Hi Leonard,
I did a bit of analysis on possible integration of this FLIP with Kafka
source. Please find the sample code and explanations below:
public class KafkaDynamicSource implements ScanTableSource,
SupportsReadingMetadata, SupportsWatermarkPushDown,
SupportsPartitioning<TopicPartition>
{
// other parts of the codebase
/** Returns a list of data units in each partition. */
Optional<List<TopicPartition>> sourcePartitions() {
// Note that here, our "data unit" is TopicPartition. If we
read from multiple topics, then partitioned-read is not possible.
// Otherwise, we derive all partitions for the topic.
if (topics.size() > 1 || topics.empty()) {
// Flink will fallback to the non-partitioned read
return Optional.Empty();
}
return deriveTopicPartitions(getTopicMetadata(adminClient,
new HashSet<>(topics.get(0))));
}
void applyPartitionedRead() {
// that this has no impact in Kafka source
}
}
Since in Kafka source each topic partition can be consumed by only one
Flink source instance, things become easier (than File source for example).
Note that, mapping each source partitions to exactly one Flink source is
the main target of this FLIP. This is by design supported in Kafka source.
One another thing to mention is that, we need to make sure is that the
mapping (between Kafka partitions and Flink source instances)
is always deterministic under the same Kafka configuration.
Hope that answers your question.
Regards,
Jeyhun
On Wed, Apr 3, 2024 at 6:53 PM Jeyhun Karimov <je.kari...@gmail.com> wrote:
> Hi Leonard,
>
> Thanks a lot for your comments. Please find my answers below:
>
> (1)The FLIP motivation section says Kafka broker is already partitioned
>> w.r.t. some key[s] , Is this the main use case in Kafka world? Partitioning
>> by key fields is not the default partitioner of Kafka default
>> partitioner[1] IIUC.
>
>
> - I see your point. The main usecase for Kafka is for ksqlDB users.
> Consider a ksqlDB query [1]
>
> CREATE STREAM products_rekeyed
> WITH (PARTITIONS=6) AS
> SELECT *
> FROM products
> PARTITION BY product_id;
>
> In this query, the output is re-partitioned to be keyed by product_id. In
> fact, as the documentation [1] (also Jim mentioned above) mentions, this
> repartitioning is a requirement for join queries.
>
>
> (2) Considering the FLIP’s optimization scope aims to both Batch and
>> Streaming pre-partitioned source, could you add a Streaming Source example
>> to help me understand the FLIP better？ I think Kafka Source is a good
>> candidates for streaming source example, file source is a good one for
>> batch source and it really helped me to follow-up the FLIP.
>
>
> - Currently, I do not have Kafka Source sample integration with this FLIP.
> My idea was to integrate first to the Flink main repo (e.g., file source in
> streaming & batch mode) and then to external connectors.
> But I can try with Kafka Source and get back.
>
> Regards,
> Jeyhun
>
> [1] https://docs.ksqldb.io/en/latest/developer-guide/joins/partition-data/
>
> On Wed, Apr 3, 2024 at 8:25 AM Leonard Xu <xbjt...@gmail.com> wrote:
>
>> Hey, Jeyhun
>>
>> Thanks for kicking off this discussion. I have two questions about
>> streaming sources:
>>
>> (1)The FLIP motivation section says Kafka broker is already partitioned
>> w.r.t. some key[s] , Is this the main use case in Kafka world? Partitioning
>> by key fields is not the default partitioner of Kafka default
>> partitioner[1] IIUC.
>>
>> (2) Considering the FLIP’s optimization scope aims to both Batch and
>> Streaming pre-partitioned source, could you add a Streaming Source example
>> to help me understand the FLIP better？ I think Kafka Source is a good
>> candidates for streaming source example, file source is a good one for
>> batch source and it really helped me to follow-up the FLIP.
>>
>> Best,
>> Leonard
>> [1]
>> https://github.com/apache/kafka/blob/trunk/clients/src/main/java/org/apache/kafka/clients/producer/internals/DefaultPartitioner.java#L31
>>
>>
>>
>> > 2024年4月3日 上午5:53，Jeyhun Karimov <je.kari...@gmail.com> 写道：
>> >
>> > Hi Lincoln,
>> >
>> > Thanks a lot for your comments. Please find my answers below.
>> >
>> >
>> > 1. Is this flip targeted only at batch scenarios or does it include
>> >> streaming?
>> >> (The flip and the discussion did not explicitly mention this, but in
>> the
>> >> draft pr, I only
>> >> saw the implementation for batch scenarios
>> >>
>> >>
>> https://github.com/apache/flink/pull/24437/files#diff-a6d71dd7d9bf0e7776404f54473b504e1de1240e93f820214fa5d1f082fb30c8
>> >> <
>> >>
>> https://github.com/apache/flink/pull/24437/files#diff-a6d71dd7d9bf0e7776404f54473b504e1de1240e93f820214fa5d1f082fb30c8%EF%BC%89
>> >>>
>> >> )
>> >> If we expect this also apply to streaming, then we need to consider the
>> >> stricter
>> >> shuffle restrictions of streaming compared to batch (if support is
>> >> considered,
>> >> more discussion is needed here, let’s not expand for now). If it only
>> >> applies to batch,
>> >> it is recommended to clarify in the flip.
>> >
>> >
>> > - The FLIP targets both streaming and batch scenarios.
>> > Could you please elaborate more on what you mean by additional
>> > restrictions?
>> >
>> >
>> > 2. In the current implementation, the optimized plan seems to have some
>> >> problems.
>> >> As described in the class comments:
>> >>
>> >>
>> https://github.com/apache/flink/blob/d6e3b51fdb9a2e565709e8d7bc619234b3768ed1/flink-table/flink-table-planner/src/main/java/org/apache/flink/table/planner/plan/rules/physical/batch/RemoveRedundantShuffleRule.java#L60
>> >
>> > BatchPhysicalHashAggregate (local)
>> >
>> > +- BatchPhysicalLocalHashAggregate (local)
>> >> +- BatchPhysicalTableSourceScan
>> >> The `BatchPhysicalLocalHashAggregate` here is redundant (in the case of
>> >> one-phase
>> >> hashAgg, localAgg is not necessary, which is the scenario currently
>> handled
>> >> by
>> >> `RemoveRedundantLocalHashAggRule` and other rules)
>> >
>> >
>> > - Yes, you are completely right. Note that the PR you referenced is
>> just a
>> > quick PoC.
>> > Redundant operators you mentioned exist because
>> > `RemoveRedundantShuffleRule` just removes the Exchange operator,
>> > without modifying upstream/downstream operators.
>> > As I mentioned, the implementation is just a PoC and the end
>> implementation
>> > will make sure that existing redundancy elimination rules remove
>> redundant
>> > operators.
>> >
>> >
>> > Also, in the draft pr,
>> >> the optimization of `testShouldEliminatePartitioning1` &
>> >> `testShouldEliminatePartitioning2`
>> >> seems didn't take effect?
>> >>
>> >>
>> https://github.com/apache/flink/blob/d6e3b51fdb9a2e565709e8d7bc619234b3768ed1/flink-table/flink-table-planner/src/test/resources/org/apache/flink/connector/file/table/BatchFileSystemTableSourceTest.xml#L38
>> >
>> >
>> > - Note that in this example, Exchange operator have a
>> > property KEEP_INPUT_AS_IS that indicates that data distribution is the
>> same
>> > as its input.
>> > Since we have redundant operators (as shown above, two aggregate
>> operators)
>> > one of the rules (not in this FLIP)
>> > adds this Exchange operator with KEEP_INPUT_AS_IS in between.
>> > Similar to my comment above, the end implementation will be except from
>> > redundant operators.
>> >
>> > In conjunction with question 2, I am wondering if we have a better
>> choice
>> >> (of course, not simply adding the current `PHYSICAL_OPT_RULES`'s
>> >> `RemoveRedundantLocalXXRule`s
>> >> to the `PHYSICAL_REWRITE`).
>> >> For example, let the source actively provide some traits (including
>> >> `FlinkRelDistribution`
>> >> and `RelCollation`) to the planner. The advantage of doing this is to
>> >> directly reuse the
>> >> current shuffle remove optimization (as `FlinkExpandConversionRule`
>> >> implemented),
>> >> and according to the data distribution characteristics provided by the
>> >> source, the planner
>> >> may choose a physical operator with a cheaper costs (for example,
>> according
>> >> to `RelCollation`,
>> >> the planner can use sortAgg, no need for a separate local sort
>> operation).
>> >> WDYT?
>> >
>> >
>> > - Good point. Makes sense to me. I will check FlinkExpandConversionRule
>> to
>> > be utilized in the implementation.
>> >
>> >
>> > Regards,
>> > Jeyhun
>> >
>> >
>> >
>> > On Tue, Apr 2, 2024 at 6:01 PM Lincoln Lee <lincoln.8...@gmail.com>
>> wrote:
>> >
>> >> Hi Jeyhun,
>> >>
>> >> Thank you for driving this, it would be very useful optimization!
>> >>
>> >> Sorry for joining the discussion now(I originally planned to reply
>> earlier,
>> >> but
>> >> happened to be during my vacation). I have two questions:
>> >>
>> >> 1. Is this flip targeted only at batch scenarios or does it include
>> >> streaming?
>> >> (The flip and the discussion did not explicitly mention this, but in
>> the
>> >> draft pr, I only
>> >> saw the implementation for batch scenarios
>> >>
>> >>
>> https://github.com/apache/flink/pull/24437/files#diff-a6d71dd7d9bf0e7776404f54473b504e1de1240e93f820214fa5d1f082fb30c8
>> >> <
>> >>
>> https://github.com/apache/flink/pull/24437/files#diff-a6d71dd7d9bf0e7776404f54473b504e1de1240e93f820214fa5d1f082fb30c8%EF%BC%89
>> >>>
>> >> )
>> >> If we expect this also apply to streaming, then we need to consider the
>> >> stricter
>> >> shuffle restrictions of streaming compared to batch (if support is
>> >> considered,
>> >> more discussion is needed here, let’s not expand for now). If it only
>> >> applies to batch,
>> >> it is recommended to clarify in the flip.
>> >>
>> >> 2. In the current implementation, the optimized plan seems to have some
>> >> problems.
>> >> As described in the class comments:
>> >>
>> >>
>> https://github.com/apache/flink/blob/d6e3b51fdb9a2e565709e8d7bc619234b3768ed1/flink-table/flink-table-planner/src/main/java/org/apache/flink/table/planner/plan/rules/physical/batch/RemoveRedundantShuffleRule.java#L60
>> >>
>> >> BatchPhysicalHashAggregate (local)
>> >>
>> >> +- BatchPhysicalLocalHashAggregate (local)
>> >>
>> >> +- BatchPhysicalTableSourceScan
>> >> The `BatchPhysicalLocalHashAggregate` here is redundant (in the case of
>> >> one-phase
>> >> hashAgg, localAgg is not necessary, which is the scenario currently
>> handled
>> >> by
>> >> `RemoveRedundantLocalHashAggRule` and other rules). Also, in the
>> draft pr,
>> >> the
>> >> optimization of `testShouldEliminatePartitioning1` &
>> >> `testShouldEliminatePartitioning2`
>> >> seems didn't take effect?
>> >>
>> >>
>> https://github.com/apache/flink/blob/d6e3b51fdb9a2e565709e8d7bc619234b3768ed1/flink-table/flink-table-planner/src/test/resources/org/apache/flink/connector/file/table/BatchFileSystemTableSourceTest.xml#L38
>> >>
>> >> In conjunction with question 2, I am wondering if we have a better
>> choice
>> >> (of course,
>> >> not simply adding the current `PHYSICAL_OPT_RULES`'s
>> >> `RemoveRedundantLocalXXRule`s
>> >> to the `PHYSICAL_REWRITE`).
>> >> For example, let the source actively provide some traits (including
>> >> `FlinkRelDistribution`
>> >> and `RelCollation`) to the planner. The advantage of doing this is to
>> >> directly reuse the
>> >> current shuffle remove optimization (as `FlinkExpandConversionRule`
>> >> implemented),
>> >> and according to the data distribution characteristics provided by the
>> >> source, the planner
>> >> may choose a physical operator with a cheaper costs (for example,
>> according
>> >> to `RelCollation`,
>> >> the planner can use sortAgg, no need for a separate local sort
>> operation).
>> >> WDYT?
>> >>
>> >>
>> >> Best,
>> >> Lincoln Lee
>> >>
>> >>
>> >> Jeyhun Karimov <je.kari...@gmail.com> 于2024年4月1日周一 18:00写道：
>> >>
>> >>> Hi everyone,
>> >>>
>> >>> Thanks for your valuable feedback!
>> >>>
>> >>> The discussion on this FLIP has been going on for a while.
>> >>> I would like to start a vote after 48 hours.
>> >>>
>> >>> Please let me know if you have any concerns or any further
>> >>> questions/comments.
>> >>>
>> >>> Regards,
>> >>> Jeyhun
>> >>>
>> >>>
>> >>> On Thu, Mar 21, 2024 at 6:01 PM Jeyhun Karimov <je.kari...@gmail.com>
>> >>> wrote:
>> >>>
>> >>>> Hi Lorenzo,
>> >>>>
>> >>>> Thanks a lot for your comments. Please find my answers below:
>> >>>>
>> >>>>
>> >>>> For the interface `SupportsPartitioning`, why returning `Optional`?
>> >>>>> If one decides to implement that, partitions must exist (at maximum,
>> >>>>> return and empty list). Returning `Optional` seem just to complicate
>> >> the
>> >>>>> logic of the code using that interface.
>> >>>>
>> >>>>
>> >>>> - The reasoning behind the use of Optional is that sometimes (e.g.,
>> in
>> >>>> HiveTableSource) the partitioning info is in catalog.
>> >>>> Therefore, we return Optional.empty(), so that the list of
>> partitions
>> >>> is
>> >>>> queried from the catalog.
>> >>>>
>> >>>>
>> >>>> I foresee the using code doing something like: "if the source
>> supports
>> >>>>> partitioning, get the partitions, but if they don't exist, raise a
>> >>> runtime
>> >>>>> exception". Let's simply make that safe at compile time and
>> guarantee
>> >>> the
>> >>>>> code that partitions exist.
>> >>>>
>> >>>>
>> >>>> - Yes, once partitions cannot be found, neither from catalog nor from
>> >> the
>> >>>> interface implementation, then we raise an exception during query
>> >> compile
>> >>>> time.
>> >>>>
>> >>>>
>> >>>> Another thing is that you show Hive-like partitioning in your FS
>> >>>>> structure, do you think it makes sense to add a note about
>> >>> auto-discovery
>> >>>>> of partitions?
>> >>>>
>> >>>>
>> >>>> - Yes, the FLIP contains just an example partitioning for filesystem
>> >>>> connector. Each connector already "knows" about autodiscovery of its
>> >>>> partitions. And we rely on this fact.
>> >>>> For example, partition discovery is different between kafka and
>> >>>> filesystem sources. So, we do not handle the manual discovery of
>> >>>> partitions. Please correct me if I misunderstood your question.
>> >>>>
>> >>>>
>> >>>> In other terms, it looks a bit counterintuitive that the user
>> >>> implementing
>> >>>>> the source has to specify which partitions exist statically (and
>> they
>> >>> can
>> >>>>> change at runtime), while the source itself knows the data provider
>> >> and
>> >>> can
>> >>>>> directly implement a method `discoverPartitions`. Then Flink would
>> >> take
>> >>>>> care of invoking that method when needed.
>> >>>>
>> >>>>
>> >>>> We utilize table option SOURCE_MONITOR_INTERVAL to check whether
>> >>>> partitions are static or not. So, a user still should give Flink a
>> hint
>> >>>> about partitions being static or not. With static partitions Flink
>> can
>> >> do
>> >>>> more optimizations.
>> >>>>
>> >>>> Please let me know if my replies answer your questions or if you have
>> >>> more
>> >>>> comments.
>> >>>>
>> >>>> Regards,
>> >>>> Jeyhun
>> >>>>
>> >>>>
>> >>>>
>> >>>> On Thu, Mar 21, 2024 at 10:03 AM <lorenzo.affe...@ververica.com>
>> >> wrote:
>> >>>>
>> >>>>> Hello Jeyhun,
>> >>>>> I really like the proposal and definitely makes sense to me.
>> >>>>>
>> >>>>> I have a couple of nits here and there:
>> >>>>>
>> >>>>> For the interface `SupportsPartitioning`, why returning `Optional`?
>> >>>>> If one decides to implement that, partitions must exist (at maximum,
>> >>>>> return and empty list). Returning `Optional` seem just to complicate
>> >> the
>> >>>>> logic of the code using that interface.
>> >>>>>
>> >>>>> I foresee the using code doing something like: "if the source
>> supports
>> >>>>> partitioning, get the partitions, but if they don't exist, raise a
>> >>> runtime
>> >>>>> exception". Let's simply make that safe at compile time and
>> guarantee
>> >>> the
>> >>>>> code that partitions exist.
>> >>>>>
>> >>>>> Another thing is that you show Hive-like partitioning in your FS
>> >>>>> structure, do you think it makes sense to add a note about
>> >>> auto-discovery
>> >>>>> of partitions?
>> >>>>>
>> >>>>> In other terms, it looks a bit counterintuitive that the user
>> >>>>> implementing the source has to specify which partitions exist
>> >> statically
>> >>>>> (and they can change at runtime), while the source itself knows the
>> >> data
>> >>>>> provider and can directly implement a method `discoverPartitions`.
>> >> Then
>> >>>>> Flink would take care of invoking that method when needed.
>> >>>>> On Mar 15, 2024 at 22:09 +0100, Jeyhun Karimov <
>> je.kari...@gmail.com
>> >>> ,
>> >>>>> wrote:
>> >>>>>
>> >>>>> Hi Benchao,
>> >>>>>
>> >>>>> Thanks for your comments.
>> >>>>>
>> >>>>> 1. What the parallelism would you take? E.g., 128 + 256 => 128? What
>> >>>>>
>> >>>>> if we cannot have a good greatest common divisor, like 127 + 128,
>> >>>>> could we just utilize one side's pre-partitioned attribute, and let
>> >>>>> another side just do the shuffle?
>> >>>>>
>> >>>>>
>> >>>>>
>> >>>>> There are two cases we need to consider:
>> >>>>>
>> >>>>> 1. Static Partition (no partitions are added during the query
>> >> execution)
>> >>>>> is
>> >>>>> enabled AND both sources implement "SupportsPartitionPushdown"
>> >>>>>
>> >>>>> In this case, we are sure that no new partitions will be added at
>> >>> runtime.
>> >>>>> So, we have a chance equalize both sources' partitions and
>> >> parallelism,
>> >>>>> IFF
>> >>>>> both sources implement "SupportsPartitionPushdown" interface.
>> >>>>> To achieve so, first we will fetch the existing partitions from
>> >> source1
>> >>>>> (say p_s1) and from source2 (say p_s2).
>> >>>>> Then, we find the intersection of these two partition sets (say
>> >>>>> p_intersect) and pushdown these partitions:
>> >>>>>
>> >>>>> SupportsPartitionPushDown::applyPartitions(p_intersect) // make sure
>> >>> that
>> >>>>> only specific partitions are read
>> >>>>> SupportsPartitioning::applyPartitionedRead(p_intersect) //
>> partitioned
>> >>>>> read
>> >>>>> with filtered partitions
>> >>>>>
>> >>>>> Lastly, we need to change the parallelism of 1) source1, 2) source2,
>> >> and
>> >>>>> 3)
>> >>>>> all of their downstream operators until (and including) their first
>> >>> common
>> >>>>> ancestor (e.g., join) to be equal to the number of partitions (size
>> of
>> >>>>> p_intersect).
>> >>>>>
>> >>>>> 2. All other cases
>> >>>>>
>> >>>>> In all other cases, the parallelism of both sources and their
>> >> downstream
>> >>>>> operators until their common ancestor would be equal to the
>> MIN(p_s1,
>> >>>>> p_s2).
>> >>>>> That is, minimum of the partition size of source1 and partition size
>> >> of
>> >>>>> source2 will be selected as the parallelism.
>> >>>>> Coming back to your example, if source1 parallelism is 127 and
>> source2
>> >>>>> parallelism is 128, then we will first check the partition size of
>> >>> source1
>> >>>>> and source2.
>> >>>>> Say partition size of source1 is 100 and partition size of source2
>> is
>> >>> 90.
>> >>>>> Then, we would set the parallelism for source1, source2, and all of
>> >>> their
>> >>>>> downstream operators until (and including) the join operator
>> >>>>> to 90 (min(100, 90)).
>> >>>>> We also plan to implement a cost based decision instead of the
>> >>> rule-based
>> >>>>> one (the ones explained above - MIN rule).
>> >>>>> One possible result of the cost based estimation is to keep the
>> >>> partitions
>> >>>>> on one side and perform the shuffling on another source.
>> >>>>>
>> >>>>>
>> >>>>>
>> >>>>> 2. In our current shuffle remove design (FlinkExpandConversionRule),
>> >>>>>
>> >>>>> we don't consider parallelism, we just remove unnecessary shuffles
>> >>>>> according to the distribution columns. After this FLIP, the
>> >>>>> parallelism may be bundled with source's partitions, then how will
>> >>>>> this optimization accommodate with FlinkExpandConversionRule, will
>> you
>> >>>>> also change downstream operator's parallelisms if we want to also
>> >>>>> remove subsequent shuffles?
>> >>>>>
>> >>>>>
>> >>>>>
>> >>>>>
>> >>>>> - From my understanding of FlinkExpandConversionRule, its removal
>> >> logic
>> >>> is
>> >>>>> agnostic to operator parallelism.
>> >>>>> So, if FlinkExpandConversionRule decides to remove a shuffle
>> >> operation,
>> >>>>> then this FLIP will search another possible shuffle (the one closest
>> >> to
>> >>>>> the
>> >>>>> source) to remove.
>> >>>>> If there is such an opportunity, this FLIP will remove the shuffle.
>> >> So,
>> >>>>> from my understanding FlinkExpandConversionRule and this
>> optimization
>> >>> rule
>> >>>>> can work together safely.
>> >>>>> Please correct me if I misunderstood your question.
>> >>>>>
>> >>>>>
>> >>>>>
>> >>>>> Regarding the new optimization rule, have you also considered to
>> allow
>> >>>>>
>> >>>>> some non-strict mode like FlinkRelDistribution#requireStrict? For
>> >>>>> example, source is pre-partitioned by a, b columns, if we are
>> >>>>> consuming this source, and do a aggregate on a, b, c, can we utilize
>> >>>>> this optimization?
>> >>>>>
>> >>>>>
>> >>>>>
>> >>>>> - Good point. Yes, there are some cases that non-strict mode will
>> >> apply.
>> >>>>> For example:
>> >>>>>
>> >>>>> - pre-partitioned columns and aggregate columns are the same but
>> have
>> >>>>> different order (e.g., source pre-partitioned w.r.t. a,b and
>> aggregate
>> >>> has
>> >>>>> a GROUP BY b,a)
>> >>>>> - columns in the Exchange operator is a list-prefix of
>> pre-partitoned
>> >>>>> columns of source (e.g., source is pre-partitioned w.r.t. a,b,c and
>> >>>>> Exchange's partition columns are a,b)
>> >>>>>
>> >>>>> Please let me know if the above answers your questions or if you
>> have
>> >>> any
>> >>>>> other comments.
>> >>>>>
>> >>>>> Regards,
>> >>>>> Jeyhun
>> >>>>>
>> >>>>> On Thu, Mar 14, 2024 at 12:48 PM Benchao Li <libenc...@apache.org>
>> >>> wrote:
>> >>>>>
>> >>>>> Thanks Jeyhun for bringing up this discussion, it is really exiting,
>> >>>>> +1 for the general idea.
>> >>>>>
>> >>>>> We also introduced a similar concept in Flink Batch internally to
>> cope
>> >>>>> with bucketed tables in Hive, it is a very important improvement.
>> >>>>>
>> >>>>>> One thing to note is that for join queries, the parallelism of each
>> >>> join
>> >>>>>> source might be different. This might result in
>> >>>>>> inconsistencies while using the pre-partitioned/pre-divided data
>> >>> (e.g.,
>> >>>>>> different mappings of partitions to source operators).
>> >>>>>> Therefore, it is the job of planner to detect this and adjust the
>> >>>>>> parallelism. With that having in mind,
>> >>>>>> the rest (how the split assigners perform) is consistent among many
>> >>>>>> sources.
>> >>>>>
>> >>>>>
>> >>>>> Could you elaborate a little more on this. I added my two cents here
>> >>>>> about this part:
>> >>>>> 1. What the parallelism would you take? E.g., 128 + 256 => 128? What
>> >>>>> if we cannot have a good greatest common divisor, like 127 + 128,
>> >>>>> could we just utilize one side's pre-partitioned attribute, and let
>> >>>>> another side just do the shuffle?
>> >>>>> 2. In our current shuffle remove design (FlinkExpandConversionRule),
>> >>>>> we don't consider parallelism, we just remove unnecessary shuffles
>> >>>>> according to the distribution columns. After this FLIP, the
>> >>>>> parallelism may be bundled with source's partitions, then how will
>> >>>>> this optimization accommodate with FlinkExpandConversionRule, will
>> you
>> >>>>> also change downstream operator's parallelisms if we want to also
>> >>>>> remove subsequent shuffles?
>> >>>>>
>> >>>>>
>> >>>>> Regarding the new optimization rule, have you also considered to
>> allow
>> >>>>> some non-strict mode like FlinkRelDistribution#requireStrict? For
>> >>>>> example, source is pre-partitioned by a, b columns, if we are
>> >>>>> consuming this source, and do a aggregate on a, b, c, can we utilize
>> >>>>> this optimization?
>> >>>>>
>> >>>>> Jane Chan <qingyue....@gmail.com> 于2024年3月14日周四 15:24写道：
>> >>>>>
>> >>>>>>
>> >>>>>> Hi Jeyhun,
>> >>>>>>
>> >>>>>> Thanks for your clarification.
>> >>>>>>
>> >>>>>
>> >>>>>>> Once a new partition is detected, we add it to our existing
>> >> mapping.
>> >>>>>
>> >>>>> Our
>> >>>>>
>> >>>>>> mapping looks like Map<Integer, Set<Integer>>
>> >>>>>
>> >>>>> subtaskToPartitionAssignment,
>> >>>>>
>> >>>>>> where it maps each source subtaskID to zero or more partitions.
>> >>>>>>
>> >>>>>> I understand your point. **It would be better if you could sync the
>> >>>>>> content to the FLIP**.
>> >>>>>>
>> >>>>>> Another thing is I'm curious about what the physical plan looks
>> >> like.
>> >>> Is
>> >>>>>> there any specific info that will be added to the table source
>> (like
>> >>>>>> filter/project pushdown)? It would be great if you could attach an
>> >>>>>
>> >>>>> example
>> >>>>>
>> >>>>>> to the FLIP.
>> >>>>>>
>> >>>>>> Bests,
>> >>>>>> Jane
>> >>>>>>
>> >>>>>> On Wed, Mar 13, 2024 at 9:11 PM Jeyhun Karimov <
>> >> je.kari...@gmail.com>
>> >>>>>
>> >>>>> wrote:
>> >>>>>
>> >>>>>>
>> >>>>>
>> >>>>>>> Hi Jane,
>> >>>>>>>
>> >>>>>>> Thanks for your comments.
>> >>>>>>>
>> >>>>>>>
>> >>>>>>> 1. Concerning the `sourcePartitions()` method, the partition
>> >>>>>
>> >>>>> information
>> >>>>>
>> >>>>>>>> returned during the optimization phase may not be the same as
>> >> the
>> >>>>>
>> >>>>>>> partition
>> >>>>>
>> >>>>>>>> information during runtime execution. For long-running jobs,
>> >>>>>
>> >>>>> partitions
>> >>>>>
>> >>>>>>> may
>> >>>>>
>> >>>>>>>> be continuously created. Is this FLIP equipped to handle
>> >>> scenarios?
>> >>>>>
>> >>>>>>>
>> >>>>>>>
>> >>>>>>> - Good point. This scenario is definitely supported.
>> >>>>>>> Once a new partition is added, or in general, new splits are
>> >>>>>>> discovered,
>> >>>>>>> PartitionAwareSplitAssigner::addSplits(Collection<FileSourceSplit>
>> >>>>>>> newSplits)
>> >>>>>>> method will be called. Inside that method, we are able to detect
>> >> if
>> >>> a
>> >>>>>
>> >>>>> split
>> >>>>>
>> >>>>>>> belongs to existing partitions or there is a new partition.
>> >>>>>>> Once a new partition is detected, we add it to our existing
>> >> mapping.
>> >>>>>
>> >>>>> Our
>> >>>>>
>> >>>>>>> mapping looks like Map<Integer, Set<Integer>>
>> >>>>>
>> >>>>> subtaskToPartitionAssignment,
>> >>>>>
>> >>>>>>> where
>> >>>>>>> it maps each source subtaskID to zero or more partitions.
>> >>>>>>>
>> >>>>>>> 2. Regarding the `RemoveRedundantShuffleRule` optimization rule, I
>> >>>>>
>> >>>>>>>> understand that it is also necessary to verify whether the hash
>> >>> key
>> >>>>>
>> >>>>>>> within
>> >>>>>
>> >>>>>>>> the Exchange node is consistent with the partition key defined
>> >> in
>> >>>>> the
>> >>>>>
>> >>>>>>> table
>> >>>>>
>> >>>>>>>> source that implements `SupportsPartitioning`.
>> >>>>>
>> >>>>>>>
>> >>>>>>>
>> >>>>>>> - Yes, I overlooked that point, fixed. Actually, the rule is much
>> >>>>>>> complicated. I tried to simplify it in the FLIP. Good point.
>> >>>>>>>
>> >>>>>>>
>> >>>>>>> 3. Could you elaborate on the desired physical plan and
>> >> integration
>> >>>>>
>> >>>>> with
>> >>>>>
>> >>>>>>>> `CompiledPlan` to enhance the overall functionality?
>> >>>>>
>> >>>>>>>
>> >>>>>>>
>> >>>>>>> - For compiled plan, PartitioningSpec will be used, with a json
>> >> tag
>> >>>>>>> "Partitioning". As a result, in the compiled plan, the source
>> >>> operator
>> >>>>>
>> >>>>> will
>> >>>>>
>> >>>>>>> have
>> >>>>>>> "abilities" : [ { "type" : "Partitioning" } ] as part of the
>> >>> compiled
>> >>>>>
>> >>>>> plan.
>> >>>>>
>> >>>>>>> More about the implementation details below:
>> >>>>>>>
>> >>>>>>> --------------------------------
>> >>>>>>> PartitioningSpec class
>> >>>>>>> --------------------------------
>> >>>>>>> @JsonTypeName("Partitioning")
>> >>>>>>> public final class PartitioningSpec extends SourceAbilitySpecBase
>> >> {
>> >>>>>>> // some code here
>> >>>>>>> @Override
>> >>>>>>> public void apply(DynamicTableSource tableSource,
>> >>>>>
>> >>>>> SourceAbilityContext
>> >>>>>
>> >>>>>>> context) {
>> >>>>>>> if (tableSource instanceof SupportsPartitioning) {
>> >>>>>>> ((SupportsPartitioning<?>)
>> >>>>>
>> >>>>> tableSource).applyPartitionedRead();
>> >>>>>
>> >>>>>>> } else {
>> >>>>>>> throw new TableException(
>> >>>>>>> String.format(
>> >>>>>>> "%s does not support
>> >>>>>
>> >>>>> SupportsPartitioning.",
>> >>>>>
>> >>>>>>> tableSource.getClass().getName()));
>> >>>>>>> }
>> >>>>>>> }
>> >>>>>>> // some code here
>> >>>>>>> }
>> >>>>>>>
>> >>>>>>> --------------------------------
>> >>>>>>> SourceAbilitySpec class
>> >>>>>>> --------------------------------
>> >>>>>>> @JsonTypeInfo(use = JsonTypeInfo.Id.NAME, include =
>> >>>>>>> JsonTypeInfo.As.PROPERTY, property = "type")
>> >>>>>>> @JsonSubTypes({
>> >>>>>>> @JsonSubTypes.Type(value = FilterPushDownSpec.class),
>> >>>>>>> @JsonSubTypes.Type(value = LimitPushDownSpec.class),
>> >>>>>>> @JsonSubTypes.Type(value = PartitionPushDownSpec.class),
>> >>>>>>> @JsonSubTypes.Type(value = ProjectPushDownSpec.class),
>> >>>>>>> @JsonSubTypes.Type(value = ReadingMetadataSpec.class),
>> >>>>>>> @JsonSubTypes.Type(value = WatermarkPushDownSpec.class),
>> >>>>>>> @JsonSubTypes.Type(value = SourceWatermarkSpec.class),
>> >>>>>>> @JsonSubTypes.Type(value = AggregatePushDownSpec.class),
>> >>>>>>> + @JsonSubTypes.Type(value = PartitioningSpec.class)
>> >>>>>
>> >>>>> //
>> >>>>>
>> >>>>>>> new added
>> >>>>>>>
>> >>>>>>>
>> >>>>>>>
>> >>>>>>> Please let me know if that answers your questions or if you have
>> >>> other
>> >>>>>>> comments.
>> >>>>>>>
>> >>>>>>> Regards,
>> >>>>>>> Jeyhun
>> >>>>>>>
>> >>>>>>>
>> >>>>>>> On Tue, Mar 12, 2024 at 8:56 AM Jane Chan <qingyue....@gmail.com>
>> >>>>>
>> >>>>> wrote:
>> >>>>>
>> >>>>>>>
>> >>>>>
>> >>>>>>>> Hi Jeyhun,
>> >>>>>>>>
>> >>>>>>>> Thank you for leading the discussion. I'm generally +1 with this
>> >>>>>
>> >>>>>>> proposal,
>> >>>>>
>> >>>>>>>> along with some questions. Please see my comments below.
>> >>>>>>>>
>> >>>>>>>> 1. Concerning the `sourcePartitions()` method, the partition
>> >>>>>
>> >>>>> information
>> >>>>>
>> >>>>>>>> returned during the optimization phase may not be the same as
>> >> the
>> >>>>>
>> >>>>>>> partition
>> >>>>>
>> >>>>>>>> information during runtime execution. For long-running jobs,
>> >>>>>
>> >>>>> partitions
>> >>>>>
>> >>>>>>> may
>> >>>>>
>> >>>>>>>> be continuously created. Is this FLIP equipped to handle
>> >>> scenarios?
>> >>>>>>>>
>> >>>>>>>> 2. Regarding the `RemoveRedundantShuffleRule` optimization
>> >> rule, I
>> >>>>>>>> understand that it is also necessary to verify whether the hash
>> >>> key
>> >>>>>
>> >>>>>>> within
>> >>>>>
>> >>>>>>>> the Exchange node is consistent with the partition key defined
>> >> in
>> >>>>> the
>> >>>>>
>> >>>>>>> table
>> >>>>>
>> >>>>>>>> source that implements `SupportsPartitioning`.
>> >>>>>>>>
>> >>>>>>>> 3. Could you elaborate on the desired physical plan and
>> >>> integration
>> >>>>>
>> >>>>> with
>> >>>>>
>> >>>>>>>> `CompiledPlan` to enhance the overall functionality?
>> >>>>>>>>
>> >>>>>>>> Best,
>> >>>>>>>> Jane
>> >>>>>>>>
>> >>>>>>>> On Tue, Mar 12, 2024 at 11:11 AM Jim Hughes
>> >>>>>
>> >>>>> <jhug...@confluent.io.invalid
>> >>>>>
>> >>>>>>>>
>> >>>>>>>> wrote:
>> >>>>>>>>
>> >>>>>
>> >>>>>>>>> Hi Jeyhun,
>> >>>>>>>>>
>> >>>>>>>>> I like the idea! Given FLIP-376[1], I wonder if it'd make
>> >> sense
>> >>> to
>> >>>>>>>>> generalize FLIP-434 to be about "pre-divided" data to cover
>> >>>>>
>> >>>>> "buckets"
>> >>>>>
>> >>>>>>> and
>> >>>>>
>> >>>>>>>>> "partitions" (and maybe even situations where a data source is
>> >>>>>
>> >>>>>>>> partitioned
>> >>>>>
>> >>>>>>>>> and bucketed).
>> >>>>>>>>>
>> >>>>>>>>> Separate from that, the page mentions TPC-H Q1 as an example.
>> >>> For
>> >>>>>
>> >>>>> a
>> >>>>>
>> >>>>>>>> join,
>> >>>>>
>> >>>>>>>>> any two tables joined on the same bucket key should provide a
>> >>>>>
>> >>>>> concrete
>> >>>>>
>> >>>>>>>>> example of a join. Systems like Kafka Streams/ksqlDB call this
>> >>>>>>>>> "co-partitioning"; for those systems, it is a requirement
>> >> placed
>> >>>>>
>> >>>>> on the
>> >>>>>
>> >>>>>>>>> input sources. For Flink, with FLIP-434, the proposed planner
>> >>> rule
>> >>>>>>>>> could remove the shuffle.
>> >>>>>>>>>
>> >>>>>>>>> Definitely a fun idea; I look forward to hearing more!
>> >>>>>>>>>
>> >>>>>>>>> Cheers,
>> >>>>>>>>>
>> >>>>>>>>> Jim
>> >>>>>>>>>
>> >>>>>>>>>
>> >>>>>>>>> 1.
>> >>>>>>>>>
>> >>>>>>>>>
>> >>>>>
>> >>>>>>>>
>> >>>>>
>> >>>>>>>
>> >>>>>
>> >>>>>
>> >>>>>
>> >>>
>> >>
>> https://cwiki.apache.org/confluence/display/FLINK/FLIP-376%3A+Add+DISTRIBUTED+BY+clause
>> >>>>>
>> >>>>>>>>> 2.
>> >>>>>>>>>
>> >>>>>>>>>
>> >>>>>
>> >>>>>>>>
>> >>>>>
>> >>>>>>>
>> >>>>>
>> >>>>>
>> >>>>>
>> >>>
>> >>
>> https://docs.ksqldb.io/en/latest/developer-guide/joins/partition-data/#co-partitioning-requirements
>> >>>>>
>> >>>>>>>>>
>> >>>>>>>>> On Sun, Mar 10, 2024 at 3:38 PM Jeyhun Karimov <
>> >>>>>
>> >>>>> je.kari...@gmail.com>
>> >>>>>
>> >>>>>>>>> wrote:
>> >>>>>>>>>
>> >>>>>
>> >>>>>>>>>> Hi devs,
>> >>>>>>>>>>
>> >>>>>>>>>> I’d like to start a discussion on FLIP-434: Support
>> >>>>>
>> >>>>> optimizations for
>> >>>>>
>> >>>>>>>>>> pre-partitioned data sources [1].
>> >>>>>>>>>>
>> >>>>>>>>>> The FLIP introduces taking advantage of pre-partitioned data
>> >>>>>
>> >>>>> sources
>> >>>>>
>> >>>>>>>> for
>> >>>>>
>> >>>>>>>>>> SQL/Table API (it is already supported as experimental
>> >> feature
>> >>>>> in
>> >>>>>>>>>> DataStream API [2]).
>> >>>>>>>>>>
>> >>>>>>>>>>
>> >>>>>>>>>> Please find more details in the FLIP wiki document [1].
>> >>>>>>>>>> Looking forward to your feedback.
>> >>>>>>>>>>
>> >>>>>>>>>> Regards,
>> >>>>>>>>>> Jeyhun
>> >>>>>>>>>>
>> >>>>>>>>>> [1]
>> >>>>>>>>>>
>> >>>>>>>>>>
>> >>>>>
>> >>>>>>>>>
>> >>>>>
>> >>>>>>>>
>> >>>>>
>> >>>>>>>
>> >>>>>
>> >>>>>
>> >>>>>
>> >>>
>> >>
>> https://cwiki.apache.org/confluence/display/FLINK/FLIP-434%3A+Support+optimizations+for+pre-partitioned+data+sources
>> >>>>>
>> >>>>>>>>>> [2]
>> >>>>>>>>>>
>> >>>>>>>>>>
>> >>>>>
>> >>>>>>>>>
>> >>>>>
>> >>>>>>>>
>> >>>>>
>> >>>>>>>
>> >>>>>
>> >>>>>
>> >>>>>
>> >>>
>> >>
>> https://nightlies.apache.org/flink/flink-docs-master/docs/dev/datastream/experimental/
>> >>>>>
>> >>>>>>>>>>
>> >>>>>
>> >>>>>>>>>
>> >>>>>
>> >>>>>>>>
>> >>>>>
>> >>>>>>>
>> >>>>>
>> >>>>>
>> >>>>>
>> >>>>>
>> >>>>> --
>> >>>>>
>> >>>>> Best,
>> >>>>> Benchao Li
>> >>>>>
>> >>>>>
>> >>>
>> >>
>>
>>
