org.apache.spark.sql.jdbc.JdbcDialects.scala Maven / Gradle / Ivy
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* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You under the Apache License, Version 2.0
* (the "License"); you may not use this file except in compliance with
* the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
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* See the License for the specific language governing permissions and
* limitations under the License.
*/
package org.apache.spark.sql.jdbc
import java.sql.Connection
import org.apache.spark.annotation.DeveloperApi
import org.apache.spark.sql.types._
/**
* :: DeveloperApi ::
* A database type definition coupled with the jdbc type needed to send null
* values to the database.
* @param databaseTypeDefinition The database type definition
* @param jdbcNullType The jdbc type (as defined in java.sql.Types) used to
* send a null value to the database.
*/
@DeveloperApi
case class JdbcType(databaseTypeDefinition : String, jdbcNullType : Int)
/**
* :: DeveloperApi ::
* Encapsulates everything (extensions, workarounds, quirks) to handle the
* SQL dialect of a certain database or jdbc driver.
* Lots of databases define types that aren't explicitly supported
* by the JDBC spec. Some JDBC drivers also report inaccurate
* information---for instance, BIT(n>1) being reported as a BIT type is quite
* common, even though BIT in JDBC is meant for single-bit values. Also, there
* does not appear to be a standard name for an unbounded string or binary
* type; we use BLOB and CLOB by default but override with database-specific
* alternatives when these are absent or do not behave correctly.
*
* Currently, the only thing done by the dialect is type mapping.
* `getCatalystType` is used when reading from a JDBC table and `getJDBCType`
* is used when writing to a JDBC table. If `getCatalystType` returns `null`,
* the default type handling is used for the given JDBC type. Similarly,
* if `getJDBCType` returns `(null, None)`, the default type handling is used
* for the given Catalyst type.
*/
@DeveloperApi
abstract class JdbcDialect extends Serializable {
/**
* Check if this dialect instance can handle a certain jdbc url.
* @param url the jdbc url.
* @return True if the dialect can be applied on the given jdbc url.
* @throws NullPointerException if the url is null.
*/
def canHandle(url : String): Boolean
/**
* Get the custom datatype mapping for the given jdbc meta information.
* @param sqlType The sql type (see java.sql.Types)
* @param typeName The sql type name (e.g. "BIGINT UNSIGNED")
* @param size The size of the type.
* @param md Result metadata associated with this type.
* @return The actual DataType (subclasses of [[org.apache.spark.sql.types.DataType]])
* or null if the default type mapping should be used.
*/
def getCatalystType(
sqlType: Int, typeName: String, size: Int, md: MetadataBuilder): Option[DataType] = None
/**
* Retrieve the jdbc / sql type for a given datatype.
* @param dt The datatype (e.g. [[org.apache.spark.sql.types.StringType]])
* @return The new JdbcType if there is an override for this DataType
*/
def getJDBCType(dt: DataType): Option[JdbcType] = None
/**
* Quotes the identifier. This is used to put quotes around the identifier in case the column
* name is a reserved keyword, or in case it contains characters that require quotes (e.g. space).
*/
def quoteIdentifier(colName: String): String = {
s""""$colName""""
}
/**
* Get the SQL query that should be used to find if the given table exists. Dialects can
* override this method to return a query that works best in a particular database.
* @param table The name of the table.
* @return The SQL query to use for checking the table.
*/
def getTableExistsQuery(table: String): String = {
s"SELECT * FROM $table WHERE 1=0"
}
/**
* Override connection specific properties to run before a select is made. This is in place to
* allow dialects that need special treatment to optimize behavior.
* @param connection The connection object
* @param properties The connection properties. This is passed through from the relation.
*/
def beforeFetch(connection: Connection, properties: Map[String, String]): Unit = {
}
}
/**
* :: DeveloperApi ::
* Registry of dialects that apply to every new jdbc [[org.apache.spark.sql.DataFrame]].
*
* If multiple matching dialects are registered then all matching ones will be
* tried in reverse order. A user-added dialect will thus be applied first,
* overwriting the defaults.
*
* Note that all new dialects are applied to new jdbc DataFrames only. Make
* sure to register your dialects first.
*/
@DeveloperApi
object JdbcDialects {
/**
* Register a dialect for use on all new matching jdbc [[org.apache.spark.sql.DataFrame]].
* Reading an existing dialect will cause a move-to-front.
*
* @param dialect The new dialect.
*/
def registerDialect(dialect: JdbcDialect) : Unit = {
dialects = dialect :: dialects.filterNot(_ == dialect)
}
/**
* Unregister a dialect. Does nothing if the dialect is not registered.
*
* @param dialect The jdbc dialect.
*/
def unregisterDialect(dialect : JdbcDialect) : Unit = {
dialects = dialects.filterNot(_ == dialect)
}
private[this] var dialects = List[JdbcDialect]()
registerDialect(MySQLDialect)
registerDialect(PostgresDialect)
registerDialect(DB2Dialect)
registerDialect(MsSqlServerDialect)
registerDialect(DerbyDialect)
registerDialect(OracleDialect)
/**
* Fetch the JdbcDialect class corresponding to a given database url.
*/
private[sql] def get(url: String): JdbcDialect = {
val matchingDialects = dialects.filter(_.canHandle(url))
matchingDialects.length match {
case 0 => NoopDialect
case 1 => matchingDialects.head
case _ => new AggregatedDialect(matchingDialects)
}
}
}
/**
* NOOP dialect object, always returning the neutral element.
*/
private object NoopDialect extends JdbcDialect {
override def canHandle(url : String): Boolean = true
}