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Sparklyr extension making Flint time series library functionalities (https://github.com/twosigma/flint) easily accessible through R

R 85.98% Shell 14.02%
flint time-series r spark data-science rstats apache-spark sparklyr remote-clusters distributed

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sparklyr.flint's Issues

Azure Databricks java.lang.ClassNotFoundException: com.twosigma.flint.timeseries.TimeSeriesRDDBuilder

Error

# Step 2: specify how the Spark dataframe should be interpreted as a time series by Flint
ts_rdd <- fromSDF(sdf, is_sorted = TRUE, time_unit = "SECONDS", time_column = "t")
Error : java.lang.ClassNotFoundException: com.twosigma.flint.timeseries.TimeSeriesRDDBuilder

Expectation

That I can use basic sparklyr.flint functions on Azure Databricks without classpath errors by using install.packages("sparklyr.flint").

Details

I've created a "Library" with flint-0.6.0 from Maven and installed it onto my cluster, detached and reattached my notebook, called library(sparklyr.flint) before spark_connect() and it still can't find the library.

Config

  • Databricks 7.3 ML LTS (1 driver, 1 worker)
  • Spark 3.0.1
  • sparklyr 1.6.2
  • sparklyr.flint 0.2.1

Reproducible code

install.packages("sparklyr")
install.packages("sparklyr.flint")
library(sparklyr)
library(sparklyr.flint)

# Step 0: decide which Spark version to use, how to connect to Spark, etc
# spark_version <- "3.0.0"
Sys.setenv(SPARK_HOME = "~/spark/spark-3.0.1-bin-hadoop3.2")
sc <- spark_connect(method = "databricks")

example_time_series <- data.frame(
  t = c(1, 3, 4, 6, 7, 10, 15, 16, 18, 19),
  v = c(4, -2, NA, 5, NA, 1, -4, 5, NA, 3)
)

# Step 1: import example time series data into a Spark dataframe
sdf <- copy_to(sc, example_time_series, overwrite = TRUE)

# Step 2: specify how the Spark dataframe should be interpreted as a time series by Flint
ts_rdd <- fromSDF(sdf, is_sorted = TRUE, time_unit = "SECONDS", time_column = "t")
Error : java.lang.ClassNotFoundException: com.twosigma.flint.timeseries.TimeSeriesRDDBuilder Error : java.lang.ClassNotFoundException: com.twosigma.flint.timeseries.TimeSeriesRDDBuilder
	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)

R interface for asof join

ASOF join is another highly desirable feature supported by Flint, aside from its wide range of summarizer functionalities.

streaming

need to find the best way to apply the summarizer functionalities to streaming use cases

create blog post for initial CRAN release of sparklyr.flint

As there appears to be some interest in the open-source community in using sparklyr.flint shortly after it became available on CRAN 7 days ago, which is definitely encouraging to see:

> library(dlstats)
> cran_stats("sparklyr.flint")
       start        end downloads        package
1 2020-08-01 2020-08-31        59 sparklyr.flint

documentation

This project is under active development and some bits of documentation are not created yet (e.g., some function parameters were not documented)

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