
I'm then flattening the json using explode. This causes an error since I have some empty files. How do I tell it to ignore empty files of somehow filter them out?
I can detect individual files checking if the head is empty but I need to do this on the collection of files iterated in the dataframe with the use of the wildcard path.
So the answer seems to be that I need to provide a schema explicitly because it can't infer one from empty file - as you would expect!
e.g.
val schemadf = sqlContext.read.json(schemapath) //infer schema from file with data or do manually
val schema = schemadf.schema
val raw = sqlContext.read.schema(schema).json(monthfile)
val prep = raw.withColumn("MyArray", explode($"MyArray"))
.select($"ID", $"name", $"CreatedAt")
display(prep)
I want to remove header from a file. But, since the file will be split into partitions, I can't just drop the first item. So I was using a filter function to figure it out and here below is the code I am using :
val noHeaderRDD = baseRDD.filter(line=>!line.contains("REPORTDATETIME"));
and the error I am getting says "error not found value line "what could be the issue here with this code?
I don't think anybody answered the obvious, whereby line.contains also possible:
val noHeaderRDD = baseRDD.filter(line => !(line contains("REPORTDATETIME")))
You were nearly there, just a syntax issue, but that is significant of course!
Using textFile as below:
val rdd = sc.textFile(<<path>>)
rdd.filter(x => !x.startsWith(<<"Header Text">>))
Or
In Spark 2.0:
spark.read.option("header","true").csv("filePath")
This question is related to this.
I am processing an S3 folder containing csv.gz files in Spark. Each csv.gz file has a header that contains column names. This has been solved by the above SO link and the solution looks like this:
val rdd = sc.textFile("s3://.../my-s3-path").mapPartitions(_.drop(1))
The problem now is that it looks like some of the files have newline ('\n') at the end (we assume we are not sure which file). So when converting the RDD to DataFrame, I'm getting some error. The question now is:
How do I get rid of the last line of each file if it is '\n'?
Why not a simple filter:
val rdd = sc.textFile("s3...").filter(line => !line.equalsIgnoreCase("\n")).mapPartition...
Or filter any empty line:
val rdd = sc.textFile("s3...").filter(line => !line.trim().isEmpty)...
i am trying to upload a csv file into a tempTable such that I can query on it and I am having two issues.
First: I tried uploading the csv to a DataFrame, and this csv has some empty fields.... and I didn't find a way to do it. I found someone posting in another post to use :
val df = sqlContext.read.format("com.databricks.spark.csv").option("header", "true").load("cars.csv")
but it gives me an error saying "Failed to load class for data source: com.databricks.spark.csv"
Then I uploaded the file and read it as a text file, without the headings as:
val sqlContext = new org.apache.spark.sql.SQLContext(sc);
import sqlContext.implicits._;
case class cars(id: Int, name: String, licence: String);
val carsDF = sc.textFile("../myTests/cars.csv").map(_.split(",")).map(p => cars( p(0).trim.toInt, p(1).trim, p(2).trim) ).toDF();
carsDF.registerTempTable("cars");
val dgp = sqlContext.sql("SELECT * FROM cars");
dgp.show()
gives an error because one of the licence field is empty... I tried to control this issue when I build the data frame but did not work.
I can obviously go into the csv file but and fix by adding a null to it but U do not want to do this because of there are a lot of fields it could be problematic. I want to fix it programmatically either when i create the dataframe or the class...
any other thoughts please let me know as well
To be able to use spark-csv you have to make sure it is available. In an interactive mode the simplest solution is to use packages argument when you start shell:
bin/spark-shell --packages com.databricks:spark-csv_2.10:1.1.0
Regarding manual parsing working with csv files, especially malformed like cars.csv, requires much more work than simply splitting on commas. Some things to consider:
how to detect csv dialect, including method of string quoting
how to handle quotes and new line characters inside strings
how handle malformed lines
In case of example file you have to at least:
filter empty lines
read header
map lines to fields providing default value if field is missing
Here you go. Remember to check the delimiter for your CSV.
// create spark session
val spark = org.apache.spark.sql.SparkSession.builder
.master("local")
.appName("Spark CSV Reader")
.getOrCreate;
// read csv
val df = spark.read
.format("csv")
.option("header", "true") //reading the headers
.option("mode", "DROPMALFORMED")
.option("delimiter", ",")
.load("/your/csv/dir/simplecsv.csv")
// create a table from dataframe
df.createOrReplaceTempView("tableName")
// run your sql query
val sqlResults = spark.sql("SELECT * FROM tableName")
// display sql results
display(sqlResults)