Ad. 1 Yes, you are right in principle.
Let me though clarify my proposal a bit. The proposed sort-style
execution aims at a generic KeyedProcessFunction were all the
"aggregations" are actually performed in the user code. It tries to
improve the performance by actually removing the need to use RocksDB e.g.:
private static final class Summer<K>
extends KeyedProcessFunction<K, Tuple2<K, Integer>,
Tuple2<K, Integer>> {
....
@Override
public void processElement(
Tuple2<K, Integer> value,
Context ctx,
Collector<Tuple2<K, Integer>> out) throws Exception {
if (!Objects.equals(timerRegistered.value(), Boolean.TRUE)) {
ctx.timerService().registerEventTimeTimer(Long.MAX_VALUE);
timerRegistered.update(true);
}
Integer v = counter.value();
Integer incomingValue = value.f1;
if (v != null) {
v += incomingValue;
} else {
v = incomingValue;
}
counter.update(v);
}
....
}
Therefore I don't think the first part of your reply with separating the
write and read workload applies here. We do not aim to create a
competing API with the Table API. We think operations such as joins or
analytical aggregations should be performed in Table API.
As for the second part I agree it would be nice to fall back to the
sorting approach only if a certain threshold of memory in a State
Backend is used. This has some problems though. We would need a way to
estimate the size of the occupied memory to tell when the threshold is
reached. That is not easily doable by default e.g. in a
MemoryStateBackend, as we do not serialize the values in the state
backend by default. We would have to add that, but this would add the
overhead of the serialization.
This proposal aims at the cases where we do have a large state that will
not fit into the memory and without the change users are forced to use
RocksDB. If the state fits in memory I agree it will be better to do
hash-based aggregations e.g. using the MemoryStateBackend. Therefore I
think it is important to give users the choice to use one or the other
approach. We might discuss which approach should be the default for
RuntimeMode.BATCH proposed in FLIP-134. Should it be hash-based with
user configured state backend or sorting-based with a single key at a
time backend. Moreover we could think if we should let users choose the
sort vs hash "state backend" per operator. Would that suffice?
Ad. 2
I still think we can just use the first X bytes of the serialized form
as the normalized key and fallback to comparing full keys on clashes. It
is because we are actually not interested in a logical order, but we
care only about the "grouping" aspect of the sorting. Therefore I think
its enough to compare only parts of the full key as the normalized key.
Thanks again for the really nice and thorough feedback!
Best,
Dawid
On 08/09/2020 14:47, Kurt Young wrote:
> Regarding #1, yes the state backend is definitely hash-based execution.
> However there are some differences between
> batch hash-based execution. The key difference is *random access &
> read/write mixed workload". For example, by using
> state backend in streaming execution, one have to mix the read and write
> operations and all of them are actually random
> access. But in a batch hash execution, we could divide the phases into
> write and read. For example, we can build the
> hash table first, with only write operations. And once the build is done,
> we can start to read and trigger the user codes.
> Take hash aggregation which blink planner implemented as an example, during
> building phase, as long as the hash map
> could fit into memory, we will update the accumulators directly in the hash
> map. And once we are running out of memory,
> we then fall back to sort based execution. It improves the performance a
> lot if the incoming data can be processed in
> memory.
>
> Regarding #2, IIUC you are actually describing a binary format of key, not
> normalized key which is used in DataSet. I will
> take String for example. If we have lots of keys with length all greater
> than, let's say 20. In your proposal, you will encode
> the whole string in the prefix of your composed data ( <key> + <timestamp>
> + <record> ). And when you compare
> records, you will actually compare the *whole* key of the record. For
> normalized key, it's fixed-length in this case, IIRC it will
> take 8 bytes to represent the string. And the sorter will store the
> normalized key and offset in a dedicated array. When doing
> the sorting, it only sorts this *small* array. If the normalized keys are
> different, you could immediately tell which is greater from
> normalized keys. You only have to compare the full keys if the normalized
> keys are equal and you know in this case the normalized
> key couldn't represent the full key. The reason why Dataset is doing this
> is it's super cache efficient by sorting the *small* array.
> The idea is borrowed from this paper [1]. Let me know if I missed or
> misunderstood anything.
>
> [1] https://dl.acm.org/doi/10.5555/615232.615237 (AlphaSort: a
> cache-sensitive parallel external sort)
>
> Best,
> Kurt
>
>
> On Tue, Sep 8, 2020 at 5:05 PM Dawid Wysakowicz <dwysakow...@apache.org>
> wrote:
>
>> Hey Kurt,
>>
>> Thank you for comments!
>>
>> Ad. 1 I might have missed something here, but as far as I see it is that
>> using the current execution stack with regular state backends (RocksDB
>> in particular if we want to have spilling capabilities) is equivalent to
>> hash-based execution. I can see a different spilling state backend
>> implementation in the future, but I think it is not batch specifc. Or am
>> I missing something?
>>
>> Ad. 2 Totally agree that normalized keys are important to the
>> performance. I think though TypeComparators are not a necessity to have
>> that. Actually this proposal is heading towards only ever performing
>> "normalized keys" comparison. I have not included in the proposal the
>> binary format which we will use for sorting (partially because I forgot,
>> and partially because I thought it was too much of an implementation
>> detail). Let me include it here though, as it might clear the situation
>> a bit here.
>>
>> In DataSet, at times we have KeySelectors which extract keys based on
>> field indices or names. This allows in certain situation to extract the
>> key from serialized records. Compared to DataSet, in DataStream, the key
>> is always described with a black-box KeySelector, or differently with a
>> function which extracts a key from a deserialized record. In turn there
>> is no way to create a comparator that could compare records by
>> extracting the key from a serialized record (neither with, nor without
>> key normalization). We suggest that the input for the sorter will be
>>
>> <key> + <timestamp> + <record>
>>
>> Without having the key prepended we would have to deserialize the record
>> for every key comparison.
>>
>> Therefore if we agree that we perform binary comparison for keys (which
>> are always prepended), it is actually equivalent to a DataSet with
>> TypeComparators that support key normalization.
>>
>> Let me know if that is clear, or I have missed something here.
>>
>> Best,
>>
>> Dawid
>>
>> On 08/09/2020 03:39, Kurt Young wrote:
>>> Hi Dawid, thanks for bringing this up, it's really exciting to see that
>>> batch execution is introduced in DataStream. From the flip, it seems
>>> we are sticking with sort based execution mode (at least for now), which
>>> will sort the whole input data before any *keyed* operation is
>>> executed. I have two comments here:
>>>
>>> 1. Do we want to introduce hash-based execution in the future? Sort is a
>>> safe choice but not the best in lots of cases. IIUC we only need
>>> to make sure that before the framework finishes dealing with one key, the
>>> operator doesn't see any data belonging to other keys, thus
>>> hash-based execution would also do the trick. Oon tricky thing the
>>> framework might need to deal with is memory constraint and spilling
>>> in the hash map, but Flink also has some good knowledge about these
>> stuff.
>>> 2. Going back to sort-based execution and how to sort keys. From my
>>> experience, the performance of sorting would be one the most important
>>> things if we want to achieve good performance of batch execution. And
>>> normalized keys are actually the key of the performance of sorting.
>>> If we want to get rid of TypeComparator, I think we still need to find a
>>> way to introduce this back.
>>>
>>> Best,
>>> Kurt
>>>
>>>
>>> On Tue, Sep 8, 2020 at 3:04 AM Aljoscha Krettek <aljos...@apache.org>
>> wrote:
>>>> Yes, I think we can address the problem of indeterminacy in a separate
>>>> FLIP because we're already in it.
>>>>
>>>> Aljoscha
>>>>
>>>> On 07.09.20 17:00, Dawid Wysakowicz wrote:
>>>>> @Seth That's a very good point. I agree that RocksDB has the same
>>>>> problem. I think we can use the same approach for the sorted shuffles
>>>>> then. @Aljoscha I agree we should think about making it more resilient,
>>>>> as I guess users might have problems already if they use keys with
>>>>> non-deterministic binary representation. How do you feel about
>>>>> addressing that separately purely to limit the scope of this FLIP?
>>>>>
>>>>> @Aljoscha I tend to agree with you that the best place to actually
>> place
>>>>> the sorting would be in the InputProcessor(s). If there are no more
>>>>> suggestions in respect to that issue. I'll put this proposal for
>> voting.
>>>>> @all Thank you for the feedback so far. I'd like to start a voting
>>>>> thread on the proposal tomorrow. Therefore I'd appreciate if you
>> comment
>>>>> before that, if you still have some outstanding ideas.
>>>>>
>>>>> Best,
>>>>>
>>>>> Dawid
>>>>>
>>>>> On 04/09/2020 17:13, Aljoscha Krettek wrote:
>>>>>> Seth is right, I was just about to write that as well. There is a
>>>>>> problem, though, because some of our TypeSerializers are not
>>>>>> deterministic even though we use them as if they were. Beam excludes
>>>>>> the FloatCoder, for example, and the AvroCoder in certain cases. I'm
>>>>>> pretty sure there is also weirdness going on in our KryoSerializer.
>>>>>>
>>>>>> On 04.09.20 14:59, Seth Wiesman wrote:
>>>>>>> There is already an implicit assumption the TypeSerializer for keys
>> is
>>>>>>> stable/deterministic, RocksDB compares keys using their serialized
>> byte
>>>>>>> strings. I think this is a non-issue (or at least it's not changing
>> the
>>>>>>> status quo).
>>>>>>>
>>>>>>> On Fri, Sep 4, 2020 at 6:39 AM Timo Walther <twal...@apache.org>
>>>> wrote:
>>>>>>>> +1 for getting rid of the TypeComparator interface and rely on the
>>>>>>>> serialized representation for grouping.
>>>>>>>>
>>>>>>>> Adding a new type to DataStream API is quite difficult at the moment
>>>>>>>> due
>>>>>>>> to too many components that are required: TypeInformation (tries to
>>>>>>>> deal
>>>>>>>> with logical fields for TypeComparators), TypeSerializer (incl. it's
>>>>>>>> snapshot interfaces), and TypeComparator (with many methods and
>>>>>>>> internals such normalized keys etc.).
>>>>>>>>
>>>>>>>> If necessary, we can add more simple comparison-related methods to
>> the
>>>>>>>> TypeSerializer interface itself in the future (like
>>>>>>>> TypeSerializer.isDeterministic).
>>>>>>>>
>>>>>>>> Regards,
>>>>>>>> Timo
>>>>>>>>
>>>>>>>>
>>>>>>>> On 04.09.20 11:48, Aljoscha Krettek wrote:
>>>>>>>>> Thanks for publishing the FLIP!
>>>>>>>>>
>>>>>>>>> On 2020/09/01 06:49:06, Dawid Wysakowicz <dwysakow...@apache.org>
>>>>>>>>> wrote:
>>>>>>>>>> 1. How to sort/group keys? What representation of the key
>>>>>>>>>> should we
>>>>>>>>>> use? Should we sort on the binary form or should we depend
>> on
>>>>>>>>>> Comparators being available.
>>>>>>>>> Initially, I suggested to Dawid (in private) to do the
>>>>>>>>> sorting/grouping
>>>>>>>> by using the binary representation. Then my opinion switched and I
>>>>>>>> thought
>>>>>>>> we should use TypeComparator/Comparator because that's what the
>>>>>>>> DataSet API
>>>>>>>> uses. After talking to Stephan, I'm again encouraged in my opinion
>>>>>>>> to use
>>>>>>>> the binary representation because it means we can eventually get rid
>>>>>>>> of the
>>>>>>>> TypeComparator interface, which is a bit complicated, and because we
>>>>>>>> don't
>>>>>>>> need any good order in our sort, we only need the grouping.
>>>>>>>>> This comes with some problems, though: we need to ensure that the
>>>>>>>> TypeSerializer of the type we're sorting is stable/deterministic.
>>>>>>>> Beam has
>>>>>>>> infrastructure for this in the form of Coder.verifyDeterministic()
>> [1]
>>>>>>>> which we don't have right now and should add if we go down this
>> path.
>>>>>>>>>> 2. Where in the stack should we apply the sorting (this
>> rather a
>>>>>>>>>> discussion about internals)
>>>>>>>>> Here, I'm gravitating towards the third option of implementing it
>>>>>>>>> in the
>>>>>>>> layer of the StreamTask, which probably means implementing a custom
>>>>>>>> InputProcessor. I think it's best to do it in this layer because we
>>>>>>>> would
>>>>>>>> not mix concerns of different layers as we would if we implemented
>>>>>>>> this as
>>>>>>>> a custom StreamOperator. I think this solution is also best when it
>>>>>>>> comes
>>>>>>>> to multi-input operators.
>>>>>>>>>> 3. How should we deal with custom implementations of
>>>>>>>>>> StreamOperators
>>>>>>>>> I think the cleanest solution would be to go through the complete
>>>>>>>> operator lifecycle for every key, because then the watermark would
>> not
>>>>>>>> oscillate between -Inf and +Inf and we would not break the
>> semantical
>>>>>>>> guarantees that we gave to operators so far, in that the watermark
>> is
>>>>>>>> strictly monotonically increasing. However, I don't think this
>>>>>>>> solution is
>>>>>>>> feasible because it would come with too much overhead. We should
>>>>>>>> solve this
>>>>>>>> problem via documentation and maybe educate people to not query the
>>>>>>>> current
>>>>>>>> watermark or not rely on the watermark being monotonically
>>>>>>>> increasing in
>>>>>>>> operator implementations to allow the framework more freedoms in how
>>>>>>>> user
>>>>>>>> programs are executed.
>>>>>>>>> Aljoscha
>>>>>>>>>
>>>>>>>>> [1]
>> https://github.com/apache/beam/blob/master/sdks/java/core/src/main/java/org/apache/beam/sdk/coders/Coder.java#L184
>>
signature.asc
Description: OpenPGP digital signature
