Sorry, my code actually was
df_one = df.select('col1', 'col2')
df_two = df.select('col1', 'col3')
But in Spark 1.4.0 this does not seem to make any difference anyway and
the problem is the same with both versions.
On 2015-04-21 17:04, ayan guha wrote:
your code should be
df_one = df.select('col1', 'col2')
df_two = df.select('col1', 'col3')
Your current code is generating a tupple, and of course df_1 and df_2
are
different, so join is yielding to cartesian.
Best
Ayan
On Wed, Apr 22, 2015 at 12:42 AM, Karlson <ksonsp...@siberie.de> wrote:
Hi,
can anyone confirm (and if so elaborate on) the following problem?
When I join two DataFrames that originate from the same source
DataFrame,
the resulting DF will explode to a huge number of rows. A quick
example:
I load a DataFrame with n rows from disk:
df = sql_context.parquetFile('data.parquet')
Then I create two DataFrames from that source.
df_one = df.select(['col1', 'col2'])
df_two = df.select(['col1', 'col3'])
Finally I want to (inner) join them back together:
df_joined = df_one.join(df_two, df_one['col1'] == df_two['col2'],
'inner')
The key in col1 is unique. The resulting DataFrame should have n rows,
however it does have n*n rows.
That does not happen, when I load df_one and df_two from disk
directly. I
am on Spark 1.3.0, but this also happens on the current 1.4.0
snapshot.
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