From 2bc348e92c458ea36872ac43a2583370d1f3eb41 Mon Sep 17 00:00:00 2001 From: Prashant Sharma Date: Fri, 23 Aug 2013 09:41:32 +0530 Subject: Linking custom receiver guide --- docs/streaming-programming-guide.md | 3 +++ 1 file changed, 3 insertions(+) (limited to 'docs') diff --git a/docs/streaming-programming-guide.md b/docs/streaming-programming-guide.md index 8cd1b0cd66..a74c17bdb7 100644 --- a/docs/streaming-programming-guide.md +++ b/docs/streaming-programming-guide.md @@ -301,6 +301,9 @@ dstream.checkpoint(checkpointInterval) // checkpointInterval must be a multiple For DStreams that must be checkpointed (that is, DStreams created by `updateStateByKey` and `reduceByKeyAndWindow` with inverse function), the checkpoint interval of the DStream is by default set to a multiple of the DStream's sliding interval such that its at least 10 seconds. +## Customizing Receiver +Spark comes with a built in support for most common usage scenarios where input stream source can be either a network socket stream to support for a few message queues. Apart from that it is also possible to supply your own custom receiver via a convenient API. Find more details at [Custom Receiver Guide](streaming-custom-receivers.html) + # Performance Tuning Getting the best performance of a Spark Streaming application on a cluster requires a bit of tuning. This section explains a number of the parameters and configurations that can tuned to improve the performance of you application. At a high level, you need to consider two things:
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