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From Nicholas Chammas <nicholas.cham...@gmail.com>
Subject Re: eager execution and debuggability
Date Wed, 09 May 2018 02:22:23 GMT
This may be technically impractical, but it would be fantastic if we could
make it easier to debug Spark programs without needing to rely on eager
execution. Sprinkling .count() and .checkpoint() at various points in my
code is still a debugging technique I use, but it always makes me wish
Spark could point more directly to the offending transformation when
something goes wrong.

Is it somehow possible to have each individual operator (is that the
correct term?) in a DAG include metadata pointing back to the line of code
that generated the operator? That way when an action triggers an error, the
failing operation can point to the relevant line of code — even if it’s a
transformation — and not just the action on the tail end that triggered the
error.

I don’t know how feasible this is, but addressing it would directly solve
the issue of linking failures to the responsible transformation, as opposed
to leaving the user to break up a chain of transformations with several
debug actions. And this would benefit new and experienced users alike.

Nick

2018년 5월 8일 (화) 오후 7:09, Ryan Blue rblue@netflix.com.invalid
<http://mailto:rblue@netflix.com.invalid>님이 작성:

I've opened SPARK-24215 to track this.
>
> On Tue, May 8, 2018 at 3:58 PM, Reynold Xin <rxin@databricks.com> wrote:
>
>> Yup. Sounds great. This is something simple Spark can do and provide huge
>> value to the end users.
>>
>>
>> On Tue, May 8, 2018 at 3:53 PM Ryan Blue <rblue@netflix.com> wrote:
>>
>>> 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
>>>
>>
>
>
> --
> Ryan Blue
> Software Engineer
> Netflix
>
​

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