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 > > > > >
