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From Ryan Blue <rb...@netflix.com.INVALID>
Subject Re: eager execution and debuggability
Date Tue, 08 May 2018 22:52:45 GMT
Would be great if it is something more turn-key.

We can easily add the __repr__ and _repr_html_ methods and behavior to
PySpark classes. We could also add a configuration property to determine
whether the dataset evaluation is eager or not. That would make it turn-key
for anyone running PySpark in Jupyter.

For JVM languages, we could also add a dependency on jvm-repr and do the
same thing.

rb
​

On Tue, May 8, 2018 at 3:47 PM, Reynold Xin <rxin@databricks.com> wrote:

> s/underestimated/overestimated/
>
> On Tue, May 8, 2018 at 3:44 PM Reynold Xin <rxin@databricks.com> wrote:
>
>> Marco,
>>
>> There is understanding how Spark works, and there is finding bugs early
>> in their own program. One can perfectly understand how Spark works and
>> still find it valuable to get feedback asap, and that's why we built eager
>> analysis in the first place.
>>
>> Also I'm afraid you've significantly underestimated the level of
>> technical sophistication of users. In many cases they struggle to get
>> anything to work, and performance optimization of their programs is
>> secondary to getting things working. As John Ousterhout says, "the greatest
>> performance improvement of all is when a system goes from not-working to
>> working".
>>
>> I really like Ryan's approach. Would be great if it is something more
>> turn-key.
>>
>>
>>
>>
>>
>>
>> On Tue, May 8, 2018 at 2:35 PM Marco Gaido <marcogaido91@gmail.com>
>> wrote:
>>
>>> I am not sure how this is useful. For students, it is important to
>>> understand how Spark works. This can be critical in many decision they have
>>> to take (whether and what to cache for instance) in order to have
>>> performant Spark application. Creating a eager execution probably can help
>>> them having something running more easily, but let them also using Spark
>>> knowing less about how it works, thus they are likely to write worse
>>> application and to have more problems in debugging any kind of problem
>>> which may later (in production) occur (therefore affecting their experience
>>> with the tool).
>>>
>>> Moreover, as Ryan also mentioned, there are tools/ways to force the
>>> execution, helping in the debugging phase. So they can achieve without a
>>> big effort the same result, but with a big difference: they are aware of
>>> what is really happening, which may help them later.
>>>
>>> Thanks,
>>> Marco
>>>
>>> 2018-05-08 21:37 GMT+02:00 Ryan Blue <rblue@netflix.com.invalid>:
>>>
>>>> At Netflix, we use Jupyter notebooks and consoles for interactive
>>>> sessions. For anyone interested, this mode of interaction is really easy
to
>>>> add in Jupyter and PySpark. You would just define a different
>>>> *repr_html* or *repr* method for Dataset that runs a take(10) or
>>>> take(100) and formats the result.
>>>>
>>>> That way, the output of a cell or console execution always causes the
>>>> dataframe to run and get displayed for that immediate feedback. But, there
>>>> is no change to Spark’s behavior because the action is run by the REPL,
and
>>>> only when a dataframe is a result of an execution in order to display it.
>>>> Intermediate results wouldn’t be run, but that gives users a way to avoid
>>>> too many executions and would still support method chaining in the
>>>> dataframe API (which would be horrible with an aggressive execution model).
>>>>
>>>> There are ways to do this in JVM languages as well if you are using a
>>>> Scala or Java interpreter (see jvm-repr
>>>> <https://github.com/jupyter/jvm-repr>). This is actually what we do
in
>>>> our Spark-based SQL interpreter to display results.
>>>>
>>>> rb
>>>> ​
>>>>
>>>> On Tue, May 8, 2018 at 12:05 PM, Koert Kuipers <koert@tresata.com>
>>>> wrote:
>>>>
>>>>> yeah we run into this all the time with new hires. they will send
>>>>> emails explaining there is an error in the .write operation and they
are
>>>>> debugging the writing to disk, focusing on that piece of code :)
>>>>>
>>>>> unrelated, but another frequent cause for confusion is cascading
>>>>> errors. like the FetchFailedException. they will be debugging the reducer
>>>>> task not realizing the error happened before that, and the
>>>>> FetchFailedException is not the root cause.
>>>>>
>>>>>
>>>>> On Tue, May 8, 2018 at 2:52 PM, Reynold Xin <rxin@databricks.com>
>>>>> wrote:
>>>>>
>>>>>> Similar to the thread yesterday about improving ML/DL integration,
>>>>>> I'm sending another email on what I've learned recently from Spark
users. I
>>>>>> recently talked to some educators that have been teaching Spark in
their
>>>>>> (top-tier) university classes. They are some of the most important
users
>>>>>> for adoption because of the multiplicative effect they have on the
future
>>>>>> generation.
>>>>>>
>>>>>> To my surprise the single biggest ask they want is to enable eager
>>>>>> execution mode on all operations for teaching and debuggability:
>>>>>>
>>>>>> (1) Most of the students are relatively new to programming, and they
>>>>>> need multiple iterations to even get the most basic operation right.
In
>>>>>> these cases, in order to trigger an error, they would need to explicitly
>>>>>> add actions, which is non-intuitive.
>>>>>>
>>>>>> (2) If they don't add explicit actions to every operation and there
>>>>>> is a mistake, the error pops up somewhere later where an action is
>>>>>> triggered. This is in a different position from the code that causes
the
>>>>>> problem, and difficult for students to correlate the two.
>>>>>>
>>>>>> I suspect in the real world a lot of Spark users also struggle in
>>>>>> similar ways as these students. While eager execution is really not
>>>>>> practical in big data, in learning environments or in development
against
>>>>>> small, sampled datasets it can be pretty helpful.
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>>
>>>>>
>>>>
>>>>
>>>> --
>>>> Ryan Blue
>>>> Software Engineer
>>>> Netflix
>>>>
>>>
>>>


-- 
Ryan Blue
Software Engineer
Netflix

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