This question already has answers here:
Resolving dependency problems in Apache Spark
(7 answers)
Closed 4 years ago.
I have a spark job that needs to run nightly. However, I had to update to spark 2.1 from 1.6. Now I am receiving an error:
java.lang.NoSuchMethodError: org/apache/spark/sql/DataFrameReader.load()Lorg/apache/spark/sql/DataFrame; (loaded from file:/usr/local/src/spark21master/spark-2.1.2-bin-2.7.3/jars/spark-sql_2.11-2.1.2.jar by sun.misc.Launcher$AppClassLoader#305de464) called from class com.ibm.cit.tennis.ServiceStat$ (loaded from file:/tmp/spark-21-ego-master/work/spark-driver-8073f84b-6c09-4d7d-83f5-2c99527eaa1c/spark-service-stat_2.11-1.0.jar by org.apache.spark.util.MutableURLClassLoader#ee80a89b).
In my SBT my build file, I have the following configs:
scalaVersion := "2.11.8"
val sparkVersion = "2.1.2"
libraryDependencies ++= Seq(
"org.apache.spark" %% "spark-core" % sparkVersion % sparkDependencyScope,
"org.apache.spark" %% "spark-sql" % sparkVersion % sparkDependencyScope,
"org.apache.spark" %% "spark-mllib" % sparkVersion % sparkDependencyScope,
"org.apache.spark" %% "spark-streaming" % sparkVersion % sparkDependencyScope,
"org.apache.spark" %% "spark-hive" % sparkVersion % sparkDependencyScope,
"org.apache.spark" %% "spark-repl" % sparkVersion % sparkDependencyScope
"org.apache.spark" %% "spark-graphx" % sparkVersion % sparkDependencyScope
)
I am building with Scala 2.11.8 and Java 1.8.0.
Any help would be appreciated,
Aaron.
NoSuchMethodError exception is a sign of a version mismatch. I suspect you're still trying to use Spark 1.6 to launch your app. It is not clear what is the value of sparkDependencyScope in your example. Normally Spark dependencies are specified with "provided" scope, i.e. the installed version of the Spark runtime.
"org.apache.spark" %% "spark-core" % sparkVersion % "provided"
Try running
spark-submit --version to figure out which Spark launcher version is used. If it is not what you expect, make sure Spark 2.1.2 is installed and in PATH.
The issue has been solved. The libraries were cached in an environment. After the creation of a new environment, SBT was able to pull the latest sources.
Also, I had to add:
conf.set("spark.sql.crossJoin.enabled", "true")
Related
This question already has an answer here:
How to define Kafka (data source) dependencies for Spark Streaming?
(1 answer)
Closed 2 years ago.
I'm new to Spark (using v2.4.5), and am still trying to figure out the correct way to add external dependencies. When trying to add Kafka streaming to my project, my build.sbt looked liked this:
name := "Stream Handler"
version := "1.0"
scalaVersion := "2.11.12"
libraryDependencies ++= Seq(
"org.apache.spark" %% "spark-core" % "2.4.5" % "provided",
"org.apache.spark" % "spark-streaming_2.11" % "2.4.5" % "provided",
"org.apache.spark" % "spark-streaming-kafka-0-10_2.11" % "2.4.5"
)
This builds successfully, but when running with spark-submit, I get a java.lang.NoClassDefFoundError with KafkaUtils.
I was able to get my code working by passing in the dependency thru the --packages option like this:
$ spark-submit [other_args] --packages "org.apache.spark:org.apache.spark:spark-streaming-kafka-0-10_2.11:2.4.5"
Ideally I would like to set up all the dependencies in the build.sbt, but I'm not sure what I'm doing wrong. Any advice would be appreciated!
your "org.apache.spark" % "spark-streaming-kafka-0-10_2.11" % "2.4.5" is wrong.
change that to below like mvnrepo.. https://mvnrepository.com/artifact/org.apache.spark/spark-streaming-kafka-0-10
libraryDependencies += "org.apache.spark" %% "spark-streaming-kafka-0-10" % "2.4.5"
This question already has an answer here:
sbt unresolved dependency for spark-cassandra-connector 2.0.2
(1 answer)
Closed 5 years ago.
I am trying to set up my first Scala project with IntelliJ Idea on Ubuntu 16.04. I need the Spark library and I think I have installed correctly in my computer, however I am not able to refer it in the project dependencies. In particular, I have added the following code in my build.sbt:
libraryDependencies ++= Seq(
"org.apache.spark" % "spark-core" % "2.1.1",
"org.apache.spark" % "spark-sql" % "2.1.1")
However sbt complains about not finding the correct packages (Unresolved Dependencies error, org.apache.spark#spark-core;2.1.1: not found and org.apache.spark#spark-sql;2.1.1: not found):
I think that the versions of the packages are incorrect (I copied the previous code from the web, just to try).
How can I determine the correct packages versions?
If you use % you have to define the exact version as
libraryDependencies ++= Seq(
"org.apache.spark" % "spark-core_2.10" % "2.1.1",
"org.apache.spark" % "spark-sql_2.10" % "2.1.1")
And if you don't want to define the version and let sbt take the correct version then you need to define %% as
libraryDependencies ++= Seq(
"org.apache.spark" %% "spark-core" % "2.1.1",
"org.apache.spark" %% "spark-sql" % "2.1.1")
you can check of installed version by doing
spark-submit --version
And by going to maven dependency
spark depends on an old version of guava.
i build my spark project with sbt assembly, excluding spark using provided, and including the latest version of guava.
However, when running sbt-assembly, the guava dependency is excluded also from the jar.
my build.sbt:
libraryDependencies ++= Seq(
"org.apache.spark" %% "spark-core" % sparkVersion % "provided",
"org.apache.spark" %% "spark-sql" % sparkVersion % "provided",
"org.apache.spark" %% "spark-mllib" % sparkVersion % "provided",
"com.google.guava" % "guava" % "11.0"
)
if i remove the % "provided", then both spark and guava is included.
so, how can i exclude spark and include guava?
You are looking for shading options. See here but basically you need to add shading instructions. Something like this:
assemblyShadeRules in assembly := Seq(
ShadeRule.rename("com.google.guava.**" -> "my_conf.#1")
.inLibrary("com.google.guava" % "config" % "11.0")
.inProject
)
There is also the corresponding maven-shade-plugin for those who prefer maven.
I am trying to compile and create a jar for Spark kinesis stream scala application provided by spark itself in the given link.
Kinesis word count sample
Following is my sbt file, It has all the dependencies and its compiling fine for simple programs.
name := "stream parser"
version := "1.0"
val sparkVersion = "2.0.2"
libraryDependencies ++= Seq(
"org.apache.spark" %% "spark-core" % sparkVersion % "provided",
"org.apache.spark" %% "spark-streaming" % sparkVersion % "provided",
"org.apache.spark" %% "spark-sql" % sparkVersion % "provided",
"org.apache.spark" %% "spark-streaming-kinesis-asl" % sparkVersion % "provided"
)
But the kinesis sample throwing following error while compiling in my Ubuntu system.
trait Logging in package internal cannot be accessed in package org.apache.spark.internal
[error] object KinesisWordCountASL extends Logging {
[error] ^
Logging class import
import org.apache.spark.internal.Logging
Any Idea what could be the problem ?
According to spark repo 2.0 and above Logging class is included in package org.apache.spark.internal spark In previous releases it is in package org.apache.spark I'd suggest run your example setting up spark 2.0.0 or later. Or make changes according usage of appropiate libraries.
Same example with build.sbt is compiling without any issues
scalaVersion := "2.10.6"
libraryDependencies ++= Seq(
"org.apache.spark" %% "spark-core" % "2.0.2",
"org.apache.spark" %% "spark-sql" % "2.0.2",
"org.apache.spark" %% "spark-streaming" % "2.0.2",
"org.apache.spark" %% "spark-streaming-kinesis-asl" % "2.0.2",
"com.amazonaws" % "amazon-kinesis-producer" % "0.10.2"
)
I'm trying to save data from Spark to Cassandra in Scala using saveToCassandra for an RDD or save with a dataframe (both result in the same error). The full message is:
java.lang.ClassCastException:
com.datastax.driver.core.DefaultResultSetFuture cannot be cast to
com.google.common.util.concurrent.ListenableFuture
I've followed along with the code here and still seem to get the error.
I'm using Cassandra 3.6, Spark 1.6.1, and spark-cassandra-connector 1.6. Let me know if there's anything else I can provide to help with the debugging.
I had similar exception and fixed it after changing in build.sbt scala version:
scalaVersion := "2.10.6"
and library dependencies:
libraryDependencies ++= Seq(
"com.datastax.spark" %% "spark-cassandra-connector" % "1.6.0",
"com.datastax.cassandra" % "cassandra-driver-core" % "3.0.2",
"org.apache.spark" %% "spark-core" % "1.6.1" % "provided",
"org.apache.spark" %% "spark-sql" % "1.6.1" % "provided"
)
With this configuration example from 5-minute quick start guide works fine.