If you wait for an inactivity period before ending a test case, you get the choice of using a long timeout, resulting in slow tests, or a short timeout, resulting in brittle tests. Both options will make developers waste time, and harm developer productivity.
I suggest that you terminate the test case based on the test predicate getting fulfilled, with a long timeout in case of test failure. I presume that your application produces output to a channel, e.g. database or Kafka topic, which the test oracle can inspect for test completeness. The flow becomes: 1. Start the stream source component and output component locally, e.g. Kafka+Cassandra 2. Start the Spark streaming application, typically with a local master. 3. Feed the input into the stream source, e.g. a Kafka test topic. 4. Let the test oracle loop, polling for a condition to be met on the output, e.g. existence of a database entry or a message on the Kafka output topic. Sleep for a short period (10 ms) between polls, and fail the test if the condition is not met after a long time (30 s). 5. Terminate the streaming application. 6. Terminate the stream source and output components. I have used this strategy for testing both financial transaction systems and spark streaming applications, and it has resulted in both fast and reliable tests, without strong coupling between production code and test code. The base strategy fails if you need to test for the absence of an output event, e.g. when my streaming event sees message X, it should filter it and not produce output. You then need to send another input event (poison pill pattern), and terminate on the output effects of the poison pill event. If you test with multiple streaming executors, remember that there are no order guarantees between executors; you will need to either make sure that all executors receive poison pills, and test for the pill effects of all of them. Starting the source+output components and the Spark context can be slow. I recommend that you provide the option to reuse the test fixture between test cases for speed. For example, if you start and stop the fixture once for each test class, rather than once per test method, you save a lot of time. Regards, Lars Albertsson Data engineering consultant www.mapflat.com +46 70 7687109 On Fri, Feb 26, 2016 at 8:24 AM, Mao, Wei <wei....@intel.com> wrote: > I would argue against making it configurable unless there is real production > use case. If it’s just for test, there are bunch of ways to achieve it. For > example, you can mark if test streaming is finished globally, and stop ssc > on another thread when status of that mark changed. > > > > Back to original exception, blindly calling “Option.get” is always not a > good practice. It would be better to pre-validate or use > getOption/getOrElse. > > > > Thanks, > > William > > > > From: Cheng, Hao [mailto:hao.ch...@intel.com] > Sent: Thursday, February 25, 2016 1:03 AM > To: Daniel Siegmann; Ashutosh Kumar > Cc: Hemant Bhanawat; Ted Yu; Femi Anthony; user > Subject: RE: Spark Streaming - graceful shutdown when stream has no more > data > > > > This is very interesting, how to shutdown the streaming job gracefully once > no input data for some time. > > > > A doable solution probably you can count the input data by using the > Accumulator, and anther thread (in master node) will always to get the > latest accumulator value, if there is no value change from the accumulator > for sometime, then shutdown the streaming job. > > > > From: Daniel Siegmann [mailto:daniel.siegm...@teamaol.com] > Sent: Wednesday, February 24, 2016 12:30 AM > To: Ashutosh Kumar <kmr.ashutos...@gmail.com> > Cc: Hemant Bhanawat <hemant9...@gmail.com>; Ted Yu <yuzhih...@gmail.com>; > Femi Anthony <femib...@gmail.com>; user <user@spark.apache.org> > Subject: Re: Spark Streaming - graceful shutdown when stream has no more > data > > > > During testing you will typically be using some finite data. You want the > stream to shut down automatically when that data has been consumed so your > test shuts down gracefully. > > Of course once the code is running in production you'll want it to keep > waiting for new records. So whether the stream shuts down when there's no > more data should be configurable. > > > > On Tue, Feb 23, 2016 at 11:09 AM, Ashutosh Kumar <kmr.ashutos...@gmail.com> > wrote: > > Just out of curiosity I will like to know why a streaming program should > shutdown when no new data is arriving? I think it should keep waiting for > arrival of new records. > > Thanks > > Ashutosh > > > > On Tue, Feb 23, 2016 at 9:17 PM, Hemant Bhanawat <hemant9...@gmail.com> > wrote: > > A guess - parseRecord is returning None in some case (probaly empty lines). > And then entry.get is throwing the exception. > > You may want to filter the None values from accessLogDStream before you run > the map function over it. > > Hemant > > > Hemant Bhanawat > > www.snappydata.io > > > > On Tue, Feb 23, 2016 at 6:00 PM, Ted Yu <yuzhih...@gmail.com> wrote: > > Which line is line 42 in your code ? > > > > When variable lines becomes empty, you can stop your program. > > > > Cheers > > > On Feb 23, 2016, at 12:25 AM, Femi Anthony <femib...@gmail.com> wrote: > > I am working on Spark Streaming API and I wish to stream a set of > pre-downloaded web log files continuously to simulate a real-time stream. I > wrote a script that gunzips the compressed logs and pipes the output to nc > on port 7777. > > The script looks like this: > > BASEDIR=/home/mysuer/data/datamining/internet_traffic_archive > > zipped_files=`find $BASEDIR -name "*.gz"` > > > > for zfile in $zipped_files > > do > > echo "Unzipping $zfile..." > > gunzip -c $zfile | nc -l -p 7777 -q 20 > > > > done > > I have streaming code written in Scala that processes the streams. It works > well for the most part, but when its run out of files to stream I get the > following error in Spark: > > 16/02/19 23:04:35 WARN ReceiverSupervisorImpl: > > Restarting receiver with delay 2000 ms: Socket data stream had no more data > > 16/02/19 23:04:35 ERROR ReceiverTracker: Deregistered receiver for stream 0: > > Restarting receiver with delay 2000ms: Socket data stream had no more data > > 16/02/19 23:04:35 WARN BlockManager: Block input-0-1455941075600 replicated > to only 0 peer(s) instead of 1 peers > > .... > > 16/02/19 23:04:40 ERROR Executor: Exception in task 2.0 in stage 15.0 (TID > 47) > > java.util.NoSuchElementException: None.get > > at scala.None$.get(Option.scala:313) > > at scala.None$.get(Option.scala:311) > > at > com.femibyte.learningsparkaddexamples.scala.StreamingLogEnhanced$$anonfun$2.apply(StreamingLogEnhanced.scala:42) > > at > com.femibyte.learningsparkaddexamples.scala.StreamingLogEnhanced$$anonfun$2.apply(StreamingLogEnhanced.scala:42) > > How to I implement a graceful shutdown so that the program exits gracefully > when it no longer detects any data in the stream ? > > My Spark Streaming code looks like this: > > object StreamingLogEnhanced { > > def main(args: Array[String]) { > > val master = args(0) > > val conf = new > > SparkConf().setMaster(master).setAppName("StreamingLogEnhanced") > > // Create a StreamingContext with a n second batch size > > val ssc = new StreamingContext(conf, Seconds(10)) > > // Create a DStream from all the input on port 7777 > > val log = Logger.getLogger(getClass.getName) > > > > sys.ShutdownHookThread { > > log.info("Gracefully stopping Spark Streaming Application") > > ssc.stop(true, true) > > log.info("Application stopped") > > } > > val lines = ssc.socketTextStream("localhost", 7777) > > // Create a count of log hits by ip > > var ipCounts=countByIp(lines) > > ipCounts.print() > > > > // start our streaming context and wait for it to "finish" > > ssc.start() > > // Wait for 600 seconds then exit > > ssc.awaitTermination(10000*600) > > ssc.stop() > > } > > > > def countByIp(lines: DStream[String]) = { > > val parser = new AccessLogParser > > val accessLogDStream = lines.map(line => parser.parseRecord(line)) > > val ipDStream = accessLogDStream.map(entry => > > (entry.get.clientIpAddress, 1)) > > ipDStream.reduceByKey((x, y) => x + y) > > } > > > > } > > Thanks for any suggestions in advance. > > > > > > > > --------------------------------------------------------------------- To unsubscribe, e-mail: user-unsubscr...@spark.apache.org For additional commands, e-mail: user-h...@spark.apache.org
