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It’s been some time since jOOQ 3.15 has been launched with its revolutionary normal SQL MULTISET emulation function. A factor that has been lengthy overdue and which I promised on twitter a couple of occasions is to run a couple of benchmarks evaluating the efficiency of varied approaches to nesting to-many relationships with jOOQ.
This text will present, that in some actual world situations on modest information set sizes, jOOQ’s MULTISET emulation performs about in addition to
- manually working single be part of queries and manually deduplicating outcomes
- manually working a number of queries per nest stage and matching ends in the shopper
In distinction, all the above carry out a lot better than the dreaded N+1 “method” (or slightly, accident), all of the whereas being far more readable and maintainable.
The conclusion is:
- For jOOQ customers to freely use
MULTISETeach time smallish information units are used (i.e. a nested loop be part of can be OK, too) - For jOOQ customers to make use of
MULTISETfastidiously the place large-ish information units are used (i.e. a hash be part of or merge be part of can be higher, e.g. in experiences) - For ORM distributors to want the a number of queries per nest stage method in case they’re in full management of their SQL to materialise predefined object graphs
Benchmark concept
As all the time, we’re querying the well-known Sakila database. There are two kinds of queries that I’ve examined on this benchmark.
A question that double-nests baby collections (DN = DoubleNesting)
The end result might be of the shape:
file DNCategory (String title) {}
file DNFilm (lengthy id, String title, Record<DNCategory> classes) {}
file DNName (String firstName, String lastName) {}
file DNActor (lengthy id, DNName title, Record<DNFilm> movies) {}
So, the end result might be actors and their movies and their classes per movie. If a single be part of is being executed, this could trigger a number of duplication within the information (though regrettably, in our take a look at information set, every movie solely has a single class)
A question that nests two baby collections in a single dad or mum (MCC = A number of Youngster Collections)
The end result might be of the shape:
file MCCName (String firstName, String lastName) {}
file MCCActor (lengthy id, MCCName title) {}
file MCCCategory (String title) {}
file MCCFilm (
lengthy id,
String title,
Record<MCCActor> actors,
Record<MCCCategory> classes
) {}
So, the end result might be movies and their actors in addition to their classes. That is exhausting to deduplicate with a single be part of, due to the cartesian product between ACTOR × CATEGORY. However different approaches with a number of queries nonetheless work, in addition to MULTISET, in fact, which would be the most handy choice
Information set measurement
Along with the above distinction of use-cases, the benchmark may even attempt to pull in both:
- Your entire information set (we’ve 1000
FILMentries in addition to 200ACTORentries, so not an enormous information set), the place hash joins are typically higher - Solely the subset for both
ACTOR_ID = 1orFILM_ID = 1, respectively, the place nested loop joins are typically higher
The expectation right here is {that a} JOIN tends to carry out higher on bigger end result units, as a result of the RDBMS will want a hash be part of algorithm. It’s unlikely the MULTISET emulation will be reworked right into a hash be part of or merge be part of, provided that it makes use of JSON_ARRAYAGG which is perhaps tough to remodel into one thing completely different, which remains to be equal.
Benchmark checks
The next issues might be benchmarked for every mixture of the above matrix:
- A single
MULTISETquestion with its 3 out there emulations utilizingXML(the place out there),JSON,JSONB - A single
JOINquestion that creates a cartesian product between dad or mum and youngsters - An method that runs 2 queries fetching all the required information into shopper reminiscence, and performs the nesting within the shopper, thereafter
- A naive N+1 “shopper facet nested loop be part of,” which is horrible however not unlikely to occur in actual world shopper code, both with jOOQ (much less seemingly, however nonetheless attainable), or with a lazy loading ORM (extra seemingly, as a result of “unintended”)
The complete benchmark logic might be posted on the finish of this text.
1. Single MULTISET question (DN)
The question appears to be like like this:
return state.ctx.choose(
ACTOR.ACTOR_ID,
// Nested file for the actor title
row(
ACTOR.FIRST_NAME,
ACTOR.LAST_NAME
).mapping(DNName::new),
// First stage nested assortment for movies per actor
multiset(
choose(
FILM_ACTOR.FILM_ID,
FILM_ACTOR.movie().TITLE,
// Second stage nested assortment for classes per movie
multiset(
choose(FILM_CATEGORY.class().NAME)
.from(FILM_CATEGORY)
.the place(FILM_CATEGORY.FILM_ID.eq(FILM_ACTOR.FILM_ID))
).convertFrom(r -> r.map(mapping(DNCategory::new)))
)
.from(FILM_ACTOR)
.the place(FILM_ACTOR.ACTOR_ID.eq(ACTOR.ACTOR_ID))
).convertFrom(r -> r.map(mapping(DNFilm::new))))
.from(ACTOR)
// Both fetch all information or filter ACTOR_ID = 1
.the place(state.filter ? ACTOR.ACTOR_ID.eq(1L) : noCondition())
.fetch(mapping(DNActor::new));
To be taught extra about the particular MULTISET syntax and the ad-hoc conversion function, please seek advice from earlier weblog posts explaining the small print. The identical is true for the implicit JOIN function, which I’ll be utilizing on this submit to maintain SQL a bit extra terse.
2. Single JOIN question (DN)
We are able to do all the things with a single be part of as effectively. On this instance, I’m utilizing a purposeful type to remodel the flat end result into the doubly nested assortment in a sort secure approach. It’s a bit quirky, maybe there are higher methods to do that with non-JDK APIs. Since I wouldn’t anticipate this to be efficiency related, I feel it’s ok:
// The question is simple. Simply be part of all the things from
// ACTOR -> FILM -> CATEGORY by way of the connection tables
return state.ctx.choose(
FILM_ACTOR.ACTOR_ID,
FILM_ACTOR.actor().FIRST_NAME,
FILM_ACTOR.actor().LAST_NAME,
FILM_ACTOR.FILM_ID,
FILM_ACTOR.movie().TITLE,
FILM_CATEGORY.class().NAME)
.from(FILM_ACTOR)
.be part of(FILM_CATEGORY).on(FILM_ACTOR.FILM_ID.eq(FILM_CATEGORY.FILM_ID))
.the place(state.filter ? FILM_ACTOR.ACTOR_ID.eq(1L) : noCondition())
// Now comes the tough half. We first use JDK Collectors to group
// outcomes by ACTOR
.gather(groupingBy(
r -> new DNActor(
r.value1(),
new DNName(r.value2(), r.value3()),
// dummy FILM record, we will not simply gather them right here, but
null
),
// For every actor, produce an inventory of FILM, once more with a dummy
// CATEGORY record as we will not gather them right here but
groupingBy(r -> new DNFilm(r.value4(), r.value5(), null))
))
// Set<Entry<DNActor, Map<DNFilm, Record<Record6<...>>>>>
.entrySet()
.stream()
// Re-map the DNActor file into itself, however this time, add the
// nested DNFilm record.
.map(a -> new DNActor(
a.getKey().id(),
a.getKey().title(),
a.getValue()
.entrySet()
.stream()
.map(f -> new DNFilm(
f.getKey().id(),
f.getKey().title(),
f.getValue().stream().map(
c -> new DNCategory(c.value6())
).toList()
))
.toList()
))
.toList();
Probably, this instance may very well be improved to keep away from the dummy assortment placeholders within the first gather() name, though that will most likely require further file varieties or structural tuple varieties like these from jOOλ. I saved it “easy” for this instance, although I’ll take your options within the feedback.
3. Two queries merged in reminiscence (DN)
A wonderfully nice answer is to run a number of queries (however not N+1 queries!), i.e. one question per stage of nesting. This isn’t all the time attainable, or optimum, however on this case, there’s an inexpensive answer.
I’m spelling out the prolonged Record5<...> varieties to point out the precise varieties on this weblog submit. You need to use var to revenue from kind inference, in fact. All of those queries use File.value5() and related accessors to revenue from index based mostly entry, simply to be truthful, stopping the sector lookup, which isn’t mandatory within the benchmark.
// Simple question to get ACTORs and their FILMs
Outcome<Record5<Lengthy, String, String, Lengthy, String>> actorAndFilms =
state.ctx
.choose(
FILM_ACTOR.ACTOR_ID,
FILM_ACTOR.actor().FIRST_NAME,
FILM_ACTOR.actor().LAST_NAME,
FILM_ACTOR.FILM_ID,
FILM_ACTOR.movie().TITLE)
.from(FILM_ACTOR)
.the place(state.filter ? FILM_ACTOR.ACTOR_ID.eq(1L) : noCondition())
.fetch();
// Create a FILM.FILM_ID => CATEGORY.NAME lookup
// That is simply fetching all of the movies and their classes.
// Optionally, filter for the earlier FILM_ID record
Map<Lengthy, Record<DNCategory>> categoriesPerFilm = state.ctx
.choose(
FILM_CATEGORY.FILM_ID,
FILM_CATEGORY.class().NAME)
.from(FILM_CATEGORY)
.the place(state.filter
? FILM_CATEGORY.FILM_ID.in(actorAndFilms.map(r -> r.value4()))
: noCondition())
.gather(intoGroups(
r -> r.value1(),
r -> new DNCategory(r.value2())
));
// Group once more by ACTOR and FILM, utilizing the earlier dummy
// assortment trick
return actorAndFilms
.gather(groupingBy(
r -> new DNActor(
r.value1(),
new DNName(r.value2(), r.value3()),
null
),
groupingBy(r -> new DNFilm(r.value4(), r.value5(), null))
))
.entrySet()
.stream()
// Then exchange the dummy collections
.map(a -> new DNActor(
a.getKey().id(),
a.getKey().title(),
a.getValue()
.entrySet()
.stream()
.map(f -> new DNFilm(
f.getKey().id(),
f.getKey().title(),
// And use the CATEGORY per FILM lookup
categoriesPerFilm.get(f.getKey().id())
))
.toList()
))
.toList();
Whew. Unwieldy. Actually, the MULTISET method is to be most well-liked from a readability perspective? All this mapping to middleman structural information varieties will be heavy, particularly in the event you make a typo and the compiler journeys.
4. N+1 queries (DN)
This naive answer is hopefully not what you’re doing principally in manufacturing, however we’ve all finished it sooner or later (sure, responsible!), so right here it’s. A minimum of, the logic is extra readable than the earlier ones, it’s as simple as the unique MULTISET instance, the truth is, as a result of it does virtually the identical factor because the MULTISET instance, however as an alternative of doing all the things in SQL, it correlates the subqueries within the shopper:
// Fetch all ACTORs
return state.ctx
.choose(ACTOR.ACTOR_ID, ACTOR.FIRST_NAME, ACTOR.LAST_NAME)
.from(ACTOR)
.the place(state.filter ? ACTOR.ACTOR_ID.eq(1L) : noCondition())
.fetch(a -> new DNActor(
a.value1(),
new DNName(a.value2(), a.value3()),
// And for every ACTOR, fetch all FILMs
state.ctx
.choose(FILM_ACTOR.FILM_ID, FILM_ACTOR.movie().TITLE)
.from(FILM_ACTOR)
.the place(FILM_ACTOR.ACTOR_ID.eq(a.value1()))
.fetch(f -> new DNFilm(
f.value1(),
f.value2(),
// And for every FILM, fetch all CATEGORY-s
state.ctx
.choose(FILM_CATEGORY.class().NAME)
.from(FILM_CATEGORY)
.the place(FILM_CATEGORY.FILM_ID.eq(f.value1()))
.fetch(r -> new DNCategory(r.value1()))
))
));
1. Single MULTISET question (MCC)
Now, we’ll repeat the train once more to gather information right into a extra tree like information construction, the place the dad or mum kind has a number of baby collections, one thing that’s far more tough to do with JOIN queries. Piece of cake with MULTISET, which nests the collections straight in SQL:
return state.ctx
.choose(
FILM.FILM_ID,
FILM.TITLE,
// Get all ACTORs for every FILM
multiset(
choose(
FILM_ACTOR.ACTOR_ID,
row(
FILM_ACTOR.actor().FIRST_NAME,
FILM_ACTOR.actor().LAST_NAME
).mapping(MCCName::new)
)
.from(FILM_ACTOR)
.the place(FILM_ACTOR.FILM_ID.eq(FILM.FILM_ID))
).convertFrom(r -> r.map(mapping(MCCActor::new))),
// Get all CATEGORY-s for every FILM
multiset(
choose(FILM_CATEGORY.class().NAME)
.from(FILM_CATEGORY)
.the place(FILM_CATEGORY.FILM_ID.eq(FILM.FILM_ID))
).convertFrom(r -> r.map(mapping(MCCCategory::new))))
.from(FILM)
.the place(state.filter ? FILM.FILM_ID.eq(1L) : noCondition())
.fetch(mapping(MCCFilm::new));
Once more, to be taught extra about the particular MULTISET syntax and the ad-hoc conversion function, please seek advice from earlier weblog posts explaining the small print. The identical is true for the implicit JOIN function, which I’ll be utilizing on this submit to maintain SQL a bit extra terse.
2. Single JOIN Question (MCC)
Such a nesting could be very exhausting to do with a single JOIN question, as a result of there might be a cartesian product between ACTOR and CATEGORY, which can be exhausting to deduplicate after the very fact. On this case, it will be attainable, as a result of we all know that every ACTOR is listed solely as soon as per FILM, and so is every CATEGORY. However what if this wasn’t the case? It won’t be attainable to appropriately take away duplicates, as a result of we wouldn’t have the ability to distinguish:
- Duplicates originating from the
JOINcartesian product - Duplicates originating from the underlying information set
As it’s exhausting (most likely not inconceivable) to ensure correctness, it’s futile to check efficiency right here.
3. Two queries merged in reminiscence (MCC)
That is once more fairly an inexpensive implementation of this type of nesting utilizing abnormal JOIN queries.
It’s most likely what most ORMs do that don’t but help MULTISET like assortment nesting. It’s completely cheap to make use of this method when the ORM is in full management of the generated queries (e.g. when fetching pre-defined object graphs). However when permitting customized queries, this method received’t work effectively for complicated queries. For instance, JPQL’s JOIN FETCH syntax might use this method behind the scenes, however this prevents JPQL from supporting non-trivial queries the place JOIN FETCH is utilized in derived tables or correlated subqueries, and itself joins derived tables, and so forth. Appropriate me if I’m mistaken, however I feel that appears to be extremely exhausting to get proper, to remodel complicated nested queries into a number of particular person queries which are executed one after the opposite, solely to then reassemble outcomes.
In any case, it’s an method that works effectively for ORMs who’re answerable for their SQL, however is laborious for finish customers to implement manually.
// Simple question to get ACTORs and their FILMs
Outcome<Record5<Lengthy, String, Lengthy, String, String>> filmsAndActors =
state.ctx
.choose(
FILM_ACTOR.FILM_ID,
FILM_ACTOR.movie().TITLE,
FILM_ACTOR.ACTOR_ID,
FILM_ACTOR.actor().FIRST_NAME,
FILM_ACTOR.actor().LAST_NAME)
.from(FILM_ACTOR)
.the place(state.filter ? FILM_ACTOR.FILM_ID.eq(1L) : noCondition())
.fetch();
// Create a FILM.FILM_ID => CATEGORY.NAME lookup
// That is simply fetching all of the movies and their classes.
Map<Lengthy, Record<MCCCategory>> categoriesPerFilm = state.ctx
.choose(
FILM_CATEGORY.FILM_ID,
FILM_CATEGORY.class().NAME)
.from(FILM_CATEGORY)
.the place(FILM_CATEGORY.FILM_ID.in(
filmsAndActors.map(r -> r.value1())
))
.and(state.filter ? FILM_CATEGORY.FILM_ID.eq(1L) : noCondition())
.gather(intoGroups(
r -> r.value1(),
r -> new MCCCategory(r.value2())
));
// Group once more by ACTOR and FILM, utilizing the earlier dummy
// assortment trick
return filmsAndActors
.gather(groupingBy(
r -> new MCCFilm(r.value1(), r.value2(), null, null),
groupingBy(r -> new MCCActor(
r.value3(),
new MCCName(r.value4(), r.value5())
))
))
.entrySet()
.stream()
// This time, the nesting of CATEGORY-s is less complicated as a result of
// we do not have to nest them once more deeply
.map(f -> new MCCFilm(
f.getKey().id(),
f.getKey().title(),
new ArrayList<>(f.getValue().keySet()),
categoriesPerFilm.get(f.getKey().id())
))
.toList();
As you cam see, it nonetheless looks like a chore to do all of this grouping and nesting manually, ensuring all of the intermediate structural varieties are right, however at the least the MCC case is a bit easier than the earlier DN case as a result of the nesting is much less deep.
However everyone knows, we’ll ultimately mix the approaches and nest tree constructions of arbitrary complexity.
4. N+1 queries (MCC)
Once more, don’t do that at dwelling (or in manufacturing), however we’ve all been there and right here’s what a number of functions do both explicitly (disgrace on the writer!), or implicitly (disgrace on the ORM for permitting the writer to place disgrace on the writer!)
// Fetch all FILMs
return state.ctx
.choose(FILM.FILM_ID, FILM.TITLE)
.from(FILM)
.the place(state.filter ? FILM.FILM_ID.eq(1L) : noCondition())
.fetch(f -> new MCCFilm(
f.value1(),
f.value2(),
// For every FILM, fetch all ACTORs
state.ctx
.choose(
FILM_ACTOR.ACTOR_ID,
FILM_ACTOR.actor().FIRST_NAME,
FILM_ACTOR.actor().LAST_NAME)
.from(FILM_ACTOR)
.the place(FILM_ACTOR.FILM_ID.eq(f.value1()))
.fetch(a -> new MCCActor(
a.value1(),
new MCCName(a.value2(), a.value3())
)),
// For every FILM, fetch additionally all CATEGORY-s
state.ctx
.choose(FILM_CATEGORY.class().NAME)
.from(FILM_CATEGORY)
.the place(FILM_CATEGORY.FILM_ID.eq(f.value1()))
.fetch(c -> new MCCCategory(c.value1()))
));
Algorithmic complexities
Earlier than we transfer on to the benchmark outcomes, please be very cautious along with your interpretation, as all the time.
The purpose of this benchmark wasn’t to discover a clear winner (or put disgrace on a clear loser). The purpose of this benchmark was to verify if the MULTISET method has any vital and apparent profit and/or disadvantage over the opposite, extra handbook and unwieldy approaches.
Don’t conclude that if one thing is 1.5x or 3x quicker than one thing else, that it’s higher. It could be on this case, but it surely might not be in numerous circumstances, e.g.
- When the info set is smaller
- When the info set is greater
- When the info set is distributed in another way (e.g. many extra classes per movie, or a much less common variety of movies per actor (sakila information units had been generated slightly uniformly))
- When switching distributors
- When switching variations
- When you will have extra load on the system
- When your queries are extra various (benchmarks are likely to run solely single queries, which significantly revenue from caching within the database server!)
So, once more, as with each benchmark end result, be very cautious along with your interpretation.
The N+1 case
Even the N+1 case, which may flip into one thing horrible isn’t all the time the mistaken selection.
As we all know from Massive O Notation, issues with unhealthy algorithmic complexities seem solely when N is large, not when it’s small.
- The algorithmic complexity of a single nested assortment is
O(N * log M), i.e.Noccasions trying up values in an index forMvalues (assuming there’s an index) - The algorithmic complexity of a doubly nested assortment, nevertheless, is way worse, it’s
O(N * log M * ? * log L), i.e.Noccasions trying up values in an index forMvalues, after which?occasions (is determined by the distribution) trying up values in an index forLvalues.
Higher hope all of those values are very small. If they’re, you’re nice. In the event that they aren’t, you’ll discover in manufacturing on a weekend.
The MULTISET case
Whereas I preserve advocating MULTISET because the holy grail as a result of it’s so highly effective, handy, kind secure, and fairly performant, as we’ll see subsequent, it isn’t the holy grail like all the things else we ever hoped for promising holy-graily-ness.
Whereas it is perhaps theoretically attainable to implement some hash be part of type nested assortment algorithm within the MULTISET case, I think that the emulations, which presently use XMLAGG, JSON_ARRAYAGG or related constructs, received’t be optimised this fashion, and as such, we’ll get correlated subqueries, which is actually N+1, however 100% on the server facet.
As an increasing number of individuals use SQL/JSON options, these is perhaps optimised additional sooner or later, although. I wouldn’t maintain my breath for RDBMS distributors investing time to enhance SQL/XML an excessive amount of (regrettably).
We are able to confirm the execution plan by working an EXPLAIN (on PostgreSQL) on the question generated by jOOQ for the doubly nested assortment case:
clarify choose
actor.actor_id,
row (actor.first_name, actor.last_name),
(
choose coalesce(
json_agg(json_build_array(v0, v1, v2)),
json_build_array()
)
from (
choose
film_actor.film_id as v0,
alias_75379701.title as v1,
(
choose coalesce(
json_agg(json_build_array(v0)),
json_build_array()
)
from (
choose alias_130639425.title as v0
from (
film_category
be part of class as alias_130639425
on film_category.category_id =
alias_130639425.category_id
)
the place film_category.film_id = film_actor.film_id
) as t
) as v2
from (
film_actor
be part of movie as alias_75379701
on film_actor.film_id = alias_75379701.film_id
)
the place film_actor.actor_id = actor.actor_id
) as t
)
from actor
the place actor.actor_id = 1
The result’s:
QUERY PLAN
-----------------------------------------------------------------------------------------------------------------------------
Seq Scan on actor (value=0.00..335.91 rows=1 width=72)
Filter: (actor_id = 1)
SubPlan 2
-> Mixture (value=331.40..331.41 rows=1 width=32)
-> Hash Be part of (value=5.09..73.73 rows=27 width=23)
Hash Cond: (alias_75379701.film_id = film_actor.film_id)
-> Seq Scan on movie alias_75379701 (value=0.00..66.00 rows=1000 width=23)
-> Hash (value=4.75..4.75 rows=27 width=8)
-> Index Solely Scan utilizing film_actor_pkey on film_actor (value=0.28..4.75 rows=27 width=8)
Index Cond: (actor_id = actor.actor_id)
SubPlan 1
-> Mixture (value=9.53..9.54 rows=1 width=32)
-> Hash Be part of (value=8.30..9.52 rows=1 width=7)
Hash Cond: (alias_130639425.category_id = film_category.category_id)
-> Seq Scan on class alias_130639425 (value=0.00..1.16 rows=16 width=15)
-> Hash (value=8.29..8.29 rows=1 width=8)
-> Index Solely Scan utilizing film_category_pkey on film_category (value=0.28..8.29 rows=1 width=8)
Index Cond: (film_id = film_actor.film_id)
As anticipated, two nested scalar subqueries. Don’t get side-tracked by the hash joins within the subqueries. These are anticipated, as a result of we’re becoming a member of between e.g. FILM and FILM_ACTOR, or between CATEGORY and FILM_CATEGORY within the subquery. However this doesn’t have an effect on how the 2 subqueries are correlated to the outer-most question, the place we can not use any hash joins.
So, we’ve an N+1 scenario, simply with out the latency of working a server roundtrip each time! The algorithmic complexity is identical, however the fixed overhead per merchandise has been eliminated, permitting for larger N earlier than it hurts – nonetheless the method will fail ultimately, similar to having too many JOIN on giant information units is inefficient in RDBMS that don’t help hash be part of or merge be part of, however solely nested loop be part of (e.g. older MySQL variations).
Future variations of jOOQ might help MULTISET extra natively on Oracle and PostgreSQL. It’s already supported natively in Informix, which has normal SQL MULTISET help. In PostgreSQL, issues may very well be finished utilizing ARRAY(<subquery>) and ARRAY_AGG(), which is perhaps extra clear to the optimiser than JSON_AGG. Whether it is, I’ll undoubtedly comply with up with one other weblog submit.
The only JOIN question case
I might anticipate this method to work OK-ish, if the nested collections aren’t too large (i.e. there isn’t an excessive amount of duplicate information). As soon as the nested collections develop larger, the deduplication will bear fairly some prices as:
- Extra redundant information needs to be produced on the server facet (requiring extra reminiscence and CPU)
- Extra redundant information needs to be transferred over the wire
- Extra deduplication must be finished on the shopper facet (requiring extra reminiscence and CPU)
All in all, this method appears foolish for complicated nesting, however doable for a single nested assortment. This benchmark doesn’t take a look at big deduplications.
The 1-query-per-nest-level case
I might anticipate the very unwieldy 1-query-per-nest-level case to be essentially the most performant as N scales. It’s additionally comparatively simple for an ORM to implement, in case the ORM is in full management of the generated SQL and doesn’t should respect any consumer question necessities. It received’t work effectively if blended into consumer question syntax, and it’s exhausting to do for customers manually each time.
Nevertheless, it’s an “after-the-fact” assortment nesting method, that means that it solely works effectively if some assumptions in regards to the unique question will be maintained. E.g. JOIN FETCH in JPQL solely takes you this far. It could have been an OK workaround to nesting collections and making the idea out there for easy circumstances, however I’m optimistic JPA / JPQL will evolve and likewise undertake MULTISET based mostly approaches. In any case, MULTISET has been a SQL normal for ORDBMS for ages now.
The long-term answer for nesting collections can solely be to nest them straight in SQL, and make all of the logic out there to the optimiser for its varied selections.
Benchmark outcomes
Lastly, some outcomes! I’ve run the benchmark on these 4 RDBMS:
- MySQL
- Oracle
- PostgreSQL
- SQL Server
I didn’t run it on Db2, which may’t correlate derived tables but, a vital function for correlated MULTISET subqueries in jOOQ in 3.15 – 3.17’s MULTISET emulation (see https://github.com/jOOQ/jOOQ/points/12045 for particulars).
As all the time, since benchmark outcomes can’t be revealed for business RDBMS, I’m not publishing precise occasions, solely relative occasions, the place the slowest execution is 1, and quicker executions are multiples of 1. Think about some precise unit of measurement, like operations/second, solely it’s not per second however per undisclosed unit of time. That approach, the RDBMS can solely be in contrast with themselves, not with one another.
MySQL:
Benchmark (filter) Mode Cnt Rating Error Items
MultisetVsJoinBenchmark.doubleNestingJoin true thrpt 7 4413.48 ± 448.63 ops/time-unit
MultisetVsJoinBenchmark.doubleNestingMultisetJSON true thrpt 7 2524.96 ± 402.38 ops/time-unit
MultisetVsJoinBenchmark.doubleNestingMultisetJSONB true thrpt 7 2738.62 ± 332.37 ops/time-unit
MultisetVsJoinBenchmark.doubleNestingNPlusOneQueries true thrpt 7 265.37 ± 42.98 ops/time-unit
MultisetVsJoinBenchmark.doubleNestingTwoQueries true thrpt 7 2256.38 ± 363.18 ops/time-unit
MultisetVsJoinBenchmark.doubleNestingJoin false thrpt 7 266.27 ± 13.31 ops/time-unit
MultisetVsJoinBenchmark.doubleNestingMultisetJSON false thrpt 7 54.98 ± 2.25 ops/time-unit
MultisetVsJoinBenchmark.doubleNestingMultisetJSONB false thrpt 7 54.05 ± 1.58 ops/time-unit
MultisetVsJoinBenchmark.doubleNestingNPlusOneQueries false thrpt 7 1.00 ± 0.11 ops/time-unit
MultisetVsJoinBenchmark.doubleNestingTwoQueries false thrpt 7 306.23 ± 11.64 ops/time-unit
MultisetVsJoinBenchmark.multipleChildCollectionsMultisetJSON true thrpt 7 3058.68 ± 722.24 ops/time-unit
MultisetVsJoinBenchmark.multipleChildCollectionsMultisetJSONB true thrpt 7 3179.18 ± 333.77 ops/time-unit
MultisetVsJoinBenchmark.multipleChildCollectionsNPlusOneQueries true thrpt 7 1845.75 ± 167.26 ops/time-unit
MultisetVsJoinBenchmark.multipleChildCollectionsTwoQueries true thrpt 7 2425.76 ± 579.73 ops/time-unit
MultisetVsJoinBenchmark.multipleChildCollectionsMultisetJSON false thrpt 7 91.78 ± 2.65 ops/time-unit
MultisetVsJoinBenchmark.multipleChildCollectionsMultisetJSONB false thrpt 7 92.48 ± 2.25 ops/time-unit
MultisetVsJoinBenchmark.multipleChildCollectionsNPlusOneQueries false thrpt 7 2.84 ± 0.48 ops/time-unit
MultisetVsJoinBenchmark.multipleChildCollectionsTwoQueries false thrpt 7 171.66 ± 19.8 ops/time-unit
Oracle:
Benchmark (filter) Mode Cnt Rating Error Items
MultisetVsJoinBenchmark.doubleNestingJoin true thrpt 7 669.54 ± 28.35 ops/time-unit
MultisetVsJoinBenchmark.doubleNestingMultisetJSON true thrpt 7 419.13 ± 23.60 ops/time-unit
MultisetVsJoinBenchmark.doubleNestingMultisetJSONB true thrpt 7 432.40 ± 17.76 ops/time-unit
MultisetVsJoinBenchmark.doubleNestingMultisetXML true thrpt 7 351.42 ± 18.70 ops/time-unit
MultisetVsJoinBenchmark.doubleNestingNPlusOneQueries true thrpt 7 251.73 ± 30.19 ops/time-unit
MultisetVsJoinBenchmark.doubleNestingTwoQueries true thrpt 7 548.80 ± 117.40 ops/time-unit
MultisetVsJoinBenchmark.doubleNestingJoin false thrpt 7 15.59 ± 1.86 ops/time-unit
MultisetVsJoinBenchmark.doubleNestingMultisetJSON false thrpt 7 2.41 ± 0.07 ops/time-unit
MultisetVsJoinBenchmark.doubleNestingMultisetJSONB false thrpt 7 2.40 ± 0.07 ops/time-unit
MultisetVsJoinBenchmark.doubleNestingMultisetXML false thrpt 7 1.91 ± 0.06 ops/time-unit
MultisetVsJoinBenchmark.doubleNestingNPlusOneQueries false thrpt 7 1.00 ± 0.12 ops/time-unit
MultisetVsJoinBenchmark.doubleNestingTwoQueries false thrpt 7 13.63 ± 1.57 ops/time-unit
MultisetVsJoinBenchmark.multipleChildCollectionsMultisetJSON true thrpt 7 1217.79 ± 89.87 ops/time-unit
MultisetVsJoinBenchmark.multipleChildCollectionsMultisetJSONB true thrpt 7 1214.07 ± 76.10 ops/time-unit
MultisetVsJoinBenchmark.multipleChildCollectionsMultisetXML true thrpt 7 702.11 ± 87.37 ops/time-unit
MultisetVsJoinBenchmark.multipleChildCollectionsNPlusOneQueries true thrpt 7 919.47 ± 340.63 ops/time-unit
MultisetVsJoinBenchmark.multipleChildCollectionsTwoQueries true thrpt 7 1194.05 ± 179.92 ops/time-unit
MultisetVsJoinBenchmark.multipleChildCollectionsMultisetJSON false thrpt 7 2.89 ± 0.08 ops/time-unit
MultisetVsJoinBenchmark.multipleChildCollectionsMultisetJSONB false thrpt 7 3.00 ± 0.05 ops/time-unit
MultisetVsJoinBenchmark.multipleChildCollectionsMultisetXML false thrpt 7 1.04 ± 0.17 ops/time-unit
MultisetVsJoinBenchmark.multipleChildCollectionsNPlusOneQueries false thrpt 7 1.52 ± 0.08 ops/time-unit
MultisetVsJoinBenchmark.multipleChildCollectionsTwoQueries false thrpt 7 13.00 ± 1.96 ops/time-unit
PostgreSQL:
Benchmark (filter) Mode Cnt Rating Error Items
MultisetVsJoinBenchmark.doubleNestingJoin true thrpt 7 4128.21 ± 398.82 ops/time-unit
MultisetVsJoinBenchmark.doubleNestingMultisetJSON true thrpt 7 3187.88 ± 409.30 ops/time-unit
MultisetVsJoinBenchmark.doubleNestingMultisetJSONB true thrpt 7 3064.69 ± 154.75 ops/time-unit
MultisetVsJoinBenchmark.doubleNestingMultisetXML true thrpt 7 1973.44 ± 166.22 ops/time-unit
MultisetVsJoinBenchmark.doubleNestingNPlusOneQueries true thrpt 7 267.15 ± 34.01 ops/time-unit
MultisetVsJoinBenchmark.doubleNestingTwoQueries true thrpt 7 2081.03 ± 317.95 ops/time-unit
MultisetVsJoinBenchmark.doubleNestingJoin false thrpt 7 275.95 ± 6.80 ops/time-unit
MultisetVsJoinBenchmark.doubleNestingMultisetJSON false thrpt 7 53.94 ± 1.06 ops/time-unit
MultisetVsJoinBenchmark.doubleNestingMultisetJSONB false thrpt 7 45.00 ± 0.52 ops/time-unit
MultisetVsJoinBenchmark.doubleNestingMultisetXML false thrpt 7 25.11 ± 1.01 ops/time-unit
MultisetVsJoinBenchmark.doubleNestingNPlusOneQueries false thrpt 7 1.00 ± 0.07 ops/time-unit
MultisetVsJoinBenchmark.doubleNestingTwoQueries false thrpt 7 306.11 ± 35.40 ops/time-unit
MultisetVsJoinBenchmark.multipleChildCollectionsMultisetJSON true thrpt 7 4710.47 ± 194.37 ops/time-unit
MultisetVsJoinBenchmark.multipleChildCollectionsMultisetJSONB true thrpt 7 4391.78 ± 223.62 ops/time-unit
MultisetVsJoinBenchmark.multipleChildCollectionsMultisetXML true thrpt 7 2740.73 ± 186.70 ops/time-unit
MultisetVsJoinBenchmark.multipleChildCollectionsNPlusOneQueries true thrpt 7 1792.94 ± 134.50 ops/time-unit
MultisetVsJoinBenchmark.multipleChildCollectionsTwoQueries true thrpt 7 2821.82 ± 252.34 ops/time-unit
MultisetVsJoinBenchmark.multipleChildCollectionsMultisetJSON false thrpt 7 68.45 ± 2.58 ops/time-unit
MultisetVsJoinBenchmark.multipleChildCollectionsMultisetJSONB false thrpt 7 58.59 ± 0.58 ops/time-unit
MultisetVsJoinBenchmark.multipleChildCollectionsMultisetXML false thrpt 7 15.58 ± 0.35 ops/time-unit
MultisetVsJoinBenchmark.multipleChildCollectionsNPlusOneQueries false thrpt 7 2.71 ± 0.16 ops/time-unit
MultisetVsJoinBenchmark.multipleChildCollectionsTwoQueries false thrpt 7 163.03 ± 7.54 ops/time-unit
SQL Server:
Benchmark (filter) Mode Cnt Rating Error Items
MultisetVsJoinBenchmark.doubleNestingJoin true thrpt 7 4081.85 ± 1029.84 ops/time-unit
MultisetVsJoinBenchmark.doubleNestingMultisetJSON true thrpt 7 1243.17 ± 84.24 ops/time-unit
MultisetVsJoinBenchmark.doubleNestingMultisetJSONB true thrpt 7 1254.13 ± 56.94 ops/time-unit
MultisetVsJoinBenchmark.doubleNestingMultisetXML true thrpt 7 1077.23 ± 61.50 ops/time-unit
MultisetVsJoinBenchmark.doubleNestingNPlusOneQueries true thrpt 7 264.45 ± 16.12 ops/time-unit
MultisetVsJoinBenchmark.doubleNestingTwoQueries true thrpt 7 1608.92 ± 145.75 ops/time-unit
MultisetVsJoinBenchmark.doubleNestingJoin false thrpt 7 359.08 ± 20.88 ops/time-unit
MultisetVsJoinBenchmark.doubleNestingMultisetJSON false thrpt 7 8.41 ± 0.06 ops/time-unit
MultisetVsJoinBenchmark.doubleNestingMultisetJSONB false thrpt 7 8.32 ± 0.15 ops/time-unit
MultisetVsJoinBenchmark.doubleNestingMultisetXML false thrpt 7 7.24 ± 0.08 ops/time-unit
MultisetVsJoinBenchmark.doubleNestingNPlusOneQueries false thrpt 7 1.00 ± 0.09 ops/time-unit
MultisetVsJoinBenchmark.doubleNestingTwoQueries false thrpt 7 376.02 ± 7.60 ops/time-unit
MultisetVsJoinBenchmark.multipleChildCollectionsMultisetJSON true thrpt 7 1735.23 ± 178.30 ops/time-unit
MultisetVsJoinBenchmark.multipleChildCollectionsMultisetJSONB true thrpt 7 1736.01 ± 92.26 ops/time-unit
MultisetVsJoinBenchmark.multipleChildCollectionsMultisetXML true thrpt 7 1339.68 ± 137.47 ops/time-unit
MultisetVsJoinBenchmark.multipleChildCollectionsNPlusOneQueries true thrpt 7 1264.50 ± 343.56 ops/time-unit
MultisetVsJoinBenchmark.multipleChildCollectionsTwoQueries true thrpt 7 1057.54 ± 130.13 ops/time-unit
MultisetVsJoinBenchmark.multipleChildCollectionsMultisetJSON false thrpt 7 7.90 ± 0.05 ops/time-unit
MultisetVsJoinBenchmark.multipleChildCollectionsMultisetJSONB false thrpt 7 7.85 ± 0.15 ops/time-unit
MultisetVsJoinBenchmark.multipleChildCollectionsMultisetXML false thrpt 7 5.06 ± 0.18 ops/time-unit
MultisetVsJoinBenchmark.multipleChildCollectionsNPlusOneQueries false thrpt 7 1.77 ± 0.28 ops/time-unit
MultisetVsJoinBenchmark.multipleChildCollectionsTwoQueries false thrpt 7 255.08 ± 19.22 ops/time-unit
Benchmark conclusion
As will be seen in most RDBMS:
- All RDBMS produced related outcomes.
- The added latency of N+1 virtually all the time contributed a major efficiency penalty. An exception is the place we’ve a
filter = trueand a number of baby collections, in case of which the dad or mumNis 1 (duh) and solely a single nest stage is carried out. MULTISETcarried out even higher than the one questionJOINbased mostly method or the 1-query-per-nest-level method whenfilter = trueand with a number of baby collections, most likely due to the extra compact information format.- The
XMLbased mostlyMULTISETemulation is all the time the slowest among the many emulations, seemingly as a result of it requires extra formatting. (In a single Oracle case, theXMLbased mostlyMULTISETemulation was even slower than the abnormal N+1 method). JSONBis a bit slower in PostgreSQL thanJSON, seemingly as a result ofJSONis a purely textual content based mostly format, with none submit processing / cleanup.JSONB‘s benefit will not be with projection-only queries, however with storage, comparability, and different operations. For many usages,JSONBmight be higher. For projection solely,JSONis healthier (jOOQ 3.17 will make this the default for theMULTISETemulation)- It’s value noting that jOOQ serialises data as JSON arrays, not JSON objects, to be able to keep away from transferring repetitive column names, and supply positional index when deserialising the array.
- For giant-ish information units (the place
filter = false), the N+1 issue of aMULTISETcorrelated subquery can turn out to be an issue (as a result of nature of algorithmic complexity), because it prevents utilizing extra environment friendly hash joins. In these circumstances, the one questionJOINbased mostly method or 1-query-per-nest-level method are higher
Briefly:
MULTISETcan be utilized each time a nested loop be part of is perfect.- If a hash be part of or merge be part of can be extra optimum, then the one question
JOINmethod or the 1-query-per-nest-level method are likely to carry out higher (although they’ve their very own caveats as complexity grows)
The profit in comfort and correctness is certainly value it for small information units. For bigger information units, proceed utilizing JOIN. As all the time, there’s no silver bullet.
Issues this weblog submit didn’t examine
A number of issues weren’t investigated by this weblog submit, together with:
- The serialisation overhead within the server. Extraordinary JDBC
ResultSetare likely to revenue from a binary community protocol between server and shopper. WithJSONorXML, that good thing about protocol compactness goes away, and a scientific overhead is produced. To what extent this performs a job has not been investigated. - The identical is true on the shopper facet, the place nested
JSONorXMLpaperwork must be deserialised. Whereas the next VisualVM screenshot reveals that there’s some overhead, it isn’t vital in comparison with the execution time. Additionally, it isn’t a major quantity extra overhead than what jOOQ already produces when mapping betweenResultSetand jOOQ information constructions. I imply, clearly, utilizing JDBC straight will be quicker in the event you’re doing it proper, however then you definitely take away all of the comfort jOOQ creates.
Benchmark code
Lastly, do you have to want to reproduce this benchmark, or adapt it to your individual wants, right here’s the code.
I’ve used JMH for the benchmark. Whereas that is clearly not a “micro benchmark,” I like JMH’s method to benchmarking, together with:
- Straightforward configuration
- Warmup penalty is eliminated by doing warmup iterations
- Statistics are collected to deal with outlier results
Clearly, all of the model use jOOQ for question constructing, execution, mapping, to realize truthful and significant outcomes. It will be attainable to make use of JDBC straight within the non-MULTISET approaches, however that wouldn’t be a good comparability of ideas.
The benchmark assumes availability of a SAKILA database occasion, in addition to generated code, much like this jOOQ demo.
package deal org.jooq.take a look at.benchmarks.native;
import static java.util.stream.Collectors.groupingBy;
import static org.jooq.Data.intoGroups;
import static org.jooq.Data.mapping;
import static org.jooq.instance.db.postgres.Tables.*;
import static org.jooq.impl.DSL.multiset;
import static org.jooq.impl.DSL.noCondition;
import static org.jooq.impl.DSL.row;
import static org.jooq.impl.DSL.choose;
import java.io.InputStream;
import java.sql.Connection;
import java.sql.DriverManager;
import java.util.ArrayList;
import java.util.Record;
import java.util.Map;
import java.util.Properties;
import java.util.operate.Client;
import org.jooq.DSLContext;
import org.jooq.Record5;
import org.jooq.Outcome;
import org.jooq.conf.NestedCollectionEmulation;
import org.jooq.conf.Settings;
import org.jooq.impl.DSL;
import org.openjdk.jmh.annotations.Benchmark;
import org.openjdk.jmh.annotations.Fork;
import org.openjdk.jmh.annotations.Stage;
import org.openjdk.jmh.annotations.Measurement;
import org.openjdk.jmh.annotations.Param;
import org.openjdk.jmh.annotations.Scope;
import org.openjdk.jmh.annotations.Setup;
import org.openjdk.jmh.annotations.State;
import org.openjdk.jmh.annotations.TearDown;
import org.openjdk.jmh.annotations.Warmup;
@Fork(worth = 1)
@Warmup(iterations = 3, time = 3)
@Measurement(iterations = 7, time = 3)
public class MultisetVsJoinBenchmark {
@State(Scope.Benchmark)
public static class BenchmarkState {
Connection connection;
DSLContext ctx;
@Param({ "true", "false" })
boolean filter;
@Setup(Stage.Trial)
public void setup() throws Exception {
strive (InputStream is = BenchmarkState.class.getResourceAsStream("/config.mysql.properties")) {
Properties p = new Properties();
p.load(is);
Class.forName(p.getProperty("db.mysql.driver"));
connection = DriverManager.getConnection(
p.getProperty("db.mysql.url"),
p.getProperty("db.mysql.username"),
p.getProperty("db.mysql.password")
);
}
ctx = DSL.utilizing(connection, new Settings()
.withExecuteLogging(false)
.withRenderSchema(false));
}
@TearDown(Stage.Trial)
public void teardown() throws Exception {
connection.shut();
}
}
file DNName(String firstName, String lastName) {}
file DNCategory(String title) {}
file DNFilm(lengthy id, String title, Record<DNCategory> classes) {}
file DNActor(lengthy id, DNName title, Record<DNFilm> movies) {}
@Benchmark
public Record<DNActor> doubleNestingMultisetXML(BenchmarkState state) {
state.ctx.settings().setEmulateMultiset(NestedCollectionEmulation.XML);
return doubleNestingMultiset0(state);
}
@Benchmark
public Record<DNActor> doubleNestingMultisetJSON(BenchmarkState state) {
state.ctx.settings().setEmulateMultiset(NestedCollectionEmulation.JSON);
return doubleNestingMultiset0(state);
}
@Benchmark
public Record<DNActor> doubleNestingMultisetJSONB(BenchmarkState state) {
state.ctx.settings().setEmulateMultiset(NestedCollectionEmulation.JSONB);
return doubleNestingMultiset0(state);
}
non-public Record<DNActor> doubleNestingMultiset0(BenchmarkState state) {
return state.ctx
.choose(
ACTOR.ACTOR_ID,
row(
ACTOR.FIRST_NAME,
ACTOR.LAST_NAME
).mapping(DNName::new),
multiset(
choose(
FILM_ACTOR.FILM_ID,
FILM_ACTOR.movie().TITLE,
multiset(
choose(FILM_CATEGORY.class().NAME)
.from(FILM_CATEGORY)
.the place(FILM_CATEGORY.FILM_ID.eq(FILM_ACTOR.FILM_ID))
).convertFrom(r -> r.map(mapping(DNCategory::new)))
)
.from(FILM_ACTOR)
.the place(FILM_ACTOR.ACTOR_ID.eq(ACTOR.ACTOR_ID))
).convertFrom(r -> r.map(mapping(DNFilm::new))))
.from(ACTOR)
.the place(state.filter ? ACTOR.ACTOR_ID.eq(1L) : noCondition())
.fetch(mapping(DNActor::new));
}
@Benchmark
public Record<DNActor> doubleNestingJoin(BenchmarkState state) {
return state.ctx
.choose(
FILM_ACTOR.ACTOR_ID,
FILM_ACTOR.actor().FIRST_NAME,
FILM_ACTOR.actor().LAST_NAME,
FILM_ACTOR.FILM_ID,
FILM_ACTOR.movie().TITLE,
FILM_CATEGORY.class().NAME)
.from(FILM_ACTOR)
.be part of(FILM_CATEGORY).on(FILM_ACTOR.FILM_ID.eq(FILM_CATEGORY.FILM_ID))
.the place(state.filter ? FILM_ACTOR.ACTOR_ID.eq(1L) : noCondition())
.gather(groupingBy(
r -> new DNActor(r.value1(), new DNName(r.value2(), r.value3()), null),
groupingBy(r -> new DNFilm(r.value4(), r.value5(), null))
))
.entrySet()
.stream()
.map(a -> new DNActor(
a.getKey().id(),
a.getKey().title(),
a.getValue()
.entrySet()
.stream()
.map(f -> new DNFilm(
f.getKey().id(),
f.getKey().title(),
f.getValue().stream().map(c -> new DNCategory(c.value6())).toList()
))
.toList()
))
.toList();
}
@Benchmark
public Record<DNActor> doubleNestingTwoQueries(BenchmarkState state) {
Outcome<Record5<Lengthy, String, String, Lengthy, String>> actorAndFilms = state.ctx
.choose(
FILM_ACTOR.ACTOR_ID,
FILM_ACTOR.actor().FIRST_NAME,
FILM_ACTOR.actor().LAST_NAME,
FILM_ACTOR.FILM_ID,
FILM_ACTOR.movie().TITLE)
.from(FILM_ACTOR)
.the place(state.filter ? FILM_ACTOR.ACTOR_ID.eq(1L) : noCondition())
.fetch();
Map<Lengthy, Record<DNCategory>> categoriesPerFilm = state.ctx
.choose(
FILM_CATEGORY.FILM_ID,
FILM_CATEGORY.class().NAME)
.from(FILM_CATEGORY)
.the place(state.filter
? FILM_CATEGORY.FILM_ID.in(actorAndFilms.map(r -> r.value4()))
: noCondition())
.gather(intoGroups(
r -> r.value1(),
r -> new DNCategory(r.value2())
));
return actorAndFilms
.gather(groupingBy(
r -> new DNActor(r.value1(), new DNName(r.value2(), r.value3()), null),
groupingBy(r -> new DNFilm(r.value4(), r.value5(), null))
))
.entrySet()
.stream()
.map(a -> new DNActor(
a.getKey().id(),
a.getKey().title(),
a.getValue()
.entrySet()
.stream()
.map(f -> new DNFilm(
f.getKey().id(),
f.getKey().title(),
categoriesPerFilm.get(f.getKey().id())
))
.toList()
))
.toList();
}
@Benchmark
public Record<DNActor> doubleNestingNPlusOneQueries(BenchmarkState state) {
return state.ctx
.choose(ACTOR.ACTOR_ID, ACTOR.FIRST_NAME, ACTOR.LAST_NAME)
.from(ACTOR)
.the place(state.filter ? ACTOR.ACTOR_ID.eq(1L) : noCondition())
.fetch(a -> new DNActor(
a.value1(),
new DNName(a.value2(), a.value3()),
state.ctx
.choose(FILM_ACTOR.FILM_ID, FILM_ACTOR.movie().TITLE)
.from(FILM_ACTOR)
.the place(FILM_ACTOR.ACTOR_ID.eq(a.value1()))
.fetch(f -> new DNFilm(
f.value1(),
f.value2(),
state.ctx
.choose(FILM_CATEGORY.class().NAME)
.from(FILM_CATEGORY)
.the place(FILM_CATEGORY.FILM_ID.eq(f.value1()))
.fetch(r -> new DNCategory(r.value1()))
))
));
}
file MCCName(String firstName, String lastName) {}
file MCCCategory(String title) {}
file MCCActor(lengthy id, MCCName title) {}
file MCCFilm(lengthy id, String title, Record<MCCActor> actors, Record<MCCCategory> classes) {}
@Benchmark
public Record<MCCFilm> multipleChildCollectionsMultisetXML(BenchmarkState state) {
state.ctx.settings().setEmulateMultiset(NestedCollectionEmulation.XML);
return multipleChildCollectionsMultiset0(state);
}
@Benchmark
public Record<MCCFilm> multipleChildCollectionsMultisetJSON(BenchmarkState state) {
state.ctx.settings().setEmulateMultiset(NestedCollectionEmulation.JSON);
return multipleChildCollectionsMultiset0(state);
}
@Benchmark
public Record<MCCFilm> multipleChildCollectionsMultisetJSONB(BenchmarkState state) {
state.ctx.settings().setEmulateMultiset(NestedCollectionEmulation.JSONB);
return multipleChildCollectionsMultiset0(state);
}
non-public Record<MCCFilm> multipleChildCollectionsMultiset0(BenchmarkState state) {
return state.ctx
.choose(
FILM.FILM_ID,
FILM.TITLE,
multiset(
choose(
FILM_ACTOR.ACTOR_ID,
row(
FILM_ACTOR.actor().FIRST_NAME,
FILM_ACTOR.actor().LAST_NAME
).mapping(MCCName::new)
)
.from(FILM_ACTOR)
.the place(FILM_ACTOR.FILM_ID.eq(FILM.FILM_ID))
).convertFrom(r -> r.map(mapping(MCCActor::new))),
multiset(
choose(
FILM_CATEGORY.class().NAME
)
.from(FILM_CATEGORY)
.the place(FILM_CATEGORY.FILM_ID.eq(FILM.FILM_ID))
).convertFrom(r -> r.map(mapping(MCCCategory::new))))
.from(FILM)
.the place(state.filter ? FILM.FILM_ID.eq(1L) : noCondition())
.fetch(mapping(MCCFilm::new));
}
@Benchmark
public Record<MCCFilm> multipleChildCollectionsTwoQueries(BenchmarkState state) {
Outcome<Record5<Lengthy, String, Lengthy, String, String>> filmsAndActors = state.ctx
.choose(
FILM_ACTOR.FILM_ID,
FILM_ACTOR.movie().TITLE,
FILM_ACTOR.ACTOR_ID,
FILM_ACTOR.actor().FIRST_NAME,
FILM_ACTOR.actor().LAST_NAME)
.from(FILM_ACTOR)
.the place(state.filter ? FILM_ACTOR.FILM_ID.eq(1L) : noCondition())
.fetch();
Map<Lengthy, Record<MCCCategory>> categoriesPerFilm = state.ctx
.choose(
FILM_CATEGORY.FILM_ID,
FILM_CATEGORY.class().NAME)
.from(FILM_CATEGORY)
.the place(FILM_CATEGORY.FILM_ID.in(
filmsAndActors.map(r -> r.value1())
))
.and(state.filter ? FILM_CATEGORY.FILM_ID.eq(1L) : noCondition())
.gather(intoGroups(
r -> r.value1(),
r -> new MCCCategory(r.value2())
));
return filmsAndActors
.gather(groupingBy(
r -> new MCCFilm(r.value1(), r.value2(), null, null),
groupingBy(r -> new MCCActor(r.value3(), new MCCName(r.value4(), r.value5())))
))
.entrySet()
.stream()
.map(f -> new MCCFilm(
f.getKey().id(),
f.getKey().title(),
new ArrayList<>(f.getValue().keySet()),
categoriesPerFilm.get(f.getKey().id())
))
.toList();
}
@Benchmark
public Record<MCCFilm> multipleChildCollectionsNPlusOneQueries(BenchmarkState state) {
return state.ctx
.choose(FILM.FILM_ID, FILM.TITLE)
.from(FILM)
.the place(state.filter ? FILM.FILM_ID.eq(1L) : noCondition())
.fetch(f -> new MCCFilm(
f.value1(),
f.value2(),
state.ctx
.choose(
FILM_ACTOR.ACTOR_ID,
FILM_ACTOR.actor().FIRST_NAME,
FILM_ACTOR.actor().LAST_NAME)
.from(FILM_ACTOR)
.the place(FILM_ACTOR.FILM_ID.eq(f.value1()))
.fetch(a -> new MCCActor(
a.value1(),
new MCCName(a.value2(), a.value3())
)),
state.ctx
.choose(FILM_CATEGORY.class().NAME)
.from(FILM_CATEGORY)
.the place(FILM_CATEGORY.FILM_ID.eq(f.value1()))
.fetch(c -> new MCCCategory(c.value1()))
));
}
}
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