Hi all,
Regarding naming `cache()` vs `materialize()`. One more explanation why I think
`materialize()` is more natural to me is that I think of all “Table”s in
Table-API as views. They behave the same way as SQL views, the only difference
for me is that their live scope is short - current session which is limited by
different execution model. That’s why “cashing” a view for me is just
materialising it.
However I see and I understand your point of view. Coming from
DataSet/DataStream and generally speaking non-SQL world, `cache()` is more
natural. But keep in mind that `.cache()` will/might not only be used in
interactive programming and not only in batching. But naming is one issue, and
not that critical to me. Especially that once we implement proper materialised
views, we can always deprecate/rename `cache()` if we deem so.
For me the more important issue is of not having the `void cache()` with side
effects. Exactly for the reasons that you have mentioned. True: results might
be non deterministic if underlying source table are changing. Problem is that
`void cache()` implicitly changes the semantic of subsequent uses of the
cached/materialized Table. It can cause “wtf” moment for a user if he inserts
“b.cache()” call in some place in his code and suddenly some other random
places are behaving differently. If `materialize()` or `cache()` returns a
Table handle, we force user to explicitly use the cache which removes the
“random” part from the "suddenly some other random places are behaving
differently”.
This argument and others that I’ve raised (greater flexibility/allowing user to
explicitly bypass the cache) are independent of `cache()` vs `materialize()`
discussion.
> Does that mean one can also insert into the CachedTable? This sounds pretty
> confusing.
I don’t know, probably initially we should make CachedTable read-only. I don’t
find it more confusing than the fact that user can not write to views or
materialised views in SQL or that user currently can not write to a Table.
Piotrek
> On 30 Nov 2018, at 17:38, Xingcan Cui <xingc...@gmail.com> wrote:
>
> Hi all,
>
> I agree with @Becket that `cache()` and `materialize()` should be considered
> as two different methods where the later one is more sophisticated.
>
> According to my understanding, the initial idea is just to introduce a simple
> cache or persist mechanism, but as the TableAPI is a high-level API, it’s
> naturally for as to think in a SQL way.
>
> Maybe we can add the `cache()` method to the DataSet API and force users to
> translate a Table to a Dataset before caching it. Then the users should
> manually register the cached dataset to a table again (we may need some table
> replacement mechanisms for datasets with an identical schema but different
> contents here). After all, it’s the dataset rather than the dynamic table
> that need to be cached, right?
>
> Best,
> Xingcan
>
>> On Nov 30, 2018, at 10:57 AM, Becket Qin <becket....@gmail.com> wrote:
>>
>> Hi Piotrek and Jark,
>>
>> Thanks for the feedback and explanation. Those are good arguments. But I
>> think those arguments are mostly about materialized view. Let me try to
>> explain the reason I believe cache() and materialize() are different.
>>
>> I think cache() and materialize() have quite different implications. An
>> analogy I can think of is save()/publish(). When users call cache(), it is
>> just like they are saving an intermediate result as a draft of their work,
>> this intermediate result may not have any realistic meaning. Calling
>> cache() does not mean users want to publish the cached table in any manner.
>> But when users call materialize(), that means "I have something meaningful
>> to be reused by others", now users need to think about the validation,
>> update & versioning, lifecycle of the result, etc.
>>
>> Piotrek's suggestions on variations of the materialize() methods are very
>> useful. It would be great if Flink have them. The concept of materialized
>> view is actually a pretty big feature, not to say the related stuff like
>> triggers/hooks you mentioned earlier. I think the materialized view itself
>> should be discussed in a more thorough and systematic manner. And I found
>> that discussion is kind of orthogonal and way beyond interactive
>> programming experience.
>>
>> The example you gave was interesting. I still have some questions, though.
>>
>> Table source = … // some source that scans files from a directory
>>> “/foo/bar/“
>>> Table t1 = source.groupBy(…).select(…).where(…) ….;
>>> Table t2 = t1.materialize() // (or `cache()`)
>>
>> t2.count() // initialise cache (if it’s lazily initialised)
>>> int a1 = t1.count()
>>> int b1 = t2.count()
>>> // something in the background (or we trigger it) writes new files to
>>> /foo/bar
>>> int a2 = t1.count()
>>> int b2 = t2.count()
>>> t2.refresh() // possible future extension, not to be implemented in the
>>> initial version
>>>
>>
>> what if someone else added some more files to /foo/bar at this point? In
>> that case, a3 won't equals to b3, and the result become non-deterministic,
>> right?
>>
>> int a3 = t1.count()
>>> int b3 = t2.count()
>>> t2.drop() // another possible future extension, manual “cache” dropping
>>
>>
>> When we talk about interactive programming, in most cases, we are talking
>> about batch applications. A fundamental assumption of such case is that the
>> source data is complete before the data processing begins, and the data
>> will not change during the data processing. IMO, if additional rows needs
>> to be added to some source during the processing, it should be done in ways
>> like union the source with another table containing the rows to be added.
>>
>> There are a few cases that computations are executed repeatedly on the
>> changing data source.
>>
>> For example, people may run a ML training job every hour with the samples
>> newly added in the past hour. In that case, the source data between will
>> indeed change. But still, the data remain unchanged within one run. And
>> usually in that case, the result will need versioning, i.e. for a given
>> result, it tells that the result is a result from the source data by a
>> certain timestamp.
>>
>> Another example is something like data warehouse. In this case, there are a
>> few source of original/raw data. On top of those sources, many materialized
>> view / queries / reports / dashboards can be created to generate derived
>> data. Those derived data needs to be updated when the underlying original
>> data changes. In that case, the processing logic that derives the original
>> data needs to be executed repeatedly to update those reports/views. Again,
>> all those derived data also need to have version management, such as
>> timestamp.
>>
>> In any of the above two cases, during a single run of the processing logic,
>> the data cannot change. Otherwise the behavior of the processing logic may
>> be undefined. In the above two examples, when writing the processing logic,
>> Users can use .cache() to hint Flink that those results should be saved to
>> avoid repeated computation. And then for the result of my application
>> logic, I'll call materialize(), so that these results could be managed by
>> the system with versioning, metadata management, lifecycle management,
>> ACLs, etc.
>>
>> It is true we can use materialize() to do the cache() job, but I am really
>> reluctant to shoehorn cache() into materialize() and force users to worry
>> about a bunch of implications that they needn't have to. I am absolutely on
>> your side that redundant API is bad. But it is equally frustrating, if not
>> more, that the same API does different things.
>>
>> Thanks,
>>
>> Jiangjie (Becket) Qin
>>
>>
>> On Fri, Nov 30, 2018 at 10:34 PM Shaoxuan Wang <wshaox...@gmail.com> wrote:
>>
>>> Thanks Piotrek,
>>> You provided a very good example, it explains all the confusions I have.
>>> It is clear that there is something we have not considered in the initial
>>> proposal. We intend to force the user to reuse the cached/materialized
>>> table, if its cache() method is executed. We did not expect that user may
>>> want to re-executed the plan from the source table. Let me re-think about
>>> it and get back to you later.
>>>
>>> In the meanwhile, this example/observation also infers that we cannot fully
>>> involve the optimizer to decide the plan if a cache/materialize is
>>> explicitly used, because weather to reuse the cache data or re-execute the
>>> query from source data may lead to different results. (But I guess
>>> optimizer can still help in some cases ---- as long as it does not
>>> re-execute from the varied source, we should be safe).
>>>
>>> Regards,
>>> Shaoxuan
>>>
>>>
>>>
>>> On Fri, Nov 30, 2018 at 9:13 PM Piotr Nowojski <pi...@data-artisans.com>
>>> wrote:
>>>
>>>> Hi Shaoxuan,
>>>>
>>>> Re 2:
>>>>
>>>>> Table t3 = methodThatAppliesOperators(t1) // t1 is modified to-> t1’
>>>>
>>>> What do you mean that “ t1 is modified to-> t1’ ” ? That
>>>> `methodThatAppliesOperators()` method has changed it’s plan?
>>>>
>>>> I was thinking more about something like this:
>>>>
>>>> Table source = … // some source that scans files from a directory
>>>> “/foo/bar/“
>>>> Table t1 = source.groupBy(…).select(…).where(…) ….;
>>>> Table t2 = t1.materialize() // (or `cache()`)
>>>>
>>>> t2.count() // initialise cache (if it’s lazily initialised)
>>>>
>>>> int a1 = t1.count()
>>>> int b1 = t2.count()
>>>>
>>>> // something in the background (or we trigger it) writes new files to
>>>> /foo/bar
>>>>
>>>> int a2 = t1.count()
>>>> int b2 = t2.count()
>>>>
>>>> t2.refresh() // possible future extension, not to be implemented in the
>>>> initial version
>>>>
>>>> int a3 = t1.count()
>>>> int b3 = t2.count()
>>>>
>>>> t2.drop() // another possible future extension, manual “cache” dropping
>>>>
>>>> assertTrue(a1 == b1) // same results, but b1 comes from the “cache"
>>>> assertTrue(b1 == b2) // both values come from the same cache
>>>> assertTrue(a2 > b2) // b2 comes from cache, a2 re-executed full table
>>> scan
>>>> and has more data
>>>> assertTrue(b3 > b2) // b3 comes from refreshed cache
>>>> assertTrue(b3 == a2 == a3)
>>>>
>>>> Piotrek
>>>>
>>>>> On 30 Nov 2018, at 10:22, Jark Wu <imj...@gmail.com> wrote:
>>>>>
>>>>> Hi,
>>>>>
>>>>> It is an very interesting and useful design!
>>>>>
>>>>> Here I want to share some of my thoughts:
>>>>>
>>>>> 1. Agree with that cache() method should return some Table to avoid
>>> some
>>>>> unexpected problems because of the mutable object.
>>>>> All the existing methods of Table are returning a new Table instance.
>>>>>
>>>>> 2. I think materialize() would be more consistent with SQL, this makes
>>> it
>>>>> possible to support the same feature for SQL (materialize view) and
>>> keep
>>>>> the same API for users in the future.
>>>>> But I'm also fine if we choose cache().
>>>>>
>>>>> 3. In the proposal, a TableService (or FlinkService?) is used to cache
>>>> the
>>>>> result of the (intermediate) table.
>>>>> But the name of TableService may be a bit general which is not quite
>>>>> understanding correctly in the first glance (a metastore for tables?).
>>>>> Maybe a more specific name would be better, such as TableCacheSerive
>>> or
>>>>> TableMaterializeSerivce or something else.
>>>>>
>>>>> Best,
>>>>> Jark
>>>>>
>>>>>
>>>>> On Thu, 29 Nov 2018 at 21:16, Fabian Hueske <fhue...@gmail.com> wrote:
>>>>>
>>>>>> Hi,
>>>>>>
>>>>>> Thanks for the clarification Becket!
>>>>>>
>>>>>> I have a few thoughts to share / questions:
>>>>>>
>>>>>> 1) I'd like to know how you plan to implement the feature on a plan /
>>>>>> planner level.
>>>>>>
>>>>>> I would imaging the following to happen when Table.cache() is called:
>>>>>>
>>>>>> 1) immediately optimize the Table and internally convert it into a
>>>>>> DataSet/DataStream. This is necessary, to avoid that operators of
>>> later
>>>>>> queries on top of the Table are pushed down.
>>>>>> 2) register the DataSet/DataStream as a DataSet/DataStream-backed
>>> Table
>>>> X
>>>>>> 3) add a sink to the DataSet/DataStream. This is the materialization
>>> of
>>>> the
>>>>>> Table X
>>>>>>
>>>>>> Based on your proposal the following would happen:
>>>>>>
>>>>>> Table t1 = ....
>>>>>> t1.cache(); // cache() returns void. The logical plan of t1 is
>>> replaced
>>>> by
>>>>>> a scan of X. There is also a reference to the materialization of X.
>>>>>>
>>>>>> t1.count(); // this executes the program, including the
>>>> DataSet/DataStream
>>>>>> that backs X and the sink that writes the materialization of X
>>>>>> t1.count(); // this executes the program, but reads X from the
>>>>>> materialization.
>>>>>>
>>>>>> My question is, how do you determine when whether the scan of t1
>>> should
>>>> go
>>>>>> against the DataSet/DataStream program and when against the
>>>>>> materialization?
>>>>>> AFAIK, there is no hook that will tell you that a part of the program
>>>> was
>>>>>> executed. Flipping a switch during optimization or plan generation is
>>>> not
>>>>>> sufficient as there is no guarantee that the plan is also executed.
>>>>>>
>>>>>> Overall, this behavior is somewhat similar to what I proposed in
>>>>>> FLINK-8950, which does not include persisting the table, but just
>>>>>> optimizing and reregistering it as DataSet/DataStream scan.
>>>>>>
>>>>>> 2) I think Piotr has a point about the implicit behavior and side
>>>> effects
>>>>>> of the cache() method if it does not return anything.
>>>>>> Consider the following example:
>>>>>>
>>>>>> Table t1 = ???
>>>>>> Table t2 = methodThatAppliesOperators(t1);
>>>>>> Table t3 = methodThatAppliesOtherOperators(t1);
>>>>>>
>>>>>> In this case, the behavior/performance of the plan that results from
>>> the
>>>>>> second method call depends on whether t1 was modified by the first
>>>> method
>>>>>> or not.
>>>>>> This is the classic issue of mutable vs. immutable objects.
>>>>>> Also, as Piotr pointed out, it might also be good to have the original
>>>> plan
>>>>>> of t1, because in some cases it is possible to push filters down such
>>>> that
>>>>>> evaluating the query from scratch might be more efficient than
>>> accessing
>>>>>> the cache.
>>>>>> Moreover, a CachedTable could extend Table() and offer a method
>>>> refresh().
>>>>>> This sounds quite useful in an interactive session mode.
>>>>>>
>>>>>> 3) Regarding the name, I can see both arguments. IMO, materialize()
>>>> seems
>>>>>> to be more future proof.
>>>>>>
>>>>>> Best, Fabian
>>>>>>
>>>>>> Am Do., 29. Nov. 2018 um 12:56 Uhr schrieb Shaoxuan Wang <
>>>>>> wshaox...@gmail.com>:
>>>>>>
>>>>>>> Hi Piotr,
>>>>>>>
>>>>>>> Thanks for sharing your ideas on the method naming. We will think
>>> about
>>>>>>> your suggestions. But I don't understand why we need to change the
>>>> return
>>>>>>> type of cache().
>>>>>>>
>>>>>>> Cache() is a physical operation, it does not change the logic of
>>>>>>> the `Table`. On the tableAPI layer, we should not introduce a new
>>> table
>>>>>>> type unless the logic of table has been changed. If we introduce a
>>> new
>>>>>>> table type `CachedTable`, we need create the same set of methods of
>>>>>> `Table`
>>>>>>> for it. I don't think it is worth doing this. Or can you please
>>>> elaborate
>>>>>>> more on what could be the "implicit behaviours/side effects" you are
>>>>>>> thinking about?
>>>>>>>
>>>>>>> Regards,
>>>>>>> Shaoxuan
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>> On Thu, Nov 29, 2018 at 7:05 PM Piotr Nowojski <
>>>> pi...@data-artisans.com>
>>>>>>> wrote:
>>>>>>>
>>>>>>>> Hi Becket,
>>>>>>>>
>>>>>>>> Thanks for the response.
>>>>>>>>
>>>>>>>> 1. I wasn’t saying that materialised view must be mutable or not.
>>> The
>>>>>>> same
>>>>>>>> thing applies to caches as well. To the contrary, I would expect
>>> more
>>>>>>>> consistency and updates from something that is called “cache” vs
>>>>>>> something
>>>>>>>> that’s a “materialised view”. In other words, IMO most caches do not
>>>>>>> serve
>>>>>>>> you invalid/outdated data and they handle updates on their own.
>>>>>>>>
>>>>>>>> 2. I don’t think that having in the future two very similar concepts
>>>> of
>>>>>>>> `materialized` view and `cache` is a good idea. It would be
>>> confusing
>>>>>> for
>>>>>>>> the users. I think it could be handled by variations/overloading of
>>>>>>>> materialised view concept. We could start with:
>>>>>>>>
>>>>>>>> `MaterializedTable materialize()` - immutable, session life scope
>>>>>>>> (basically the same semantic as you are proposing
>>>>>>>>
>>>>>>>> And then in the future (if ever) build on top of that/expand it
>>> with:
>>>>>>>>
>>>>>>>> `MaterializedTable materialize(refreshTime=…)` or `MaterializedTable
>>>>>>>> materialize(refreshHook=…)`
>>>>>>>>
>>>>>>>> Or with cross session support:
>>>>>>>>
>>>>>>>> `MaterializedTable materializeInto(connector=…)` or
>>> `MaterializedTable
>>>>>>>> materializeInto(tableFactory=…)`
>>>>>>>>
>>>>>>>> I’m not saying that we should implement cross session/refreshing now
>>>> or
>>>>>>>> even in the near future. I’m just arguing that naming current
>>>> immutable
>>>>>>>> session life scope method `materialize()` is more future proof and
>>>> more
>>>>>>>> consistent with SQL (on which after all table-api is heavily basing
>>>>>> on).
>>>>>>>>
>>>>>>>> 3. Even if we agree on naming it `cache()`, I would still insist on
>>>>>>>> `cache()` returning `CachedTable` handle to avoid implicit
>>>>>>> behaviours/side
>>>>>>>> effects and to give both us & users more flexibility.
>>>>>>>>
>>>>>>>> Piotrek
>>>>>>>>
>>>>>>>>> On 29 Nov 2018, at 06:20, Becket Qin <becket....@gmail.com> wrote:
>>>>>>>>>
>>>>>>>>> Just to add a little bit, the materialized view is probably more
>>>>>>> similar
>>>>>>>> to
>>>>>>>>> the persistent() brought up earlier in the thread. So it is usually
>>>>>>> cross
>>>>>>>>> session and could be used in a larger scope. For example, a
>>>>>>> materialized
>>>>>>>>> view created by user A may be visible to user B. It is probably
>>>>>>> something
>>>>>>>>> we want to have in the future. I'll put it in the future work
>>>>>> section.
>>>>>>>>>
>>>>>>>>> Thanks,
>>>>>>>>>
>>>>>>>>> Jiangjie (Becket) Qin
>>>>>>>>>
>>>>>>>>> On Thu, Nov 29, 2018 at 9:47 AM Becket Qin <becket....@gmail.com>
>>>>>>> wrote:
>>>>>>>>>
>>>>>>>>>> Hi Piotrek,
>>>>>>>>>>
>>>>>>>>>> Thanks for the explanation.
>>>>>>>>>>
>>>>>>>>>> Right now we are mostly thinking of the cached table as
>>> immutable. I
>>>>>>> can
>>>>>>>>>> see the Materialized view would be useful in the future. That
>>> said,
>>>>>> I
>>>>>>>> think
>>>>>>>>>> a simple cache mechanism is probably still needed. So to me,
>>> cache()
>>>>>>> and
>>>>>>>>>> materialize() should be two separate method as they address
>>>>>> different
>>>>>>>>>> needs. Materialize() is a higher level concept usually implying
>>>>>>>> periodical
>>>>>>>>>> update, while cache() has much simpler semantic. For example, one
>>>>>> may
>>>>>>>>>> create a materialized view and use cache() method in the
>>>>>> materialized
>>>>>>>> view
>>>>>>>>>> creation logic. So that during the materialized view update, they
>>> do
>>>>>>> not
>>>>>>>>>> need to worry about the case that the cached table is also
>>> changed.
>>>>>>>> Maybe
>>>>>>>>>> under the hood, materialized() and cache() could share some
>>>>>> mechanism,
>>>>>>>> but
>>>>>>>>>> I think a simple cache() method would be handy in a lot of cases.
>>>>>>>>>>
>>>>>>>>>> Thanks,
>>>>>>>>>>
>>>>>>>>>> Jiangjie (Becket) Qin
>>>>>>>>>>
>>>>>>>>>> On Mon, Nov 26, 2018 at 9:38 PM Piotr Nowojski <
>>>>>>> pi...@data-artisans.com
>>>>>>>>>
>>>>>>>>>> wrote:
>>>>>>>>>>
>>>>>>>>>>> Hi Becket,
>>>>>>>>>>>
>>>>>>>>>>>> Is there any extra thing user can do on a MaterializedTable that
>>>>>>> they
>>>>>>>>>>> cannot do on a Table?
>>>>>>>>>>>
>>>>>>>>>>> Maybe not in the initial implementation, but various DBs offer
>>>>>>>> different
>>>>>>>>>>> ways to “refresh” the materialised view. Hooks, triggers, timers,
>>>>>>>> manually
>>>>>>>>>>> etc. Having `MaterializedTable` would help us to handle that in
>>> the
>>>>>>>> future.
>>>>>>>>>>>
>>>>>>>>>>>> After users call *table.cache(), *users can just use that table
>>>>>> and
>>>>>>> do
>>>>>>>>>>> anything that is supported on a Table, including SQL.
>>>>>>>>>>>
>>>>>>>>>>> This is some implicit behaviour with side effects. Imagine if
>>> user
>>>>>>> has
>>>>>>>> a
>>>>>>>>>>> long and complicated program, that touches table `b` multiple
>>>>>> times,
>>>>>>>> maybe
>>>>>>>>>>> scattered around different methods. If he modifies his program by
>>>>>>>> inserting
>>>>>>>>>>> in one place
>>>>>>>>>>>
>>>>>>>>>>> b.cache()
>>>>>>>>>>>
>>>>>>>>>>> This implicitly alters the semantic and behaviour of his code all
>>>>>>> over
>>>>>>>>>>> the place, maybe in a ways that might cause problems. For example
>>>>>>> what
>>>>>>>> if
>>>>>>>>>>> underlying data is changing?
>>>>>>>>>>>
>>>>>>>>>>> Having invisible side effects is also not very clean, for example
>>>>>>> think
>>>>>>>>>>> about something like this (but more complicated):
>>>>>>>>>>>
>>>>>>>>>>> Table b = ...;
>>>>>>>>>>>
>>>>>>>>>>> If (some_condition) {
>>>>>>>>>>> processTable1(b)
>>>>>>>>>>> }
>>>>>>>>>>> else {
>>>>>>>>>>> processTable2(b)
>>>>>>>>>>> }
>>>>>>>>>>>
>>>>>>>>>>> // do more stuff with b
>>>>>>>>>>>
>>>>>>>>>>> And user adds `b.cache()` call to only one of the `processTable1`
>>>>>> or
>>>>>>>>>>> `processTable2` methods.
>>>>>>>>>>>
>>>>>>>>>>> On the other hand
>>>>>>>>>>>
>>>>>>>>>>> Table materialisedB = b.materialize()
>>>>>>>>>>>
>>>>>>>>>>> Avoids (at least some of) the side effect issues and forces user
>>> to
>>>>>>>>>>> explicitly use `materialisedB` where it’s appropriate and forces
>>>>>> user
>>>>>>>> to
>>>>>>>>>>> think what does it actually mean. And if something doesn’t work
>>> in
>>>>>>> the
>>>>>>>> end
>>>>>>>>>>> for the user, he will know what has he changed instead of blaming
>>>>>>>> Flink for
>>>>>>>>>>> some “magic” underneath. In the above example, after
>>> materialising
>>>>>> b
>>>>>>> in
>>>>>>>>>>> only one of the methods, he should/would realise about the issue
>>>>>> when
>>>>>>>>>>> handling the return value `MaterializedTable` of that method.
>>>>>>>>>>>
>>>>>>>>>>> I guess it comes down to personal preferences if you like things
>>> to
>>>>>>> be
>>>>>>>>>>> implicit or not. The more power is the user, probably the more
>>>>>> likely
>>>>>>>> he is
>>>>>>>>>>> to like/understand implicit behaviour. And we as Table API
>>>>>> designers
>>>>>>>> are
>>>>>>>>>>> the most power users out there, so I would proceed with caution
>>> (so
>>>>>>>> that we
>>>>>>>>>>> do not end up in the crazy perl realm with it’s lovely implicit
>>>>>>> method
>>>>>>>>>>> arguments ;) <https://stackoverflow.com/a/14922656/8149051>)
>>>>>>>>>>>
>>>>>>>>>>>> Table API to also support non-relational processing cases,
>>> cache()
>>>>>>>>>>> might be slightly better.
>>>>>>>>>>>
>>>>>>>>>>> I think even such extended Table API could benefit from sticking
>>>>>>>> to/being
>>>>>>>>>>> consistent with SQL where both SQL and Table API are basically
>>> the
>>>>>>>> same.
>>>>>>>>>>>
>>>>>>>>>>> One more thing. `MaterializedTable materialize()` could be more
>>>>>>>>>>> powerful/flexible allowing the user to operate both on
>>> materialised
>>>>>>>> and not
>>>>>>>>>>> materialised view at the same time for whatever reasons
>>> (underlying
>>>>>>>> data
>>>>>>>>>>> changing/better optimisation opportunities after pushing down
>>> more
>>>>>>>> filters
>>>>>>>>>>> etc). For example:
>>>>>>>>>>>
>>>>>>>>>>> Table b = …;
>>>>>>>>>>>
>>>>>>>>>>> MaterlizedTable mb = b.materialize();
>>>>>>>>>>>
>>>>>>>>>>> Val min = mb.min();
>>>>>>>>>>> Val max = mb.max();
>>>>>>>>>>>
>>>>>>>>>>> Val user42 = b.filter(‘userId = 42);
>>>>>>>>>>>
>>>>>>>>>>> Could be more efficient compared to `b.cache()` if
>>> `filter(‘userId
>>>>>> =
>>>>>>>>>>> 42);` allows for much more aggressive optimisations.
>>>>>>>>>>>
>>>>>>>>>>> Piotrek
>>>>>>>>>>>
>>>>>>>>>>>> On 26 Nov 2018, at 12:14, Fabian Hueske <fhue...@gmail.com>
>>>>>> wrote:
>>>>>>>>>>>>
>>>>>>>>>>>> I'm not suggesting to add support for Ignite. This was just an
>>>>>>>> example.
>>>>>>>>>>>> Plasma and Arrow sound interesting, too.
>>>>>>>>>>>> For the sake of this proposal, it would be up to the user to
>>>>>>>> implement a
>>>>>>>>>>>> TableFactory and corresponding TableSource / TableSink classes
>>> to
>>>>>>>>>>> persist
>>>>>>>>>>>> and read the data.
>>>>>>>>>>>>
>>>>>>>>>>>> Am Mo., 26. Nov. 2018 um 12:06 Uhr schrieb Flavio Pompermaier <
>>>>>>>>>>>> pomperma...@okkam.it>:
>>>>>>>>>>>>
>>>>>>>>>>>>> What about to add also Apache Plasma + Arrow as an alternative
>>> to
>>>>>>>>>>> Apache
>>>>>>>>>>>>> Ignite?
>>>>>>>>>>>>> [1]
>>>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>
>>>>>>
>>> https://arrow.apache.org/blog/2017/08/08/plasma-in-memory-object-store/
>>>>>>>>>>>>>
>>>>>>>>>>>>> On Mon, Nov 26, 2018 at 11:56 AM Fabian Hueske <
>>>>>> fhue...@gmail.com>
>>>>>>>>>>> wrote:
>>>>>>>>>>>>>
>>>>>>>>>>>>>> Hi,
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> Thanks for the proposal!
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> To summarize, you propose a new method Table.cache(): Table
>>> that
>>>>>>>> will
>>>>>>>>>>>>>> trigger a job and write the result into some temporary storage
>>>>>> as
>>>>>>>>>>> defined
>>>>>>>>>>>>>> by a TableFactory.
>>>>>>>>>>>>>> The cache() call blocks while the job is running and
>>> eventually
>>>>>>>>>>> returns a
>>>>>>>>>>>>>> Table object that represents a scan of the temporary table.
>>>>>>>>>>>>>> When the "session" is closed (closing to be defined?), the
>>>>>>> temporary
>>>>>>>>>>>>> tables
>>>>>>>>>>>>>> are all dropped.
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> I think this behavior makes sense and is a good first step
>>>>>> towards
>>>>>>>>>>> more
>>>>>>>>>>>>>> interactive workloads.
>>>>>>>>>>>>>> However, its performance suffers from writing to and reading
>>>>>> from
>>>>>>>>>>>>> external
>>>>>>>>>>>>>> systems.
>>>>>>>>>>>>>> I think this is OK for now. Changes that would significantly
>>>>>>> improve
>>>>>>>>>>> the
>>>>>>>>>>>>>> situation (i.e., pinning data in-memory across jobs) would
>>> have
>>>>>>>> large
>>>>>>>>>>>>>> impacts on many components of Flink.
>>>>>>>>>>>>>> Users could use in-memory filesystems or storage grids (Apache
>>>>>>>>>>> Ignite) to
>>>>>>>>>>>>>> mitigate some of the performance effects.
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> Best, Fabian
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> Am Mo., 26. Nov. 2018 um 03:38 Uhr schrieb Becket Qin <
>>>>>>>>>>>>>> becket....@gmail.com
>>>>>>>>>>>>>>> :
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> Thanks for the explanation, Piotrek.
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> Is there any extra thing user can do on a MaterializedTable
>>>>>> that
>>>>>>>> they
>>>>>>>>>>>>>>> cannot do on a Table? After users call *table.cache(), *users
>>>>>> can
>>>>>>>>>>> just
>>>>>>>>>>>>>> use
>>>>>>>>>>>>>>> that table and do anything that is supported on a Table,
>>>>>>> including
>>>>>>>>>>> SQL.
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> Naming wise, either cache() or materialize() sounds fine to
>>> me.
>>>>>>>>>>> cache()
>>>>>>>>>>>>>> is
>>>>>>>>>>>>>>> a bit more general than materialize(). Given that we are
>>>>>>> enhancing
>>>>>>>>>>> the
>>>>>>>>>>>>>>> Table API to also support non-relational processing cases,
>>>>>>> cache()
>>>>>>>>>>>>> might
>>>>>>>>>>>>>> be
>>>>>>>>>>>>>>> slightly better.
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> Thanks,
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> Jiangjie (Becket) Qin
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> On Fri, Nov 23, 2018 at 11:25 PM Piotr Nowojski <
>>>>>>>>>>>>> pi...@data-artisans.com
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> wrote:
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> Hi Becket,
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> Ops, sorry I didn’t notice that you intend to reuse existing
>>>>>>>>>>>>>>>> `TableFactory`. I don’t know why, but I assumed that you
>>> want
>>>>>> to
>>>>>>>>>>>>>> provide
>>>>>>>>>>>>>>> an
>>>>>>>>>>>>>>>> alternate way of writing the data.
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> Now that I hopefully understand the proposal, maybe we could
>>>>>>>> rename
>>>>>>>>>>>>>>>> `cache()` to
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> void materialize()
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> or going step further
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> MaterializedTable materialize()
>>>>>>>>>>>>>>>> MaterializedTable createMaterializedView()
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> ?
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> The second option with returning a handle I think is more
>>>>>>> flexible
>>>>>>>>>>>>> and
>>>>>>>>>>>>>>>> could provide features such as “refresh”/“delete” or
>>> generally
>>>>>>>>>>>>> speaking
>>>>>>>>>>>>>>>> manage the the view. In the future we could also think about
>>>>>>>> adding
>>>>>>>>>>>>>> hooks
>>>>>>>>>>>>>>>> to automatically refresh view etc. It is also more explicit
>>> -
>>>>>>>>>>>>>>>> materialization returning a new table handle will not have
>>> the
>>>>>>>> same
>>>>>>>>>>>>>>>> implicit side effects as adding a simple line of code like
>>>>>>>>>>>>> `b.cache()`
>>>>>>>>>>>>>>>> would have.
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> It would also be more SQL like, making it more intuitive for
>>>>>>> users
>>>>>>>>>>>>>>> already
>>>>>>>>>>>>>>>> familiar with the SQL.
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> Piotrek
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> On 23 Nov 2018, at 14:53, Becket Qin <becket....@gmail.com
>>>>
>>>>>>>> wrote:
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> Hi Piotrek,
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> For the cache() method itself, yes, it is equivalent to
>>>>>>> creating
>>>>>>>> a
>>>>>>>>>>>>>>>> BUILT-IN
>>>>>>>>>>>>>>>>> materialized view with a lifecycle. That functionality is
>>>>>>> missing
>>>>>>>>>>>>>>> today,
>>>>>>>>>>>>>>>>> though. Not sure if I understand your question. Do you mean
>>>>>> we
>>>>>>>>>>>>>> already
>>>>>>>>>>>>>>>> have
>>>>>>>>>>>>>>>>> the functionality and just need a syntax sugar?
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> What's more interesting in the proposal is do we want to
>>> stop
>>>>>>> at
>>>>>>>>>>>>>>> creating
>>>>>>>>>>>>>>>>> the materialized view? Or do we want to extend that in the
>>>>>>> future
>>>>>>>>>>>>> to
>>>>>>>>>>>>>> a
>>>>>>>>>>>>>>>> more
>>>>>>>>>>>>>>>>> useful unified data store distributed with Flink? And do we
>>>>>>> want
>>>>>>>> to
>>>>>>>>>>>>>>> have
>>>>>>>>>>>>>>>> a
>>>>>>>>>>>>>>>>> mechanism allow more flexible user job pattern with their
>>> own
>>>>>>>> user
>>>>>>>>>>>>>>>> defined
>>>>>>>>>>>>>>>>> services. These considerations are much more architectural.
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> Thanks,
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> Jiangjie (Becket) Qin
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> On Fri, Nov 23, 2018 at 6:01 PM Piotr Nowojski <
>>>>>>>>>>>>>>> pi...@data-artisans.com>
>>>>>>>>>>>>>>>>> wrote:
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> Hi,
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> Interesting idea. I’m trying to understand the problem.
>>>>>> Isn’t
>>>>>>>> the
>>>>>>>>>>>>>>>>>> `cache()` call an equivalent of writing data to a sink and
>>>>>>> later
>>>>>>>>>>>>>>> reading
>>>>>>>>>>>>>>>>>> from it? Where this sink has a limited live scope/live
>>> time?
>>>>>>> And
>>>>>>>>>>>>> the
>>>>>>>>>>>>>>>> sink
>>>>>>>>>>>>>>>>>> could be implemented as in memory or a file sink?
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> If so, what’s the problem with creating a materialised
>>> view
>>>>>>>> from a
>>>>>>>>>>>>>>> table
>>>>>>>>>>>>>>>>>> “b” (from your document’s example) and reusing this
>>>>>>> materialised
>>>>>>>>>>>>>> view
>>>>>>>>>>>>>>>>>> later? Maybe we are lacking mechanisms to clean up
>>>>>>> materialised
>>>>>>>>>>>>>> views
>>>>>>>>>>>>>>>> (for
>>>>>>>>>>>>>>>>>> example when current session finishes)? Maybe we need some
>>>>>>>>>>>>> syntactic
>>>>>>>>>>>>>>>> sugar
>>>>>>>>>>>>>>>>>> on top of it?
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> Piotrek
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> On 23 Nov 2018, at 07:21, Becket Qin <
>>> becket....@gmail.com
>>>>>>>
>>>>>>>>>>>>> wrote:
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> Thanks for the suggestion, Jincheng.
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> Yes, I think it makes sense to have a persist() with
>>>>>>>>>>>>>>> lifecycle/defined
>>>>>>>>>>>>>>>>>>> scope. I just added a section in the future work for
>>> this.
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> Thanks,
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> Jiangjie (Becket) Qin
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> On Fri, Nov 23, 2018 at 1:55 PM jincheng sun <
>>>>>>>>>>>>>>> sunjincheng...@gmail.com
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> wrote:
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>> Hi Jiangjie,
>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>> Thank you for the explanation about the name of
>>>>>> `cache()`, I
>>>>>>>>>>>>>>>> understand
>>>>>>>>>>>>>>>>>> why
>>>>>>>>>>>>>>>>>>>> you designed this way!
>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>> Another idea is whether we can specify a lifecycle for
>>>>>> data
>>>>>>>>>>>>>>>> persistence?
>>>>>>>>>>>>>>>>>>>> For example, persist (LifeCycle.SESSION), so that the
>>> user
>>>>>>> is
>>>>>>>>>>>>> not
>>>>>>>>>>>>>>>>>> worried
>>>>>>>>>>>>>>>>>>>> about data loss, and will clearly specify the time range
>>>>>> for
>>>>>>>>>>>>>> keeping
>>>>>>>>>>>>>>>>>> time.
>>>>>>>>>>>>>>>>>>>> At the same time, if we want to expand, we can also
>>> share
>>>>>>> in a
>>>>>>>>>>>>>>> certain
>>>>>>>>>>>>>>>>>>>> group of session, for example:
>>>>>>> LifeCycle.SESSION_GROUP(...), I
>>>>>>>>>>>>> am
>>>>>>>>>>>>>>> not
>>>>>>>>>>>>>>>>>> sure,
>>>>>>>>>>>>>>>>>>>> just an immature suggestion, for reference only!
>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>> Bests,
>>>>>>>>>>>>>>>>>>>> Jincheng
>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>> Becket Qin <becket....@gmail.com> 于2018年11月23日周五
>>>>>> 下午1:33写道：
>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>> Re: Jincheng,
>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>> Thanks for the feedback. Regarding cache() v.s.
>>>>>> persist(),
>>>>>>>>>>>>>>>> personally I
>>>>>>>>>>>>>>>>>>>>> find cache() to be more accurately describing the
>>>>>> behavior,
>>>>>>>>>>>>> i.e.
>>>>>>>>>>>>>>> the
>>>>>>>>>>>>>>>>>>>> Table
>>>>>>>>>>>>>>>>>>>>> is cached for the session, but will be deleted after
>>> the
>>>>>>>>>>>>> session
>>>>>>>>>>>>>> is
>>>>>>>>>>>>>>>>>>>> closed.
>>>>>>>>>>>>>>>>>>>>> persist() seems a little misleading as people might
>>> think
>>>>>>> the
>>>>>>>>>>>>>> table
>>>>>>>>>>>>>>>>>> will
>>>>>>>>>>>>>>>>>>>>> still be there even after the session is gone.
>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>> Great point about mixing the batch and stream
>>> processing
>>>>>> in
>>>>>>>> the
>>>>>>>>>>>>>>> same
>>>>>>>>>>>>>>>>>> job.
>>>>>>>>>>>>>>>>>>>>> We should absolutely move towards that goal. I imagine
>>>>>> that
>>>>>>>>>>>>> would
>>>>>>>>>>>>>>> be
>>>>>>>>>>>>>>>> a
>>>>>>>>>>>>>>>>>>>> huge
>>>>>>>>>>>>>>>>>>>>> change across the board, including sources, operators
>>> and
>>>>>>>>>>>>>>>>>> optimizations,
>>>>>>>>>>>>>>>>>>>> to
>>>>>>>>>>>>>>>>>>>>> name some. Likely we will need several separate
>>> in-depth
>>>>>>>>>>>>>>> discussions.
>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>> Thanks,
>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>> Jiangjie (Becket) Qin
>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>> On Fri, Nov 23, 2018 at 5:14 AM Xingcan Cui <
>>>>>>>>>>>>> xingc...@gmail.com>
>>>>>>>>>>>>>>>>>> wrote:
>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>>> Hi all,
>>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>>> @Shaoxuan, I think the lifecycle or access domain are
>>>>>> both
>>>>>>>>>>>>>>>> orthogonal
>>>>>>>>>>>>>>>>>>>> to
>>>>>>>>>>>>>>>>>>>>>> the cache problem. Essentially, this may be the first
>>>>>> time
>>>>>>>> we
>>>>>>>>>>>>>> plan
>>>>>>>>>>>>>>>> to
>>>>>>>>>>>>>>>>>>>>>> introduce another storage mechanism other than the
>>>>>> state.
>>>>>>>>>>>>> Maybe
>>>>>>>>>>>>>>> it’s
>>>>>>>>>>>>>>>>>>>>> better
>>>>>>>>>>>>>>>>>>>>>> to first draw a big picture and then concentrate on a
>>>>>>>> specific
>>>>>>>>>>>>>>> part?
>>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>>> @Becket, yes, actually I am more concerned with the
>>>>>>>> underlying
>>>>>>>>>>>>>>>>>> service.
>>>>>>>>>>>>>>>>>>>>>> This seems to be quite a major change to the existing
>>>>>>>>>>>>> codebase.
>>>>>>>>>>>>>> As
>>>>>>>>>>>>>>>> you
>>>>>>>>>>>>>>>>>>>>>> claimed, the service should be extendible to support
>>>>>> other
>>>>>>>>>>>>>>>> components
>>>>>>>>>>>>>>>>>>>> and
>>>>>>>>>>>>>>>>>>>>>> we’d better discussed it in another thread.
>>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>>> All in all, I also eager to enjoy the more interactive
>>>>>>> Table
>>>>>>>>>>>>>> API,
>>>>>>>>>>>>>>> in
>>>>>>>>>>>>>>>>>>>> case
>>>>>>>>>>>>>>>>>>>>>> of a general and flexible enough service mechanism.
>>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>>> Best,
>>>>>>>>>>>>>>>>>>>>>> Xingcan
>>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>>>> On Nov 22, 2018, at 10:16 AM, Xiaowei Jiang <
>>>>>>>>>>>>>> xiaow...@gmail.com>
>>>>>>>>>>>>>>>>>>>>> wrote:
>>>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>>>> Relying on a callback for the temp table for clean up
>>>>>> is
>>>>>>>> not
>>>>>>>>>>>>>> very
>>>>>>>>>>>>>>>>>>>>>> reliable.
>>>>>>>>>>>>>>>>>>>>>>> There is no guarantee that it will be executed
>>>>>>>> successfully.
>>>>>>>>>>>>> We
>>>>>>>>>>>>>>> may
>>>>>>>>>>>>>>>>>>>>> risk
>>>>>>>>>>>>>>>>>>>>>>> leaks when that happens. I think that it's safer to
>>>>>> have
>>>>>>> an
>>>>>>>>>>>>>>>>>>>> association
>>>>>>>>>>>>>>>>>>>>>>> between temp table and session id. So we can always
>>>>>> clean
>>>>>>>> up
>>>>>>>>>>>>>> temp
>>>>>>>>>>>>>>>>>>>>> tables
>>>>>>>>>>>>>>>>>>>>>>> which are no longer associated with any active
>>>>>> sessions.
>>>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>>>> Regards,
>>>>>>>>>>>>>>>>>>>>>>> Xiaowei
>>>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>>>> On Thu, Nov 22, 2018 at 12:55 PM jincheng sun <
>>>>>>>>>>>>>>>>>>>>> sunjincheng...@gmail.com>
>>>>>>>>>>>>>>>>>>>>>>> wrote:
>>>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>>>>> Hi Jiangjie&Shaoxuan,
>>>>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>>>>> Thanks for initiating this great proposal！
>>>>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>>>>> Interactive Programming is very useful and user
>>>>>> friendly
>>>>>>>> in
>>>>>>>>>>>>>> case
>>>>>>>>>>>>>>>> of
>>>>>>>>>>>>>>>>>>>>> your
>>>>>>>>>>>>>>>>>>>>>>>> examples.
>>>>>>>>>>>>>>>>>>>>>>>> Moreover， especially when a business has to be
>>>>>> executed
>>>>>>> in
>>>>>>>>>>>>>>> several
>>>>>>>>>>>>>>>>>>>>>> stages
>>>>>>>>>>>>>>>>>>>>>>>> with dependencies，such as the pipeline of Flink ML,
>>> in
>>>>>>>> order
>>>>>>>>>>>>>> to
>>>>>>>>>>>>>>>>>>>>> utilize
>>>>>>>>>>>>>>>>>>>>>> the
>>>>>>>>>>>>>>>>>>>>>>>> intermediate calculation results we have to submit a
>>>>>> job
>>>>>>>> by
>>>>>>>>>>>>>>>>>>>>>> env.execute().
>>>>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>>>>> About the `cache()` , I think is better to named
>>>>>>>>>>>>> `persist()`,
>>>>>>>>>>>>>>> And
>>>>>>>>>>>>>>>>>>>> The
>>>>>>>>>>>>>>>>>>>>>>>> Flink framework determines whether we internally
>>> cache
>>>>>>> in
>>>>>>>>>>>>>> memory
>>>>>>>>>>>>>>>> or
>>>>>>>>>>>>>>>>>>>>>> persist
>>>>>>>>>>>>>>>>>>>>>>>> to the storage system，Maybe save the data into state
>>>>>>>> backend
>>>>>>>>>>>>>>>>>>>>>>>> (MemoryStateBackend or RocksDBStateBackend etc.)
>>>>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>>>>> BTW, from the points of my view in the future,
>>> support
>>>>>>> for
>>>>>>>>>>>>>>>> streaming
>>>>>>>>>>>>>>>>>>>>> and
>>>>>>>>>>>>>>>>>>>>>>>> batch mode switching in the same job will also
>>> benefit
>>>>>>> in
>>>>>>>>>>>>>>>>>>>> "Interactive
>>>>>>>>>>>>>>>>>>>>>>>> Programming", I am looking forward to your JIRAs
>>> and
>>>>>>>> FLIP!
>>>>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>>>>> Best,
>>>>>>>>>>>>>>>>>>>>>>>> Jincheng
>>>>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>>>>> Becket Qin <becket....@gmail.com> 于2018年11月20日周二
>>>>>>>> 下午9:56写道：
>>>>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>>>>>> Hi all,
>>>>>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>>>>>> As a few recent email threads have pointed out, it
>>>>>> is a
>>>>>>>>>>>>>>> promising
>>>>>>>>>>>>>>>>>>>>>>>>> opportunity to enhance Flink Table API in various
>>>>>>>> aspects,
>>>>>>>>>>>>>>>>>>>> including
>>>>>>>>>>>>>>>>>>>>>>>>> functionality and ease of use among others. One of
>>>>>> the
>>>>>>>>>>>>>>> scenarios
>>>>>>>>>>>>>>>>>>>>> where
>>>>>>>>>>>>>>>>>>>>>> we
>>>>>>>>>>>>>>>>>>>>>>>>> feel Flink could improve is interactive
>>> programming.
>>>>>> To
>>>>>>>>>>>>>> explain
>>>>>>>>>>>>>>>> the
>>>>>>>>>>>>>>>>>>>>>>>> issues
>>>>>>>>>>>>>>>>>>>>>>>>> and facilitate the discussion on the solution, we
>>> put
>>>>>>>>>>>>>> together
>>>>>>>>>>>>>>>> the
>>>>>>>>>>>>>>>>>>>>>>>>> following document with our proposal.
>>>>>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>
>>>>>>>
>>>>>>
>>>>
>>> https://docs.google.com/document/d/1d4T2zTyfe7hdncEUAxrlNOYr4e5IMNEZLyqSuuswkA0/edit?usp=sharing
>>>>>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>>>>>> Feedback and comments are very welcome!
>>>>>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>>>>>> Thanks,
>>>>>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>>>>>> Jiangjie (Becket) Qin
>>>>>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>>>>
>>>>>>>>
>>>>>>>>
>>>>>>>
>>>>>>
>>>>
>>>>
>>>
>
>
