Hi everyone!
Thanks for driving such a valuable improvement!
I do think that single cache implementation would be a nice opportunity for
users. And it will break the "FOR SYSTEM_TIME AS OF proc_time" semantics
anyway - doesn't matter how it will be implemented.
Putting myself in the user's shoes, I can say that:
1) I would prefer to have the opportunity to cut off the cache size by
simply filtering unnecessary data. And the most handy way to do it is apply
it inside LookupRunners. It would be a bit harder to pass it through the
LookupJoin node to TableFunction. And Alexander correctly mentioned that
filter pushdown still is not implemented for jdbc/hive/hbase.
2) The ability to set the different caching parameters for different tables
is quite important. So I would prefer to set it through DDL rather than
have similar ttla, strategy and other options for all lookup tables.
3) Providing the cache into the framework really deprives us of
extensibility (users won't be able to implement their own cache). But most
probably it might be solved by creating more different cache strategies and
a wider set of configurations.
All these points are much closer to the schema proposed by Alexander.
Qingshen Ren, please correct me if I'm not right and all these facilities
might be simply implemented in your architecture?
Best regards,
Roman Boyko
e.: ro.v.bo...@gmail.com
On Wed, 4 May 2022 at 21:01, Martijn Visser <martijnvis...@apache.org>
wrote:
> Hi everyone,
>
> I don't have much to chip in, but just wanted to express that I really
> appreciate the in-depth discussion on this topic and I hope that others
> will join the conversation.
>
> Best regards,
>
> Martijn
>
> On Tue, 3 May 2022 at 10:15, Александр Смирнов <smirale...@gmail.com>
> wrote:
>
> > Hi Qingsheng, Leonard and Jark,
> >
> > Thanks for your detailed feedback! However, I have questions about
> > some of your statements (maybe I didn't get something?).
> >
> > > Caching actually breaks the semantic of "FOR SYSTEM_TIME AS OF
> proc_time”
> >
> > I agree that the semantics of "FOR SYSTEM_TIME AS OF proc_time" is not
> > fully implemented with caching, but as you said, users go on it
> > consciously to achieve better performance (no one proposed to enable
> > caching by default, etc.). Or by users do you mean other developers of
> > connectors? In this case developers explicitly specify whether their
> > connector supports caching or not (in the list of supported options),
> > no one makes them do that if they don't want to. So what exactly is
> > the difference between implementing caching in modules
> > flink-table-runtime and in flink-table-common from the considered
> > point of view? How does it affect on breaking/non-breaking the
> > semantics of "FOR SYSTEM_TIME AS OF proc_time"?
> >
> > > confront a situation that allows table options in DDL to control the
> > behavior of the framework, which has never happened previously and should
> > be cautious
> >
> > If we talk about main differences of semantics of DDL options and
> > config options("table.exec.xxx"), isn't it about limiting the scope of
> > the options + importance for the user business logic rather than
> > specific location of corresponding logic in the framework? I mean that
> > in my design, for example, putting an option with lookup cache
> > strategy in configurations would be the wrong decision, because it
> > directly affects the user's business logic (not just performance
> > optimization) + touches just several functions of ONE table (there can
> > be multiple tables with different caches). Does it really matter for
> > the user (or someone else) where the logic is located, which is
> > affected by the applied option?
> > Also I can remember DDL option 'sink.parallelism', which in some way
> > "controls the behavior of the framework" and I don't see any problem
> > here.
> >
> > > introduce a new interface for this all-caching scenario and the design
> > would become more complex
> >
> > This is a subject for a separate discussion, but actually in our
> > internal version we solved this problem quite easily - we reused
> > InputFormat class (so there is no need for a new API). The point is
> > that currently all lookup connectors use InputFormat for scanning the
> > data in batch mode: HBase, JDBC and even Hive - it uses class
> > PartitionReader, that is actually just a wrapper around InputFormat.
> > The advantage of this solution is the ability to reload cache data in
> > parallel (number of threads depends on number of InputSplits, but has
> > an upper limit). As a result cache reload time significantly reduces
> > (as well as time of input stream blocking). I know that usually we try
> > to avoid usage of concurrency in Flink code, but maybe this one can be
> > an exception. BTW I don't say that it's an ideal solution, maybe there
> > are better ones.
> >
> > > Providing the cache in the framework might introduce compatibility
> issues
> >
> > It's possible only in cases when the developer of the connector won't
> > properly refactor his code and will use new cache options incorrectly
> > (i.e. explicitly provide the same options into 2 different code
> > places). For correct behavior all he will need to do is to redirect
> > existing options to the framework's LookupConfig (+ maybe add an alias
> > for options, if there was different naming), everything will be
> > transparent for users. If the developer won't do refactoring at all,
> > nothing will be changed for the connector because of backward
> > compatibility. Also if a developer wants to use his own cache logic,
> > he just can refuse to pass some of the configs into the framework, and
> > instead make his own implementation with already existing configs and
> > metrics (but actually I think that it's a rare case).
> >
> > > filters and projections should be pushed all the way down to the table
> > function, like what we do in the scan source
> >
> > It's the great purpose. But the truth is that the ONLY connector that
> > supports filter pushdown is FileSystemTableSource
> > (no database connector supports it currently). Also for some databases
> > it's simply impossible to pushdown such complex filters that we have
> > in Flink.
> >
> > > only applying these optimizations to the cache seems not quite useful
> >
> > Filters can cut off an arbitrarily large amount of data from the
> > dimension table. For a simple example, suppose in dimension table
> > 'users'
> > we have column 'age' with values from 20 to 40, and input stream
> > 'clicks' that is ~uniformly distributed by age of users. If we have
> > filter 'age > 30',
> > there will be twice less data in cache. This means the user can
> > increase 'lookup.cache.max-rows' by almost 2 times. It will gain a
> > huge
> > performance boost. Moreover, this optimization starts to really shine
> > in 'ALL' cache, where tables without filters and projections can't fit
> > in memory, but with them - can. This opens up additional possibilities
> > for users. And this doesn't sound as 'not quite useful'.
> >
> > It would be great to hear other voices regarding this topic! Because
> > we have quite a lot of controversial points, and I think with the help
> > of others it will be easier for us to come to a consensus.
> >
> > Best regards,
> > Smirnov Alexander
> >
> >
> > пт, 29 апр. 2022 г. в 22:33, Qingsheng Ren <renqs...@gmail.com>:
> > >
> > > Hi Alexander and Arvid,
> > >
> > > Thanks for the discussion and sorry for my late response! We had an
> > internal discussion together with Jark and Leonard and I’d like to
> > summarize our ideas. Instead of implementing the cache logic in the table
> > runtime layer or wrapping around the user-provided table function, we
> > prefer to introduce some new APIs extending TableFunction with these
> > concerns:
> > >
> > > 1. Caching actually breaks the semantic of "FOR SYSTEM_TIME AS OF
> > proc_time”, because it couldn’t truly reflect the content of the lookup
> > table at the moment of querying. If users choose to enable caching on the
> > lookup table, they implicitly indicate that this breakage is acceptable
> in
> > exchange for the performance. So we prefer not to provide caching on the
> > table runtime level.
> > >
> > > 2. If we make the cache implementation in the framework (whether in a
> > runner or a wrapper around TableFunction), we have to confront a
> situation
> > that allows table options in DDL to control the behavior of the
> framework,
> > which has never happened previously and should be cautious. Under the
> > current design the behavior of the framework should only be specified by
> > configurations (“table.exec.xxx”), and it’s hard to apply these general
> > configs to a specific table.
> > >
> > > 3. We have use cases that lookup source loads and refresh all records
> > periodically into the memory to achieve high lookup performance (like
> Hive
> > connector in the community, and also widely used by our internal
> > connectors). Wrapping the cache around the user’s TableFunction works
> fine
> > for LRU caches, but I think we have to introduce a new interface for this
> > all-caching scenario and the design would become more complex.
> > >
> > > 4. Providing the cache in the framework might introduce compatibility
> > issues to existing lookup sources like there might exist two caches with
> > totally different strategies if the user incorrectly configures the table
> > (one in the framework and another implemented by the lookup source).
> > >
> > > As for the optimization mentioned by Alexander, I think filters and
> > projections should be pushed all the way down to the table function, like
> > what we do in the scan source, instead of the runner with the cache. The
> > goal of using cache is to reduce the network I/O and pressure on the
> > external system, and only applying these optimizations to the cache seems
> > not quite useful.
> > >
> > > I made some updates to the FLIP[1] to reflect our ideas. We prefer to
> > keep the cache implementation as a part of TableFunction, and we could
> > provide some helper classes (CachingTableFunction,
> AllCachingTableFunction,
> > CachingAsyncTableFunction) to developers and regulate metrics of the
> cache.
> > Also, I made a POC[2] for your reference.
> > >
> > > Looking forward to your ideas!
> > >
> > > [1]
> >
> https://cwiki.apache.org/confluence/display/FLINK/FLIP-221+Abstraction+for+lookup+source+cache+and+metric
> > > [2] https://github.com/PatrickRen/flink/tree/FLIP-221
> > >
> > > Best regards,
> > >
> > > Qingsheng
> > >
> > > On Tue, Apr 26, 2022 at 4:45 PM Александр Смирнов <
> smirale...@gmail.com>
> > wrote:
> > >>
> > >> Thanks for the response, Arvid!
> > >>
> > >> I have few comments on your message.
> > >>
> > >> > but could also live with an easier solution as the first step:
> > >>
> > >> I think that these 2 ways are mutually exclusive (originally proposed
> > >> by Qingsheng and mine), because conceptually they follow the same
> > >> goal, but implementation details are different. If we will go one way,
> > >> moving to another way in the future will mean deleting existing code
> > >> and once again changing the API for connectors. So I think we should
> > >> reach a consensus with the community about that and then work together
> > >> on this FLIP, i.e. divide the work on tasks for different parts of the
> > >> flip (for example, LRU cache unification / introducing proposed set of
> > >> metrics / further work…). WDYT, Qingsheng?
> > >>
> > >> > as the source will only receive the requests after filter
> > >>
> > >> Actually if filters are applied to fields of the lookup table, we
> > >> firstly must do requests, and only after that we can filter responses,
> > >> because lookup connectors don't have filter pushdown. So if filtering
> > >> is done before caching, there will be much less rows in cache.
> > >>
> > >> > @Alexander unfortunately, your architecture is not shared. I don't
> > know the
> > >>
> > >> > solution to share images to be honest.
> > >>
> > >> Sorry for that, I’m a bit new to such kinds of conversations :)
> > >> I have no write access to the confluence, so I made a Jira issue,
> > >> where described the proposed changes in more details -
> > >> https://issues.apache.org/jira/browse/FLINK-27411.
> > >>
> > >> Will happy to get more feedback!
> > >>
> > >> Best,
> > >> Smirnov Alexander
> > >>
> > >> пн, 25 апр. 2022 г. в 19:49, Arvid Heise <ar...@apache.org>:
> > >> >
> > >> > Hi Qingsheng,
> > >> >
> > >> > Thanks for driving this; the inconsistency was not satisfying for
> me.
> > >> >
> > >> > I second Alexander's idea though but could also live with an easier
> > >> > solution as the first step: Instead of making caching an
> > implementation
> > >> > detail of TableFunction X, rather devise a caching layer around X.
> So
> > the
> > >> > proposal would be a CachingTableFunction that delegates to X in case
> > of
> > >> > misses and else manages the cache. Lifting it into the operator
> model
> > as
> > >> > proposed would be even better but is probably unnecessary in the
> > first step
> > >> > for a lookup source (as the source will only receive the requests
> > after
> > >> > filter; applying projection may be more interesting to save memory).
> > >> >
> > >> > Another advantage is that all the changes of this FLIP would be
> > limited to
> > >> > options, no need for new public interfaces. Everything else remains
> an
> > >> > implementation of Table runtime. That means we can easily
> incorporate
> > the
> > >> > optimization potential that Alexander pointed out later.
> > >> >
> > >> > @Alexander unfortunately, your architecture is not shared. I don't
> > know the
> > >> > solution to share images to be honest.
> > >> >
> > >> > On Fri, Apr 22, 2022 at 5:04 PM Александр Смирнов <
> > smirale...@gmail.com>
> > >> > wrote:
> > >> >
> > >> > > Hi Qingsheng! My name is Alexander, I'm not a committer yet, but
> I'd
> > >> > > really like to become one. And this FLIP really interested me.
> > >> > > Actually I have worked on a similar feature in my company’s Flink
> > >> > > fork, and we would like to share our thoughts on this and make
> code
> > >> > > open source.
> > >> > >
> > >> > > I think there is a better alternative than introducing an abstract
> > >> > > class for TableFunction (CachingTableFunction). As you know,
> > >> > > TableFunction exists in the flink-table-common module, which
> > provides
> > >> > > only an API for working with tables – it’s very convenient for
> > importing
> > >> > > in connectors. In turn, CachingTableFunction contains logic for
> > >> > > runtime execution, so this class and everything connected with it
> > >> > > should be located in another module, probably in
> > flink-table-runtime.
> > >> > > But this will require connectors to depend on another module,
> which
> > >> > > contains a lot of runtime logic, which doesn’t sound good.
> > >> > >
> > >> > > I suggest adding a new method ‘getLookupConfig’ to
> LookupTableSource
> > >> > > or LookupRuntimeProvider to allow connectors to only pass
> > >> > > configurations to the planner, therefore they won’t depend on
> > runtime
> > >> > > realization. Based on these configs planner will construct a
> lookup
> > >> > > join operator with corresponding runtime logic (ProcessFunctions
> in
> > >> > > module flink-table-runtime). Architecture looks like in the pinned
> > >> > > image (LookupConfig class there is actually yours CacheConfig).
> > >> > >
> > >> > > Classes in flink-table-planner, that will be responsible for this
> –
> > >> > > CommonPhysicalLookupJoin and his inheritors.
> > >> > > Current classes for lookup join in flink-table-runtime -
> > >> > > LookupJoinRunner, AsyncLookupJoinRunner, LookupJoinRunnerWithCalc,
> > >> > > AsyncLookupJoinRunnerWithCalc.
> > >> > >
> > >> > > I suggest adding classes LookupJoinCachingRunner,
> > >> > > LookupJoinCachingRunnerWithCalc, etc.
> > >> > >
> > >> > > And here comes another more powerful advantage of such a solution.
> > If
> > >> > > we have caching logic on a lower level, we can apply some
> > >> > > optimizations to it. LookupJoinRunnerWithCalc was named like this
> > >> > > because it uses the ‘calc’ function, which actually mostly
> consists
> > of
> > >> > > filters and projections.
> > >> > >
> > >> > > For example, in join table A with lookup table B condition ‘JOIN …
> > ON
> > >> > > A.id = B.id AND A.age = B.age + 10 WHERE B.salary > 1000’ ‘calc’
> > >> > > function will contain filters A.age = B.age + 10 and B.salary >
> > 1000.
> > >> > >
> > >> > > If we apply this function before storing records in cache, size of
> > >> > > cache will be significantly reduced: filters = avoid storing
> useless
> > >> > > records in cache, projections = reduce records’ size. So the
> initial
> > >> > > max number of records in cache can be increased by the user.
> > >> > >
> > >> > > What do you think about it?
> > >> > >
> > >> > >
> > >> > > On 2022/04/19 02:47:11 Qingsheng Ren wrote:
> > >> > > > Hi devs,
> > >> > > >
> > >> > > > Yuan and I would like to start a discussion about FLIP-221[1],
> > which
> > >> > > introduces an abstraction of lookup table cache and its standard
> > metrics.
> > >> > > >
> > >> > > > Currently each lookup table source should implement their own
> > cache to
> > >> > > store lookup results, and there isn’t a standard of metrics for
> > users and
> > >> > > developers to tuning their jobs with lookup joins, which is a
> quite
> > common
> > >> > > use case in Flink table / SQL.
> > >> > > >
> > >> > > > Therefore we propose some new APIs including cache, metrics,
> > wrapper
> > >> > > classes of TableFunction and new table options. Please take a look
> > at the
> > >> > > FLIP page [1] to get more details. Any suggestions and comments
> > would be
> > >> > > appreciated!
> > >> > > >
> > >> > > > [1]
> > >> > >
> >
> https://cwiki.apache.org/confluence/display/FLINK/FLIP-221+Abstraction+for+lookup+source+cache+and+metric
> > >> > > >
> > >> > > > Best regards,
> > >> > > >
> > >> > > > Qingsheng
> > >> > > >
> > >> > > >
> > >> > >
> > >
> > >
> > >
> > > --
> > > Best Regards,
> > >
> > > Qingsheng Ren
> > >
> > > Real-time Computing Team
> > > Alibaba Cloud
> > >
> > > Email: renqs...@gmail.com
> >
>
