Hi Yuan and Lincoln, thanks a lot for the attention. I would answer the email one by one.
To Yuan > How shall we deal with CDC data? If there is CDC data in the pipeline, IMHO, shuffle by join key will cause CDC data disorder. Will it be better to use primary key in this case? Good question. The problem could not only exists in CDC data source, but also exists when the input stream is not insert-only stream (for example, the result of unbounded aggregate or regular join). I think use hash by primary key is not a good choise. It could not raise the cache hit because cache key is look up key instead of primary key of input. To avoid wrong result, hash lookup Join requires that the input stream should be insert_only stream or its upsert keys contains lookup keys. I've added this limitation to FLIP, thanks a lot for reminding. > If the shuffle keys can be customized when users have the knowledge about distribution of data? I'm not sure I understand your question. Do you mean to support user defined partitioner function on keys just like flink DataStream sql? If yes, I'm afraid there is no plan to support this feature yet because the feature involves many things, for example: 1. sql syntax 2. user defined partitioner API 3. RelDistribution type extension and Flink RelDistribution extension 4. FlinkExpandConversionRule 5. Exchange execNode extension 6. .... It needs well designed and more discussion. If this is a strong requirement, we would drive another discussion on this point individually. In this FLIP, I would first support hash shuffle. WDYT? Or do you mean support hash by other keys instead of lookup key? If yes, would you please tell me a specific user case? We need to fetch the record from external storage of dimension table by look up key, so those dimension table source uses look up keys as cache key. We could only increase the cache ratio by shuffle lookup keys. I need more use cases to understand this requirement. > Some connectors such as hive, caches all data in LookupFunction. How to decrease the valid cache data size if data can be shuffled? Very good idea. There are two types of cache. For Key-Value storage, such as Redis/HBase, the lookup table source stores the visited lookup keys and it's record into cache lazily. For other storage without keys, such as hive, each task loads all data into cache eagerly in the initialize phase. After introduce hash partitioner, for key-value storages, there is no need to change; for hive, each task could only load part of cache instead of load all cache. We have implemented this optimization in our internal version. The core idea is push the partitioner information down to the lookup table source. When loading data into caches, each task could only store those records which look keys are sent to current task. We called this 'HashPartitionedCache'. I have added this point into the Lookup Join requirements list in the motivation of the FLIP, but I would not do this point in this FLIP right now. If this is a strong requirement, we need drive another discussion on this topic individually because this point involves many extension on API. Best, Jing Zhang Lincoln Lee <lincoln.8...@gmail.com> 于2021年12月29日周三 10:01写道： > Hi Jing, > Thanks for bringing up this discussion! Agree that this join hints > should benefit both bounded and unbounded cases as Martin mentioned. > I also agree that implementing the query hint is the right way for a more > general purpose since the dynamic table options has a limited scope. > Some points I'd like to share are: > 1. Regarding the hint name ‘USE_HASH’, could we consider more candidates? > Things are a little different from RDBMS in the distributed world, and we > also aim to solve the data skew problem, so all these incoming hints names > should be considered together. > 2. As you mentioned in the flip, this solution depends on future changes to > calcite (and also upgrading calcite would be another possible big change: > at least calicite-1.30 vs 1.26, are we preparing to accept this big > change?). Is there another possible way to minimize the change in calcite? > As I know there're more limitations than `Correlate`. > > Best, > Lincoln Lee > > > Jing Zhang <beyond1...@gmail.com> 于2021年12月28日周二 23:04写道： > > > Hi Martijn, > > Thanks a lot for your attention. > > I'm sorry I didn't explain the motivation clearly. I would like to > explain > > it in detail, and then give response on your questions. > > A lookup join is typically used to enrich a table with data that is > queried > > from an external system. Many Lookup table sources introduce cache in > order > > to reduce the RPC call, such as JDBC, CSV, HBase connectors. > > For those connectors, we could raise cache hit ratio by routing the same > > lookup keys to the same task instance. This is the purpose of > > > > > https://cwiki.apache.org/confluence/display/FLINK/FLIP-204%3A+Introduce+Hash+Lookup+Join > > . > > Other cases might benefit from Hash distribution, such as batch hash join > > as you mentioned. It is a cool idea, however it is not the purpose of > this > > FLIP, we could discuss this in FLINK-20670 > > <https://issues.apache.org/jira/browse/FLINK-20670>.😀 > > > > > - When I was reading about this topic [1] I was wondering if this > feature > > would be more beneficial for bounded use cases and not so much for > > unbounded use cases. What do you think? > > > > As mentioned before, the purpose of Hash Lookup Join is to increase the > > cache hit ratio which is different from Oracle Hash Join. However we > could > > use the similar hint syntax. > > > > > - If I look at the current documentation for SQL Hints in Flink [2], I > > notice that all of the hints there are located at the end of the SQL > > statement. In the FLIP, the use_hash is defined directly after the > 'SELECT' > > keyword. Can we somehow make this consistent for the user? Or should the > > user be able to specify hints anywhere in its SQL statement? > > > > Calcite supports hints in two locations [3]: > > Query Hint: right after the SELECT keyword; > > Table Hint: right after the referenced table name. > > Now Flink has supported dynamic table options based on the Hint framework > > of Calcite which is mentioned in doc[2]. > > Besides, query hints are also important, it could give a hint for > > optimizers to choose a better plan. Almost all popular databases and > > big-data engines support sql query hints, such as oracle, hive, spark and > > so on. > > I think using query hints in this case is more natural for users, WDYT? > > > > I have updated the motivation part in the FLIP, > > Thanks for the feedback! > > > > [1] https://logicalread.com/oracle-11g-hash-joins-mc02/#.V5Wm4_mnoUI > > [2] > > > > > https://nightlies.apache.org/flink/flink-docs-stable/docs/dev/table/sql/queries/hints/ > > [3] https://calcite.apache.org/docs/reference.html#sql-hints > > > > Best, > > Jing Zhang > > > > Martijn Visser <mart...@ververica.com> 于2021年12月28日周二 22:02写道： > > > > > Hi Jing, > > > > > > Thanks a lot for the explanation and the FLIP. I definitely learned > > > something when reading more about `use_hash`. My interpretation would > be > > > that the primary benefit of a hash lookup join would be improved > > > performance by allowing the user to explicitly optimise the planner. > > > > > > I have a couple of questions: > > > > > > - When I was reading about this topic [1] I was wondering if this > feature > > > would be more beneficial for bounded use cases and not so much for > > > unbounded use cases. What do you think? > > > - If I look at the current documentation for SQL Hints in Flink [2], I > > > notice that all of the hints there are located at the end of the SQL > > > statement. In the FLIP, the use_hash is defined directly after the > > 'SELECT' > > > keyword. Can we somehow make this consistent for the user? Or should > the > > > user be able to specify hints anywhere in its SQL statement? > > > > > > Best regards, > > > > > > Martijn > > > > > > [1] https://logicalread.com/oracle-11g-hash-joins-mc02/#.V5Wm4_mnoUI > > > [2] > > > > > > > > > https://nightlies.apache.org/flink/flink-docs-stable/docs/dev/table/sql/queries/hints/ > > > > > > > > > On Tue, 28 Dec 2021 at 08:17, Jing Zhang <beyond1...@gmail.com> wrote: > > > > > > > Hi everyone, > > > > Look up join > > > > < > > > > > > > > > > https://nightlies.apache.org/flink/flink-docs-master/docs/dev/table/sql/queries/joins/#lookup-join > > > > >[1] > > > > is > > > > commonly used feature in Flink SQL. We have received many > optimization > > > > requirements on look up join. For example: > > > > 1. Enforces left side of lookup join do a hash partitioner to raise > > cache > > > > hint ratio > > > > 2. Solves the data skew problem after introduces hash lookup join > > > > 3. Enables mini-batch optimization to reduce RPC call > > > > > > > > Next we will solve these problems one by one. Firstly, we would > focus > > on > > > > point 1, and continue to discuss point 2 and point 3 later. > > > > > > > > There are many similar requirements from user mail list and JIRA > about > > > hash > > > > Lookup Join, for example: > > > > 1. FLINK-23687 <https://issues.apache.org/jira/browse/FLINK-23687> - > > > > Introduce partitioned lookup join to enforce input of LookupJoin to > > hash > > > > shuffle by lookup keys > > > > 2. FLINK-25396 <https://issues.apache.org/jira/browse/FLINK-25396> - > > > > lookupjoin source table for pre-partitioning > > > > 3. FLINK-25262 <https://issues.apache.org/jira/browse/FLINK-25262> - > > > > Support to send data to lookup table for KeyGroupStreamPartitioner > way > > > for > > > > SQL. > > > > > > > > In this FLIP, I would like to start a discussion about Hash Lookup > > Join. > > > > The core idea is introducing a 'USE_HASH' hint in query. This syntax > > is > > > > directly user-oriented and therefore requires careful design. > > > > There are two ways about how to propagate this hint to LookupJoin in > > > > optimizer. We need further discussion to do final decide. Anyway, the > > > > difference between the two solution is only about the internal > > > > implementation and has no impact on the user. > > > > > > > > For more detail on the proposal: > > > > > > > > > > > > > > https://cwiki.apache.org/confluence/display/FLINK/FLIP-204%3A+Introduce+Hash+Lookup+Join > > > > > > > > > > > > Looking forward to your feedback, thanks. > > > > > > > > Best, > > > > Jing Zhang > > > > > > > > [1] > > > > > > > > > > > > > > https://nightlies.apache.org/flink/flink-docs-master/docs/dev/table/sql/queries/joins/#lookup-join > > > > > > > > > >
