add some additional material to doc
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@ -351,13 +351,14 @@ logger.hibernate.name = org.hibernate.SQL
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logger.hibernate.level = debug
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logger.hibernate.level = debug
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----
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----
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You can make the logged SQL more readable by enabling one or both of the following settings:
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You can make the SQL logged to the console more readable by enabling formatting or highlighting.
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.Setting for SQL logging to the console
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.Settings for SQL logging to the console
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[cols=",2"]
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[cols=",2"]
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|===
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|===
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| Configuration property name | Purpose
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| Configuration property name | Purpose
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| `hibernate.show_sql` | If `true`, log SQL directly to the console
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| `hibernate.format_sql` | If `true`, log SQL in a multiline, indented format
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| `hibernate.format_sql` | If `true`, log SQL in a multiline, indented format
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| `hibernate.highlight_sql` | If `true`, log SQL with syntax highlighting via ANSI escape codes
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| `hibernate.highlight_sql` | If `true`, log SQL with syntax highlighting via ANSI escape codes
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|===
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|===
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@ -391,7 +392,7 @@ Please refer to the Javadoc for these interfaces for more information about the
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=== Nationalized character data in SQL Server
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=== Nationalized character data in SQL Server
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_By default,_ SQL Server's `char` and `varchar` types don't accommodate Unicode data. So, if you're working with SQL Server, you might need to force Hibernate to use the `nchar` and `nvarchar` types.
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_By default,_ SQL Server's `char` and `varchar` types don't accommodate Unicode data. But a Java string may contain any Unicode character. So, if you're working with SQL Server, you might need to force Hibernate to use the `nchar` and `nvarchar` column types.
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.Setting the use of nationalized character data
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.Setting the use of nationalized character data
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[cols=",2"]
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[cols=",2"]
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@ -406,3 +407,6 @@ _By default,_ SQL Server's `char` and `varchar` types don't accommodate Unicode
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====
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====
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Alternatively, you can configure SQL Server to use the UTF-8 enabled collation `_UTF8`.
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Alternatively, you can configure SQL Server to use the UTF-8 enabled collation `_UTF8`.
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====
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====
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On the other hand, if only _some_ columns store nationalized data, use the `@Nationalized` annotation to indicate fields of your entities which map these columns.
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@ -897,6 +897,26 @@ This is quite normal.
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A one-to-one association is the usual way we implement subtyping in a fully-normalized relational model.
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A one-to-one association is the usual way we implement subtyping in a fully-normalized relational model.
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====
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====
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There are three annotations for mapping associations: `@ManyToOne`, `@OneToMany`, and `@ManyToMany`.
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They share some common annotation members:
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.Association-defining annotation members
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[cols=",4,3"]
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|===
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| Member | Interpretation | Default value
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| `cascade` | Persistence operations which should <<cascade,cascade>> to the associated entity (a list of ``CascadeType``s) | `{}`
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| `fetch` | Whether the association is eagerly <<association-fetching,fetched>> or may be <<proxies-and-lazy-fetching,proxied>>
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a|
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- `LAZY` for `@OneToMany` and `@ManyToMany`
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- `EAGER` for `@ManyToOne` 💀💀💀
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| `targetEntity` | The associated entity class | Determined from the attribute type declaration
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| `optional` | For `@ManyToOne` or `@OneToOne` associations, whether the association can be `null` | `true`
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| `mappedBy` | For a bidirectional association, an attribute of the associated entity which maps the association | By default, the association is assumed unidirectional
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|===
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We'll explain the effect of these members as we consider the various types of association mapping.
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Let's begin with the most common association multiplicity.
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Let's begin with the most common association multiplicity.
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[[many-to-one]]
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[[many-to-one]]
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@ -652,3 +652,38 @@ InputStream bytes = book.images.getBinaryStream();
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----
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----
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Of course, the behavior here depends very much on the JDBC driver, and so we really can't promise that this is a sensible thing to do on your database.
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Of course, the behavior here depends very much on the JDBC driver, and so we really can't promise that this is a sensible thing to do on your database.
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[[mapping-formulas]]
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=== Mapping to formulas
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Hibernate lets us map an attribute of an entity to a SQL formula involving columns of the mapped table.
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Thus, the attribute is a sort of "derived" value.
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.Annotations for mapping formulas
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[cols=",5"]
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|===
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| Annotation | Purpose
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| `@Formula` | Map an attribute to a SQL formula
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| `@JoinFormula` | Map an association to a SQL formula
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| `@DiscriminatorFormula` | Use a SQL formula as the discriminator in <<mapping-inheritance,single table inheritance>>.
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|===
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For example:
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[source,java]
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----
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@Entity
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class Order {
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...
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@Column(name = "sub_total", scale=2, precision=8)
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BigDecimal subTotal;
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@Column(name = "tax", scale=4, precision=4)
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BigDecimal taxRate;
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@Formula("sub_total * (1.0 + tax)")
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BigDecimal totalWithTax;
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...
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}
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----
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@ -394,6 +394,28 @@ List<Publisher> allpubs =
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----
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----
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====
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====
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[TIP]
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====
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For "reference" data, that is, for data which is expected to always be found in the second-level cache, it's a good idea to _prime_ the cache at startup.
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There's a really easy way to do this: just execute a query immediately after obtaining the
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`EntityManager` or `SessionFactory`.
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[source,java]
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----
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SessionFactory sf = setupHibernate(new Configuration()).buildSessionFactory();
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// prime the second-level cache
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sf.inSession(s -> {
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s.createSelectionQuery("from Countries"))
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.setReadOnly(true)
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.getResultList();
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s.createSelectionQuery("from Product where discontinued = false"))
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.setReadOnly(true)
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.getResultList();
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});
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----
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====
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Very occasionally, it's necessary or advantageous to control the cache explicitly, for example, to evict some data that we know to be stale.
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Very occasionally, it's necessary or advantageous to control the cache explicitly, for example, to evict some data that we know to be stale.
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The `Cache` interface allows programmatic eviction of cached items.
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The `Cache` interface allows programmatic eviction of cached items.
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