SPARKSQL-使用DataFrames和JDBC更新MySql表

Question

我试图插入和更新MySql上使用SparkSQLDataFrames和JDBC连接的一些数据。

我已经成功地使用SaveMode插入新数据。追加。有没有办法从SparkSQL更新MySql Table中已经存在的数据？

我要插入的代码是：

myDataFrame。写模式（SaveMode.Append）。jdbc（JDBCurl、mySqlTable、connectionProperties）

如果我切换到保存模式。覆盖它会删除完整的表并创建一个新表，我正在寻找MySql中提供的类似“ON DUPLICATE KEY UPDATE”的内容

Answer 1

覆盖org.apache.spark.sql.execution.datasources.jdbcJdbcUtils.scala插入到以替换到

import java.sql.{Connection, Driver, DriverManager, PreparedStatement, ResultSet, SQLException}

import scala.collection.JavaConverters._
import scala.util.control.NonFatal
import com.typesafe.scalalogging.Logger
import org.apache.spark.sql.catalyst.InternalRow
import org.apache.spark.sql.execution.datasources.jdbc.{DriverRegistry, DriverWrapper, JDBCOptions}
import org.apache.spark.sql.jdbc.{JdbcDialect, JdbcDialects, JdbcType}
import org.apache.spark.sql.types._
import org.apache.spark.sql.{DataFrame, Row}

/**
  * Util functions for JDBC tables.
  */
object UpdateJdbcUtils {

  val logger = Logger(this.getClass)

  /**
    * Returns a factory for creating connections to the given JDBC URL.
    *
    * @param options - JDBC options that contains url, table and other information.
    */
  def createConnectionFactory(options: JDBCOptions): () => Connection = {
    val driverClass: String = options.driverClass
    () => {
      DriverRegistry.register(driverClass)
      val driver: Driver = DriverManager.getDrivers.asScala.collectFirst {
        case d: DriverWrapper if d.wrapped.getClass.getCanonicalName == driverClass => d
        case d if d.getClass.getCanonicalName == driverClass => d
      }.getOrElse {
        throw new IllegalStateException(
          s"Did not find registered driver with class $driverClass")
      }
      driver.connect(options.url, options.asConnectionProperties)
    }
  }

  /**
    * Returns a PreparedStatement that inserts a row into table via conn.
    */
  def insertStatement(conn: Connection, table: String, rddSchema: StructType, dialect: JdbcDialect)
  : PreparedStatement = {
    val columns = rddSchema.fields.map(x => dialect.quoteIdentifier(x.name)).mkString(",")
    val placeholders = rddSchema.fields.map(_ => "?").mkString(",")
    val sql = s"REPLACE INTO $table ($columns) VALUES ($placeholders)"
    conn.prepareStatement(sql)
  }

  /**
    * Retrieve standard jdbc types.
    *
    * @param dt The datatype (e.g. [[org.apache.spark.sql.types.StringType]])
    * @return The default JdbcType for this DataType
    */
  def getCommonJDBCType(dt: DataType): Option[JdbcType] = {
    dt match {
      case IntegerType => Option(JdbcType("INTEGER", java.sql.Types.INTEGER))
      case LongType => Option(JdbcType("BIGINT", java.sql.Types.BIGINT))
      case DoubleType => Option(JdbcType("DOUBLE PRECISION", java.sql.Types.DOUBLE))
      case FloatType => Option(JdbcType("REAL", java.sql.Types.FLOAT))
      case ShortType => Option(JdbcType("INTEGER", java.sql.Types.SMALLINT))
      case ByteType => Option(JdbcType("BYTE", java.sql.Types.TINYINT))
      case BooleanType => Option(JdbcType("BIT(1)", java.sql.Types.BIT))
      case StringType => Option(JdbcType("TEXT", java.sql.Types.CLOB))
      case BinaryType => Option(JdbcType("BLOB", java.sql.Types.BLOB))
      case TimestampType => Option(JdbcType("TIMESTAMP", java.sql.Types.TIMESTAMP))
      case DateType => Option(JdbcType("DATE", java.sql.Types.DATE))
      case t: DecimalType => Option(
        JdbcType(s"DECIMAL(${t.precision},${t.scale})", java.sql.Types.DECIMAL))
      case _ => None
    }
  }

  private def getJdbcType(dt: DataType, dialect: JdbcDialect): JdbcType = {
    dialect.getJDBCType(dt).orElse(getCommonJDBCType(dt)).getOrElse(
      throw new IllegalArgumentException(s"Can't get JDBC type for ${dt.simpleString}"))
  }

  // A `JDBCValueGetter` is responsible for getting a value from `ResultSet` into a field
  // for `MutableRow`. The last argument `Int` means the index for the value to be set in
  // the row and also used for the value in `ResultSet`.
  private type JDBCValueGetter = (ResultSet, InternalRow, Int) => Unit

  // A `JDBCValueSetter` is responsible for setting a value from `Row` into a field for
  // `PreparedStatement`. The last argument `Int` means the index for the value to be set
  // in the SQL statement and also used for the value in `Row`.
  private type JDBCValueSetter = (PreparedStatement, Row, Int) => Unit

  /**
    * Saves a partition of a DataFrame to the JDBC database.  This is done in
    * a single database transaction (unless isolation level is "NONE")
    * in order to avoid repeatedly inserting data as much as possible.
    *
    * It is still theoretically possible for rows in a DataFrame to be
    * inserted into the database more than once if a stage somehow fails after
    * the commit occurs but before the stage can return successfully.
    *
    * This is not a closure inside saveTable() because apparently cosmetic
    * implementation changes elsewhere might easily render such a closure
    * non-Serializable.  Instead, we explicitly close over all variables that
    * are used.
    */
  def savePartition(
                     getConnection: () => Connection,
                     table: String,
                     iterator: Iterator[Row],
                     rddSchema: StructType,
                     nullTypes: Array[Int],
                     batchSize: Int,
                     dialect: JdbcDialect,
                     isolationLevel: Int): Iterator[Byte] = {
    val conn = getConnection()
    var committed = false

    var finalIsolationLevel = Connection.TRANSACTION_NONE
    if (isolationLevel != Connection.TRANSACTION_NONE) {
      try {
        val metadata = conn.getMetaData
        if (metadata.supportsTransactions()) {
          // Update to at least use the default isolation, if any transaction level
          // has been chosen and transactions are supported
          val defaultIsolation = metadata.getDefaultTransactionIsolation
          finalIsolationLevel = defaultIsolation
          if (metadata.supportsTransactionIsolationLevel(isolationLevel)) {
            // Finally update to actually requested level if possible
            finalIsolationLevel = isolationLevel
          } else {
            logger.warn(s"Requested isolation level $isolationLevel is not supported; " +
              s"falling back to default isolation level $defaultIsolation")
          }
        } else {
          logger.warn(s"Requested isolation level $isolationLevel, but transactions are unsupported")
        }
      } catch {
        case NonFatal(e) => logger.warn("Exception while detecting transaction support", e)
      }
    }
    val supportsTransactions = finalIsolationLevel != Connection.TRANSACTION_NONE

    try {
      if (supportsTransactions) {
        conn.setAutoCommit(false) // Everything in the same db transaction.
        conn.setTransactionIsolation(finalIsolationLevel)
      }
      val stmt = insertStatement(conn, table, rddSchema, dialect)
      val setters: Array[JDBCValueSetter] = rddSchema.fields.map(_.dataType)
        .map(makeSetter(conn, dialect, _))
      val numFields = rddSchema.fields.length

      try {
        var rowCount = 0
        while (iterator.hasNext) {
          val row = iterator.next()
          var i = 0
          while (i < numFields) {
            if (row.isNullAt(i)) {
              stmt.setNull(i + 1, nullTypes(i))
            } else {
              setters(i).apply(stmt, row, i)
            }
            i = i + 1
          }
          stmt.addBatch()
          rowCount += 1
          if (rowCount % batchSize == 0) {
            stmt.executeBatch()
            rowCount = 0
          }
        }
        if (rowCount > 0) {
          stmt.executeBatch()
        }
      } finally {
        stmt.close()
      }
      if (supportsTransactions) {
        conn.commit()
      }
      committed = true
      Iterator.empty
    } catch {
      case e: SQLException =>
        val cause = e.getNextException
        if (cause != null && e.getCause != cause) {
          if (e.getCause == null) {
            e.initCause(cause)
          } else {
            e.addSuppressed(cause)
          }
        }
        throw e
    } finally {
      if (!committed) {
        // The stage must fail.  We got here through an exception path, so
        // let the exception through unless rollback() or close() want to
        // tell the user about another problem.
        if (supportsTransactions) {
          conn.rollback()
        }
        conn.close()
      } else {
        // The stage must succeed.  We cannot propagate any exception close() might throw.
        try {
          conn.close()
        } catch {
          case e: Exception => logger.warn("Transaction succeeded, but closing failed", e)
        }
      }
    }
  }

  /**
    * Saves the RDD to the database in a single transaction.
    */
  def saveTable(
                 df: DataFrame,
                 url: String,
                 table: String,
                 options: JDBCOptions) {
    val dialect = JdbcDialects.get(url)
    val nullTypes: Array[Int] = df.schema.fields.map { field =>
      getJdbcType(field.dataType, dialect).jdbcNullType
    }

    val rddSchema = df.schema
    val getConnection: () => Connection = createConnectionFactory(options)
    val batchSize = options.batchSize
    val isolationLevel = options.isolationLevel
    df.foreachPartition(iterator => savePartition(
      getConnection, table, iterator, rddSchema, nullTypes, batchSize, dialect, isolationLevel)
    )
  }

  private def makeSetter(
                          conn: Connection,
                          dialect: JdbcDialect,
                          dataType: DataType): JDBCValueSetter = dataType match {
    case IntegerType =>
      (stmt: PreparedStatement, row: Row, pos: Int) =>
        stmt.setInt(pos + 1, row.getInt(pos))

    case LongType =>
      (stmt: PreparedStatement, row: Row, pos: Int) =>
        stmt.setLong(pos + 1, row.getLong(pos))

    case DoubleType =>
      (stmt: PreparedStatement, row: Row, pos: Int) =>
        stmt.setDouble(pos + 1, row.getDouble(pos))

    case FloatType =>
      (stmt: PreparedStatement, row: Row, pos: Int) =>
        stmt.setFloat(pos + 1, row.getFloat(pos))

    case ShortType =>
      (stmt: PreparedStatement, row: Row, pos: Int) =>
        stmt.setInt(pos + 1, row.getShort(pos))

    case ByteType =>
      (stmt: PreparedStatement, row: Row, pos: Int) =>
        stmt.setInt(pos + 1, row.getByte(pos))

    case BooleanType =>
      (stmt: PreparedStatement, row: Row, pos: Int) =>
        stmt.setBoolean(pos + 1, row.getBoolean(pos))

    case StringType =>
      (stmt: PreparedStatement, row: Row, pos: Int) =>
        stmt.setString(pos + 1, row.getString(pos))

    case BinaryType =>
      (stmt: PreparedStatement, row: Row, pos: Int) =>
        stmt.setBytes(pos + 1, row.getAs[Array[Byte]](pos))

    case TimestampType =>
      (stmt: PreparedStatement, row: Row, pos: Int) =>
        stmt.setTimestamp(pos + 1, row.getAs[java.sql.Timestamp](pos))

    case DateType =>
      (stmt: PreparedStatement, row: Row, pos: Int) =>
        stmt.setDate(pos + 1, row.getAs[java.sql.Date](pos))

    case t: DecimalType =>
      (stmt: PreparedStatement, row: Row, pos: Int) =>
        stmt.setBigDecimal(pos + 1, row.getDecimal(pos))

    case ArrayType(et, _) =>
      // remove type length parameters from end of type name
      val typeName = getJdbcType(et, dialect).databaseTypeDefinition
        .toLowerCase.split("\\(")(0)
      (stmt: PreparedStatement, row: Row, pos: Int) =>
        val array = conn.createArrayOf(
          typeName,
          row.getSeq[AnyRef](pos).toArray)
        stmt.setArray(pos + 1, array)

    case _ =>
      (_: PreparedStatement, _: Row, pos: Int) =>
        throw new IllegalArgumentException(
          s"Can't translate non-null value for field $pos")
  }
}

用法：

val url = s"jdbc:mysql://$host/$database?useUnicode=true&characterEncoding=UTF-8"

val parameters: Map[String, String] = Map(
  "url" -> url,
  "dbtable" -> table,
  "driver" -> "com.mysql.jdbc.Driver",
  "numPartitions" -> numPartitions.toString,
  "user" -> user,
  "password" -> password
)
val options = new JDBCOptions(parameters)

for (d <- data) {
  UpdateJdbcUtils.saveTable(d, url, table, options)
}

ps：注意死锁，不要频繁更新数据，只是在紧急情况下重新运行时使用，我认为这就是spark不支持这位官员的原因。

Answer 2

遗憾的是没有保存模式。Upsert模式在Spark中用于upserting等非常常见的情况。

zero322在总体上是正确的，但我认为提供这种替换功能应该是可能的（在性能上有所妥协）。

我还想为这个案例提供一些java代码。当然，它的性能不如spark内置的，但它应该是满足您需求的良好基础。只需根据您的需要进行修改：

myDF.repartition(20); //one connection per partition, see below

myDF.foreachPartition((Iterator<Row> t) -> {
            Connection conn = DriverManager.getConnection(
                    Constants.DB_JDBC_CONN,
                    Constants.DB_JDBC_USER,
                    Constants.DB_JDBC_PASS);

            conn.setAutoCommit(true);
            Statement statement = conn.createStatement();

            final int batchSize = 100000;
            int i = 0;
            while (t.hasNext()) {
                Row row = t.next();
                try {
                    // better than REPLACE INTO, less cycles
                    statement.addBatch(("INSERT INTO mytable " + "VALUES ("
                            + "'" + row.getAs("_id") + "', 
                            + "'" + row.getStruct(1).get(0) + "'
                            + "')  ON DUPLICATE KEY UPDATE _id='" + row.getAs("_id") + "';"));
                    //conn.commit();

                    if (++i % batchSize == 0) {
                        statement.executeBatch();
                    }
                } catch (SQLIntegrityConstraintViolationException e) {
                    //should not occur, nevertheless
                    //conn.commit();
                } catch (SQLException e) {
                    e.printStackTrace();
                } finally {
                    //conn.commit();
                    statement.executeBatch();
                }
            }
            int[] ret = statement.executeBatch();

            System.out.println("Ret val: " + Arrays.toString(ret));
            System.out.println("Update count: " + statement.getUpdateCount());
            //conn.commit();

            statement.close();
            conn.close();

Answer 3

这是不可能的。目前（Spark 1.6.0/2.2.0快照）SparkDataFrameWriter仅支持四种写入模式：

• SaveMode。覆盖：覆盖现有数据

您可以手动插入，例如使用mapPartitions（因为您希望UPSERT操作应该是幂等的，因此易于实现）、写入临时表并手动执行UPSERT，或者使用触发器。

一般来说，实现批处理操作的upsert行为并保持良好的性能绝非易事。您必须记住，在一般情况下，会有多个并发事务（每个分区一个），因此您必须确保不会有写冲突（通常通过使用特定于应用程序的分区）或提供适当的恢复过程。在实践中，最好执行并批量写入临时表，并直接在数据库中解析upsert部分。