Re: [HACKERS] [WIP]Vertical Clustered Index (columnar storeextension) - Mailing list pgsql-hackers

On 30.12.2016 06:55, Haribabu Kommi wrote:

Hi All,

Fujitsu was interested in developing a columnar storage extension with minimal

changes the server backend.

We  in PostgresPRO are also very interested in developing vertical storage (VS) for Postgres.
And after considering many alternatives, we came to the conclusion that approach based on representing columnar store as access method (index)
is the most promising one.

It allows to:
1. Implement VS as extension without affecting Postgres core.
2. Have both ROS and WOS.
3. Create multiple projections (as in Vertica).
4. Optimize insert speed by support batch inserts and use flexible recovery model for VS.

So it is very similar with your approach. But there are few differences:

1. Our intention is to completely eliminate changes in Postgres core.

You wrote:

Yes, it is a mix of both index and table access methods. The current design

of Vertical clustered index needs both access methods, because of this reason

we used both access methods.

But I still do not completely understand why it is not possible to use VS in index only scans without any changes and standard Postgres executor?
Why it is not possible to rely on standard rules of applying indexes in Postgres optimizer based on costs provided by our AM implementation?


2. You are accessing VS pages through Postgres buffer manager. It certainly have a lot of advantages. First of all it significantly simplifies implementation of VS and allows to reuse Postgres cache and lock managers.
But is all leads to some limitation:
- For VS it is preferable to have larger pages (in Vertica size of page can be several megabytes).
- VS is optimized for sequential access, so caching pages in buffer manager is no needed and can only cause leaching of other useful pages from cache.
- It makes it not possible to implement in-memory version of VS.
- Access to buffer manager adds extra synchronization overhead which becomes noticeable at MPP systems.

So I wonder if you have considered approach with VS specific implementation of storage layer?

3. To take all advantages of vertical model, we should provide vector execution.
Without it columnar store can only reduce amount of fetched data by selective fetch of accessed columns and better compression of them.
But this is what existed cstore_fdw extension for Postgres also does.

We are going to use executor hooks or custom nodes to implement vector operations for some nodes (filter, grand aggregate, aggregation with group by,...).
Something similar with  https://github.com/citusdata/postgres_vectorization_test

What is your vision of optimizing executor to work with VS?

4. How do you consider adding parallelism support to VS? Should it be handled inside VS implementation? Or should we use standard Postgres parallel execution (parallel index-only scan)?

Thanks in advance,
Kosntantin

The columnar store is implemented as an extension using index access methods.

This can be easily enhanced with pluggable storage methods once they are available.

A new index method (VCI) is added to create columnar index on the table.

The following is the basic design idea of the columnar extension, 

This has the on-disk columnar representation. So, even after crash, 

the columnar format is recovered to the state when it was crashed.

To provide performance benefit for both read and write operations,

the data is stored in two formats

1) write optimized storage (WOS) 

2) read optimized storage (ROS).

This is useful for the users where there is a great chance of data modification

that is newly added instead of the old data.

WOS

====

write optimized storage is the data of all columns that are part of VCI are

stored in a row wise format. All the newly added data is stored in WOS

relation with xmin/xmax information also. If user wants to update/delete the

newly added data, it doesn't affect the performance much compared to

deleting the data from columnar storage.

The tuples which don't have multiple copies or frozen data will be moved

from WOS to ROS periodically by the background worker process or autovauum

process. Every column data is stored separately in it's relation file. There

is no transaction information is present in ROS. The data in ROS can be

referred with tuple ID.

In this approach, the column data is present in both heap and columnar

storage.

ROS

====

This is the place, where all the column data is stored in columnar format.

The data from WOS to ROS is converted by background workers continously based

on the tuple visibility check. Whenever the tuple is frozen and it gets moved

from WOS to ROS.

The Data in ROS is stored in extents. One extent contains of 262,144 rows. Because

of fixed number of records in an extent it is easy to map the heap record to the columnar

record with TID to CRID map.

Insert

=====

The insert operation is just like inserting a data into an index.

Select

=====

Because of two storage formats, during the select operation, the data in WOS

is converted into Local ROS for the statement to be executed. The conversion

cost depends upon the number of tuples present in the WOS file. This

may add some performance overhead for select statements. The life of the Local

ROS is till the end of query context.

Delete

=====

During the delete operation, whenever the data is deleted in heap at the same

time the data in WOS file is marked as deleted similar like heap. But in case

if the data is already migrated from WOS to ROS, then we will maintain some

delete vector to store the details of tuple id, transaction information and etc.

During the data read from ROS file, it is verified against delete vector and 

confirms whether the record is visible or not? All the delete vectors

data is applied to ROS periodically.

More details of internal relations and their usage is available in the README.

Still it needs more updates to explain full details of the columnar index design.

The concept of Vertical clustered index columnar extension is from Fujitsu Labs, Japan.

Following is the brief schedule of patches that are required

for a better performing columnar store.

1. Minimal server changes (new relkind "CSTORE" option)

2. Base storage patch

3. Support for moving data from WOS to ROS

4. Local ROS support

5. Custom scan support to read the data from ROS and Local ROS

6. Background worker support for data movement

7. Expression state support in VCI

8. Aggregation support in VCI 

9. Pg_dump support for the new type of relations

10. psql \d command support for CSTORE relations

11. Parallelism support

12. Compression support

13. In-memory support with dynamic shared memory

Currently I attached only patches for 1 and 2. These will provide the

basic changes that are required in PostgreSQL core to the extension

to work.

I have to rebase/rewrite the rest of the patches to the latest master, 

and share them with community. 

Any Comments on the approach?

 

Regards,

Hari Babu

Fujitsu Australia

-- 
Konstantin Knizhnik
Postgres Professional: http://www.postgrespro.com
The Russian Postgres Company