Difference between spark_session and sqlContext on loading a local file
I'm tried to load a local file as dataframe with using spark_session and sqlContext.
df = spark_session.read...load(localpath)
It couldn't read local files. df is empty.
But, after creating sqlcontext from spark_context, it could load a local file.
sqlContext = SQLContext(spark_context)
df = sqlContext.read...load(localpath)
It worked fine. But I can't understand why. What is the cause ?
Envionment: Windows10, spark 2.2.1
EDIT
Finally I've resolved this problem. The root cause is version difference between PySpark installed with pip and PySpark installed in local file system. PySpark failed to start because of py4j failing.
apache-spark pyspark
asked Nov 28 '18 at 10:19
hiroponhiropon
9462928
add a comment |
I'm tried to load a local file as dataframe with using spark_session and sqlContext.
df = spark_session.read...load(localpath)
It couldn't read local files. df is empty.
But, after creating sqlcontext from spark_context, it could load a local file.
sqlContext = SQLContext(spark_context)
df = sqlContext.read...load(localpath)
It worked fine. But I can't understand why. What is the cause ?
Envionment: Windows10, spark 2.2.1
EDIT
Finally I've resolved this problem. The root cause is version difference between PySpark installed with pip and PySpark installed in local file system. PySpark failed to start because of py4j failing.
apache-spark pyspark
asked Nov 28 '18 at 10:19
hiroponhiropon
9462928
Almost the same issue stackoverflow.com/q/48026195/2565527
– hiropon
Nov 28 '18 at 10:55
add a comment |
I'm tried to load a local file as dataframe with using spark_session and sqlContext.
df = spark_session.read...load(localpath)
It couldn't read local files. df is empty.
But, after creating sqlcontext from spark_context, it could load a local file.
sqlContext = SQLContext(spark_context)
df = sqlContext.read...load(localpath)
It worked fine. But I can't understand why. What is the cause ?
Envionment: Windows10, spark 2.2.1
EDIT
Finally I've resolved this problem. The root cause is version difference between PySpark installed with pip and PySpark installed in local file system. PySpark failed to start because of py4j failing.
apache-spark pyspark
asked Nov 28 '18 at 10:19
hiroponhiropon
9462928
I'm tried to load a local file as dataframe with using spark_session and sqlContext.
df = spark_session.read...load(localpath)
It couldn't read local files. df is empty.
But, after creating sqlcontext from spark_context, it could load a local file.
sqlContext = SQLContext(spark_context)
df = sqlContext.read...load(localpath)
It worked fine. But I can't understand why. What is the cause ?
Envionment: Windows10, spark 2.2.1
EDIT
Finally I've resolved this problem. The root cause is version difference between PySpark installed with pip and PySpark installed in local file system. PySpark failed to start because of py4j failing.
apache-spark pyspark
apache-spark pyspark
asked Nov 28 '18 at 10:19
hiroponhiropon
9462928
asked Nov 28 '18 at 10:19
hiroponhiropon
9462928
edited Jan 10 at 2:18
hiropon
asked Nov 28 '18 at 10:19
hiroponhiropon
9462928
asked Nov 28 '18 at 10:19
hiroponhiropon
9462928
asked Nov 28 '18 at 10:19
hiroponhiropon
9462928
9462928
Almost the same issue stackoverflow.com/q/48026195/2565527
– hiropon
Nov 28 '18 at 10:55
add a comment |
Almost the same issue stackoverflow.com/q/48026195/2565527
– hiropon
Nov 28 '18 at 10:55
Almost the same issue stackoverflow.com/q/48026195/2565527
– hiropon
Nov 28 '18 at 10:55
Almost the same issue stackoverflow.com/q/48026195/2565527
– hiropon
Nov 28 '18 at 10:55
add a comment |
1 Answer
1
active
oldest
votes
I am pasting a sample code that might help. We have used this to create a Sparksession object and read a local file with it:
import org.apache.spark.sql.SparkSession
object SetTopBox_KPI1_1 {
def main(args: Array[String]): Unit = {
if(args.length < 2) {
System.err.println("SetTopBox Data Analysis <Input-File> OR <Output-File> is missing")
System.exit(1)
}
val spark = SparkSession.builder().appName("KPI1_1").getOrCreate()
val record = spark.read.textFile(args(0)).rdd
.....
On the whole, in Spark 2.2 the preferred way to use Spark is by creating a SparkSession object.
answered Nov 28 '18 at 12:35
BDABDA
25610
add a comment |
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1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
I am pasting a sample code that might help. We have used this to create a Sparksession object and read a local file with it:
import org.apache.spark.sql.SparkSession
object SetTopBox_KPI1_1 {
def main(args: Array[String]): Unit = {
if(args.length < 2) {
System.err.println("SetTopBox Data Analysis <Input-File> OR <Output-File> is missing")
System.exit(1)
}
val spark = SparkSession.builder().appName("KPI1_1").getOrCreate()
val record = spark.read.textFile(args(0)).rdd
.....
On the whole, in Spark 2.2 the preferred way to use Spark is by creating a SparkSession object.
answered Nov 28 '18 at 12:35
BDABDA
25610
add a comment |
I am pasting a sample code that might help. We have used this to create a Sparksession object and read a local file with it:
import org.apache.spark.sql.SparkSession
object SetTopBox_KPI1_1 {
def main(args: Array[String]): Unit = {
if(args.length < 2) {
System.err.println("SetTopBox Data Analysis <Input-File> OR <Output-File> is missing")
System.exit(1)
}
val spark = SparkSession.builder().appName("KPI1_1").getOrCreate()
val record = spark.read.textFile(args(0)).rdd
.....
On the whole, in Spark 2.2 the preferred way to use Spark is by creating a SparkSession object.
answered Nov 28 '18 at 12:35
BDABDA
25610
add a comment |
I am pasting a sample code that might help. We have used this to create a Sparksession object and read a local file with it:
import org.apache.spark.sql.SparkSession
object SetTopBox_KPI1_1 {
def main(args: Array[String]): Unit = {
if(args.length < 2) {
System.err.println("SetTopBox Data Analysis <Input-File> OR <Output-File> is missing")
System.exit(1)
}
val spark = SparkSession.builder().appName("KPI1_1").getOrCreate()
val record = spark.read.textFile(args(0)).rdd
.....
On the whole, in Spark 2.2 the preferred way to use Spark is by creating a SparkSession object.
answered Nov 28 '18 at 12:35
BDABDA
25610
I am pasting a sample code that might help. We have used this to create a Sparksession object and read a local file with it:
import org.apache.spark.sql.SparkSession
object SetTopBox_KPI1_1 {
def main(args: Array[String]): Unit = {
if(args.length < 2) {
System.err.println("SetTopBox Data Analysis <Input-File> OR <Output-File> is missing")
System.exit(1)
}
val spark = SparkSession.builder().appName("KPI1_1").getOrCreate()
val record = spark.read.textFile(args(0)).rdd
.....
On the whole, in Spark 2.2 the preferred way to use Spark is by creating a SparkSession object.
answered Nov 28 '18 at 12:35
BDABDA
25610
answered Nov 28 '18 at 12:35
BDABDA
25610
answered Nov 28 '18 at 12:35
BDABDA
25610
answered Nov 28 '18 at 12:35
BDABDA
25610
25610
add a comment |
add a comment |
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Almost the same issue stackoverflow.com/q/48026195/2565527
– hiropon
Nov 28 '18 at 10:55