19th September 2021
Redshift is a sorted relational database and, historically, the act
of sorting a table, being expensive, was a manually issued SQL command;
a Redshift user had to issue the VACUUM
command to sort a
table.
Additionally, VACUUM
has a second purpose, of expunging
from a table all rows which could never be read by any current or future
query.
This second purpose arises from the use of MVCC to implement ACID (I’ve given Wikipedia links so you can look up the acronyms; don’t try to actually read those pages to understand the concepts, it’s impossible).
With MVCC, each table holds two extra columns maintained by the
database which indicate when a row is valid from and when it is valid
until; and so when for example an UPDATE
occurs, a row
being updated has its validity end set to now and then a copy of the row
is made, incorporating the changes from the update which has its
validity start set to now and its validity end as NULL (which means
ongoing).
The whole point of this is that a query when it runs knows the time at which is began, and it will only see the version of rows which were valid at that time.
In the end of course what happens is that tables end up with rows
which have their validity end set, but time has passed and now every
query running is after their validity end, and so those rows
are now completely useless - no query can ever see them again - and it
is the job of VACUUM
to finally, permanently expunge such
rows from a table.
The VACUUM
command in Redshift can be run so that it
performs only sorting, or performs only expunging deleted rows, or that
it does both (a “full” vacuum).
(In fact, because of the way VACUUM
works, a sort-only
delete at best takes as long as a full vacuum, but the more deleted rows
you have, the faster the full vacuum will be. I’ll explain it all in a
later white paper about VACUUM
. For now, suffice to say,
never use sort-only vacuum. Only use delete, or full.)
Recently, in late 2018, automatic background delete-only vacuum was introduced.
More recently still, in late 2019, automatic background sort-only vacuum was introduced.
Both are mandatory; they cannot be disabled.
Both are being described in the documentation as taking care of
VACUUM
such that system administrators and developers no
longer need concern themselves with this task.
However, discussion over time with fellow Redshift admin has led to a consensus that auto-vacuum runs too infrequently to make a difference - or, indeed, even to be noticed at all.
Unfortunately, I have also spoken to a lot of Redshift admin and
developers and there is a common theme where they are assuming, without
any form of examination of their cluster or tables, that auto-vacuum
will completely take care of all vacuum requirements; they have read the
docs, and stopped thinking about VACUUM
.
This white paper then is an investigation to provide some evidence into the behaviour and characteristics of auto-vacuum.
Ten tables are created. The tables are identical. They have one
bigint
column and consist of exactly full raw encoded
blocks, and are key distributed. There is one value in the column for
every slice, such that each slice has the same number of blocks. There
are 50 blocks per slice (on a two node, four slice test cluster). The
tables are all fully vacuumed and analyzed.
The cluster is configured with one queue of ten slots. Once all the tables are ready, ten queries are concurrently issued, one per table, which updates every row in each table, where the new value keeps each row on the slice it is already on; the idea here is to get the updates done as quickly as possible, while also keeping the cluster busy, so that auto-vacuum doesn’t start before the test starts.
At this point we have each table having 200 blocks of sorted but deleted rows, and 200 blocks of unsorted rows.
We then examine the system tables every 10 seconds, over a period of 300 seconds, to see if any vacuums occur (which will we detect by changes to the number of sorted and unsorted blocks).
We then delete all the tables and remake them, but increase the polling interval by 10 seconds.
This process continues until the final polling interval of 60 seconds.
We record when delete vacuums occur and when sort vacuums occur (which polling iteration of which polling interval).
The entire set of tests is repeated five times.
(The reason for this test design is that during experimentation it became clear even the slightest load on the cluster (say, examining the system tables every 10 seconds) seemed enough to forestall auto-vacuum completely.)
See Appendix A for the Python
pprint
dump of the results dictionary.
The X axis indicates the test iteration (there were five).
The Y axis indicate the poll iteration (each polling interval tests for 300 seconds).
There are ten test tables, and the value in each field is “number of tables delete vacuumed / number of tables sort vacuumed”.
As the number of tables having been vacuumed rises, the number rises from 0 (no tables vacuumed) to 10 (all tables vacuumed).
Test duration was 15,388 seconds (includes server bring-up and shut-down time).
Poll Iteration | Iteration #0 | Iteration #1 | Iteration #2 | Iteration #3 | Iteration #4 |
---|---|---|---|---|---|
0 | 0/0 | 0/0 | 0/0 | 0/0 | 0/0 |
1 | 0/0 | 0/0 | 0/0 | 0/0 | 0/0 |
2 | 0/0 | 0/0 | 0/0 | 0/0 | 0/0 |
3 | 0/0 | 0/0 | 0/0 | 0/0 | 0/0 |
4 | 0/0 | 0/0 | 0/0 | 0/0 | 0/0 |
5 | 0/0 | 0/0 | 0/0 | 0/0 | 0/0 |
6 | 0/0 | 0/0 | 0/0 | 0/0 | 0/0 |
7 | 0/0 | 0/0 | 0/0 | 0/0 | 0/0 |
8 | 0/0 | 0/0 | 0/0 | 0/0 | 0/0 |
9 | 0/0 | 0/0 | 0/0 | 0/0 | 0/0 |
10 | 0/0 | 0/0 | 0/0 | 0/0 | 0/0 |
11 | 0/0 | 0/0 | 0/0 | 0/0 | 0/0 |
12 | 0/0 | 0/0 | 0/0 | 0/0 | 0/0 |
13 | 0/0 | 0/0 | 0/0 | 0/0 | 0/0 |
14 | 0/0 | 0/0 | 0/0 | 0/0 | 0/0 |
15 | 0/0 | 0/0 | 0/0 | 0/0 | 0/0 |
16 | 0/0 | 0/0 | 0/0 | 0/0 | 0/0 |
17 | 0/0 | 0/0 | 0/0 | 0/0 | 0/0 |
18 | 0/0 | 0/0 | 0/0 | 0/0 | 0/0 |
19 | 0/0 | 0/0 | 0/0 | 0/0 | 0/0 |
20 | 0/0 | 0/0 | 0/0 | 0/0 | 0/0 |
21 | 0/0 | 0/0 | 0/0 | 0/0 | 0/0 |
22 | 0/0 | 0/0 | 0/0 | 0/0 | 0/0 |
23 | 0/0 | 0/0 | 0/0 | 0/0 | 0/0 |
24 | 0/0 | 0/0 | 0/0 | 0/0 | 0/0 |
25 | 0/0 | 0/0 | 0/0 | 0/0 | 0/0 |
26 | 0/0 | 0/0 | 0/0 | 0/0 | 0/0 |
27 | 0/0 | 0/0 | 0/0 | 0/0 | 0/0 |
28 | 0/0 | 0/0 | 0/0 | 0/0 | 0/0 |
29 | 0/0 | 0/0 | 0/0 | 0/0 | 0/0 |
Poll Iteration | Iteration #0 | Iteration #1 | Iteration #2 | Iteration #3 | Iteration #4 |
---|---|---|---|---|---|
0 | 0/0 | 0/0 | 0/0 | 0/0 | 0/0 |
1 | 0/0 | 0/0 | 0/0 | 0/0 | 0/0 |
2 | 0/0 | 0/0 | 0/0 | 0/0 | 0/0 |
3 | 0/0 | 0/0 | 0/0 | 0/0 | 0/0 |
4 | 0/0 | 0/0 | 0/0 | 0/0 | 0/0 |
5 | 0/0 | 0/0 | 0/0 | 0/0 | 0/0 |
6 | 0/0 | 0/0 | 0/0 | 0/0 | 0/0 |
7 | 0/0 | 0/0 | 0/0 | 0/0 | 0/0 |
8 | 0/0 | 0/0 | 0/0 | 0/0 | 0/0 |
9 | 0/0 | 0/0 | 0/0 | 0/0 | 0/0 |
10 | 0/0 | 0/0 | 0/0 | 0/0 | 0/0 |
11 | 0/0 | 0/0 | 0/0 | 0/0 | 0/0 |
12 | 0/0 | 0/0 | 0/0 | 0/0 | 0/0 |
13 | 0/0 | 0/0 | 0/0 | 0/0 | 0/0 |
14 | 0/0 | 0/0 | 0/0 | 0/0 | 0/0 |
Poll Iteration | Iteration #0 | Iteration #1 | Iteration #2 | Iteration #3 | Iteration #4 |
---|---|---|---|---|---|
0 | 0/0 | 0/0 | 0/0 | 0/0 | 0/0 |
1 | 0/0 | 0/0 | 0/0 | 0/0 | 0/0 |
2 | 0/0 | 0/0 | 0/0 | 0/0 | 0/0 |
3 | 0/0 | 0/0 | 0/0 | 0/0 | 0/0 |
4 | 0/0 | 0/0 | 0/0 | 0/0 | 0/0 |
5 | 0/0 | 0/0 | 0/0 | 0/0 | 0/0 |
6 | 0/0 | 0/0 | 0/0 | 0/0 | 0/0 |
7 | 0/0 | 0/0 | 0/0 | 0/0 | 0/0 |
8 | 0/0 | 0/0 | 0/0 | 0/0 | 0/0 |
9 | 0/0 | 0/0 | 0/0 | 0/0 | 0/0 |
Poll Iteration | Iteration #0 | Iteration #1 | Iteration #2 | Iteration #3 | Iteration #4 |
---|---|---|---|---|---|
0 | 0/0 | 0/0 | 0/0 | 0/0 | 0/0 |
1 | 0/0 | 0/0 | 0/0 | 10/0 | 0/0 |
2 | 10/0 | 0/0 | 0/0 | 10/0 | 0/0 |
3 | 10/0 | 0/0 | 0/0 | 10/0 | 0/0 |
4 | 10/0 | 0/0 | 0/0 | 10/0 | 0/0 |
5 | 10/0 | 0/0 | 0/0 | 10/0 | 0/0 |
6 | 10/0 | 0/0 | 0/0 | 10/0 | 0/0 |
7 | 10/0 | 0/0 | 0/0 | 10/0 | 0/0 |
Poll Iteration | Iteration #0 | Iteration #1 | Iteration #2 | Iteration #3 | Iteration #4 |
---|---|---|---|---|---|
0 | 0/0 | 0/0 | 0/0 | 0/0 | 0/0 |
1 | 0/0 | 0/0 | 0/0 | 0/0 | 0/0 |
2 | 0/0 | 2/0 | 0/0 | 0/0 | 10/0 |
3 | 0/0 | 10/0 | 0/0 | 0/0 | 10/0 |
4 | 0/0 | 10/0 | 0/0 | 0/0 | 10/0 |
5 | 0/0 | 10/0 | 0/0 | 0/0 | 10/0 |
Poll Iteration | Iteration #0 | Iteration #1 | Iteration #2 | Iteration #3 | Iteration #4 |
---|---|---|---|---|---|
0 | 0/0 | 0/0 | 0/0 | 0/0 | 0/0 |
1 | 0/0 | 0/0 | 0/0 | 0/0 | 0/0 |
2 | 0/0 | 0/0 | 0/0 | 0/0 | 0/0 |
3 | 0/0 | 0/0 | 0/0 | 0/0 | 0/0 |
4 | 0/0 | 0/0 | 10/0 | 0/0 | 0/0 |
First, it is essential to appreciate that where auto-vacuum is a black box, it is impossible to know what factors are involved in its operation, and so all discussion and conclusions are almost completely tentative, since they are based on the observations from this one particular test method, which could well have missed any number of factors involved in the operation of auto-vacuum.
The fact that this is the case, is, of course, a central part of this discussion : you cannot knowingly correctly design systems when they contain black boxes, and even more so, black boxes controlled by third parties and subject without warning to unannounced change.
Turning to the results, the first obvious behaviour which seems apparent is that shorter polling intervals appear to forestall auto-vacuum.
This is remarkable.
To put it in other words, issuing a single query every 30 seconds, and this query being on system tables and so not even touching the test tables, always prevented auto-vacuum from running at all.
To put the results then as whole into words; with 10s, 20s and 30s polling intervals, auto-vacuum did not run at all.
With 40s and 50s intervals, auto-vacuum ran in two of the five test iterations (in both cases after one or two polling intervals).
With 60s intervals, auto-vacuum ran in one of the five iterations, and then on the final polling interval of that test iteration.
Five test iterations all together take 30 minutes.
On the face of it, this explains the consensus amongst fellow Redshift admin that auto-vacuum runs so infrequently as not even to be detectable.
On a real cluster, with dozens or even hundreds of users and many hundreds or thousands of tables, and a literally never-ending flow of queries, auto-vacuum as it is now looks to sink without a trace.
The second obvious behaviour is that a sorting vacuum was never observed; only delete vacuums occurred.
AWS sells auto-vacuum pretty hard;
With Redshift automatic table sort, an administrator no longer needs to worry about tracking what to sort and when to sort. Redshift does it automatically. Redshift runs the sorting in the background and re-organizes the data in tables to maintain sort order and provide optimal performance.
Given this admonition, it is hardly surprising I find now, as I do, that new Redshift administrators and developers imagine auto-vacuum has completely solved the vacuum problem, and they give it not one more thought.
Given what we find of auto-vacuum behaviour in this investigation, these people have been misled, and auto-vacuum, and its documentation, has contributed greatly to the incorrect operation of Redshift clusters around the world.
Coming back to auto-vacuum itself, what we see here then in this is that auto-vacuum is a black box; we have no idea of its internal workings, and so when the externally visible behaviour makes no obvious and inherent sense, we have no idea why what we see is happening.
It is of course impossible to knowingly design correct systems when they contain black boxes.
This however is not a problem for Redshift users who do not know how to knowingly design a correct system, and although I may be wrong, I suspect auto-vacuum was actually intended for them.
In my experience, not all but very nearly almost all Redshift administrators and developers do not know how to correctly operate a Redshift cluster.
My almost invariable experience as a consultant is beginning a contract, explaining to the client how Redshift works internally, and so what it can actually be used for, at which point they then are able by themselves to see that their use of Redshift is completely inappropriate (clients have always treat Redshift as if it were an unsorted row-store database but with an unlimited capacity for users, tables, queries, etc), and then to see what use cases they do have which are appropriate for Redshift, while also then understanding what they will have to do to operate Redshift correctly so that it will be able to fulfil those use cases.
Sometimes incorrect operation of Redshift is simply due to admin or users being assigned a task beyond their current capabilities, but fundamentally I think it is due to what I consider the complete lack of meaningful documentation published by AWS.
It is not possible to know how to correctly operate Redshift, even when it is perfectly within the administrators’ or developers’ capabilities, because the information necessary to do so simply is not available.
At this point, it is useful to bring Snowflake into the picture.
Snowflake, like Redshift, is sorted column-store. It is, however, completely automated. There is literally no configuration, except for how much you want to pay for hardware. Everything is configured and run by algorithms.
If you do not know how to correctly operate a sorted column-store database, Snowflake is great. The automation will do a lot better than you will.
If you do know how to correctly operate a sorted column-store database, Snowflake is awful. The automation will do a lot worse than you will.
Unfortunately, of course, with Snowflake, there are no options : you can have any colour you like, as long as it’s black. It’s automation and only automation. What you get is what the automation can deliver, and nothing else.
With Redshift, historically, there was zero automation.
You had to know what you were doing.
If you did not, Redshift was a disaster.
If you did, Redshift was staggeringly amazing.
My view on Snowflake vs Redshift is that if you don’t know what you’re doing, Snowflake is a lot better, but if you do know what you’re doing, Redshift is a lot better; and so as data volumes become large, Snowflake’s relative inefficiency becomes increasingly expensive compared to a correctly operated Redshift (that’s the ugly).
This brings us back to auto-vacuum.
Auto-vacuum is a black box, an algorithm.
It will give you what it gives you, nothing more, nothing less.
If you have no idea what you’re doing, auto-vacuum is in principle a hell of a lot better than nothing (but currently in practise, where it seems to run so infrequently, it actually seems currently to be basically worthless).
If you do know what you’re doing, you will do a million times better than any black box algorithm.
This leads us then though to the question of where AWS is going with Redshift.
Since Snowflake became successful, Redshift development has focused on the introduction of black boxes, to automate various aspects of its behaviour.
To put it in other words, Redshift has for a few years been focusing on damage limitation for those users who have no idea how to operate Redshift correctly.
Some of these users are going to have no idea what to do no matter what, but I am of the view that no matter how capable users are, they can have no idea what to do because of the complete lack of meaningful documentation from AWS.
The right way to solve this problem is to publish the necessary information, rather than to take the dev team away from introducing features which improve Redshift for its actual proper use case, and have them rather develop mechanisms to ameliorate the self-harm uninformed users inflict upon themselves.
Snowflake, in my view, does not actually compete with Redshift, because Redshift is when correctly operated simply much better at sorted column-store than Snowflake, such that there is no competition; rather, what we’re actually seeing here is Snowflake competing successfully with the lack of information published by AWS about how to operate Redshift correctly.
Redshift instead of playing to its strengths, and improving its feature set for its actual proper use case and documenting itself so users know how to operate it correctly, is playing the game of its opponent, by instead trying only to introduce mechanisms to limit the self-harm uninformed users inflict upon themselves; and I am of the view it’s never going to win this game, because Snowflake is a new design, intended from the start to work in this way, and Redshift to me behaves like a large, unmaintainable source code base, and so I think has a dozen tons of technical design and implementation legacy.
With ten 400mb tables, each of which contains 200mb of sorted but deleted rows, and 200mb of unsorted rows, where for each test run the system tables were polled over a period of 300 seconds to check for auto-vacuum having occurred, and where as the tests were re-run the test conditions were varied and the polling interval became once every 10s, then once every 20s and then once every 30s, auto-vacuum never occurred, over all five iterations of the test for each polling interval.
With an interval of 40s and 50s, auto-vacuum occurred in two of five test iterations.
With an interval of 60s, auto-vacuum occurred in one of the five test iterations.
My hypothesis is that the load caused by the query examining the system tables was enough to forestall auto-vacuum.
In all cases, the only vacuum which ran was vacuum delete. A sorting vacuum was never observed.
On the face of it, and given that auto-vacuum is a black box, so it is impossible to judge the fairness of the test method, it appears auto-vacuum runs so infrequently as to be of negligible consequence.
This aligns with the general consensus amongst the skilled Redshift admin I have spoken to about auto-vacuum.
Auto-vacuum cannot be disabled, and when updated will be without warning or announcement. Currently, auto-vacuum seems inconsequential, and so neither harmful nor helpful to either unskilled or skilled Redshift users, but its behaviour could change at any time.
Note these results are completely unprocessed; they are a raw dump of the results, so the original, wholly unprocessed data, is available.
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'tests': {'dc2.large': {2: {}}},
'versions': {'dc2.large': {2: 'PostgreSQL 8.0.2 on i686-pc-linux-gnu, '
'compiled by GCC gcc (GCC) 3.4.2 20041017 (Red '
'Hat 3.4.2-6.fc3), Redshift 1.0.30840'}}}
As part of writing this white paper, I came to re-read the various official documentation pages relating to vacuum and auto-vacuum.
Of particular interest is the page Vacuuming
tables. The first heading on this page is “Automatic table sort” and
here the text discusses at some length the column
vacuum_sort_benefit
in the system table
[SVV_TABLE_INFO][2]
.
1 https://docs.aws.amazon.com/redshift/latest/dg/t_Reclaiming_storage_space202.html
2 https://docs.aws.amazon.com/redshift/latest/dg/r_SVV_TABLE_INFO.html
The prose states that this column indicates how much a table would benefit from sorting, and that the column is computed by Redshift based on scans performed on the table.
None of this actually has anything to the heading, “Automatic table sort” - at least, not by anything written in the text. It is however rather suggestive that so much has been written about this column under this heading - such that I wonder if the value in this column is being used by Redshift to decide which tables to auto-vacuum.
Some while ago, I was interested to see how this column was computed.
I obtained the SQL for SVV_TABLE_INFO
(it’s a view) and,
well, my first reaction was astonishment at how enormous the view SQL is
- 16.5 kilobytes, joining to twelve system tables.
You can see the SQL here, in the System Table Explorer (scroll down to get to the SQL dump).
3 https://www.redshift-observatory.ch/system_table_explorer/2021-09-15_1.0.30840_html/pg_catalog.svv_table_info.html
I doubt the correctness of any SQL this long and varied. Moreover, although I may be wrong, I absolutely doubt the existence of a meaningful test suite for this view. I know from experience it would be a nightmare to write, and it’s simply not something which from past experience I would expect from the Redshift team anyway.
Having followed the code down, it turns out this column is taken
directly from a system table which is a real system table, but to which
you have no access (a lot of the system tables can only be accessed by
the rdsdb
user, which is owned and controlled by AWS).
In other words, the value in vacuum_sort_benefit
is
computed by a black box, and it might be that the output of this black
box is fed into the black box of auto-vacuum, so that it knows
which table to vacuum next (and then must decide for itself
when to vacuum).
I am a C programmer - kernel development, high performance computing, networking, data structures and so on.
I read the C. J. Date book, the classic text on relational database theory, and having learned the principles, wrote a relational database from scratch in C, which purely by chance set me up quite nicely for what came next, moving into data engineering in late 2011, when I joined as the back-end engineer two friends in their startup.
In that startup, I began using Redshift the day it came out, in 2012 (we had been trying to get into the beta programme).
We were early, heavy users for a year and a half, and I ending up
having monthly one-to-one meetings with one of the Redshift team
managers, where one or two features which are in Redshift today
originate from suggestions made in those meetings, such as the
distribution style ALL
.
Once that was done, after a couple of years of non-Redshift data engineering work, I returned to Redshift work, and then in about mid-2018 contracted with a publisher to write a book about Redshift.
The book was largely written but it became apparent I wanted to do a lot of things which couldn’t be done with a book - republish on every new Redshift release, for example - and so in the end I stepped back from the contract and developed the web-site, where I publish investigation into, and ongoing monitoring of, Redshift.
So for many years now I’ve been investigating Redshift sub-systems full-time, one by one, and this site and these investigations are as far as I know the and the only source of this kind of information about Redshift.
I provide consultancy services for Redshift - advice, design, training, getting failing systems back on their feet pronto, the usual gamut - but in particular offer a Redshift cluster cost reduction service, where the fee is and only is one month of the savings made.
Broadly speaking, to give guidance, savings are expected fall into one of two categories; either something like 20%, or something like 80%. The former is for systems where the business use case is such that Redshift cannot be operated correctly, and this outcome requires no fundamental re-engineering work, the latter is for systems where Redshift can be operated correctly, and usually requires fundamental re-engineering work (which you may or may not wish to engage in, despite the cost savings, in which case we’re back to the 20%).
Details and contact information are on the web-site.