From bdc60569196e9ae4e9086c3e514a406a9e8b23a6 Mon Sep 17 00:00:00 2001
From: ymahajan <ymahajan@snappydata.io>
Date: Wed, 19 Apr 2017 20:08:31 -0700
Subject: [PATCH] Fixed typos in docs
## What changes were proposed in this pull request?
Typos at a couple of place in the docs.
## How was this patch tested?
build including docs
Please review http://spark.apache.org/contributing.html before opening a pull request.
Author: ymahajan <ymahajan@snappydata.io>
Closes #17690 from ymahajan/master.
---
docs/sql-programming-guide.md | 2 +-
docs/structured-streaming-programming-guide.md | 2 +-
2 files changed, 2 insertions(+), 2 deletions(-)
diff --git a/docs/sql-programming-guide.md b/docs/sql-programming-guide.md
index 28942b68fa..490c1ce8a7 100644
--- a/docs/sql-programming-guide.md
+++ b/docs/sql-programming-guide.md
@@ -571,7 +571,7 @@ be created by calling the `table` method on a `SparkSession` with the name of th
For file-based data source, e.g. text, parquet, json, etc. you can specify a custom table path via the
`path` option, e.g. `df.write.option("path", "/some/path").saveAsTable("t")`. When the table is dropped,
the custom table path will not be removed and the table data is still there. If no custom table path is
-specifed, Spark will write data to a default table path under the warehouse directory. When the table is
+specified, Spark will write data to a default table path under the warehouse directory. When the table is
dropped, the default table path will be removed too.
Starting from Spark 2.1, persistent datasource tables have per-partition metadata stored in the Hive metastore. This brings several benefits:
diff --git a/docs/structured-streaming-programming-guide.md b/docs/structured-streaming-programming-guide.md
index 3cf7151819..5b18cf2f3c 100644
--- a/docs/structured-streaming-programming-guide.md
+++ b/docs/structured-streaming-programming-guide.md
@@ -778,7 +778,7 @@ windowedCounts = words \
In this example, we are defining the watermark of the query on the value of the column "timestamp",
and also defining "10 minutes" as the threshold of how late is the data allowed to be. If this query
is run in Update output mode (discussed later in [Output Modes](#output-modes) section),
-the engine will keep updating counts of a window in the Resule Table until the window is older
+the engine will keep updating counts of a window in the Result Table until the window is older
than the watermark, which lags behind the current event time in column "timestamp" by 10 minutes.
Here is an illustration.
--
GitLab