使用 rsparkling 在 Databricks 上启动 H2O 上下文

Posted 2023-03-23

【中文标题】使用 rsparkling 在 Databricks 上启动 H2O 上下文

【英文标题】：Start H2O context on Databricks with rsparkling

【发布时间】：2021-07-15 23:27:35

【问题描述】：

问题

我想分别在 Azure Databricks 中的多节点集群上以交互方式和通过 RStudio 和 R 笔记本在作业中使用 H2O 的苏打水。我可以在我的本地计算机上的 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。似乎有一个经典的类路径问题。

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)

我的尝试

设置：单节点 Azure Databricks 集群，7.6 ML（包括 Apache Spark 3.0.1，Scala 2.12），带有“Standard_F4s”驱动程序（我的用例是多节点，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）

按照这里的指示：http://docs.h2o.ai/sparkling-water/3.0/latest-stable/doc/deployment/rsparkling_azure_dbc.html

对于苏打水 3.32.1.1-1-3.0，请选择 Spark 3.0.2

Spark 3.0.2 不能作为集群使用，在我的其余方法中选择了 3.0.1

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)'

【问题讨论】：

根据您的错误，您没有在 R 上导入苏打水，library(rsparkling)

@NeemaMashayekhi，我认为该包应该已在 Dockerfile 的最后一行“# connect to H2O cluster with Sparkling Water context”下导入，请参阅library(rsparkling) 向右滚动。谢谢！

【参考方案1】：

就我而言，我需要将“Library”安装到我的 Databricks 工作区、集群或作业中。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：

点击主页图标 点击“共享”>“创建”>“库” 点击“Maven”（作为“库源”） 点击“坐标”框旁边的“搜索包”链接 点击下拉框并选择“Maven Central” 在“查询”框中输入ai.h2o.sparkling-water-package 选择与您的rsparkling 版本匹配的最近的“工件ID”和“发布”，对我来说ai.h2o:sparkling-water-package_2.12:3.32.0.5-1-3.0 点击“选项”下的“选择” 单击“创建”以创建库 谢天谢地，当作为 Databricks 作业运行时，不需要对我的 Databricks R Notebook 进行任何更改

# 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)