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.NET for Apache Spark C# Samples: Streamingis a free, open-source, and cross-platform big data analytics framework.In the Streaming folder, we provide C# samples which will help you get started with the big data analytics scenario known asstructured streaming. Stream processing means we're analyzing data live as it's being produced.
ProblemThese samples are examples of stream processing since we're processing data in real time. SolutionYou'll see there are four different samples included in the Streaming folder:.
word count on data streamed from any source (i.e. Netcat). word count on data with windowing logic. word count on data streamed from Kafka. sample counting number of characters in each string read from a stream; demonstrating power of UDFs + stream processingWhile the steps below apply to most stream processing apps, some of the specific code snippets or submission instructions pertain specifically to the example. Create a Spark SessionIn any Spark application, we need to establish a new SparkSession, which is the entry point to programming Spark with the Dataset and DataFrame API. SparkSession spark = SparkSession.
AppName( 'Streaming example with a UDF '). GetOrCreate;By calling on the spark object created above, we can access Spark and DataFrame functionality throughout our program. Establish and Connect to Data StreamDepending upon which sample you choose to run, you need to setup a connection to a data stream. One popular way to test out stream processing is through netcat.
Establish Stream: Netcatnetcat (also known as nc) allows you to read from and write to network connections. We'll establish a networkconnection with netcat through a terminal window., extract the file from the zip download, and append thedirectory you extracted to your 'PATH' environment variable.To start a new connection, open a command prompt. For Linux users, run nc -lk 9999 to connect to localhost on port 9999.Windows users can run nc -vvv -l -p 9999 to connect to localhost port 9999. The result should look something like this:Our Spark program will be listening for input we type into this command prompt. Connect to Stream: ReadStreamThe ReadStream method returns a DataStreamReader that can be used to read streaming data in as a DataFrame.
We'll include the host and port information so that our Spark app knows where to expect its streaming data. DataFrame arrayDF = lines.
Select( Explode( udfArray( lines 'value ')));In the above code snippet from, we apply udfArray to each value in our DataFrame (which represents each string read in from our netcat terminal). We then apply the SparkSQL method Explode to put each entry of our array in its own row. Finally, we use Select to place the columns we've produced in the new DataFrame arrayDF. Display Your StreamWe can use DataFrame.WriteStream to establish characteristics of our output, such as printing our results to the console and only displaying the most recent output and not all of our previous output as well. StreamingQuery query = arrayDf.
Format( 'console '). Running Your CodeStructured streaming in Spark processes data through a series of small batches.When you run your program, the command prompt where we established the netcat will allow you to start typing.In our example, when you hit enter after entering data in the command prompt, Spark will consider that a batch and run the UDF.Check out the directions for building and running this app on. Windows Example:After starting a new netcat session, open a new terminal and run your spark-submit command, similar to the following.
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Spark -submit -class org.apache.spark.deploy.dotnet.DotnetRunner -master local /path /to /microsoft -spark -.jar Microsoft.Spark.CSharp.Examples.exe Sql.Streaming.StructuredNetworkCharacterCount localhost 9999Note: Be sure to update the above command with the actual path to your Microsoft Spark jar file. The above command also assumes your netcat server is running on localhost port 9999. Additional ResourcesTo learn more about structured streaming with.NET for Apache Spark, check out from the.NET for Apache Spark 101 video series to see a streaming demo coded and ran live.You can also from the.NET for Apache Spark session at.NET Conf 2019!