用起泡启动数据库上的H2O上下文

Question

问题

我想在Azure中的多节点集群上使用H2O的闪闪发光水，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H2O集群和rocker/verse:4.0.3上的databricksruntime/rbase:latest (以及databricksruntime/standard) Docker容器上启动一个闪闪发光的水上下文，但目前还不能在Databricks集群上启动。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)

我试过的

安装:单节点Azure Databricks集群，7.6ML(包括ApacheSpark3.0.1，Scala2.12)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"作为数据库中的.jar位置RStudio)

• 这里有以下说明：dbc.html

如欲饮用起泡水3.32.1.1-1-3.0，请选择星火3.0.2

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

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)'

Answer 1

在我的例子中，我需要在我的Databricks工作区、集群或作业中安装一个"图书馆“。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. 点击主页图标
2. 单击“共享”>“创建”>“库”
3. 单击"Maven“(作为”库源“)
4. 单击“坐标”框旁边的“搜索包”链接
5. 单击下拉框并选择"Maven Central“
6. 在“查询”框中输入ai.h2o.sparkling-water-package
7. 为me ai.h2o:sparkling-water-package_2.12:3.32.0.5-1-3.0选择与您的ai.h2o:sparkling-water-package_2.12:3.32.0.5-1-3.0版本相匹配的最新“伪Id”和"Release“
8. 单击“选项”下的“选择”
9. 单击“创建”创建库
   • 谢天谢地，在作为Databricks作业运行时，这不需要对我的Databricks R笔记本进行任何更改

# 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)