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https://github.com/mvysny/vok-orm
Mapping rows from a SQL database to POJOs in its simplest form
https://github.com/mvysny/vok-orm
database jdbc jdbc-orm-framework kotlin pojos sql-databases sql2o vok-orm
Last synced: about 2 months ago
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Mapping rows from a SQL database to POJOs in its simplest form
- Host: GitHub
- URL: https://github.com/mvysny/vok-orm
- Owner: mvysny
- License: mit
- Created: 2018-02-23T15:11:04.000Z (almost 7 years ago)
- Default Branch: master
- Last Pushed: 2024-08-23T16:11:46.000Z (4 months ago)
- Last Synced: 2024-10-24T02:26:16.070Z (about 2 months ago)
- Topics: database, jdbc, jdbc-orm-framework, kotlin, pojos, sql-databases, sql2o, vok-orm
- Language: Kotlin
- Homepage:
- Size: 572 KB
- Stars: 23
- Watchers: 3
- Forks: 4
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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# Vaadin-On-Kotlin database mapping library
`vok-orm` allows you to load the data from database rows into objects (POJOs)
and write the data back into the database. No JPA dirty tricks are used: no runtime
enhancements, no lazy loading, no `DetachedExceptions`, no change tracking
behind the scenes - everything happens explicitly. No compiler
plugin is needed - `vok-orm` uses Kotlin language features to add a standard
set of finders to your entities. You can add any number of business logic methods as
you like to your entities; the database transaction is easy to launch simply by calling the
global `db {}` function.
No dependency injection framework is required - the library works in all
sorts of environments.
> vok-orm uses the [jdbi-orm](https://gitlab.com/mvysny/jdbi-orm) and [JDBI](http://jdbi.org/) under the belt,
and introduces first-class Kotlin support on top of those frameworks.
## Supported Databases
vok-orm currently supports MySQL, MariaDB, PostgreSQL, H2, Microsoft SQL and CockroachDB.
Other databases are untested - they might or might not work.
## Usage
Just add the following lines to your Gradle script, to include this library in your project:
```groovy
repositories {
mavenCentral()
}
dependencies {
compile("com.github.mvysny.vokorm:vok-orm:x.y")
}
```
> Note: obtain the newest version from the tag name at the top of the page
Maven: (it's very simple since vok-orm is in Maven Central):
```xml
com.github.mvysny.vokorm
vok-orm
x.y
```
See the [vok-orm-playground](https://gitlab.com/mvysny/vok-orm-playground) for a very simple example project
using `vok-orm`.
Compatibility Chart:
| vok-orm version | validation API | Min. Java version |
|-----------------|--------------------|-------------------|
| 1.x | javax.validation | 8+ |
| 2.x | jakarta.validation | 11+ |
## Usage Examples
Say that we have a table containing a list of beverage categories, such as Cider or Beer. The H2 DDL for such table is simple:
```sql92
create TABLE CATEGORY (
id bigint auto_increment PRIMARY KEY,
name varchar(200) NOT NULL
);
create UNIQUE INDEX idx_category_name ON CATEGORY(name);
```
> **Note:** We expect that the programmer wants to write the DDL scripts herself, to make full
use of the DDL capabilities of the underlying database.
We will therefore not hide the DDL behind some type-safe generator API.
Such entity can be mapped to a data class as follows:
```kotlin
data class Category(override var id: Long? = null, var name: String = "") : KEntity
```
(the `id` is nullable since its value is initially `null` until the category is
actually created and the id is assigned by the database).
The `Category` class is just a simple data class: there are no hidden private fields added by
runtime enhancements, no hidden lazy loading - everything is pre-fetched upfront. Because of that,
the class can be passed around the application freely as a DTO (data transfer object),
without the fear of failing with
`DetachedException` when accessing properties. Since `Entity` is `Serializable`, you can
also store the entity into a session.
> The Category class (or any entity class for that matter) must have all fields
pre-initialized, so that Kotlin creates a zero-arg constructor.
Zero-arg constructor is mandated by JDBI, in order for JDBI to be able to construct
instances of entity class for every row returned.
By implementing the `KEntity` interface, we are telling vok-orm that the primary key is of type `Long`;
this will be important later on when using Dao.
The [KEntity](src/main/kotlin/com/github/vokorm/KEntity.kt) interface brings in three useful methods:
* `save()` which either creates a new row by generating the INSERT statement
(if the ID is null), or updates the row by generating the UPDATE statement (if the ID is not null)
* `create()` for special cases when the ID is pre-known (social security number)
and `save()` wouldn't work. More info in the 'Pre-known IDs' chapter.
* `delete()` which deletes the row identified by the `id` primary key from the database.
* `validate()` validates the bean. By default all `javax.validation` annotations
are validated; you can override this method to provide further bean-level validations.
Please read the 'Validation' chapter below, for further details.
> There are two interfaces you can use: Entity and KEntity. Both work the same way, however
Entity is tailored towards Java developers and is not as pleasant to use with Kotlin as KEntity is.
The INSERT/UPDATE statement is automatically constructed by the `save()` method,
simply by enumerating all non-transient and non-ignored properties of
the bean using reflection and fetching their values. See the [KEntity](src/main/kotlin/com/github/vokorm/KEntity.kt)
sources for more details.
You can annotate the `Category` class with the `@Table(dbname = "Categories")` annotation, to specify a different table name.
The category can now be created easily:
```kotlin
Category(name = "Beer").save()
```
But how do we specify the target database where to store the category in?
### Connecting to a database
As a bare minimum, you need to specify the JDBC URL and a couple of config parameters as follows:
```kotlin
JdbiOrm.setDataSource(HikariDataSource(HikariConfig()))
```
to the `JdbiOrm.setDataSource()` first. It's a [Hikari-CP](https://brettwooldridge.github.io/HikariCP/)
configuration file which contains lots of other options as well.
It comes pre-initialized with sensible default settings.
> Hikari-CP is a JDBC connection pool which manages a pool of JDBC connections
since they are "expensive" to create - it takes some time to establish the TCP-IP connection for example.
Typically all projects use some sort of JDBC connection pooling; we'll use Hikari-CP in this
tutorial however you can use whichever pool you wish, or no pool at all. You can also use DataSource
offered by Spring or JavaEE.
For example, to use an in-memory H2 database, just add H2 onto the classpath as
a Gradle dependency: `compile 'com.h2database:h2:1.4.196'`. Then,
configure vok-orm as follows:
```kotlin
val cfg = HikariConfig().apply {
jdbcUrl = "jdbc:h2:mem:test;DB_CLOSE_DELAY=-1"
username = "sa"
password = ""
}
JdbiOrm.setDataSource(HikariDataSource(cfg))
```
After you have configured the JDBC URL, just call `JdbiOrm.setDataSource(HikariDataSource(cfg))` which will initialize
Hikari-CP's connection pool. After the connection pool is initialized, you can simply call
the `db{}` function to run the
block in a database transaction. The `db{}` function will acquire new connection from the
connection pool; then it will start a transaction and it will provide you with means to execute SQL commands:
```kotlin
db {
handle.createUpdate("delete from Category where id = :id")
.bind("id", id)
.execute()
}
```
You can call this function from anywhere; you don't need to use dependency injection or anything like that.
That is precisely how the `save()` function saves the bean - it simply calls the `db {}` function and executes
an appropriate INSERT/UPDATE statement.
The function will automatically roll back the transaction on any exception thrown out from the block (both checked and unchecked).
After you're done, call `JdbiOrm.destroy()` to close the pool.
> You can call methods of this library from anywhere. You don't need to be running inside of the JavaEE or Spring container or
any container at all - you can actually use this library from a plain JavaSE main method.
Full example of a `main()` method that does all of the above:
```kotlin
fun main(args: Array) {
val cfg = HikariConfig().apply {
jdbcUrl = "jdbc:h2:mem:test;DB_CLOSE_DELAY=-1"
username = "sa"
password = ""
}
JdbiOrm.setDataSource(HikariDataSource(cfg))
db {
con.createQuery("create TABLE CATEGORY (id bigint auto_increment PRIMARY KEY, name varchar(200) NOT NULL );").executeUpdate()
}
db {
(0..100).forEach { Category(name = "cat $it").save() }
}
JdbiOrm.destroy()
}
```
See the [vok-orm-playground](https://gitlab.com/mvysny/vok-orm-playground)
project which contains such `main` method, all JDBC drivers pre-loaded and
simple instructions on how to query different database kinds.
> *Note*: for the sake of simplicity we're running the `CREATE TABLE` as a query. For a persistent database
it's definitely better to use [Flyway](https://flywaydb.org/) as described below.
### Finding Categories
The so-called finder (or Dao) methods actually resemble factory methods since they also produce instances of Categories. The best place for such
methods is on the `Category` class itself. We can write all of the necessary finders ourselves, by using the `db{}`
method as stated above; however vok-orm already provides a set of handy methods for you. All you need
to do is for the companion object to extend the `Dao` class:
```kotlin
data class Category(override var id: Long? = null, var name: String = "") : KEntity {
companion object : Dao(Category::class.java)
}
```
Since Category's companion object extends the `Dao` class, Category will now be outfitted
with several useful finder methods (static extension methods
that are attached to the [Dao](src/main/kotlin/com/github/vokorm/Dao.kt) interface itself):
* `Category.findAll()` will return a list of all categories
* `Category.getById(25L)` will fetch a category with the ID of 25, failing if there is no such category
* `Category.findById(25L)` will fetch a category with ID of 25, returning `null` if there is no such category
* `Category.deleteAll()` will delete all categories
* `Category.deleteById(42L)` will delete a category with ID of 42
* `Category.count()` will return the number of rows in the Category table.
* `Category.findBy { "name = :name1 or name = :name2"("name1" to "Beer", "name2" to "Cider") }` will find all categories with the name of "Beer" or "Cider".
This is an example of a parametrized select, from which you only need to provide the WHERE clause.
* `Category.deleteBy { (Category::name eq "Beer") or (Category::name eq "Cider") }` will delete all categories
matching given criteria. This is an example of a statically-typed matching criteria which
is converted into the WHERE clause.
* `Category.singleBy { "name = :name"("name" to "Beer") }` will fetch exactly one matching category, failing if there is no such category or there are more than one.
* `Category.findSingleBy { "name = :name"("name" to "Beer") }` will fetch one matching category, failing if there are more than one. Returns `null` if there is none.
* `Category.count { "name = :name"("name" to "Beer") }` will return the number of rows in the Category table matching given query.
In the spirit of type safety, the finder methods will only accept `Long` (or whatever is the type of
the primary key in the `KEntity` implementation clause).
You can of course add your own custom finder methods into the Category companion object. For example:
```kotlin
data class Category(override var id: Long? = null, var name: String = "") : KEntity {
companion object : Dao {
fun findByName(name: String): Category? = findSingleBy { Category::name eq name }
fun getByName(name: String): Category = singleBy { Category::name eq name }
fun existsWithName(name: String): Boolean = count { Category::name eq name } > 0
}
}
```
> **Note**: If you don't want to use the KEntity interface for some reason
(for example when the table has no primary key), you can still include
useful finder methods by making the companion object to implement the `DaoOfAny`
interface. The finder methods such as `findById()` will accept
`Any` as a primary key.
### Adding Reviews
Let's add the second table, the "Review" table. The Review table is a list of reviews for
various drinks; it back-references the drink category as a foreign key into the `Category` table:
```sql92
create TABLE REVIEW (
id bigint auto_increment PRIMARY KEY,
beverageName VARCHAR(200) not null,
score TINYINT NOT NULL,
date DATE not NULL,
category BIGINT,
count TINYINT not null
);
alter table Review add CONSTRAINT r_score_range CHECK (score >= 1 and score <= 5);
alter table Review add FOREIGN KEY (category) REFERENCES Category(ID);
alter table Review add CONSTRAINT r_count_range CHECK (count >= 1 and count <= 99);
create INDEX idx_beverage_name ON Review(beverageName);
```
The mapping class is as follows:
```kotlin
/**
* Represents a beverage review.
* @property score the score, 1..5, 1 being worst, 5 being best
* @property date when the review was done
* @property category the beverage category [Category.id]
* @property count times tasted, 1..99
*/
data class Review(override var id: Long? = null,
var score: Int = 1,
var beverageName: String = "",
var date: LocalDate = LocalDate.now(),
var category: Long? = null,
var count: Int = 1) : KEntity {
companion object : Dao(Review::class.java)
}
```
Now if we want to delete a category, we need to first set the `Review.category` value to `null` for all reviews that
are linked to that very category, otherwise
we will get a foreign constraint violation. It's quite easy: just override the `delete()` method in the
`Category` class as follows:
```kotlin
data class Category(/*...*/) : KEntity {
// ...
override fun delete() {
db {
if (id != null) {
handle.createQuery("update Review set category = NULL where category=:catId")
.bind("catId", id!!)
.executeUpdate()
}
super.delete()
}
}
}
```
> **Note:** for all slightly more complex queries it's a good practice to simply use the JDBI API - we will simply pass in the SQL command as a String to JDBI.
As you can see, you can use the JDBI connection yourself, to execute any kind of SELECT/UPDATE/INSERT/DELETE statements as you like.
For example you can define static finder or computation method into the `Review` companion object:
```kotlin
companion object : Dao(Review::class.java) {
/**
* Computes the total sum of [count] for all reviews belonging to given [categoryId].
* @return the total sum, 0 or greater.
*/
fun getTotalCountForReviewsInCategory(categoryId: Long): Long = db {
handle.createQuery("select sum(r.count) from Review r where r.category = :catId")
.bind("catId", categoryId)
.mapTo(Long::class.java).one() ?: 0L
}
}
```
Then we can outfit the Category itself with this functionality, by adding an extension method to compute this value:
```kotlin
fun Category.getTotalCountForReviews(): Long = Review.getTotalCountForReviewsInCategory(id!!)
```
Note how freely and simply we can add useful business logic methods to entities. It's because:
* the entities are just plain old classes with no hidden fields and no runtime enhancements, and
* because we can invoke `db{}` freely from anywhere. You don't need transaction annotations and injected entity managers,
and you don't need huge container such as Spring or JavaEE which must instantiate your classes
in order to activate those annotations and injections.
Those are things of the past.
### Auto-generated IDs vs pre-provided IDs
There are generally three cases for entity ID generation:
* IDs generated by the database when the `INSERT` statement is executed
* Natural IDs, such as a NaturalPerson with ID pre-provided by the government (social security number etc).
* IDs created by the application, for example via `UUID.randomUUID()`
The `save()` method is designed to work out-of-the-box only for the first case (IDs auto-generated by the database). In this
case, `save()` emits `INSERT` when the ID is null, and `UPDATE` when the ID is not null.
When the ID is pre-provided, you can only use `save()` method to update a row in the database; using `save()` to create a
row in the database will throw an exception. In order to create an
entity with a pre-provided ID, you need to use the `create()` method:
```kotlin
NaturalPerson(id = "12345678", name = "Albedo").create()
```
For entities with IDs created by the application you can make `save()` work properly, by overriding the `create()` method
in your entity as follows:
```kotlin
override fun create(validate: Boolean) {
id = UUID.randomUUID()
super.create(validate)
}
```
Even better, you can inherit from the `Entity` interface as follows:
```kotlin
interface UuidEntity : KEntity {
override fun create(validate: Boolean) {
id = UUID.randomUUID()
super.create(validate)
}
}
```
And simply make all of your entities implement the `UuidEntity` interface.
### Joins
When we display a list of reviews (say, in a Vaadin Grid), we want to display an actual category name instead of the numeric category ID.
We can take advantage of JDBI simply matching all SELECT column names into bean fields; all we have to do is to:
* create a new class which contains both the `Review` field and add the `categoryName` field which will carry the category name information;
* write a SELECT that will return all of the `Review` fields, and, additionally, the `categoryName` field
Let's thus create a `ReviewWithCategory` class:
```kotlin
class ReviewWithCategory : Serializable {
@field:Nested
var review: Review = Review()
@field:ColumnName("categoryName")
var categoryName: String? = null
}
```
> Note the `@ColumnName` annotation which tells vok-orm that the field is named differently in the database. Often the database naming schema
is different from Kotlin's naming schema, for example `NUMBER_OF_PETS` would be represented by the `numberOfPets` in the Kotlin class.
You can use database aliases, for example `SELECT NUMBER_OF_PETS AS numberOfPets`. However note that you can't then add a `WHERE` clause on
the `numberOfPets` alias - that's not supported by SQL databases. See [Issue #5](https://github.com/mvysny/vok-orm/issues/5) for more details.
Currently we don't use WHERE in our examples so you're free to use aliases, but aliases do not work with Data Loaders and therefore it's a good
practice to use `@ColumnName` instead of SQL aliases.
Now we can add a new finder function into `ReviewWithCategory`'s companion object:
```kotlin
companion object : DaoOfAny(ReviewWithCategory::class.java) {
//...
fun findReviews(): List = db {
handle.createQuery("""select r.*, c.name
FROM Review r left join Category c on r.category = c.id
ORDER BY r.name""")
.map(getRowMapper())
.list()
}
}
```
It also makes sense to add this function to `Review`'s companion object:
```kotlin
companion object : Dao(Review::class.java) {
//...
fun findReviews() = ReviewWithCategory.findReviews()
}
```
We can take JDBI's mapping capabilities to full use: we can craft any SELECT we want,
and then we can create a holder class that will not be an entity itself, but will merely hold the result of that SELECT.
The only thing that matters is that the class will have properties named exactly as the columns in the SELECT statement (or properly aliased
using the `@ColumnName` annotation):
```kotlin
data class Beverage(@field:ColumnName("beverageName") var name: String = "", @field:ColumnName("name") var category: String? = null) : Serializable {
companion object {
fun findAll(): List = db {
handle.createQuery("select r.beverageName, c.name from Review r left join Category c on r.category = c.id")
.map(FieldMapper.of(Beverage::class.java))
.list()
}
}
}
```
We just have to make sure that all of the `Beverage`'s fields are pre-initialized, so that the `Beverage` class has a zero-arg constructor.
If not, JDBI will throw an exception in runtime, stating that the `Beverage` class has no zero-arg constructor.
## Validations
Often the database entities are connected to UI forms which need to provide sensible
validation errors to the users as they enter invalid values. The validation
could be done on the database level, but databases tend to provide unlocalized
cryptic error messages. Also, some validations are either impossible to do, or very hard
to do on the database level. That's why `vok-orm` provides additional validation
layer for your entities.
`vok-orm` uses [JSR303 Java Standard for Validation](https://en.wikipedia.org/wiki/Bean_Validation); you can
quickly skim over [JSR303 tutorial](https://dzone.com/articles/bean-validation-made-simple) to see how to start
using the validation.
In a nutshell, you annotate your KEntity's fields with validation annotations; the fields are
then checked for valid values with the JSR303 Validator (invoked when
`entity.validate()`/`entity.save()`/`entity.create()` is called). The validation is
also mentioned in [Vaadin-on-Kotlin Forms](http://www.vaadinonkotlin.eu/forms.html) documentation.
For example:
```kotlin
data class Person(
override var id: Long? = null,
@field:NotNull
@field:Size(min = 1, max = 200)
var name: String? = null,
@field:NotNull
@field:Min(15)
@field:Max(100)
var age: Int? = null) : KEntity
val p = Person(name = "John", age = 10)
p.validate() // throws an exception since age must be at least 15
```
*Important note:* the validation is an optional feature in `vok-orm`, and by default
the validation is disabled. This fact is advertised in the `vok-orm` logs as the following message:
> JSR 303 Validator Provider was not found on your classpath, disabling entity validation
In order to activate the entity validations, you need to add a JSR303 Validation Provider jar
to your classpath. Just use Hibernate-Validator (don't worry it will not pull in Hibernate nor
JPA) and add this to your `build.gradle`:
```groovy
dependencies {
compile("org.hibernate.validator:hibernate-validator:6.0.17.Final")
// EL is required: http://hibernate.org/validator/documentation/getting-started/
compile("org.glassfish:javax.el:3.0.1-b08")
}
```
You can check out the [vok-orm-playground](https://gitlab.com/mvysny/vok-orm-playground) which
has validations enabled and all necessary jars included.
## Data Loaders
The support for [Data Loader](https://gitlab.com/mvysny/vok-dataloader) is deprecated and removed.
## Vaadin
For Vaadin integration please see [jdbi-orm-vaadin](https://gitlab.com/mvysny/jdbi-orm-vaadin)
which supports vok-orm too, and it provides support for all sorts of data providers including
entity, POJO and joins/custom SQL statements.
For Vaadin example apps, please take a look at:
* [beverage-buddy-vok](https://github.com/mvysny/beverage-buddy-vok): A full-blown Vaadin Kotlin app which demoes vok-orm-based CRUD
* [vok-security-demo](https://github.com/mvysny/vok-security-demo): uses vok-orm to load users
* [vaadin-kotlin-pwa](https://github.com/mvysny/vaadin-kotlin-pwa): demoes Vaadin+Kotlin+CRUD as well
## Full-Text Filters
In order for the `FullTextFilter` filter to work, you must create a proper full-text index
in your database for the column being matched. Please see the documentation for
individual databases below.
To customize the SQL scripts being generated, you can create a delegate
`FilterToSqlConverter` which is able to handle `FullTextFilter`s and passes through
all other filters to the default `VokOrm.filterToSqlConverter`.
The `FullTextFilter` class cleans up the user input, removes any non-alphabetic and non-digit
characters and turns user input into a set of words. The words will never contain
characters such as `+` `/` `*` which are often used by the full text engine.
Therefore, it's easy to join the words and produce a full-text query.
In order for the Filter converter to know which syntax to produce, you must set
your database variant in `VokOrm.databaseVariant`. The default one is 'Unknown'
and since there is no full-text matching in SQL92, by default all `FullTextFilter`
conversion will fail.
### H2
Full-Text searches are supported. vok-orm uses FullTextLucene implementation since
the native H2 implementation can't do partial matches (e.g. filter `car` won't match `carousel`).
The following WHERE clauses are produced by default:
```sql
$idColumn IN (SELECT CAST(FT.KEYS[1] AS BIGINT) AS ID FROM FTL_SEARCH_DATA(:$parameterName, 0, 0) FT WHERE FT.`TABLE`='${meta.databaseTableName.toUpperCase()}')
```
You need to call the following to init the engine and create the index:
```sql
CREATE ALIAS IF NOT EXISTS FTL_INIT FOR "org.h2.fulltext.FullTextLucene.init";
CALL FTL_INIT();
CALL FTL_CREATE_INDEX('PUBLIC', 'TEST', 'NAME'); -- Adds index on the 'NAME' column of the 'TEST' table.
```
Make sure to use upper-case table+column names otherwise H2 will complain that the column/table doesn't exist.
You will need to add Lucene on the classpath:
```gradle
compile("org.apache.lucene:lucene-analyzers-common:5.5.5")
compile("org.apache.lucene:lucene-queryparser:5.5.5")
```
Limitations:
* Only tables with non-composite `BIGINT` primary keys are supported. You can
lift this limitation by implementing your own `FilterToSqlConverter` and constructing
your own query.
See [H2 Full-Text search](https://www.h2database.com/html/tutorial.html#fulltext) for
more info.
### PostgreSQL
The following WHERE clauses are produced by default:
`to_tsvector('english', $databaseColumnName) @@ to_tsquery('english', 'fat:* cat:*')`.
A full-text index is not necessary in order for PostgreSQL to properly match records,
however the performance will be horrible. I recommend to create the index, e.g.
`CREATE INDEX pgweb_idx ON Test USING GIN (to_tsvector('english', name))`.
See [PostgreSQL Full-Text search](https://www.postgresql.org/docs/9.5/textsearch-tables.html#TEXTSEARCH-TABLES-INDEX)
for more info.
### MySQL/MariaDB
You'll need to create a [FULLTEXT index](https://dev.mysql.com/doc/refman/8.0/en/fulltext-search.html)
for the column, otherwise MySQL will match nothing.
By default the following WHERE clauses are produced: e.g. when searching for "fat cat",
this is emitted: `MATCH($databaseColumnName) AGAINST ("+fat* +cat*" IN BOOLEAN MODE)`.
MySQL has a number of quirks to look after:
* Sometimes MySQL will use another index instead of a full-text index: [MySQL sporadic MATCH AGAINST behaviour with unique index](https://stackoverflow.com/questions/45281641/mysql-sporadic-match-against-behaviour-with-unique-index)
Either delete the offending index, or use [NativeFilter] and `IGNORE INDEX ()`
Why the filter is using BOOLEAN mode instead of NATURAL LANGUAGE mode:
* Random treating of words as stopwords because they're present in more than 50% of the rows: [MySQL Natural Language](https://dev.mysql.com/doc/refman/5.5/en/fulltext-natural-language.html).
* No way to match word beginnings.
## Aliases
Often database columns follow different naming convention than bean fields, e.g. database `CUSTOMER_NAME` should be mapped to the
`CustomerAddress::customerName` field. The first thing to try is to use aliases in the SQL itself, for example
```sql
select c.CUSTOMER_NAME as customerName from Customer c ...;
```
The problem with this approach is twofold:
* Databases can't sort nor filter based on aliased column;
please see [Issue 5](https://github.com/mvysny/vok-orm/issues/5) for more details.
Using such queries with `SqlDataLoader` and trying to pass in filter such as `buildFilter { "customerName ILIKE cn"("cn" to "Foo%") }` will cause
the select command to fail with `SqlException`.
* INSERTs/UPDATEs issued by your entity `Dao` will fail since they will use the bean field names instead of actual column name
and will emit `INSERT INTO Customer (customerName) values ($1)` instead of `INSERT INTO Customer (CUSTOMER_NAME) values ($1)`
Therefore, instead of database-based aliases it's better to use the `@ColumnName` annotation on your beans, both natural entities
such as `Customer` and projection-only entities such as `CustomerAddress`:
```kotlin
data class Customer(@field:ColumnName("CUSTOMER_NAME") var name: String? = null) : KEntity
data class CustomerAddress(@field:ColumnName("CUSTOMER_NAME") var customerName: String? = null)
```
The `@ColumnName` annotation is honored both by `Dao`s and by all data loaders.
## A main() method Example
Using the vok-orm library from a JavaSE main method;
see the [vok-orm-playground](https://gitlab.com/mvysny/vok-orm-playground) for a very simple example project
using `vok-orm`.
```kotlin
data class Person(
override var id: Long? = null,
var name: String = "",
var age: Int = 0,
var dateOfBirth: LocalDate? = null,
var recordCreatedAt: Instant? = null
) : KEntity {
override fun save(validate: Boolean) {
if (id == null) {
recordCreatedAt = Instant.now()
}
super.save(validate)
}
companion object : Dao(Person::class.java)
}
fun main(args: Array) {
val cfg = HikariConfig().apply {
jdbcUrl = "jdbc:h2:mem:test;DB_CLOSE_DELAY=-1"
}
JdbiOrm.setDataSource(HikariDataSource(cfg))
db {
con.createQuery(
"""create table if not exists Test (
id bigint primary key auto_increment,
name varchar not null,
age integer not null,
dateOfBirth date,
recordCreatedAt timestamp
)"""
).executeUpdate()
}
// runs SELECT * FROM Person
// prints []
println(Person.findAll())
// runs INSERT INTO Person (name, age, recordCreatedAt) values (:p1, :p2, :p3)
Person(name = "John", age = 42).save()
// runs SELECT * FROM Person
// prints [Person(id=1, name=John, age=42, dateOfBirth=null, recordCreatedAt=2011-12-03T10:15:30Z)]
println(Person.findAll())
// runs SELECT * FROM Person where id=:id
// prints John
println(Person.getById(1L).name)
// mass-saves 11 persons in a single transaction.
db { (0..10).forEach { Person(name = "person $it", age = it).save() } }
JdbiOrm.destroy()
}
```
# Using Flyway to migrate the database
[Flyway](https://flywaydb.org/) is able to run DDL scripts on given database and track which scripts already ran.
This way, you can simply add more scripts and Flyway will apply them, to migrate the database to the newest version.
This even works in a cluster since Flyway will obtain a database lock, locking out other members of the cluster attempting
to upgrade.
Let's use the Category example from above. We need Flyway to run two scripts, to initialize the database:
one creates the table, while other creates the indices.
You don't need to use Flyway plugin. Just add the following Gradle dependency to your project:
```gradle
compile "org.flywaydb:flyway-core:6.0.7"
```
Flyway expects the migration scripts named in a certain format, to know the order in which to execute them.
Create the `db.migration` package in your `src/main/resources` and put two files there: the `V01__CreateCategory.sql`
file:
```sql92
create TABLE CATEGORY (
id bigint auto_increment PRIMARY KEY,
name varchar(200) NOT NULL
);
```
The next one will be `V02__CreateIndexCategoryName.sql`:
```sql92
create UNIQUE INDEX idx_category_name ON CATEGORY(name);
```
In order to run the migrations, just run the following after `JdbiOrm.setDataSource()`:
```kotlin
val flyway = Flyway()
flyway.dataSource = JdbiOrm.getDataSource()
flyway.migrate()
```
# Using with Spring or JavaEE
By default VoK-ORM connects to the JDBC database directly and uses its own instance of
Hikari-CP to pool JDBC connections. That of course doesn't work with containers such as Spring or
JavaEE which manage JDBC resources themselves.
It is very easy to use VoK-ORM with Spring or JavaEE. All you need is to obtain
an instance of `DataSource` when your server boots up, then simply set it to
JdbiOrm via `JdbiOrm.setDataSource()`. VoK-ORM will then simply poll Spring or JavaEE
DataSource for connections; Spring/JavaEE will then make sure the connections are pooled properly.
You don't even need to call `JdbiOrm.destroy()` on Spring/JavaEE app shutdown:
all `JdbiOrm.destroy()` does is that it closes the `DataSource`, however Spring/JavaEE
will do that for us.
# `vok-orm` design principles
`vok-orm` is a very simple object-relational mapping library, built around the following ideas:
* Simplicity is the most valued property; working with plain SQL commands is preferred over having a type-safe
query language. If you want a type-safe database mapping library, try [Exposed](https://github.com/JetBrains/Exposed).
* The database is the source of truth. JVM objects are nothing more than DTOs,
merely capture snapshots of the JDBC `ResultSet` rows. The entities are populated by the
means of reflection: for every column in
the JDBC `ResultSet` an appropriate setter is invoked, to populate the data.
* The entities are real POJOs: they do not track modifications, they do not automatically store modified
values back into the database. They are not runtime-enhanced and can be final.
* A switch from one type of database to another never happens. We understand that the programmer
wants to exploit the full potential of the database, by writing SQLs tailored for that particular database.
`vok-orm` should not attempt to generate SELECTs on behalf of the programmer (except for the very basic ones related to CRUD);
instead it should simply allow SELECTs to be passed as Strings, and then map the result
to an object of programmer's choosing.
As such, `vok-orm` has much in common with the [ActiveJDBC](https://github.com/javalite/activejdbc) project, in terms
of design principles. The advantage of `vok-orm` is that it doesn't require any instrumentation to work
(instead it uses Kotlin language features), and it's even simpler than ActiveJDBC.
Please read [Back to Base - make SQL great again](http://mavi.logdown.com/posts/5771422)
for the complete explanation of ideas behind this framework.
This framework uses [JDBI](http://jdbi.org/) to map data from the JDBC `ResultSet` to POJOs; in addition it provides a very simple
mechanism to store/update the data back to the database.
## Comparison with other database-related libraries
* [ActiveJDBC](https://javalite.io/activejdbc) has much in common with jdbi-orm; the advantage of jdbi-orm
is that we do not require any instrumentation to work (we use only Java language features).
* [JOOQ](https://www.jooq.org/) is great but requires initial generation of java code from your database scheme
(you write your entities by hand with jdbi-orm), and promotes type-safe query building instead of plain SQLs.
There's the usual set of problems coming with generated classes: you can't add your custom utility functions to those,
you can't add validation annotations, etc.
If you don't mind that, go for JOOQ - it's definitely more popular than jdbi-orm.
* JPA: just no. We want real POJOs, not a dynamically-enhanced thing managed by the Entity Manager. Also see below.
* Spring JdbcTemplate: not bad but it depends on Spring; jdbi-orm must be able to work on pure JVM, without Spring.
## Why not JPA
JPA promises simplicity of usage by providing an object-oriented API. However, this is achieved by
creating a *virtual object database* layer over a relational database; that creates much complexity
under the hood which leaks in various ways. In short, JPA is a double failure: it chose the wrong abstraction,
and implemented it poorly.
There are major issues in JPA which cannot be overlooked:
* [Vaadin-on-Kotlin Issue #3 Remove JPA](https://github.com/mvysny/vaadin-on-kotlin/issues/3)
* [Back to Base - make SQL great again](http://mavi.logdown.com/posts/5771422)
* [Do-It-Yourself ORM as an Alternative to Hibernate](https://blog.philipphauer.de/do-it-yourself-orm-alternative-hibernate-drawbacks/)
We strive to erase the virtual object database layer. We acknowledge the existence of
the relational database; we only provide tools to ease the use of the database from a
statically-typed OOP language.
## Running tests
Running `./gradlew` or `./gradlew test` will run all tests on all databases (given that
Docker is available on the host system). To run the tests on H2 only
(the test suite will run much faster), run with `./gradlew -Dh2only=true`
# License
Licensed under the [MIT License](https://opensource.org/licenses/MIT).
Copyright (c) 2017-2018 Martin Vysny
All rights reserved.
Permission is hereby granted, free of charge, to any person obtaining
a copy of this software and associated documentation files (the
"Software"), to deal in the Software without restriction, including
without limitation the rights to use, copy, modify, merge, publish,
distribute, sublicense, and/or sell copies of the Software, and to
permit persons to whom the Software is furnished to do so, subject to
the following conditions:
The above copyright notice and this permission notice shall be
included in all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE
LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION
OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION
WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.