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.
>>>>>>>>>>>>>
>>>>>>>>>>>>> In order to address the issues mentioned in case 2 and
>>> inspired by
>>>>>>>> the
>>>>>>>>>>>>> discussion so far, I am thinking about using hint to allow user
>>>>>>>>>>>> explicitly
>>>>>>>>>>>>> ignore cache. Although we do not have hint yet, but we probably
>>>>>>>> should
>>>>>>>>>>>> have
>>>>>>>>>>>>> one. So the code becomes:
>>>>>>>>>>>>>
>>>>>>>>>>>>> *Case 3: returning this table*
>>>>>>>>>>>>> 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.hint("ignoreCache").filter(...) // DAG will be used
>>> instead
>>>>>>> of
>>>>>>>>> the
>>>>>>>>>>>>> cache.
>>>>>>>>>>>>>
>>>>>>>>>>>>> a.unCache()
>>>>>>>>>>>>> a.unCache() // no-op
>>>>>>>>>>>>>
>>>>>>>>>>>>> We could also let cache() return this table to allow chained
>>>>>>> method
>>>>>>>>>>>> calls.
>>>>>>>>>>>>> Do you think this API addresses the concerns?
>>>>>>>>>>>>>
>>>>>>>>>>>>> Thanks,
>>>>>>>>>>>>>
>>>>>>>>>>>>> Jiangjie (Becket) Qin
>>>>>>>>>>>>>
>>>>>>>>>>>>>
>>>>>>>>>>>>> On Wed, Dec 5, 2018 at 10:55 AM Jark Wu <imj...@gmail.com>
>>> wrote:
>>>>>>>>>>>>>
>>>>>>>>>>>>>> Hi，
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> All the recent discussions are focused on whether there is a
>>>>>>>> problem
>>>>>>>>> if
>>>>>>>>>>>>>> cache() not return a Table.
>>>>>>>>>>>>>> It seems that returning a Table explicitly is more clear (and
>>>>>>>> safe?).
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> So whether there are any problems if cache() returns a Table?
>>>>>>>>> @Becket
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> Best,
>>>>>>>>>>>>>> Jark
>>>>>>>>>>>>>>
>>>>>>>>>>>>>> On Tue, 4 Dec 2018 at 22:27, Till Rohrmann <
>>> trohrm...@apache.org
>>>>>>>>
>>>>>>>>>>>> wrote:
>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> It's true that b, c, d and e will all read from the original
>>> DAG
>>>>>>>>> that
>>>>>>>>>>>>>>> generates a. But all subsequent operators (when running
>>> multiple
>>>>>>>>>>>> queries)
>>>>>>>>>>>>>>> which reference cachedTableA should not need to reproduce `a`
>>>>>>> but
>>>>>>>>>>>>>> directly
>>>>>>>>>>>>>>> consume the intermediate result.
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> 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 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()`.
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> Cheers,
>>>>>>>>>>>>>>> Till
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>> On Tue, Dec 4, 2018 at 2:33 PM Becket Qin <
>>> becket....@gmail.com
>>>>>>>>
>>>>>>>>>>>> wrote:
>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> Hi Till,
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> Thanks for the clarification. I am still a little confused.
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> If cache() returns a CachedTable, the example might become:
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> b = a.map(...)
>>>>>>>>>>>>>>>> c = a.map(...)
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> cachedTableA = a.cache()
>>>>>>>>>>>>>>>> d = cachedTableA.map(...)
>>>>>>>>>>>>>>>> e = a.map()
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> In the above case, if cache() is lazily evaluated, b, c, d
>>> and
>>>>>>> e
>>>>>>>>> are
>>>>>>>>>>>>>> all
>>>>>>>>>>>>>>>> going to be reading from the original DAG that generates a.
>>> But
>>>>>>>>> with
>>>>>>>>>>>> a
>>>>>>>>>>>>>>>> naive expectation, d should be reading from the cache. This
>>>>>>> seems
>>>>>>>>> not
>>>>>>>>>>>>>>>> solving the potential confusion you raised, right?
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> Just to be clear, my understanding are all based on the
>>>>>>>> assumption
>>>>>>>>>>>> that
>>>>>>>>>>>>>>> the
>>>>>>>>>>>>>>>> tables are immutable. Therefore, after a.cache(), a the
>>>>>>>>>>>> c*achedTableA*
>>>>>>>>>>>>>>> and
>>>>>>>>>>>>>>>> original table *a * should be completely interchangeable.
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> That said, I think a valid argument is optimization. There
>>> are
>>>>>>>>> indeed
>>>>>>>>>>>>>>> cases
>>>>>>>>>>>>>>>> that reading from the original DAG could be faster than
>>> reading
>>>>>>>>> from
>>>>>>>>>>>>>> the
>>>>>>>>>>>>>>>> cache. For example, in the following example:
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> a.filter(f1' > 100)
>>>>>>>>>>>>>>>> a.cache()
>>>>>>>>>>>>>>>> b = a.filter(f1' < 100)
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> Ideally the optimizer should be intelligent enough to decide
>>>>>>>> which
>>>>>>>>>>>> way
>>>>>>>>>>>>>> is
>>>>>>>>>>>>>>>> faster, without user intervention. In this case, it will
>>>>>>> identify
>>>>>>>>>>>> that
>>>>>>>>>>>>>> b
>>>>>>>>>>>>>>>> would just be an empty table, thus skip reading from the
>>> cache
>>>>>>>>>>>>>>> completely.
>>>>>>>>>>>>>>>> But I agree that returning a CachedTable would give user the
>>>>>>>>> control
>>>>>>>>>>>> of
>>>>>>>>>>>>>>>> when to use cache, even though I still feel that letting the
>>>>>>>>>>>> optimizer
>>>>>>>>>>>>>>>> handle this is a better option in long run.
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> Thanks,
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> Jiangjie (Becket) Qin
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>> On Tue, Dec 4, 2018 at 6:51 PM Till Rohrmann <
>>>>>>>> trohrm...@apache.org
>>>>>>>>>>
>>>>>>>>>>>>>>> wrote:
>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> Yes you are right Becket that it still depends on the
>>> actual
>>>>>>>>>>>>>> execution
>>>>>>>>>>>>>>> of
>>>>>>>>>>>>>>>>> the job whether a consumer reads from a cached result or
>>> not.
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> My point was actually about the properties of a (cached vs.
>>>>>>>>>>>>>> non-cached)
>>>>>>>>>>>>>>>> and
>>>>>>>>>>>>>>>>> not about the execution. I would not make cache trigger the
>>>>>>>>>>>> execution
>>>>>>>>>>>>>>> of
>>>>>>>>>>>>>>>>> the job because one loses some flexibility by eagerly
>>>>>>> triggering
>>>>>>>>> the
>>>>>>>>>>>>>>>>> execution.
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> I tried to argue for an explicit CachedTable which is
>>> returned
>>>>>>>> by
>>>>>>>>>>>> the
>>>>>>>>>>>>>>>>> cache() method like Piotr did in order to make the API more
>>>>>>>>>>>> explicit.
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> Cheers,
>>>>>>>>>>>>>>>>> Till
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>> On Mon, Dec 3, 2018 at 4:23 PM Becket Qin <
>>>>>>> becket....@gmail.com
>>>>>>>>>
>>>>>>>>>>>>>>> wrote:
>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> Hi Till,
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> That is a good example. Just a minor correction, in this
>>>>>>> case,
>>>>>>>>> b, c
>>>>>>>>>>>>>>>> and d
>>>>>>>>>>>>>>>>>> will all consume from a non-cached a. This is because
>>> cache
>>>>>>>> will
>>>>>>>>>>>>>> only
>>>>>>>>>>>>>>>> be
>>>>>>>>>>>>>>>>>> created on the very first job submission that generates
>>> the
>>>>>>>> table
>>>>>>>>>>>>>> to
>>>>>>>>>>>>>>> be
>>>>>>>>>>>>>>>>>> cached.
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> If I understand correctly, this is example is about
>>> whether
>>>>>>>>>>>>>> .cache()
>>>>>>>>>>>>>>>>> method
>>>>>>>>>>>>>>>>>> should be eagerly evaluated or lazily evaluated. In
>>> another
>>>>>>>> word,
>>>>>>>>>>>>>> if
>>>>>>>>>>>>>>>>>> cache() method actually triggers a job that creates the
>>>>>>> cache,
>>>>>>>>>>>>>> there
>>>>>>>>>>>>>>>> will
>>>>>>>>>>>>>>>>>> be no such confusion. Is that right?
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> In the example, although d will not consume from the
>>> cached
>>>>>>>> Table
>>>>>>>>>>>>>>> while
>>>>>>>>>>>>>>>>> it
>>>>>>>>>>>>>>>>>> looks supposed to, from correctness perspective the code
>>> will
>>>>>>>>> still
>>>>>>>>>>>>>>>>> return
>>>>>>>>>>>>>>>>>> correct result, assuming that tables are immutable.
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> Personally I feel it is OK because users probably won't
>>>>>>> really
>>>>>>>>>>>>>> worry
>>>>>>>>>>>>>>>>> about
>>>>>>>>>>>>>>>>>> whether the table is cached or not. And lazy cache could
>>>>>>> avoid
>>>>>>>>> some
>>>>>>>>>>>>>>>>>> unnecessary caching if a cached table is never created in
>>> the
>>>>>>>>> user
>>>>>>>>>>>>>>>>>> application. But I am not opposed to do eager evaluation
>>> of
>>>>>>>>> cache.
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> Thanks,
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> Jiangjie (Becket) Qin
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>> On Mon, Dec 3, 2018 at 10:01 PM Till Rohrmann <
>>>>>>>>>>>>>> trohrm...@apache.org>
>>>>>>>>>>>>>>>>>> wrote:
>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> Another argument for Piotr's point is that lazily
>>> changing
>>>>>>>>>>>>>>> properties
>>>>>>>>>>>>>>>>> of
>>>>>>>>>>>>>>>>>> a
>>>>>>>>>>>>>>>>>>> node affects all down stream consumers but does not
>>>>>>>> necessarily
>>>>>>>>>>>>>>> have
>>>>>>>>>>>>>>>> to
>>>>>>>>>>>>>>>>>>> happen before these consumers are defined. From a user's
>>>>>>>>>>>>>>> perspective
>>>>>>>>>>>>>>>>> this
>>>>>>>>>>>>>>>>>>> can be quite confusing:
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> b = a.map(...)
>>>>>>>>>>>>>>>>>>> c = a.map(...)
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> a.cache()
>>>>>>>>>>>>>>>>>>> d = a.map(...)
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> now b, c and d will consume from a cached operator. In
>>> this
>>>>>>>>> case,
>>>>>>>>>>>>>>> the
>>>>>>>>>>>>>>>>>> user
>>>>>>>>>>>>>>>>>>> would most likely expect that only d reads from a cached
>>>>>>>> result.
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> Cheers,
>>>>>>>>>>>>>>>>>>> Till
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>> On Mon, Dec 3, 2018 at 11:32 AM Piotr Nowojski <
>>>>>>>>>>>>>>>>> pi...@data-artisans.com>
>>>>>>>>>>>>>>>>>>> wrote:
>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>> Hey Shaoxuan and Becket,
>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>> Can you explain a bit more one what are the side
>>> effects?
>>>>>>> So
>>>>>>>>>>>>>>> far
>>>>>>>>>>>>>>>> my
>>>>>>>>>>>>>>>>>>>>> understanding is that such side effects only exist if a
>>>>>>>> table
>>>>>>>>>>>>>>> is
>>>>>>>>>>>>>>>>>>> mutable.
>>>>>>>>>>>>>>>>>>>>> Is that the case?
>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>> Not only that. There are also performance implications
>>> and
>>>>>>>>>>>>>> those
>>>>>>>>>>>>>>>> are
>>>>>>>>>>>>>>>>>>>> another implicit side effects of using `void cache()`.
>>> As I
>>>>>>>>>>>>>> wrote
>>>>>>>>>>>>>>>>>> before,
>>>>>>>>>>>>>>>>>>>> reading from cache might not always be desirable, thus
>>> it
>>>>>>> can
>>>>>>>>>>>>>>> cause
>>>>>>>>>>>>>>>>>>>> performance degradation and I’m fine with that - user's
>>> or
>>>>>>>>>>>>>>>>> optimiser’s
>>>>>>>>>>>>>>>>>>>> choice. What I do not like is that this implicit side
>>>>>>> effect
>>>>>>>>>>>>>> can
>>>>>>>>>>>>>>>>>> manifest
>>>>>>>>>>>>>>>>>>>> in completely different part of code, that wasn’t
>>> touched
>>>>>>> by
>>>>>>>> a
>>>>>>>>>>>>>>> user
>>>>>>>>>>>>>>>>>> while
>>>>>>>>>>>>>>>>>>>> he was adding `void cache()` call somewhere else. And
>>> even
>>>>>>> if
>>>>>>>>>>>>>>>> caching
>>>>>>>>>>>>>>>>>>>> improves performance, it’s still a side effect of `void
>>>>>>>>>>>>>> cache()`.
>>>>>>>>>>>>>>>>>> Almost
>>>>>>>>>>>>>>>>>>>> from the definition `void` methods have only side
>>> effects.
>>>>>>>> As I
>>>>>>>>>>>>>>>> wrote
>>>>>>>>>>>>>>>>>>>> before, there are couple of scenarios where this might
>>> be
>>>>>>>>>>>>>>>> undesirable
>>>>>>>>>>>>>>>>>>>> and/or unexpected, for example:
>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>> 1.
>>>>>>>>>>>>>>>>>>>> Table b = …;
>>>>>>>>>>>>>>>>>>>> b.cache()
>>>>>>>>>>>>>>>>>>>> x = b.join(…)
>>>>>>>>>>>>>>>>>>>> y = b.count()
>>>>>>>>>>>>>>>>>>>> // ...
>>>>>>>>>>>>>>>>>>>> // 100
>>>>>>>>>>>>>>>>>>>> // hundred
>>>>>>>>>>>>>>>>>>>> // lines
>>>>>>>>>>>>>>>>>>>> // of
>>>>>>>>>>>>>>>>>>>> // code
>>>>>>>>>>>>>>>>>>>> // later
>>>>>>>>>>>>>>>>>>>> z = b.filter(…).groupBy(…) // this might be even hidden
>>> in
>>>>>>> a
>>>>>>>>>>>>>>>>> different
>>>>>>>>>>>>>>>>>>>> method/file/package/dependency
>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>> 2.
>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>> Table b = ...
>>>>>>>>>>>>>>>>>>>> If (some_condition) {
>>>>>>>>>>>>>>>>>>>> foo(b)
>>>>>>>>>>>>>>>>>>>> }
>>>>>>>>>>>>>>>>>>>> Else {
>>>>>>>>>>>>>>>>>>>> bar(b)
>>>>>>>>>>>>>>>>>>>> }
>>>>>>>>>>>>>>>>>>>> z = b.filter(…).groupBy(…)
>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>> Void foo(Table b) {
>>>>>>>>>>>>>>>>>>>> b.cache()
>>>>>>>>>>>>>>>>>>>> // do something with b
>>>>>>>>>>>>>>>>>>>> }
>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>> In both above examples, `b.cache()` will implicitly
>>> affect
>>>>>>>>>>>>>>>> (semantic
>>>>>>>>>>>>>>>>>> of a
>>>>>>>>>>>>>>>>>>>> program in case of sources being mutable and
>>> performance)
>>>>>>> `z
>>>>>>>> =
>>>>>>>>>>>>>>>>>>>> b.filter(…).groupBy(…)` which might be far from obvious.
>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>> On top of that, there is still this argument of mine
>>> that
>>>>>>>>>>>>>> having
>>>>>>>>>>>>>>> a
>>>>>>>>>>>>>>>>>>>> `MaterializedTable` or `CachedTable` handle is more
>>>>>>> flexible
>>>>>>>>>>>>>> for
>>>>>>>>>>>>>>> us
>>>>>>>>>>>>>>>>> for
>>>>>>>>>>>>>>>>>>> the
>>>>>>>>>>>>>>>>>>>> future and for the user (as a manual option to bypass
>>> cache
>>>>>>>>>>>>>>> reads).
>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>> But Jiangjie is correct,
>>>>>>>>>>>>>>>>>>>>> the source table in batching should be immutable. It is
>>>>>>> the
>>>>>>>>>>>>>>>> user’s
>>>>>>>>>>>>>>>>>>>>> responsibility to ensure it, otherwise even a regular
>>>>>>>>>>>>>> failover
>>>>>>>>>>>>>>>> may
>>>>>>>>>>>>>>>>>> lead
>>>>>>>>>>>>>>>>>>>>> to inconsistent results.
>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>> Yes, I agree that’s what perfect world/good deployment
>>>>>>> should
>>>>>>>>>>>>>> be.
>>>>>>>>>>>>>>>> But
>>>>>>>>>>>>>>>>>> its
>>>>>>>>>>>>>>>>>>>> often isn’t and while I’m not trying to fix this (since
>>> the
>>>>>>>>>>>>>>> proper
>>>>>>>>>>>>>>>>> fix
>>>>>>>>>>>>>>>>>> is
>>>>>>>>>>>>>>>>>>>> to support transactions), I’m just trying to minimise
>>>>>>>> confusion
>>>>>>>>>>>>>>> for
>>>>>>>>>>>>>>>>> the
>>>>>>>>>>>>>>>>>>>> users that are not fully aware what’s going on and
>>> operate
>>>>>>> in
>>>>>>>>>>>>>>> less
>>>>>>>>>>>>>>>>> then
>>>>>>>>>>>>>>>>>>>> perfect setup. And if something bites them after adding
>>>>>>>>>>>>>>> `b.cache()`
>>>>>>>>>>>>>>>>>> call,
>>>>>>>>>>>>>>>>>>>> to make sure that they at least know all of the places
>>> that
>>>>>>>>>>>>>>> adding
>>>>>>>>>>>>>>>>> this
>>>>>>>>>>>>>>>>>>>> line can affect.
>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>> Thanks, Piotrek
>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>> On 1 Dec 2018, at 15:39, Becket Qin <
>>> becket....@gmail.com
>>>>>>>>
>>>>>>>>>>>>>>>> wrote:
>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>> Hi Piotrek,
>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>> Thanks again for the clarification. Some more replies
>>> are
>>>>>>>>>>>>>>>>> following.
>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>> But keep in mind that `.cache()` will/might not only be
>>>>>>> used
>>>>>>>>>>>>>> in
>>>>>>>>>>>>>>>>>>>> interactive
>>>>>>>>>>>>>>>>>>>>>> programming and not only in batching.
>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>> It is true. Actually in stream processing, cache() has
>>> the
>>>>>>>>>>>>>> same
>>>>>>>>>>>>>>>>>>> semantic
>>>>>>>>>>>>>>>>>>>> as
>>>>>>>>>>>>>>>>>>>>> batch processing. The semantic is following:
>>>>>>>>>>>>>>>>>>>>> For a table created via a series of computation, save
>>> that
>>>>>>>>>>>>>>> table
>>>>>>>>>>>>>>>>> for
>>>>>>>>>>>>>>>>>>>> later
>>>>>>>>>>>>>>>>>>>>> reference to avoid running the computation logic to
>>>>>>>>>>>>>> regenerate
>>>>>>>>>>>>>>>> the
>>>>>>>>>>>>>>>>>>> table.
>>>>>>>>>>>>>>>>>>>>> Once the application exits, drop all the cache.
>>>>>>>>>>>>>>>>>>>>> This semantic is same for both batch and stream
>>>>>>> processing.
>>>>>>>>>>>>>> The
>>>>>>>>>>>>>>>>>>>> difference
>>>>>>>>>>>>>>>>>>>>> is that stream applications will only run once as they
>>> are
>>>>>>>>>>>>>> long
>>>>>>>>>>>>>>>>>>> running.
>>>>>>>>>>>>>>>>>>>>> And the batch applications may be run multiple times,
>>>>>>> hence
>>>>>>>>>>>>>> the
>>>>>>>>>>>>>>>>> cache
>>>>>>>>>>>>>>>>>>> may
>>>>>>>>>>>>>>>>>>>>> be created and dropped each time the application runs.
>>>>>>>>>>>>>>>>>>>>> Admittedly, there will probably be some resource
>>>>>>> management
>>>>>>>>>>>>>>>>>>> requirements
>>>>>>>>>>>>>>>>>>>>> for the streaming cached table, such as time based /
>>> size
>>>>>>>>>>>>>> based
>>>>>>>>>>>>>>>>>>>> retention,
>>>>>>>>>>>>>>>>>>>>> to address the infinite data issue. But such
>>> requirement
>>>>>>>> does
>>>>>>>>>>>>>>> not
>>>>>>>>>>>>>>>>>>> change
>>>>>>>>>>>>>>>>>>>>> the semantic.
>>>>>>>>>>>>>>>>>>>>> You are right that interactive programming is just one
>>> use
>>>>>>>>>>>>>> case
>>>>>>>>>>>>>>>> of
>>>>>>>>>>>>>>>>>>>> cache().
>>>>>>>>>>>>>>>>>>>>> It is not the only use case.
>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>> For me the more important issue is of not having the
>>> `void
>>>>>>>>>>>>>>>> cache()`
>>>>>>>>>>>>>>>>>>> with
>>>>>>>>>>>>>>>>>>>>>> side effects.
>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>> This is indeed the key point. The argument around
>>> whether
>>>>>>>>>>>>>>> cache()
>>>>>>>>>>>>>>>>>>> should
>>>>>>>>>>>>>>>>>>>>> return something already indicates that cache() and
>>>>>>>>>>>>>>> materialize()
>>>>>>>>>>>>>>>>>>> address
>>>>>>>>>>>>>>>>>>>>> different issues.
>>>>>>>>>>>>>>>>>>>>> Can you explain a bit more one what are the side
>>> effects?
>>>>>>> So
>>>>>>>>>>>>>>> far
>>>>>>>>>>>>>>>> my
>>>>>>>>>>>>>>>>>>>>> understanding is that such side effects only exist if a
>>>>>>>> table
>>>>>>>>>>>>>>> is
>>>>>>>>>>>>>>>>>>> mutable.
>>>>>>>>>>>>>>>>>>>>> Is that the case?
>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>> 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.
>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>> I don't think anyone should insert something to a
>>> cache.
>>>>>>> By
>>>>>>>>>>>>>>>>>> definition
>>>>>>>>>>>>>>>>>>>> the
>>>>>>>>>>>>>>>>>>>>> cache should only be updated when the corresponding
>>>>>>> original
>>>>>>>>>>>>>>>> table
>>>>>>>>>>>>>>>>> is
>>>>>>>>>>>>>>>>>>>>> updated. What I am wondering is that given the
>>> following
>>>>>>> two
>>>>>>>>>>>>>>>> facts:
>>>>>>>>>>>>>>>>>>>>> 1. If and only if a table is mutable (with something
>>> like
>>>>>>>>>>>>>>>>> insert()),
>>>>>>>>>>>>>>>>>> a
>>>>>>>>>>>>>>>>>>>>> CachedTable may have implicit behavior.
>>>>>>>>>>>>>>>>>>>>> 2. A CachedTable extends a Table.
>>>>>>>>>>>>>>>>>>>>> We can come to the conclusion that a CachedTable is
>>>>>>> mutable
>>>>>>>>>>>>>> and
>>>>>>>>>>>>>>>>> users
>>>>>>>>>>>>>>>>>>> can
>>>>>>>>>>>>>>>>>>>>> insert into the CachedTable directly. This is where I
>>>>>>>> thought
>>>>>>>>>>>>>>>>>>> confusing.
>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>> Thanks,
>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>> Jiangjie (Becket) Qin
>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>> On Sat, Dec 1, 2018 at 2:45 AM Piotr Nowojski <
>>>>>>>>>>>>>>>>>> pi...@data-artisans.com
>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>> wrote:
>>>>>>>>>>>>>>>>>>>>>
>>>>>>>>>>>>>>>>>>>>>> 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 ha
>>>
>>>
