Hi Piotr,
1. `env.getCacheService().releaseCacheFor(cachedT);` vs
`cachedT.releaseCache();`
It doesn't matter which signature we provide. To those who write the
function, "releasing the cache" is not a "side effect", it is exactly what
they wanted. Even if they know that they may be releasing someone else's
cache at the same time, there is nothing they can do about it.
2. re: option 3.
I don't think `.cache()` is mutating the original table object at all. This
is exactly the same as `void t.writeToSink()`, we can even name it
`writeToCache()` if you think that would make it less misleading.
3. ref count or not.
I tend to agree that the "side effect" of releasing a cache is probably not
a big problem. So I think option 4 (as below) is acceptable.
Table cache() - create cache of a table, returning table with a hint.
void uncache() - drop the cache of the table if there is any.
Table.hint("ignoreCache").foo() - absolutely ignore cache even if it exists.
This will eventually go to a consistent state after we have automatic
caching enabled. i.e. after `b = a.cache()`, `a.foo()` and `b.foo()` are
exactly the same.
Thanks,
Jiangjie (Becket) Qin
On Wed, Jan 9, 2019 at 8:31 PM Piotr Nowojski <pi...@da-platform.com> wrote:
> Hi,
>
> I know that it still can have side effects and that’s why I wrote:
>
> > Something like this might be a better (not perfect, but just a bit
> better):
>
> My point was that this:
>
> void foo(Table t) {
> val cachedT = t.cache();
> ...
> env.getCacheService().releaseCacheFor(cachedT);
> }
>
> Should communicate the potential side effects to the user in a better way
> compared to:
>
> void foo(Table t) {
> val cachedT = t.cache();
> …
> cachedT.releaseCache();
> }
>
> Your option 3. has the problem of API class being mutable on `.cache()`
> calls.
>
> As I wrote before, we could use reference counting on `Table` or
> `CachedTable` returned from Option 4., but:
>
> > I think that introducing ref counting could be confusing and it will be
> > error prone, since Flink-table’s users are not used to closing/releasing
> > resources.
>
> I have a feeling that the inconvenience for the users in all of the use
> cases where they do not care about releasing the cache manually (which I
> would expect to be the vast majority), would overshadow potential benefits
> of using ref counting. And it’s not like ref counting can not cause
> problems on it’s own, with users wondering “why my cache wasn’t released?"
> (Because of dangling/not closed reference).
>
> Piotrek
>
> > On 8 Jan 2019, at 14:06, Becket Qin <becket....@gmail.com> wrote:
> >
> > Just to clarify, when I say foo() like below, I assume that foo() must
> have
> > a way to release its own cache, so it must have access to table env.
> >
> > void foo(Table t) {
> > ...
> > t.cache(); // create cache for t
> > ...
> > env.getCacheService().releaseCacheFor(t); // release cache for t
> > }
> >
> > Thanks,
> >
> > Jiangjie (Becket) Qin
> >
> > On Tue, Jan 8, 2019 at 9:04 PM Becket Qin <becket....@gmail.com> wrote:
> >
> >> Hi Piotr,
> >>
> >> I don't think it is feasible to ask every third party library to have
> >> method signature with CacheService as an argument.
> >>
> >> And even that signature does not really solve the problem. Imagine
> >> function foo() looks like following:
> >>
> >> void foo(Table t) {
> >> ...
> >> t.cache(); // create cache for t
> >> ...
> >> env.getCacheService().releaseCacheFor(t); // release cache for t
> >> }
> >>
> >> From function foo()'s perspective, it created a cache and released it.
> >> However, if someone invokes foo like this:
> >> {
> >> Table src = ...
> >> Table t = src.select(...).cache()
> >> foo(t)
> >> // t is uncached by foo() already.
> >> }
> >>
> >> So the "side effect" still exists.
> >>
> >> I think the only safe way to ensure there is no side effect while
> sharing
> >> the cache is to use ref count.
> >>
> >> BTW, the discussion we are having here is exactly the reason that I
> prefer
> >> option 3. From technical perspective option 3 solves all the concerns.
> >>
> >> Thanks,
> >>
> >> Jiangjie (Becket) Qin
> >>
> >>
> >> On Tue, Jan 8, 2019 at 8:41 PM Piotr Nowojski <pi...@da-platform.com>
> >> wrote:
> >>
> >>> Hi,
> >>>
> >>> I think that introducing ref counting could be confusing and it will be
> >>> error prone, since Flink-table’s users are not used to
> closing/releasing
> >>> resources. I was more objecting placing the
> >>> `uncache()`/`dropCache()`/`releaseCache()` (releaseCache sounds best
> to me)
> >>> as a method in the “Table”. It might be not obvious that it will drop
> the
> >>> cache for all of the usages of the given table. For example:
> >>>
> >>> public void foo(Table t) {
> >>> // …
> >>> t.releaseCache();
> >>> }
> >>>
> >>> public void bar(Table t) {
> >>> // ...
> >>> }
> >>>
> >>> Table a = …
> >>> val cachedA = a.cache()
> >>>
> >>> foo(cachedA)
> >>> bar(cachedA)
> >>>
> >>>
> >>> My problem with above example is that `t.releaseCache()` call is not
> >>> doing the best possible job in communicating to the user that it will
> have
> >>> a side effects for other places, like `bar(cachedA)` call. Something
> like
> >>> this might be a better (not perfect, but just a bit better):
> >>>
> >>> public void foo(Table t, CacheService cacheService) {
> >>> // …
> >>> cacheService.releaseCacheFor(t);
> >>> }
> >>>
> >>> Table a = …
> >>> val cachedA = a.cache()
> >>>
> >>> foo(cachedA, env.getCacheService())
> >>> bar(cachedA)
> >>>
> >>>
> >>> Also from another perspective, maybe placing `releaseCache()` method in
> >>> Table might not be the best separation of concerns - `releaseCache()`
> >>> method seams significantly different compared to other existing
> methods.
> >>>
> >>> Piotrek
> >>>
> >>>> On 8 Jan 2019, at 12:28, Becket Qin <becket....@gmail.com> wrote:
> >>>>
> >>>> Hi Piotr,
> >>>>
> >>>> You are right. There might be two intuitive meanings when users call
> >>>> 'a.uncache()', namely:
> >>>> 1. release the resource
> >>>> 2. Do not use cache for the next operation.
> >>>>
> >>>> Case (1) would likely be the dominant use case. So I would suggest we
> >>>> dedicate uncache() method to case (1), i.e. for resource release, but
> >>> not
> >>>> for ignoring cache.
> >>>>
> >>>> For case 2, i.e. explicitly ignoring cache (which is rare), users may
> >>> use
> >>>> something like 'hint("ignoreCache")'. I think this is better as it is
> a
> >>>> little weird for users to call `a.uncache()` while they may not even
> >>> know
> >>>> if the table is cached at all.
> >>>>
> >>>> Assuming we let `uncache()` to only release resource, one possibility
> is
> >>>> using ref count to mitigate the side effect. That means a ref count is
> >>>> incremented on `cache()` and decremented on `uncache()`. That means
> >>>> `uncache()` does not physically release the resource immediately, but
> >>> just
> >>>> means the cache could be released.
> >>>> That being said, I am not sure if this is really a better solution as
> it
> >>>> seems a little counter intuitive. Maybe calling it releaseCache()
> help a
> >>>> little bit?
> >>>>
> >>>> Thanks,
> >>>>
> >>>> Jiangjie (Becket) Qin
> >>>>
> >>>>
> >>>>
> >>>>
> >>>> On Tue, Jan 8, 2019 at 5:36 PM Piotr Nowojski <pi...@da-platform.com>
> >>> wrote:
> >>>>
> >>>>> Hi Becket,
> >>>>>
> >>>>> With `uncache` there are probably two features that we can think
> about:
> >>>>>
> >>>>> a)
> >>>>>
> >>>>> Physically dropping the cached table from the storage, freeing up the
> >>>>> resources
> >>>>>
> >>>>> b)
> >>>>>
> >>>>> Hinting the optimizer to not cache the reads for the next query/table
> >>>>>
> >>>>> a) Has the issue as I wrote before, that it seemed to be an operation
> >>>>> inherently “flawed" with having side effects.
> >>>>>
> >>>>> I’m not sure how it would be best to express. We could make it work:
> >>>>>
> >>>>> 1. via a method on a Table as you proposed:
> >>>>>
> >>>>> void Table#dropCache()
> >>>>> void Table#uncache()
> >>>>>
> >>>>> 2. Operation on the environment
> >>>>>
> >>>>> env.dropCacheFor(table) // or some other argument that allows user to
> >>>>> identify the desired cache
> >>>>>
> >>>>> 3. Extending (from your original design doc) `setTableService` method
> >>> to
> >>>>> return some control handle like:
> >>>>>
> >>>>> TableServiceControl setTableService(TableFactory tf,
> >>>>> TableProperties properties,
> >>>>> TempTableCleanUpCallback cleanUpCallback);
> >>>>>
> >>>>> (TableServiceControl? TableService? TableServiceHandle?
> CacheService?)
> >>>>>
> >>>>> And having the drop cache method there:
> >>>>>
> >>>>> TableServiceControl#dropCache(table)
> >>>>>
> >>>>> Out of those options, option 1 might have a disadvantage of kind of
> not
> >>>>> making the user aware, that this is a global operation with side
> >>> effects.
> >>>>> Like the old example of:
> >>>>>
> >>>>> public void foo(Table t) {
> >>>>> // …
> >>>>> t.dropCache();
> >>>>> }
> >>>>>
> >>>>> It might not be immediately obvious that `t.dropCache()` is some kind
> >>> of
> >>>>> global operation, with side effects visible outside of the `foo`
> >>> function.
> >>>>>
> >>>>> On the other hand, both option 2 and 3, might have greater chance of
> >>>>> catching user’s attention:
> >>>>>
> >>>>> public void foo(Table t, CacheService cacheService) {
> >>>>> // …
> >>>>> cacheService.dropCache(t);
> >>>>> }
> >>>>>
> >>>>> b) could be achieved quite easily:
> >>>>>
> >>>>> Table a = …
> >>>>> val notCached1 = a.doNotCache()
> >>>>> val cachedA = a.cache()
> >>>>> val notCached2 = cachedA.doNotCache() // equivalent of notCached1
> >>>>>
> >>>>> `doNotCache()` would behave similarly to `cache()` - return a copy of
> >>> the
> >>>>> table with removed “cache” hint and/or added “never cache” hint.
> >>>>>
> >>>>> Piotrek
> >>>>>
> >>>>>
> >>>>>> On 8 Jan 2019, at 03:17, Becket Qin <becket....@gmail.com> wrote:
> >>>>>>
> >>>>>> Hi Piotr,
> >>>>>>
> >>>>>> Thanks for the proposal and detailed explanation. I like the idea of
> >>>>>> returning a new hinted Table without modifying the original table.
> >>> This
> >>>>>> also leave the room for users to benefit from future implicit
> caching.
> >>>>>>
> >>>>>> Just to make sure I get the full picture. In your proposal, there
> will
> >>>>> also
> >>>>>> be a 'void Table#uncache()' method to release the cache, right?
> >>>>>>
> >>>>>> Thanks,
> >>>>>>
> >>>>>> Jiangjie (Becket) Qin
> >>>>>>
> >>>>>> On Mon, Jan 7, 2019 at 11:50 PM Piotr Nowojski <
> pi...@da-platform.com
> >>>>
> >>>>>> wrote:
> >>>>>>
> >>>>>>> Hi Becket!
> >>>>>>>
> >>>>>>> After further thinking I tend to agree that my previous proposal
> >>>>> (*Option
> >>>>>>> 2*) indeed might not be if would in the future introduce automatic
> >>>>> caching.
> >>>>>>> However I would like to propose a slightly modified version of it:
> >>>>>>>
> >>>>>>> *Option 4*
> >>>>>>>
> >>>>>>> Adding `cache()` method with following signature:
> >>>>>>>
> >>>>>>> Table Table#cache();
> >>>>>>>
> >>>>>>> Without side-effects, and `cache()` call do not modify/change
> >>> original
> >>>>>>> Table in any way.
> >>>>>>> It would return a copy of original table, with added hint for the
> >>>>>>> optimizer to cache the table, so that the future accesses to the
> >>>>> returned
> >>>>>>> table might be cached or not.
> >>>>>>>
> >>>>>>> Assuming that we are talking about a setup, where we do not have
> >>>>> automatic
> >>>>>>> caching enabled (possible future extension).
> >>>>>>>
> >>>>>>> Example #1:
> >>>>>>>
> >>>>>>> ```
> >>>>>>> Table a = …
> >>>>>>> a.foo() // not cached
> >>>>>>>
> >>>>>>> val cachedTable = a.cache();
> >>>>>>>
> >>>>>>> cachedA.bar() // maybe cached
> >>>>>>> a.foo() // same as before - effectively not cached
> >>>>>>> ```
> >>>>>>>
> >>>>>>> Both the first and the second `a.foo()` operations would behave in
> >>> the
> >>>>>>> exactly same way. Again, `a.cache()` call doesn’t affect `a`
> itself.
> >>> If
> >>>>> `a`
> >>>>>>> was not hinted for caching before `a.cache();`, then both `a.foo()`
> >>>>> calls
> >>>>>>> wouldn’t use cache.
> >>>>>>>
> >>>>>>> Returned `cachedA` would be hinted with “cache” hint, so probably
> >>>>>>> `cachedA.bar()` would go through cache (unless optimiser decides
> the
> >>>>>>> opposite)
> >>>>>>>
> >>>>>>> Example #2
> >>>>>>>
> >>>>>>> ```
> >>>>>>> Table a = …
> >>>>>>>
> >>>>>>> a.foo() // not cached
> >>>>>>>
> >>>>>>> val b = a.cache();
> >>>>>>>
> >>>>>>> a.foo() // same as before - effectively not cached
> >>>>>>> b.foo() // maybe cached
> >>>>>>>
> >>>>>>> val c = b.cache();
> >>>>>>>
> >>>>>>> a.foo() // same as before - effectively not cached
> >>>>>>> b.foo() // same as before - effectively maybe cached
> >>>>>>> c.foo() // maybe cached
> >>>>>>> ```
> >>>>>>>
> >>>>>>> Now, assuming that we have some future “automatic caching
> >>> optimisation”:
> >>>>>>>
> >>>>>>> Example #3
> >>>>>>>
> >>>>>>> ```
> >>>>>>> env.enableAutomaticCaching()
> >>>>>>> Table a = …
> >>>>>>>
> >>>>>>> a.foo() // might be cached, depending if `a` was selected to
> >>> automatic
> >>>>>>> caching
> >>>>>>>
> >>>>>>> val b = a.cache();
> >>>>>>>
> >>>>>>> a.foo() // same as before - might be cached, if `a` was selected to
> >>>>>>> automatic caching
> >>>>>>> b.foo() // maybe cached
> >>>>>>> ```
> >>>>>>>
> >>>>>>>
> >>>>>>> More or less this is the same behaviour as:
> >>>>>>>
> >>>>>>> Table a = ...
> >>>>>>> val b = a.filter(x > 20)
> >>>>>>>
> >>>>>>> calling `filter` hasn’t changed or altered `a` in anyway. If `a`
> was
> >>>>>>> previously filtered:
> >>>>>>>
> >>>>>>> Table src = …
> >>>>>>> val a = src.filter(x > 20)
> >>>>>>> val b = a.filter(x > 20)
> >>>>>>>
> >>>>>>> then yes, `a` and `b` will be the same. But the point is that
> neither
> >>>>>>> `filter` nor `cache` changes the original `a` table.
> >>>>>>>
> >>>>>>> One thing is that indeed, physically dropping cache operation, will
> >>> have
> >>>>>>> side effects and it will in a way mutate the cached table
> references.
> >>>>> But
> >>>>>>> this is I think unavoidable in any solution - the same issue as
> >>> calling
> >>>>>>> `.close()`, or calling destructor in C++.
> >>>>>>>
> >>>>>>> Piotrek
> >>>>>>>
> >>>>>>>> On 7 Jan 2019, at 10:41, Becket Qin <becket....@gmail.com> wrote:
> >>>>>>>>
> >>>>>>>> Happy New Year, everybody!
> >>>>>>>>
> >>>>>>>> I would like to resume this discussion thread. At this point, We
> >>> have
> >>>>>>>> agreed on the first step goal of interactive programming. The open
> >>>>>>>> discussion is the exact API. More specifically, what should
> >>> *cache()*
> >>>>>>>> method return and what is the semantic. There are three options:
> >>>>>>>>
> >>>>>>>> *Option 1*
> >>>>>>>> *void cache()* OR *Table cache()* which returns the original table
> >>> for
> >>>>>>>> chained calls.
> >>>>>>>> *void uncache() *releases the cache.
> >>>>>>>> *Table.hint(ignoreCache).foo()* to ignore cache for operation
> foo().
> >>>>>>>>
> >>>>>>>> - Semantic: a.cache() hints that table 'a' should be cached.
> >>> Optimizer
> >>>>>>>> decides whether the cache will be used or not.
> >>>>>>>> - pros: simple and no confusion between CachedTable and original
> >>> table
> >>>>>>>> - cons: A table may be cached / uncached in a method invocation,
> >>> while
> >>>>>>> the
> >>>>>>>> caller does not know about this.
> >>>>>>>>
> >>>>>>>> *Option 2*
> >>>>>>>> *CachedTable cache()*
> >>>>>>>> *CachedTable *extends *Table *with an additional *uncache()*
> method
> >>>>>>>>
> >>>>>>>> - Semantic: After *val cachedA = a.cache()*, *cachedA.foo()* will
> >>>>> always
> >>>>>>>> use cache. *a.bar() *will always use original DAG.
> >>>>>>>> - pros: No potential side effects in method invocation.
> >>>>>>>> - cons: Optimizer has no chance to kick in. Future optimization
> will
> >>>>>>> become
> >>>>>>>> a behavior change and need users to change the code.
> >>>>>>>>
> >>>>>>>> *Option 3*
> >>>>>>>> *CacheHandle cache()*
> >>>>>>>> *CacheHandle.release() *to release a cache handle on the table. If
> >>> all
> >>>>>>>> cache handles are released, the cache could be removed.
> >>>>>>>> *Table.hint(ignoreCache).foo()* to ignore cache for operation
> foo().
> >>>>>>>>
> >>>>>>>> - Semantic: *a.cache() *hints that 'a' should be cached. Optimizer
> >>>>>>> decides
> >>>>>>>> whether the cache will be used or not. Cache is released either no
> >>>>> handle
> >>>>>>>> is on it, or the user program exits.
> >>>>>>>> - pros: No potential side effect in method invocation. No
> confusion
> >>>>>>> between
> >>>>>>>> cached table v.s original table.
> >>>>>>>> - cons: An additional CacheHandle exposed to the users.
> >>>>>>>>
> >>>>>>>>
> >>>>>>>> Personally I prefer option 3 for the following reasons:
> >>>>>>>> 1. It is simple. Vast majority of the users would just call
> >>>>>>>> *a.cache()* followed
> >>>>>>>> by *a.foo(),* *a.bar(), etc. *
> >>>>>>>> 2. There is no semantic ambiguity and semantic change if we decide
> >>> to
> >>>>> add
> >>>>>>>> implicit cache in the future.
> >>>>>>>> 3. There is no side effect in the method calls.
> >>>>>>>> 4. Admittedly we need to expose one more CacheHandle class to the
> >>>>> users.
> >>>>>>>> But it is not that difficult to understand given similar well
> known
> >>>>>>> concept
> >>>>>>>> like ref count (we can name it CacheReference if that is easier to
> >>>>>>>> understand). So I think it is fine.
> >>>>>>>>
> >>>>>>>>
> >>>>>>>> Thanks,
> >>>>>>>>
> >>>>>>>> Jiangjie (Becket) Qin
> >>>>>>>>
> >>>>>>>>
> >>>>>>>> On Thu, Dec 13, 2018 at 11:23 AM Becket Qin <becket....@gmail.com
> >
> >>>>>>> wrote:
> >>>>>>>>
> >>>>>>>>> Hi Piotrek,
> >>>>>>>>>
> >>>>>>>>> 1. Regarding optimization.
> >>>>>>>>> Sure there are many cases that the decision is hard to make. But
> >>> that
> >>>>>>> does
> >>>>>>>>> not make it any easier for the users to make those decisions. I
> >>>>> imagine
> >>>>>>> 99%
> >>>>>>>>> of the users would just naively use cache. I am not saying we can
> >>>>>>> optimize
> >>>>>>>>> in all the cases. But as long as we agree that at least in
> certain
> >>>>>>> cases (I
> >>>>>>>>> would argue most cases), optimizer can do a little better than an
> >>>>>>> average
> >>>>>>>>> user who likely knows little about Flink internals, we should not
> >>> push
> >>>>>>> the
> >>>>>>>>> burden of optimization to users.
> >>>>>>>>>
> >>>>>>>>> BTW, it seems some of your concerns are related to the
> >>>>> implementation. I
> >>>>>>>>> did not mention the implementation of the caching service because
> >>> that
> >>>>>>>>> should not affect the API semantic. Not sure if this helps, but
> >>>>> imagine
> >>>>>>> the
> >>>>>>>>> default implementation has one StorageNode service colocating
> with
> >>>>> each
> >>>>>>> TM.
> >>>>>>>>> It could be running within the TM process or in a standalone
> >>> process,
> >>>>>>>>> depending on configuration.
> >>>>>>>>>
> >>>>>>>>> The StorageNode uses memory + spill-to-disk mechanism. The cached
> >>> data
> >>>>>>>>> will just be written to the local StorageNode service. If the
> >>>>>>> StorageNode
> >>>>>>>>> is running within the TM process, the in-memory cache could just
> be
> >>>>>>> objects
> >>>>>>>>> so we save some serde cost. A later job referring to the cached
> >>> Table
> >>>>>>> will
> >>>>>>>>> be scheduled in a locality aware manner, i.e. run in the TM whose
> >>> peer
> >>>>>>>>> StorageNode hosts the data.
> >>>>>>>>>
> >>>>>>>>>
> >>>>>>>>> 2. Semantic
> >>>>>>>>> I am not sure why introducing a new hintCache() or
> >>>>>>>>> env.enableAutomaticCaching() method would avoid the consequence
> of
> >>>>>>> semantic
> >>>>>>>>> change.
> >>>>>>>>>
> >>>>>>>>> If the auto optimization is not enabled by default, users still
> >>> need
> >>>>> to
> >>>>>>>>> make code change to all existing programs in order to get the
> >>> benefit.
> >>>>>>>>> If the auto optimization is enabled by default, advanced users
> who
> >>>>> know
> >>>>>>>>> that they really want to use cache will suddenly lose the
> >>> opportunity
> >>>>>>> to do
> >>>>>>>>> so, unless they change the code to disable auto optimization.
> >>>>>>>>>
> >>>>>>>>>
> >>>>>>>>> 3. side effect
> >>>>>>>>> The CacheHandle is not only for where to put uncache(). It is to
> >>> solve
> >>>>>>> the
> >>>>>>>>> implicit performance impact by moving the uncache() to the
> >>>>> CacheHandle.
> >>>>>>>>>
> >>>>>>>>> - If users wants to leverage cache, they can call a.cache().
> After
> >>>>>>>>> that, unless user explicitly release that CacheHandle, a.foo()
> will
> >>>>>>> always
> >>>>>>>>> leverage cache if needed (optimizer may choose to ignore cache if
> >>>>> that
> >>>>>>>>> helps accelerate the process). Any function call will not be able
> >>> to
> >>>>>>>>> release the cache because they do not have that CacheHandle.
> >>>>>>>>> - If some advanced users do not want to use cache at all, they
> will
> >>>>>>>>> call a.hint(ignoreCache).foo(). This will for sure ignore cache
> and
> >>>>>>> use the
> >>>>>>>>> original DAG to process.
> >>>>>>>>>
> >>>>>>>>>
> >>>>>>>>>> In vast majority of the cases, users wouldn't really care
> whether
> >>> the
> >>>>>>>>>> cache is used or not.
> >>>>>>>>>> I wouldn’t agree with that, because “caching” (if not purely in
> >>>>> memory
> >>>>>>>>>> caching) would add additional IO costs. It’s similar as saying
> >>> that
> >>>>>>> users
> >>>>>>>>>> would not see a difference between Spark/Flink and MapReduce
> >>>>> (MapReduce
> >>>>>>>>>> writes data to disks after every map/reduce stage).
> >>>>>>>>>
> >>>>>>>>> What I wanted to say is that in most cases, after users call
> >>> cache(),
> >>>>>>> they
> >>>>>>>>> don't really care about whether auto optimization has decided to
> >>>>> ignore
> >>>>>>> the
> >>>>>>>>> cache or not, as long as the program runs faster.
> >>>>>>>>>
> >>>>>>>>> Thanks,
> >>>>>>>>>
> >>>>>>>>> Jiangjie (Becket) Qin
> >>>>>>>>>
> >>>>>>>>>
> >>>>>>>>>
> >>>>>>>>>
> >>>>>>>>>
> >>>>>>>>>
> >>>>>>>>>
> >>>>>>>>>
> >>>>>>>>> On Wed, Dec 12, 2018 at 10:50 PM Piotr Nowojski <
> >>>>>>> pi...@data-artisans.com>
> >>>>>>>>> wrote:
> >>>>>>>>>
> >>>>>>>>>> Hi,
> >>>>>>>>>>
> >>>>>>>>>> Thanks for the quick answer :)
> >>>>>>>>>>
> >>>>>>>>>> Re 1.
> >>>>>>>>>>
> >>>>>>>>>> I generally agree with you, however couple of points:
> >>>>>>>>>>
> >>>>>>>>>> a) the problem with using automatic caching is bigger, because
> you
> >>>>> will
> >>>>>>>>>> have to decide, how do you compare IO vs CPU costs and if you
> pick
> >>>>>>> wrong,
> >>>>>>>>>> additional IO costs might be enormous or even can crash your
> >>> system.
> >>>>>>> This
> >>>>>>>>>> is more difficult problem compared to let say join reordering,
> >>> where
> >>>>>>> the
> >>>>>>>>>> only issue is to have good statistics that can capture
> >>> correlations
> >>>>>>> between
> >>>>>>>>>> columns (when you reorder joins number of IO operations do not
> >>>>> change)
> >>>>>>>>>> c) your example is completely independent of caching.
> >>>>>>>>>>
> >>>>>>>>>> Query like this:
> >>>>>>>>>>
> >>>>>>>>>> src1.filte('f1 > 10).join(src2.filter('f2 < 30), `f1
> >>> ===`f2).as('f3,
> >>>>>>>>>> …).filter(‘f3 > 30)
> >>>>>>>>>>
> >>>>>>>>>> Should/could be optimised to empty result immediately, without
> the
> >>>>> need
> >>>>>>>>>> for any cache/materialisation and that should work even without
> >>> any
> >>>>>>>>>> statistics provided by the connector.
> >>>>>>>>>>
> >>>>>>>>>> For me prerequisite to any serious cost-based optimisations
> would
> >>> be
> >>>>>>> some
> >>>>>>>>>> reasonable benchmark coverage of the code (tpch?). Otherwise
> that
> >>>>>>> would be
> >>>>>>>>>> equivalent of adding not tested code, since we wouldn’t be able
> to
> >>>>>>> verify
> >>>>>>>>>> our assumptions, like how does the writing of 10 000 records to
> >>>>>>>>>> cache/RocksDB/Kafka/CSV file compare to
> >>> joining/filtering/processing
> >>>>> of
> >>>>>>>>>> lets say 1000 000 rows.
> >>>>>>>>>>
> >>>>>>>>>> Re 2.
> >>>>>>>>>>
> >>>>>>>>>> I wasn’t proposing to change the semantic later. I was proposing
> >>> that
> >>>>>>> we
> >>>>>>>>>> start now:
> >>>>>>>>>>
> >>>>>>>>>> CachedTable cachedA = a.cache()
> >>>>>>>>>> cachedA.foo() // Cache is used
> >>>>>>>>>> a.bar() // Original DAG is used
> >>>>>>>>>>
> >>>>>>>>>> And then later we can think about adding for example
> >>>>>>>>>>
> >>>>>>>>>> CachedTable cachedA = a.hintCache()
> >>>>>>>>>> cachedA.foo() // Cache might be used
> >>>>>>>>>> a.bar() // Original DAG is used
> >>>>>>>>>>
> >>>>>>>>>> Or
> >>>>>>>>>>
> >>>>>>>>>> env.enableAutomaticCaching()
> >>>>>>>>>> a.foo() // Cache might be used
> >>>>>>>>>> a.bar() // Cache might be used
> >>>>>>>>>>
> >>>>>>>>>> Or (I would still not like this option):
> >>>>>>>>>>
> >>>>>>>>>> a.hintCache()
> >>>>>>>>>> a.foo() // Cache might be used
> >>>>>>>>>> a.bar() // Cache might be used
> >>>>>>>>>>
> >>>>>>>>>> Or whatever else that will come to our mind. Even if we add some
> >>>>>>>>>> automatic caching in the future, keeping implicit (`CachedTable
> >>>>>>> cache()`)
> >>>>>>>>>> caching will still be useful, at least in some cases.
> >>>>>>>>>>
> >>>>>>>>>> Re 3.
> >>>>>>>>>>
> >>>>>>>>>>> 2. The source tables are immutable during one run of batch
> >>>>> processing
> >>>>>>>>>> logic.
> >>>>>>>>>>> 3. The cache is immutable during one run of batch processing
> >>> logic.
> >>>>>>>>>>
> >>>>>>>>>>> I think assumption 2 and 3 are by definition what batch
> >>> processing
> >>>>>>>>>> means,
> >>>>>>>>>>> i.e the data must be complete before it is processed and should
> >>> not
> >>>>>>>>>> change
> >>>>>>>>>>> when the processing is running.
> >>>>>>>>>>
> >>>>>>>>>> I agree that this is how batch systems SHOULD be working.
> However
> >>> I
> >>>>>>> know
> >>>>>>>>>> from my previous experience that it’s not always the case.
> >>> Sometimes
> >>>>>>> users
> >>>>>>>>>> are just working on some non transactional storage, which can be
> >>>>>>> (either
> >>>>>>>>>> constantly or occasionally) being modified by some other
> processes
> >>>>> for
> >>>>>>>>>> whatever the reasons (fixing the data, updating, adding new data
> >>>>> etc).
> >>>>>>>>>>
> >>>>>>>>>> But even if we ignore this point (data immutability),
> performance
> >>>>> side
> >>>>>>>>>> effect issue of your proposal remains. If user calls `void
> >>> a.cache()`
> >>>>>>> deep
> >>>>>>>>>> inside some private method, it will have implicit side effects
> on
> >>>>> other
> >>>>>>>>>> parts of his program that might not be obvious.
> >>>>>>>>>>
> >>>>>>>>>> Re `CacheHandle`.
> >>>>>>>>>>
> >>>>>>>>>> If I understand it correctly, it only addresses the issue where
> to
> >>>>>>> place
> >>>>>>>>>> method `uncache`/`dropCache`.
> >>>>>>>>>>
> >>>>>>>>>> Btw,
> >>>>>>>>>>
> >>>>>>>>>>> In vast majority of the cases, users wouldn't really care
> whether
> >>>>> the
> >>>>>>>>>> cache is used or not.
> >>>>>>>>>>
> >>>>>>>>>> I wouldn’t agree with that, because “caching” (if not purely in
> >>>>> memory
> >>>>>>>>>> caching) would add additional IO costs. It’s similar as saying
> >>> that
> >>>>>>> users
> >>>>>>>>>> would not see a difference between Spark/Flink and MapReduce
> >>>>> (MapReduce
> >>>>>>>>>> writes data to disks after every map/reduce stage).
> >>>>>>>>>>
> >>>>>>>>>> Piotrek
> >>>>>>>>>>
> >>>>>>>>>>> On 12 Dec 2018, at 14:28, Becket Qin <becket....@gmail.com>
> >>> wrote:
> >>>>>>>>>>>
> >>>>>>>>>>> Hi Piotrek,
> >>>>>>>>>>>
> >>>>>>>>>>> Not sure if you noticed, in my last email, I was proposing
> >>>>>>> `CacheHandle
> >>>>>>>>>>> cache()` to avoid the potential side effect due to function
> >>> calls.
> >>>>>>>>>>>
> >>>>>>>>>>> Let's look at the disagreement in your reply one by one.
> >>>>>>>>>>>
> >>>>>>>>>>>
> >>>>>>>>>>> 1. Optimization chances
> >>>>>>>>>>>
> >>>>>>>>>>> Optimization is never a trivial work. This is exactly why we
> >>> should
> >>>>>>> not
> >>>>>>>>>> let
> >>>>>>>>>>> user manually do that. Databases have done huge amount of work
> in
> >>>>> this
> >>>>>>>>>>> area. At Alibaba, we rely heavily on many optimization rules to
> >>>>> boost
> >>>>>>>>>> the
> >>>>>>>>>>> SQL query performance.
> >>>>>>>>>>>
> >>>>>>>>>>> In your example, if I filling the filter conditions in a
> certain
> >>>>> way,
> >>>>>>>>>> the
> >>>>>>>>>>> optimization would become obvious.
> >>>>>>>>>>>
> >>>>>>>>>>> Table src1 = … // read from connector 1
> >>>>>>>>>>> Table src2 = … // read from connector 2
> >>>>>>>>>>>
> >>>>>>>>>>> Table a = src1.filte('f1 > 10).join(src2.filter('f2 < 30), `f1
> >>> ===
> >>>>>>>>>>> `f2).as('f3, ...)
> >>>>>>>>>>> a.cache() // write cache to connector 3, when writing the
> >>> records,
> >>>>>>>>>> remember
> >>>>>>>>>>> min and max of `f1
> >>>>>>>>>>>
> >>>>>>>>>>> a.filter('f3 > 30) // There is no need to read from any
> connector
> >>>>>>>>>> because
> >>>>>>>>>>> `a` does not contain any record whose 'f3 is greater than 30.
> >>>>>>>>>>> env.execute()
> >>>>>>>>>>> a.select(…)
> >>>>>>>>>>>
> >>>>>>>>>>> BTW, it seems to me that adding some basic statistics is fairly
> >>>>>>>>>>> straightforward and the cost is pretty marginal if not
> >>> ignorable. In
> >>>>>>>>>> fact
> >>>>>>>>>>> it is not only needed for optimization, but also for cases such
> >>> as
> >>>>> ML,
> >>>>>>>>>>> where some algorithms may need to decide their parameter based
> on
> >>>>> the
> >>>>>>>>>>> statistics of the data.
> >>>>>>>>>>>
> >>>>>>>>>>>
> >>>>>>>>>>> 2. Same API, one semantic now, another semantic later.
> >>>>>>>>>>>
> >>>>>>>>>>> I am trying to understand what is the semantic of `CachedTable
> >>>>>>> cache()`
> >>>>>>>>>> you
> >>>>>>>>>>> are proposing. IMO, we should avoid designing an API whose
> >>> semantic
> >>>>>>>>>> will be
> >>>>>>>>>>> changed later. If we have a "CachedTable cache()" method, then
> >>> the
> >>>>>>>>>> semantic
> >>>>>>>>>>> should be very clearly defined upfront and do not change later.
> >>> It
> >>>>>>>>>> should
> >>>>>>>>>>> never be "right now let's go with semantic 1, later we can
> >>> silently
> >>>>>>>>>> change
> >>>>>>>>>>> it to semantic 2 or 3". Such change could result in bad
> >>> consequence.
> >>>>>>> For
> >>>>>>>>>>> example, let's say we decide go with semantic 1:
> >>>>>>>>>>>
> >>>>>>>>>>> CachedTable cachedA = a.cache()
> >>>>>>>>>>> cachedA.foo() // Cache is used
> >>>>>>>>>>> a.bar() // Original DAG is used.
> >>>>>>>>>>>
> >>>>>>>>>>> Now majority of the users would be using cachedA.foo() in their
> >>>>> code.
> >>>>>>>>>> And
> >>>>>>>>>>> some advanced users will use a.bar() to explicitly skip the
> >>> cache.
> >>>>>>> Later
> >>>>>>>>>>> on, we added smart optimization and change the semantic to
> >>> semantic
> >>>>> 2:
> >>>>>>>>>>>
> >>>>>>>>>>> CachedTable cachedA = a.cache()
> >>>>>>>>>>> cachedA.foo() // Cache is used
> >>>>>>>>>>> a.bar() // Cache MIGHT be used, and Flink may decide to skip
> >>> cache
> >>>>> if
> >>>>>>>>>> it is
> >>>>>>>>>>> faster.
> >>>>>>>>>>>
> >>>>>>>>>>> Now most of the users who were writing cachedA.foo() will not
> >>>>> benefit
> >>>>>>>>>> from
> >>>>>>>>>>> this optimization at all, unless they change their code to use
> >>>>> a.foo()
> >>>>>>>>>>> instead. And those advanced users suddenly lose the option to
> >>>>>>> explicitly
> >>>>>>>>>>> ignore cache unless they change their code (assuming we care
> >>> enough
> >>>>> to
> >>>>>>>>>>> provide something like hint(useCache)). If we don't define the
> >>>>>>> semantic
> >>>>>>>>>>> carefully, our users will have to change their code again and
> >>> again
> >>>>>>>>>> while
> >>>>>>>>>>> they shouldn't have to.
> >>>>>>>>>>>
> >>>>>>>>>>>
> >>>>>>>>>>> 3. side effect.
> >>>>>>>>>>>
> >>>>>>>>>>> Before we talk about side effect, we have to agree on the
> >>>>> assumptions.
> >>>>>>>>>> The
> >>>>>>>>>>> assumptions I have are following:
> >>>>>>>>>>> 1. We are talking about batch processing.
> >>>>>>>>>>> 2. The source tables are immutable during one run of batch
> >>>>> processing
> >>>>>>>>>> logic.
> >>>>>>>>>>> 3. The cache is immutable during one run of batch processing
> >>> logic.
> >>>>>>>>>>>
> >>>>>>>>>>> I think assumption 2 and 3 are by definition what batch
> >>> processing
> >>>>>>>>>> means,
> >>>>>>>>>>> i.e the data must be complete before it is processed and should
> >>> not
> >>>>>>>>>> change
> >>>>>>>>>>> when the processing is running.
> >>>>>>>>>>>
> >>>>>>>>>>> As far as I am aware of, I don't know any batch processing
> system
> >>>>>>>>>> breaking
> >>>>>>>>>>> those assumptions. Even for relational database tables, where
> >>>>> queries
> >>>>>>>>>> can
> >>>>>>>>>>> run with concurrent modifications, necessary locking are still
> >>>>>>> required
> >>>>>>>>>> to
> >>>>>>>>>>> ensure the integrity of the query result.
> >>>>>>>>>>>
> >>>>>>>>>>> Please let me know if you disagree with the above assumptions.
> If
> >>>>> you
> >>>>>>>>>> agree
> >>>>>>>>>>> with these assumptions, with the `CacheHandle cache()` API in
> my
> >>>>> last
> >>>>>>>>>>> email, do you still see side effects?
> >>>>>>>>>>>
> >>>>>>>>>>> Thanks,
> >>>>>>>>>>>
> >>>>>>>>>>> Jiangjie (Becket) Qin
> >>>>>>>>>>>
> >>>>>>>>>>>
> >>>>>>>>>>> On Wed, Dec 12, 2018 at 7:11 PM Piotr Nowojski <
> >>>>>>> pi...@data-artisans.com
> >>>>>>>>>>>
> >>>>>>>>>>> wrote:
> >>>>>>>>>>>
> >>>>>>>>>>>> Hi Becket,
> >>>>>>>>>>>>
> >>>>>>>>>>>>> Regarding the chance of optimization, it might not be that
> >>> rare.
> >>>>>>> Some
> >>>>>>>>>>>> very
> >>>>>>>>>>>>> simple statistics could already help in many cases. For
> >>> example,
> >>>>>>>>>> simply
> >>>>>>>>>>>>> maintaining max and min of each fields can already eliminate
> >>> some
> >>>>>>>>>>>>> unnecessary table scan (potentially scanning the cached
> table)
> >>> if
> >>>>>>> the
> >>>>>>>>>>>>> result is doomed to be empty. A histogram would give even
> >>> further
> >>>>>>>>>>>>> information. The optimizer could be very careful and only
> >>> ignores
> >>>>>>>>>> cache
> >>>>>>>>>>>>> when it is 100% sure doing that is cheaper. e.g. only when a
> >>>>> filter
> >>>>>>> on
> >>>>>>>>>>>> the
> >>>>>>>>>>>>> cache will absolutely return nothing.
> >>>>>>>>>>>>
> >>>>>>>>>>>> I do not see how this might be easy to achieve. It would
> require
> >>>>> tons
> >>>>>>>>>> of
> >>>>>>>>>>>> effort to make it work and in the end you would still have a
> >>>>> problem
> >>>>>>> of
> >>>>>>>>>>>> comparing/trading CPU cycles vs IO. For example:
> >>>>>>>>>>>>
> >>>>>>>>>>>> Table src1 = … // read from connector 1
> >>>>>>>>>>>> Table src2 = … // read from connector 2
> >>>>>>>>>>>>
> >>>>>>>>>>>> Table a = src1.filter(…).join(src2.filter(…), …)
> >>>>>>>>>>>> a.cache() // write cache to connector 3
> >>>>>>>>>>>>
> >>>>>>>>>>>> a.filter(…)
> >>>>>>>>>>>> env.execute()
> >>>>>>>>>>>> a.select(…)
> >>>>>>>>>>>>
> >>>>>>>>>>>> Decision whether it’s better to:
> >>>>>>>>>>>> A) read from connector1/connector2, filter/map and join them
> >>> twice
> >>>>>>>>>>>> B) read from connector1/connector2, filter/map and join them
> >>> once,
> >>>>>>> pay
> >>>>>>>>>> the
> >>>>>>>>>>>> price of writing to connector 3 and then reading from it
> >>>>>>>>>>>>
> >>>>>>>>>>>> Is very far from trivial. `a` can end up much larger than
> `src1`
> >>>>> and
> >>>>>>>>>>>> `src2`, writes to connector 3 might be extremely slow, reads
> >>> from
> >>>>>>>>>> connector
> >>>>>>>>>>>> 3 can be slower compared to reads from connector 1 & 2, … .
> You
> >>>>>>> really
> >>>>>>>>>> need
> >>>>>>>>>>>> to have extremely good statistics to correctly asses size of
> the
> >>>>>>>>>> output and
> >>>>>>>>>>>> it would still be failing many times (correlations etc). And
> >>> keep
> >>>>> in
> >>>>>>>>>> mind
> >>>>>>>>>>>> that at the moment we do not have ANY statistics at all. More
> >>> than
> >>>>>>>>>> that, it
> >>>>>>>>>>>> would require significantly more testing and setting up some
> >>>>>>>>>> benchmarks to
> >>>>>>>>>>>> make sure that we do not brake it with some regressions.
> >>>>>>>>>>>>
> >>>>>>>>>>>> That’s why I’m strongly opposing this idea - at least let’s
> not
> >>>>>>> starts
> >>>>>>>>>>>> with this. If we first start with completely manual/explicit
> >>>>> caching,
> >>>>>>>>>>>> without any magic, it would be a significant improvement for
> the
> >>>>>>> users
> >>>>>>>>>> for
> >>>>>>>>>>>> a fraction of the development cost. After implementing that,
> >>> when
> >>>>> we
> >>>>>>>>>>>> already have all of the working pieces, we can start working
> on
> >>>>> some
> >>>>>>>>>>>> optimisations rules. As I wrote before, if we start with
> >>>>>>>>>>>>
> >>>>>>>>>>>> `CachedTable cache()`
> >>>>>>>>>>>>
> >>>>>>>>>>>> We can later work on follow up stories to make it automatic.
> >>>>> Despite
> >>>>>>>>>> that
> >>>>>>>>>>>> I don’t like this implicit/side effect approach with `void`
> >>> method,
> >>>>>>>>>> having
> >>>>>>>>>>>> explicit `CachedTable cache()` wouldn’t even prevent as from
> >>> later
> >>>>>>>>>> adding
> >>>>>>>>>>>> `void hintCache()` method, with the exact semantic that you
> >>> want.
> >>>>>>>>>>>>
> >>>>>>>>>>>> On top of that I re-rise again that having implicit `void
> >>>>>>>>>>>> cache()/hintCache()` has other side effects and problems with
> >>> non
> >>>>>>>>>> immutable
> >>>>>>>>>>>> data, and being annoying when used secretly inside methods.
> >>>>>>>>>>>>
> >>>>>>>>>>>> Explicit `CachedTable cache()` just looks like much less
> >>>>>>> controversial
> >>>>>>>>>> MVP
> >>>>>>>>>>>> and if we decide to go further with this topic, it’s not a
> >>> wasted
> >>>>>>>>>> effort,
> >>>>>>>>>>>> but just lies on a stright path to more advanced/complicated
> >>>>>>> solutions
> >>>>>>>>>> in
> >>>>>>>>>>>> the future. Are there any drawbacks of starting with
> >>> `CachedTable
> >>>>>>>>>> cache()`
> >>>>>>>>>>>> that I’m missing?
> >>>>>>>>>>>>
> >>>>>>>>>>>> Piotrek
> >>>>>>>>>>>>
> >>>>>>>>>>>>> On 12 Dec 2018, at 09:30, Jeff Zhang <zjf...@gmail.com>
> wrote:
> >>>>>>>>>>>>>
> >>>>>>>>>>>>> Hi Becket,
> >>>>>>>>>>>>>
> >>>>>>>>>>>>> Introducing CacheHandle seems too complicated. That means
> users
> >>>>> have
> >>>>>>>>>> to
> >>>>>>>>>>>>> maintain Handler properly.
> >>>>>>>>>>>>>
> >>>>>>>>>>>>> And since cache is just a hint for optimizer, why not just
> >>> return
> >>>>>>>>>> Table
> >>>>>>>>>>>>> itself for cache method. This hint info should be kept in
> >>> Table I
> >>>>>>>>>>>> believe.
> >>>>>>>>>>>>>
> >>>>>>>>>>>>> So how about adding method cache and uncache for Table, and
> >>> both
> >>>>>>>>>> return
> >>>>>>>>>>>>> Table. Because what cache and uncache did is just adding some
> >>> hint
> >>>>>>>>>> info
> >>>>>>>>>>>>> into Table.
> >>>>>>>>>>>>>
> >>>>>>>>>>>>>
> >>>>>>>>>>>>>
> >>>>>>>>>>>>>
> >>>>>>>>>>>>> Becket Qin <becket....@gmail.com> 于2018年12月12日周三 上午11:25写道：
> >>>>>>>>>>>>>
> >>>>>>>>>>>>>> Hi Till and Piotrek,
> >>>>>>>>>>>>>>
> >>>>>>>>>>>>>> Thanks for the clarification. That solves quite a few
> >>> confusion.
> >>>>> My
> >>>>>>>>>>>>>> understanding of how cache works is same as what Till
> >>> describe.
> >>>>>>> i.e.
> >>>>>>>>>>>>>> cache() is a hint to Flink, but it is not guaranteed that
> >>> cache
> >>>>>>>>>> always
> >>>>>>>>>>>>>> exist and it might be recomputed from its lineage.
> >>>>>>>>>>>>>>
> >>>>>>>>>>>>>> Is this the core of our disagreement here? That you would
> like
> >>>>> this
> >>>>>>>>>>>>>>> “cache()” to be mostly hint for the optimiser?
> >>>>>>>>>>>>>>
> >>>>>>>>>>>>>> Semantic wise, yes. That's also why I think materialize()
> has
> >>> a
> >>>>>>> much
> >>>>>>>>>>>> larger
> >>>>>>>>>>>>>> scope than cache(), thus it should be a different method.
> >>>>>>>>>>>>>>
> >>>>>>>>>>>>>> Regarding the chance of optimization, it might not be that
> >>> rare.
> >>>>>>> Some
> >>>>>>>>>>>> very
> >>>>>>>>>>>>>> simple statistics could already help in many cases. For
> >>> example,
> >>>>>>>>>> simply
> >>>>>>>>>>>>>> maintaining max and min of each fields can already eliminate
> >>> some
> >>>>>>>>>>>>>> unnecessary table scan (potentially scanning the cached
> >>> table) if
> >>>>>>> the
> >>>>>>>>>>>>>> result is doomed to be empty. A histogram would give even
> >>> further
> >>>>>>>>>>>>>> information. The optimizer could be very careful and only
> >>> ignores
> >>>>>>>>>> cache
> >>>>>>>>>>>>>> when it is 100% sure doing that is cheaper. e.g. only when a
> >>>>> filter
> >>>>>>>>>> on
> >>>>>>>>>>>> the
> >>>>>>>>>>>>>> cache will absolutely return nothing.
> >>>>>>>>>>>>>>
> >>>>>>>>>>>>>> Given the above clarification on cache, I would like to
> >>> revisit
> >>>>> the
> >>>>>>>>>>>>>> original "void cache()" proposal and see if we can improve
> on
> >>> top
> >>>>>>> of
> >>>>>>>>>>>> that.
> >>>>>>>>>>>>>>
> >>>>>>>>>>>>>> What do you think about the following modified interface?
> >>>>>>>>>>>>>>
> >>>>>>>>>>>>>> Table {
> >>>>>>>>>>>>>> /**
> >>>>>>>>>>>>>> * This call hints Flink to maintain a cache of this table
> and
> >>>>>>>>>> leverage
> >>>>>>>>>>>>>> it for performance optimization if needed.
> >>>>>>>>>>>>>> * Note that Flink may still decide to not use the cache if
> it
> >>> is
> >>>>>>>>>>>> cheaper
> >>>>>>>>>>>>>> by doing so.
> >>>>>>>>>>>>>> *
> >>>>>>>>>>>>>> * A CacheHandle will be returned to allow user release the
> >>> cache
> >>>>>>>>>>>>>> actively. The cache will be deleted if there
> >>>>>>>>>>>>>> * is no unreleased cache handlers to it. When the
> >>>>> TableEnvironment
> >>>>>>>>>> is
> >>>>>>>>>>>>>> closed. The cache will also be deleted
> >>>>>>>>>>>>>> * and all the cache handlers will be released.
> >>>>>>>>>>>>>> *
> >>>>>>>>>>>>>> * @return a CacheHandle referring to the cache of this
> table.
> >>>>>>>>>>>>>> */
> >>>>>>>>>>>>>> CacheHandle cache();
> >>>>>>>>>>>>>> }
> >>>>>>>>>>>>>>
> >>>>>>>>>>>>>> CacheHandle {
> >>>>>>>>>>>>>> /**
> >>>>>>>>>>>>>> * Close the cache handle. This method does not necessarily
> >>>>> deletes
> >>>>>>>>>> the
> >>>>>>>>>>>>>> cache. Instead, it simply decrements the reference counter
> to
> >>> the
> >>>>>>>>>> cache.
> >>>>>>>>>>>>>> * When the there is no handle referring to a cache. The
> cache
> >>>>> will
> >>>>>>>>>> be
> >>>>>>>>>>>>>> deleted.
> >>>>>>>>>>>>>> *
> >>>>>>>>>>>>>> * @return the number of open handles to the cache after this
> >>>>> handle
> >>>>>>>>>>>> has
> >>>>>>>>>>>>>> been released.
> >>>>>>>>>>>>>> */
> >>>>>>>>>>>>>> int release()
> >>>>>>>>>>>>>> }
> >>>>>>>>>>>>>>
> >>>>>>>>>>>>>> The rationale behind this interface is following:
> >>>>>>>>>>>>>> In vast majority of the cases, users wouldn't really care
> >>> whether
> >>>>>>> the
> >>>>>>>>>>>> cache
> >>>>>>>>>>>>>> is used or not. So I think the most intuitive way is letting
> >>>>>>> cache()
> >>>>>>>>>>>> return
> >>>>>>>>>>>>>> nothing. So nobody needs to worry about the difference
> between
> >>>>>>>>>>>> operations
> >>>>>>>>>>>>>> on CacheTables and those on the "original" tables. This will
> >>> make
> >>>>>>>>>> maybe
> >>>>>>>>>>>>>> 99.9% of the users happy. There were two concerns raised for
> >>> this
> >>>>>>>>>>>> approach:
> >>>>>>>>>>>>>> 1. In some rare cases, users may want to ignore cache,
> >>>>>>>>>>>>>> 2. A table might be cached/uncached in a third party
> function
> >>>>> while
> >>>>>>>>>> the
> >>>>>>>>>>>>>> caller does not know.
> >>>>>>>>>>>>>>
> >>>>>>>>>>>>>> For the first issue, users can use hint("ignoreCache") to
> >>>>>>> explicitly
> >>>>>>>>>>>> ignore
> >>>>>>>>>>>>>> cache.
> >>>>>>>>>>>>>> For the second issue, the above proposal lets cache()
> return a
> >>>>>>>>>>>> CacheHandle,
> >>>>>>>>>>>>>> the only method in it is release(). Different CacheHandles
> >>> will
> >>>>>>>>>> refer to
> >>>>>>>>>>>>>> the same cache, if a cache no longer has any cache handle,
> it
> >>>>> will
> >>>>>>> be
> >>>>>>>>>>>>>> deleted. This will address the following case:
> >>>>>>>>>>>>>> {
> >>>>>>>>>>>>>> val handle1 = a.cache()
> >>>>>>>>>>>>>> process(a)
> >>>>>>>>>>>>>> a.select(...) // cache is still available, handle1 has not
> >>> been
> >>>>>>>>>>>> released.
> >>>>>>>>>>>>>> }
> >>>>>>>>>>>>>>
> >>>>>>>>>>>>>> void process(Table t) {
> >>>>>>>>>>>>>> val handle2 = t.cache() // new handle to cache
> >>>>>>>>>>>>>> t.select(...) // optimizer decides cache usage
> >>>>>>>>>>>>>> t.hint("ignoreCache").select(...) // cache is ignored
> >>>>>>>>>>>>>> handle2.release() // release the handle, but the cache may
> >>> still
> >>>>> be
> >>>>>>>>>>>>>> available if there are other handles
> >>>>>>>>>>>>>> ...
> >>>>>>>>>>>>>> }
> >>>>>>>>>>>>>>
> >>>>>>>>>>>>>> Does the above modified approach look reasonable to you?
> >>>>>>>>>>>>>>
> >>>>>>>>>>>>>> Cheers,
> >>>>>>>>>>>>>>
> >>>>>>>>>>>>>> Jiangjie (Becket) Qin
> >>>>>>>>>>>>>>
> >>>>>>>>>>>>>>
> >>>>>>>>>>>>>>
> >>>>>>>>>>>>>>
> >>>>>>>>>>>>>>
> >>>>>>>>>>>>>>
> >>>>>>>>>>>>>>
> >>>>>>>>>>>>>> On Tue, Dec 11, 2018 at 6:44 PM Till Rohrmann <
> >>>>>>> trohrm...@apache.org>
> >>>>>>>>>>>>>> wrote:
> >>>>>>>>>>>>>>
> >>>>>>>>>>>>>>> Hi Becket,
> >>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>> I was aiming at semantics similar to 1. I actually thought
> >>> that
> >>>>>>>>>>>> `cache()`
> >>>>>>>>>>>>>>> would tell the system to materialize the intermediate
> result
> >>> so
> >>>>>>> that
> >>>>>>>>>>>>>>> subsequent queries don't need to reprocess it. This means
> >>> that
> >>>>> the
> >>>>>>>>>>>> usage
> >>>>>>>>>>>>>> of
> >>>>>>>>>>>>>>> the cached table in this example
> >>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>> {
> >>>>>>>>>>>>>>> val cachedTable = a.cache()
> >>>>>>>>>>>>>>> val b1 = cachedTable.select(…)
> >>>>>>>>>>>>>>> val b2 = cachedTable.foo().select(…)
> >>>>>>>>>>>>>>> val b3 = cachedTable.bar().select(...)
> >>>>>>>>>>>>>>> val c1 = a.select(…)
> >>>>>>>>>>>>>>> val c2 = a.foo().select(…)
> >>>>>>>>>>>>>>> val c3 = a.bar().select(...)
> >>>>>>>>>>>>>>> }
> >>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>> strongly depends on interleaved calls which trigger the
> >>>>> execution
> >>>>>>> of
> >>>>>>>>>>>> sub
> >>>>>>>>>>>>>>> queries. So for example, if there is only a single
> >>> env.execute
> >>>>>>> call
> >>>>>>>>>> at
> >>>>>>>>>>>>>> the
> >>>>>>>>>>>>>>> end of block, then b1, b2, b3, c1, c2 and c3 would all be
> >>>>>>> computed
> >>>>>>>>>> by
> >>>>>>>>>>>>>>> reading directly from the sources (given that there is
> only a
> >>>>>>> single
> >>>>>>>>>>>>>>> JobGraph). It just happens that the result of `a` will be
> >>> cached
> >>>>>>>>>> such
> >>>>>>>>>>>>>> that
> >>>>>>>>>>>>>>> we skip the processing of `a` when there are subsequent
> >>> queries
> >>>>>>>>>> reading
> >>>>>>>>>>>>>>> from `cachedTable`. If for some reason the system cannot
> >>>>>>> materialize
> >>>>>>>>>>>> the
> >>>>>>>>>>>>>>> table (e.g. running out of disk space, ttl expired), then
> it
> >>>>> could
> >>>>>>>>>> also
> >>>>>>>>>>>>>>> happen that we need to reprocess `a`. In that sense
> >>>>> `cachedTable`
> >>>>>>>>>>>> simply
> >>>>>>>>>>>>>> is
> >>>>>>>>>>>>>>> an identifier for the materialized result of `a` with the
> >>>>> lineage
> >>>>>>>>>> how
> >>>>>>>>>>>> to
> >>>>>>>>>>>>>>> reprocess it.
> >>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>> Cheers,
> >>>>>>>>>>>>>>> Till
> >>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>> On Tue, Dec 11, 2018 at 11:01 AM Piotr Nowojski <
> >>>>>>>>>>>> pi...@data-artisans.com
> >>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>> wrote:
> >>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>> Hi Becket,
> >>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>> {
> >>>>>>>>>>>>>>>>> val cachedTable = a.cache()
> >>>>>>>>>>>>>>>>> val b = cachedTable.select(...)
> >>>>>>>>>>>>>>>>> val c = a.select(...)
> >>>>>>>>>>>>>>>>> }
> >>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>> Semantic 1. b uses cachedTable as user demanded so. c
> uses
> >>>>>>>>>> original
> >>>>>>>>>>>>>> DAG
> >>>>>>>>>>>>>>>> as
> >>>>>>>>>>>>>>>>> user demanded so. In this case, the optimizer has no
> >>> chance to
> >>>>>>>>>>>>>>> optimize.
> >>>>>>>>>>>>>>>>> Semantic 2. b uses cachedTable as user demanded so. c
> >>> leaves
> >>>>> the
> >>>>>>>>>>>>>>>> optimizer
> >>>>>>>>>>>>>>>>> to choose whether the cache or DAG should be used. In
> this
> >>>>> case,
> >>>>>>>>>> user
> >>>>>>>>>>>>>>>> lose
> >>>>>>>>>>>>>>>>> the option to NOT use cache.
> >>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>> As you can see, neither of the options seem perfect.
> >>> However,
> >>>>> I
> >>>>>>>>>> guess
> >>>>>>>>>>>>>>> you
> >>>>>>>>>>>>>>>>> and Till are proposing the third option:
> >>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>> Semantic 3. b leaves the optimizer to choose whether
> cache
> >>> or
> >>>>>>> DAG
> >>>>>>>>>>>>>>> should
> >>>>>>>>>>>>>>>> be
> >>>>>>>>>>>>>>>>> used. c always use the DAG.
> >>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>> I am pretty sure that me, Till, Fabian and others were all
> >>>>>>>>>> proposing
> >>>>>>>>>>>>>> and
> >>>>>>>>>>>>>>>> advocating in favour of semantic “1”. No cost based
> >>> optimiser
> >>>>>>>>>>>> decisions
> >>>>>>>>>>>>>>> at
> >>>>>>>>>>>>>>>> all.
> >>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>> {
> >>>>>>>>>>>>>>>> val cachedTable = a.cache()
> >>>>>>>>>>>>>>>> val b1 = cachedTable.select(…)
> >>>>>>>>>>>>>>>> val b2 = cachedTable.foo().select(…)
> >>>>>>>>>>>>>>>> val b3 = cachedTable.bar().select(...)
> >>>>>>>>>>>>>>>> val c1 = a.select(…)
> >>>>>>>>>>>>>>>> val c2 = a.foo().select(…)
> >>>>>>>>>>>>>>>> val c3 = a.bar().select(...)
> >>>>>>>>>>>>>>>> }
> >>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>> All b1, b2 and b3 are reading from cache, while c1, c2 and
> >>> c3
> >>>>> are
> >>>>>>>>>>>>>>>> re-executing whole plan for “a”.
> >>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>> In the future we could discuss going one step further,
> >>>>>>> introducing
> >>>>>>>>>>>> some
> >>>>>>>>>>>>>>>> global optimisation (that can be manually
> enabled/disabled):
> >>>>>>>>>>>>>> deduplicate
> >>>>>>>>>>>>>>>> plan nodes/deduplicate sub queries/re-use sub queries
> >>>>> results/or
> >>>>>>>>>>>>>> whatever
> >>>>>>>>>>>>>>>> we could call it. It could do two things:
> >>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>> 1. Automatically try to deduplicate fragments of the plan
> >>> and
> >>>>>>> share
> >>>>>>>>>>>> the
> >>>>>>>>>>>>>>>> result using CachedTable - in other words automatically
> >>> insert
> >>>>>>>>>>>>>>> `CachedTable
> >>>>>>>>>>>>>>>> cache()` calls.
> >>>>>>>>>>>>>>>> 2. Automatically make decision to bypass explicit
> >>> `CachedTable`
> >>>>>>>>>> access
> >>>>>>>>>>>>>>>> (this would be the equivalent of what you described as
> >>>>> “semantic
> >>>>>>>>>> 3”).
> >>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>> However as I wrote previously, I have big doubts if such
> >>>>>>> cost-based
> >>>>>>>>>>>>>>>> optimisation would work (this applies also to “Semantic
> >>> 2”). I
> >>>>>>>>>> would
> >>>>>>>>>>>>>>> expect
> >>>>>>>>>>>>>>>> it to do more harm than good in so many cases, that it
> >>> wouldn’t
> >>>>>>>>>> make
> >>>>>>>>>>>>>>> sense.
> >>>>>>>>>>>>>>>> Even assuming that we calculate statistics perfectly (this
> >>>>> ain’t
> >>>>>>>>>> gonna
> >>>>>>>>>>>>>>>> happen), it’s virtually impossible to correctly estimate
> >>>>> correct
> >>>>>>>>>>>>>> exchange
> >>>>>>>>>>>>>>>> rate of CPU cycles vs IO operations as it is changing so
> >>> much
> >>>>>>> from
> >>>>>>>>>>>>>>>> deployment to deployment.
> >>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>> Is this the core of our disagreement here? That you would
> >>> like
> >>>>>>> this
> >>>>>>>>>>>>>>>> “cache()” to be mostly hint for the optimiser?
> >>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>> Piotrek
> >>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>> On 11 Dec 2018, at 06:00, Becket Qin <
> becket....@gmail.com
> >>>>
> >>>>>>>>>> wrote:
> >>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>> Another potential concern for semantic 3 is that. In the
> >>>>> future,
> >>>>>>>>>> we
> >>>>>>>>>>>>>> may
> >>>>>>>>>>>>>>>> add
> >>>>>>>>>>>>>>>>> automatic caching to Flink. e.g. cache the intermediate
> >>>>> results
> >>>>>>> at
> >>>>>>>>>>>>>> the
> >>>>>>>>>>>>>>>>> shuffle boundary. If our semantic is that reference to
> the
> >>>>>>>>>> original
> >>>>>>>>>>>>>>> table
> >>>>>>>>>>>>>>>>> means skipping cache, those users may not be able to
> >>> benefit
> >>>>>>> from
> >>>>>>>>>> the
> >>>>>>>>>>>>>>>>> implicit cache.
> >>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>> On Tue, Dec 11, 2018 at 12:10 PM Becket Qin <
> >>>>>>> becket....@gmail.com
> >>>>>>>>>>>
> >>>>>>>>>>>>>>>> wrote:
> >>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>>> Hi Piotrek,
> >>>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>>> Thanks for the reply. Thought about it again, I might
> have
> >>>>>>>>>>>>>>> misunderstood
> >>>>>>>>>>>>>>>>>> your proposal in earlier emails. Returning a CachedTable
> >>>>> might
> >>>>>>>>>> not
> >>>>>>>>>>>>>> be
> >>>>>>>>>>>>>>> a
> >>>>>>>>>>>>>>>> bad
> >>>>>>>>>>>>>>>>>> idea.
> >>>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>>> I was more concerned about the semantic and its
> >>> intuitiveness
> >>>>>>>>>> when a
> >>>>>>>>>>>>>>>>>> CachedTable is returned. i..e, if cache() returns
> >>>>> CachedTable.
> >>>>>>>>>> What
> >>>>>>>>>>>>>>> are
> >>>>>>>>>>>>>>>> the
> >>>>>>>>>>>>>>>>>> semantic in the following code:
> >>>>>>>>>>>>>>>>>> {
> >>>>>>>>>>>>>>>>>> val cachedTable = a.cache()
> >>>>>>>>>>>>>>>>>> val b = cachedTable.select(...)
> >>>>>>>>>>>>>>>>>> val c = a.select(...)
> >>>>>>>>>>>>>>>>>> }
> >>>>>>>>>>>>>>>>>> What is the difference between b and c? At the first
> >>> glance,
> >>>>> I
> >>>>>>>>>> see
> >>>>>>>>>>>>>> two
> >>>>>>>>>>>>>>>>>> options:
> >>>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>>> Semantic 1. b uses cachedTable as user demanded so. c
> uses
> >>>>>>>>>> original
> >>>>>>>>>>>>>>> DAG
> >>>>>>>>>>>>>>>> as
> >>>>>>>>>>>>>>>>>> user demanded so. In this case, the optimizer has no
> >>> chance
> >>>>> to
> >>>>>>>>>>>>>>> optimize.
> >>>>>>>>>>>>>>>>>> Semantic 2. b uses cachedTable as user demanded so. c
> >>> leaves
> >>>>>>> the
> >>>>>>>>>>>>>>>> optimizer
> >>>>>>>>>>>>>>>>>> to choose whether the cache or DAG should be used. In
> this
> >>>>>>> case,
> >>>>>>>>>>>>>> user
> >>>>>>>>>>>>>>>> lose
> >>>>>>>>>>>>>>>>>> the option to NOT use cache.
> >>>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>>> As you can see, neither of the options seem perfect.
> >>>>> However, I
> >>>>>>>>>>>>>> guess
> >>>>>>>>>>>>>>>> you
> >>>>>>>>>>>>>>>>>> and Till are proposing the third option:
> >>>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>>> Semantic 3. b leaves the optimizer to choose whether
> >>> cache or
> >>>>>>> DAG
> >>>>>>>>>>>>>>> should
> >>>>>>>>>>>>>>>>>> be used. c always use the DAG.
> >>>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>>> This does address all the concerns. It is just that from
> >>>>>>>>>>>>>> intuitiveness
> >>>>>>>>>>>>>>>>>> perspective, I found that asking user to explicitly use
> a
> >>>>>>>>>>>>>> CachedTable
> >>>>>>>>>>>>>>>> while
> >>>>>>>>>>>>>>>>>> the optimizer might choose to ignore is a little weird.
> >>> That
> >>>>>>> was
> >>>>>>>>>>>>>> why I
> >>>>>>>>>>>>>>>> did
> >>>>>>>>>>>>>>>>>> not think about that semantic. But given there is
> material
> >>>>>>>>>> benefit,
> >>>>>>>>>>>>>> I
> >>>>>>>>>>>>>>>> think
> >>>>>>>>>>>>>>>>>> this semantic is acceptable.
> >>>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>>> 1. If we want to let optimiser make decisions whether to
> >>> use
> >>>>>>>>>> cache
> >>>>>>>>>>>>>> or
> >>>>>>>>>>>>>>>> not,
> >>>>>>>>>>>>>>>>>>> then why do we need “void cache()” method at all? Would
> >>> It
> >>>>>>>>>>>>>>> “increase”
> >>>>>>>>>>>>>>>> the
> >>>>>>>>>>>>>>>>>>> chance of using the cache? That’s sounds strange. What
> >>> would
> >>>>>>> be
> >>>>>>>>>> the
> >>>>>>>>>>>>>>>>>>> mechanism of deciding whether to use the cache or not?
> >>> If we
> >>>>>>>>>> want
> >>>>>>>>>>>>>> to
> >>>>>>>>>>>>>>>>>>> introduce such kind automated optimisations of “plan
> >>> nodes
> >>>>>>>>>>>>>>>> deduplication”
> >>>>>>>>>>>>>>>>>>> I would turn it on globally, not per table, and let the
> >>>>>>>>>> optimiser
> >>>>>>>>>>>>>> do
> >>>>>>>>>>>>>>>> all of
> >>>>>>>>>>>>>>>>>>> the work.
> >>>>>>>>>>>>>>>>>>> 2. We do not have statistics at the moment for any
> >>> use/not
> >>>>> use
> >>>>>>>>>>>>>> cache
> >>>>>>>>>>>>>>>>>>> decision.
> >>>>>>>>>>>>>>>>>>> 3. Even if we had, I would be veeerryy sceptical
> whether
> >>>>> such
> >>>>>>>>>> cost
> >>>>>>>>>>>>>>>> based
> >>>>>>>>>>>>>>>>>>> optimisations would work properly and I would still
> >>> insist
> >>>>>>>>>> first on
> >>>>>>>>>>>>>>>>>>> providing explicit caching mechanism (`CachedTable
> >>> cache()`)
> >>>>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>>> We are absolutely on the same page here. An explicit
> >>> cache()
> >>>>>>>>>> method
> >>>>>>>>>>>>>> is
> >>>>>>>>>>>>>>>>>> necessary not only because optimizer may not be able to
> >>> make
> >>>>>>> the
> >>>>>>>>>>>>>> right
> >>>>>>>>>>>>>>>>>> decision, but also because of the nature of interactive
> >>>>>>>>>> programming.
> >>>>>>>>>>>>>>> For
> >>>>>>>>>>>>>>>>>> example, if users write the following code in Scala
> shell:
> >>>>>>>>>>>>>>>>>> val b = a.select(...)
> >>>>>>>>>>>>>>>>>> val c = b.select(...)
> >>>>>>>>>>>>>>>>>> val d = c.select(...).writeToSink(...)
> >>>>>>>>>>>>>>>>>> tEnv.execute()
> >>>>>>>>>>>>>>>>>> There is no way optimizer will know whether b or c will
> be
> >>>>> used
> >>>>>>>>>> in
> >>>>>>>>>>>>>>> later
> >>>>>>>>>>>>>>>>>> code, unless users hint explicitly.
> >>>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>>> At the same time I’m not sure if you have responded to
> our
> >>>>>>>>>>>>>> objections
> >>>>>>>>>>>>>>> of
> >>>>>>>>>>>>>>>>>>> `void cache()` being implicit/having side effects,
> which
> >>> me,
> >>>>>>>>>> Jark,
> >>>>>>>>>>>>>>>> Fabian,
> >>>>>>>>>>>>>>>>>>> Till and I think also Shaoxuan are supporting.
> >>>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>>> Is there any other side effects if we use semantic 3
> >>>>> mentioned
> >>>>>>>>>>>>>> above?
> >>>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>>> Thanks,
> >>>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>>> JIangjie (Becket) Qin
> >>>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>>> On Mon, Dec 10, 2018 at 7:54 PM Piotr Nowojski <
> >>>>>>>>>>>>>>> pi...@data-artisans.com
> >>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>>> wrote:
> >>>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>>>> Hi Becket,
> >>>>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>>>> Sorry for not responding long time.
> >>>>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>>>> Regarding case1.
> >>>>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>>>> There wouldn’t be no “a.unCache()” method, but I would
> >>>>> expect
> >>>>>>>>>> only
> >>>>>>>>>>>>>>>>>>> `cachedTableA1.dropCache()`. Dropping `cachedTableA1`
> >>>>> wouldn’t
> >>>>>>>>>>>>>> affect
> >>>>>>>>>>>>>>>>>>> `cachedTableA2`. Just as in any other database dropping
> >>>>>>>>>> modifying
> >>>>>>>>>>>>>> one
> >>>>>>>>>>>>>>>>>>> independent table/materialised view does not affect
> >>> others.
> >>>>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>>>>> What I meant is that assuming there is already a
> cached
> >>>>>>> table,
> >>>>>>>>>>>>>>> ideally
> >>>>>>>>>>>>>>>>>>> users need
> >>>>>>>>>>>>>>>>>>>> not to specify whether the next query should read from
> >>> the
> >>>>>>>>>> cache
> >>>>>>>>>>>>>> or
> >>>>>>>>>>>>>>>> use
> >>>>>>>>>>>>>>>>>>> the
> >>>>>>>>>>>>>>>>>>>> original DAG. This should be decided by the optimizer.
> >>>>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>>>> 1. If we want to let optimiser make decisions whether
> to
> >>> use
> >>>>>>>>>> cache
> >>>>>>>>>>>>>> or
> >>>>>>>>>>>>>>>>>>> not, then why do we need “void cache()” method at all?
> >>> Would
> >>>>>>> It
> >>>>>>>>>>>>>>>> “increase”
> >>>>>>>>>>>>>>>>>>> the chance of using the cache? That’s sounds strange.
> >>> What
> >>>>>>>>>> would be
> >>>>>>>>>>>>>>> the
> >>>>>>>>>>>>>>>>>>> mechanism of deciding whether to use the cache or not?
> >>> If we
> >>>>>>>>>> want
> >>>>>>>>>>>>>> to
> >>>>>>>>>>>>>>>>>>> introduce such kind automated optimisations of “plan
> >>> nodes
> >>>>>>>>>>>>>>>> deduplication”
> >>>>>>>>>>>>>>>>>>> I would turn it on globally, not per table, and let the
> >>>>>>>>>> optimiser
> >>>>>>>>>>>>>> do
> >>>>>>>>>>>>>>>> all of
> >>>>>>>>>>>>>>>>>>> the work.
> >>>>>>>>>>>>>>>>>>> 2. We do not have statistics at the moment for any
> >>> use/not
> >>>>> use
> >>>>>>>>>>>>>> cache
> >>>>>>>>>>>>>>>>>>> decision.
> >>>>>>>>>>>>>>>>>>> 3. Even if we had, I would be veeerryy sceptical
> whether
> >>>>> such
> >>>>>>>>>> cost
> >>>>>>>>>>>>>>>> based
> >>>>>>>>>>>>>>>>>>> optimisations would work properly and I would still
> >>> insist
> >>>>>>>>>> first on
> >>>>>>>>>>>>>>>>>>> providing explicit caching mechanism (`CachedTable
> >>> cache()`)
> >>>>>>>>>>>>>>>>>>> 4. As Till wrote, having explicit `CachedTable cache()`
> >>>>>>> doesn’t
> >>>>>>>>>>>>>>>>>>> contradict future work on automated cost based caching.
> >>>>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>>>> At the same time I’m not sure if you have responded to
> >>> our
> >>>>>>>>>>>>>> objections
> >>>>>>>>>>>>>>>> of
> >>>>>>>>>>>>>>>>>>> `void cache()` being implicit/having side effects,
> which
> >>> me,
> >>>>>>>>>> Jark,
> >>>>>>>>>>>>>>>> Fabian,
> >>>>>>>>>>>>>>>>>>> Till and I think also Shaoxuan are supporting.
> >>>>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>>>> Piotrek
> >>>>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>>>>> On 5 Dec 2018, at 12:42, Becket Qin <
> >>> becket....@gmail.com>
> >>>>>>>>>> wrote:
> >>>>>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>>>>> Hi Till,
> >>>>>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>>>>> It is true that after the first job submission, there
> >>> will
> >>>>> be
> >>>>>>>>>> no
> >>>>>>>>>>>>>>>>>>> ambiguity
> >>>>>>>>>>>>>>>>>>>> in terms of whether a cached table is used or not.
> That
> >>> is
> >>>>>>> the
> >>>>>>>>>>>>>> same
> >>>>>>>>>>>>>>>> for
> >>>>>>>>>>>>>>>>>>> the
> >>>>>>>>>>>>>>>>>>>> cache() without returning a CachedTable.
> >>>>>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>>>>> Conceptually one could think of cache() as
> introducing a
> >>>>>>>>>> caching
> >>>>>>>>>>>>>>>>>>> operator
> >>>>>>>>>>>>>>>>>>>>> from which you need to consume from if you want to
> >>> benefit
> >>>>>>>>>> from
> >>>>>>>>>>>>>> the
> >>>>>>>>>>>>>>>>>>> caching
> >>>>>>>>>>>>>>>>>>>>> functionality.
> >>>>>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>>>>> I am thinking a little differently. I think it is a
> hint
> >>>>> (as
> >>>>>>>>>> you
> >>>>>>>>>>>>>>>>>>> mentioned
> >>>>>>>>>>>>>>>>>>>> later) instead of a new operator. I'd like to be
> careful
> >>>>>>> about
> >>>>>>>>>> the
> >>>>>>>>>>>>>>>>>>> semantic
> >>>>>>>>>>>>>>>>>>>> of the API. A hint is a property set on an existing
> >>>>> operator,
> >>>>>>>>>> but
> >>>>>>>>>>>>>> is
> >>>>>>>>>>>>>>>> not
> >>>>>>>>>>>>>>>>>>>> itself an operator as it does not really manipulate
> the
> >>>>> data.
> >>>>>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>>>>> I agree, ideally the optimizer makes this kind of
> >>> decision
> >>>>>>>>>> which
> >>>>>>>>>>>>>>>>>>>>> intermediate result should be cached. But especially
> >>> when
> >>>>>>>>>>>>>> executing
> >>>>>>>>>>>>>>>>>>> ad-hoc
> >>>>>>>>>>>>>>>>>>>>> queries the user might better know which results need
> >>> to
> >>>>> be
> >>>>>>>>>>>>>> cached
> >>>>>>>>>>>>>>>>>>> because
> >>>>>>>>>>>>>>>>>>>>> Flink might not see the full DAG. In that sense, I
> >>> would
> >>>>>>>>>> consider
> >>>>>>>>>>>>>>> the
> >>>>>>>>>>>>>>>>>>>>> cache() method as a hint for the optimizer. Of
> course,
> >>> in
> >>>>>>> the
> >>>>>>>>>>>>>>> future
> >>>>>>>>>>>>>>>> we
> >>>>>>>>>>>>>>>>>>>>> might add functionality which tries to automatically
> >>> cache
> >>>>>>>>>>>>>> results
> >>>>>>>>>>>>>>>>>>> (e.g.
> >>>>>>>>>>>>>>>>>>>>> caching the latest intermediate results until so and
> so
> >>>>> much
> >>>>>>>>>>>>>> space
> >>>>>>>>>>>>>>> is
> >>>>>>>>>>>>>>>>>>>>> used). But this should hopefully not contradict with
> >>>>>>>>>> `CachedTable
> >>>>>>>>>>>>>>>>>>> cache()`.
> >>>>>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>>>>> I agree that cache() method is needed for exactly the
> >>>>> reason
> >>>>>>>>>> you
> >>>>>>>>>>>>>>>>>>> mentioned,
> >>>>>>>>>>>>>>>>>>>> i.e. Flink cannot predict what users are going to
> write
> >>>>>>> later,
> >>>>>>>>>> so
> >>>>>>>>>>>>>>>> users
> >>>>>>>>>>>>>>>>>>>> need to tell Flink explicitly that this table will be
> >>> used
> >>>>>>>>>> later.
> >>>>>>>>>>>>>>>> What I
> >>>>>>>>>>>>>>>>>>>> meant is that assuming there is already a cached
> table,
> >>>>>>> ideally
> >>>>>>>>>>>>>>> users
> >>>>>>>>>>>>>>>>>>> need
> >>>>>>>>>>>>>>>>>>>> not to specify whether the next query should read from
> >>> the
> >>>>>>>>>> cache
> >>>>>>>>>>>>>> or
> >>>>>>>>>>>>>>>> use
> >>>>>>>>>>>>>>>>>>> the
> >>>>>>>>>>>>>>>>>>>> original DAG. This should be decided by the optimizer.
> >>>>>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>>>>> To explain the difference between returning / not
> >>>>> returning a
> >>>>>>>>>>>>>>>>>>> CachedTable,
> >>>>>>>>>>>>>>>>>>>> I want compare the following two case:
> >>>>>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>>>>> *Case 1: returning a CachedTable*
> >>>>>>>>>>>>>>>>>>>> b = a.map(...)
> >>>>>>>>>>>>>>>>>>>> val cachedTableA1 = a.cache()
> >>>>>>>>>>>>>>>>>>>> val cachedTableA2 = a.cache()
> >>>>>>>>>>>>>>>>>>>> b.print() // Just to make sure a is cached.
> >>>>>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>>>>> c = a.filter(...) // User specify that the original
> DAG
> >>> is
> >>>>>>>>>> used?
> >>>>>>>>>>>>>> Or
> >>>>>>>>>>>>>>>> the
> >>>>>>>>>>>>>>>>>>>> optimizer decides whether DAG or cache should be used?
> >>>>>>>>>>>>>>>>>>>> d = cachedTableA1.filter() // User specify that the
> >>> cached
> >>>>>>>>>> table
> >>>>>>>>>>>>>> is
> >>>>>>>>>>>>>>>>>>> used.
> >>>>>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>>>>> a.unCache() // Can cachedTableA still be used
> >>> afterwards?
> >>>>>>>>>>>>>>>>>>>> cachedTableA1.uncache() // Can cachedTableA2 still be
> >>> used?
> >>>>>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>>>>> *Case 2: not returning a CachedTable*
> >>>>>>>>>>>>>>>>>>>> b = a.map()
> >>>>>>>>>>>>>>>>>>>> a.cache()
> >>>>>>>>>>>>>>>>>>>> a.cache() // no-op
> >>>>>>>>>>>>>>>>>>>> b.print() // Just to make sure a is cached
> >>>>>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>>>>> c = a.filter(...) // Optimizer decides whether the
> >>> cache or
> >>>>>>> DAG
> >>>>>>>>>>>>>>> should
> >>>>>>>>>>>>>>>>>>> be
> >>>>>>>>>>>>>>>>>>>> used
> >>>>>>>>>>>>>>>>>>>> d = a.filter(...) // Optimizer decides whether the
> >>> cache or
> >>>>>>> DAG
> >>>>>>>>>>>>>>> should
> >>>>>>>>>>>>>>>>>>> be
> >>>>>>>>>>>>>>>>>>>> used
> >>>>>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>>>>> a.unCache()
> >>>>>>>>>>>>>>>>>>>> a.unCache() // no-op
> >>>>>>>>>>>>>>>>>>>>
> >>>>>>>>>>>>>>>>>>>> In case 1, semantic wise, optimizer lose the option to
> >>>>> choose
> >>>>>>>>>>>>>>> between
> >>>>>>>>>>>>>>>>>>> DAG
> >>>>>>>>>>>>>>>>>>>> and cache. And the unCache() call becomes tricky.
> >>>>>>>>>>>>>>>>>>>> In case 2, users do not need to worry about whether
> >>> cache
> >>>>> or
> >>>>>>>>>> DAG
> >>>>>>>>>>>>>> is
> >>>>>>>>>>>>>>>>>>> used.
> >>>>>>>>>>>>>>>>>>>> And the unCache() semantic is clear. However, the
> >>> caveat is
> >>>>>>>>>> that
> >>>>>>>>>>>>>>> users
> >>>>>>>>>>>>>>>>>>>> cannot explicitly ignore the cache.
> >>>>>>>>>>>>>>>>>>
> >>
> >>
>
>
