The Efficiency of Varied To-Many Nesting Algorithms – Java, SQL and jOOQ.

It’s been some time since jOOQ 3.15 has been launched with its revolutionary commonplace SQL MULTISET emulation function. A factor that has been lengthy overdue and which I promised on twitter a couple of instances 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 eventualities 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 degree and matching ends in the consumer

In distinction, all the above carry out a lot better than the dreaded N+1 “method” (or relatively, accident), all of the whereas being rather more readable and maintainable.

The conclusion is:

• For jOOQ customers to freely use MULTISET every time smallish information units are used (i.e. a nested loop be part of could be OK, too)
• For jOOQ customers to make use of MULTISET rigorously the place large-ish information units are used (i.e. a hash be part of or merge be part of could be higher, e.g. in stories)
• For ORM distributors to want the a number of queries per nest degree method in case they’re in full management of their SQL to materialise predefined object graphs

Benchmark concept

As at all times, we’re querying the well-known Sakila database. There are two forms of queries that I’ve examined on this benchmark.

A question that double-nests youngster collections (DN = DoubleNesting)

The outcome will likely be of the shape:

file DNCategory (String title) {}
file DNFilm (lengthy id, String title, Record classes) {}
file DNName (String firstName, String lastName) {}
file DNActor (lengthy id, DNName title, Record movies) {}

So, the outcome will likely be actors and their movies and their classes per movie. If a single be part of is being executed, this could trigger plenty 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 youngster collections in a single guardian (MCC = A number of Little one Collections)

The outcome will likely 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 actors, 
    Record classes
) {}

So, the outcome will likely be movies and their actors in addition to their classes. That is arduous 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, after all, which would be the most handy possibility

Information set dimension

Along with the above distinction of use-cases, the benchmark can even attempt to pull in both:

• Your entire information set (we’ve got 1000 FILM entries in addition to 200 ACTOR entries, so not an enormous information set), the place hash joins are usually higher
• Solely the subset for both ACTOR_ID = 1 or FILM_ID = 1, respectively, the place nested loop joins are usually higher

The expectation right here is {that a} JOIN tends to carry out higher on bigger outcome 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, on condition that it makes use of JSON_ARRAYAGG which may be tough to rework into one thing completely different, which continues to be equal.

Benchmark checks

The next issues will likely be benchmarked for every mixture of the above matrix:

1. A single MULTISET question with its 3 out there emulations utilizing XML (the place out there), JSON, JSONB
2. A single JOIN question that creates a cartesian product between guardian and kids
3. An method that runs 2 queries fetching all the required information into consumer reminiscence, and performs the nesting within the consumer, thereafter
4. A naive N+1 “consumer aspect nested loop be part of,” which is horrible however not unlikely to occur in actual world consumer code, both with jOOQ (much less seemingly, however nonetheless doable), or with a lazy loading ORM (extra seemingly, as a result of “unintended”)

The total benchmark logic will likely be posted on the finish of this text.

1. Single MULTISET question (DN)

The question seems to be 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 degree nested assortment for movies per actor
    multiset(
        choose(
            FILM_ACTOR.FILM_ID,
            FILM_ACTOR.movie().TITLE,

            // Second degree 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 study extra about the particular MULTISET syntax and the ad-hoc conversion function, please consult with earlier weblog posts explaining the main points. The identical is true for the implicit JOIN function, which I’ll be utilizing on this put up to maintain SQL a bit extra terse.

2. Single JOIN question (DN)

We will do every thing with a single be part of as properly. On this instance, I’m utilizing a purposeful model to rework the flat outcome into the doubly nested assortment in a kind protected approach. It’s a bit quirky, maybe there are higher methods to do that with non-JDK APIs. Since I wouldn’t count on this to be efficiency related, I believe it’s ok:

// The question is easy. Simply be part of every thing 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 difficult half. We first use JDK Collectors to group
// outcomes by ACTOR
.acquire(groupingBy(
    r -> new DNActor(
        r.value1(), 
        new DNName(r.value2(), r.value3()), 

        // dummy FILM record, we will not simply acquire them right here, but
        null 
    ),

    // For every actor, produce an inventory of FILM, once more with a dummy
    // CATEGORY record as we will not acquire them right here but
    groupingBy(r -> new DNFilm(r.value4(), r.value5(), null))
))

// Set>>>> 
.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 could possibly be improved to keep away from the dummy assortment placeholders within the first acquire() name, though that might in all probability require extra file varieties or structural tuple varieties like these from jOOλ. I saved it “easy” for this instance, although I’ll take your recommendations within the feedback.

3. Two queries merged in reminiscence (DN)

A superbly nice answer is to run a number of queries (however not N+1 queries!), i.e. one question per degree of nesting. This isn’t at all times doable, or optimum, however on this case, there’s an inexpensive answer.

I’m spelling out the prolonged Record5<...> varieties to indicate the precise varieties on this weblog put up. You need to use var to revenue from kind inference, after all. All of those queries use Report.value5() and comparable accessors to revenue from index based mostly entry, simply to be truthful, stopping the sphere lookup, which isn’t mandatory within the benchmark.

// Simple question to get ACTORs and their FILMs
End result> 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> 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())
    .acquire(intoGroups(
        r -> r.value1(),
        r -> new DNCategory(r.value2())
    ));

// Group once more by ACTOR and FILM, utilizing the earlier dummy
// assortment trick
return actorAndFilms
    .acquire(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 change 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. Definitely, the MULTISET method is to be most popular 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 largely in manufacturing, however we’ve all carried out it sooner or later (sure, responsible!), so right here it’s. At the least, the logic is extra readable than the earlier ones, it’s as simple as the unique MULTISET instance, in actual fact, as a result of it does virtually the identical factor because the MULTISET instance, however as an alternative of doing every thing in SQL, it correlates the subqueries within the consumer:

// 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 guardian kind has a number of youngster collections, one thing that’s rather 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 study extra about the particular MULTISET syntax and the ad-hoc conversion function, please consult with earlier weblog posts explaining the main points. The identical is true for the implicit JOIN function, which I’ll be utilizing on this put up to maintain SQL a bit extra terse.

2. Single JOIN Question (MCC)

The sort of nesting may be very arduous to do with a single JOIN question, as a result of there will likely be a cartesian product between ACTOR and CATEGORY, which can be arduous to deduplicate after the very fact. On this case, it could be doable, 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 doable to accurately take away duplicates, as a result of we wouldn’t be capable of distinguish:

• Duplicates originating from the JOIN cartesian product
• Duplicates originating from the underlying information set

As it’s arduous (in all probability not not possible) 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 unusual JOIN queries.

It’s in all probability what most ORMs do that don’t but assist 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 gained’t work properly 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. Right me if I’m incorrect, however I believe that appears to be extremely arduous to get proper, to rework complicated nested queries into a number of particular person queries which can be executed one after the opposite, solely to then reassemble outcomes.

In any case, it’s an method that works properly for ORMs who’re accountable for their SQL, however is laborious for finish customers to implement manually.

// Simple question to get ACTORs and their FILMs
End result> 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> 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())
    .acquire(intoGroups(
        r -> r.value1(),
        r -> new MCCCategory(r.value2())
    ));

// Group once more by ACTOR and FILM, utilizing the earlier dummy
// assortment trick
return filmsAndActors
    .acquire(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 easier 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 appears like a chore to do all of this grouping and nesting manually, ensuring all of the intermediate structural varieties are appropriate, however not less than the MCC case is a bit less complicated than the earlier DN case as a result of the nesting is much less deep.

However everyone knows, we’ll finally mix the approaches and nest tree buildings of arbitrary complexity.

4. N+1 queries (MCC)

Once more, don’t do that at residence (or in manufacturing), however we’ve all been there and right here’s what plenty 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 together with your interpretation, as at all times.

The aim of this benchmark wasn’t to discover a clear winner (or put disgrace on a clear loser). The aim of this benchmark was to test 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 sooner than one thing else, that it’s higher. It could be on this case, nevertheless it is probably not in several circumstances, e.g.

• When the information set is smaller
• When the information set is greater
• When the information set is distributed in a different way (e.g. many extra classes per movie, or a much less common variety of movies per actor (sakila information units have been generated relatively uniformly))
• When switching distributors
• When switching variations
• When you will have extra load on the system
• When your queries are extra various (benchmarks are inclined to run solely single queries, which drastically revenue from caching within the database server!)

So, once more, as with each benchmark outcome, be very cautious together with your interpretation.

The N+1 case

Even the N+1 case, which might flip into one thing horrible isn’t at all times the incorrect selection.

As we all know from Massive O Notation, issues with unhealthy algorithmic complexities seem solely when N is huge, not when it’s small.

• The algorithmic complexity of a single nested assortment is O(N * log M), i.e. N instances wanting up values in an index for M values (assuming there’s an index)
• The algorithmic complexity of a doubly nested assortment, nevertheless, is far worse, it’s O(N * log M * ? * log L), i.e. N instances wanting up values in an index for M values, after which ? instances (depends upon the distribution) wanting up values in an index for L values.

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 maintain advocating MULTISET because the holy grail as a result of it’s so highly effective, handy, kind protected, and fairly performant, as we’ll see subsequent, it isn’t the holy grail like every thing else we ever hoped for promising holy-graily-ness.

Whereas it may be theoretically doable to implement some hash be part of model nested assortment algorithm within the MULTISET case, I think that the emulations, which presently use XMLAGG, JSON_ARRAYAGG or comparable constructs, gained’t be optimised this fashion, and as such, we’ll get correlated subqueries, which is actually N+1, however 100% on the server aspect.

As increasingly folks use SQL/JSON options, these may be 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 will 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  (price=0.00..335.91 rows=1 width=72)                                                                       
  Filter: (actor_id = 1)                                                                                                     
  SubPlan 2                                                                                                                  
    ->  Mixture  (price=331.40..331.41 rows=1 width=32)                                                                     
          ->  Hash Be a part of  (price=5.09..73.73 rows=27 width=23)                                                                 
                Hash Cond: (alias_75379701.film_id = film_actor.film_id)                                                     
                ->  Seq Scan on movie alias_75379701  (price=0.00..66.00 rows=1000 width=23)                                   
                ->  Hash  (price=4.75..4.75 rows=27 width=8)                                                                  
                      ->  Index Solely Scan utilizing film_actor_pkey on film_actor  (price=0.28..4.75 rows=27 width=8)             
                            Index Cond: (actor_id = actor.actor_id)                                                          
          SubPlan 1                                                                                                          
            ->  Mixture  (price=9.53..9.54 rows=1 width=32)                                                                 
                  ->  Hash Be a part of  (price=8.30..9.52 rows=1 width=7)                                                            
                        Hash Cond: (alias_130639425.category_id = film_category.category_id)                                 
                        ->  Seq Scan on class alias_130639425  (price=0.00..1.16 rows=16 width=15)                         
                        ->  Hash  (price=8.29..8.29 rows=1 width=8)                                                           
                              ->  Index Solely Scan utilizing film_category_pkey on film_category  (price=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’t use any hash joins.

So, we’ve got an N+1 scenario, simply with out the latency of working a server roundtrip each time! The algorithmic complexity is similar, however the fixed overhead per merchandise has been eliminated, permitting for greater N earlier than it hurts – nonetheless the method will fail finally, identical to having too many JOIN on massive information units is inefficient in RDBMS that don’t assist 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 assist MULTISET extra natively on Oracle and PostgreSQL. It’s already supported natively in Informix, which has commonplace SQL MULTISET assist. In PostgreSQL, issues could possibly be carried out utilizing ARRAY() and ARRAY_AGG(), which may be extra clear to the optimiser than JSON_AGG. Whether it is, I’ll undoubtedly observe up with one other weblog put up.

The only JOIN question case

I’d count on this method to work OK-ish, if the nested collections aren’t too huge (i.e. there isn’t an excessive amount of duplicate information). As soon as the nested collections develop greater, the deduplication will bear fairly some prices as:

• Extra redundant information needs to be produced on the server aspect (requiring extra reminiscence and CPU)
• Extra redundant information needs to be transferred over the wire
• Extra deduplication must be carried out on the consumer aspect (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 enormous deduplications.

The 1-query-per-nest-level case

I’d count on the very unwieldy 1-query-per-nest-level case to be probably 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 must respect any person question necessities. It gained’t work properly if blended into person question syntax, and it’s arduous to do for customers manually each time.

Nevertheless, it’s an “after-the-fact” assortment nesting method, which means that it solely works properly if some assumptions concerning the authentic 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 constructive JPA / JPQL will evolve and likewise undertake MULTISET based mostly approaches. In any case, MULTISET has been a SQL commonplace 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 might’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 at all times, since benchmark outcomes can’t be printed for industrial RDBMS, I’m not publishing precise instances, solely relative instances, the place the slowest execution is 1, and sooner 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   Models
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   Models
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   Models
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  Models
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 comparable outcomes.
• The added latency of N+1 virtually at all times contributed a major efficiency penalty. An exception is the place we’ve got a filter = true and a number of youngster collections, in case of which the guardian N is 1 (duh) and solely a single nest degree is applied.
• MULTISET carried out even higher than the only question JOIN based mostly method or the 1-query-per-nest-level method when filter = true and with a number of youngster collections, in all probability due to the extra compact information format.
• The XML based mostly MULTISET emulation is at all times the slowest among the many emulations, seemingly as a result of it requires extra formatting. (In a single Oracle case, the XML based mostly MULTISET emulation was even slower than the unusual N+1 method).
• JSONB is a bit slower in PostgreSQL than JSON, seemingly as a result of JSON is a purely textual content based mostly format, with none put up processing / cleanup. JSONB‘s benefit will not be with projection-only queries, however with storage, comparability, and different operations. For many usages, JSONB might be higher. For projection solely, JSON is best (jOOQ 3.17 will make this the default for the MULTISET emulation)
• It’s value noting that jOOQ serialises information as JSON arrays, not JSON objects, so as to keep away from transferring repetitive column names, and supply positional index when deserialising the array.
• For big-ish information units (the place filter = false), the N+1 issue of a MULTISET correlated subquery can turn into an issue (because of the nature of algorithmic complexity), because it prevents utilizing extra environment friendly hash joins. In these circumstances, the only question JOIN based mostly method or 1-query-per-nest-level method are higher

Briefly:

• MULTISET can be utilized every time a nested loop be part of is perfect.
• If a hash be part of or merge be part of could be extra optimum, then the only question JOIN method or the 1-query-per-nest-level method are inclined to carry out higher (although they’ve their very own caveats as complexity grows)

The profit in comfort and correctness is unquestionably value it for small information units. For bigger information units, proceed utilizing JOIN. As at all times, there’s no silver bullet.

Issues this weblog put up didn’t examine

A couple of issues weren’t investigated by this weblog put up, together with:

• The serialisation overhead within the server. Atypical JDBC ResultSet are inclined to revenue from a binary community protocol between server and consumer. With JSON or XML, that advantage of 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 consumer aspect, the place nested JSON or XML paperwork should 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 between ResultSet and jOOQ information buildings. I imply, clearly, utilizing JDBC straight will be sooner in the event you’re doing it proper, however you then 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 personal 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:

• Simple 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 could be doable 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.End result;
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 {
            attempt (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 classes) {}
    file DNActor(lengthy id, DNName title, Record movies) {}

    @Benchmark
    public Record doubleNestingMultisetXML(BenchmarkState state) {
        state.ctx.settings().setEmulateMultiset(NestedCollectionEmulation.XML);
        return doubleNestingMultiset0(state);
    }

    @Benchmark
    public Record doubleNestingMultisetJSON(BenchmarkState state) {
        state.ctx.settings().setEmulateMultiset(NestedCollectionEmulation.JSON);
        return doubleNestingMultiset0(state);
    }

    @Benchmark
    public Record doubleNestingMultisetJSONB(BenchmarkState state) {
        state.ctx.settings().setEmulateMultiset(NestedCollectionEmulation.JSONB);
        return doubleNestingMultiset0(state);
    }

    personal Record 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 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())
            .acquire(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 doubleNestingTwoQueries(BenchmarkState state) {
        End result> 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> 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())
            .acquire(intoGroups(
                r -> r.value1(),
                r -> new DNCategory(r.value2())
            ));

        return actorAndFilms
            .acquire(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 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 actors, Record classes) {}

    @Benchmark
    public Record multipleChildCollectionsMultisetXML(BenchmarkState state) {
        state.ctx.settings().setEmulateMultiset(NestedCollectionEmulation.XML);
        return multipleChildCollectionsMultiset0(state);
    }

    @Benchmark
    public Record multipleChildCollectionsMultisetJSON(BenchmarkState state) {
        state.ctx.settings().setEmulateMultiset(NestedCollectionEmulation.JSON);
        return multipleChildCollectionsMultiset0(state);
    }

    @Benchmark
    public Record multipleChildCollectionsMultisetJSONB(BenchmarkState state) {
        state.ctx.settings().setEmulateMultiset(NestedCollectionEmulation.JSONB);
        return multipleChildCollectionsMultiset0(state);
    }

    personal Record 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 multipleChildCollectionsTwoQueries(BenchmarkState state) {
        End result> 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> 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())
            .acquire(intoGroups(
                r -> r.value1(),
                r -> new MCCCategory(r.value2())
            ));

        return filmsAndActors
            .acquire(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 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()))
            ));
    }
}