Home Programming 3.17.0 Launch with Computed Columns, Audit Columns, Sample Matching, Reactive Transactions and Kotlin Coroutine Help – Java, SQL and jOOQ.

3.17.0 Launch with Computed Columns, Audit Columns, Sample Matching, Reactive Transactions and Kotlin Coroutine Help – Java, SQL and jOOQ.

3.17.0 Launch with Computed Columns, Audit Columns, Sample Matching, Reactive Transactions and Kotlin Coroutine Help – Java, SQL and jOOQ.


This launch contiues the work from earlier releases round extra refined SQL transformation capabilities, together with:

  • Shopper aspect computed columns for each learn and write operations
  • Audit columns
  • Sample matching SQL transformations
  • Extra implicit JOIN capabilities

Shopper aspect computed columns

A floor breaking new core characteristic out there in all industrial distributions is
the brand new consumer aspect computed columns characteristic, constructing on high of jOOQ 3.16’s
industrial assist for readonly columns and server aspect computed columns.

Not all RDBMS assist computed columns (e.g. utilizing the usual SQL syntax
GENERATED ALWAYS AS), and in the event that they do, they won’t assist them in each STORED (computed on write) and VIRTUAL (computed on learn) variants. jOOQ can now emulate each options on the consumer aspect, by remodeling your SQL queries:

  • VIRTUAL impacts SELECT and the RETURNING clause of DML statements. To make use of those, mix them with the brand new artificial column era characteristic.

Not like their server aspect counterparts, these consumer aspect options can produce arbitrary expressions, together with:

  • Implicit joins
  • Scalar subqueries
  • MULTISET subqueries
  • Way more

Consider this as “views” written in jOOQ, on a per-column foundation. An expecially helpful characteristic mixture is to mix these computed columns with the brand new visibility modifier that permits for conserving computed columns (or the underlying base columns) non-public and thus invisible to person code.

Extra about this characteristic right here

Audit columns

A particular case of STORED consumer aspect computed columns are audit columns, whose most elementary implementation comes within the type of:


Different approaches to auditing exist, together with delicate deletion, further meta information, (bi)temporal versioning, however these columns are among the many hottest approaches, making this industrial solely comfort characteristic very helpful to a whole lot of prospects.

Extra about this characteristic right here

Java 17 baseline for the jOOQ Open Supply Version

Java 17 has been the newest LTS, and it consists of a whole lot of actually cool options, together with:

  • sealed varieties (important for sample matching)
  • information
  • instanceof sample matching
  • textual content blocks
  • swap expressions

jOOQ 3.16’s experimental new Question Object Mannequin (QOM) API experiments with sealed varieties, which can be adopted extra usually as soon as the QOM API is finalized.

To get broader person suggestions on these enhancements, in addition to to embrace Java’s new LTS replace cadence, we’ve determined to make Java 17 the baseline for the jOOQ 3.17 Open Supply Version, persevering with our Java 8 and 11 assist within the industrial jOOQ distributions.

The next older jOOQ releases will proceed to obtain upgrades for some time:

  • jOOQ 3.14: The final launch with Java 8 assist within the jOOQ Open Supply
    Version and Java 6 assist within the jOOQ Enterprise Version
  • jOOQ 3.15 and three.16: The final releases with Java 11 assist within the jOOQ Open
    Supply Version.

PostgreSQL information kind assist

The jooq-postgres-extensions module, which contained assist for the HSTORE kind, now has much more assist for PostgreSQL particular information varieties, together with array kinds of every of:

  • CIDR
  • INET
  • RANGE (together with all of the specialisations for INT4, INT8, and so on.)

As a way to revenue from these information varieties, simply add the org.jooq:jooq-postgres-extensions module to your code era and runtime dependencies, and the kinds are generated routinely.

Implicit JOIN enhancements

On this launch, we experimented with a number of new implicit JOIN options, together with assist for implicit JOIN in DML statements. The present implementation produces correlated subqueries the place JOIN isn’t supported in DML statements.

We’ve additionally experimented with making a “comfort syntax” for different generally used correlated subqueries, comparable to EXISTS(...) subqueries or MULTISET(...) subqueries. The experiment has been very attention-grabbing. The prototype, nevertheless, was rejected. See the discussions right here:

Future jOOQ variations will implement the specified comfort within the type of extra implicit JOIN performance, providing the characteristic additionally as an implicit to-many JOIN.

A leftover from the prototype is the truth that now you can extra simply venture expressions aside from traditional Subject<T> in your SELECT clause, specifically:

  • Desk<R> now extends SelectField<R>
  • Situation now extends Subject<Boolean>

This implies you’ll be able to write a question like this:

Outcome<Record3<CustomerRecord, AddressRecord, Boolean>> outcome =

      // Venture a CustomerRecord straight

      // Venture an AddressRecord from an implicit JOIN

      // Venture a boolean expression, as an alternative of wrapping it with area()

Sample matching SQL Transformations

SQL transformations have been a strategic characteristic set to current jOOQ releases, providing further compatibility between SQL dialects to industrial prospects, comparable to, for instance:

  • Reworking Oracle’s ROWNUM into equal window capabilities or LIMIT clauses.
  • Turning desk lists together with Oracle’s (+) operator into ANSI JOIN syntax.

This launch ships with a brand new industrial solely characteristic that straight transforms the brand new Question Object Mannequin (QOM)’s expression tree previous to rendering. It does so by making use of sample matching to the expression tree. Some assorted examples embody:

  • LTRIM(RTRIM(x)) into TRIM(x)
  • x != a AND x != b into x NOT IN (a, b)
  • x IN (a, b, c) AND x IN (b, c, d) into x IN (b, c)
  • NOT (NOT (x = 1)) into x = 1
  • NOT (x = 1) into x != 1

And way more. The first use-cases for this performance are:

  • SQL linting, e.g. as a part of an ExecuteListener
  • SQL auto cleanup, together with in a ParsingConnection
  • Dialect migration, when upgrading database variations, or transferring between dialects
  • Patching particular SQL options

For extra details about the characteristic, see right here

Observe that this characteristic can be out there free of charge on-line at https://www.jooq.org/translate

Reactive and kotlin coroutine assist

Lots of minor enhancements have been applied. Just a few extra important ones

  • R2DBC 0.9.1.RELEASE is now supported
  • A brand new reactive transaction API has been added, which gives the identical nested
    transaction semantics as the present blocking transaction API, see additionally:
  • jOOQ’s reactive streams bindings by way of the Writer SPI are actually
    bridged routinely to kotlin coroutines within the new
    org.jooq:jooq-kotlin-coroutines module utilizing the standard utilites
    org.jetbrains.kotlinx:kotlinx-coroutines-core and
  • The org.jooq:jooq-kotlin extensions module now has further
    extension capabilities for extra MULTISET and different nesting associated
  • All the blocking execution API is now annotated with
    org.jetbrains.annotations.Blocking to assist reactive jOOQ customers
    keep away from unintentionally blocking on a question, when utilizing IntelliJ. As well as, we
    now annotate experimental and inner API with the ApiStatus
    annotation from the identical bundle.

Full launch notes right here



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