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
