Query Compilation

20th February 2022 (updated 1st May 2024)

Abstract

Redshift converts SQL queries to C++ and this compiles to multiple binaries (known as segments) which are then distributed to, and executed by, the worker nodes. Historically, and still in some regions, this work is performed on the leader node and then compiling a segment on an unloaded cluster typically takes about 4 to 8 seconds, where a complex query might have ten segments. More recently, a compiled segment cache was introduced, which appears by its performance and rapid expiry of segments to be on the leader node, and if a segment is in this cache, it is not compiled but served and extremely quickly (thousandths of a second) from the cache. More recently still, an ancillary system was introduced, the compile off-load system, which off-loads compilation work from the leader-node. This system appears to have its own cache and when a query is compiled, if one or more segments are not in the cache, then there is a slow first segment (typically the usual 4 to 8 seconds), during which all segments are compiled in parallel, and after this the leader node the obtains segments from the compile off-load system at the rate of about a tenth of a second each. However, if all segments are in the compile off-load segment cache, then the slow first segment does not occur. In all cases, increasing the number of concurrently compiled queries increases compile time, but in a somewhat better than linear fashion. Finally, a serious vulnerability was discovered, where by a normal unprivileged user can instantly crash Redshift. This is achieved by creating an empty table with a sufficiently large number of columns, and then issuing a query which has one window function per column, the core issue I suspect actually being simply to issue a query with between (inclusive both ends) 980 and 1664 segments.

Introduction

The Redshift leader node is responsible for converting the SQL queries it receives into C++ files, which are compiled into executable binaries (known as segments) and distributed to the worker nodes, which then execute those segments to perform the query.

Query compilation is performed by Redshift because Redshift is intended to be able to issue timely SQL on Big Data, and this requirement although primarily met by supporting sorting (and sorting must be used, and used correctly, to allow timely SQL on Big Data), other design choices are either essential (support for a cluster) or helpful, such as query compilation.

Some years ago, a compiled segment cache was added to Redshift.

More recently still, an ancillary system, the compile off-load system, was added, such that the leader node no longer performs compilation, but rather farms that work out to the compile off-load system which compiles all segments in parallel and then returns them to the leader node.

This white paper then investigates query compilation, describing the behaviour and characteristics of leader node compilation and off-load compilation.

Test Method

To begin with, it is necessary to ensure readers are familiar with the way in which queries are organized in Redshift.

The SQL of a query is issued by a user to the leader node. The leader node analyses the SQL and converts it into streams, segments and steps, these being Redshift-specific concepts used to organize query compilation and execution.

The highest level concept is that of the stream. A query is converted into a series of streams, which execute serially. Each stream stores the results of its work to disk for the next stream to load.

Streams are composed of segments. All the segments in a stream execute concurrently. A segment is a single executable binary, compiled from the original SQL; there is then necessarily one compilation per segment.

Segments in turn are composed of steps. All the steps in a segment execute concurrently, and so all the steps in all the segments in a single stream execute concurrently (remember that streams execute serially). A step is a fundamental operation, such as scanning a table, or sorting rows, or broadcasting rows to other nodes.

The steps in a stream form the nodes on a pyramid-shaped graph through which rows flow from the bottom to the top; the bottom steps perform tasks such as reading rows from tables, the middle nodes perform processing work such as sorting or aggregation, and the very peak of the pyramid is the segment (which always runs on the leader node and so is a sort of pseudo-segment, as leader node segments are not compiled and are not affected by WLM configuration, but run as SQL normally does on Postgres) which returns the final results of the query to the caller.

(It may be of interest to mention here that a slice is a single running copy of a stream, where each node will run multiple copies of that stream, the number of copies being the number of slices. Each slice maintains its own part of each table, and performs its work on those rows only. A slice is in no way a hardware concept.)

Moving on then to the test method, Redshift for many years has provided a compiled segment cache and so - in theory - compilation is avoided if the same query is re-issued.

AWS have said various things about the cache, but in my experience, everything from AWS is vague, misleading and relentlessly positive, and by this dangerous; it leads to imagining things are very different to how they are. I note what AWS says, and then I go and find out the truth by investigating; I’ve yet to find a single statement from AWS which I actually thought actually accurately conveying the truth of a matter.

In any event, there is no option to disable the compiled segment cache, which makes testing very difficult, because on the face of it, you can only run each test query once; after that, it will be served from cache.

Not being able to disable the compiled segment cache is a major design flaw in Redshift, because the compiled segment cache is empty for a new Redshift version when it is released (cached binaries are marked with the version of Redshift they come from), and you need to be able - but by this you are not able - to check your system will continue to function even when the cache is empty and so every segment is going to compiled, both in terms of load on the leader node, and general system-wide delay, and that delay in regions which do not support compilation off-load can be very long indeed.

I spent some time looking at ways to circumvent the cache, but I found only a partial solution, such that the first segment would always be recompiled, rather than all segments, which was not good enough.

So then the compiled segment cache presents problems when it comes to running the test script with this white paper. Firstly, purely by chance, it may be segments I compile have already been compiled by others and are served from the cache. Secondly, I can only ever compile any given query once, which is awkward.

As we will see though when we come to discuss the test method implementation, there are partial ways around these problems, enough to allow useful testing.

First we must discuss what we want to test.

To begin with, note that query compilation is a purely leader node affair. Worker nodes are not involved, and so WLM configuration is irrelevant (since it does not apply to the leader node), except in that WLM configuration does set the total number of slots in the system, and so the maximum number of queries which can be compiling on the leader node at any one time.

Now, it used to be all compilation occurred on the leader node. However, two or three years ago I think it was, but in some regions only, Redshift introduced “compilation off-load”, which is to say, a fleet of helpers of some kind (lambda functions?), such that the leader node now hands over compilation work to those helpers.

Obviously, we want to test compilation behaviour in a region with compile off-load, and a region without.

Next, it would be interesting to vary the number of queries being compiled concurrently, so we can get an idea of how performance varies under load.

It would be interesting to also vary the number of segments in the test queries, so we can see how long compilation takes as queries require more and more compilation work.

Finally, we also want to generally explore and characteristic the behaviour of query compilation.

Turning then to test method implementation, varying whether compile off-load is in use or not is easy; change region. The test script, once complete, will be run once in a region with compile off-load and once in a region without.

Varying the number of queries being compiled concurrently is on the face of it easy enough; in the Python script, prepare absolutely everything before executing (SQL produced, target tables made, a per-thread connection to the database established, etc), then have one thread per query to execute, and start those threads in a tight for loop.

This in and of itself works, but the problem is if you issue the same query in each thread, the first query which compiles populates the cache and the others immediately begin to be served from the cache; the queries have to be different.

This question also needs an answer with regard to varying the number of segments per query, because here we also need to ensure previously issued queries do not cause later queries to be served from the cache.

After experimentation, the method used to control the number of segments in a query is to create an empty test table (no rows are needed for compilation to occur), which will have a SELECT issued upon it, where we vary the number of columns in the table and where every column has a window function issued upon it.

Each window function causes two segments to be created, and - critically - varying the number of columns in the table causes full compilation; nothing is served from the cache.

(There are two segments in addition to the two per window function; one is the initial table scan, and one is the final segment which returns rows to the caller, but this final segment in fact executes on the leader node and so is not compiled.)

It also turns out then it is possible to ensure queries are different to each other even if they have operate on a table with the same number of columns by using different window functions; having compiled any given window function on a column does not allow the cache to service compilation of other, different windows functions on a column.

This is how we vary the number of concurrent queries issued, while avoiding the cache, on tables with the same number of columns. However, there are not many window functions (about 25 or so), so there is a limit to how much we can vary the number of queries issued concurrently.

Nevertheless, despite these efforts, it is flatly impossible to obtain a clean, fully compiled test run; by whatever means, some segments are normally served, and it’s not clear how or why.

As such, each test run indicates how many segments are in the query, and for the queries the test issues (remembering we vary the number of queries), the mean and standard deviation of that mean are provided for the number of compiled segments; as such, where we always know the number of compilations, we can properly judge the results of that test.

So, all in all, what we end up with is this;

The cluster configuration is always one queue, with 100% memory, and 30 slots (as Redshift has a bug where-by no more than 30 queries can run in total on the whole cluster if 30 queries are running in a single queue, so the maximum number of slots we can test is 30, not 50.)

The set of tests are;

All tests are performed in a region with compilation off-load, and then again without.
All tests are run twice, so that we have one set of results with compilation, and with served from cache.
The number of queries issue concurrently is varied, over the sequence [1, 2, 4, 12].

The reason for only varying over four values is that a different window function is needed for every query. The problem is that say 30 queries are issued concurrently, and the queries are identical, as soon as one of those queries has compiled a segment, that segment will be serviced from the cache for the other queries.

So every query has to be unique - has to use a different window function - and there are only 25 or so window functions. Accordingly, I’ve picked a spread of numbers of concurrent queries to try to give as much insight as possible into performance behaviour.

The test queries are issued repeatedly, where the number of columns in the test table is varied. The sequence of the number of table columns is different for regions with and without compilation off-load, as it was found the test took about an hour to run with off-load, but about a twenty-one hours to run without and during the most intensive tests (12 queries, 256 columns) disconnected the script.

As such, with compile off-load, the test varies over the following sequence of table columns [1, 2, 4, 8, 16, 32, 64, 128, 256], but without compile off-load, only [1, 2, 4, 8, 16, 32]. Without has smaller tables only, in part to reduce test time which otherwise is excessively long (about a day), and in part because intermittent disconnects were experienced with 64 or more columns.

Here the number of segments in the query is changing (two per column), and this forces compilation, so a new window function does not need to be used.

To obtain the results of compilation, I turn to the system table SVL_COMPILE, which contains one row per segment, providing information about the compilation of the segment, such as whether it compiled or came from cache and when compilation or cache retrieval began and ended.

Normally of course we would perform every test five times and discard the slowest and fastest result, but that is not possible due to the lack of an option to disable the compiled segment cache; we get to run each query we want to compile just once.

Finally, note the test script has support for four regions (eu-central-1, us-east-1, us-east-2 and eu-north-1) as it knows for these regions which have compile off-load and which do not. Where the volume of output is large, I run a single region at a time, which requires editing the regions list at the end of the script.

(Finally, note, in principle, I should also test to see if Concurrency Scaling Clusters utilize compilation off-load, but I consider their use always a mistake, so I have no motivation to do that work. If you’re using CSC, it means your queries are not correct for the sorting orders of your tables (if they were correct, your query performance would be so great you would not need CSC), which means you’re operating Redshift incorrectly. What you need to do is operate Redshift correctly, not use an expensive band-aid.)

Results

See Appendix A : Raw Data Dump for the Python pprint dump of the results dictionary.

Notes on the tests;

There are results from three regions. Initially I planned to test a region with compile off-load and a region without, but I discovered in the course of investigation unexpected behaviour which to demonstrate requires results for a third region, as will be explained in the Discussion.
Regions with compilation off-load go up to and including 256 columns. Regions without only go up to and including 32, because otherwise the test script takes much too long to run (in fact, going up to 64 is okay with regard to how long the test takes, but 64 columns usually causes the cluster to disconnect the session or crash, so I brought it back down to 32).
Each individual test (defined by the set of the region and cluster, the number of concurrent queries and the number of segments in the query) is run twice, the first time being expected to compile, and the second expected to be served from the compiled segment cache, so we can examine both situations.
The results here are for dc2.large. During the development of this paper, which took about two months, I normally used all three small node types. Their results are essentially identical - the same behaviours and characteristics - the only difference being that where the node types have different performances, their compile times vary; dc2.large and ds2.xlarge being almost the same, ra3.xlplus, with its more modern hardware, being significantly but still only incrementally faster, and bear in mind being significantly faster when serving from cache or with compilation off-load doesn’t mean very much in real terms, because those operations are already very fast.

Notes on table columns;

“Segments” has the format “64 (31.83/1.46)”. The first number is the number of segments in the query (remember though the final segment is always on the leader node, and so is never compiled). Then, in the brackets, we have first the mean number of compiled segments, and then the standard deviation of that mean. The compiled segment cache makes it impossible to fully control compilation, so it is necessary to show in the data how much compilation actually occurred for a given test run.

The second run of each individual test, being served from the cache, should always have in the brackets “(0.00/0.00)”, as no compilation should occur. If any compilation does occur, the compiled segment cache failed, because we are running the exact same query twice.

Finally, remember also for the tests where only a single query is run, the standard deviation is necessarily always zero.
“Total Time” is the mean total time and the standard deviation of that mean, in seconds, for compilation; from the start of the first segment, which begins compilation, to the end of the last segment, which finishes compilation. (Remember that we are issuing multiple queries simultaneously, so they run concurrently, and so total time is a mean because each query produces its own total time).
“Compile Times” are the mean compilation time and the standard deviation of that mean, in seconds, for all segments which were compiled.
“Cache Times” are the mean serving-from-cache time and the standard deviation of that mean, in seconds, for all segments which were not compiled, but were served from the cache.
“OOM” means “Out of Memory” (and is followed in brackets by the number of test queries which failed in this way). When too many queries are being compiled at once, with or without compile off-load, the leader node runs out of memory, and queries start to fail. If any query fails, the results for all queries for that test are thrown away.

eu-north-1

dc2.large, 2 nodes (1.0.34928)

1 Concurrent Queries
Segments	Total Time	Compile Times	Cache Times
4 (3.00/0.00)	24.142985/0.000000	8.045265/0.164552	0.000009/0.000000
4 (0.00/0.00)	0.004124/0.000000	0.000000/0.000000	0.000057/0.000030
6 (5.00/0.00)	32.204706/0.000000	6.437644/0.471851	0.000012/0.000000
6 (0.00/0.00)	0.014511/0.000000	0.000000/0.000000	0.000045/0.000023
10 (9.00/0.00)	56.769054/0.000000	6.303836/0.521740	0.000013/0.000000
10 (0.00/0.00)	0.021747/0.000000	0.000000/0.000000	0.000070/0.000070
18 (17.00/0.00)	104.898972/0.000000	6.165553/0.496166	0.000008/0.000000
18 (0.00/0.00)	0.056376/0.000000	0.000000/0.000000	0.000040/0.000020
34 (34.00/0.00)	208.902144/0.000000	6.137960/0.376504	0.000000/0.000000
34 (0.00/0.00)	0.143642/0.000000	0.000000/0.000000	0.000043/0.000018
66 (66.00/0.00)	391.023386/0.000000	5.915541/0.431908	0.000000/0.000000
66 (0.00/0.00)	0.439463/0.000000	0.000000/0.000000	0.000051/0.000020

2 Concurrent Queries
Segments	Total Time	Compile Times	Cache Times
4 (2.00/1.00)	18.559837/0.377666	9.275491/0.348223	0.000044/0.000026
4 (0.00/0.00)	0.006286/0.001871	0.000000/0.000000	0.000042/0.000030
6 (3.00/1.00)	29.965021/0.490979	9.982479/0.733660	0.000039/0.000013
6 (0.00/0.00)	0.013462/0.000430	0.000000/0.000000	0.000045/0.000025
10 (5.00/1.00)	50.996083/0.774142	10.190931/0.416915	0.000046/0.000019
10 (0.00/0.00)	0.034248/0.003217	0.000000/0.000000	0.000055/0.000043
18 (9.00/1.00)	94.228252/0.348787	10.457489/0.504110	0.000058/0.000045
18 (0.00/0.00)	0.092749/0.003563	0.000000/0.000000	0.000126/0.000458
34 (17.00/1.00)	180.026265/0.443273	10.573291/0.452336	0.000226/0.000867
34 (0.00/0.00)	0.217371/0.013383	0.000000/0.000000	0.000066/0.000062
66 (33.00/1.00)	348.596157/0.361177	10.537306/0.447504	0.000067/0.000063
66 (0.00/0.00)	0.657334/0.017184	0.000000/0.000000	0.000077/0.000039

4 Concurrent Queries
Segments	Total Time	Compile Times	Cache Times
4 (2.25/0.83)	38.702055/1.623959	17.196888/0.986832	0.000030/0.000023
4 (0.00/0.00)	0.010193/0.002174	0.000000/0.000000	0.000049/0.000025
6 (4.25/1.48)	74.899428/2.037646	17.616471/0.800973	0.000833/0.001943
6 (0.00/0.00)	0.042238/0.009401	0.000000/0.000000	0.000128/0.000393
10 (8.00/2.35)	155.713405/2.280672	19.456441/1.024729	0.000040/0.000022
10 (0.00/0.00)	0.067866/0.008240	0.000000/0.000000	0.000054/0.000042
18 (14.75/3.96)	307.256454/4.124277	20.818707/1.117811	0.000041/0.000018
18 (0.00/0.00)	0.180169/0.013446	0.000000/0.000000	0.000078/0.000149
34 (28.75/7.40)	590.790427/6.925831	20.530826/1.016894	0.000046/0.000016
34 (0.00/0.00)	0.424517/0.367104	0.000000/0.000000	0.000089/0.000162
66 (56.75/14.31)	1261.091837/15.158997	22.190295/1.069941	0.000304/0.000901
66 (0.00/0.00)	1.308156/0.042008	0.000000/0.000000	0.000125/0.000354

12 Concurrent Queries
Segments	Total Time	Compile Times	Cache Times
4 (2.00/0.91)	61.386046/2.377553	30.684262/1.457319	0.000043/0.000029
4 (0.00/0.00)	0.024429/0.439729	0.000000/0.000000	0.000058/0.000048
6 (3.08/0.86)	112.867784/2.570187	34.892393/1.208840	0.000516/0.002068
6 (0.00/0.00)	0.106579/0.362921	0.000000/0.000000	0.000121/0.000621
10 (5.58/1.75)	262.572158/5.129281	45.949583/2.352368	0.000178/0.000796
10 (0.00/0.00)	0.203089/0.042719	0.000000/0.000000	0.000145/0.000658
18 (10.25/2.92)	482.068491/8.433345	45.250360/2.887237	0.000310/0.001476
18 (0.00/0.00)	0.547780/0.084150	0.000000/0.000000	0.000197/0.000961
34 (19.42/5.50)	874.490022/9.206106	44.358130/2.435816	0.000272/0.067699
34 (0.00/0.00)	1.125988/0.178849	0.000000/0.000000	0.000272/0.001051
66 (38.00/11.22)	1884.483992/15.813361	48.822731/2.656171	0.000307/0.001336
66 (0.00/0.00)	3.251278/0.228089	0.000000/0.000000	0.000349/0.001470

us-east-1

dc2.large, 2 nodes (1.0.34922)

1 Concurrent Queries
Segments	Total Time	Compile Times	Cache Times
4 (2.00/0.00)	5.285583/0.000000	2.638695/0.165136	0.001139/0.001133
4 (0.00/0.00)	0.003092/0.000000	0.000000/0.000000	0.000035/0.000014
6 (4.00/0.00)	4.976540/0.000000	1.240458/0.055642	0.001362/0.001354
6 (0.00/0.00)	0.010732/0.000000	0.000000/0.000000	0.000032/0.000013
10 (9.00/0.00)	12.885971/0.000000	1.428980/0.124065	0.002815/0.000000
10 (0.00/0.00)	0.021754/0.000000	0.000000/0.000000	0.000040/0.000020
18 (14.00/0.00)	6.358469/0.000000	0.448651/0.364444	0.002216/0.000121
18 (0.00/0.00)	0.054255/0.000000	0.000000/0.000000	0.000038/0.000013
34 (28.00/0.00)	8.427189/0.000000	0.293964/0.243573	0.002364/0.001239
34 (0.00/0.00)	0.134097/0.000000	0.000000/0.000000	0.000038/0.000012
66 (48.00/0.00)	25.449643/0.000000	0.518797/0.292893	0.002134/0.000578
66 (0.00/0.00)	0.425203/0.000000	0.000000/0.000000	0.000054/0.000023
130 (99.00/0.00)	20.320209/0.000000	0.188207/0.227645	0.002221/0.000482
130 (0.00/0.00)	1.437882/0.000000	0.000000/0.000000	0.000076/0.000037
258 (238.00/0.00)	30.366711/0.000000	0.103382/0.217567	0.003130/0.004130
258 (0.00/0.00)	5.229575/0.000000	0.000000/0.000000	0.000127/0.000066
514 (505.00/0.00)	50.299579/0.000000	0.055166/0.166604	0.002282/0.000289
514 (0.00/0.00)	24.033789/0.000000	0.000000/0.000000	0.000283/0.000536

2 Concurrent Queries
Segments	Total Time	Compile Times	Cache Times
4 (1.50/0.50)	7.010902/0.159372	4.669733/0.128289	0.000614/0.001152
4 (0.00/0.00)	0.004477/0.000009	0.000000/0.000000	0.000035/0.000012
6 (3.00/1.00)	5.495396/0.033583	1.827905/0.144639	0.000074/0.000080
6 (0.00/0.00)	0.019441/0.004188	0.000000/0.000000	0.000035/0.000013
10 (4.50/0.50)	5.821796/0.012034	1.287489/0.481879	0.000305/0.000842
10 (0.00/0.00)	0.040933/0.001795	0.000000/0.000000	0.000046/0.000017
18 (9.00/1.00)	6.289746/0.004207	0.690481/0.280537	0.000045/0.000016
18 (0.00/0.00)	0.086549/0.008949	0.000000/0.000000	0.000054/0.000052
34 (16.50/0.50)	6.818131/0.303221	0.399195/0.247036	0.000134/0.000511
34 (0.00/0.00)	0.217018/0.006395	0.000000/0.000000	0.000058/0.000026
66 (32.50/0.50)	18.888259/0.074604	0.560829/0.347018	0.000261/0.001292
66 (0.00/0.00)	0.774451/0.022710	0.000000/0.000000	0.000085/0.000050
130 (64.50/0.50)	21.880755/0.744224	0.292471/0.304722	0.000157/0.000675
130 (0.00/0.00)	2.238058/0.030903	0.000000/0.000000	0.000119/0.000057
258 (128.50/0.50)	19.671221/0.075895	0.080655/0.256524	0.000199/0.000686
258 (0.00/0.00)	8.129139/0.070221	0.000000/0.000000	0.000199/0.000110
514 (257.00/1.00)	63.600862/1.182877	0.124424/0.221353	0.000243/0.039664
514 (0.00/0.00)	30.901956/0.129890	0.000000/0.000000	0.000348/0.000201

4 Concurrent Queries
Segments	Total Time	Compile Times	Cache Times
4 (1.50/0.50)	4.215933/0.449714	2.799792/0.708839	0.000936/0.001408
4 (0.00/0.00)	0.006115/0.000602	0.000000/0.000000	0.000050/0.000030
6 (3.75/1.09)	4.866568/0.457982	1.289883/0.464315	0.001261/0.001745
6 (0.00/0.00)	0.030044/0.008534	0.000000/0.000000	0.000042/0.000014
10 (6.25/1.48)	5.238486/0.210825	0.830118/0.306621	0.001431/0.001753
10 (0.00/0.00)	0.059356/0.013937	0.000000/0.000000	0.000064/0.000065
18 (12.75/2.95)	6.745562/0.634387	0.515361/0.358395	0.001816/0.002329
18 (0.00/0.00)	0.144303/0.002532	0.000000/0.000000	0.000072/0.000117
34 (26.25/6.02)	9.257150/0.352524	0.335100/0.361574	0.001121/0.001471
34 (0.00/0.00)	0.367226/0.388770	0.000000/0.000000	0.000112/0.000515
66 (46.00/8.28)	29.629540/1.814069	0.618224/0.408968	0.001475/0.099845
66 (0.00/0.00)	1.267633/0.345479	0.000000/0.000000	0.000145/0.000557
130 (99.50/21.12)	49.813576/0.991975	0.445538/0.405511	0.001681/0.003390
130 (0.00/0.00)	4.287951/0.303141	0.000000/0.000000	0.000242/0.000724
258 (199.75/41.48)	82.713721/2.378772	0.317721/0.347036	0.001593/0.002062
258 (0.00/0.00)	16.343165/0.290888	0.000000/0.000000	0.000375/0.000880
514 (404.00/90.29)	194.887104/4.349836	0.306530/0.353421	0.001469/0.001791
514 (0.00/0.00)	62.202337/0.550155	0.000000/0.000000	0.000637/0.019880

12 Concurrent Queries
Segments	Total Time	Compile Times	Cache Times
4 (1.42/0.64)	4.483092/0.401051	3.123023/0.475741	0.000889/0.001496
4 (0.00/0.00)	0.016386/0.008802	0.000000/0.000000	0.000113/0.000337
6 (2.50/1.19)	5.792873/0.396980	2.257046/0.450936	0.000717/0.002001
6 (0.00/0.00)	0.072171/0.018682	0.000000/0.000000	0.000398/0.001795
10 (4.67/1.31)	6.471693/0.373077	1.335608/0.521275	0.000727/0.110662
10 (0.00/0.00)	0.173172/0.406593	0.000000/0.000000	0.000139/0.000801
18 (8.67/0.94)	8.078763/0.253910	0.886633/0.404569	0.000603/0.004066
18 (0.00/0.00)	0.352926/0.326458	0.000000/0.000000	0.000139/0.000565
34 (16.75/2.45)	12.470317/0.421592	0.670584/0.422723	0.000505/0.002581
34 (0.00/0.00)	0.897465/0.139820	0.000000/0.000000	0.000261/0.001589
66 (33.58/3.25)	22.838389/0.399691	0.563627/0.443681	0.000698/0.002900
66 (0.00/0.00)	2.846313/0.450861	0.000000/0.000000	0.000274/0.001420
130 (69.42/11.05)	50.989803/1.170322	0.555011/0.487345	0.000634/0.003020
130 (0.00/0.00)	9.649971/0.426905	0.000000/0.000000	0.000388/0.001500
258 (131.92/8.86)	371.671581/7.956138	2.486043/1.141436	0.000550/0.023195
258 (0.00/0.00)	35.345906/0.736346	0.000000/0.000000	0.000595/0.016300
OOM (2)	-	-	-
OOM (12)	-	-	-

us-east-2

dc2.large, 2 nodes (1.0.34928)

1 Concurrent Queries
Segments	Total Time	Compile Times	Cache Times
4 (4.00/0.00)	0.198977/0.000000	0.048615/0.045695	0.000000/0.000000
4 (0.00/0.00)	0.003485/0.000000	0.000000/0.000000	0.000035/0.000008
6 (5.00/0.00)	0.301373/0.000000	0.058343/0.042180	0.000007/0.000000
6 (0.00/0.00)	0.011044/0.000000	0.000000/0.000000	0.000035/0.000010
10 (8.00/0.00)	0.498589/0.000000	0.058994/0.288004	0.001989/0.000042
10 (0.00/0.00)	0.021213/0.000000	0.000000/0.000000	0.000035/0.000012
18 (14.00/0.00)	0.938671/0.000000	0.062242/0.226718	0.001460/0.000842
18 (0.00/0.00)	0.053172/0.000000	0.000000/0.000000	0.000038/0.000012
34 (23.00/0.00)	1.840768/0.000000	0.072222/0.249354	0.001870/0.000595
34 (0.00/0.00)	0.139761/0.000000	0.000000/0.000000	0.000042/0.000015
66 (50.00/0.00)	3.680881/0.000000	0.063812/0.176141	0.001919/0.000503
66 (0.00/0.00)	0.425057/0.000000	0.000000/0.000000	0.000051/0.000019
130 (105.00/0.00)	7.823294/0.000000	0.059228/0.191254	0.002016/0.000444
130 (0.00/0.00)	1.443827/0.000000	0.000000/0.000000	0.000075/0.000035
258 (227.00/0.00)	17.809934/0.000000	0.053239/0.198224	0.002150/0.000403
258 (0.00/0.00)	5.225350/0.000000	0.000000/0.000000	0.000121/0.000064
514 (507.00/0.00)	44.160361/0.000000	0.043393/0.170409	0.002137/0.000160
514 (0.00/0.00)	19.898536/0.000000	0.000000/0.000000	0.000217/0.000126

2 Concurrent Queries
Segments	Total Time	Compile Times	Cache Times
4 (1.50/0.50)	0.188509/0.022931	0.120428/0.041097	0.000622/0.001171
4 (0.00/0.00)	0.004659/0.000496	0.000000/0.000000	0.000040/0.000017
6 (3.00/1.00)	0.287374/0.048492	0.091556/0.035418	0.000045/0.000022
6 (0.00/0.00)	0.010933/0.001672	0.000000/0.000000	0.000042/0.000018
10 (5.00/1.00)	0.488043/0.051648	0.092353/0.030841	0.000057/0.000051
10 (0.00/0.00)	0.047539/0.000444	0.000000/0.000000	0.000056/0.000042
18 (9.00/1.00)	0.885166/0.026666	0.089761/0.284495	0.000041/0.000014
18 (0.00/0.00)	0.147972/0.003961	0.000000/0.000000	0.000052/0.000025
34 (16.50/0.50)	1.788960/0.049815	0.095453/0.294557	0.000171/0.000560
34 (0.00/0.00)	0.221311/0.006630	0.000000/0.000000	0.000059/0.000025
66 (33.00/1.00)	3.707373/0.070375	0.091329/0.277960	0.000052/0.000018
66 (0.00/0.00)	0.680772/0.013344	0.000000/0.000000	0.000097/0.000125
130 (65.00/1.00)	8.386364/0.515440	0.092160/0.248243	0.000095/0.000266
130 (0.00/0.00)	2.712771/0.039818	0.000000/0.000000	0.000127/0.000075
258 (128.50/0.50)	20.207090/0.373737	0.086171/0.269498	0.000135/0.000206
258 (0.00/0.00)	8.199255/0.454374	0.000000/0.000000	0.000194/0.000106
514 (256.50/0.50)	58.299812/0.291161	0.075931/0.238570	0.000247/0.039535
514 (0.00/0.00)	31.233244/0.044053	0.000000/0.000000	0.000354/0.000237

4 Concurrent Queries
Segments	Total Time	Compile Times	Cache Times
4 (2.50/1.12)	0.374970/0.156945	0.144969/0.071827	0.001090/0.001467
4 (0.00/0.00)	0.007901/0.002186	0.000000/0.000000	0.000036/0.000013
6 (3.00/1.00)	0.682202/0.348325	0.219724/0.340942	0.001214/0.001782
6 (0.00/0.00)	0.026591/0.003956	0.000000/0.000000	0.000053/0.000032
10 (7.50/2.29)	1.330001/0.333357	0.169439/0.350318	0.000564/0.001058
10 (0.00/0.00)	0.061224/0.006489	0.000000/0.000000	0.000200/0.000935
18 (14.25/3.77)	2.651723/0.449411	0.176757/0.328284	0.000917/0.001549
18 (0.00/0.00)	0.183089/0.369498	0.000000/0.000000	0.000133/0.000605
34 (25.75/5.80)	5.472492/0.287536	0.193014/0.367469	0.001319/0.002540
34 (0.00/0.00)	0.420983/0.016156	0.000000/0.000000	0.000154/0.000595
66 (49.50/10.14)	11.286011/0.701355	0.189548/0.320161	0.002032/0.109990
66 (0.00/0.00)	1.306735/0.431190	0.000000/0.000000	0.000131/0.000274
130 (102.25/22.12)	24.165633/0.255078	0.170899/0.330322	0.001357/0.002422
130 (0.00/0.00)	4.458234/0.296962	0.000000/0.000000	0.000190/0.000403
258 (200.50/41.91)	55.475499/0.371010	0.154436/0.327572	0.001652/0.059239
258 (0.00/0.00)	16.432558/0.212557	0.000000/0.000000	0.000323/0.027911
514 (406.50/89.25)	142.321784/0.954985	0.125955/0.294146	0.001524/0.002137
514 (0.00/0.00)	63.513135/0.462147	0.000000/0.000000	0.000658/0.019886

12 Concurrent Queries
Segments	Total Time	Compile Times	Cache Times
4 (1.58/1.11)	0.671669/0.242798	0.396958/0.318334	0.000241/0.000739
4 (0.00/0.00)	0.021786/0.009389	0.000000/0.000000	0.000079/0.000195
6 (2.58/0.86)	1.284020/0.270685	0.443209/0.376319	0.000375/0.001045
6 (0.00/0.00)	0.082796/0.028357	0.000000/0.000000	0.000521/0.003076
10 (4.58/1.38)	2.469451/0.369001	0.485940/0.419551	0.000399/0.001007
10 (0.00/0.00)	0.146997/0.032216	0.000000/0.000000	0.000182/0.001308
18 (8.58/0.76)	5.078213/0.260966	0.528122/0.424881	0.000803/0.002618
18 (0.00/0.00)	0.360724/0.051108	0.000000/0.000000	0.000234/0.000899
34 (17.25/2.01)	9.997962/0.422902	0.504136/0.425279	0.001182/0.005861
34 (0.00/0.00)	0.952919/0.143394	0.000000/0.000000	0.000344/0.044552
66 (34.00/3.58)	20.421071/0.436939	0.483639/0.418861	0.000661/0.003494
66 (0.00/0.00)	2.917125/0.281375	0.000000/0.000000	0.000377/0.001671
130 (67.42/6.58)	42.938870/0.479051	0.433346/0.429261	0.000586/0.002650
130 (0.00/0.00)	9.930965/0.179020	0.000000/0.000000	0.000379/0.001482
258 (129.75/2.92)	100.776749/0.948303	0.376936/0.413962	0.000788/0.003368
258 (0.00/0.00)	35.966557/0.506836	0.000000/0.000000	0.000573/0.032134
OOM (2)	-	-	-
OOM (12)	-	-	-

Discussion

To begin with, let’s examine what happens when a query is compiled on the leader node, which is to say without off-load, which is how Redshift originally compiled and still does in some regions. All start/end times are HH:MM:SS.ssssss, durations are in seconds.

Six segment query, `eu-north-1`, `dc2.large`, without compile off-load
Segment	Start	End	Duration	Cached
0	14:54:10.325439	14:54:18.848517	8.523078	No
1	14:54:18.848564	14:54:21.720273	2.871709	No
2	14:54:21.728164	14:54:30.658418	8.930254	No
3	14:54:30.658448	14:54:33.457195	2.798747	No
4	14:54:33.465668	14:54:42.530103	9.064435	No
5	14:54:42.530133	14:54:42.530145	0.000012	Yes

We see that compilation is as has been stated in the Test Method serial; each segment completes compilation before compilation of the following segment begins. We see here also that this simple six segment query took about 30 seconds to compile before it ran (and this on an empty table, so the run time is as close to zero seconds as you can get), which illustrates the overhead of compilation.

Note the pattern of slow/fast compilation is I specific to the query I’m using to test. I’m using a window function per column; each window function turns into two segments, but it looks like one takes most of the time to compile, and the other not much at all. Normal queries have pretty much equal compilation times for all segments.

We can see this in the step-plan for a six segment query, which is provided in Appendix B : Query Results and Step Plan.

The first segment reads from the table, all of the following segment except the final segment are identical pairs of segments, the first (with the slow compilation) performing the steps scan-project-window-project-distribute, the second performing only the steps scan-sort. The final segment runs on the leader node and gathers the results from the worker nodes and returns them to the user (and this happens with every query issued to Redshift, which is why this final segment is always cached).

Leader node compilation times vary by node type, as the node types vary in processor performance, in line with the processor performance benchmarks from the Cross-Region Benchmarks white paper, which is to say, dc2.large and ds2.xlarge are almost the same, but ra3.xlplus with its more modern hardware is significantly (but still, only incrementally) faster.

(Note that for compilation offload and compiled segment cache, being significantly faster doesn’t mean much, because both operations are already extremely fast; if you have say ten segments, a typical large query, and each takes 0.1, or 0.0001 seconds, taking off 30% really doesn’t matter.)

This serial compilation behaviour remains true even with off-load, except now rather than compilation, which the leader node no longer performs, the work being performed by the leader node is the serial retrieval of compiled segments from the compilation off-load helpers.

Six segment query, `us-east-1`, `dc2.large`, with compile off-load.
Segment	Start	End	Duration	Cached
0	8:22:56.453312	8:23:01.011365	4.558053	No
1	8:23:01.011419	8:23:01.014135	0.002716	Yes
2	8:23:01.019596	8:23:01.126234	0.106638	No
3	8:23:01.126262	8:23:01.333220	0.206958	No
4	8:23:01.339641	8:23:01.429824	0.090183	No
5	8:23:01.429844	8:23:01.429852	0.000008	Yes

Notice that the first segment was slow, but the later segments were fast.

In the results for us-east-1, every query which compiles has at least one segment, usually but not absolutely always the first segment, which takes between 4 and 8 seconds to compile, but no more than just two or three such segments.

For example, the 66 segment query on us-east-1 shows the following slow segments;

Segment	Start	End	Duration	Cached
0	8:24:13:809157	8:24:18:386261	4.577104	No
19	8:24:20:294238	8:24:22:527682	2.233444	No
32	8:24:30:119882	8:24:37:522842	7.402960	No

This query is unusual to have so many slow segments. Normally there is only the one - even the 258 an 514 segment queries only had one slow segment each.

However, in the results for us-east-2, there are never slow segments, not even one; every segment compiles in under one second.

Six segment query, `us-east-2`, `dc2.large`, with compile off-load
Segment	Start	End	Duration	Cached
0	10:53:50.335628	10:53:50.421669	0.086041	No
1	10:53:50.421728	10:53:50.430473	0.008745	Yes
2	10:53:50.434302	10:53:50.529503	0.095201	No
3	10:53:50.529528	10:53:50.534576	0.005048	No
4	10:53:50.540293	10:53:50.636975	0.096682	No
5	10:53:50.636994	10:53:50.637001	0.000007	Yes

Furthermore, just to add to the mystery, the eu-central-1 region from the beginning of working on this white paper and then for over a month, was like us-east-1 and had at least one slow segment per query; then it changed, and became like us-east-2, and no longer ever had any slow segments in any query.

I will try to explain this, but to do so, first, I must describe queries which are served fully from the compiled segment cache.

Six segment query, `us-east-1`, `dc2.large`, served from cache
Segment	Start	End	Duration	Cached
0	9:13:05.879516	9:13:05.879570	0.000054	Yes
1	9:13:05.879904	9:13:05.879926	0.000022	Yes
2	9:13:05.883134	9:13:05.883172	0.000038	Yes
3	9:13:05.883406	9:13:05.883425	0.000019	Yes
4	9:13:05.889886	9:13:05.889927	0.000041	Yes
5	9:13:05.890226	9:13:05.890248	0.000022	Yes

We see here again that the loading of segments from the cache remains serial, but now the time taken to obtain a segment is very short indeed.

Queries served entirely from cache never have slow segments.

So, what’s going on with all this? the slow segments turning up when compile off-load is in use?

Well, there’s a salient point which I suspect may be key to it all : with compile off-load, when is compilation occurring?

Consider - on the leader node, which is pretty powerful beast, a single segment takes typically between 4 and 8 seconds to compile. If I look at us-east-2, segments are being provided in less than a tenth of a seconds. There’s no way a segment is being compiled in so short a time, and if they’re not being compiled, then they must be being returning from a cache of previously compiled segments.

I can imagine for us-east-1 that the delay for the first segment is the time in which compilation occurs on all the compile off-load helpers, and after that we see the very short time required to process an already compiled segment; but then how do I explain what I see in us-east-2? and how do I explain the additional segments which by their long duration appear to compile on the leader node?

Giving the timing information, it simply cannot be that compilation is actually occurring for the us-east-2 queries. That must then be that those segments are actually being returned from a cache; and that it turn would mean the compile column in SVL_COMPILE actually means either compiled on the leader node, or, compiled by or served from cache by the compile off-load system.

I would then explain why it is the first segment which is often the slow segment; because here compilation is actually occurring, and it is during the time taken by the first segment that the compile off-load helpers are performing compilation.

One seeming issue with this theory is with the compiled segment cache, for we can see above that when the leader node issues a query where the compile column in SVL_COMPILE is always 0 (“not compiled”, i.e. cached), the time taken to obtain a segment is incredibly short, about 0.00003 seconds.

This to me is so short a time that this must be caching local to the leader node; and that fits with what I’ve seen, which is that if a query is immediately re-issued, it is served entirely from this cache, but if there is a reasonable delay of any kind, some segments are compiled.

What’s more, the compiled segment cache came years before compile off-load; so there was certainly a system which was operated purely by the leader node for compiled segments.

I can imagine that the compile off-load system for some reason then does not use this existing system, and so has its own cache, and the time taken when a query is being compiled to go through the compile off-load system, into its cache, and back again, is the roughly 0.1 seconds that we see in us-east-1 and us-east-2, rather than the 0.00003 seconds we see when the leader node cache is used.

This then finally leave the questions of why segments other than the first segment occasionally take about 4 to 8 seconds, and of why we see different behaviour in these two regions.

With regard to the latter, the salient fact is that the time taken looks exactly like the time taken for the leader node to compile a segment. My guess my guess is that now and then, a compile off-load helper fails; and in this situation, the leader node compiles the segment.

With regard to the latter, it seems to me entirely reasonable to expect the compile off-load system to vary in capacity between regions (us-east-1, a massively used region, must surely have more capacity than, say, af-south-1), and also regions vary in the number of Redshift clusters, so it may be reasonable that cross-region differences are observed, and that upgrades to these systems are responsible for the change in behaviour in eu-central-1; the compile off-load cache was increased in capacity, and so overnight, the segments created by queries run by the test script then remained in cache for long enough that the next time I ran the test script, they were not being compiled.

In any event, regardless of how correct these explanations are, we can say from observation that even with compile off-load, one or a few segments are slow to compile, and that frequency with which the first segment is slow appears to depend on region.

Changing subject, looking at the time taken for each segment with compile of-load, these times are so short they evidently represent the time taken by the leader node to process a given segment, either to obtain it over the network from the compile off-load helper, or, if in fact is done as part of the compilation work, to read it from local disk.

The variation in the times between segments then I think represents differences in the physical size of the binary which is the segment, where obviously the larger the binary, the longer it takes to copy over the network, or read from local disk.

Having then now examined and classified the different types of query compilation, we can consider Redshift’s behaviour as the number of queries being concurrently compiled increases.

First, let’s look at eu-north-1, which is to say, without compile off-load.

The times here are the mean average time (and standard deviation) to compile a single segment.

Single Segment Mean Compile Times, `eu-north-1`, `dc2.large`
Segments	Compile Time (1Q)	Compile Time (2Q)
4	8.045265/0.164552	9.275491/0.348223
6	6.437644/0.471851	9.982479/0.733660
10	6.303836/0.521740	10.190931/0.416915
18	6.165553/0.496166	10.457489/0.504110
34	6.137960/0.376504	10.573291/0.452336
66	5.915541/0.431908	10.537306/0.447504

Segments	Compile Time (4Q)	Compile Time (12Q)
4	17.196888/0.986832	30.684262/1.457319
6	17.616471/0.800973	34.892393/1.208840
10	19.456441/1.024729	45.949583/2.352368
18	20.818707/1.117811	45.250360/2.887237
34	20.530826/1.016894	44.358130/2.435816
66	22.190295/1.069941	48.822731/2.656171

When examining the figures, remember that each query complies its segments serially; so when we have say four queries, it means we have four concurrent compiles running; when we have twelve queries, twelve concurrent compiles. As the number of segments changed, a query has more compilation work to do, but it will be doing all its compilations serially.

As such, we should only find the number of concurrent queries affects compilation time.

However, although this is mainly true, we can see it’s not completely true.

What we see in the figures is that when we keep the number of segments fixed, the time taken to compile a single segment is a very good deal better than linear with the number of concurrent queries; with a single query, the typical compile time is a bit more than 6 seconds, and with 12 concurrent queries, about 45 seconds - a linear increase in time would mean 12 * 6 = 72 seconds.

As we increase the number of segments from 4 to 66, compiles times for 1 and 2 queries do not change, but for 4 and 12 queries, they increase; with 4 queries from about 17 seconds to about 22 seconds, for 12 queries from about 30 seconds to about 48 seconds.

Probably this is due to memory contention and a greater degree of swapping to disk; remember that if we fire off enough concurrent compiles, queries will begin to fail with out-of-memory errors.

Second, let’s look at us-east-2, which is to say, with no slow segments. (Regions with a slow segment are difficult to look at because the slow segment makes queries with few segments look as if they have much longer mean compile times than they really do.)

Single Segment Mean Compile Times, `us-east-1`, `dc2.large`
Segments	Compile Time (1Q)	Compile Time (2Q)
4	0.048615/0.045695	0.120428/0.041097
6	0.058343/0.042180	0.091556/0.035418
10	0.058994/0.288004	0.092353/0.030841
18	0.062242/0.226718	0.089761/0.284495
34	0.072222/0.249354	0.095453/0.294557
66	0.063812/0.176141	0.091329/0.277960
130	0.059228/0.191254	0.092160/0.248243
258	0.053239/0.198224	0.086171/0.269498
514	0.043393/0.170409	0.075931/0.238570

Segments	Compile Time (4Q)	Compile Time (12Q)
4	0.144969/0.071827	0.396958/0.318334
6	0.219724/0.340942	0.443209/0.376319
10	0.169439/0.350318	0.485940/0.419551
18	0.176757/0.328284	0.528122/0.424881
34	0.193014/0.367469	0.504136/0.425279
66	0.189548/0.320161	0.483639/0.418861
130	0.170899/0.330322	0.433346/0.429261
258	0.154436/0.327572	0.376936/0.413962
514	0.125955/0.294146	OOM

Starting by looking at performance for a single query, the mean compile time for a single segment is about 0.05 seconds. This is a staggeringly large improvement over the leader node compile time of about 6 seconds.

There’s an inexplicable U-shaped performance curve, where 4 segments and 514 segments give the fastest two times, and 34 segments gives the slowest. I have no idea why this should be so; and we see this across each of the different number of concurrent queries being compiled.

Looking next at the times when 12 queries are concurrently compiling, we see that the time taken has increased from about 0.05 seconds to about 0.45 seconds (again, with the inexplicable U-shaped performance curve).

This is a slow down by about a factor of 9, which is roughly how many more queries there are (12 vs 1).

We see a slowdown also in the times taken to retrieve segments from the compiled segment cache, but here the factor of slowdown is about half the number of queries.

Cache Times, `us-east-1`, `dc2.large`
Segments	Cache Time (1Q)	Cache Time (2Q)
4	0.000035/0.000014	0.000035/0.000012
6	0.000032/0.000013	0.000035/0.000013
10	0.000040/0.000020	0.000046/0.000017
18	0.000038/0.000013	0.000054/0.000052
34	0.000038/0.000012	0.000058/0.000026
66	0.000054/0.000023	0.000085/0.000050
130	0.000076/0.000037	0.000119/0.000057
258	0.000127/0.000066	0.000199/0.000110
514	0.000283/0.000536	0.000348/0.000201

Segments	Cache Time (4Q)	Cache Time (12Q)
4	0.000050/0.000030	0.000113/0.000337
6	0.000042/0.000014	0.000398/0.001795
10	0.000064/0.000065	0.000139/0.000801
18	0.000072/0.000117	0.000139/0.000565
34	0.000112/0.000515	0.000261/0.001589
66	0.000145/0.000557	0.000274/0.001420
130	0.000242/0.000724	0.000388/0.001500
258	0.000375/0.000880	0.000595/0.016300
514	0.000637/0.019880	OOM

I suspect what’s happening here is that the leader node can in fact only process or collect a single segment at a time, be it from a compile off-load helper or from the cache, no matter how many queries are compiling in parallel. It’s not like when the leader node itself is actually compiling and there really are multiple compilations occurring in parallel.

As such, although the time taken to actually retrieve any given segment in fact is always the same, the apparent time rises, because there’s more and more delay where the leader node is busy servicing other queries.

When the number of concurrent queries is high, and the number of segments is high, compilation - even without any slow segments - can still be some seconds. A 10 segment query, with 12 concurrent queries, with no slow segments, is going to take about 5 seconds to compile.

In any event, this remains light-years better than leader node based compilation.

At the time of writing, regions with/without compile off-load are (by my testing, not the AWS docs);

With	Without
ap-northeast-1	af-south-1
ap-northeast-2	ap-east-1
ap-south-1	ap-northeast-3
ap-southeast-1	ap-southeast-3
ap-southeast-2	eu-north-1
ca-central-1	eu-south-1
eu-central-1	eu-west-3
eu-west-1	me-south-1
eu-west-2
sa-east-1
us-east-1
us-east-2
us-west-1
us-west-2

Conclusions

It has to be assumed the reader is aware of the query and compilation model used by Redshift, because it’s too much material to reproduce here. This is described at the start of the Test Method.

Historically, segment compilation was performed by the leader node and this still the case in a minority of regions. Apart from resource limits (compilation of course uses memory and so if too many compilations run concurrently, out of memory errors occur and queries abort) any number of queries can be compiling in parallel; but for any given query, its segments are compiled serially, so each query has only one compilation occurring at any one time.

All the segments of a query are compiled before the query executes (there has been some confusion over this, due to one or two Redshift developer comments, which implied a stream compiled its segments only when it came to execute).

Typically, when compiled on the leader node, on an unloaded dc2.large, a segment takes between about 4 and 8 seconds to compile, depending on its complexity. As the number of concurrently compiling queries rises, compile times naturally become longer - there is more load on the leader node - but the rate of slow down is quite a bit better than linear; for example, with a single query, where a segment would take about 6 seconds, with twelve such queries compiling concurrently, a segment takes about 45 seconds, rather than 72 seconds.

A typical large query might have ten segments, so on an unloaded leader node take about 60 seconds, but on a loaded leader node (say, 12 queries) take 450 seconds (about eight minutes).

Some years ago, a compiled segment cache was introduced. I suspect this cache is on the leader node, as it is extremely fast, typically about 0.00003 seconds per segment, and because it seems to expire segments fairly quickly, which implies it is limited in size. As before, segments are obtained serially.

More recently, in a small but over time increasing number of regions, an ancillary system was introduced, such that the leader node itself no longer performs compilation, but hands the compilation work to the ancillary system and then obtains compiled segments from it. This ancillary system is known as “compile off-load”.

Compile off-load evidently from how it behaves was been designed to fit into the existing leader node compilation system with minimal disruption and as such what we see now is that, as before, any number of queries can be compiling in parallel, but now for each query, the leader node is at any time obtaining only a single compiled segment from the ancillary system.

In other words, it’s the same design - any number of queries concurrently, one segment at a time per query - but now the leader node has replaced compiling each segment with obtaining it from the ancillary system.

What’s different now though is that there is a slow first segment, during which time all the compile off-load helpers compile their segment, and once this is complete then the time taken to obtain each segment from the ancillary system is extremely short, typically in a tenth or a few tenths of a second.

Furthermore, it looks like the compilation off-load system has its own cache, and can serve segments from this cache, and when all compile off-load helpers serve from cache, then the slow first segment does not occur.

As more and more queries compile concurrently, the time taken to obtain a segment from the compile off-load system rises, by a factor approximately equal to the number of concurrently compiling queries. I think this is because the leader node in fact can only obtain one segment at a time from the compile off-load system; it cannot obtain one segment per compiling query in parallel. This is a loss of parallelism, but we’re still only talking about the basic time going from 0.1 seconds to something like 0.05 seconds (for 12 concurrent queries). If we imagine a moderately extreme case, of 10 queries each with 10 segments and no slow segments, then we’re looking at about 5 seconds compilation per query.

This behaviour is also seen for the leader node cache, which goes from something like 0.00003 seconds per segment to something like 0.0003 seconds per segment with 12 concurrent queries.

Unexpected Findings

When you investigate Redshift, there are always unexpected findings.

On a two node dc2.large cluster, issuing a query with one window function per column on a table with 491 to 832 (inclusive both ends) columns crashes the cluster.

I suspect really the key issue is the number of segments (which is for these queries almost exactly twice the number of table columns) and so this then would really be that a query with about 980 to 1664 segments crashes a cluster.

The exact number of columns of the lower bound varies a bit, by one or two columns, over Redshift versions, presumably as each version uses a bit more or a bit less memory on the leader node. The upper bound appears stable, as it originates from a non-memory based limitation.

I’ve not explored if this occurs when the cluster size or node type changes, but to the extent it occurs, it allows normal, unprivileged users to simply and easily crash clusters; make a table, issue a query, share and enjoy.
On a two node dc2.large cluster, if you issue one window function on every column in a table, and you have 833 or more columns, and the error “target lists can have at most 1664 entries” is thrown.

This error catches the query before it executes, and so I suspect prevents the query from crashing the cluster as in #1, above.

Each window function here is producing two segments, so we can see 833 columns, which means 833 window functions, produces 1666 segments, just over the limit mentioned in the error. There is however one additional segment, for returning rows to the client, but this runs on the leader node and appears then not to contribute to the “1664 entry” limit.

This means the purported 1600 column limit for tables can be supplanted before being reached by other, undocumented limits.
In STL_QUERYTEXT, when a row has 200 characters of text and the final character is a space, the next line has an additional space character inserted at its beginning. This breaks searching for such queries by their text, as the text you search for no longer matches the stored text. On the face of it, this system table can never have been tested, since this bug would immediately have been found. (Later use in other white papers of the various query text system tables has found a wide range of issues with text deformation, not just this one issue.)
It became clear during this white paper that the compiled segment cache is on a per-region basis (as well as per-Redshift version). It might even be on a per-AZ basis, but I’ve not tested for this. I also wonder if it is on a per-node-type basis, but I’ve not tested for that, either - this is not a white paper investigating the cache :-)

Revision History

v1

Initial release.

v2

Minor change to abstract.

v3

Corrected an awful typo in the Conclusion, where I had written “0.0003” when I meant to write “0.00003”.

v4

Changed to Redshift Research Project (AWS have a copyright on “Amazon Redshift”).

v5

Added “About the Author”. made site name in title a link, and made each chapter start a new page.
Updated links to amazonredshiftresearcproject.org to redshiftresearcproject.org.

Appendix A : Raw Data Dump

This appendix normally is the raw data dump from the test script.

However, the raw data from the query compilation test script is about 37 megabytes, and this is too large for the PDF generator to handle.

As such, the raw results have been made available as a separate file, available here.

I have however included the results from eu-north-1, as this is a region without compile off-load and so only has up to 32 columns in the tables being tested, and so has a much smaller set of results.

eu-north-1

{'proofs': {'dc2.large': {2: {}}},
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                                                'mean_total_count': 4,
                                                'reuse_time_mean': 57.0,
                                                'reuse_time_stddev': 29.8911416223238,
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                                                'time_total_mean': 4124.0,
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                                                'reuse_time_mean': 45.0,
                                                'reuse_time_stddev': 22.6876488578995,
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                                                'time_total_mean': 14511.0,
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                                                                                datetime.timedelta(microseconds=56),
                                                                                0],
                                                                               [18,
                                                                                datetime.datetime(2022, 2, 13, 17, 13, 57, 136239),
                                                                                datetime.datetime(2022, 2, 13, 17, 14, 30, 415533),
                                                                                datetime.timedelta(seconds=33, microseconds=279294),
                                                                                1],
                                                                               [19,
                                                                                datetime.datetime(2022, 2, 13, 17, 13, 57, 136150),
                                                                                datetime.datetime(2022, 2, 13, 17, 13, 57, 136208),
                                                                                datetime.timedelta(microseconds=58),
                                                                                0],
                                                                               [20,
                                                                                datetime.datetime(2022, 2, 13, 17, 14, 30, 472570),
                                                                                datetime.datetime(2022, 2, 13, 17, 15, 18, 662265),
                                                                                datetime.timedelta(seconds=48, microseconds=189695),
                                                                                1],
                                                                               [21,
                                                                                datetime.datetime(2022, 2, 13, 17, 14, 30, 472472),
                                                                                datetime.datetime(2022, 2, 13, 17, 14, 30, 472528),
                                                                                datetime.timedelta(microseconds=56),
                                                                                0],
                                                                               [22,
                                                                                datetime.datetime(2022, 2, 13, 17, 15, 18, 740856),
                                                                                datetime.datetime(2022, 2, 13, 17, 16, 14, 869240),
                                                                                datetime.timedelta(seconds=56, microseconds=128384),
                                                                                1],
                                                                               [23,
                                                                                datetime.datetime(2022, 2, 13, 17, 15, 18, 740769),
                                                                                datetime.datetime(2022, 2, 13, 17, 15, 18, 740823),
                                                                                datetime.timedelta(microseconds=54),
                                                                                0],
                                                                               [24,
                                                                                datetime.datetime(2022, 2, 13, 17, 16, 14, 948136),
                                                                                datetime.datetime(2022, 2, 13, 17, 17, 17, 549663),
                                                                                datetime.timedelta(seconds=62, microseconds=601527),
                                                                                1],
                                                                               [25,
                                                                                datetime.datetime(2022, 2, 13, 17, 16, 14, 945843),
                                                                                datetime.datetime(2022, 2, 13, 17, 16, 14, 948100),
                                                                                datetime.timedelta(microseconds=2257),
                                                                                0],
                                                                               [26,
                                                                                datetime.datetime(2022, 2, 13, 17, 17, 17, 606381),
                                                                                datetime.datetime(2022, 2, 13, 17, 18, 41, 94728),
                                                                                datetime.timedelta(seconds=83, microseconds=488347),
                                                                                1],
                                                                               [27,
                                                                                datetime.datetime(2022, 2, 13, 17, 17, 17, 606289),
                                                                                datetime.datetime(2022, 2, 13, 17, 17, 17, 606349),
                                                                                datetime.timedelta(microseconds=60),
                                                                                0],
                                                                               [28,
                                                                                datetime.datetime(2022, 2, 13, 17, 18, 41, 143001),
                                                                                datetime.datetime(2022, 2, 13, 17, 19, 49, 303808),
                                                                                datetime.timedelta(seconds=68, microseconds=160807),
                                                                                1],
                                                                               [29,
                                                                                datetime.datetime(2022, 2, 13, 17, 18, 41, 142916),
                                                                                datetime.datetime(2022, 2, 13, 17, 18, 41, 142970),
                                                                                datetime.timedelta(microseconds=54),
                                                                                0],
                                                                               [30,
                                                                                datetime.datetime(2022, 2, 13, 17, 19, 49, 364841),
                                                                                datetime.datetime(2022, 2, 13, 17, 21, 51, 476893),
                                                                                datetime.timedelta(seconds=122, microseconds=112052),
                                                                                1],
                                                                               [31,
                                                                                datetime.datetime(2022, 2, 13, 17, 19, 49, 364745),
                                                                                datetime.datetime(2022, 2, 13, 17, 19, 49, 364806),
                                                                                datetime.timedelta(microseconds=61),
                                                                                0],
                                                                               [32,
                                                                                datetime.datetime(2022, 2, 13, 17, 21, 51, 543229),
                                                                                datetime.datetime(2022, 2, 13, 17, 23, 25, 426776),
                                                                                datetime.timedelta(seconds=93, microseconds=883547),
                                                                                1],
                                                                               [33,
                                                                                datetime.datetime(2022, 2, 13, 17, 21, 51, 543139),
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                                                                                datetime.timedelta(microseconds=57),
                                                                                0],
                                                                               [34,
                                                                                datetime.datetime(2022, 2, 13, 17, 23, 25, 490970),
                                                                                datetime.datetime(2022, 2, 13, 17, 24, 17, 941239),
                                                                                datetime.timedelta(seconds=52, microseconds=450269),
                                                                                1],
                                                                               [35,
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                                                                                datetime.timedelta(microseconds=58),
                                                                                0],
                                                                               [36,
                                                                                datetime.datetime(2022, 2, 13, 17, 24, 18, 17245),
                                                                                datetime.datetime(2022, 2, 13, 17, 25, 25, 342434),
                                                                                datetime.timedelta(seconds=67, microseconds=325189),
                                                                                1],
                                                                               [37,
                                                                                datetime.datetime(2022, 2, 13, 17, 24, 18, 4952),
                                                                                datetime.datetime(2022, 2, 13, 17, 24, 18, 17209),
                                                                                datetime.timedelta(microseconds=12257),
                                                                                0],
                                                                               [38,
                                                                                datetime.datetime(2022, 2, 13, 17, 25, 25, 420447),
                                                                                datetime.datetime(2022, 2, 13, 17, 26, 22, 470345),
                                                                                datetime.timedelta(seconds=57, microseconds=49898),
                                                                                1],
                                                                               [39,
                                                                                datetime.datetime(2022, 2, 13, 17, 25, 25, 420351),
                                                                                datetime.datetime(2022, 2, 13, 17, 25, 25, 420404),
                                                                                datetime.timedelta(microseconds=53),
                                                                                0],
                                                                               [40,
                                                                                datetime.datetime(2022, 2, 13, 17, 26, 22, 515947),
                                                                                datetime.datetime(2022, 2, 13, 17, 27, 2, 920725),
                                                                                datetime.timedelta(seconds=40, microseconds=404778),
                                                                                1],
                                                                               [41,
                                                                                datetime.datetime(2022, 2, 13, 17, 26, 22, 515861),
                                                                                datetime.datetime(2022, 2, 13, 17, 26, 22, 515915),
                                                                                datetime.timedelta(microseconds=54),
                                                                                0],
                                                                               [42,
                                                                                datetime.datetime(2022, 2, 13, 17, 27, 2, 983330),
                                                                                datetime.datetime(2022, 2, 13, 17, 27, 34, 292066),
                                                                                datetime.timedelta(seconds=31, microseconds=308736),
                                                                                1],
                                                                               [43,
                                                                                datetime.datetime(2022, 2, 13, 17, 27, 2, 983232),
                                                                                datetime.datetime(2022, 2, 13, 17, 27, 2, 983292),
                                                                                datetime.timedelta(microseconds=60),
                                                                                0],
                                                                               [44,
                                                                                datetime.datetime(2022, 2, 13, 17, 27, 34, 323132),
                                                                                datetime.datetime(2022, 2, 13, 17, 28, 25, 230452),
                                                                                datetime.timedelta(seconds=50, microseconds=907320),
                                                                                1],
                                                                               [45,
                                                                                datetime.datetime(2022, 2, 13, 17, 27, 34, 323046),
                                                                                datetime.datetime(2022, 2, 13, 17, 27, 34, 323098),
                                                                                datetime.timedelta(microseconds=52),
                                                                                0],
                                                                               [46,
                                                                                datetime.datetime(2022, 2, 13, 17, 28, 25, 290991),
                                                                                datetime.datetime(2022, 2, 13, 17, 29, 36, 792975),
                                                                                datetime.timedelta(seconds=71, microseconds=501984),
                                                                                1],
                                                                               [47,
                                                                                datetime.datetime(2022, 2, 13, 17, 28, 25, 290901),
                                                                                datetime.datetime(2022, 2, 13, 17, 28, 25, 290958),
                                                                                datetime.timedelta(microseconds=57),
                                                                                0],
                                                                               [48,
                                                                                datetime.datetime(2022, 2, 13, 17, 29, 36, 853826),
                                                                                datetime.datetime(2022, 2, 13, 17, 30, 34, 452931),
                                                                                datetime.timedelta(seconds=57, microseconds=599105),
                                                                                1],
                                                                               [49,
                                                                                datetime.datetime(2022, 2, 13, 17, 29, 36, 853741),
                                                                                datetime.datetime(2022, 2, 13, 17, 29, 36, 853791),
                                                                                datetime.timedelta(microseconds=50),
                                                                                0],
                                                                               [50,
                                                                                datetime.datetime(2022, 2, 13, 17, 30, 34, 491262),
                                                                                datetime.datetime(2022, 2, 13, 17, 32, 3, 466301),
                                                                                datetime.timedelta(seconds=88, microseconds=975039),
                                                                                1],
                                                                               [51,
                                                                                datetime.datetime(2022, 2, 13, 17, 30, 34, 491174),
                                                                                datetime.datetime(2022, 2, 13, 17, 30, 34, 491226),
                                                                                datetime.timedelta(microseconds=52),
                                                                                0],
                                                                               [52,
                                                                                datetime.datetime(2022, 2, 13, 17, 32, 3, 529302),
                                                                                datetime.datetime(2022, 2, 13, 17, 33, 11, 940663),
                                                                                datetime.timedelta(seconds=68, microseconds=411361),
                                                                                1],
                                                                               [53,
                                                                                datetime.datetime(2022, 2, 13, 17, 32, 3, 529214),
                                                                                datetime.datetime(2022, 2, 13, 17, 32, 3, 529269),
                                                                                datetime.timedelta(microseconds=55),
                                                                                0],
                                                                               [54,
                                                                                datetime.datetime(2022, 2, 13, 17, 33, 11, 981648),
                                                                                datetime.datetime(2022, 2, 13, 17, 34, 20, 365233),
                                                                                datetime.timedelta(seconds=68, microseconds=383585),
                                                                                1],
                                                                               [55,
                                                                                datetime.datetime(2022, 2, 13, 17, 33, 11, 981568),
                                                                                datetime.datetime(2022, 2, 13, 17, 33, 11, 981617),
                                                                                datetime.timedelta(microseconds=49),
                                                                                0],
                                                                               [56,
                                                                                datetime.datetime(2022, 2, 13, 17, 34, 20, 433897),
                                                                                datetime.datetime(2022, 2, 13, 17, 35, 6, 669285),
                                                                                datetime.timedelta(seconds=46, microseconds=235388),
                                                                                1],
                                                                               [57,
                                                                                datetime.datetime(2022, 2, 13, 17, 34, 20, 433796),
                                                                                datetime.datetime(2022, 2, 13, 17, 34, 20, 433852),
                                                                                datetime.timedelta(microseconds=56),
                                                                                0],
                                                                               [58,
                                                                                datetime.datetime(2022, 2, 13, 17, 35, 6, 722963),
                                                                                datetime.datetime(2022, 2, 13, 17, 36, 4, 500224),
                                                                                datetime.timedelta(seconds=57, microseconds=777261),
                                                                                1],
                                                                               [59,
                                                                                datetime.datetime(2022, 2, 13, 17, 35, 6, 722876),
                                                                                datetime.datetime(2022, 2, 13, 17, 35, 6, 722929),
                                                                                datetime.timedelta(microseconds=53),
                                                                                0],
                                                                               [60,
                                                                                datetime.datetime(2022, 2, 13, 17, 36, 4, 546059),
                                                                                datetime.datetime(2022, 2, 13, 17, 36, 52, 691111),
                                                                                datetime.timedelta(seconds=48, microseconds=145052),
                                                                                1],
                                                                               [61,
                                                                                datetime.datetime(2022, 2, 13, 17, 36, 4, 545974),
                                                                                datetime.datetime(2022, 2, 13, 17, 36, 4, 546027),
                                                                                datetime.timedelta(microseconds=53),
                                                                                0],
                                                                               [62,
                                                                                datetime.datetime(2022, 2, 13, 17, 36, 52, 744391),
                                                                                datetime.datetime(2022, 2, 13, 17, 37, 25, 291256),
                                                                                datetime.timedelta(seconds=32, microseconds=546865),
                                                                                1],
                                                                               [63,
                                                                                datetime.datetime(2022, 2, 13, 17, 36, 52, 744301),
                                                                                datetime.datetime(2022, 2, 13, 17, 36, 52, 744353),
                                                                                datetime.timedelta(microseconds=52),
                                                                                0],
                                                                               [64,
                                                                                datetime.datetime(2022, 2, 13, 17, 37, 25, 349975),
                                                                                datetime.datetime(2022, 2, 13, 17, 38, 9, 154823),
                                                                                datetime.timedelta(seconds=43, microseconds=804848),
                                                                                1],
                                                                               [65,
                                                                                datetime.datetime(2022, 2, 13, 17, 37, 25, 349886),
                                                                                datetime.datetime(2022, 2, 13, 17, 37, 25, 349938),
                                                                                datetime.timedelta(microseconds=52),
                                                                                0]],
                                                         'oom': False,
                                                         'qid': 1326,
                                                         'reuse_count': 34,
                                                         'total_count': 66},
                                                    'compile_time_mean': 48822731.0,
                                                    'compile_time_stddev': 2656171.32001046,
                                                    'mean_compile_count': 38.0,
                                                    'mean_reuse_count': 28.0,
                                                    'mean_total_count': 66,
                                                    'reuse_time_mean': 307.0,
                                                    'reuse_time_stddev': 1336.31636943531,
                                                    'sd_compile_count': 11.2175457803092,
                                                    'time_total_mean': 1884483992.0,
                                                    'time_total_stddev': 15813361.4870594}}}}}},
 '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 1.0.34928'}}}

Appendix B : Query Results and Step Plan

This appendix contains the raw results and step-plan for six segment query, with no first slow segment, in us-east-2.

The step-plan plan is identical when there is a slow first segment.

Apologies for the font being so small, but anything off-page cannot be seen or copied, so is lost. At least this way you can copy and paste to a text editor and view the material.

 'Compiled': {1: {2: {0: {'compile_count': 4,
                          'full_compile_data': [[0,
                                                 datetime.datetime(2022, 2, 19, 20, 28, 56, 639752),
                                                 datetime.datetime(2022, 2, 19, 20, 28, 56, 807496),
                                                 datetime.timedelta(microseconds=167744),
                                                 1],
                                                [1,
                                                 datetime.datetime(2022, 2, 19, 20, 28, 56, 807546),
                                                 datetime.datetime(2022, 2, 19, 20, 28, 56, 809193),
                                                 datetime.timedelta(microseconds=1647),
                                                 0],
                                                [2,
                                                 datetime.datetime(2022, 2, 19, 20, 28, 56, 812303),
                                                 datetime.datetime(2022, 2, 19, 20, 28, 56, 863141),
                                                 datetime.timedelta(microseconds=50838),
                                                 1],
                                                [3,
                                                 datetime.datetime(2022, 2, 19, 20, 28, 56, 863172),
                                                 datetime.datetime(2022, 2, 19, 20, 28, 56, 867896),
                                                 datetime.timedelta(microseconds=4724),
                                                 1],
                                                [4,
                                                 datetime.datetime(2022, 2, 19, 20, 28, 56, 871778),
                                                 datetime.datetime(2022, 2, 19, 20, 28, 56, 912451),
                                                 datetime.timedelta(microseconds=40673),
                                                 1],
                                                [5,
                                                 datetime.datetime(2022, 2, 19, 20, 28, 56, 912504),
                                                 datetime.datetime(2022, 2, 19, 20, 28, 56, 912513),
                                                 datetime.timedelta(microseconds=9),
                                                 0]],
                          'oom': False,
                          'qid': 678,
                          'reuse_count': 2,
                          'total_count': 6},
                      'compile_time_mean': 65994.0,
                      'compile_time_stddev': 61192.1810341886,
                      'mean_compile_count': 4.0,
                      'mean_reuse_count': 2.0,
                      'mean_total_count': 6,
                      'reuse_time_mean': 828.0,
                      'reuse_time_stddev': 818.991661071777,
                      'sd_compile_count': 0.0,
                      'time_total_mean': 272761.0,
                      'time_total_stddev': 0.0}}}}

 qid | stream | segment | step | node_id | slice_id | step_type  | rows | bytes | seg_start_time  | seg_duration | schematable_name |                notes                 
-----+--------+---------+------+---------+----------+------------+------+-------+-----------------+--------------+------------------+--------------------------------------
 678 |      0 |       0 |    0 |       0 |        0 | scan       |    0 |     0 | 20:28:56.802296 |     0.000476 | table_0          | scan data from user table
 678 |      0 |       0 |    0 |       0 |        1 | scan       |    0 |     0 | 20:28:56.80199  |     0.000763 | table_0          | scan data from user table
 678 |      0 |       0 |    0 |       1 |        2 | scan       |    0 |     0 | 20:28:56.802924 |     0.000407 | table_0          | scan data from user table
 678 |      0 |       0 |    0 |       1 |        3 | scan       |    0 |     0 | 20:28:56.802386 |     0.000934 | table_0          | scan data from user table
 678 |      0 |       0 |    1 |       0 |        0 | project    |    0 |       | 20:28:56.802296 |     0.000476 |                  | 
 678 |      0 |       0 |    1 |       0 |        1 | project    |    0 |       | 20:28:56.80199  |     0.000763 |                  | 
 678 |      0 |       0 |    1 |       1 |        2 | project    |    0 |       | 20:28:56.802924 |     0.000407 |                  | 
 678 |      0 |       0 |    1 |       1 |        3 | project    |    0 |       | 20:28:56.802386 |     0.000934 |                  | 
 678 |      0 |       0 |    2 |       0 |        0 | distribute |    0 |     0 | 20:28:56.802296 |     0.000476 |                  | 
 678 |      0 |       0 |    2 |       0 |        1 | distribute |    0 |     0 | 20:28:56.80199  |     0.000763 |                  | 
 678 |      0 |       0 |    2 |       1 |        2 | distribute |    0 |     0 | 20:28:56.802924 |     0.000407 |                  | 
 678 |      0 |       0 |    2 |       1 |        3 | distribute |    0 |     0 | 20:28:56.802386 |     0.000934 |                  | 
 678 |      0 |       1 |    0 |       0 |        0 | scan       |    0 |     0 | 20:28:56.799457 |     0.004018 |                  | scan data from network to temp table
 678 |      0 |       1 |    0 |       0 |        1 | scan       |    0 |     0 | 20:28:56.799458 |     0.004086 |                  | scan data from network to temp table
 678 |      0 |       1 |    0 |       1 |        2 | scan       |    0 |     0 | 20:28:56.800151 |     0.004621 |                  | scan data from network to temp table
 678 |      0 |       1 |    0 |       1 |        3 | scan       |    0 |     0 | 20:28:56.800149 |     0.004605 |                  | scan data from network to temp table
 678 |      0 |       1 |    1 |       0 |        0 | sort       |    0 |     0 | 20:28:56.799457 |     0.004018 |                  | sorted in memory
 678 |      0 |       1 |    1 |       0 |        1 | sort       |    0 |     0 | 20:28:56.799458 |     0.004086 |                  | sorted in memory
 678 |      0 |       1 |    1 |       1 |        2 | sort       |    0 |     0 | 20:28:56.800151 |     0.004621 |                  | sorted in memory
 678 |      0 |       1 |    1 |       1 |        3 | sort       |    0 |     0 | 20:28:56.800149 |     0.004605 |                  | sorted in memory
 678 |      1 |       2 |    0 |       0 |        0 | scan       |    0 |     0 | 20:28:56.859425 |      0.00015 |                  | scan data from temp table
 678 |      1 |       2 |    0 |       0 |        1 | scan       |    0 |     0 | 20:28:56.859488 |     0.000111 |                  | scan data from temp table
 678 |      1 |       2 |    0 |       1 |        2 | scan       |    0 |     0 | 20:28:56.859817 |     0.000154 |                  | scan data from temp table
 678 |      1 |       2 |    0 |       1 |        3 | scan       |    0 |     0 | 20:28:56.859888 |     0.000105 |                  | scan data from temp table
 678 |      1 |       2 |    1 |       0 |        0 | project    |    0 |       | 20:28:56.859425 |      0.00015 |                  | 
 678 |      1 |       2 |    1 |       0 |        1 | project    |    0 |       | 20:28:56.859488 |     0.000111 |                  | 
 678 |      1 |       2 |    1 |       1 |        2 | project    |    0 |       | 20:28:56.859817 |     0.000154 |                  | 
 678 |      1 |       2 |    1 |       1 |        3 | project    |    0 |       | 20:28:56.859888 |     0.000105 |                  | 
 678 |      1 |       2 |    2 |       0 |        0 | window     |    0 |       | 20:28:56.859425 |      0.00015 |                  | window function in memory
 678 |      1 |       2 |    2 |       0 |        1 | window     |    0 |       | 20:28:56.859488 |     0.000111 |                  | window function in memory
 678 |      1 |       2 |    2 |       1 |        2 | window     |    0 |       | 20:28:56.859817 |     0.000154 |                  | window function in memory
 678 |      1 |       2 |    2 |       1 |        3 | window     |    0 |       | 20:28:56.859888 |     0.000105 |                  | window function in memory
 678 |      1 |       2 |    3 |       0 |        0 | project    |    0 |       | 20:28:56.859425 |      0.00015 |                  | 
 678 |      1 |       2 |    3 |       0 |        1 | project    |    0 |       | 20:28:56.859488 |     0.000111 |                  | 
 678 |      1 |       2 |    3 |       1 |        2 | project    |    0 |       | 20:28:56.859817 |     0.000154 |                  | 
 678 |      1 |       2 |    3 |       1 |        3 | project    |    0 |       | 20:28:56.859888 |     0.000105 |                  | 
 678 |      1 |       2 |    4 |       0 |        0 | distribute |    0 |     0 | 20:28:56.859425 |      0.00015 |                  | 
 678 |      1 |       2 |    4 |       0 |        1 | distribute |    0 |     0 | 20:28:56.859488 |     0.000111 |                  | 
 678 |      1 |       2 |    4 |       1 |        2 | distribute |    0 |     0 | 20:28:56.859817 |     0.000154 |                  | 
 678 |      1 |       2 |    4 |       1 |        3 | distribute |    0 |     0 | 20:28:56.859888 |     0.000105 |                  | 
 678 |      1 |       3 |    0 |       0 |        0 | scan       |    0 |     0 | 20:28:56.857951 |     0.002335 |                  | scan data from network to temp table
 678 |      1 |       3 |    0 |       0 |        1 | scan       |    0 |     0 | 20:28:56.858076 |     0.002199 |                  | scan data from network to temp table
 678 |      1 |       3 |    0 |       1 |        2 | scan       |    0 |     0 | 20:28:56.858699 |     0.002266 |                  | scan data from network to temp table
 678 |      1 |       3 |    0 |       1 |        3 | scan       |    0 |     0 | 20:28:56.858577 |     0.003376 |                  | scan data from network to temp table
 678 |      1 |       3 |    1 |       0 |        0 | sort       |    0 |     0 | 20:28:56.857951 |     0.002335 |                  | sorted in memory
 678 |      1 |       3 |    1 |       0 |        1 | sort       |    0 |     0 | 20:28:56.858076 |     0.002199 |                  | sorted in memory
 678 |      1 |       3 |    1 |       1 |        2 | sort       |    0 |     0 | 20:28:56.858699 |     0.002266 |                  | sorted in memory
 678 |      1 |       3 |    1 |       1 |        3 | sort       |    0 |     0 | 20:28:56.858577 |     0.003376 |                  | sorted in memory
 678 |      2 |       4 |    0 |       0 |        0 | scan       |    0 |     0 | 20:28:56.903008 |      8.8e-05 |                  | scan data from temp table
 678 |      2 |       4 |    0 |       0 |        1 | scan       |    0 |     0 | 20:28:56.903105 |     0.000113 |                  | scan data from temp table
 678 |      2 |       4 |    0 |       1 |        2 | scan       |    0 |     0 | 20:28:56.903439 |     0.000119 |                  | scan data from temp table
 678 |      2 |       4 |    0 |       1 |        3 | scan       |    0 |     0 | 20:28:56.903507 |     0.000105 |                  | scan data from temp table
 678 |      2 |       4 |    1 |       0 |        0 | project    |    0 |       | 20:28:56.903008 |      8.8e-05 |                  | 
 678 |      2 |       4 |    1 |       0 |        1 | project    |    0 |       | 20:28:56.903105 |     0.000113 |                  | 
 678 |      2 |       4 |    1 |       1 |        2 | project    |    0 |       | 20:28:56.903439 |     0.000119 |                  | 
 678 |      2 |       4 |    1 |       1 |        3 | project    |    0 |       | 20:28:56.903507 |     0.000105 |                  | 
 678 |      2 |       4 |    2 |       0 |        0 | window     |    0 |       | 20:28:56.903008 |      8.8e-05 |                  | window function in memory
 678 |      2 |       4 |    2 |       0 |        1 | window     |    0 |       | 20:28:56.903105 |     0.000113 |                  | window function in memory
 678 |      2 |       4 |    2 |       1 |        2 | window     |    0 |       | 20:28:56.903439 |     0.000119 |                  | window function in memory
 678 |      2 |       4 |    2 |       1 |        3 | window     |    0 |       | 20:28:56.903507 |     0.000105 |                  | window function in memory
 678 |      2 |       4 |    3 |       0 |        0 | project    |    0 |       | 20:28:56.903008 |      8.8e-05 |                  | 
 678 |      2 |       4 |    3 |       0 |        1 | project    |    0 |       | 20:28:56.903105 |     0.000113 |                  | 
 678 |      2 |       4 |    3 |       1 |        2 | project    |    0 |       | 20:28:56.903439 |     0.000119 |                  | 
 678 |      2 |       4 |    3 |       1 |        3 | project    |    0 |       | 20:28:56.903507 |     0.000105 |                  | 
 678 |      2 |       4 |    4 |       0 |        0 | project    |    0 |       | 20:28:56.903008 |      8.8e-05 |                  | 
 678 |      2 |       4 |    4 |       0 |        1 | project    |    0 |       | 20:28:56.903105 |     0.000113 |                  | 
 678 |      2 |       4 |    4 |       1 |        2 | project    |    0 |       | 20:28:56.903439 |     0.000119 |                  | 
 678 |      2 |       4 |    4 |       1 |        3 | project    |    0 |       | 20:28:56.903507 |     0.000105 |                  | 
 678 |      2 |       4 |    5 |       0 |        0 | return     |    0 |     0 | 20:28:56.903008 |      8.8e-05 |                  | 
 678 |      2 |       4 |    5 |       0 |        1 | return     |    0 |     0 | 20:28:56.903105 |     0.000113 |                  | 
 678 |      2 |       4 |    5 |       1 |        2 | return     |    0 |     0 | 20:28:56.903439 |     0.000119 |                  | 
 678 |      2 |       4 |    5 |       1 |        3 | return     |    0 |     0 | 20:28:56.903507 |     0.000105 |                  | 
 678 |      2 |       5 |    0 |         |    12812 | scan       |    0 |     0 | 20:28:56.913386 |      0.00096 |                  | scan data from network to temp table
 678 |      2 |       5 |    1 |         |    12812 | return     |    0 |     0 | 20:28:56.913386 |      0.00096 |                  |

About the Author

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.

Redshift Cluster Cost Reduction Service

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.