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From "Thakrar, Jayesh" <jthak...@conversantmedia.com>
Subject Re: [Discuss] Datasource v2 support for manipulating partitions
Date Thu, 20 Sep 2018 15:11:50 GMT
Here’s what can be done in PostgreSQL

You can create a partitioned table with a partition clause, e.g.
CREATE TABLE measurement (.....) PARTITION BY RANGE (logdate)

You can create a partitioned table by creating tables as partitions of a partitioned table,
e.g.
CREATE TABLE measurement_y2006m02 PARTITION OF measurement FOR VALUES FROM ('2006-02-01')
TO ('2006-03-01')

Each “partition” is like a table and can be managed just like a table.

And ofcourse you can have nested partitioning.

As for partition management, you can attach/detach partitions by converting a regular table
into a table partition and a table partition into a regular table using the ALTER TABLE statement

ALTER TABLE measurement ATTACH/DETACH PARTITION

There are similar options in Oracle.
In Oracle, converting a table into a partition and vice-versa is referred to as  “partition
exchange”.
However unlike Postgres, table partitions are not treated as regular tables.


As for partition management relevance in Spark API, here are some thoughts:

Reading data from a table supporting predicate pushdown
W/o explicit partition specification, we would need to rely on partition pruning to select
the appropriate partitions
However if we can provide a mechanism to specify the partition(s), that would be great –
and it would need to be translated into appropriate SQL clauses under the covers

Writing data to a table supporting partitions
I think there is no current way to support the above Postgres/Oracle ways of creating partition
tables or doing table exchanges intelligently.
So probably options or some appropriate interfaces would be required
And the above ALTER TABLE equivalent work can be done as part of the commit (provided an appropriate
interface is supported).

Here are Dale’s comments earlier from the thread
“So if we are not hiding them from the user, we need to allow users to
    manipulate them. Either by representing them generically in the API,
    allowing pass-through commands to manipulate them, or by some other means.”

I think we need to mull over this and also look beyond RDBMSes – say, S3 for applicability.

In essence, I think partitions matter because they allow partition pruning (= less resource
intensive) during read and allow partition setup and appropriately targeting during write.


From: Ryan Blue <rblue@netflix.com>
Reply-To: "rblue@netflix.com" <rblue@netflix.com>
Date: Wednesday, September 19, 2018 at 4:35 PM
To: "Thakrar, Jayesh" <jthakrar@conversantmedia.com>
Cc: "tigerquoll@outlook.com" <tigerquoll@outlook.com>, Spark Dev List <dev@spark.apache.org>
Subject: Re: [Discuss] Datasource v2 support for manipulating partitions

What does partition management look like in those systems and what are the options we would
standardize in an API?

On Wed, Sep 19, 2018 at 2:16 PM Thakrar, Jayesh <jthakrar@conversantmedia.com<mailto:jthakrar@conversantmedia.com>>
wrote:
I think partition management feature would be very useful in RDBMSes that support it – e.g.
Oracle, PostgreSQL, and DB2.
In some cases add partitions can be explicit and can/may be done outside of data loads.
But in some other cases, it may/can need to be done implicitly when supported  by the platform.
Similar to the static/dynamic partition loading in Hive and Oracle.

So in short, I agree that partition management should be an optional interface.

From: Ryan Blue <rblue@netflix.com<mailto:rblue@netflix.com>>
Reply-To: "rblue@netflix.com<mailto:rblue@netflix.com>" <rblue@netflix.com<mailto:rblue@netflix.com>>
Date: Wednesday, September 19, 2018 at 2:58 PM
To: "Thakrar, Jayesh" <jthakrar@conversantmedia.com<mailto:jthakrar@conversantmedia.com>>
Cc: "tigerquoll@outlook.com<mailto:tigerquoll@outlook.com>" <tigerquoll@outlook.com<mailto:tigerquoll@outlook.com>>,
Spark Dev List <dev@spark.apache.org<mailto:dev@spark.apache.org>>
Subject: Re: [Discuss] Datasource v2 support for manipulating partitions

I'm open to exploring the idea of adding partition management as a catalog API. The approach
we're taking is to have an interface for each concern a catalog might implement, like TableCatalog
(proposed in SPARK-24252), but also FunctionCatalog for stored functions and possibly PartitionedTableCatalog
for explicitly partitioned tables.

That could definitely be used to implement ALTER TABLE ADD/DROP PARTITION for Hive tables,
although I'm not sure that we would want to continue exposing partitions for simple tables.
I know that this is important for storage systems like Kudu, but I think it is needlessly
difficult and annoying for simple tables that are partitioned by a regular transformation
like Hive tables. That's why Iceberg hides partitioning outside of table configuration. That
also avoids problems where SELECT DISTINCT queries are wrong because a partition exists but
has no data.

How useful is this outside of Kudu? Is it something that we should provide an API for, or
is it specific enough to Kudu that Spark shouldn't include it in the API for all sources?

rb


On Tue, Sep 18, 2018 at 7:38 AM Thakrar, Jayesh <jthakrar@conversantmedia.com<mailto:jthakrar@conversantmedia.com>>
wrote:
Totally agree with you Dale, that there are situations for efficiency, performance and better
control/visibility/manageability that we need to expose partition management.

So as described, I suggested two things - the ability to do it in the current V2 API form
via options and appropriate implementation in datasource reader/writer.

And for long term, suggested that partition management can be made part of metadata/catalog
management - SPARK-24252 (DataSourceV2: Add catalog support)?


On 9/17/18, 8:26 PM, "tigerquoll" <tigerquoll@outlook.com<mailto:tigerquoll@outlook.com>>
wrote:

    Hi Jayesh,
    I get where you are coming from - partitions are just an implementation
    optimisation that we really shouldn’t be bothering the end user with.
    Unfortunately that view is like saying RPC is like a procedure call, and
    details of the network transport should be hidden from the end user. CORBA
    tried this approach for RPC and failed for the same reason that no major
    vendor of DBMS systems that support partitions try to hide them from the end
    user.  They have a substantial real world effect that is impossible to hide
    from the user (in particular when writing/modifying the data source).  Any
    attempt to “take care” of partitions automatically invariably guesses wrong
    and ends up frustrating the end user (as “substantial real world effect”
    turns to “show stopping performance penalty” if the user attempts to fight
    against a partitioning scheme she has no idea exists)

    So if we are not hiding them from the user, we need to allow users to
    manipulate them. Either by representing them generically in the API,
    allowing pass-through commands to manipulate them, or by some other means.

    Regards,
    Dale.




    --
    Sent from: http://apache-spark-developers-list.1001551.n3.nabble.com/



--
Ryan Blue
Software Engineer
Netflix


--
Ryan Blue
Software Engineer
Netflix
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