spark-dev mailing list archives

Site index · List index
Message view « Date » · « Thread »
Top « Date » · « Thread »
From Wenchen Fan <cloud0...@gmail.com>
Subject Re: [VOTE] [SPIP] SPARK-15689: Data Source API V2
Date Thu, 07 Sep 2017 02:32:44 GMT
Hi Ryan,

Yea I agree with you that we should discuss some substantial details during
the vote, and I addressed your comments about schema inference API in my
new PR, please take a look.

I've also called a new vote for the read path, please vote there, thanks!

On Thu, Sep 7, 2017 at 7:55 AM, Ryan Blue <rblue@netflix.com> wrote:

> I'm all for keeping this moving and not getting too far into the details
> (like naming), but I think the substantial details should be clarified
> first since they are in the proposal that's being voted on.
>
> I would prefer moving the write side to a separate SPIP, too, since there
> isn't much detail in the proposal and I think we should be more deliberate
> with things like schema evolution.
>
> On Thu, Aug 31, 2017 at 10:33 AM, Wenchen Fan <cloud0fan@gmail.com> wrote:
>
>> Hi Ryan,
>>
>> I think for a SPIP, we should not worry too much about details, as we can
>> discuss them during PR review after the vote pass.
>>
>> I think we should focus more on the overall design, like James did. The
>> interface mix-in vs plan push down discussion was great, hope we can get a
>> consensus on this topic soon. The current proposal is, we keep the
>> interface mix-in framework, and add an unstable plan push down trait.
>>
>> For details like interface names, sort push down vs sort propagate, etc.,
>> I think they should not block the vote, as they can be updated/improved
>> within the current interface mix-in framework.
>>
>> About separating read/write proposals, we should definitely send
>> individual PRs for read/write when developing data source v2. I'm also OK
>> with voting on the read side first. The write side is way simpler than the
>> read side, I think it's more important to get agreement on the read side
>> first.
>>
>> BTW, I do appreciate your feedbacks/comments on the prototype, let's keep
>> the discussion there. In the meanwhile, let's have more discussion on the
>> overall framework, and drive this project together.
>>
>> Wenchen
>>
>>
>>
>> On Thu, Aug 31, 2017 at 6:22 AM, Ryan Blue <rblue@netflix.com> wrote:
>>
>>> Maybe I'm missing something, but the high-level proposal consists of:
>>> Goals, Non-Goals, and Proposed API. What is there to discuss other than the
>>> details of the API that's being proposed? I think the goals make sense, but
>>> goals alone aren't enough to approve a SPIP.
>>>
>>> On Wed, Aug 30, 2017 at 2:46 PM, Reynold Xin <rxin@databricks.com>
>>> wrote:
>>>
>>>> So we seem to be getting into a cycle of discussing more about the
>>>> details of APIs than the high level proposal. The details of APIs are
>>>> important to debate, but those belong more in code reviews.
>>>>
>>>> One other important thing is that we should avoid API design by
>>>> committee. While it is extremely useful to get feedback, understand the use
>>>> cases, we cannot do API design by incorporating verbatim the union of
>>>> everybody's feedback. API design is largely a tradeoff game. The most
>>>> expressive API would also be harder to use, or sacrifice backward/forward
>>>> compatibility. It is as important to decide what to exclude as what to
>>>> include.
>>>>
>>>> Unlike the v1 API, the way Wenchen's high level V2 framework is
>>>> proposed makes it very easy to add new features (e.g. clustering
>>>> properties) in the future without breaking any APIs. I'd rather us shipping
>>>> something useful that might not be the most comprehensive set, than
>>>> debating about every single feature we should add and then creating
>>>> something super complicated that has unclear value.
>>>>
>>>>
>>>>
>>>> On Wed, Aug 30, 2017 at 6:37 PM, Ryan Blue <rblue@netflix.com> wrote:
>>>>
>>>>> -1 (non-binding)
>>>>>
>>>>> Sometimes it takes a VOTE thread to get people to actually read and
>>>>> comment, so thanks for starting this one… but there’s still discussion
>>>>> happening on the prototype API, which it hasn’t been updated. I’d
like to
>>>>> see the proposal shaped by the ongoing discussion so that we have a better,
>>>>> more concrete plan. I think that’s going to produces a better SPIP.
>>>>>
>>>>> The second reason for -1 is that I think the read- and write-side
>>>>> proposals should be separated. The PR
>>>>> <https://github.com/cloud-fan/spark/pull/10> currently has “write
>>>>> path” listed as a TODO item and most of the discussion I’ve seen
is on the
>>>>> read side. I think it would be better to separate the read and write
APIs
>>>>> so we can focus on them individually.
>>>>>
>>>>> An example of why we should focus on the write path separately is that
>>>>> the proposal says this:
>>>>>
>>>>> Ideally partitioning/bucketing concept should not be exposed in the
>>>>> Data Source API V2, because they are just techniques for data skipping
and
>>>>> pre-partitioning. However, these 2 concepts are already widely used in
>>>>> Spark, e.g. DataFrameWriter.partitionBy and DDL syntax like ADD PARTITION.
>>>>> To be consistent, we need to add partitioning/bucketing to Data Source
V2 .
>>>>> . .
>>>>>
>>>>> Essentially, the some APIs mix DDL and DML operations. I’d like to
>>>>> consider ways to fix that problem instead of carrying the problem forward
>>>>> to Data Source V2. We can solve this by adding a high-level API for DDL
and
>>>>> a better write/insert API that works well with it. Clearly, that discussion
>>>>> is independent of the read path, which is why I think separating the
two
>>>>> proposals would be a win.
>>>>>
>>>>> rb
>>>>> ​
>>>>>
>>>>> On Wed, Aug 30, 2017 at 4:28 AM, Reynold Xin <rxin@databricks.com>
>>>>> wrote:
>>>>>
>>>>>> That might be good to do, but seems like orthogonal to this effort
>>>>>> itself. It would be a completely different interface.
>>>>>>
>>>>>> On Wed, Aug 30, 2017 at 1:10 PM Wenchen Fan <cloud0fan@gmail.com>
>>>>>> wrote:
>>>>>>
>>>>>>> OK I agree with it, how about we add a new interface to push
down
>>>>>>> the query plan, based on the current framework? We can mark the
>>>>>>> query-plan-push-down interface as unstable, to save the effort
of designing
>>>>>>> a stable representation of query plan and maintaining forward
compatibility.
>>>>>>>
>>>>>>> On Wed, Aug 30, 2017 at 10:53 AM, James Baker <j.baker@outlook.com>
>>>>>>> wrote:
>>>>>>>
>>>>>>>> I'll just focus on the one-by-one thing for now - it's the
thing
>>>>>>>> that blocks me the most.
>>>>>>>>
>>>>>>>> I think the place where we're most confused here is on the
cost of
>>>>>>>> determining whether I can push down a filter. For me, in
order to work out
>>>>>>>> whether I can push down a filter or satisfy a sort, I might
have to read
>>>>>>>> plenty of data. That said, it's worth me doing this because
I can use this
>>>>>>>> information to avoid reading >>that much data.
>>>>>>>>
>>>>>>>> If you give me all the orderings, I will have to read that
data
>>>>>>>> many times (we stream it to avoid keeping it in memory).
>>>>>>>>
>>>>>>>> There's also a thing where our typical use cases have many
filters
>>>>>>>> (20+ is common). So, it's likely not going to work to pass
us all the
>>>>>>>> combinations. That said, if I can tell you a cost, I know
what optimal
>>>>>>>> looks like, why can't I just pick that myself?
>>>>>>>>
>>>>>>>> The current design is friendly to simple datasources, but
does not
>>>>>>>> have the potential to support this.
>>>>>>>>
>>>>>>>> So the main problem we have with datasources v1 is that it's
>>>>>>>> essentially impossible to leverage a bunch of Spark features
- I don't get
>>>>>>>> to use bucketing or row batches or all the nice things that
I really want
>>>>>>>> to use to get decent performance. Provided I can leverage
these in a
>>>>>>>> moderately supported way which won't break in any given commit,
I'll be
>>>>>>>> pretty happy with anything that lets me opt out of the restrictions.
>>>>>>>>
>>>>>>>> My suggestion here is that if you make a mode which works
well for
>>>>>>>> complicated use cases, you end up being able to write simple
mode in terms
>>>>>>>> of it very easily. So we could actually provide two APIs,
one that lets
>>>>>>>> people who have more interesting datasources leverage the
cool Spark
>>>>>>>> features, and one that lets people who just want to implement
basic
>>>>>>>> features do that - I'd try to include some kind of layering
here. I could
>>>>>>>> probably sketch out something here if that'd be useful?
>>>>>>>>
>>>>>>>> James
>>>>>>>>
>>>>>>>> On Tue, 29 Aug 2017 at 18:59 Wenchen Fan <cloud0fan@gmail.com>
>>>>>>>> wrote:
>>>>>>>>
>>>>>>>>> Hi James,
>>>>>>>>>
>>>>>>>>> Thanks for your feedback! I think your concerns are all
valid, but
>>>>>>>>> we need to make a tradeoff here.
>>>>>>>>>
>>>>>>>>> > Explicitly here, what I'm looking for is a convenient
mechanism
>>>>>>>>> to accept a fully specified set of arguments
>>>>>>>>>
>>>>>>>>> The problem with this approach is: 1) if we wanna add
more
>>>>>>>>> arguments in the future, it's really hard to do without
changing
>>>>>>>>> the existing interface. 2) if a user wants to implement
a very simple data
>>>>>>>>> source, he has to look at all the arguments and understand
them, which may
>>>>>>>>> be a burden for him.
>>>>>>>>> I don't have a solution to solve these 2 problems, comments
are
>>>>>>>>> welcome.
>>>>>>>>>
>>>>>>>>>
>>>>>>>>> > There are loads of cases like this - you can imagine
someone
>>>>>>>>> being able to push down a sort before a filter is applied,
but not
>>>>>>>>> afterwards. However, maybe the filter is so selective
that it's better to
>>>>>>>>> push down the filter and not handle the sort. I don't
get to make this
>>>>>>>>> decision, Spark does (but doesn't have good enough information
to do it
>>>>>>>>> properly, whilst I do). I want to be able to choose the
parts I push down
>>>>>>>>> given knowledge of my datasource - as defined the APIs
don't let me do
>>>>>>>>> that, they're strictly more restrictive than the V1 APIs
in this way.
>>>>>>>>>
>>>>>>>>> This is true, the current framework applies push downs
one by one,
>>>>>>>>> incrementally. If a data source wanna go back to accept
a sort push down
>>>>>>>>> after it accepts a filter push down, it's impossible
with the current data
>>>>>>>>> source V2.
>>>>>>>>> Fortunately, we have a solution for this problem. At
Spark side,
>>>>>>>>> actually we do have a fully specified set of arguments
waiting to
>>>>>>>>> be pushed down, but Spark doesn't know which is the best
order to push them
>>>>>>>>> into data source. Spark can try every combination and
ask the data source
>>>>>>>>> to report a cost, then Spark can pick the best combination
with the lowest
>>>>>>>>> cost. This can also be implemented as a cost report interface,
so that
>>>>>>>>> advanced data source can implement it for optimal performance,
and simple
>>>>>>>>> data source doesn't need to care about it and keep simple.
>>>>>>>>>
>>>>>>>>>
>>>>>>>>> The current design is very friendly to simple data source,
and has
>>>>>>>>> the potential to support complex data source, I prefer
the current design
>>>>>>>>> over the plan push down one. What do you think?
>>>>>>>>>
>>>>>>>>>
>>>>>>>>> On Wed, Aug 30, 2017 at 5:53 AM, James Baker <j.baker@outlook.com>
>>>>>>>>> wrote:
>>>>>>>>>
>>>>>>>>>> Yeah, for sure.
>>>>>>>>>>
>>>>>>>>>> With the stable representation - agree that in the
general case
>>>>>>>>>> this is pretty intractable, it restricts the modifications
that you can do
>>>>>>>>>> in the future too much. That said, it shouldn't be
as hard if you restrict
>>>>>>>>>> yourself to the parts of the plan which are supported
by the datasources V2
>>>>>>>>>> API (which after all, need to be translateable properly
into the future to
>>>>>>>>>> support the mixins proposed). This should have a
pretty small scope in
>>>>>>>>>> comparison. As long as the user can bail out of nodes
they don't
>>>>>>>>>> understand, they should be ok, right?
>>>>>>>>>>
>>>>>>>>>> That said, what would also be fine for us is a place
to plug into
>>>>>>>>>> an unstable query plan.
>>>>>>>>>>
>>>>>>>>>> Explicitly here, what I'm looking for is a convenient
mechanism
>>>>>>>>>> to accept a fully specified set of arguments (of
which I can choose to
>>>>>>>>>> ignore some), and return the information as to which
of them I'm ignoring.
>>>>>>>>>> Taking a query plan of sorts is a way of doing this
which IMO is intuitive
>>>>>>>>>> to the user. It also provides a convenient location
to plug in things like
>>>>>>>>>> stats. Not at all married to the idea of using a
query plan here; it just
>>>>>>>>>> seemed convenient.
>>>>>>>>>>
>>>>>>>>>> Regarding the users who just want to be able to pump
data into
>>>>>>>>>> Spark, my understanding is that replacing isolated
nodes in a query plan is
>>>>>>>>>> easy. That said, our goal here is to be able to push
down as much as
>>>>>>>>>> possible into the underlying datastore.
>>>>>>>>>>
>>>>>>>>>> To your second question:
>>>>>>>>>>
>>>>>>>>>> The issue is that if you build up pushdowns incrementally
and not
>>>>>>>>>> all at once, you end up having to reject pushdowns
and filters that you
>>>>>>>>>> actually can do, which unnecessarily increases overheads.
>>>>>>>>>>
>>>>>>>>>> For example, the dataset
>>>>>>>>>>
>>>>>>>>>> a b c
>>>>>>>>>> 1 2 3
>>>>>>>>>> 1 3 3
>>>>>>>>>> 1 3 4
>>>>>>>>>> 2 1 1
>>>>>>>>>> 2 0 1
>>>>>>>>>>
>>>>>>>>>> can efficiently push down sort(b, c) if I have already
applied
>>>>>>>>>> the filter a = 1, but otherwise will force a sort
in Spark. On the PR I
>>>>>>>>>> detail a case I see where I can push down two equality
filters iff I am
>>>>>>>>>> given them at the same time, whilst not being able
to one at a time.
>>>>>>>>>>
>>>>>>>>>> There are loads of cases like this - you can imagine
someone
>>>>>>>>>> being able to push down a sort before a filter is
applied, but not
>>>>>>>>>> afterwards. However, maybe the filter is so selective
that it's better to
>>>>>>>>>> push down the filter and not handle the sort. I don't
get to make this
>>>>>>>>>> decision, Spark does (but doesn't have good enough
information to do it
>>>>>>>>>> properly, whilst I do). I want to be able to choose
the parts I push down
>>>>>>>>>> given knowledge of my datasource - as defined the
APIs don't let me do
>>>>>>>>>> that, they're strictly more restrictive than the
V1 APIs in this way.
>>>>>>>>>>
>>>>>>>>>> The pattern of not considering things that can be
done in bulk
>>>>>>>>>> bites us in other ways. The retrieval methods end
up being trickier to
>>>>>>>>>> implement than is necessary because frequently a
single operation provides
>>>>>>>>>> the result of many of the getters, but the state
is mutable, so you end up
>>>>>>>>>> with odd caches.
>>>>>>>>>>
>>>>>>>>>> For example, the work I need to do to answer unhandledFilters
in
>>>>>>>>>> V1 is roughly the same as the work I need to do to
buildScan, so I want to
>>>>>>>>>> cache it. This means that I end up with code that
looks like:
>>>>>>>>>>
>>>>>>>>>> public final class CachingFoo implements Foo {
>>>>>>>>>>     private final Foo delegate;
>>>>>>>>>>
>>>>>>>>>>     private List<Filter> currentFilters = emptyList();
>>>>>>>>>>     private Supplier<Bar> barSupplier =
>>>>>>>>>> newSupplier(currentFilters);
>>>>>>>>>>
>>>>>>>>>>     public CachingFoo(Foo delegate) {
>>>>>>>>>>         this.delegate = delegate;
>>>>>>>>>>     }
>>>>>>>>>>
>>>>>>>>>>     private Supplier<Bar> newSupplier(List<Filter>
filters) {
>>>>>>>>>>         return Suppliers.memoize(() ->
>>>>>>>>>> delegate.computeBar(filters));
>>>>>>>>>>     }
>>>>>>>>>>
>>>>>>>>>>     @Override
>>>>>>>>>>     public Bar computeBar(List<Filter> filters)
{
>>>>>>>>>>         if (!filters.equals(currentFilters)) {
>>>>>>>>>>             currentFilters = filters;
>>>>>>>>>>             barSupplier = newSupplier(filters);
>>>>>>>>>>         }
>>>>>>>>>>
>>>>>>>>>>         return barSupplier.get();
>>>>>>>>>>     }
>>>>>>>>>> }
>>>>>>>>>>
>>>>>>>>>> which caches the result required in unhandledFilters
on the
>>>>>>>>>> expectation that Spark will call buildScan afterwards
and get to use the
>>>>>>>>>> result..
>>>>>>>>>>
>>>>>>>>>> This kind of cache becomes more prominent, but harder
to deal
>>>>>>>>>> with in the new APIs. As one example here, the state
I will need in order
>>>>>>>>>> to compute accurate column stats internally will
likely be a subset of the
>>>>>>>>>> work required in order to get the read tasks, tell
you if I can handle
>>>>>>>>>> filters, etc, so I'll want to cache them for reuse.
However, the cached
>>>>>>>>>> information needs to be appropriately invalidated
when I add a new filter
>>>>>>>>>> or sort order or limit, and this makes implementing
the APIs harder and
>>>>>>>>>> more error-prone.
>>>>>>>>>>
>>>>>>>>>> One thing that'd be great is a defined contract of
the order in
>>>>>>>>>> which Spark calls the methods on your datasource
(ideally this contract
>>>>>>>>>> could be implied by the way the Java class structure
works, but otherwise I
>>>>>>>>>> can just throw).
>>>>>>>>>>
>>>>>>>>>> James
>>>>>>>>>>
>>>>>>>>>> On Tue, 29 Aug 2017 at 02:56 Reynold Xin <rxin@databricks.com>
>>>>>>>>>> wrote:
>>>>>>>>>>
>>>>>>>>>>> James,
>>>>>>>>>>>
>>>>>>>>>>> Thanks for the comment. I think you just pointed
out a trade-off
>>>>>>>>>>> between expressiveness and API simplicity, compatibility
and evolvability.
>>>>>>>>>>> For the max expressiveness, we'd want the ability
to expose full query
>>>>>>>>>>> plans, and let the data source decide which part
of the query plan can be
>>>>>>>>>>> pushed down.
>>>>>>>>>>>
>>>>>>>>>>> The downside to that (full query plan push down)
are:
>>>>>>>>>>>
>>>>>>>>>>> 1. It is extremely difficult to design a stable
representation
>>>>>>>>>>> for logical / physical plan. It is doable, but
we'd be the first to do
>>>>>>>>>>> it. I'm not sure of any mainstream databases
being able to do that in the
>>>>>>>>>>> past. The design of that API itself, to make
sure we have a good story for
>>>>>>>>>>> backward and forward compatibility, would probably
take months if not
>>>>>>>>>>> years. It might still be good to do, or offer
an experimental trait without
>>>>>>>>>>> compatibility guarantee that uses the current
Catalyst internal logical
>>>>>>>>>>> plan.
>>>>>>>>>>>
>>>>>>>>>>> 2. Most data source developers simply want a
way to offer some
>>>>>>>>>>> data, without any pushdown. Having to understand
query plans is a burden
>>>>>>>>>>> rather than a gift.
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>> Re: your point about the proposed v2 being worse
than v1 for
>>>>>>>>>>> your use case.
>>>>>>>>>>>
>>>>>>>>>>> Can you say more? You used the argument that
in v2 there are
>>>>>>>>>>> more support for broader pushdown and as a result
it is harder to
>>>>>>>>>>> implement. That's how it is supposed to be. If
a data source simply
>>>>>>>>>>> implements one of the trait, it'd be logically
identical to v1. I don't see
>>>>>>>>>>> why it would be worse or better, other than v2
provides much stronger
>>>>>>>>>>> forward compatibility guarantees than v1.
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>> On Tue, Aug 29, 2017 at 4:54 AM, James Baker
<
>>>>>>>>>>> j.baker@outlook.com> wrote:
>>>>>>>>>>>
>>>>>>>>>>>> Copying from the code review comments I just
submitted on the
>>>>>>>>>>>> draft API (https://github.com/cloud-fan/
>>>>>>>>>>>> spark/pull/10#pullrequestreview-59088745):
>>>>>>>>>>>>
>>>>>>>>>>>> Context here is that I've spent some time
implementing a Spark
>>>>>>>>>>>> datasource and have had some issues with
the current API which are made
>>>>>>>>>>>> worse in V2.
>>>>>>>>>>>>
>>>>>>>>>>>> The general conclusion I’ve come to here
is that this is very
>>>>>>>>>>>> hard to actually implement (in a similar
but more aggressive way than
>>>>>>>>>>>> DataSource V1, because of the extra methods
and dimensions we get in V2).
>>>>>>>>>>>>
>>>>>>>>>>>> In DataSources V1 PrunedFilteredScan, the
issue is that you are
>>>>>>>>>>>> passed in the filters with the buildScan
method, and then passed in again
>>>>>>>>>>>> with the unhandledFilters method.
>>>>>>>>>>>>
>>>>>>>>>>>> However, the filters that you can’t handle
might be data
>>>>>>>>>>>> dependent, which the current API does not
handle well. Suppose I can handle
>>>>>>>>>>>> filter A some of the time, and filter B some
of the time. If I’m passed in
>>>>>>>>>>>> both, then either A and B are unhandled,
or A, or B, or neither. The work I
>>>>>>>>>>>> have to do to work this out is essentially
the same as I have to do while
>>>>>>>>>>>> actually generating my RDD (essentially I
have to generate my partitions),
>>>>>>>>>>>> so I end up doing some weird caching work.
>>>>>>>>>>>>
>>>>>>>>>>>> This V2 API proposal has the same issues,
but perhaps moreso.
>>>>>>>>>>>> In PrunedFilteredScan, there is essentially
one degree of freedom for
>>>>>>>>>>>> pruning (filters), so you just have to implement
caching between
>>>>>>>>>>>> unhandledFilters and buildScan. However,
here we have many degrees of
>>>>>>>>>>>> freedom; sorts, individual filters, clustering,
sampling, maybe
>>>>>>>>>>>> aggregations eventually - and these operations
are not all commutative, and
>>>>>>>>>>>> computing my support one-by-one can easily
end up being more expensive than
>>>>>>>>>>>> computing all in one go.
>>>>>>>>>>>>
>>>>>>>>>>>> For some trivial examples:
>>>>>>>>>>>>
>>>>>>>>>>>> - After filtering, I might be sorted, whilst
before filtering I
>>>>>>>>>>>> might not be.
>>>>>>>>>>>>
>>>>>>>>>>>> - Filtering with certain filters might affect
my ability to
>>>>>>>>>>>> push down others.
>>>>>>>>>>>>
>>>>>>>>>>>> - Filtering with aggregations (as mooted)
might not be possible
>>>>>>>>>>>> to push down.
>>>>>>>>>>>>
>>>>>>>>>>>> And with the API as currently mooted, I need
to be able to go
>>>>>>>>>>>> back and change my results because they might
change later.
>>>>>>>>>>>>
>>>>>>>>>>>> Really what would be good here is to pass
all of the filters
>>>>>>>>>>>> and sorts etc all at once, and then I return
the parts I can’t handle.
>>>>>>>>>>>>
>>>>>>>>>>>> I’d prefer in general that this be implemented
by passing some
>>>>>>>>>>>> kind of query plan to the datasource which
enables this kind of
>>>>>>>>>>>> replacement. Explicitly don’t want to give
the whole query plan - that
>>>>>>>>>>>> sounds painful - would prefer we push down
only the parts of the query plan
>>>>>>>>>>>> we deem to be stable. With the mix-in approach,
I don’t think we can
>>>>>>>>>>>> guarantee the properties we want without
a two-phase thing - I’d really
>>>>>>>>>>>> love to be able to just define a straightforward
union type which is our
>>>>>>>>>>>> supported pushdown stuff, and then the user
can transform and return it.
>>>>>>>>>>>>
>>>>>>>>>>>> I think this ends up being a more elegant
API for consumers,
>>>>>>>>>>>> and also far more intuitive.
>>>>>>>>>>>>
>>>>>>>>>>>> James
>>>>>>>>>>>>
>>>>>>>>>>>> On Mon, 28 Aug 2017 at 18:00 蒋星博 <jiangxb1987@gmail.com>
wrote:
>>>>>>>>>>>>
>>>>>>>>>>>>> +1 (Non-binding)
>>>>>>>>>>>>>
>>>>>>>>>>>>> Xiao Li <gatorsmile@gmail.com>于2017年8月28日
周一下午5:38写道:
>>>>>>>>>>>>>
>>>>>>>>>>>>>> +1
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> 2017-08-28 12:45 GMT-07:00 Cody Koeninger
<cody@koeninger.org
>>>>>>>>>>>>>> >:
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> Just wanted to point out that
because the jira isn't labeled
>>>>>>>>>>>>>>> SPIP, it
>>>>>>>>>>>>>>> won't have shown up linked from
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> http://spark.apache.org/improvement-proposals.html
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> On Mon, Aug 28, 2017 at 2:20
PM, Wenchen Fan <
>>>>>>>>>>>>>>> cloud0fan@gmail.com> wrote:
>>>>>>>>>>>>>>> > Hi all,
>>>>>>>>>>>>>>> >
>>>>>>>>>>>>>>> > It has been almost 2 weeks
since I proposed the data
>>>>>>>>>>>>>>> source V2 for
>>>>>>>>>>>>>>> > discussion, and we already
got some feedbacks on the JIRA
>>>>>>>>>>>>>>> ticket and the
>>>>>>>>>>>>>>> > prototype PR, so I'd like
to call for a vote.
>>>>>>>>>>>>>>> >
>>>>>>>>>>>>>>> > The full document of the
Data Source API V2 is:
>>>>>>>>>>>>>>> > https://docs.google.com/docume
>>>>>>>>>>>>>>> nt/d/1n_vUVbF4KD3gxTmkNEon5qdQ-Z8qU5Frf6WMQZ6jJVM/edit
>>>>>>>>>>>>>>> >
>>>>>>>>>>>>>>> > Note that, this vote should
focus on high-level
>>>>>>>>>>>>>>> design/framework, not
>>>>>>>>>>>>>>> > specified APIs, as we can
always change/improve specified
>>>>>>>>>>>>>>> APIs during
>>>>>>>>>>>>>>> > development.
>>>>>>>>>>>>>>> >
>>>>>>>>>>>>>>> > The vote will be up for
the next 72 hours. Please reply
>>>>>>>>>>>>>>> with your vote:
>>>>>>>>>>>>>>> >
>>>>>>>>>>>>>>> > +1: Yeah, let's go forward
and implement the SPIP.
>>>>>>>>>>>>>>> > +0: Don't really care.
>>>>>>>>>>>>>>> > -1: I don't think this is
a good idea because of the
>>>>>>>>>>>>>>> following technical
>>>>>>>>>>>>>>> > reasons.
>>>>>>>>>>>>>>> >
>>>>>>>>>>>>>>> > Thanks!
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> ------------------------------------------------------------
>>>>>>>>>>>>>>> ---------
>>>>>>>>>>>>>>> To unsubscribe e-mail: dev-unsubscribe@spark.apache.org
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>
>>>>>>>
>>>>>
>>>>>
>>>>> --
>>>>> Ryan Blue
>>>>> Software Engineer
>>>>> Netflix
>>>>>
>>>>
>>>>
>>>
>>>
>>> --
>>> Ryan Blue
>>> Software Engineer
>>> Netflix
>>>
>>
>>
>
>
> --
> Ryan Blue
> Software Engineer
> Netflix
>

Mime
View raw message