Zedstore - compressed in-core columnar storage

Heikki and I have been hacking recently for few weeks to implement
in-core columnar storage for PostgreSQL. Here's the design and initial
implementation of Zedstore, compressed in-core columnar storage (table
access method). Attaching the patch and link to github branch [1] to
follow along.

The objective is to gather feedback on design and approach to the
same. The implementation has core basic pieces working but not close
to complete.

Big thank you to Andres, Haribabu and team for the table access method
API's. Leveraged the API's for implementing zedstore, and proves API
to be in very good shape. Had to enhance the same minimally but
in-general didn't had to touch executor much.

Motivations / Objectives

* Performance improvement for queries selecting subset of columns
(reduced IO).
* Reduced on-disk footprint compared to heap table. Shorter tuple
headers and also leveraging compression of similar type data
* Be first-class citizen in the Postgres architecture (tables data can
just independently live in columnar storage)
* Fully MVCC compliant
* All Indexes supported
* Hybrid row-column store, where some columns are stored together, and
others separately. Provide flexibility of granularity on how to
divide the columns. Columns accessed together can be stored
together.
* Provide better control over bloat (similar to zheap)
* Eliminate need for separate toast tables
* Faster add / drop column or changing data type of column by avoiding
full rewrite of the table.

High-level Design - B-trees for the win!
========================================

To start simple, let's ignore column store aspect for a moment and
consider it as compressed row store. The column store is natural
extension of this concept, explained in next section.

The basic on-disk data structure leveraged is a B-tree, indexed by
TID. BTree being a great data structure, fast and versatile. Note this
is not referring to existing Btree indexes, but instead net new
separate BTree for table data storage.

TID - logical row identifier:
TID is just a 48-bit row identifier. The traditional division into
block and offset numbers is meaningless. In order to find a tuple with
a given TID, one must always descend the B-tree. Having logical TID
provides flexibility to move the tuples around different pages on page
splits or page merges can be performed.

The internal pages of the B-tree are super simple and boring. Each
internal page just stores an array of TID and downlink pairs. Let's
focus on the leaf level. Leaf blocks have short uncompressed header,
followed by btree items. Two kinds of items exist:

- plain item, holds one tuple or one datum, uncompressed payload
- a "container item", holds multiple plain items, compressed payload

+-----------------------------
| Fixed-size page header:
|
| LSN
| TID low and hi key (for Lehman & Yao B-tree operations)
| left and right page pointers
|
| Items:
|
| TID | size | flags | uncompressed size | lastTID | payload (container
item)
| TID | size | flags | uncompressed size | lastTID | payload (container
item)
| TID | size | flags | undo pointer | payload (plain item)
| TID | size | flags | undo pointer | payload (plain item)
| ...
|
+----------------------------

Row store
---------

The tuples are stored one after another, sorted by TID. For each
tuple, we store its 48-bit TID, a undo record pointer, and the actual
tuple data uncompressed.

In uncompressed form, the page can be arbitrarily large. But after
compression, it must fit into a physical 8k block. If on insert or
update of a tuple, the page cannot be compressed below 8k anymore, the
page is split. Note that because TIDs are logical rather than physical
identifiers, we can freely move tuples from one physical page to
another during page split. A tuple's TID never changes.

The buffer cache caches compressed blocks. Likewise, WAL-logging,
full-page images etc. work on compressed blocks. Uncompression is done
on-the-fly, as and when needed in backend-private memory, when
reading. For some compressions like rel encoding or delta encoding
tuples can be constructed directly from compressed data.

Column store
------------

A column store uses the same structure but we have *multiple* B-trees,
one for each column, all indexed by TID. The B-trees for all columns
are stored in the same physical file.

A metapage at block 0, has links to the roots of the B-trees. Leaf
pages look the same, but instead of storing the whole tuple, stores
just a single attribute. To reconstruct a row with given TID, scan
descends down the B-trees for all the columns using that TID, and
fetches all attributes. Likewise, a sequential scan walks all the
B-trees in lockstep.

So, in summary can imagine Zedstore as forest of B-trees, one for each
column, all indexed by TIDs.

This way of laying out the data also easily allows for hybrid
row-column store, where some columns are stored together, and others
have a dedicated B-tree. Need to have user facing syntax to allow
specifying how to group the columns.

Main reasons for storing data this way
--------------------------------------

* Layout the data/tuples in mapped fashion instead of keeping the
logical to physical mapping separate from actual data. So, keep the
meta-data and data logically in single stream of file, avoiding the
need for separate forks/files to store meta-data and data.

* Stick to fixed size physical blocks. Variable size blocks pose need
for increased logical to physical mapping maintenance, plus
restrictions on concurrency of writes and reads to files. Hence
adopt compression to fit fixed size blocks instead of other way
round.

MVCC
----
MVCC works very similar to zheap for zedstore. Undo record pointers
are used to implement MVCC. Transaction information if not directly
stored with the data. In zheap, there's a small, fixed, number of
"transaction slots" on each page, but zedstore has undo pointer with
each item directly; in normal cases, the compression squeezes this
down to almost nothing.

Implementation
==============

Insert:
Inserting a new row, splits the row into datums. Then for first column
decide which block to insert the same to, and pick a TID for it, and
write undo record for the same. Rest of the columns are inserted using
that same TID and point to same undo position.

Compression:
Items are added to Btree in uncompressed form. If page is full and new
item can't be added, compression kicks in. Existing uncompressed items
(plain items) of the page are passed to compressor for
compression. Already compressed items are added back as is. Page is
rewritten with compressed data with new item added to it. If even
after compression, can't add item to page, then page split happens.

Toast:
When an overly large datum is stored, it is divided into chunks, and
each chunk is stored on a dedicated toast page within the same
physical file. The toast pages of a datum form list, each page has a
next/prev pointer.

Select:
Property is added to Table AM to convey if column projection is
leveraged by AM for scans. While scanning tables with AM leveraging
this property, executor parses the plan. Leverages the target list and
quals to find the required columns for query. This list is passed down
to AM on beginscan. Zedstore uses this column projection list to only
pull data from selected columns. Virtual tuple table slot is used to
pass back the datums for subset of columns.

Current table am API requires enhancement here to pass down column
projection to AM. The patch showcases two different ways for the same.

* For sequential scans added new beginscan_with_column_projection()
API. Executor checks AM property and if it leverages column
projection uses this new API else normal beginscan() API.

* For index scans instead of modifying the begin scan API, added new
API to specifically pass column projection list after calling begin
scan to populate the scan descriptor but before fetching the tuples.

Index Support:
Building index also leverages columnar storage and only scans columns
required to build the index. Indexes work pretty similar to heap
tables. Data is inserted into tables and TID for the tuple same gets
stored in index. On index scans, required column Btrees are scanned
for given TID and datums passed back using virtual tuple.

Page Format:
ZedStore table contains different kinds of pages, all in the same
file. Kinds of pages are meta-page, per-attribute btree internal and
leaf pages, UNDO log page, and toast pages. Each page type has its own
distinct data storage format.

Block 0 is always a metapage. It contains the block numbers of the
other data structures stored within the file, like the per-attribute
B-trees, and the UNDO log.

Enhancements to design:
=======================

Instead of compressing all the tuples on a page in one batch, we could
store a small "dictionary", e.g. in page header or meta-page, and use
it to compress each tuple separately. That could make random reads and
updates of individual tuples faster.

When adding column, just need to create new Btree for newly added
column and linked to meta-page. No existing content needs to be
rewritten.

When the column is dropped, can scan the B-tree of that column, and
immediately mark all the pages as free in the FSM. But we don't
actually have to scan the leaf level: all leaf tuples have a downlink
in the parent, so we can scan just the internal pages. Unless the
column is very wide, that's only a small fraction of the data. That
makes the space immediately reusable for new insertions, but it won't
return the space to the Operating System. In order to do that, we'd
still need to defragment, moving pages from the end of the file closer
to the beginning, and truncate the file.

In this design, we only cache compressed pages in the page cache. If
we want to cache uncompressed pages instead, or in addition to that,
we need to invent a whole new kind of a buffer cache that can deal
with the variable-size blocks.

If you do a lot of updates, the file can get fragmented, with lots of
unused space on pages. Losing the correlation between TIDs and
physical order is also bad, because it will make SeqScans slow, as
they're not actually doing sequential I/O anymore. We can write a
defragmenter to fix things up. Half-empty pages can be merged, and
pages can be moved to restore TID/physical correlation. This format
doesn't have the same MVCC problems with moving tuples around that the
Postgres heap does, so it can be fairly easily be done on-line.

Min-Max values can be stored for block to easily skip scanning if
column values doesn't fall in range.

Notes about current patch
=========================

Basic (core) functionality is implemented to showcase and play with.

Two compression algorithms are supported Postgres pg_lzcompress and
lz4. Compiling server with --with-lz4 enables the LZ4 compression for
zedstore else pg_lzcompress is default. Definitely LZ4 is super fast
at compressing and uncompressing.

Not all the table AM API's are implemented. For the functionality not
implmented yet will ERROR out with not supported. Zedstore Table can
be created using command:

CREATE TABLE <name> (column listing) USING zedstore;

Bulk load can be performed using COPY. INSERT, SELECT, UPDATE and
DELETES work. Btree indexes can be created. Btree and bitmap index
scans work. Test in src/test/regress/sql/zedstore.sql showcases all
the functionality working currently. Updates are currently implemented
as cold, means always creates new items and not performed in-place.

TIDs currently can't leverage the full 48 bit range but instead need
to limit to values which are considered valid ItemPointers. Also,
MaxHeapTuplesPerPage pose restrictions on the values currently it can
have. Refer [7] for the same.

Extremely basic UNDO logging has be implemented just for MVCC
perspective. MVCC is missing tuple lock right now. Plus, doesn't
actually perform any undo yet. No WAL logging exist currently hence
its not crash safe either.

Helpful functions to find how many pages of each type is present in
zedstore table and also to find compression ratio is provided.

Test mentioned in thread "Column lookup in a row performance" [6],
good example query for zedstore locally on laptop using lz4 shows

postgres=# SELECT AVG(i199) FROM (select i199 from layout offset 0) x; --
heap
avg
---------------------
500000.500000000000
(1 row)

Time: 4679.026 ms (00:04.679)

postgres=# SELECT AVG(i199) FROM (select i199 from zlayout offset 0) x; --
zedstore
avg
---------------------
500000.500000000000
(1 row)

Time: 379.710 ms

Important note:
---------------
Planner has not been modified yet to leverage the columnar
storage. Hence, plans using "physical tlist" optimization or such good
for row store miss out to leverage the columnar nature
currently. Hence, can see the need for subquery with OFFSET 0 above to
disable the optimization and scan only required column.

The current proposal and discussion is more focused on AM layer work
first. Hence, currently intentionally skips to discuss the planner or
executor "feature" enhancements like adding vectorized execution and
family of features.

Previous discussions or implementations for column store Vertical
cluster index [2], Incore columnar storage [3] and [4], cstore_fdw [5]
were refered to distill down objectives and come up with design and
implementations to avoid any earlier concerns raised. Learnings from
Greenplum Database column store also leveraged while designing and
implementing the same.

Credit: Design is moslty brain child of Heikki, or actually his
epiphany to be exact. I acted as idea bouncing board and contributed
enhancements to the same. We both are having lot of fun writing the
code for this.

References
1] https://github.com/greenplum-db/postgres/tree/zedstore
2]
https://www.postgresql.org/message-id/flat/CAJrrPGfaC7WC9NK6PTTy6YN-NN%2BhCy8xOLAh2doYhVg5d6HsAA%40mail.gmail.com
3]
https://www.postgresql.org/message-id/flat/20150611230316.GM133018%40postgresql.org
4]
https://www.postgresql.org/message-id/flat/20150831225328.GM2912%40alvherre.pgsql
5] https://github.com/citusdata/cstore_fdw
6]
https://www.postgresql.org/message-id/flat/CAOykqKfko-n5YiBJtk-ocVdp%2Bj92Apu5MJBwbGGh4awRY5NCuQ%40mail.gmail.com
7]
https://www.postgresql.org/message-id/d0fc97bd-7ec8-2388-e4a6-0fda86d71a43%40iki.fi