Open-source InfluxDB connector for Apache Spark on top of Chronicler.
At the beginning add required module to your build.sbt:
// For RDD
libraryDependencies += "com.github.fsanaulla" %% "chronicler-spark-rdd" % <version>
// For Dataset
libraryDependencies += "com.github.fsanaulla" %% "chronicler-spark-ds" % <version>
// For Structured Streaming
libraryDependencies += "com.github.fsanaulla" %% "chronicler-spark-structured-streaming" % <version>
// For DStream
libraryDependencies += "com.github.fsanaulla" %% "chronicler-spark-streaming" % <version>
Default configuration:
final case class InfluxConfig(
host: String,
port: Int = 8086,
credentials: Option[InfluxCredentials] = None,
compress: Boolean = false,
ssl: Boolean = false)
It's recommended to enable data compression to decrease network traffic.
For RDD[T]:
import com.github.fsanaulla.chronicler.spark.rdd._
val rdd: RDD[T] = _
rdd.saveToInfluxDBMeas("dbName", "measurementName")
// to save with dynamicly generated measurement
rdd.saveToInfluxDB("dbName")
For Dataset[T]:
import com.github.fsanaulla.chronicler.spark.ds._
val ds: Dataset[T] = _
ds.saveToInfluxDBMeas("dbName", "measurementName")
// to save with dynamicly generated measurement
ds.saveToInfluxDB("dbName")
For DataStreamWriter[T]
import com.github.fsanaulla.chronicler.spark.structured.streaming._
val structStream: DataStreamWriter[T] = _
val saved = structStream.saveToInfluxDBMeas("dbName", "measurementName")
// to save with dynamicly generated measurement
val saved = structStream.saveToInfluxDB("dbName")
..
saved.start().awaitTermination()
For DStream[T]:
import com.github.fsanaulla.chronicler.spark.streaming._
val stream: DStream[T] = _
stream.saveToInfluxDBMeas("dbName", "measurementName")
stream,saveToInfluxDB("dbName")
