22nd August 2022 (updated 1st May 2024)
Postgres, and from it Redshift, implements and uses a computing method known as MVCC (Multi-Version Concurrent Control). MVCC has a number of functions, purposes and benefits, one of which is that it provides most of what is required for ACID (atomicity, consistency, isolation, durability), which is central to modern databases.
An aspect of MVCC is the notion of the state of a transaction. A Postgres, and by the looks of it also a Redshift, transaction can be in progress, aborted or committed. In Postgres, where Postgres runs on a single machine only, changing the state of a transaction is a trivial operation (atomic compare-and-swap in shared memory). In Redshift, where there is a cluster and a node can fail at any time, changing the state of a transaction is a non-trivial operation which requires co-ordination between nodes, so that the “right thing” happens, no matter what nodes fail when; consider, for example, what happens if a node fails while you’re in the process of dealing with a node failure? (I don’t know the current method used for this, but certainly in the early days of Redshift the Paxos protocol was used - I spoke at the time to the engineer who implemented it in Redshift.)
As a part of this mechanism to move a transaction’s state from in progress to committed, there is a mechanism known as the commit queue. This is from what’s seen in the system tables theorised to be a single FIFO queue, through which all transactions pass; when a transaction needs to commit, it enters the queue, waits for its turn, and then when its turn comes, the necessary work to perform the commit occurs.
This is a queue entirely separate from the usual WLM queues. You can have as many WLM queues as Redshift can support - they are queuing and issuing queries; and for all transactions, regardless of which queue or queues they execute in, they will once they have finished working need to enter the commit queue.
This document investigates the commit queue, firstly to determine performance over different node types and cluster sizes (as there is a possibility larger clusters commit more slowly), and secondly, to try to prove that the commit queue is a single queue and does indeed execute a single commit at a time.
The test method basically sets up a table and performs an insert, and
then examines STL_COMMIT_STATS to find out how long the
commit took.
The test query is issued five times, with the slowest and fastest results being discarded.
The test table is dropped, created and repopulated for every iteration, and for every test query; which is to say, all the test tables are identical, but each table only ever has one query issued on it.
A number of factors are varied, with the test query being run on every combination of the following;
dc2.large, ds2.xlarge,
ra3.xlplus)All blocks, always, are exactly full of rows. The table has
key distribution (even has off-by-one bugs, so
cannot be used), with the value used in the distribution key being
chosen to ensure each slice has an equal number of records. The table
has a single column, with the data type bigint and
not null specified, encoding is raw, and the
table is fully vacuumed and analyzed. Result caching is disabled, and
the analysis threshold set to 0.
How long a query took to execute is taken directly from the system tables, and so mark the times internal to Redshift as to when the query execution started and ended (queuing and compile times are excluded).
The goal is to find out if the commit queue is a single queue, and if it commits a single query at a time.
As before, this test is run on the three different cluster types.
Each cluster is configured to have three queues with one slot each, with 49%, 49% and 2% memory, respectively. The test actually wants two queues, but I wanted them to have equal memory, so I needed a third queue. The third queue (which is the default queue) is not used at all.
The first queue has a single large insert query issued, which is known from experience to need (depending on cluster type) many seconds to commit.
As soon as this query is issued (and remember it will not commit until it finishes inserting), the other queue has issued to it a non-stop flow of queries which perform a very small write, which take about a second to commit.
When the large query returns, having completed (which means having committed), we then stop issuing the small queries.
We then examine STL_COMMIT_STATS, and we look to see,
from the timestamps of the work done by each node for each transaction,
if two transactions ever overlapped on one node; if they do not, then
when the large query came to commit, the small queries were being
blocked, and there then must be only a single queue, and it must be it
executes only a single commit at a time.
(We have to examine timestamps on a per-node basis, rather than a per-transaction basis, because the timestamps for each node are from the clock on each node, and those clocks cannot be perfectly synchronized; there is from this, where we might end up comparing a slow clock on one node with a fast clock on another, sometimes an artefact where it looks like work is occurring in parallel when in fact it is not.)
See Appendix A for the Python
pprint dump of the results dictionary.
The X-axis is the number of blocks in the table, per column (there is only one column), per slice.
The Y-axis is the number of blocks inserted into the table, again per column (but there is only the one), per slice.
The idea here is to keep the workload per slice stable, so we can see what happens as the number of slices in a cluster rises.
The individual results are “mean/standard deviation” of the duration
of the commit phase, for all nodes, in seconds
(STL_COMMIT_STATS gives information on a per-node basis,
not a per-slice basis).
As ever, five trials occurred, with the slowest and fastest being discarded.
Test duration was 3,324 seconds.
| 1 | 10 | 100 | |
|---|---|---|---|
| 1 | 0.10/0.00 | 0.10/0.00 | 0.11/0.01 |
| 10 | 0.18/0.03 | 0.19/0.02 | 0.22/0.01 |
| 100 | 3.55/0.08 | 3.37/0.19 | 3.78/0.11 |
| 200 | 7.32/0.41 | 7.40/0.10 | 7.44/0.22 |
| 1 | 10 | 100 | |
|---|---|---|---|
| 1 | 0.11/0.00 | 0.11/0.00 | 0.11/0.00 |
| 10 | 0.17/0.02 | 0.23/0.04 | 0.15/0.03 |
| 100 | 3.18/0.15 | 3.51/0.09 | 3.16/0.16 |
| 200 | 7.27/0.12 | 7.24/0.42 | 7.44/0.18 |
| 1 | 10 | 100 | |
|---|---|---|---|
| 1 | 0.10/0.00 | 0.10/0.00 | 0.11/0.00 |
| 10 | 0.15/0.02 | 0.18/0.03 | 0.17/0.01 |
| 100 | 3.26/0.22 | 3.17/0.16 | 3.55/0.19 |
| 200 | 7.34/0.15 | 7.21/0.22 | 7.65/0.07 |
| 1 | 10 | 100 | |
|---|---|---|---|
| 1 | 0.21/0.00 | 0.21/0.01 | 0.22/0.00 |
| 10 | 0.40/0.03 | 0.40/0.02 | 0.42/0.04 |
| 100 | 2.16/0.09 | 2.35/0.11 | 2.37/0.05 |
| 200 | 4.68/0.09 | 4.58/0.15 | 4.39/0.15 |
| 1 | 10 | 100 | |
|---|---|---|---|
| 1 | 0.26/0.01 | 0.28/0.04 | 0.28/0.01 |
| 10 | 0.46/0.02 | 0.52/0.04 | 0.46/0.02 |
| 100 | 3.38/0.18 | 2.88/0.33 | 2.95/0.18 |
| 200 | 7.48/0.20 | 4.67/0.62 | 7.00/0.50 |
| 1 | 10 | 100 | |
|---|---|---|---|
| 1 | 0.27/0.01 | 0.31/0.01 | 0.28/0.02 |
| 10 | 0.50/0.00 | 0.53/0.02 | 0.55/0.04 |
| 100 | 3.13/0.16 | 3.51/0.07 | 3.29/0.21 |
| 200 | 6.13/0.24 | 6.22/0.34 | 6.13/0.06 |
| 1 | 10 | 100 | |
|---|---|---|---|
| 1 | 0.84/0.04 | 0.48/0.02 | 0.53/0.05 |
| 10 | 0.87/0.01 | 0.57/0.01 | 0.54/0.01 |
| 100 | 1.46/0.17 | 1.62/0.18 | 1.58/0.21 |
| 200 | 2.32/0.18 | 2.83/0.54 | 2.15/0.15 |
| 1 | 10 | 100 | |
|---|---|---|---|
| 1 | 0.48/0.01 | 0.49/0.01 | 0.56/0.03 |
| 10 | 0.59/0.01 | 0.51/0.03 | 0.59/0.04 |
| 100 | 1.47/0.01 | 1.39/0.08 | 1.39/0.06 |
| 200 | 2.32/0.23 | 2.05/0.01 | 2.06/0.08 |
| 1 | 10 | 100 | |
|---|---|---|---|
| 1 | 0.60/0.07 | 0.48/0.01 | 0.47/0.01 |
| 10 | 0.66/0.13 | 0.54/0.00 | 0.55/0.01 |
| 100 | 1.34/0.03 | 1.54/0.17 | 1.42/0.02 |
| 200 | 2.52/0.08 | 2.49/0.11 | 2.08/0.10 |
| Node | Start Queue | Start Work | End Flush | End Staging | End Local | Start Global | End Time |
|---|---|---|---|---|---|---|---|
| leader | 0.001398 | 0.002113 | 7.562822 | 7.562907 | |||
| worker#0 | 0.000000 | 7.556499 | 7.557350 | 7.558815 | 7.560003 | 7.560818 | |
| worker#1 | 0.001192 | 7.550308 | 7.551323 | 7.552987 | 7.560387 | 7.561187 | |
| worker#2 | 0.005975 | 7.243325 | 7.244267 | 7.245893 | 7.560355 | 7.561285 | |
| worker#3 | 0.000376 | 7.023369 | 7.024926 | 7.029212 | 7.560516 | 7.561396 |
| Node | Start Queue | Start Work | End Flush | End Staging | End Local | Start Global | End Time |
|---|---|---|---|---|---|---|---|
| leader | 0.001353 | 0.002862 | 4.181659 | 4.181716 | |||
| worker#0 | 0.000000 | 3.833250 | 3.839024 | 4.169154 | 4.178382 | 4.179440 | |
| worker#1 | 0.001056 | 4.120788 | 4.154199 | 4.178504 | 4.179360 | 4.180276 |
| Node | Start Queue | Start Work | End Flush | End Staging | End Local | Start Global | End Time |
|---|---|---|---|---|---|---|---|
| leader | 0.000000 | 0.024787 | 7.661154 | 7.661198 | |||
| worker#0 | 0.011801 | 7.138532 | 7.140267 | 7.647116 | 7.648065 | 7.648988 | |
| worker#1 | 0.009710 | 7.136275 | 7.138151 | 7.639574 | 7.645915 | 7.646864 | |
| worker#2 | 0.015661 | 5.786670 | 5.791286 | 6.356112 | 7.651727 | 7.653689 |
| Node | Start Queue | Start Work | End Flush | End Staging | End Local | Start Global | End Time |
|---|---|---|---|---|---|---|---|
| leader | 0.000000 | 0.148287 | 5.695788 | 5.695861 | |||
| worker#0 | 0.139752 | 3.764309 | 3.765709 | 3.840431 | 5.686664 | 5.687704 | |
| worker#1 | 0.151825 | 4.789478 | 4.791013 | 5.419762 | 5.698756 | 5.699941 | |
| worker#2 | 0.164160 | 5.156860 | 5.168473 | 5.710041 | 5.711127 | 5.712334 | |
| worker#3 | 0.151675 | 5.171510 | 5.401974 | 5.694301 | 5.698566 | 5.699609 |
| Node | Start Queue | Start Work | End Flush | End Staging | End Local | Start Global | End Time |
|---|---|---|---|---|---|---|---|
| leader | 0.000000 | 0.000340 | 2.197454 | 2.197519 | |||
| worker#0 | 0.005052 | 2.149740 | 2.150524 | 2.197577 | 2.198422 | 2.200337 | |
| worker#1 | 0.010430 | 1.645728 | 1.646635 | 1.682417 | 2.200012 | 2.201798 | |
| worker#2 | 0.023643 | 1.970819 | 1.971518 | 2.020031 | 2.198829 | 2.200653 | |
| worker#3 | 0.022025 | 1.517918 | 1.519292 | 1.560860 | 2.197425 | 2.198897 |
leader : 1
worker#0 : 1
worker#1 : 1leader : 1
worker#0 : 1
worker#1 : 1
worker#2 : 1leader : 1
worker#0 : 1
worker#1 : 1
worker#2 : 1
worker#3 : 1leader : 1
worker#0 : 1
worker#1 : 1leader : 1
worker#0 : 1
worker#1 : 1
worker#2 : 1leader : 1
worker#0 : 1
worker#1 : 1
worker#2 : 1
worker#3 : 1leader : 1
worker#0 : 1
worker#1 : 1leader : 1
worker#0 : 1
worker#1 : 1
worker#2 : 1leader : 1
worker#0 : 1
worker#1 : 1
worker#2 : 1
worker#3 : 1First, it must be noted the test script has only examined the smallest type of each node type, and then only two, three and four node clusters. I have heard solid evidence from a fellow Redshift admin that as cluster sizes get quite a lot larger (over a hundred slices), commit times become disproportionately slower; something I will test once I’m contracting again and am happy to blow some hundreds of dollars on a few test runs.
Now, it is immediately apparent from the performance test results that;
dc2.large cluster takes 7.44 seconds to
commit 200 blocks per slice, where a four node cluster of the same type,
also committing 200 blocks per slice, takes 7.65 seconds.dc2.large, ds2.xlarge and
ra3.xlplus, the ra3.xlplus node has the
fastest commit, and by some way, being about four times faster than
dc2.large (ra3 nodes have about twice the
hardware of the dc2 nodes).There is something odd happening with the ds2.xlarge
nodes, which is not happening with the other node types. These are the
mean/standard deviation benchmarks for the 100 blocks present/200 blocks
inserted case, for the different node types;
| # Nodes | ds2.xlarge | dc2.large | ra3.xlplus |
|---|---|---|---|
| 2 | 4.39/0.15 | 7.44/0.22 | 2.15/0.15 |
| 3 | 7.00/0.50 | 7.44/0.18 | 2.06/0.08 |
| 4 | 6.13/0.06 | 7.65/0.07 | 2.08/0.10 |
We see the ds2.xlarge type expressed a lot more variance
in performance than the other node types. The particular test run used
for this white paper shows in fact quite a small variance compared to
that seen in other test runs; I’ve seen times (with larger volumes of
data, and so longer times) for ds2.xlarge, over the three
node sizes, ranging from 13 to 30 seconds, with the three node cluster
being the slow cluster.
Remember these are not one-off tests - five iterations of each test run occur, with the slowest and fastest being discarded, so we’re looking for each set of three cluster sizes at 15 test iterations. These are consistent results.
My thought so far is that individual ds2.xlarge nodes
vary significantly in their performance, far more than other node types.
A cluster then will be composed of good nodes, and bad nodes, and
commits of course run as slowly as the slowest node. Much more so with
ds2.xlarge than other node types, it looks prudent to
benchmark a new cluster to check it’s not too bad for performance.
Finally, there is one more ds2.xlarge anomaly, which I
really have no explanation for at all. Look at the results for inserting
200 blocks with a three node ds2.xlarge cluster;
| 1 | 10 | 100 | |
|---|---|---|---|
| 200 | 7.48/0.20 | 4.67/0.62 | 7.00/0.50 |
Why was the middle run so much quicker than the other runs? and this is the mean over five runs, which together occurred over the course of about a minute.
The detailed results are provided so we can gain a more detailed
understanding of what’s happening with the work done when committing,
and also they allow a closer investigation of what’s happening with
ds2.xlarge.
In the detailed results I take the data from
STL_COMMIT_STATS and I find the earliest timestamp for a
transaction, and then convert the other timestamps into offsets from
that earliest timestamps. This makes it easier to see what’s happening
in what order.
The system table STL_COMMIT_STATS contains one row per
node per transaction, which records the start and end times of varies
phases in the commit work. It looks like the times recorded come from
the clock on each respective node, and of course those clocks are going
to deviate from each other, just a little, and this shows up; you can
for example occasionally find that a node - as far as the times recorded
in the table go - has started working on a commit before it was
queued. You see this in the two node ds2.xlarge detailed
results, where worker node 0 has the earliest timestamp, which is on the
face of it before the leader node queues the commit.
Looking at the data available in STL_COMMIT_STATS, at
the names of the columns, it looks on the face of it like a standard
two-phase commit.
So, to begin with, we see clearly the leader node queues the commit, then begins work, and then the worker nodes start work, and then there’s about 7 seconds or so of work on the worker nodes, taking all worker nodes up to “End Local”.
What looks to happen then is that the leader node waits till all worker nodes have complete their local work, and then begins the global stage, which triggers all the worker node to perform the global stage, which they do, and then all worker nodes and the leader node perform the global work, and when they’re all done, the commit is complete.
It looks to me like there is no sychronization for the end of the global phase; which is to say, we might think the leader node kicks off the global phase, the worker nodes then do that work, and when they’re done, then the leader node completes the global phase. Well, this appears not to happen, at least if we take the timestamps as they are. The leader node sychronizes the start of the global phase, but not its end.
In principle, what I think happens is this;
| Node | Start Queue | Start Work | End Flush | End Staging | End Local | Start Global | End Time |
|---|---|---|---|---|---|---|---|
| leader | 0 | 1 | 6 | 8 | |||
| worker#0 | 2 | 3 | 4 | 5 | 7 | 8 | |
| worker#1 | 2 | 3 | 4 | 5 | 7 | 8 | |
| worker#2 | 2 | 3 | 4 | 5 | 7 | 8 |
Now let’s have a look at the ds2.xlarge nodes.
Remember that the global stage is very quick, and all nodes have to wait for the local stage to complete before they can begin the global phase, so the basic measure of how quick a node is how long the local stage took.
The two node cluster takes about 4 seconds to complete the local stage. The three node cluster has two nodes taking about 7 seconds, with the other nodes taking about 5.8 seconds. The four node cluster has all threes nodes taking about 5.7 seconds, and one taking about 3.8 seconds.
What I’ve seen here over the many test runs during the development of
the white paper is that ds2.xlarge nodes vary a lot in
commit performance, just as you see here, but of course commit runs as
slowly as the slowest node, since the local stage has to complete on all
nodes before the global stage can occur.
It seems like a good idea, if you’re spinning up a
ds2.xlarge cluster, to test commit performance before
keeping the cluster.
We can see that for every test case, on every node type, with every cluster size, we only ever see a single commit in progress at any one time on a single node.
It looks then like there is a single commit queue per cluster, and it commits a single query at a time.
The number of blocks being committed is in linear proportion to the time taken for the commit to occur, except for very small commits, where the intrinsic overhead of a commit dominates the time which varies linearly to the number of blocks being committed.
The number of blocks already in a table make no difference to the commit time.
The number of nodes in a cluster, over the range of very small clusters tested, has no effect on commit performance. (Note there is solid evidence from a fellow Redshift admin that when cluster sizes begin to exceed a hundred slices, commits do become disproportionately slower.)
Node types vary considerably in their commit performance. Of the
three node types tested, ra3.xlplus took about 2 seconds,
dc2.large about 7.5 seconds, ds2.xlarge about
7 seconds.
The ds2.xlarge node type, unlike the others, showed
considerable variance in performance as the number of node changes, and
performance was not related to the actual number of nodes; the three
node cluster was slowest, the four node cluster was better, the two node
cluster best of all. I feel that ds2.xlarge nodes may vary
considerable from node to node in performance, unlike other node
types.
Commits run as slowly as the slowest node in the cluster.
The commit queue looks to be a single queue, which executes a single commit at a time.
When you investigate Redshift, there are always unexpected findings.
Commit performance for and only for ds2.xlarge type
nodes varies greatly between nodes; there seem to be good nodes and bad
nodes, and how many of each you find in a given cluster is a matter of
chance.
Commits are performed on transactions, not on queries. When a
transaction comes to commit, all its queries have completed. As such,
there is no information in the system tables about transactions which
are currently in the commit queue or which are committing. There is only
information about transactions is for after they have
committed, and is in STL_COMMIT_STATS.
The system table STL_COMMIT_STATS has multiple
transactions with the transaction ID of 0.
The rows in the system table STL_COMMIT_STATS are on
a per-node basis, and contain timestamps. The timestamps appear to come
from the node in question, and so these timestamps come from clocks
which are necessarily fractionally out-of-sync, and enough that it can
look like the events the timestamps denote have occurred in an order
other than their actual order. As such, it is necessary when
working with the timestamps not to compare timestamps across
nodes (which also means not comparing timestamps across a transaction,
since there is one row per node for each transaction).
boto3 has design and/or implementation issues, at
least as far as operating Redshift clusters, and Redshift clusters have
reliability issues with start-up. A script running a single cluster
doesn’t have much of a problem, but a script running nine clusters has
enough going it has lots of problems. It took many days of work, and
about 150 USD in cluster charges, to force a way through these
issues.
ds2.xlarge type nodes in particular had the problem
of starting up, but being impossible to connect to (either due to DNS
lookup failure, or connections simply timing out).
Temporary tables with key distribution do not
translate values to slices in the same way as normal tables. No detailed
investigation has been performed, but it looks like with a normal table,
changing a value by 1 results in a different slice being used, but with
a temporary table, values go to the same node until a value difference
of about 1500 is reached, at which point values then go to the next node
(and presumably return back to the first node after another
1500).
Note these results are completely unprocessed; they are a raw dump of the results, so the original, wholly unprocessed data, is available.
{'proofs': {'dc2.large': {2: [['leader', 1], ['worker#0', 1], ['worker#1', 1]],
3: [['leader', 1],
['worker#0', 1],
['worker#1', 1],
['worker#2', 1]],
4: [['leader', 1],
['worker#0', 1],
['worker#1', 1],
['worker#2', 1],
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'ds2.xlarge': {2: [['leader', 1], ['worker#0', 1], ['worker#1', 1]],
3: [['leader', 1],
['worker#0', 1],
['worker#1', 1],
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['worker#2', 1]],
4: [['leader', 1],
['worker#0', 1],
['worker#1', 1],
['worker#2', 1],
['worker#3', 1]]}},
'tests': {'dc2.large': {2: {1: {1: [[[-1,
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'Hat 3.4.2-6.fc3), Redshift 1.0.29551'}}}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.