Re: Quicksql

[Chunwei Lei]

; >>
> >>
> >> Juan Pan (Trista)
> >>
> >> Senior DBA & PPMC of Apache ShardingSphere(Incubating)
> >> E-mail: panj...@apache.org
> >>
> >>
> >>
> >>
> >> On 12/13/2019 17:16，Juan Pan wrote：
> >> Yes, indeed.
> >>
> >>
> >> Juan Pan (Trista)
> >>
> >> Senior DBA & PPMC of Apache ShardingSphere(Incubating)
> >> E-mail: panj...@apache.org
> >>
> >>
> >>
> >>
> >> On 12/12/2019 18:00，Alessandro Solimando >
> >> wrote：
> >> Adapters must be needed by data sources not supporting SQL, I think this
> >> is
> >> what Juan Pan was asking for.
> >>
> >> On Thu, 12 Dec 2019 at 04:05, Haisheng Yuan  wrote:
> >>
> >> Nope, it doesn't use any adapters. It just submits partial SQL query to
> >> different engines.
> >>
> >> If query contains table from single source, e.g.
> >> select count(*) from hive_table1, hive_table2 where a=b;
> >> then the whole query will be submitted to hive.
> >>
> >> Otherwise, e.g.
> >> select distinct a,b from hive_table union select distinct a,b from
> >> mysql_table;
> >>
> >> The following query will be submitted to Spark and executed by Spark:
> >> select a,b from spark_tmp_table1 union select a,b from spark_tmp_table2;
> >>
> >> spark_tmp_table1: select distinct a,b from hive_table
> >> spark_tmp_table2: select distinct a,b from mysql_table
> >>
> >> On 2019/12/11 04:27:07, "Juan Pan"  wrote:
> >> Hi Haisheng,
> >>
> >>
> >> The query on different data source will then be registered as temp
> >> spark tables (with filter or join pushed in), the whole query is
> rewritten
> >> as SQL text over these temp tables and submitted to Spark.
> >>
> >>
> >> Does it mean QuickSQL also need adaptors to make query executed on
> >> different data source?
> >>
> >>
> >> Yes, virtualization is one of Calcite’s goals. In fact, when I created
> >> Calcite I was thinking about virtualization + in-memory materialized
> >> views.
> >> Not only the Spark convention but any of the “engine” conventions
> (Drill,
> >> Flink, Beam, Enumerable) could be used to create a virtual query engine.
> >>
> >>
> >> Basically, i like and agree with Julian’s statement. It is a great idea
> >> which personally hope Calcite move towards.
> >>
> >>
> >> Give my best wishes to Calcite community.
> >>
> >>
> >> Thanks,
> >> Trista
> >>
> >>
> >> Juan Pan
> >>
> >>
> >> panj...@apache.org
> >> Juan Pan(Trista), Apache ShardingSphere
> >>
> >>
> >> On 12/11/2019 10:53，Haisheng Yuan wrote：
> >> As far as I know, users still need to register tables from other data
> >> sources before querying it. QuickSQL uses Calcite for parsing queries
> and
> >> optimizing logical expressions with several transformation rules. The
> >> query
> >> on different data source will then be registered as temp spark tables
> >> (with
> >> filter or join pushed in), the whole query is rewritten as SQL text over
> >> these temp tables and submitted to Spark.
> >>
> >> - Haisheng
> >>
> >> --
> >> 发件人：Rui Wang
> >> 日 期：2019年12月11日 06:24:45
> >> 收件人：
> >> 主 题：Re: Quicksql
> >>
> >> The co-routine model sounds fitting into Streaming cases well.
> >>
> >> I was thinking how should Enumerable interface work with streaming cases
> >> but now I should also check Interpreter.
> >>
> >>
> >> -Rui
> >>
> >> On Tue, Dec 10, 2019 at 1:33 PM Julian Hyde  wrote:
> >>
> >> The goal (or rather my goal) for the interpreter is to replace
> >> Enumerable as the quick, easy default convention.
> >>
> >> Enumerable is efficient but not that efficient (compared to engines
> >> that work on off-heap data representing batches of records). And
> >> because it generates java byte code there is a certain latency to
> >> getting a query prepared and ready to run.
> >>
> >> It basically implements the old Volcano query evaluation model. It is
> >> single-threaded (because all work happens as a result of a call to
&g

Re: Quicksql

[Siyuan Liu]

 adapters. It just submits partial SQL query to
>> different engines.
>>
>> If query contains table from single source, e.g.
>> select count(*) from hive_table1, hive_table2 where a=b;
>> then the whole query will be submitted to hive.
>>
>> Otherwise, e.g.
>> select distinct a,b from hive_table union select distinct a,b from
>> mysql_table;
>>
>> The following query will be submitted to Spark and executed by Spark:
>> select a,b from spark_tmp_table1 union select a,b from spark_tmp_table2;
>>
>> spark_tmp_table1: select distinct a,b from hive_table
>> spark_tmp_table2: select distinct a,b from mysql_table
>>
>> On 2019/12/11 04:27:07, "Juan Pan"  wrote:
>> Hi Haisheng,
>>
>>
>> The query on different data source will then be registered as temp
>> spark tables (with filter or join pushed in), the whole query is rewritten
>> as SQL text over these temp tables and submitted to Spark.
>>
>>
>> Does it mean QuickSQL also need adaptors to make query executed on
>> different data source?
>>
>>
>> Yes, virtualization is one of Calcite’s goals. In fact, when I created
>> Calcite I was thinking about virtualization + in-memory materialized
>> views.
>> Not only the Spark convention but any of the “engine” conventions (Drill,
>> Flink, Beam, Enumerable) could be used to create a virtual query engine.
>>
>>
>> Basically, i like and agree with Julian’s statement. It is a great idea
>> which personally hope Calcite move towards.
>>
>>
>> Give my best wishes to Calcite community.
>>
>>
>> Thanks,
>> Trista
>>
>>
>> Juan Pan
>>
>>
>> panj...@apache.org
>> Juan Pan(Trista), Apache ShardingSphere
>>
>>
>> On 12/11/2019 10:53，Haisheng Yuan wrote：
>> As far as I know, users still need to register tables from other data
>> sources before querying it. QuickSQL uses Calcite for parsing queries and
>> optimizing logical expressions with several transformation rules. The
>> query
>> on different data source will then be registered as temp spark tables
>> (with
>> filter or join pushed in), the whole query is rewritten as SQL text over
>> these temp tables and submitted to Spark.
>>
>> - Haisheng
>>
>> --
>> 发件人：Rui Wang
>> 日 期：2019年12月11日 06:24:45
>> 收件人：
>> 主 题：Re: Quicksql
>>
>> The co-routine model sounds fitting into Streaming cases well.
>>
>> I was thinking how should Enumerable interface work with streaming cases
>> but now I should also check Interpreter.
>>
>>
>> -Rui
>>
>> On Tue, Dec 10, 2019 at 1:33 PM Julian Hyde  wrote:
>>
>> The goal (or rather my goal) for the interpreter is to replace
>> Enumerable as the quick, easy default convention.
>>
>> Enumerable is efficient but not that efficient (compared to engines
>> that work on off-heap data representing batches of records). And
>> because it generates java byte code there is a certain latency to
>> getting a query prepared and ready to run.
>>
>> It basically implements the old Volcano query evaluation model. It is
>> single-threaded (because all work happens as a result of a call to
>> 'next()' on the root node) and cannot handle branching data-flow
>> graphs (DAGs).
>>
>> The Interpreter operates uses a co-routine model (reading from queues,
>> writing to queues, and yielding when there is no work to be done) and
>> therefore could be more efficient than enumerable in a single-node
>> multi-core system. Also, there is little start-up time, which is
>> important for small queries.
>>
>> I would love to add another built-in convention that uses Arrow as
>> data format and generates co-routines for each operator. Those
>> co-routines could be deployed in a parallel and/or distributed data
>> engine.
>>
>> Julian
>>
>> On Tue, Dec 10, 2019 at 3:47 AM Zoltan Farkas
>>  wrote:
>>
>> What is the ultimate goal of the Calcite Interpreter?
>>
>> To provide some context, I have been playing around with calcite + REST
>> (see https://github.com/zolyfarkas/jaxrs-spf4j-demo/wiki/AvroCalciteRest
>> <
>> https://github.com/zolyfarkas/jaxrs-spf4j-demo/wiki/AvroCalciteRest> for
>> detail of my experiments)
>>
>>
>> —Z
>>
>> On Dec 9, 2019, at 9:05 PM, Julian Hyde  wrote:
>>
>> Yes, virtualization is one of Calcite’s goals. In fact, when I created
>> Calcite

Re: Quicksql

[Francis Du]

Hi everyone:

Allow me to introduce my good friend Siyuan Liu, who is the leader of
Quicksql project.

I CC to him and ask him to introduce the project to us.Here is the
documentation link for

Quicksql [1].

[1].  https://quicksql.readthedocs.io/en/latest/

Regards,
Francis

Juan Pan  于2019年12月23日周一 上午11:44写道：

> Thanks Gelbana,
>
>
> Very appreciated your explanation, which sheds me some light on exploring
> Calcite. :)
>
>
> Best wishes,
> Trista
>
>
>  Juan Pan (Trista)
>
> Senior DBA & PPMC of Apache ShardingSphere(Incubating)
> E-mail: panj...@apache.org
>
>
>
>
> On 12/22/2019 05:58，Muhammad Gelbana wrote：
> I am curious how to join the tables from different datasources.
> Based on Calcite's conventions concept, the Join operator and its input
> operators should all have the same convention. If they don't, the
> convention different from the Join operator's convention will have to
> register a converter rule. This rule should produce an operator that only
> converts from that convention to the Join operator's convention.
>
> This way the Join operator will be able to handle the data obtained from
> its input operators because it understands the data structure.
>
> Thanks,
> Gelbana
>
>
> On Wed, Dec 18, 2019 at 5:08 AM Juan Pan  wrote:
>
> Some updates.
>
>
> Recently i took a look at their doc and source code, and found this
> project uses SQL parsing and Relational algebra of Calcite to get query
> plan, and also translates to spark SQL for joining different datasources,
> or corresponding query for single datasource.
>
>
> Although it copies many classes from Calcite, the idea of QuickSQL seems
> some of interests, and code is succinct.
>
>
> Best,
> Trista
>
>
> Juan Pan (Trista)
>
> Senior DBA & PPMC of Apache ShardingSphere(Incubating)
> E-mail: panj...@apache.org
>
>
>
>
> On 12/13/2019 17:16，Juan Pan wrote：
> Yes, indeed.
>
>
> Juan Pan (Trista)
>
> Senior DBA & PPMC of Apache ShardingSphere(Incubating)
> E-mail: panj...@apache.org
>
>
>
>
> On 12/12/2019 18:00，Alessandro Solimando
> wrote：
> Adapters must be needed by data sources not supporting SQL, I think this is
> what Juan Pan was asking for.
>
> On Thu, 12 Dec 2019 at 04:05, Haisheng Yuan  wrote:
>
> Nope, it doesn't use any adapters. It just submits partial SQL query to
> different engines.
>
> If query contains table from single source, e.g.
> select count(*) from hive_table1, hive_table2 where a=b;
> then the whole query will be submitted to hive.
>
> Otherwise, e.g.
> select distinct a,b from hive_table union select distinct a,b from
> mysql_table;
>
> The following query will be submitted to Spark and executed by Spark:
> select a,b from spark_tmp_table1 union select a,b from spark_tmp_table2;
>
> spark_tmp_table1: select distinct a,b from hive_table
> spark_tmp_table2: select distinct a,b from mysql_table
>
> On 2019/12/11 04:27:07, "Juan Pan"  wrote:
> Hi Haisheng,
>
>
> The query on different data source will then be registered as temp
> spark tables (with filter or join pushed in), the whole query is rewritten
> as SQL text over these temp tables and submitted to Spark.
>
>
> Does it mean QuickSQL also need adaptors to make query executed on
> different data source?
>
>
> Yes, virtualization is one of Calcite’s goals. In fact, when I created
> Calcite I was thinking about virtualization + in-memory materialized views.
> Not only the Spark convention but any of the “engine” conventions (Drill,
> Flink, Beam, Enumerable) could be used to create a virtual query engine.
>
>
> Basically, i like and agree with Julian’s statement. It is a great idea
> which personally hope Calcite move towards.
>
>
> Give my best wishes to Calcite community.
>
>
> Thanks,
> Trista
>
>
> Juan Pan
>
>
> panj...@apache.org
> Juan Pan(Trista), Apache ShardingSphere
>
>
> On 12/11/2019 10:53，Haisheng Yuan wrote：
> As far as I know, users still need to register tables from other data
> sources before querying it. QuickSQL uses Calcite for parsing queries and
> optimizing logical expressions with several transformation rules. The query
> on different data source will then be registered as temp spark tables (with
> filter or join pushed in), the whole query is rewritten as SQL text over
> these temp tables and submitted to Spark.
>
> - Haisheng
>
> --
> 发件人：Rui Wang
> 日 期：2019年12月11日 06:24:45
> 收件人：
> 主 题：Re: Quicksql
>
> The co-routine model sounds fitting into Streaming cases well.
>
> I was thinking how shoul

Re: Quicksql

[Juan Pan]

Thanks Gelbana,


Very appreciated your explanation, which sheds me some light on exploring 
Calcite. :)


Best wishes,
Trista


 Juan Pan (Trista) 
 
Senior DBA & PPMC of Apache ShardingSphere(Incubating)
E-mail: panj...@apache.org




On 12/22/2019 05:58，Muhammad Gelbana wrote：
I am curious how to join the tables from different datasources.
Based on Calcite's conventions concept, the Join operator and its input
operators should all have the same convention. If they don't, the
convention different from the Join operator's convention will have to
register a converter rule. This rule should produce an operator that only
converts from that convention to the Join operator's convention.

This way the Join operator will be able to handle the data obtained from
its input operators because it understands the data structure.

Thanks,
Gelbana


On Wed, Dec 18, 2019 at 5:08 AM Juan Pan  wrote:

Some updates.


Recently i took a look at their doc and source code, and found this
project uses SQL parsing and Relational algebra of Calcite to get query
plan, and also translates to spark SQL for joining different datasources,
or corresponding query for single datasource.


Although it copies many classes from Calcite, the idea of QuickSQL seems
some of interests, and code is succinct.


Best,
Trista


Juan Pan (Trista)

Senior DBA & PPMC of Apache ShardingSphere(Incubating)
E-mail: panj...@apache.org




On 12/13/2019 17:16，Juan Pan wrote：
Yes, indeed.


Juan Pan (Trista)

Senior DBA & PPMC of Apache ShardingSphere(Incubating)
E-mail: panj...@apache.org




On 12/12/2019 18:00，Alessandro Solimando
wrote：
Adapters must be needed by data sources not supporting SQL, I think this is
what Juan Pan was asking for.

On Thu, 12 Dec 2019 at 04:05, Haisheng Yuan  wrote:

Nope, it doesn't use any adapters. It just submits partial SQL query to
different engines.

If query contains table from single source, e.g.
select count(*) from hive_table1, hive_table2 where a=b;
then the whole query will be submitted to hive.

Otherwise, e.g.
select distinct a,b from hive_table union select distinct a,b from
mysql_table;

The following query will be submitted to Spark and executed by Spark:
select a,b from spark_tmp_table1 union select a,b from spark_tmp_table2;

spark_tmp_table1: select distinct a,b from hive_table
spark_tmp_table2: select distinct a,b from mysql_table

On 2019/12/11 04:27:07, "Juan Pan"  wrote:
Hi Haisheng,


The query on different data source will then be registered as temp
spark tables (with filter or join pushed in), the whole query is rewritten
as SQL text over these temp tables and submitted to Spark.


Does it mean QuickSQL also need adaptors to make query executed on
different data source?


Yes, virtualization is one of Calcite’s goals. In fact, when I created
Calcite I was thinking about virtualization + in-memory materialized views.
Not only the Spark convention but any of the “engine” conventions (Drill,
Flink, Beam, Enumerable) could be used to create a virtual query engine.


Basically, i like and agree with Julian’s statement. It is a great idea
which personally hope Calcite move towards.


Give my best wishes to Calcite community.


Thanks,
Trista


Juan Pan


panj...@apache.org
Juan Pan(Trista), Apache ShardingSphere


On 12/11/2019 10:53，Haisheng Yuan wrote：
As far as I know, users still need to register tables from other data
sources before querying it. QuickSQL uses Calcite for parsing queries and
optimizing logical expressions with several transformation rules. The query
on different data source will then be registered as temp spark tables (with
filter or join pushed in), the whole query is rewritten as SQL text over
these temp tables and submitted to Spark.

- Haisheng

--
发件人：Rui Wang
日 期：2019年12月11日 06:24:45
收件人：
主 题：Re: Quicksql

The co-routine model sounds fitting into Streaming cases well.

I was thinking how should Enumerable interface work with streaming cases
but now I should also check Interpreter.


-Rui

On Tue, Dec 10, 2019 at 1:33 PM Julian Hyde  wrote:

The goal (or rather my goal) for the interpreter is to replace
Enumerable as the quick, easy default convention.

Enumerable is efficient but not that efficient (compared to engines
that work on off-heap data representing batches of records). And
because it generates java byte code there is a certain latency to
getting a query prepared and ready to run.

It basically implements the old Volcano query evaluation model. It is
single-threaded (because all work happens as a result of a call to
'next()' on the root node) and cannot handle branching data-flow
graphs (DAGs).

The Interpreter operates uses a co-routine model (reading from queues,
writing to queues, and yielding when there is no work to be done) and
therefore could be more efficient than enumerable in a single-node
multi-core system. Also, there is little start-up time, which 

Re: Quicksql

[Muhammad Gelbana]

> I am curious how to join the tables from different datasources.
Based on Calcite's conventions concept, the Join operator and its input
operators should all have the same convention. If they don't, the
convention different from the Join operator's convention will have to
register a converter rule. This rule should produce an operator that only
converts from that convention to the Join operator's convention.

This way the Join operator will be able to handle the data obtained from
its input operators because it understands the data structure.

Thanks,
Gelbana


On Wed, Dec 18, 2019 at 5:08 AM Juan Pan  wrote:

> Some updates.
>
>
> Recently i took a look at their doc and source code, and found this
> project uses SQL parsing and Relational algebra of Calcite to get query
> plan, and also translates to spark SQL for joining different datasources,
> or corresponding query for single datasource.
>
>
> Although it copies many classes from Calcite, the idea of QuickSQL seems
> some of interests, and code is succinct.
>
>
> Best,
> Trista
>
>
>  Juan Pan (Trista)
>
> Senior DBA & PPMC of Apache ShardingSphere(Incubating)
> E-mail: panj...@apache.org
>
>
>
>
> On 12/13/2019 17:16，Juan Pan wrote：
> Yes, indeed.
>
>
> Juan Pan (Trista)
>
> Senior DBA & PPMC of Apache ShardingSphere(Incubating)
> E-mail: panj...@apache.org
>
>
>
>
> On 12/12/2019 18:00，Alessandro Solimando
> wrote：
> Adapters must be needed by data sources not supporting SQL, I think this is
> what Juan Pan was asking for.
>
> On Thu, 12 Dec 2019 at 04:05, Haisheng Yuan  wrote:
>
> Nope, it doesn't use any adapters. It just submits partial SQL query to
> different engines.
>
> If query contains table from single source, e.g.
> select count(*) from hive_table1, hive_table2 where a=b;
> then the whole query will be submitted to hive.
>
> Otherwise, e.g.
> select distinct a,b from hive_table union select distinct a,b from
> mysql_table;
>
> The following query will be submitted to Spark and executed by Spark:
> select a,b from spark_tmp_table1 union select a,b from spark_tmp_table2;
>
> spark_tmp_table1: select distinct a,b from hive_table
> spark_tmp_table2: select distinct a,b from mysql_table
>
> On 2019/12/11 04:27:07, "Juan Pan"  wrote:
> Hi Haisheng,
>
>
> The query on different data source will then be registered as temp
> spark tables (with filter or join pushed in), the whole query is rewritten
> as SQL text over these temp tables and submitted to Spark.
>
>
> Does it mean QuickSQL also need adaptors to make query executed on
> different data source?
>
>
> Yes, virtualization is one of Calcite’s goals. In fact, when I created
> Calcite I was thinking about virtualization + in-memory materialized views.
> Not only the Spark convention but any of the “engine” conventions (Drill,
> Flink, Beam, Enumerable) could be used to create a virtual query engine.
>
>
> Basically, i like and agree with Julian’s statement. It is a great idea
> which personally hope Calcite move towards.
>
>
> Give my best wishes to Calcite community.
>
>
> Thanks,
> Trista
>
>
> Juan Pan
>
>
> panj...@apache.org
> Juan Pan(Trista), Apache ShardingSphere
>
>
> On 12/11/2019 10:53，Haisheng Yuan wrote：
> As far as I know, users still need to register tables from other data
> sources before querying it. QuickSQL uses Calcite for parsing queries and
> optimizing logical expressions with several transformation rules. The query
> on different data source will then be registered as temp spark tables (with
> filter or join pushed in), the whole query is rewritten as SQL text over
> these temp tables and submitted to Spark.
>
> - Haisheng
>
> --
> 发件人：Rui Wang
> 日 期：2019年12月11日 06:24:45
> 收件人：
> 主 题：Re: Quicksql
>
> The co-routine model sounds fitting into Streaming cases well.
>
> I was thinking how should Enumerable interface work with streaming cases
> but now I should also check Interpreter.
>
>
> -Rui
>
> On Tue, Dec 10, 2019 at 1:33 PM Julian Hyde  wrote:
>
> The goal (or rather my goal) for the interpreter is to replace
> Enumerable as the quick, easy default convention.
>
> Enumerable is efficient but not that efficient (compared to engines
> that work on off-heap data representing batches of records). And
> because it generates java byte code there is a certain latency to
> getting a query prepared and ready to run.
>
> It basically implements the old Volcano query evaluation model. It is
> single-threaded (because all work happens as a result of a call to
> 'next()' on the root node) and 

Re: Quicksql

[Juan Pan]

Some updates.


Recently i took a look at their doc and source code, and found this project 
uses SQL parsing and Relational algebra of Calcite to get query plan, and also 
translates to spark SQL for joining different datasources, or corresponding 
query for single datasource.


Although it copies many classes from Calcite, the idea of QuickSQL seems some 
of interests, and code is succinct.


Best,
Trista 


 Juan Pan (Trista) 
 
Senior DBA & PPMC of Apache ShardingSphere(Incubating)
E-mail: panj...@apache.org




On 12/13/2019 17:16，Juan Pan wrote：
Yes, indeed.


Juan Pan (Trista)

Senior DBA & PPMC of Apache ShardingSphere(Incubating)
E-mail: panj...@apache.org




On 12/12/2019 18:00，Alessandro Solimando wrote：
Adapters must be needed by data sources not supporting SQL, I think this is
what Juan Pan was asking for.

On Thu, 12 Dec 2019 at 04:05, Haisheng Yuan  wrote:

Nope, it doesn't use any adapters. It just submits partial SQL query to
different engines.

If query contains table from single source, e.g.
select count(*) from hive_table1, hive_table2 where a=b;
then the whole query will be submitted to hive.

Otherwise, e.g.
select distinct a,b from hive_table union select distinct a,b from
mysql_table;

The following query will be submitted to Spark and executed by Spark:
select a,b from spark_tmp_table1 union select a,b from spark_tmp_table2;

spark_tmp_table1: select distinct a,b from hive_table
spark_tmp_table2: select distinct a,b from mysql_table

On 2019/12/11 04:27:07, "Juan Pan"  wrote:
Hi Haisheng,


The query on different data source will then be registered as temp
spark tables (with filter or join pushed in), the whole query is rewritten
as SQL text over these temp tables and submitted to Spark.


Does it mean QuickSQL also need adaptors to make query executed on
different data source?


Yes, virtualization is one of Calcite’s goals. In fact, when I created
Calcite I was thinking about virtualization + in-memory materialized views.
Not only the Spark convention but any of the “engine” conventions (Drill,
Flink, Beam, Enumerable) could be used to create a virtual query engine.


Basically, i like and agree with Julian’s statement. It is a great idea
which personally hope Calcite move towards.


Give my best wishes to Calcite community.


Thanks,
Trista


Juan Pan


panj...@apache.org
Juan Pan(Trista), Apache ShardingSphere


On 12/11/2019 10:53，Haisheng Yuan wrote：
As far as I know, users still need to register tables from other data
sources before querying it. QuickSQL uses Calcite for parsing queries and
optimizing logical expressions with several transformation rules. The query
on different data source will then be registered as temp spark tables (with
filter or join pushed in), the whole query is rewritten as SQL text over
these temp tables and submitted to Spark.

- Haisheng

--
发件人：Rui Wang
日 期：2019年12月11日 06:24:45
收件人：
主 题：Re: Quicksql

The co-routine model sounds fitting into Streaming cases well.

I was thinking how should Enumerable interface work with streaming cases
but now I should also check Interpreter.


-Rui

On Tue, Dec 10, 2019 at 1:33 PM Julian Hyde  wrote:

The goal (or rather my goal) for the interpreter is to replace
Enumerable as the quick, easy default convention.

Enumerable is efficient but not that efficient (compared to engines
that work on off-heap data representing batches of records). And
because it generates java byte code there is a certain latency to
getting a query prepared and ready to run.

It basically implements the old Volcano query evaluation model. It is
single-threaded (because all work happens as a result of a call to
'next()' on the root node) and cannot handle branching data-flow
graphs (DAGs).

The Interpreter operates uses a co-routine model (reading from queues,
writing to queues, and yielding when there is no work to be done) and
therefore could be more efficient than enumerable in a single-node
multi-core system. Also, there is little start-up time, which is
important for small queries.

I would love to add another built-in convention that uses Arrow as
data format and generates co-routines for each operator. Those
co-routines could be deployed in a parallel and/or distributed data
engine.

Julian

On Tue, Dec 10, 2019 at 3:47 AM Zoltan Farkas
 wrote:

What is the ultimate goal of the Calcite Interpreter?

To provide some context, I have been playing around with calcite + REST
(see https://github.com/zolyfarkas/jaxrs-spf4j-demo/wiki/AvroCalciteRest
<
https://github.com/zolyfarkas/jaxrs-spf4j-demo/wiki/AvroCalciteRest> for
detail of my experiments)


—Z

On Dec 9, 2019, at 9:05 PM, Julian Hyde  wrote:

Yes, virtualization is one of Calcite’s goals. In fact, when I created
Calcite I was thinking about virtualization + in-memory materialized
views.
Not only the Spark convention but any of the “engine” conventions (Drill,
Flink,

Re: Quicksql

[Juan Pan]

Thanks for your clarification, Haisheng.


I am curious how to join the tables from different datasources. 


Supposing there is tb1 in datasource1 and tb2 in datasource2 and the SQL is 
`select tb1.col1, tb2.col2 from tb1, tb2 where tb1.id = tb2.id`, how to join 
two of tables together and get the final result?


 Juan Pan (Trista) 
 
Senior DBA & PPMC of Apache ShardingSphere(Incubating)
E-mail: panj...@apache.org




On 12/12/2019 11:05，Haisheng Yuan wrote：
Nope, it doesn't use any adapters. It just submits partial SQL query to 
different engines.

If query contains table from single source, e.g.
select count(*) from hive_table1, hive_table2 where a=b;
then the whole query will be submitted to hive.

Otherwise, e.g.
select distinct a,b from hive_table union select distinct a,b from mysql_table;

The following query will be submitted to Spark and executed by Spark:
select a,b from spark_tmp_table1 union select a,b from spark_tmp_table2;

spark_tmp_table1: select distinct a,b from hive_table
spark_tmp_table2: select distinct a,b from mysql_table

On 2019/12/11 04:27:07, "Juan Pan"  wrote:
Hi Haisheng,


The query on different data source will then be registered as temp spark tables 
(with filter or join pushed in), the whole query is rewritten as SQL text over 
these temp tables and submitted to Spark.


Does it mean QuickSQL also need adaptors to make query executed on different 
data source?


Yes, virtualization is one of Calcite’s goals. In fact, when I created Calcite 
I was thinking about virtualization + in-memory materialized views. Not only 
the Spark convention but any of the “engine” conventions (Drill, Flink, Beam, 
Enumerable) could be used to create a virtual query engine.


Basically, i like and agree with Julian’s statement. It is a great idea which 
personally hope Calcite move towards.


Give my best wishes to Calcite community.


Thanks,
Trista


Juan Pan


panj...@apache.org
Juan Pan(Trista), Apache ShardingSphere


On 12/11/2019 10:53，Haisheng Yuan wrote：
As far as I know, users still need to register tables from other data sources 
before querying it. QuickSQL uses Calcite for parsing queries and optimizing 
logical expressions with several transformation rules. The query on different 
data source will then be registered as temp spark tables (with filter or join 
pushed in), the whole query is rewritten as SQL text over these temp tables and 
submitted to Spark.

- Haisheng

--
发件人：Rui Wang
日　期：2019年12月11日 06:24:45
收件人：
主　题：Re: Quicksql

The co-routine model sounds fitting into Streaming cases well.

I was thinking how should Enumerable interface work with streaming cases
but now I should also check Interpreter.


-Rui

On Tue, Dec 10, 2019 at 1:33 PM Julian Hyde  wrote:

The goal (or rather my goal) for the interpreter is to replace
Enumerable as the quick, easy default convention.

Enumerable is efficient but not that efficient (compared to engines
that work on off-heap data representing batches of records). And
because it generates java byte code there is a certain latency to
getting a query prepared and ready to run.

It basically implements the old Volcano query evaluation model. It is
single-threaded (because all work happens as a result of a call to
'next()' on the root node) and cannot handle branching data-flow
graphs (DAGs).

The Interpreter operates uses a co-routine model (reading from queues,
writing to queues, and yielding when there is no work to be done) and
therefore could be more efficient than enumerable in a single-node
multi-core system. Also, there is little start-up time, which is
important for small queries.

I would love to add another built-in convention that uses Arrow as
data format and generates co-routines for each operator. Those
co-routines could be deployed in a parallel and/or distributed data
engine.

Julian

On Tue, Dec 10, 2019 at 3:47 AM Zoltan Farkas
 wrote:

What is the ultimate goal of the Calcite Interpreter?

To provide some context, I have been playing around with calcite + REST
(see https://github.com/zolyfarkas/jaxrs-spf4j-demo/wiki/AvroCalciteRest <
https://github.com/zolyfarkas/jaxrs-spf4j-demo/wiki/AvroCalciteRest> for
detail of my experiments)


—Z

On Dec 9, 2019, at 9:05 PM, Julian Hyde  wrote:

Yes, virtualization is one of Calcite’s goals. In fact, when I created
Calcite I was thinking about virtualization + in-memory materialized views.
Not only the Spark convention but any of the “engine” conventions (Drill,
Flink, Beam, Enumerable) could be used to create a virtual query engine.

See e.g. a talk I gave in 2013 about Optiq (precursor to Calcite)
https://www.slideshare.net/julianhyde/optiq-a-dynamic-data-management-framework
<
https://www.slideshare.net/julianhyde/optiq-a-dynamic-data-management-framework
.

Julian



On Dec 9, 2019, at 2:29 PM, Muhammad Gelbana 
wrote:

I recently contacted one of the active co

Re: Quicksql

[Juan Pan]

Yes, indeed.


 Juan Pan (Trista) 
 
Senior DBA & PPMC of Apache ShardingSphere(Incubating)
E-mail: panj...@apache.org




On 12/12/2019 18:00，Alessandro Solimando wrote：
Adapters must be needed by data sources not supporting SQL, I think this is
what Juan Pan was asking for.

On Thu, 12 Dec 2019 at 04:05, Haisheng Yuan  wrote:

Nope, it doesn't use any adapters. It just submits partial SQL query to
different engines.

If query contains table from single source, e.g.
select count(*) from hive_table1, hive_table2 where a=b;
then the whole query will be submitted to hive.

Otherwise, e.g.
select distinct a,b from hive_table union select distinct a,b from
mysql_table;

The following query will be submitted to Spark and executed by Spark:
select a,b from spark_tmp_table1 union select a,b from spark_tmp_table2;

spark_tmp_table1: select distinct a,b from hive_table
spark_tmp_table2: select distinct a,b from mysql_table

On 2019/12/11 04:27:07, "Juan Pan"  wrote:
Hi Haisheng,


The query on different data source will then be registered as temp
spark tables (with filter or join pushed in), the whole query is rewritten
as SQL text over these temp tables and submitted to Spark.


Does it mean QuickSQL also need adaptors to make query executed on
different data source?


Yes, virtualization is one of Calcite’s goals. In fact, when I created
Calcite I was thinking about virtualization + in-memory materialized views.
Not only the Spark convention but any of the “engine” conventions (Drill,
Flink, Beam, Enumerable) could be used to create a virtual query engine.


Basically, i like and agree with Julian’s statement. It is a great idea
which personally hope Calcite move towards.


Give my best wishes to Calcite community.


Thanks,
Trista


Juan Pan


panj...@apache.org
Juan Pan(Trista), Apache ShardingSphere


On 12/11/2019 10:53，Haisheng Yuan wrote：
As far as I know, users still need to register tables from other data
sources before querying it. QuickSQL uses Calcite for parsing queries and
optimizing logical expressions with several transformation rules. The query
on different data source will then be registered as temp spark tables (with
filter or join pushed in), the whole query is rewritten as SQL text over
these temp tables and submitted to Spark.

- Haisheng

--
发件人：Rui Wang
日 期：2019年12月11日 06:24:45
收件人：
主 题：Re: Quicksql

The co-routine model sounds fitting into Streaming cases well.

I was thinking how should Enumerable interface work with streaming cases
but now I should also check Interpreter.


-Rui

On Tue, Dec 10, 2019 at 1:33 PM Julian Hyde  wrote:

The goal (or rather my goal) for the interpreter is to replace
Enumerable as the quick, easy default convention.

Enumerable is efficient but not that efficient (compared to engines
that work on off-heap data representing batches of records). And
because it generates java byte code there is a certain latency to
getting a query prepared and ready to run.

It basically implements the old Volcano query evaluation model. It is
single-threaded (because all work happens as a result of a call to
'next()' on the root node) and cannot handle branching data-flow
graphs (DAGs).

The Interpreter operates uses a co-routine model (reading from queues,
writing to queues, and yielding when there is no work to be done) and
therefore could be more efficient than enumerable in a single-node
multi-core system. Also, there is little start-up time, which is
important for small queries.

I would love to add another built-in convention that uses Arrow as
data format and generates co-routines for each operator. Those
co-routines could be deployed in a parallel and/or distributed data
engine.

Julian

On Tue, Dec 10, 2019 at 3:47 AM Zoltan Farkas
 wrote:

What is the ultimate goal of the Calcite Interpreter?

To provide some context, I have been playing around with calcite + REST
(see https://github.com/zolyfarkas/jaxrs-spf4j-demo/wiki/AvroCalciteRest
<
https://github.com/zolyfarkas/jaxrs-spf4j-demo/wiki/AvroCalciteRest> for
detail of my experiments)


—Z

On Dec 9, 2019, at 9:05 PM, Julian Hyde  wrote:

Yes, virtualization is one of Calcite’s goals. In fact, when I created
Calcite I was thinking about virtualization + in-memory materialized
views.
Not only the Spark convention but any of the “engine” conventions (Drill,
Flink, Beam, Enumerable) could be used to create a virtual query engine.

See e.g. a talk I gave in 2013 about Optiq (precursor to Calcite)

https://www.slideshare.net/julianhyde/optiq-a-dynamic-data-management-framework
<

https://www.slideshare.net/julianhyde/optiq-a-dynamic-data-management-framework
.

Julian



On Dec 9, 2019, at 2:29 PM, Muhammad Gelbana 
wrote:

I recently contacted one of the active contributors asking about the
purpose of the project and here's his reply:

From my understanding, Quicksql is a data virtualization platform. It
can
quer

Re: Quicksql

[Alessandro Solimando]

Adapters must be needed by data sources not supporting SQL, I think this is
what Juan Pan was asking for.

On Thu, 12 Dec 2019 at 04:05, Haisheng Yuan  wrote:

> Nope, it doesn't use any adapters. It just submits partial SQL query to
> different engines.
>
> If query contains table from single source, e.g.
> select count(*) from hive_table1, hive_table2 where a=b;
> then the whole query will be submitted to hive.
>
> Otherwise, e.g.
> select distinct a,b from hive_table union select distinct a,b from
> mysql_table;
>
> The following query will be submitted to Spark and executed by Spark:
> select a,b from spark_tmp_table1 union select a,b from spark_tmp_table2;
>
> spark_tmp_table1: select distinct a,b from hive_table
> spark_tmp_table2: select distinct a,b from mysql_table
>
> On 2019/12/11 04:27:07, "Juan Pan"  wrote:
> > Hi Haisheng,
> >
> >
> > > The query on different data source will then be registered as temp
> spark tables (with filter or join pushed in), the whole query is rewritten
> as SQL text over these temp tables and submitted to Spark.
> >
> >
> > Does it mean QuickSQL also need adaptors to make query executed on
> different data source?
> >
> >
> > > Yes, virtualization is one of Calcite’s goals. In fact, when I created
> Calcite I was thinking about virtualization + in-memory materialized views.
> Not only the Spark convention but any of the “engine” conventions (Drill,
> Flink, Beam, Enumerable) could be used to create a virtual query engine.
> >
> >
> > Basically, i like and agree with Julian’s statement. It is a great idea
> which personally hope Calcite move towards.
> >
> >
> > Give my best wishes to Calcite community.
> >
> >
> > Thanks,
> > Trista
> >
> >
> >  Juan Pan
> >
> >
> > panj...@apache.org
> > Juan Pan(Trista), Apache ShardingSphere
> >
> >
> > On 12/11/2019 10:53，Haisheng Yuan wrote：
> > As far as I know, users still need to register tables from other data
> sources before querying it. QuickSQL uses Calcite for parsing queries and
> optimizing logical expressions with several transformation rules. The query
> on different data source will then be registered as temp spark tables (with
> filter or join pushed in), the whole query is rewritten as SQL text over
> these temp tables and submitted to Spark.
> >
> > - Haisheng
> >
> > --
> > 发件人：Rui Wang
> > 日 期：2019年12月11日 06:24:45
> > 收件人：
> > 主 题：Re: Quicksql
> >
> > The co-routine model sounds fitting into Streaming cases well.
> >
> > I was thinking how should Enumerable interface work with streaming cases
> > but now I should also check Interpreter.
> >
> >
> > -Rui
> >
> > On Tue, Dec 10, 2019 at 1:33 PM Julian Hyde  wrote:
> >
> > The goal (or rather my goal) for the interpreter is to replace
> > Enumerable as the quick, easy default convention.
> >
> > Enumerable is efficient but not that efficient (compared to engines
> > that work on off-heap data representing batches of records). And
> > because it generates java byte code there is a certain latency to
> > getting a query prepared and ready to run.
> >
> > It basically implements the old Volcano query evaluation model. It is
> > single-threaded (because all work happens as a result of a call to
> > 'next()' on the root node) and cannot handle branching data-flow
> > graphs (DAGs).
> >
> > The Interpreter operates uses a co-routine model (reading from queues,
> > writing to queues, and yielding when there is no work to be done) and
> > therefore could be more efficient than enumerable in a single-node
> > multi-core system. Also, there is little start-up time, which is
> > important for small queries.
> >
> > I would love to add another built-in convention that uses Arrow as
> > data format and generates co-routines for each operator. Those
> > co-routines could be deployed in a parallel and/or distributed data
> > engine.
> >
> > Julian
> >
> > On Tue, Dec 10, 2019 at 3:47 AM Zoltan Farkas
> >  wrote:
> >
> > What is the ultimate goal of the Calcite Interpreter?
> >
> > To provide some context, I have been playing around with calcite + REST
> > (see https://github.com/zolyfarkas/jaxrs-spf4j-demo/wiki/AvroCalciteRest
> <
> > https://github.com/zolyfarkas/jaxrs-spf4j-demo/wiki/AvroCalciteRest> for
> > detail of my experiments)
> >
> >
> > —Z
> >
> > On Dec 9, 20

Re: Quicksql

[Haisheng Yuan]

Nope, it doesn't use any adapters. It just submits partial SQL query to 
different engines.

If query contains table from single source, e.g.
select count(*) from hive_table1, hive_table2 where a=b;
then the whole query will be submitted to hive.

Otherwise, e.g.
select distinct a,b from hive_table union select distinct a,b from mysql_table;

The following query will be submitted to Spark and executed by Spark:
select a,b from spark_tmp_table1 union select a,b from spark_tmp_table2;

spark_tmp_table1: select distinct a,b from hive_table 
spark_tmp_table2: select distinct a,b from mysql_table

On 2019/12/11 04:27:07, "Juan Pan"  wrote: 
> Hi Haisheng,
> 
> 
> > The query on different data source will then be registered as temp spark 
> > tables (with filter or join pushed in), the whole query is rewritten as SQL 
> > text over these temp tables and submitted to Spark.
> 
> 
> Does it mean QuickSQL also need adaptors to make query executed on different 
> data source? 
> 
> 
> > Yes, virtualization is one of Calcite’s goals. In fact, when I created 
> > Calcite I was thinking about virtualization + in-memory materialized views. 
> > Not only the Spark convention but any of the “engine” conventions (Drill, 
> > Flink, Beam, Enumerable) could be used to create a virtual query engine.
> 
> 
> Basically, i like and agree with Julian’s statement. It is a great idea which 
> personally hope Calcite move towards.
> 
> 
> Give my best wishes to Calcite community. 
> 
> 
> Thanks,
> Trista
> 
> 
>  Juan Pan
> 
> 
> panj...@apache.org
> Juan Pan(Trista), Apache ShardingSphere
> 
> 
> On 12/11/2019 10:53，Haisheng Yuan wrote：
> As far as I know, users still need to register tables from other data sources 
> before querying it. QuickSQL uses Calcite for parsing queries and optimizing 
> logical expressions with several transformation rules. The query on different 
> data source will then be registered as temp spark tables (with filter or join 
> pushed in), the whole query is rewritten as SQL text over these temp tables 
> and submitted to Spark.
> 
> - Haisheng
> 
> --
> 发件人：Rui Wang
> 日　期：2019年12月11日 06:24:45
> 收件人：
> 主　题：Re: Quicksql
> 
> The co-routine model sounds fitting into Streaming cases well.
> 
> I was thinking how should Enumerable interface work with streaming cases
> but now I should also check Interpreter.
> 
> 
> -Rui
> 
> On Tue, Dec 10, 2019 at 1:33 PM Julian Hyde  wrote:
> 
> The goal (or rather my goal) for the interpreter is to replace
> Enumerable as the quick, easy default convention.
> 
> Enumerable is efficient but not that efficient (compared to engines
> that work on off-heap data representing batches of records). And
> because it generates java byte code there is a certain latency to
> getting a query prepared and ready to run.
> 
> It basically implements the old Volcano query evaluation model. It is
> single-threaded (because all work happens as a result of a call to
> 'next()' on the root node) and cannot handle branching data-flow
> graphs (DAGs).
> 
> The Interpreter operates uses a co-routine model (reading from queues,
> writing to queues, and yielding when there is no work to be done) and
> therefore could be more efficient than enumerable in a single-node
> multi-core system. Also, there is little start-up time, which is
> important for small queries.
> 
> I would love to add another built-in convention that uses Arrow as
> data format and generates co-routines for each operator. Those
> co-routines could be deployed in a parallel and/or distributed data
> engine.
> 
> Julian
> 
> On Tue, Dec 10, 2019 at 3:47 AM Zoltan Farkas
>  wrote:
> 
> What is the ultimate goal of the Calcite Interpreter?
> 
> To provide some context, I have been playing around with calcite + REST
> (see https://github.com/zolyfarkas/jaxrs-spf4j-demo/wiki/AvroCalciteRest <
> https://github.com/zolyfarkas/jaxrs-spf4j-demo/wiki/AvroCalciteRest> for
> detail of my experiments)
> 
> 
> —Z
> 
> On Dec 9, 2019, at 9:05 PM, Julian Hyde  wrote:
> 
> Yes, virtualization is one of Calcite’s goals. In fact, when I created
> Calcite I was thinking about virtualization + in-memory materialized views.
> Not only the Spark convention but any of the “engine” conventions (Drill,
> Flink, Beam, Enumerable) could be used to create a virtual query engine.
> 
> See e.g. a talk I gave in 2013 about Optiq (precursor to Calcite)
> https://www.slideshare.net/julianhyde/optiq-a-dynamic-data-management-framework
> <
> https://www.slideshare.net/julianhyde/optiq-a-dynamic-data-management-framework
>

Re: Quicksql

[Juan Pan]

Hi Haisheng,


> The query on different data source will then be registered as temp spark 
> tables (with filter or join pushed in), the whole query is rewritten as SQL 
> text over these temp tables and submitted to Spark.


Does it mean QuickSQL also need adaptors to make query executed on different 
data source? 


> Yes, virtualization is one of Calcite’s goals. In fact, when I created 
> Calcite I was thinking about virtualization + in-memory materialized views. 
> Not only the Spark convention but any of the “engine” conventions (Drill, 
> Flink, Beam, Enumerable) could be used to create a virtual query engine.


Basically, i like and agree with Julian’s statement. It is a great idea which 
personally hope Calcite move towards.


Give my best wishes to Calcite community. 


Thanks,
Trista


 Juan Pan


panj...@apache.org
Juan Pan(Trista), Apache ShardingSphere


On 12/11/2019 10:53，Haisheng Yuan wrote：
As far as I know, users still need to register tables from other data sources 
before querying it. QuickSQL uses Calcite for parsing queries and optimizing 
logical expressions with several transformation rules. The query on different 
data source will then be registered as temp spark tables (with filter or join 
pushed in), the whole query is rewritten as SQL text over these temp tables and 
submitted to Spark.

- Haisheng

--
发件人：Rui Wang
日　期：2019年12月11日 06:24:45
收件人：
主　题：Re: Quicksql

The co-routine model sounds fitting into Streaming cases well.

I was thinking how should Enumerable interface work with streaming cases
but now I should also check Interpreter.


-Rui

On Tue, Dec 10, 2019 at 1:33 PM Julian Hyde  wrote:

The goal (or rather my goal) for the interpreter is to replace
Enumerable as the quick, easy default convention.

Enumerable is efficient but not that efficient (compared to engines
that work on off-heap data representing batches of records). And
because it generates java byte code there is a certain latency to
getting a query prepared and ready to run.

It basically implements the old Volcano query evaluation model. It is
single-threaded (because all work happens as a result of a call to
'next()' on the root node) and cannot handle branching data-flow
graphs (DAGs).

The Interpreter operates uses a co-routine model (reading from queues,
writing to queues, and yielding when there is no work to be done) and
therefore could be more efficient than enumerable in a single-node
multi-core system. Also, there is little start-up time, which is
important for small queries.

I would love to add another built-in convention that uses Arrow as
data format and generates co-routines for each operator. Those
co-routines could be deployed in a parallel and/or distributed data
engine.

Julian

On Tue, Dec 10, 2019 at 3:47 AM Zoltan Farkas
 wrote:

What is the ultimate goal of the Calcite Interpreter?

To provide some context, I have been playing around with calcite + REST
(see https://github.com/zolyfarkas/jaxrs-spf4j-demo/wiki/AvroCalciteRest <
https://github.com/zolyfarkas/jaxrs-spf4j-demo/wiki/AvroCalciteRest> for
detail of my experiments)


—Z

On Dec 9, 2019, at 9:05 PM, Julian Hyde  wrote:

Yes, virtualization is one of Calcite’s goals. In fact, when I created
Calcite I was thinking about virtualization + in-memory materialized views.
Not only the Spark convention but any of the “engine” conventions (Drill,
Flink, Beam, Enumerable) could be used to create a virtual query engine.

See e.g. a talk I gave in 2013 about Optiq (precursor to Calcite)
https://www.slideshare.net/julianhyde/optiq-a-dynamic-data-management-framework
<
https://www.slideshare.net/julianhyde/optiq-a-dynamic-data-management-framework
.

Julian



On Dec 9, 2019, at 2:29 PM, Muhammad Gelbana 
wrote:

I recently contacted one of the active contributors asking about the
purpose of the project and here's his reply:

From my understanding, Quicksql is a data virtualization platform. It
can
query multiple data sources altogether and in a distributed way;
Say, you
can write a SQL with a MySql table join with an Elasticsearch table.
Quicksql can recognize that, and then generate Spark code, in which
it will
fetch the MySQL/ES data as a temporary table separately, and then
join them
in Spark. The execution is in Spark so it is totally distributed.
The user
doesn't need to aware of where the table is from.


I understand that the Spark convention Calcite has attempts to
achieve the
same goal, but it isn't fully implemented yet.


On Tue, Oct 29, 2019 at 9:43 PM Julian Hyde  wrote:

Anyone know anything about Quicksql? It seems to be quite a popular
project, and they have an internal fork of Calcite.

https://github.com/Qihoo360/ <https://github.com/Qihoo360/>



https://github.com/Qihoo360/Quicksql/tree/master/analysis/src/main/java/org/apache/calcite
<

https://github.com/Qihoo360/Quicksql/tree/master/analysis/src/main/java/org/apache/calcite


Julian

Re: Re: Quicksql

[Haisheng Yuan]

As far as I know, users still need to register tables from other data sources 
before querying it. QuickSQL uses Calcite for parsing queries and optimizing 
logical expressions with several transformation rules. The query on different 
data source will then be registered as temp spark tables (with filter or join 
pushed in), the whole query is rewritten as SQL text over these temp tables and 
submitted to Spark.

- Haisheng

--
发件人：Rui Wang
日　期：2019年12月11日 06:24:45
收件人：
主　题：Re: Quicksql

The co-routine model sounds fitting into Streaming cases well.

I was thinking how should Enumerable interface work with streaming cases
but now I should also check Interpreter.


-Rui

On Tue, Dec 10, 2019 at 1:33 PM Julian Hyde  wrote:

> The goal (or rather my goal) for the interpreter is to replace
> Enumerable as the quick, easy default convention.
>
> Enumerable is efficient but not that efficient (compared to engines
> that work on off-heap data representing batches of records). And
> because it generates java byte code there is a certain latency to
> getting a query prepared and ready to run.
>
> It basically implements the old Volcano query evaluation model. It is
> single-threaded (because all work happens as a result of a call to
> 'next()' on the root node) and cannot handle branching data-flow
> graphs (DAGs).
>
> The Interpreter operates uses a co-routine model (reading from queues,
> writing to queues, and yielding when there is no work to be done) and
> therefore could be more efficient than enumerable in a single-node
> multi-core system. Also, there is little start-up time, which is
> important for small queries.
>
> I would love to add another built-in convention that uses Arrow as
> data format and generates co-routines for each operator. Those
> co-routines could be deployed in a parallel and/or distributed data
> engine.
>
> Julian
>
> On Tue, Dec 10, 2019 at 3:47 AM Zoltan Farkas
>  wrote:
> >
> > What is the ultimate goal of the Calcite Interpreter?
> >
> > To provide some context, I have been playing around with calcite + REST
> (see https://github.com/zolyfarkas/jaxrs-spf4j-demo/wiki/AvroCalciteRest <
> https://github.com/zolyfarkas/jaxrs-spf4j-demo/wiki/AvroCalciteRest> for
> detail of my experiments)
> >
> >
> > —Z
> >
> > > On Dec 9, 2019, at 9:05 PM, Julian Hyde  wrote:
> > >
> > > Yes, virtualization is one of Calcite’s goals. In fact, when I created
> Calcite I was thinking about virtualization + in-memory materialized views.
> Not only the Spark convention but any of the “engine” conventions (Drill,
> Flink, Beam, Enumerable) could be used to create a virtual query engine.
> > >
> > > See e.g. a talk I gave in 2013 about Optiq (precursor to Calcite)
> https://www.slideshare.net/julianhyde/optiq-a-dynamic-data-management-framework
> <
> https://www.slideshare.net/julianhyde/optiq-a-dynamic-data-management-framework
> >.
> > >
> > > Julian
> > >
> > >
> > >
> > >> On Dec 9, 2019, at 2:29 PM, Muhammad Gelbana 
> wrote:
> > >>
> > >> I recently contacted one of the active contributors asking about the
> > >> purpose of the project and here's his reply:
> > >>
> > >> From my understanding, Quicksql is a data virtualization platform. It
> can
> > >>> query multiple data sources altogether and in a distributed way;
> Say, you
> > >>> can write a SQL with a MySql table join with an Elasticsearch table.
> > >>> Quicksql can recognize that, and then generate Spark code, in which
> it will
> > >>> fetch the MySQL/ES data as a temporary table separately, and then
> join them
> > >>> in Spark. The execution is in Spark so it is totally distributed.
> The user
> > >>> doesn't need to aware of where the table is from.
> > >>>
> > >>
> > >> I understand that the Spark convention Calcite has attempts to
> achieve the
> > >> same goal, but it isn't fully implemented yet.
> > >>
> > >>
> > >> On Tue, Oct 29, 2019 at 9:43 PM Julian Hyde  wrote:
> > >>
> > >>> Anyone know anything about Quicksql? It seems to be quite a popular
> > >>> project, and they have an internal fork of Calcite.
> > >>>
> > >>> https://github.com/Qihoo360/ <https://github.com/Qihoo360/>
> > >>>
> > >>>
> > >>>
> https://github.com/Qihoo360/Quicksql/tree/master/analysis/src/main/java/org/apache/calcite
> > >>> <
> > >>>
> https://github.com/Qihoo360/Quicksql/tree/master/analysis/src/main/java/org/apache/calcite
> > >>>>
> > >>>
> > >>> Julian
> > >>>
> > >>>
> > >
> >
>

Re: Quicksql

[Rui Wang]

The co-routine model sounds fitting into Streaming cases well.

I was thinking how should Enumerable interface work with streaming cases
but now I should also check Interpreter.


-Rui

On Tue, Dec 10, 2019 at 1:33 PM Julian Hyde  wrote:

> The goal (or rather my goal) for the interpreter is to replace
> Enumerable as the quick, easy default convention.
>
> Enumerable is efficient but not that efficient (compared to engines
> that work on off-heap data representing batches of records). And
> because it generates java byte code there is a certain latency to
> getting a query prepared and ready to run.
>
> It basically implements the old Volcano query evaluation model. It is
> single-threaded (because all work happens as a result of a call to
> 'next()' on the root node) and cannot handle branching data-flow
> graphs (DAGs).
>
> The Interpreter operates uses a co-routine model (reading from queues,
> writing to queues, and yielding when there is no work to be done) and
> therefore could be more efficient than enumerable in a single-node
> multi-core system. Also, there is little start-up time, which is
> important for small queries.
>
> I would love to add another built-in convention that uses Arrow as
> data format and generates co-routines for each operator. Those
> co-routines could be deployed in a parallel and/or distributed data
> engine.
>
> Julian
>
> On Tue, Dec 10, 2019 at 3:47 AM Zoltan Farkas
>  wrote:
> >
> > What is the ultimate goal of the Calcite Interpreter?
> >
> > To provide some context, I have been playing around with calcite + REST
> (see https://github.com/zolyfarkas/jaxrs-spf4j-demo/wiki/AvroCalciteRest <
> https://github.com/zolyfarkas/jaxrs-spf4j-demo/wiki/AvroCalciteRest> for
> detail of my experiments)
> >
> >
> > —Z
> >
> > > On Dec 9, 2019, at 9:05 PM, Julian Hyde  wrote:
> > >
> > > Yes, virtualization is one of Calcite’s goals. In fact, when I created
> Calcite I was thinking about virtualization + in-memory materialized views.
> Not only the Spark convention but any of the “engine” conventions (Drill,
> Flink, Beam, Enumerable) could be used to create a virtual query engine.
> > >
> > > See e.g. a talk I gave in 2013 about Optiq (precursor to Calcite)
> https://www.slideshare.net/julianhyde/optiq-a-dynamic-data-management-framework
> <
> https://www.slideshare.net/julianhyde/optiq-a-dynamic-data-management-framework
> >.
> > >
> > > Julian
> > >
> > >
> > >
> > >> On Dec 9, 2019, at 2:29 PM, Muhammad Gelbana 
> wrote:
> > >>
> > >> I recently contacted one of the active contributors asking about the
> > >> purpose of the project and here's his reply:
> > >>
> > >> From my understanding, Quicksql is a data virtualization platform. It
> can
> > >>> query multiple data sources altogether and in a distributed way;
> Say, you
> > >>> can write a SQL with a MySql table join with an Elasticsearch table.
> > >>> Quicksql can recognize that, and then generate Spark code, in which
> it will
> > >>> fetch the MySQL/ES data as a temporary table separately, and then
> join them
> > >>> in Spark. The execution is in Spark so it is totally distributed.
> The user
> > >>> doesn't need to aware of where the table is from.
> > >>>
> > >>
> > >> I understand that the Spark convention Calcite has attempts to
> achieve the
> > >> same goal, but it isn't fully implemented yet.
> > >>
> > >>
> > >> On Tue, Oct 29, 2019 at 9:43 PM Julian Hyde  wrote:
> > >>
> > >>> Anyone know anything about Quicksql? It seems to be quite a popular
> > >>> project, and they have an internal fork of Calcite.
> > >>>
> > >>> https://github.com/Qihoo360/ 
> > >>>
> > >>>
> > >>>
> https://github.com/Qihoo360/Quicksql/tree/master/analysis/src/main/java/org/apache/calcite
> > >>> <
> > >>>
> https://github.com/Qihoo360/Quicksql/tree/master/analysis/src/main/java/org/apache/calcite
> > 
> > >>>
> > >>> Julian
> > >>>
> > >>>
> > >
> >
>

Re: Quicksql

[Julian Hyde]

The goal (or rather my goal) for the interpreter is to replace
Enumerable as the quick, easy default convention.

Enumerable is efficient but not that efficient (compared to engines
that work on off-heap data representing batches of records). And
because it generates java byte code there is a certain latency to
getting a query prepared and ready to run.

It basically implements the old Volcano query evaluation model. It is
single-threaded (because all work happens as a result of a call to
'next()' on the root node) and cannot handle branching data-flow
graphs (DAGs).

The Interpreter operates uses a co-routine model (reading from queues,
writing to queues, and yielding when there is no work to be done) and
therefore could be more efficient than enumerable in a single-node
multi-core system. Also, there is little start-up time, which is
important for small queries.

I would love to add another built-in convention that uses Arrow as
data format and generates co-routines for each operator. Those
co-routines could be deployed in a parallel and/or distributed data
engine.

Julian

On Tue, Dec 10, 2019 at 3:47 AM Zoltan Farkas
 wrote:
>
> What is the ultimate goal of the Calcite Interpreter?
>
> To provide some context, I have been playing around with calcite + REST (see 
> https://github.com/zolyfarkas/jaxrs-spf4j-demo/wiki/AvroCalciteRest 
>  for 
> detail of my experiments)
>
>
> —Z
>
> > On Dec 9, 2019, at 9:05 PM, Julian Hyde  wrote:
> >
> > Yes, virtualization is one of Calcite’s goals. In fact, when I created 
> > Calcite I was thinking about virtualization + in-memory materialized views. 
> > Not only the Spark convention but any of the “engine” conventions (Drill, 
> > Flink, Beam, Enumerable) could be used to create a virtual query engine.
> >
> > See e.g. a talk I gave in 2013 about Optiq (precursor to Calcite) 
> > https://www.slideshare.net/julianhyde/optiq-a-dynamic-data-management-framework
> >  
> > .
> >
> > Julian
> >
> >
> >
> >> On Dec 9, 2019, at 2:29 PM, Muhammad Gelbana  wrote:
> >>
> >> I recently contacted one of the active contributors asking about the
> >> purpose of the project and here's his reply:
> >>
> >> From my understanding, Quicksql is a data virtualization platform. It can
> >>> query multiple data sources altogether and in a distributed way; Say, you
> >>> can write a SQL with a MySql table join with an Elasticsearch table.
> >>> Quicksql can recognize that, and then generate Spark code, in which it 
> >>> will
> >>> fetch the MySQL/ES data as a temporary table separately, and then join 
> >>> them
> >>> in Spark. The execution is in Spark so it is totally distributed. The user
> >>> doesn't need to aware of where the table is from.
> >>>
> >>
> >> I understand that the Spark convention Calcite has attempts to achieve the
> >> same goal, but it isn't fully implemented yet.
> >>
> >>
> >> On Tue, Oct 29, 2019 at 9:43 PM Julian Hyde  wrote:
> >>
> >>> Anyone know anything about Quicksql? It seems to be quite a popular
> >>> project, and they have an internal fork of Calcite.
> >>>
> >>> https://github.com/Qihoo360/ 
> >>>
> >>>
> >>> https://github.com/Qihoo360/Quicksql/tree/master/analysis/src/main/java/org/apache/calcite
> >>> <
> >>> https://github.com/Qihoo360/Quicksql/tree/master/analysis/src/main/java/org/apache/calcite
> 
> >>>
> >>> Julian
> >>>
> >>>
> >
>

Re: Quicksql

[Zoltan Farkas]

What is the ultimate goal of the Calcite Interpreter? 

To provide some context, I have been playing around with calcite + REST (see 
https://github.com/zolyfarkas/jaxrs-spf4j-demo/wiki/AvroCalciteRest 
 for 
detail of my experiments)


—Z

> On Dec 9, 2019, at 9:05 PM, Julian Hyde  wrote:
> 
> Yes, virtualization is one of Calcite’s goals. In fact, when I created 
> Calcite I was thinking about virtualization + in-memory materialized views. 
> Not only the Spark convention but any of the “engine” conventions (Drill, 
> Flink, Beam, Enumerable) could be used to create a virtual query engine.
> 
> See e.g. a talk I gave in 2013 about Optiq (precursor to Calcite) 
> https://www.slideshare.net/julianhyde/optiq-a-dynamic-data-management-framework
>  
> .
> 
> Julian
> 
> 
> 
>> On Dec 9, 2019, at 2:29 PM, Muhammad Gelbana  wrote:
>> 
>> I recently contacted one of the active contributors asking about the
>> purpose of the project and here's his reply:
>> 
>> From my understanding, Quicksql is a data virtualization platform. It can
>>> query multiple data sources altogether and in a distributed way; Say, you
>>> can write a SQL with a MySql table join with an Elasticsearch table.
>>> Quicksql can recognize that, and then generate Spark code, in which it will
>>> fetch the MySQL/ES data as a temporary table separately, and then join them
>>> in Spark. The execution is in Spark so it is totally distributed. The user
>>> doesn't need to aware of where the table is from.
>>> 
>> 
>> I understand that the Spark convention Calcite has attempts to achieve the
>> same goal, but it isn't fully implemented yet.
>> 
>> 
>> On Tue, Oct 29, 2019 at 9:43 PM Julian Hyde  wrote:
>> 
>>> Anyone know anything about Quicksql? It seems to be quite a popular
>>> project, and they have an internal fork of Calcite.
>>> 
>>> https://github.com/Qihoo360/ 
>>> 
>>> 
>>> https://github.com/Qihoo360/Quicksql/tree/master/analysis/src/main/java/org/apache/calcite
>>> <
>>> https://github.com/Qihoo360/Quicksql/tree/master/analysis/src/main/java/org/apache/calcite
 
>>> 
>>> Julian
>>> 
>>> 
> 

Re: Quicksql

[Stamatis Zampetakis]

The guys at Contiamo [1] are doing similar stuff. I added the nice talk [2]
by Chris in ApacheCon to our website.

[1] https://www.contiamo.com/
[2] https://youtu.be/4JAOkLKrcYE

On Tue, Dec 10, 2019 at 3:05 AM Julian Hyde  wrote:

> Yes, virtualization is one of Calcite’s goals. In fact, when I created
> Calcite I was thinking about virtualization + in-memory materialized views.
> Not only the Spark convention but any of the “engine” conventions (Drill,
> Flink, Beam, Enumerable) could be used to create a virtual query engine.
>
> See e.g. a talk I gave in 2013 about Optiq (precursor to Calcite)
> https://www.slideshare.net/julianhyde/optiq-a-dynamic-data-management-framework
> <
> https://www.slideshare.net/julianhyde/optiq-a-dynamic-data-management-framework
> >.
>
> Julian
>
>
>
> > On Dec 9, 2019, at 2:29 PM, Muhammad Gelbana 
> wrote:
> >
> > I recently contacted one of the active contributors asking about the
> > purpose of the project and here's his reply:
> >
> > From my understanding, Quicksql is a data virtualization platform. It can
> >> query multiple data sources altogether and in a distributed way; Say,
> you
> >> can write a SQL with a MySql table join with an Elasticsearch table.
> >> Quicksql can recognize that, and then generate Spark code, in which it
> will
> >> fetch the MySQL/ES data as a temporary table separately, and then join
> them
> >> in Spark. The execution is in Spark so it is totally distributed. The
> user
> >> doesn't need to aware of where the table is from.
> >>
> >
> > I understand that the Spark convention Calcite has attempts to achieve
> the
> > same goal, but it isn't fully implemented yet.
> >
> >
> > On Tue, Oct 29, 2019 at 9:43 PM Julian Hyde  wrote:
> >
> >> Anyone know anything about Quicksql? It seems to be quite a popular
> >> project, and they have an internal fork of Calcite.
> >>
> >> https://github.com/Qihoo360/ 
> >>
> >>
> >>
> https://github.com/Qihoo360/Quicksql/tree/master/analysis/src/main/java/org/apache/calcite
> >> <
> >>
> https://github.com/Qihoo360/Quicksql/tree/master/analysis/src/main/java/org/apache/calcite
> >>>
> >>
> >> Julian
> >>
> >>
>
>

Re: Quicksql

[Julian Hyde]

Yes, virtualization is one of Calcite’s goals. In fact, when I created Calcite 
I was thinking about virtualization + in-memory materialized views. Not only 
the Spark convention but any of the “engine” conventions (Drill, Flink, Beam, 
Enumerable) could be used to create a virtual query engine.

See e.g. a talk I gave in 2013 about Optiq (precursor to Calcite) 
https://www.slideshare.net/julianhyde/optiq-a-dynamic-data-management-framework 
.

Julian



> On Dec 9, 2019, at 2:29 PM, Muhammad Gelbana  wrote:
> 
> I recently contacted one of the active contributors asking about the
> purpose of the project and here's his reply:
> 
> From my understanding, Quicksql is a data virtualization platform. It can
>> query multiple data sources altogether and in a distributed way; Say, you
>> can write a SQL with a MySql table join with an Elasticsearch table.
>> Quicksql can recognize that, and then generate Spark code, in which it will
>> fetch the MySQL/ES data as a temporary table separately, and then join them
>> in Spark. The execution is in Spark so it is totally distributed. The user
>> doesn't need to aware of where the table is from.
>> 
> 
> I understand that the Spark convention Calcite has attempts to achieve the
> same goal, but it isn't fully implemented yet.
> 
> 
> On Tue, Oct 29, 2019 at 9:43 PM Julian Hyde  wrote:
> 
>> Anyone know anything about Quicksql? It seems to be quite a popular
>> project, and they have an internal fork of Calcite.
>> 
>> https://github.com/Qihoo360/ 
>> 
>> 
>> https://github.com/Qihoo360/Quicksql/tree/master/analysis/src/main/java/org/apache/calcite
>> <
>> https://github.com/Qihoo360/Quicksql/tree/master/analysis/src/main/java/org/apache/calcite
>>> 
>> 
>> Julian
>> 
>> 

Re: Quicksql

[Muhammad Gelbana]

I recently contacted one of the active contributors asking about the
purpose of the project and here's his reply:

>From my understanding, Quicksql is a data virtualization platform. It can
> query multiple data sources altogether and in a distributed way; Say, you
> can write a SQL with a MySql table join with an Elasticsearch table.
> Quicksql can recognize that, and then generate Spark code, in which it will
> fetch the MySQL/ES data as a temporary table separately, and then join them
> in Spark. The execution is in Spark so it is totally distributed. The user
> doesn't need to aware of where the table is from.
>

I understand that the Spark convention Calcite has attempts to achieve the
same goal, but it isn't fully implemented yet.


On Tue, Oct 29, 2019 at 9:43 PM Julian Hyde  wrote:

> Anyone know anything about Quicksql? It seems to be quite a popular
> project, and they have an internal fork of Calcite.
>
> https://github.com/Qihoo360/ 
>
>
> https://github.com/Qihoo360/Quicksql/tree/master/analysis/src/main/java/org/apache/calcite
> <
> https://github.com/Qihoo360/Quicksql/tree/master/analysis/src/main/java/org/apache/calcite
> >
>
> Julian
>
>