I have been trying to get the databricks library for reading CSVs to work. I am trying to read a TSV created by hive into a spark data frame using the scala api.
Here is an example that you can run in the spark shell (I made the sample data public so it can work for you)
import org.apache.spark.sql.SQLContext
import org.apache.spark.sql.types.{StructType, StructField, StringType, IntegerType};
val sqlContext = new SQLContext(sc)
val segments = sqlContext.read.format("com.databricks.spark.csv").load("s3n://michaeldiscenza/data/test_segments")
The documentation says you can specify the delimiter but I am unclear about how to specify that option.
All of the option parameters are passed in the option() function as below:
val segments = sqlContext.read.format("com.databricks.spark.csv")
.option("delimiter", "\t")
.load("s3n://michaeldiscenza/data/test_segments")
With Spark 2.0+ use the built-in CSV connector to avoid third party dependancy and better performance:
val spark = SparkSession.builder.getOrCreate()
val segments = spark.read.option("sep", "\t").csv("/path/to/file")
You May also try to inferSchema and check for schema.
val df = spark.read.format("csv")
.option("inferSchema", "true")
.option("sep","\t")
.option("header", "true")
.load(tmp_loc)
df.printSchema()
Related
Currently I am trying to merge multiple csv files into one single file , with exact same header but different data and they are named as - data_0_1 , data_0_2..
I am using spark and scala to achive this task . Bellow is my code
import org.apache.spark.sql.functions._
import org.apache.spark.sql.types._
import org.apache.spark.sql.{Dataset, Row}
import spark.implicits._
val INPUT_BUCKET_PREFIX = "fie:/path/data/";
def getData(tableName: String): Dataset[Row] = {
spark.read
.option("header", "true")
.option("ignoreLeadingWhiteSpace", "true")
.option("ignoreTrailingWhiteSpace", "true")
.csv(INPUT_BUCKET_PREFIX + tableName)
}
getData("data*")
.coalesce(1)
.write.csv("file:/path/output")
Currently, I am able to merge all the files in the folder , but if i keep .option("header", "true") then on every combine i see header is written into outputfile multiple times, which i dont want it to happen , I want header to be written only once into outputfile. How can i achive this ?
Note : if i keep the .option("header", "true") then i see no header is written into outfile
I Am not sure do you have this option in apache-spark. But in pandas I write something like this.
header=not os.path.exists(filetar1) - This will create header only once. If the file is Already exists it will not add header
df.to_csv(filetar1, sep='|',index=False,encoding='utf-8', mode='a', header=not os.path.exists(filetar1))
Using spark 1.6
I tried following code:
val diamonds = spark.read.format("csv").option("header", "true").option("inferSchema", "true").load("/got_own/com_sep_fil.csv")
which caused the error
error: not found: value spark
In Spark 1.6 shell you get sc of type SparkContext, not spark of type SparkSession, if you want to get that functionlity you will need to instantiate a SqlContext
import org.apache.spark.sql._
val spark = new SQLContext(sc)
sqlContext is implict object SQL contect which can be used to load csv file and use com.databricks.spark.csv for mentionin csv file format
val df = sqlContext.read.format("csv").option("header", "true").option("inferSchema", "true").load("data.csv")
You need to initialize instance using SQLContext(spark version<2.0) or SparkSession(spark version>=2.0) to use methods provided by Spark.
To initialize spark instance for spark version < 2.0 use:
import org.apache.spark.sql._
val spark = new SQLContext(sc)
To initialize spark instance for spark version >= 2.0 use:
val spark = new SparkConf().setAppName("SparkSessionExample").setMaster("local")
To read the csv using spark 1.6 and databricks spark-csv package:
val df = sqlContext.read.format("com.databricks.spark.csv").option("header", "true").option("inferSchema", "true").load("data.csv")
I have following simple Scala class , which i will later modify to fit some machine learning models.
I need to create a jar file out of this as i am going to run these models in amazon-emr . I am a beginner in this process. So i first tested whether i can successfully import the following csv file and write to another file by creating a jar file using the Scala class mention below.
The csv file looks like this and its include a Date column as one of the variables.
+-------------------+-------------+-------+---------+-----+
| Date| x1 | y | x2 | x3 |
+-------------------+-------------+-------+---------+-----+
|0010-01-01 00:00:00|0.099636562E8|6405.29| 57.06|21.55|
|0010-03-31 00:00:00|0.016645123E8|5885.41| 53.54|21.89|
|0010-03-30 00:00:00|0.044308936E8|6260.95|57.080002|20.93|
|0010-03-27 00:00:00|0.124928214E8|6698.46|65.540001|23.44|
|0010-03-26 00:00:00|0.570222885E7|6768.49| 61.0|24.65|
|0010-03-25 00:00:00|0.086162414E8|6502.16|63.950001|25.24|
Data set link : https://drive.google.com/open?id=18E6nf4_lK46kl_zwYJ1CIuBOTPMriGgE
I created a jar file out of this using intelliJ IDEA. And it was successfully done.
object jar1 {
def main(args: Array[String]): Unit = {
val sc: SparkSession = SparkSession.builder()
.appName("SparkByExample")
.getOrCreate()
val data = sc.read.format("csv")
.option("header","true")
.option("inferSchema","true")
.load(args(0))
data.write.format("text").save(args(1))
}
}
After that I upload this jar file along with the csv file mentioned above in amazon-s3 and tried to ran this in a cluster of amazon-emr .
But it was failed and i got following error message :
ERROR Client: Application diagnostics message: User class threw exception: org.apache.spark.sql.AnalysisException: Text data source does not support timestamp data type.;
I am sure this error is something to do with the Date variable in the data set. But i dont know how to fix this.
Can anyone help me to figure this out ?
Updated :
I tried to open the same csv file that i mentioned earlier without the date column . In this case i am getting this error :
ERROR Client: Application diagnostics message: User class threw exception: org.apache.spark.sql.AnalysisException: Text data source does not support double data type.;
Thank you
As I paid attention later that your are going to write to a text file. Spark's .format(text) doesn't support any specific types except String/Text. So to achive a goal you need to first convert the all the types to String and store:
df.rdd.map(_.toString().replace("[","").replace("]", "")).saveAsTextFile("textfilename")
If it's you could consider other oprions to store the data as file based, then you can have benefits of types. For example using CSV or JSON.
This is working code example based on your csv file for csv.
val spark = SparkSession.builder
.appName("Simple Application")
.config("spark.master", "local")
.getOrCreate()
import spark.implicits._
import spark.sqlContext.implicits._
val df = spark.read
.format("csv")
.option("delimiter", ",")
.option("header", "true")
.option("inferSchema", "true")
.option("dateFormat", "yyyy-MM-dd")
.load("datat.csv")
df.printSchema()
df.show()
df.write
.format("csv")
.option("inferSchema", "true")
.option("header", "true")
.option("delimiter", "\t")
.option("timestampFormat", "yyyy-MM-dd HH:mm:ss")
.option("escape", "\\")
.save("another")
There is no need custom encoder/decoder.
I am able to connect to ADLS gen2 from a notebook running on Azure Databricks but am unable to connect from a job using a jar. I used the same settings as I did in the notebook, save for the use of dbutils.
I used the same setting for Spark conf from the notebook in the Scala code.
Notebook:
spark.conf.set(
"fs.azure.account.key.xxxx.dfs.core.windows.net",
dbutils.secrets.get(scope = "kv-secrets", key = "xxxxxx"))
spark.conf.set
("fs.azure.createRemoteFileSystemDuringInitialization", "true")
spark.conf.set
("fs.azure.createRemoteFileSystemDuringInitialization", "false")
val rdd = sqlContext.read.format
("csv").option("header",
"true").load(
"abfss://catalogs#xxxx.dfs.core.windows.net/test/sample.csv")
// Convert rdd to data frame using toDF; the following import is
//required to use toDF function.
val df: DataFrame = rdd.toDF()
// Write file to parquet
df.write.parquet
("abfss://catalogs#xxxx.dfs.core.windows.net/test/Sales.parquet")
Scala code:
val sc = SparkContext.getOrCreate()
val spark = SparkSession.builder().getOrCreate()
sc.getConf.setAppName("Test")
sc.getConf.set("fs.azure.account.key.xxxx.dfs.core.windows.net",
"<actual key>")
sc.getConf.set("fs.azure.account.auth.type", "OAuth")
sc.getConf.set("fs.azure.account.oauth.provider.type",
"org.apache.hadoop.fs.azurebfs.oauth2.ClientCredsTokenProvider")
sc.getConf.set("fs.azure.account.oauth2.client.id", "<app id>")
sc.getConf.set("fs.azure.account.oauth2.client.secret", "<app password>")
sc.getConf.set("fs.azure.account.oauth2.client.endpoint",
"https://login.microsoftonline.com/<tenant id>/oauth2/token")
sc.getConf.set
("fs.azure.createRemoteFileSystemDuringInitialization", "false")
val sqlContext = spark.sqlContext
val rdd = sqlContext.read.format
("csv").option("header",
"true").load
("abfss://catalogs#xxxx.dfs.core.windows.net/test/sample.csv")
// Convert rdd to data frame using toDF; the following import is
//required to use toDF function.
val df: DataFrame = rdd.toDF()
println(df.count())
// Write file to parquet
df.write.parquet
("abfss://catalogs#xxxx.dfs.core.windows.net/test/Sales.parquet")
I expected the parquet file to get written. Instead I get the following error:
19/04/20 13:58:40 ERROR Uncaught throwable from user code: Configuration property xxxx.dfs.core.windows.net not found.
at shaded.databricks.v20180920_b33d810.org.apache.hadoop.fs.azurebfs.AbfsConfiguration.getStorageAccountKey(AbfsConfiguration.java:385)
at shaded.databricks.v20180920_b33d810.org.apache.hadoop.fs.azurebfs.AzureBlobFileSystemStore.initializeClient(AzureBlobFileSystemStore.java:802)
at shaded.databricks.v20180920_b33d810.org.apache.hadoop.fs.azurebfs.AzureBlobFileSystemStore.(AzureBlobFileSystemStore.java:133)
at shaded.databricks.v20180920_b33d810.org.apache.hadoop.fs.azurebfs.AzureBlobFileSystem.initialize(AzureBlobFileSystem.java:103)
at org.apache.hadoop.fs.FileSystem.createFileSystem(FileSystem.java:2669)
Never mind, silly mistake. It should be:
val sc = SparkContext.getOrCreate()
val spark = SparkSession.builder().getOrCreate()
sc.getConf.setAppName("Test")
spark.conf.set("fs.azure.account.key.xxxx.dfs.core.windows.net",
"<actual key>")
spark.conf.set("fs.azure.account.auth.type", "OAuth")
spark.conf.set("fs.azure.account.oauth.provider.type",
"org.apache.hadoop.fs.azurebfs.oauth2.ClientCredsTokenProvider")
spark.conf.set("fs.azure.account.oauth2.client.id", "<app id>")
spark.conf.set("fs.azure.account.oauth2.client.secret", "<app password>")
spark.conf.set("fs.azure.account.oauth2.client.endpoint",
"https://login.microsoftonline.com/<tenant id>/oauth2/token")
spark.conf.set
("fs.azure.createRemoteFileSystemDuringInitialization", "false")
This question already has answers here:
Spark - load CSV file as DataFrame?
(14 answers)
Closed 5 years ago.
I'm very new to Spark and Scala(Like two hours new), I'm trying to play with a CSV data file but I cannot do it as I'm not sure how to deal with "Header row", I have searched internet for the way to load it or to skip it but I don't really know how to do that.
I'm pasting my code That I'm using, please help me.
object TaxiCaseOne{
case class NycTaxiData(Vendor_Id:String, PickUpdate:String, Droptime:String, PassengerCount:Int, Distance:Double, PickupLong:String, PickupLat:String, RateCode:Int, Flag:String, DropLong:String, DropLat:String, PaymentMode:String, Fare:Double, SurCharge:Double, Tax:Double, TripAmount:Double, Tolls:Double, TotalAmount:Double)
def mapper(line:String): NycTaxiData = {
val fields = line.split(',')
val data:NycTaxiData = NycTaxiData(fields(0), fields(1), fields(2), fields(3).toInt, fields(4).toDouble, fields(5), fields(6), fields(7).toInt, fields(8), fields(9),fields(10),fields(11),fields(12).toDouble,fields(13).toDouble,fields(14).toDouble,fields(15).toDouble,fields(16).toDouble,fields(17).toDouble)
return data
}def main(args: Array[String]) {
// Set the log level to only print errors
Logger.getLogger("org").setLevel(Level.ERROR)
// Use new SparkSession interface in Spark 2.0
val spark = SparkSession
.builder
.appName("SparkSQL")
.master("local[*]")
.config("spark.sql.warehouse.dir", "file:///C:/temp") // Necessary to work around a Windows bug in Spark 2.0.0; omit if you're not on Windows.
.getOrCreate()
val lines = spark.sparkContext.textFile("../nyc.csv")
val data = lines.map(mapper)
// Infer the schema, and register the DataSet as a table.
import spark.implicits._
val schemaData = data.toDS
schemaData.printSchema()
schemaData.createOrReplaceTempView("data")
// SQL can be run over DataFrames that have been registered as a table
val vendor = spark.sql("SELECT * FROM data WHERE Vendor_Id == 'CMT'")
val results = teenagers.collect()
results.foreach(println)
spark.stop()
}
}
If you have a CSV file you should use spark-csv to read the csv files rather than using textFile
val spark = SparkSession.builder().appName("test val spark = SparkSession
.builder
.appName("SparkSQL")
.master("local[*]")
.config("spark.sql.warehouse.dir", "file:///C:/temp") // Necessary to work around a Windows bug in Spark 2.0.0; omit if you're not on Windows.
.getOrCreate()
val df = spark.read
.format("csv")
.option("header", "true") //This identifies first line as header
.csv("../nyc.csv")
You need a spark-core and spark-sql dependency to work with this
Hope this helps!