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authorPeter Hoffmann <ph@peter-hoffmann.com>2015-06-14 11:41:16 -0700
committerReynold Xin <rxin@databricks.com>2015-06-14 11:41:16 -0700
commitf3f2a4397da164f0ddfa5d60bf441099296c4346 (patch)
treee6be327feadaa7a5957fe0d7953f2a8860fb5e06 /docs
parent53c16b92a537c392a7c3ebc3ef24c1e86cb1a7a4 (diff)
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fix read/write mixup
Author: Peter Hoffmann <ph@peter-hoffmann.com> Closes #6815 from hoffmann/patch-1 and squashes the following commits: 2abb6da [Peter Hoffmann] fix read/write mixup
Diffstat (limited to 'docs')
-rw-r--r--docs/sql-programming-guide.md4
1 files changed, 2 insertions, 2 deletions
diff --git a/docs/sql-programming-guide.md b/docs/sql-programming-guide.md
index c5ab074e44..7fed1bf882 100644
--- a/docs/sql-programming-guide.md
+++ b/docs/sql-programming-guide.md
@@ -995,11 +995,11 @@ List<String> teenagerNames = teenagers.javaRDD().map(new Function<Row, String>()
schemaPeople # The DataFrame from the previous example.
# DataFrames can be saved as Parquet files, maintaining the schema information.
-schemaPeople.read.parquet("people.parquet")
+schemaPeople.write.parquet("people.parquet")
# Read in the Parquet file created above. Parquet files are self-describing so the schema is preserved.
# The result of loading a parquet file is also a DataFrame.
-parquetFile = sqlContext.write.parquet("people.parquet")
+parquetFile = sqlContext.read.parquet("people.parquet")
# Parquet files can also be registered as tables and then used in SQL statements.
parquetFile.registerTempTable("parquetFile");