Re: [DISCUSS] Resolve ambiguous parser rule between two "create table"s

[Jungtaek Lim]

Thanks Nicholas for the side comment; you'll need to interpret "CREATE
TABLE USING HIVE FORMAT" as CREATE TABLE using "HIVE FORMAT", but yes it
may add the confusion.

Ryan, thanks for the detailed analysis and proposal. That's what I would
like to see in discussion thread.

I'm open to solutions which enable end users to specify their intention
properly - my main concern of SPARK-30098 is that it becomes unclear which
provider the query will use in create table unless USING provider is
explicitly specified. If the new proposal makes clear on this, that should
be better than now.

Replying inline:

On Thu, Mar 19, 2020 at 11:06 AM Nicholas Chammas <
nicholas.cham...@gmail.com> wrote:

> Side comment: The current docs for CREATE TABLE
> 
> add to the confusion by describing the Hive-compatible command as "CREATE
> TABLE USING HIVE FORMAT", but neither "USING" nor "HIVE FORMAT" are
> actually part of the syntax
> 
> .
>
> On Wed, Mar 18, 2020 at 8:31 PM Ryan Blue 
> wrote:
>
>> Jungtaek, it sounds like you consider the two rules to be separate
>> syntaxes with their own consistency rules. For example, if I am using the
>> Hive syntax rule, then the PARTITIONED BY clause adds new (partition)
>> columns and requires types for those columns; if I’m using the Spark syntax
>> rule with USING then PARTITIONED BY must reference existing columns and
>> cannot include types.
>>
>> I agree that this is confusing to users! We should fix it, but I don’t
>> think the right solution is to continue to have two rules with divergent
>> syntax.
>>
>> This is confusing to users because they don’t know anything about
>> separate parser rules. All the user sees is that sometimes PARTITION BY
>> requires types and sometimes it doesn’t. Yes, we could add a keyword,
>> HIVE, to signal that the syntax is borrowed from Hive for that case, but
>> that actually breaks queries that run in Hive.
>>
> That might less matter, because SPARK-30098 (and I guess your proposal as
well) enforces end users to add "USING HIVE" for their queries to enable
Hive provider in any way, even only when the query matches with rule 1
(conditional). Once they decide to create Hive table, the query might have
to be changed, or they have to change the default provider, or they have to
enable legacy config.


> I think the right solution is to unify the two syntaxes. I don’t think
>> they are so different that it isn’t possible. Here are the differences I
>> see:
>>
>>- Only in Hive:
>>   - EXTERNAL
>>   - skewSpec: SKEWED BY ...
>>   - rowFormat: ROW FORMAT DELIMITED ..., ROW FORMAT SERDE ...
>>   - createFileFormat: STORED AS ...
>>- Only in Spark:
>>   - OPTIONS property list
>>- Different syntax/interpretation:
>>   - PARTITIONED BY transformList / PARTITIONED BY colTypeList
>>
>> ":" after column name is another one only supported in Hive, though
that's relatively minor to support it in unified syntax.

>
>>-
>>
>> For the clauses that are supported in one but not the other, we can add
>> them to a unified rule as optional clauses. The AST builder would then
>> validate what makes sense or not (e.g., stored as with using or row format
>> delimited) and finally pass the remaining data on using the
>> CreateTableStatement. That statement would be handled like we do for the
>> Spark rule today, but with extra metadata to pass along. This is also a
>> step toward being able to put Hive behind the DSv2 API because we’d be able
>> to pass all of the Hive metadata clauses to the v2 catalog.
>>
>> The only difficult part is handling PARTITIONED BY. But in that case, we
>> can use two different syntaxes from the same CREATE TABLE rule. If types
>> are included, we use the Hive PARTITIONED BY syntax and convert in the
>> AST builder to normalize to a single representation.
>>
> The proposal looks promising - it may add some sort of complexity but
sounds like worth adding.

One thing to make clear - in unified syntax we only rely on explicit
provider, or default provider, right? I would concern if the proposal
automatically uses Hive provider if Hive specific clauses are being used.
Yes as I said earlier it may make end users' query to be changed, but
better than uncertain.

Btw, if the main purpose to add native syntax and change it by default is
to discontinue supporting Hive create table rule sooner, simply dropping
rule 2 with providing legacy config is still one of valid options I think.


> What do you both think? This would make the behavior more clear and take a
>> step toward getting rid of Hive-specific code.
>>
>> On Wed, Mar 18, 2020 at 4:45 PM Jungtaek Lim <
>> kabhwan.opensou...@gmail.com> wrote:
>>
>>> I'm trying to understand the reason you have been 

Re: [DISCUSS] Resolve ambiguous parser rule between two "create table"s

[Nicholas Chammas]

Side comment: The current docs for CREATE TABLE

add to the confusion by describing the Hive-compatible command as "CREATE
TABLE USING HIVE FORMAT", but neither "USING" nor "HIVE FORMAT" are
actually part of the syntax

.

On Wed, Mar 18, 2020 at 8:31 PM Ryan Blue  wrote:

> Jungtaek, it sounds like you consider the two rules to be separate
> syntaxes with their own consistency rules. For example, if I am using the
> Hive syntax rule, then the PARTITIONED BY clause adds new (partition)
> columns and requires types for those columns; if I’m using the Spark syntax
> rule with USING then PARTITIONED BY must reference existing columns and
> cannot include types.
>
> I agree that this is confusing to users! We should fix it, but I don’t
> think the right solution is to continue to have two rules with divergent
> syntax.
>
> This is confusing to users because they don’t know anything about separate
> parser rules. All the user sees is that sometimes PARTITION BY requires
> types and sometimes it doesn’t. Yes, we could add a keyword, HIVE, to
> signal that the syntax is borrowed from Hive for that case, but that
> actually breaks queries that run in Hive.
>
> I think the right solution is to unify the two syntaxes. I don’t think
> they are so different that it isn’t possible. Here are the differences I
> see:
>
>- Only in Hive:
>   - EXTERNAL
>   - skewSpec: SKEWED BY ...
>   - rowFormat: ROW FORMAT DELIMITED ..., ROW FORMAT SERDE ...
>   - createFileFormat: STORED AS ...
>- Only in Spark:
>   - OPTIONS property list
>- Different syntax/interpretation:
>   - PARTITIONED BY transformList / PARTITIONED BY colTypeList
>
> For the clauses that are supported in one but not the other, we can add
> them to a unified rule as optional clauses. The AST builder would then
> validate what makes sense or not (e.g., stored as with using or row format
> delimited) and finally pass the remaining data on using the
> CreateTableStatement. That statement would be handled like we do for the
> Spark rule today, but with extra metadata to pass along. This is also a
> step toward being able to put Hive behind the DSv2 API because we’d be able
> to pass all of the Hive metadata clauses to the v2 catalog.
>
> The only difficult part is handling PARTITIONED BY. But in that case, we
> can use two different syntaxes from the same CREATE TABLE rule. If types
> are included, we use the Hive PARTITIONED BY syntax and convert in the
> AST builder to normalize to a single representation.
>
> What do you both think? This would make the behavior more clear and take a
> step toward getting rid of Hive-specific code.
>
> On Wed, Mar 18, 2020 at 4:45 PM Jungtaek Lim 
> wrote:
>
>> I'm trying to understand the reason you have been suggesting to keep the
>> real thing unchanged but change doc instead. Could you please elaborate
>> why? End users would blame us when they hit the case their query doesn't
>> work as intended (1) and found the fact it's undocumented (2) and hard to
>> understand even from the Spark codebase (3).
>>
>> For me, addressing the root issue adopting your suggestion would be
>> "dropping the rule 2" and only supporting it with legacy config on. We
>> would say to end users, you need to enable the legacy config to leverage
>> Hive create table syntax, or just use beeline with Hive connected.
>>
>> But since we are even thinking about native syntax as a first class and
>> dropping Hive one implicitly (hide in doc) or explicitly, does it really
>> matter we require a marker (like "HIVE") in rule 2 and isolate it? It would
>> have even less confusion than Spark 2.x, since we will require end users to
>> fill the marker regarding Hive when creating Hive table, easier to classify
>> than "USING provider".
>>
>> If we think native syntax would cover many cases end users have been
>> creating Hive table in Spark (say, USING hive would simply work for them),
>> I'm OK to drop the rule 2 and lead end users to enable the legacy config if
>> really needed. If not, let's continue "fixing" the issue.
>>
>> (Another valid approach would be consolidating two rules into one, and
>> defining support of parameters per provider, e.g. EXTERNAL, STORED AS, ROW
>> FORMAT, etc. are only supported in Hive provider.)
>>
>>
>> On Wed, Mar 18, 2020 at 8:47 PM Wenchen Fan  wrote:
>>
>>> The fact that we have 2 CREATE TABLE syntax is already confusing many
>>> users. Shall we only document the native syntax? Then users don't need to
>>> worry about which rule their query fits and they don't need to spend a lot
>>> of time understanding the subtle difference between these 2 syntaxes.
>>>
>>> On Wed, Mar 18, 2020 at 7:01 PM Jungtaek Lim <
>>> 

Re: [DISCUSS] Resolve ambiguous parser rule between two "create table"s

[Ryan Blue]

Jungtaek, it sounds like you consider the two rules to be separate syntaxes
with their own consistency rules. For example, if I am using the Hive
syntax rule, then the PARTITIONED BY clause adds new (partition) columns
and requires types for those columns; if I’m using the Spark syntax rule
with USING then PARTITIONED BY must reference existing columns and cannot
include types.

I agree that this is confusing to users! We should fix it, but I don’t
think the right solution is to continue to have two rules with divergent
syntax.

This is confusing to users because they don’t know anything about separate
parser rules. All the user sees is that sometimes PARTITION BY requires
types and sometimes it doesn’t. Yes, we could add a keyword, HIVE, to
signal that the syntax is borrowed from Hive for that case, but that
actually breaks queries that run in Hive.

I think the right solution is to unify the two syntaxes. I don’t think they
are so different that it isn’t possible. Here are the differences I see:

   - Only in Hive:
  - EXTERNAL
  - skewSpec: SKEWED BY ...
  - rowFormat: ROW FORMAT DELIMITED ..., ROW FORMAT SERDE ...
  - createFileFormat: STORED AS ...
   - Only in Spark:
  - OPTIONS property list
   - Different syntax/interpretation:
  - PARTITIONED BY transformList / PARTITIONED BY colTypeList

For the clauses that are supported in one but not the other, we can add
them to a unified rule as optional clauses. The AST builder would then
validate what makes sense or not (e.g., stored as with using or row format
delimited) and finally pass the remaining data on using the
CreateTableStatement. That statement would be handled like we do for the
Spark rule today, but with extra metadata to pass along. This is also a
step toward being able to put Hive behind the DSv2 API because we’d be able
to pass all of the Hive metadata clauses to the v2 catalog.

The only difficult part is handling PARTITIONED BY. But in that case, we
can use two different syntaxes from the same CREATE TABLE rule. If types
are included, we use the Hive PARTITIONED BY syntax and convert in the AST
builder to normalize to a single representation.

What do you both think? This would make the behavior more clear and take a
step toward getting rid of Hive-specific code.

On Wed, Mar 18, 2020 at 4:45 PM Jungtaek Lim 
wrote:

> I'm trying to understand the reason you have been suggesting to keep the
> real thing unchanged but change doc instead. Could you please elaborate
> why? End users would blame us when they hit the case their query doesn't
> work as intended (1) and found the fact it's undocumented (2) and hard to
> understand even from the Spark codebase (3).
>
> For me, addressing the root issue adopting your suggestion would be
> "dropping the rule 2" and only supporting it with legacy config on. We
> would say to end users, you need to enable the legacy config to leverage
> Hive create table syntax, or just use beeline with Hive connected.
>
> But since we are even thinking about native syntax as a first class and
> dropping Hive one implicitly (hide in doc) or explicitly, does it really
> matter we require a marker (like "HIVE") in rule 2 and isolate it? It would
> have even less confusion than Spark 2.x, since we will require end users to
> fill the marker regarding Hive when creating Hive table, easier to classify
> than "USING provider".
>
> If we think native syntax would cover many cases end users have been
> creating Hive table in Spark (say, USING hive would simply work for them),
> I'm OK to drop the rule 2 and lead end users to enable the legacy config if
> really needed. If not, let's continue "fixing" the issue.
>
> (Another valid approach would be consolidating two rules into one, and
> defining support of parameters per provider, e.g. EXTERNAL, STORED AS, ROW
> FORMAT, etc. are only supported in Hive provider.)
>
>
> On Wed, Mar 18, 2020 at 8:47 PM Wenchen Fan  wrote:
>
>> The fact that we have 2 CREATE TABLE syntax is already confusing many
>> users. Shall we only document the native syntax? Then users don't need to
>> worry about which rule their query fits and they don't need to spend a lot
>> of time understanding the subtle difference between these 2 syntaxes.
>>
>> On Wed, Mar 18, 2020 at 7:01 PM Jungtaek Lim <
>> kabhwan.opensou...@gmail.com> wrote:
>>
>>> A bit correction: the example I provided for vice versa is not really a
>>> correct case for vice versa. It's actually same case (intended to use rule
>>> 2 which is not default) but different result.
>>>
>>> On Wed, Mar 18, 2020 at 7:22 PM Jungtaek Lim <
>>> kabhwan.opensou...@gmail.com> wrote:
>>>
 My concern is that although we simply think about the change to
 mark "USING provider" to be optional in rule 1, but in reality the change
 is most likely swapping the default rule for CREATE TABLE, which was "rule
 2", and now "rule 1" (it would be the happy case of migration doc if the
 swap happens as intended), and 

Re: [DISCUSS] Resolve ambiguous parser rule between two "create table"s

[Jungtaek Lim]

I'm trying to understand the reason you have been suggesting to keep the
real thing unchanged but change doc instead. Could you please elaborate
why? End users would blame us when they hit the case their query doesn't
work as intended (1) and found the fact it's undocumented (2) and hard to
understand even from the Spark codebase (3).

For me, addressing the root issue adopting your suggestion would be
"dropping the rule 2" and only supporting it with legacy config on. We
would say to end users, you need to enable the legacy config to leverage
Hive create table syntax, or just use beeline with Hive connected.

But since we are even thinking about native syntax as a first class and
dropping Hive one implicitly (hide in doc) or explicitly, does it really
matter we require a marker (like "HIVE") in rule 2 and isolate it? It would
have even less confusion than Spark 2.x, since we will require end users to
fill the marker regarding Hive when creating Hive table, easier to classify
than "USING provider".

If we think native syntax would cover many cases end users have been
creating Hive table in Spark (say, USING hive would simply work for them),
I'm OK to drop the rule 2 and lead end users to enable the legacy config if
really needed. If not, let's continue "fixing" the issue.

(Another valid approach would be consolidating two rules into one, and
defining support of parameters per provider, e.g. EXTERNAL, STORED AS, ROW
FORMAT, etc. are only supported in Hive provider.)


On Wed, Mar 18, 2020 at 8:47 PM Wenchen Fan  wrote:

> The fact that we have 2 CREATE TABLE syntax is already confusing many
> users. Shall we only document the native syntax? Then users don't need to
> worry about which rule their query fits and they don't need to spend a lot
> of time understanding the subtle difference between these 2 syntaxes.
>
> On Wed, Mar 18, 2020 at 7:01 PM Jungtaek Lim 
> wrote:
>
>> A bit correction: the example I provided for vice versa is not really a
>> correct case for vice versa. It's actually same case (intended to use rule
>> 2 which is not default) but different result.
>>
>> On Wed, Mar 18, 2020 at 7:22 PM Jungtaek Lim <
>> kabhwan.opensou...@gmail.com> wrote:
>>
>>> My concern is that although we simply think about the change to
>>> mark "USING provider" to be optional in rule 1, but in reality the change
>>> is most likely swapping the default rule for CREATE TABLE, which was "rule
>>> 2", and now "rule 1" (it would be the happy case of migration doc if the
>>> swap happens as intended), and there're still couple of things which make
>>> the query still fall into rule 2 which is non-trivial to reason about and
>>> also not easy to explain.
>>>
>>> I only mentioned ROW FORMAT and STORED AS as the case to fall into rule
>>> 2 to simplify the problem statement, but they're not only the case - using
>>> col_name1:col_type1 would make the query fall into rule 2 regardless of any
>>> other properties.
>>>
>>> What's the matter? In Spark 2.x, if end users want to use rule 1 (which
>>> is not default) and specify the parameters which are only available in rule
>>> 1, it clearly requires "USING provider" - parser will throw error if
>>> there're any mistakes on specifying parameters. End users could set
>>> intention clearly which rule the query should be bound. If the query fails
>>> to bind the rule as intended, it's simply denied.
>>>
>>> In Spark 3.x, parser may not help figuring out the case where end users
>>> intend to use rule 2 (which is not default) but some mistake on specifying
>>> parameters - it could just "silently" bound into rule 1 and it may be even
>>> executed without any error. Vice versa happens, but in odd way - e.g.
>>> CREATE EXTERNAL TABLE ... LOCATION fails with weird message CREATE EXTERNAL
>>> TABLE is not supported.
>>>
>>> It's deterministic for end users only if they're fully understand the
>>> difference between the twos and also understand how Spark would apply the
>>> rules to make the query fall into one of the rule. I'm not sure how we can
>>> only improve documentation to make things be clear, but if the approach
>>> would be explaining the difference of rules and guide the tip to make the
>>> query be bound to the specific rule, the same could be applied to parser
>>> rule to address the root cause.
>>>
>>>
>>> On Wed, Mar 18, 2020 at 6:24 PM Wenchen Fan  wrote:
>>>
 Document-wise, yes, it's confusing as a simple CREATE TABLE fits both
 native and Hive syntax. I'm fine with some changes to make it less
 confusing, as long as the user-facing behavior doesn't change. For example,
 define "ROW FORMAT" or "STORED AS" as mandatory only if the legacy config
 is false.

 I still don't get your point about what's the real problem to end
 users. There is no ambiguity as the behavior is deterministic, although we
 rely on optional fields and rule order which is bad. It's hard to document,
 but I don't think that's a big problem to end users.

Re: Spark 2.4.x and 3.x datasourcev2 api documentation & references

[MadDoxX]

Thanks for the link !

i was able to code my first datasources in microbatch and continuous by
using the v2 api...

i was wondering if there are some equivalent to SparkListener for
microbatching & continuous stream (where one could use the
spark.extra.listener) ~ monitoring :)



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Re: Scala vs PySpark Inconsistency: SQLContext/SparkSession access from DataFrame/DataSet

[Maciej Szymkiewicz]

Hi Ben,

Please note that `_sc` is not a SQLContext. It is a SparkContext, which
is used primarily for internal calls.

SQLContext is exposed through `sql_ctx`
(https://github.com/apache/spark/blob/8bfaa62f2fcc942dd99a63b20366167277bce2a1/python/pyspark/sql/dataframe.py#L80)

On 3/17/20 5:53 PM, Ben Roling wrote:
> I tried this on the users mailing list but didn't get traction.  It's
> probably more appropriate here anyway.
>
> I've noticed that DataSet.sqlContext is public in Scala but the
> equivalent (DataFrame._sc) in PySpark is named as if it should be
> treated as private.
>
> Is this intentional?  If so, what's the rationale?  If not, then it
> feels like a bug and DataFrame should have some form of public access
> back to the context/session.  I'm happy to log the bug but thought I
> would ask here first.  Thanks!

-- 
Best regards,
Maciej Szymkiewicz

Web: https://zero323.net
Keybase: https://keybase.io/zero323
Gigs: https://www.codementor.io/@zero323
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Re: [DISCUSS] Resolve ambiguous parser rule between two "create table"s

[Wenchen Fan]

The fact that we have 2 CREATE TABLE syntax is already confusing many
users. Shall we only document the native syntax? Then users don't need to
worry about which rule their query fits and they don't need to spend a lot
of time understanding the subtle difference between these 2 syntaxes.

On Wed, Mar 18, 2020 at 7:01 PM Jungtaek Lim 
wrote:

> A bit correction: the example I provided for vice versa is not really a
> correct case for vice versa. It's actually same case (intended to use rule
> 2 which is not default) but different result.
>
> On Wed, Mar 18, 2020 at 7:22 PM Jungtaek Lim 
> wrote:
>
>> My concern is that although we simply think about the change to
>> mark "USING provider" to be optional in rule 1, but in reality the change
>> is most likely swapping the default rule for CREATE TABLE, which was "rule
>> 2", and now "rule 1" (it would be the happy case of migration doc if the
>> swap happens as intended), and there're still couple of things which make
>> the query still fall into rule 2 which is non-trivial to reason about and
>> also not easy to explain.
>>
>> I only mentioned ROW FORMAT and STORED AS as the case to fall into rule 2
>> to simplify the problem statement, but they're not only the case - using
>> col_name1:col_type1 would make the query fall into rule 2 regardless of any
>> other properties.
>>
>> What's the matter? In Spark 2.x, if end users want to use rule 1 (which
>> is not default) and specify the parameters which are only available in rule
>> 1, it clearly requires "USING provider" - parser will throw error if
>> there're any mistakes on specifying parameters. End users could set
>> intention clearly which rule the query should be bound. If the query fails
>> to bind the rule as intended, it's simply denied.
>>
>> In Spark 3.x, parser may not help figuring out the case where end users
>> intend to use rule 2 (which is not default) but some mistake on specifying
>> parameters - it could just "silently" bound into rule 1 and it may be even
>> executed without any error. Vice versa happens, but in odd way - e.g.
>> CREATE EXTERNAL TABLE ... LOCATION fails with weird message CREATE EXTERNAL
>> TABLE is not supported.
>>
>> It's deterministic for end users only if they're fully understand the
>> difference between the twos and also understand how Spark would apply the
>> rules to make the query fall into one of the rule. I'm not sure how we can
>> only improve documentation to make things be clear, but if the approach
>> would be explaining the difference of rules and guide the tip to make the
>> query be bound to the specific rule, the same could be applied to parser
>> rule to address the root cause.
>>
>>
>> On Wed, Mar 18, 2020 at 6:24 PM Wenchen Fan  wrote:
>>
>>> Document-wise, yes, it's confusing as a simple CREATE TABLE fits both
>>> native and Hive syntax. I'm fine with some changes to make it less
>>> confusing, as long as the user-facing behavior doesn't change. For example,
>>> define "ROW FORMAT" or "STORED AS" as mandatory only if the legacy config
>>> is false.
>>>
>>> I still don't get your point about what's the real problem to end users.
>>> There is no ambiguity as the behavior is deterministic, although we rely on
>>> optional fields and rule order which is bad. It's hard to document, but I
>>> don't think that's a big problem to end users.
>>>
>>> For the legacy config, it does make the implementation more complicated,
>>> but it's invisible to most end users (we don't document it) and can be
>>> super useful to some users who want the queries to keep working in 3.0
>>> without rewriting.
>>>
>>> If your only concern is documentation, I totally agree that we should
>>> improve it.
>>>
>>> On Wed, Mar 18, 2020 at 4:36 PM Jungtaek Lim <
>>> kabhwan.opensou...@gmail.com> wrote:
>>>
 Thanks for sharing your view.

 I agree with you it's good for Spark to promote Spark's own CREATE
 TABLE syntax. The thing is, we still leave Hive CREATE TABLE syntax
 unchanged - it's being said as "convenience" but I'm not sure I can agree
 with.

 I'll quote my comments in SPARK-31136 here again to make the problem
 statement be clearer:

 I think the parser implementation around CREATE TABLE brings ambiguity
 which is not documented anywhere. It wasn't ambiguous because we forced to
 specify USE provider if it's not a Hive table. Now it's either default
 provider or Hive according to which options are provided, which seems to be
 non-trivial to reason about. (End users would never know, as it's
 completely from parser rule.)

 I feel this as the issue of "not breaking old behavior". The parser
 rule gets pretty much complicated due to support legacy config. Not
 breaking anything would make us be stuck eventually.

 https://github.com/apache/spark/blob/master/docs/sql-migration-guide.md

 Since Spark 3.0, CREATE TABLE without a specific provider will use the
 value of 

Re: [DISCUSS] Resolve ambiguous parser rule between two "create table"s

[Jungtaek Lim]

A bit correction: the example I provided for vice versa is not really a
correct case for vice versa. It's actually same case (intended to use rule
2 which is not default) but different result.

On Wed, Mar 18, 2020 at 7:22 PM Jungtaek Lim 
wrote:

> My concern is that although we simply think about the change to
> mark "USING provider" to be optional in rule 1, but in reality the change
> is most likely swapping the default rule for CREATE TABLE, which was "rule
> 2", and now "rule 1" (it would be the happy case of migration doc if the
> swap happens as intended), and there're still couple of things which make
> the query still fall into rule 2 which is non-trivial to reason about and
> also not easy to explain.
>
> I only mentioned ROW FORMAT and STORED AS as the case to fall into rule 2
> to simplify the problem statement, but they're not only the case - using
> col_name1:col_type1 would make the query fall into rule 2 regardless of any
> other properties.
>
> What's the matter? In Spark 2.x, if end users want to use rule 1 (which is
> not default) and specify the parameters which are only available in rule 1,
> it clearly requires "USING provider" - parser will throw error if there're
> any mistakes on specifying parameters. End users could set intention
> clearly which rule the query should be bound. If the query fails to bind
> the rule as intended, it's simply denied.
>
> In Spark 3.x, parser may not help figuring out the case where end users
> intend to use rule 2 (which is not default) but some mistake on specifying
> parameters - it could just "silently" bound into rule 1 and it may be even
> executed without any error. Vice versa happens, but in odd way - e.g.
> CREATE EXTERNAL TABLE ... LOCATION fails with weird message CREATE EXTERNAL
> TABLE is not supported.
>
> It's deterministic for end users only if they're fully understand the
> difference between the twos and also understand how Spark would apply the
> rules to make the query fall into one of the rule. I'm not sure how we can
> only improve documentation to make things be clear, but if the approach
> would be explaining the difference of rules and guide the tip to make the
> query be bound to the specific rule, the same could be applied to parser
> rule to address the root cause.
>
>
> On Wed, Mar 18, 2020 at 6:24 PM Wenchen Fan  wrote:
>
>> Document-wise, yes, it's confusing as a simple CREATE TABLE fits both
>> native and Hive syntax. I'm fine with some changes to make it less
>> confusing, as long as the user-facing behavior doesn't change. For example,
>> define "ROW FORMAT" or "STORED AS" as mandatory only if the legacy config
>> is false.
>>
>> I still don't get your point about what's the real problem to end users.
>> There is no ambiguity as the behavior is deterministic, although we rely on
>> optional fields and rule order which is bad. It's hard to document, but I
>> don't think that's a big problem to end users.
>>
>> For the legacy config, it does make the implementation more complicated,
>> but it's invisible to most end users (we don't document it) and can be
>> super useful to some users who want the queries to keep working in 3.0
>> without rewriting.
>>
>> If your only concern is documentation, I totally agree that we should
>> improve it.
>>
>> On Wed, Mar 18, 2020 at 4:36 PM Jungtaek Lim <
>> kabhwan.opensou...@gmail.com> wrote:
>>
>>> Thanks for sharing your view.
>>>
>>> I agree with you it's good for Spark to promote Spark's own CREATE TABLE
>>> syntax. The thing is, we still leave Hive CREATE TABLE syntax unchanged -
>>> it's being said as "convenience" but I'm not sure I can agree with.
>>>
>>> I'll quote my comments in SPARK-31136 here again to make the problem
>>> statement be clearer:
>>>
>>> I think the parser implementation around CREATE TABLE brings ambiguity
>>> which is not documented anywhere. It wasn't ambiguous because we forced to
>>> specify USE provider if it's not a Hive table. Now it's either default
>>> provider or Hive according to which options are provided, which seems to be
>>> non-trivial to reason about. (End users would never know, as it's
>>> completely from parser rule.)
>>>
>>> I feel this as the issue of "not breaking old behavior". The parser rule
>>> gets pretty much complicated due to support legacy config. Not breaking
>>> anything would make us be stuck eventually.
>>>
>>> https://github.com/apache/spark/blob/master/docs/sql-migration-guide.md
>>>
>>> Since Spark 3.0, CREATE TABLE without a specific provider will use the
>>> value of spark.sql.sources.default as its provider. In Spark version 2.4
>>> and earlier, it was hive. To restore the behavior before Spark 3.0, you can
>>> set spark.sql.legacy.createHiveTableByDefault.enabled to true.
>>>
>>>
>>> It's not true if "ROW FORMAT" / "STORED AS" are provided, and we didn't
>>> describe anything for this.
>>>
>>>
>>> https://github.com/apache/spark/blob/master/docs/sql-ref-syntax-ddl-create-table-datasource.md
>>>
>>> CREATE TABLE 

Re: [DISCUSS] Resolve ambiguous parser rule between two "create table"s

[Jungtaek Lim]

My concern is that although we simply think about the change to mark "USING
provider" to be optional in rule 1, but in reality the change is most
likely swapping the default rule for CREATE TABLE, which was "rule 2", and
now "rule 1" (it would be the happy case of migration doc if the swap
happens as intended), and there're still couple of things which make the
query still fall into rule 2 which is non-trivial to reason about and also
not easy to explain.

I only mentioned ROW FORMAT and STORED AS as the case to fall into rule 2
to simplify the problem statement, but they're not only the case - using
col_name1:col_type1 would make the query fall into rule 2 regardless of any
other properties.

What's the matter? In Spark 2.x, if end users want to use rule 1 (which is
not default) and specify the parameters which are only available in rule 1,
it clearly requires "USING provider" - parser will throw error if there're
any mistakes on specifying parameters. End users could set intention
clearly which rule the query should be bound. If the query fails to bind
the rule as intended, it's simply denied.

In Spark 3.x, parser may not help figuring out the case where end users
intend to use rule 2 (which is not default) but some mistake on specifying
parameters - it could just "silently" bound into rule 1 and it may be even
executed without any error. Vice versa happens, but in odd way - e.g.
CREATE EXTERNAL TABLE ... LOCATION fails with weird message CREATE EXTERNAL
TABLE is not supported.

It's deterministic for end users only if they're fully understand the
difference between the twos and also understand how Spark would apply the
rules to make the query fall into one of the rule. I'm not sure how we can
only improve documentation to make things be clear, but if the approach
would be explaining the difference of rules and guide the tip to make the
query be bound to the specific rule, the same could be applied to parser
rule to address the root cause.


On Wed, Mar 18, 2020 at 6:24 PM Wenchen Fan  wrote:

> Document-wise, yes, it's confusing as a simple CREATE TABLE fits both
> native and Hive syntax. I'm fine with some changes to make it less
> confusing, as long as the user-facing behavior doesn't change. For example,
> define "ROW FORMAT" or "STORED AS" as mandatory only if the legacy config
> is false.
>
> I still don't get your point about what's the real problem to end users.
> There is no ambiguity as the behavior is deterministic, although we rely on
> optional fields and rule order which is bad. It's hard to document, but I
> don't think that's a big problem to end users.
>
> For the legacy config, it does make the implementation more complicated,
> but it's invisible to most end users (we don't document it) and can be
> super useful to some users who want the queries to keep working in 3.0
> without rewriting.
>
> If your only concern is documentation, I totally agree that we should
> improve it.
>
> On Wed, Mar 18, 2020 at 4:36 PM Jungtaek Lim 
> wrote:
>
>> Thanks for sharing your view.
>>
>> I agree with you it's good for Spark to promote Spark's own CREATE TABLE
>> syntax. The thing is, we still leave Hive CREATE TABLE syntax unchanged -
>> it's being said as "convenience" but I'm not sure I can agree with.
>>
>> I'll quote my comments in SPARK-31136 here again to make the problem
>> statement be clearer:
>>
>> I think the parser implementation around CREATE TABLE brings ambiguity
>> which is not documented anywhere. It wasn't ambiguous because we forced to
>> specify USE provider if it's not a Hive table. Now it's either default
>> provider or Hive according to which options are provided, which seems to be
>> non-trivial to reason about. (End users would never know, as it's
>> completely from parser rule.)
>>
>> I feel this as the issue of "not breaking old behavior". The parser rule
>> gets pretty much complicated due to support legacy config. Not breaking
>> anything would make us be stuck eventually.
>>
>> https://github.com/apache/spark/blob/master/docs/sql-migration-guide.md
>>
>> Since Spark 3.0, CREATE TABLE without a specific provider will use the
>> value of spark.sql.sources.default as its provider. In Spark version 2.4
>> and earlier, it was hive. To restore the behavior before Spark 3.0, you can
>> set spark.sql.legacy.createHiveTableByDefault.enabled to true.
>>
>>
>> It's not true if "ROW FORMAT" / "STORED AS" are provided, and we didn't
>> describe anything for this.
>>
>>
>> https://github.com/apache/spark/blob/master/docs/sql-ref-syntax-ddl-create-table-datasource.md
>>
>> CREATE TABLE [ IF NOT EXISTS ] table_identifier [ ( col_name1 col_type1 [
>> COMMENT col_comment1 ], ... ) ] [USING data_source] [ OPTIONS ( key1=val1,
>> key2=val2, ... ) ] [ PARTITIONED BY ( col_name1, col_name2, ... ) ] [
>> CLUSTERED BY ( col_name3, col_name4, ... ) [ SORTED BY ( col_name [ ASC |
>> DESC ], ... ) ] INTO num_buckets BUCKETS ] [ LOCATION path ] [ COMMENT
>> table_comment ] [ TBLPROPERTIES ( 

Re: [DISCUSS] Resolve ambiguous parser rule between two "create table"s

[Wenchen Fan]

Document-wise, yes, it's confusing as a simple CREATE TABLE fits both
native and Hive syntax. I'm fine with some changes to make it less
confusing, as long as the user-facing behavior doesn't change. For example,
define "ROW FORMAT" or "STORED AS" as mandatory only if the legacy config
is false.

I still don't get your point about what's the real problem to end users.
There is no ambiguity as the behavior is deterministic, although we rely on
optional fields and rule order which is bad. It's hard to document, but I
don't think that's a big problem to end users.

For the legacy config, it does make the implementation more complicated,
but it's invisible to most end users (we don't document it) and can be
super useful to some users who want the queries to keep working in 3.0
without rewriting.

If your only concern is documentation, I totally agree that we should
improve it.

On Wed, Mar 18, 2020 at 4:36 PM Jungtaek Lim 
wrote:

> Thanks for sharing your view.
>
> I agree with you it's good for Spark to promote Spark's own CREATE TABLE
> syntax. The thing is, we still leave Hive CREATE TABLE syntax unchanged -
> it's being said as "convenience" but I'm not sure I can agree with.
>
> I'll quote my comments in SPARK-31136 here again to make the problem
> statement be clearer:
>
> I think the parser implementation around CREATE TABLE brings ambiguity
> which is not documented anywhere. It wasn't ambiguous because we forced to
> specify USE provider if it's not a Hive table. Now it's either default
> provider or Hive according to which options are provided, which seems to be
> non-trivial to reason about. (End users would never know, as it's
> completely from parser rule.)
>
> I feel this as the issue of "not breaking old behavior". The parser rule
> gets pretty much complicated due to support legacy config. Not breaking
> anything would make us be stuck eventually.
>
> https://github.com/apache/spark/blob/master/docs/sql-migration-guide.md
>
> Since Spark 3.0, CREATE TABLE without a specific provider will use the
> value of spark.sql.sources.default as its provider. In Spark version 2.4
> and earlier, it was hive. To restore the behavior before Spark 3.0, you can
> set spark.sql.legacy.createHiveTableByDefault.enabled to true.
>
>
> It's not true if "ROW FORMAT" / "STORED AS" are provided, and we didn't
> describe anything for this.
>
>
> https://github.com/apache/spark/blob/master/docs/sql-ref-syntax-ddl-create-table-datasource.md
>
> CREATE TABLE [ IF NOT EXISTS ] table_identifier [ ( col_name1 col_type1 [
> COMMENT col_comment1 ], ... ) ] [USING data_source] [ OPTIONS ( key1=val1,
> key2=val2, ... ) ] [ PARTITIONED BY ( col_name1, col_name2, ... ) ] [
> CLUSTERED BY ( col_name3, col_name4, ... ) [ SORTED BY ( col_name [ ASC |
> DESC ], ... ) ] INTO num_buckets BUCKETS ] [ LOCATION path ] [ COMMENT
> table_comment ] [ TBLPROPERTIES ( key1=val1, key2=val2, ... ) ] [ AS
> select_statement ]
>
>
>
> https://github.com/apache/spark/blob/master/docs/sql-ref-syntax-ddl-create-table-hiveformat.md
>
> CREATE [ EXTERNAL ] TABLE [ IF NOT EXISTS ] table_identifier [ (
> col_name1[:] col_type1 [ COMMENT col_comment1 ], ... ) ] [ COMMENT
> table_comment ] [ PARTITIONED BY ( col_name2[:] col_type2 [ COMMENT
> col_comment2 ], ... ) | ( col_name1, col_name2, ... ) ] [ ROW FORMAT
> row_format ] [ STORED AS file_format ] [ LOCATION path ] [ TBLPROPERTIES (
> key1=val1, key2=val2, ... ) ] [ AS select_statement ]
>
>
> At least we should describe that parser will try to match the first case
> (create table ~ using data source), and fail back to second case; even
> though we describe this it's not intuitive to reason about which rule the
> DDL query will fall into. As I commented earlier, "ROW FORMAT" and "STORED
> AS" are the options which make DDL query fall into the second case, but
> they're described as "optional" so it's hard to catch the gotcha.
>
> Furthermore, while we document the syntax as above, in reality we allow
> "EXTERNAL" in first rule (and throw error), which ends up existing DDL
> query "CREATE EXTERNAL TABLE ~ LOCATION" be broken. Even we add "USING
> hive" parser will throw error. It now requires "ROW FORMAT" or "STORED AS".
>
>
> Simply saying, do we really think end users should stop and try to match
> their query against the parser rules (or orders when we explain in the doc)
> by themselves to understand which provider the table will leverage? I'm
> sorry but I think we are making bad assumption on end users which is a
> serious problem.
>
> If we really want to promote Spark's one for CREATE TABLE, then would it
> really matter to treat Hive CREATE TABLE be "exceptional" one and try to
> isolate each other? What's the point of providing a legacy config to go
> back to the old one even we fear about breaking something to make it better
> or clearer? We do think that table provider is important (hence the change
> was done), then is it still a trivial problem whether the provider is
> affected by 

Re: [DISCUSS] Resolve ambiguous parser rule between two "create table"s

[Jungtaek Lim]

Thanks for sharing your view.

I agree with you it's good for Spark to promote Spark's own CREATE TABLE
syntax. The thing is, we still leave Hive CREATE TABLE syntax unchanged -
it's being said as "convenience" but I'm not sure I can agree with.

I'll quote my comments in SPARK-31136 here again to make the problem
statement be clearer:

I think the parser implementation around CREATE TABLE brings ambiguity
which is not documented anywhere. It wasn't ambiguous because we forced to
specify USE provider if it's not a Hive table. Now it's either default
provider or Hive according to which options are provided, which seems to be
non-trivial to reason about. (End users would never know, as it's
completely from parser rule.)

I feel this as the issue of "not breaking old behavior". The parser rule
gets pretty much complicated due to support legacy config. Not breaking
anything would make us be stuck eventually.

https://github.com/apache/spark/blob/master/docs/sql-migration-guide.md

Since Spark 3.0, CREATE TABLE without a specific provider will use the
value of spark.sql.sources.default as its provider. In Spark version 2.4
and earlier, it was hive. To restore the behavior before Spark 3.0, you can
set spark.sql.legacy.createHiveTableByDefault.enabled to true.


It's not true if "ROW FORMAT" / "STORED AS" are provided, and we didn't
describe anything for this.

https://github.com/apache/spark/blob/master/docs/sql-ref-syntax-ddl-create-table-datasource.md

CREATE TABLE [ IF NOT EXISTS ] table_identifier [ ( col_name1 col_type1 [
COMMENT col_comment1 ], ... ) ] [USING data_source] [ OPTIONS ( key1=val1,
key2=val2, ... ) ] [ PARTITIONED BY ( col_name1, col_name2, ... ) ] [
CLUSTERED BY ( col_name3, col_name4, ... ) [ SORTED BY ( col_name [ ASC |
DESC ], ... ) ] INTO num_buckets BUCKETS ] [ LOCATION path ] [ COMMENT
table_comment ] [ TBLPROPERTIES ( key1=val1, key2=val2, ... ) ] [ AS
select_statement ]


https://github.com/apache/spark/blob/master/docs/sql-ref-syntax-ddl-create-table-hiveformat.md

CREATE [ EXTERNAL ] TABLE [ IF NOT EXISTS ] table_identifier [ (
col_name1[:] col_type1 [ COMMENT col_comment1 ], ... ) ] [ COMMENT
table_comment ] [ PARTITIONED BY ( col_name2[:] col_type2 [ COMMENT
col_comment2 ], ... ) | ( col_name1, col_name2, ... ) ] [ ROW FORMAT
row_format ] [ STORED AS file_format ] [ LOCATION path ] [ TBLPROPERTIES (
key1=val1, key2=val2, ... ) ] [ AS select_statement ]


At least we should describe that parser will try to match the first case
(create table ~ using data source), and fail back to second case; even
though we describe this it's not intuitive to reason about which rule the
DDL query will fall into. As I commented earlier, "ROW FORMAT" and "STORED
AS" are the options which make DDL query fall into the second case, but
they're described as "optional" so it's hard to catch the gotcha.

Furthermore, while we document the syntax as above, in reality we allow
"EXTERNAL" in first rule (and throw error), which ends up existing DDL
query "CREATE EXTERNAL TABLE ~ LOCATION" be broken. Even we add "USING
hive" parser will throw error. It now requires "ROW FORMAT" or "STORED AS".


Simply saying, do we really think end users should stop and try to match
their query against the parser rules (or orders when we explain in the doc)
by themselves to understand which provider the table will leverage? I'm
sorry but I think we are making bad assumption on end users which is a
serious problem.

If we really want to promote Spark's one for CREATE TABLE, then would it
really matter to treat Hive CREATE TABLE be "exceptional" one and try to
isolate each other? What's the point of providing a legacy config to go
back to the old one even we fear about breaking something to make it better
or clearer? We do think that table provider is important (hence the change
was done), then is it still a trivial problem whether the provider is
affected by specifying the "optional" fields?


On Wed, Mar 18, 2020 at 4:38 PM Wenchen Fan  wrote:

> I think the general guideline is to promote Spark's own CREATE TABLE
> syntax instead of the Hive one. Previously these two rules are mutually
> exclusive because the native syntax requires the USING clause while the
> Hive syntax makes ROW FORMAT or STORED AS clause optional.
>
> It's a good move to make the USING clause optional, which makes it easier
> to write the native CREATE TABLE syntax. Unfortunately, it leads to some
> conflicts with the Hive CREATE TABLE syntax, but I don't see a serious
> problem here. If a user just writes CREATE TABLE without USING or ROW
> FORMAT or STORED AS, does it matter what table we create? Internally the
> parser rules conflict and we pick the native syntax depending on the rule
> order. But the user-facing behavior looks fine.
>
> CREATE EXTERNAL TABLE is a problem as it works in 2.4 but not in 3.0.
> Shall we simply remove EXTERNAL from the native CREATE TABLE syntax? Then
> CREATE EXTERNAL TABLE creates Hive table like 2.4.
>
> On Mon, Mar 

Re: [DISCUSS] Resolve ambiguous parser rule between two "create table"s

[Wenchen Fan]

I think the general guideline is to promote Spark's own CREATE TABLE syntax
instead of the Hive one. Previously these two rules are mutually exclusive
because the native syntax requires the USING clause while the Hive
syntax makes ROW FORMAT or STORED AS clause optional.

It's a good move to make the USING clause optional, which makes it easier
to write the native CREATE TABLE syntax. Unfortunately, it leads to some
conflicts with the Hive CREATE TABLE syntax, but I don't see a serious
problem here. If a user just writes CREATE TABLE without USING or ROW
FORMAT or STORED AS, does it matter what table we create? Internally the
parser rules conflict and we pick the native syntax depending on the rule
order. But the user-facing behavior looks fine.

CREATE EXTERNAL TABLE is a problem as it works in 2.4 but not in 3.0. Shall
we simply remove EXTERNAL from the native CREATE TABLE syntax? Then CREATE
EXTERNAL TABLE creates Hive table like 2.4.

On Mon, Mar 16, 2020 at 10:55 AM Jungtaek Lim 
wrote:

> Hi devs,
>
> I'd like to initiate discussion and hear the voices for resolving
> ambiguous parser rule between two "create table"s being brought by
> SPARK-30098 [1].
>
> Previously, "create table" parser rules were clearly distinguished via
> "USING provider", which was very intuitive and deterministic. Say, DDL
> query creates "Hive" table unless "USING provider" is specified,
> (Please refer the parser rule in branch-2.4 [2])
>
> After SPARK-30098, "create table" parser rules became ambiguous (please
> refer the parser rule in branch-3.0 [3]) - the factors differentiating two
> rules are only "ROW FORMAT" and "STORED AS" which are all defined as
> "optional". Now it relies on the "order" of parser rule which end users
> would have no idea to reason about, and very unintuitive.
>
> Furthermore, undocumented rule of EXTERNAL (added in the first rule to
> provide better message) brought more confusion (I've described the broken
> existing query via SPARK-30436 [4]).
>
> Personally I'd like to see two rules mutually exclusive, instead of trying
> to document the difference and talk end users to be careful about their
> query. I'm seeing two ways to make rules be mutually exclusive:
>
> 1. Add some identifier in create Hive table rule, like `CREATE ... "HIVE"
> TABLE ...`.
>
> pros. This is the simplest way to distinguish between two rules.
> cons. This would lead end users to change their query if they intend to
> create Hive table. (Given we will also provide legacy option I'm feeling
> this is acceptable.)
>
> 2. Define "ROW FORMAT" or "STORED AS" as mandatory one.
>
> pros. Less invasive for existing queries.
> cons. Less intuitive, because they have been optional and now become
> mandatory to fall into the second rule.
>
> Would like to hear everyone's voices; better ideas are welcome!
>
> Thanks,
> Jungtaek Lim (HeartSaVioR)
>
> 1. SPARK-30098 Use default datasource as provider for CREATE TABLE syntax
> https://issues.apache.org/jira/browse/SPARK-30098
> 2.
> https://github.com/apache/spark/blob/branch-2.4/sql/catalyst/src/main/antlr4/org/apache/spark/sql/catalyst/parser/SqlBase.g4
> 3.
> https://github.com/apache/spark/blob/branch-3.0/sql/catalyst/src/main/antlr4/org/apache/spark/sql/catalyst/parser/SqlBase.g4
> 4. https://issues.apache.org/jira/browse/SPARK-30436
>
>

Re: Spark 2.4.x and 3.x datasourcev2 api documentation & references

[Wenchen Fan]

For now you can take a look at `DataSourceV2Suite`, which contains both
Java/Scala implementations. There is also an ongoing PR to implement
catalog APIs for JDBC: https://github.com/apache/spark/pull/27345

We are still working on the user guide.

On Mon, Mar 16, 2020 at 4:59 AM MadDoxX 
wrote:

> Hi,
> i have been following closely the new proposed api, and seems until now
> some
> lack of documentation... specially for people trying to implement them in
> JAVA.
>
> Anyway to give out some reference as starting point for java developpers as
> tests classes  ?
>
> (anyway new api looks promising) !
>
>
>
>
>
> --
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>
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>
>