小贝子编程

r-使用rsparkling在Databricks上启动H2O上下文



问题

我想在Azure Databricks的多节点集群上使用H2O的Sparkling Water，分别通过RStudio和R笔记本进行交互和作业。我可以在本地机器上的rocker/verse:4.0.3和databricksruntime/rbase:latest(以及databricksruntime/standard(Docker容器上启动H2O集群和Sparkling Water上下文，但目前不在Databricks集群上。似乎存在一个典型的类路径问题。

Error : java.lang.ClassNotFoundException: ai.h2o.sparkling.H2OConf
at java.net.URLClassLoader.findClass(URLClassLoader.java:382)
at java.lang.ClassLoader.loadClass(ClassLoader.java:419)
at com.databricks.backend.daemon.driver.ClassLoaders$LibraryClassLoader.loadClass(ClassLoaders.scala:151)
at java.lang.ClassLoader.loadClass(ClassLoader.java:352)
at java.lang.Class.forName0(Native Method)
at java.lang.Class.forName(Class.java:264)
at sparklyr.StreamHandler.handleMethodCall(stream.scala:106)
at sparklyr.StreamHandler.read(stream.scala:61)
at sparklyr.BackendHandler.$anonfun$channelRead0$1(handler.scala:58)
at scala.util.control.Breaks.breakable(Breaks.scala:42)
at sparklyr.BackendHandler.channelRead0(handler.scala:39)
at sparklyr.BackendHandler.channelRead0(handler.scala:14)
at io.netty.channel.SimpleChannelInboundHandler.channelRead(SimpleChannelInboundHandler.java:99)
at io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:379)
at io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:365)
at io.netty.channel.AbstractChannelHandlerContext.fireChannelRead(AbstractChannelHandlerContext.java:357)
at io.netty.handler.codec.MessageToMessageDecoder.channelRead(MessageToMessageDecoder.java:102)
at io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:379)
at io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:365)
at io.netty.channel.AbstractChannelHandlerContext.fireChannelRead(AbstractChannelHandlerContext.java:357)
at io.netty.handler.codec.ByteToMessageDecoder.fireChannelRead(ByteToMessageDecoder.java:321)
at io.netty.handler.codec.ByteToMessageDecoder.channelRead(ByteToMessageDecoder.java:295)
at io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:379)
at io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:365)
at io.netty.channel.AbstractChannelHandlerContext.fireChannelRead(AbstractChannelHandlerContext.java:357)
at io.netty.channel.DefaultChannelPipeline$HeadContext.channelRead(DefaultChannelPipeline.java:1410)
at io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:379)
at io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:365)
at io.netty.channel.DefaultChannelPipeline.fireChannelRead(DefaultChannelPipeline.java:919)
at io.netty.channel.nio.AbstractNioByteChannel$NioByteUnsafe.read(AbstractNioByteChannel.java:163)
at io.netty.channel.nio.NioEventLoop.processSelectedKey(NioEventLoop.java:714)
at io.netty.channel.nio.NioEventLoop.processSelectedKeysOptimized(NioEventLoop.java:650)
at io.netty.channel.nio.NioEventLoop.processSelectedKeys(NioEventLoop.java:576)
at io.netty.channel.nio.NioEventLoop.run(NioEventLoop.java:493)
at io.netty.util.concurrent.SingleThreadEventExecutor$4.run(SingleThreadEventExecutor.java:989)
at io.netty.util.internal.ThreadExecutorMap$2.run(ThreadExecutorMap.java:74)
at io.netty.util.concurrent.FastThreadLocalRunnable.run(FastThreadLocalRunnable.java:30)
at java.lang.Thread.run(Thread.java:748)

我尝试过的

设置：单节点Azure Databricks集群，7.6 ML(包括Apache Spark 3.0.1、Scala 2.12(；Standard_F4s"；驱动程序(我的用例是多节点的，但我试图保持简单(

  • 设置options()，例如options(rsparkling.sparklingwater.version = "2.3.11")或options(rsparkling.sparklingwater.version = "3.0.1")

  • 设置config，例如

    conf$`sparklyr.shell.jars` <- c("/databricks/spark/R/lib/h2o/java/h2o.jar")

或sc <- sparklyr::spark_connect(method = "databricks", version = "3.0.1", config = conf, jars = c("/databricks/spark/R/lib/h2o/java/h2o.jar"))(或"~/R/x86_64-pc-linux-gnu-library/3.6/h2o/java/h2o.jar"或"~/R/x86_64-pc-linux-gnu-library/3.6/rsparkling/java/sparkling_water_assembly.jar"作为Databricks RStudio上的.jar位置(

  • 以下指示如下：http://docs.h2o.ai/sparkling-water/3.0/latest-stable/doc/deployment/rsparkling_azure_dbc.html

对于Sparkling Water 3.32.1.1-1-3.0，选择Spark 3.0.2

Spark 3.0.2不能作为集群使用，选择了3.0.1作为我的方法的其余部分

Error in h2o_context(sc) : could not find function "h2o_context"

在本地机器上工作的Dockerfile

# get the base image (https://hub.docker.com/r/databricksruntime/standard; https://github.com/databricks/containers/blob/master/ubuntu/standard/Dockerfile)
FROM databricksruntime/standard
# not needed if using `FROM databricksruntime/r-base:latest` at top
ENV DEBIAN_FRONTEND noninteractive
# go into the repo directory
RUN . /etc/environment 
# Install linux depedendencies here
&& apt-get update 
&& apt-get install libcurl4-openssl-dev libxml2-dev libssl-dev -y 
# not needed if using `FROM databricksruntime/r-base:latest` at top
&& apt-get install r-base -y
# install specific R packages
RUN R -e 'install.packages(c("httr", "xml2"))'
# sparklyr and Spark
RUN R -e 'install.packages(c("sparklyr"))'
# h2o
# RSparkling 3.32.0.5-1-3.0 requires H2O of version 3.32.0.5.
RUN R -e 'install.packages(c("statmod", "RCurl"))'
RUN R -e 'install.packages("h2o", type = "source", repos = "http://h2o-release.s3.amazonaws.com/h2o/rel-zermelo/5/R")'
# rsparkling
# RSparkling 3.32.0.5-1-3.0 is built for 3.0.
RUN R -e 'install.packages("rsparkling", type = "source", repos = "http://h2o-release.s3.amazonaws.com/sparkling-water/spark-3.0/3.32.0.5-1-3.0/R")'
# connect to H2O cluster with Sparkling Water context
RUN R -e 'library(sparklyr); sparklyr::spark_install("3.0.1", hadoop_version = "3.2"); Sys.setenv(SPARK_HOME = "~/spark/spark-3.0.1-bin-hadoop3.2"); library(rsparkling); sc <- sparklyr::spark_connect(method = "databricks", version = "3.0.1"); sparklyr::spark_version(sc); h2oConf <- H2OConf(); hc <- H2OContext.getOrCreate(h2oConf)'

在我的情况下，我需要安装一个；图书馆"；到我的Databricks工作区、集群或作业。我可以上传它，也可以让Databricks从Maven坐标中获取它。

在Databricks工作区中：

  1. 单击主页图标
  2. 点击"；共享的">quot；创建">quot；图书馆">
  3. 点击"；Maven"；(作为"Library Source"(
  4. 点击"；搜索包"；"旁边的链接；坐标"；箱子
  5. 点击下拉框并选择"；Maven Central">
  6. 将CCD_ 11输入到"；查询"；箱子
  7. 选择最近的"；工件Id"；用"；释放"；与您的rsparkling版本相匹配，对我来说是ai.h2o:sparkling-water-package_2.12:3.32.0.5-1-3.0
  8. 点击"；选择"；在"；选项">
  9. 点击"；创建"；创建库
    • 谢天谢地，当作为Databricks作业运行时，这不需要更改我的DatabricksR笔记本
# install specific R packages
install.packages(c("httr", "xml2"))
# sparklyr and Spark
install.packages(c("sparklyr"))
# h2o
# RSparkling 3.32.0.5-1-3.0 requires H2O of version 3.32.0.5.
install.packages(c("statmod", "RCurl"))
install.packages("h2o", type = "source", repos = "http://h2o-release.s3.amazonaws.com/h2o/rel-zermelo/5/R")
# rsparkling
# RSparkling 3.32.0.5-1-3.0 is built for 3.0.
install.packages("rsparkling", type = "source", repos = "http://h2o-release.s3.amazonaws.com/sparkling-water/spark-3.0/3.32.0.5-1-3.0/R")
# connect to H2O cluster with Sparkling Water context
library(sparklyr)
sparklyr::spark_install("3.0.1", hadoop_version = "3.2")
Sys.setenv(SPARK_HOME = "~/spark/spark-3.0.1-bin-hadoop3.2")
sparklyr::spark_default_version()
library(rsparkling)

SparkR::sparkR.session()
sc <- sparklyr::spark_connect(method = "databricks", version = "3.0.1")
sparklyr::spark_version(sc)
# next command will not work without adding https://mvnrepository.com/artifact/ai.h2o/sparkling-water-package_2.12/3.32.0.5-1-3.0 file as "Library" to Databricks cluster
h2oConf <- H2OConf()
hc <- H2OContext.getOrCreate(h2oConf)

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