Hi,
thanks for the KIP Eno! Here are my 2 cents:
1) I like Guozhang's proposal about removing store name from all
KTable
methods and generate internal names (however, I would do this as
overloads). Furthermore, I would not force users to call
.materialize()
if they want to query a store, but add one more method
.stateStoreName()
that returns the store name if the KTable is materialized. Thus, also
.materialize() must not necessarily have a parameter storeName (ie,
we
should have some overloads here).
I would also not allow to provide a null store name (to indicate no
materialization if not necessary) but throw an exception.
This yields some simplification (see below).
2) I also like Guozhang's proposal about KStream#toTable()
3)
3. What will happen when you call materialize on KTable that is
already
materialized? Will it create another StateStore (providing the
name
is
different), throw an Exception?
Currently an exception is thrown, but see below.
If we follow approach (1) from Guozhang, there is no need to worry
about
a second materialization and also no exception must be throws. A
call to
.materialize() basically sets a "materialized flag" (ie, idempotent
operation) and sets a new name.
4)
Rename toStream() to toKStream() for consistency.
Not sure whether that is really required. We also use
`KStreamBuilder#stream()` and `KStreamBuilder#table()`, for example,
and
don't care about the "K" prefix.
Eno's reply:
I think changing it to `toKStream` would make it absolutely clear
what
we are converting it to.
I'd say we should probably change the KStreamBuilder methods (but
not
in
this KIP).
I would keep #toStream(). (see below)
5) We should not remove any methods but only deprecate them.
A general note:
I do not understand your comments "Rejected Alternatives". You say
"Have
the KTable be the materialized view" was rejected. But your KIP
actually
does exactly this -- the changelog abstraction of KTable is secondary
after those changes and the "view" abstraction is what a KTable is.
And
just to be clear, I like this a lot:
- it aligns with the name KTable
- is aligns with stream-table-duality
- it aligns with IQ
I would say that a KTable is a "view abstraction" (as
materialization is
optional).
-Matthias
On 1/22/17 5:05 PM, Guozhang Wang wrote:
Thanks for the KIP Eno, I have a few meta comments and a few
detailed
comments:
1. I like the materialize() function in general, but I would like to
see
how other KTable functions should be updated accordingly. For example,
1)
KStreamBuilder.table(..) has a state store name parameter, and we will
always materialize the KTable unless its state store name is set to
null;
2) KTable.agg requires the result KTable to be materialized, and hence
it
also have a state store name; 3) KTable.join requires the joining
table
to
be materialized. And today we do not actually have a mechanism to
enforce
that, but will only throw an exception at runtime if it is not (e.g.
if
you
have "builder.table("topic", null).join()" a RTE will be thrown).
I'd make an extended proposal just to kick off the discussion here:
let's
remove all the state store params in other KTable functions, and if in
some
cases KTable have to be materialized (e.g. KTable resulted from
KXX.agg)
and users do not call materialize(), then we treat it as "users are
not
interested in querying it at all" and hence use an internal name
generated
for the materialized KTable; i.e. although it is materialized the
state
store is not exposed to users. And if users call materialize()
afterwards
but we have already decided to materialize it, we can replace the
internal
name with the user's provided names. Then from a user's point-view,
if
they
ever want to query a KTable, they have to call materialize() with a
given
state store name. This approach has one awkwardness though, that
serdes
and
state store names param are not separated and could be overlapped
(see
detailed comment #2 below).
2. This step does not need to be included in this KIP, but just as a
reference / future work: as we have discussed before, we may enforce
materialize KTable.join resulted KTables as well in the future. If
we
do
that, then:
a) KXX.agg resulted KTables are always materialized;
b) KTable.agg requires the aggregating KTable to always be
materialized
(otherwise we would not know the old value);
c) KTable.join resulted KTables are always materialized, and so are
the
joining KTables to always be materialized.
d) KTable.filter/mapValues resulted KTables materialization depend
on
its
parent's materialization;
By recursive induction all KTables are actually always materialized,
and
then the effect of the "materialize()" is just for specifying the
state
store names. In this scenario, we do not need to send Change<V> in
repartition topics within joins any more, but only for repartitions
topics
within aggregations. Instead, we can just send a "tombstone" without
the
old value and we do not need to calculate joins twice (one more time
when
old value is received).
3. I'm wondering if it is worth-while to add a "KStream#toTable()"
function
which is interpreted as a dummy-aggregation where the new value
always
replaces the old value. I have seen a couple of use cases of this,
for
example, users want to read a changelog topic, apply some filters,
and
then
materialize it into a KTable with state stores without creating
duplicated
changelog topics. With materialize() and toTable I'd imagine users
can
specify sth. like:
"
KStream stream = builder.stream("topic1").filter(..);
KTable table = stream.toTable(..);
table.materialize("state1");
"
And the library in this case could set store "state1" 's changelog
topic
to
be "topic1", and applying the filter on the fly while (re-)storing
its
state by reading from this topic, instead of creating a second
changelog
topic like "appID-state1-changelog" which is a semi-duplicate of
"topic1".
Detailed:
1. I'm +1 with Michael regarding "#toStream"; actually I was
thinking
about
renaming to "#toChangeLog" but after thinking a bit more I think
#toStream
is still better, and we can just mention in the javaDoc that it is
transforming its underlying changelog stream to a normal stream.
2. As Damian mentioned, there are a few scenarios where the serdes
are
already specified in a previous operation whereas it is not known
before
calling materialize, for example:
stream.groupByKey.agg(serde).materialize(serde) v.s.
table.mapValues(/*no
serde specified*/).materialize(serde). We need to specify what are
the
handling logic here.
3. We can remove "KTable#to" call as well, and enforce users to
call "
KTable.toStream.to" to be more clear.
Guozhang
On Wed, Jan 18, 2017 at 3:22 AM, Eno Thereska <
eno.there...@gmail.com>
wrote:
I think changing it to `toKStream` would make it absolutely clear
what
we
are converting it to.
I'd say we should probably change the KStreamBuilder methods (but
not
in
this KIP).
Thanks
Eno
On 17 Jan 2017, at 13:59, Michael Noll <mich...@confluent.io>
wrote:
Rename toStream() to toKStream() for consistency.
Not sure whether that is really required. We also use
`KStreamBuilder#stream()` and `KStreamBuilder#table()`, for
example,
and
don't care about the "K" prefix.
On Tue, Jan 17, 2017 at 10:55 AM, Eno Thereska <
eno.there...@gmail.com
wrote:
Thanks Damian, answers inline:
On 16 Jan 2017, at 17:17, Damian Guy <damian....@gmail.com>
wrote:
Hi Eno,
Thanks for the KIP. Some comments:
1. I'd probably rename materialized to materialize.
Ok.
2. I don't think the addition of the new Log compaction mechanism
is
necessary for this KIP, i.e, the KIP is useful without it. Maybe
that
should be a different KIP?
Agreed, already removed. Will do a separate KIP for that.
3. What will happen when you call materialize on KTable that is
already
materialized? Will it create another StateStore (providing the
name
is
different), throw an Exception?
Currently an exception is thrown, but see below.
4. Have you considered overloading the existing KTable operations
to
add
a state store name? So if a state store name is provided, then
materialize
a state store? This would be my preferred approach as i don't
think
materialize is always a valid operation.
Ok I can see your point. This will increase the KIP size since
I'll
need
to enumerate all overloaded methods, but it's not a problem.
5. The materialize method will need ta value Serde as some
operations,
i.e., mapValues, join etc can change the value types
6. https://issues.apache.org/jira/browse/KAFKA-4609 - might
mean
that
we
always need to materialize the StateStore for KTable-KTable
joins.
If
that
is the case, then the KTable Join operators will also need Serde
information.
I'll update the KIP with the serdes.
Thanks
Eno
Cheers,
Damian
On Mon, 16 Jan 2017 at 16:44 Eno Thereska <
eno.there...@gmail.com>
wrote:
Hello,
We created "KIP-114: KTable materialization and improved
semantics"
to
solidify the KTable semantics in Kafka Streams:
https://cwiki.apache.org/confluence/display/KAFKA/KIP-
114%3A+KTable+materialization+and+improved+semantics
<
https://cwiki.apache.org/confluence/display/KAFKA/KIP-
114:+KTable+materialization+and+improved+semantics
Your feedback is appreciated.
Thanks
Eno
