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<alessandro.solima...@gmail.com> 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 <hy...@apache.org> 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" <panj...@apache.org> 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<h.y...@alibaba-inc.com> 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<amaliu...@apache.org>
日 期：2019年12月11日 06:24:45
收件人：<dev@calcite.apache.org>
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 <jh...@apache.org> 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
<zolyfar...@yahoo.com.invalid> 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 <jh...@apache.org> 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 <mgelb...@apache.org>
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 <jh...@apache.org> 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
