From d0bfc6733521709e453d643582df2bdd68f28de7 Mon Sep 17 00:00:00 2001
From: Prashant Sharma <prashant@apache.org>
Date: Fri, 7 Jul 2017 23:33:12 -0700
Subject: [PATCH] [SPARK-21069][SS][DOCS] Add rate source to programming guide.
## What changes were proposed in this pull request?
SPARK-20979 added a new structured streaming source: Rate source. This patch adds the corresponding documentation to programming guide.
## How was this patch tested?
Tested by running jekyll locally.
Author: Prashant Sharma <prashant@apache.org>
Author: Prashant Sharma <prashsh1@in.ibm.com>
Closes #18562 from ScrapCodes/spark-21069/rate-source-docs.
---
docs/structured-streaming-programming-guide.md | 15 +++++++++++++++
1 file changed, 15 insertions(+)
diff --git a/docs/structured-streaming-programming-guide.md b/docs/structured-streaming-programming-guide.md
index 3bc377c9a3..8f64faadc3 100644
--- a/docs/structured-streaming-programming-guide.md
+++ b/docs/structured-streaming-programming-guide.md
@@ -499,6 +499,8 @@ There are a few built-in sources.
- **Socket source (for testing)** - Reads UTF8 text data from a socket connection. The listening server socket is at the driver. Note that this should be used only for testing as this does not provide end-to-end fault-tolerance guarantees.
+ - **Rate source (for testing)** - Generates data at the specified number of rows per second, each output row contains a `timestamp` and `value`. Where `timestamp` is a `Timestamp` type containing the time of message dispatch, and `value` is of `Long` type containing the message count, starting from 0 as the first row. This source is intended for testing and benchmarking.
+
Some sources are not fault-tolerant because they do not guarantee that data can be replayed using
checkpointed offsets after a failure. See the earlier section on
[fault-tolerance semantics](#fault-tolerance-semantics).
@@ -546,6 +548,19 @@ Here are the details of all the sources in Spark.
<td>No</td>
<td></td>
</tr>
+ <tr>
+ <td><b>Rate Source</b></td>
+ <td>
+ <code>rowsPerSecond</code> (e.g. 100, default: 1): How many rows should be generated per second.<br/><br/>
+ <code>rampUpTime</code> (e.g. 5s, default: 0s): How long to ramp up before the generating speed becomes <code>rowsPerSecond</code>. Using finer granularities than seconds will be truncated to integer seconds. <br/><br/>
+ <code>numPartitions</code> (e.g. 10, default: Spark's default parallelism): The partition number for the generated rows. <br/><br/>
+
+ The source will try its best to reach <code>rowsPerSecond</code>, but the query may be resource constrained, and <code>numPartitions</code> can be tweaked to help reach the desired speed.
+ </td>
+ <td>Yes</td>
+ <td></td>
+ </tr>
+
<tr>
<td><b>Kafka Source</b></td>
<td>
--
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