Spark-Sql源码解析之二 Sqlparser：sql –> unresolved logical plan

前面章节讲解了Spark-SQL中的核心流程，接下来主要讲解如何将sql语句转化为UnResolved Logical Plan(包含UnresolvedRelation、 UnresolvedFunction、 UnresolvedAttribute)。

protected[sql] def parseSql(sql: String): LogicalPlan = {  val ret = ddlParser.parse(sql, false)  ret}private[sql] class DDLParser(    parseQuery: String => LogicalPlan)  extends AbstractSparkSQLParser with DataTypeParser with Logging {  def parse(input: String, exceptionOnError: Boolean): LogicalPlan = {    try {//先解析看看是不是DDL语句      parse(input)    } catch {      case ddlException: DDLException => throw ddlException      case _ if !exceptionOnError => parseQuery(input)//进一步解析其它类型的语句，其parseQuery为DDLParser的构造参数      case x: Throwable => throw x    }  }}protected[sql] val ddlParser = new DDLParser(sqlParser.parse(_))//其中fallback= getSQLDialect().parse(_)protected[sql] val sqlParser = new SparkSQLParser(getSQLDialect().parse(_))private[sql] class SparkSQLParser(fallback: String => LogicalPlan) extends AbstractSparkSQLParser {override protected lazy val start: Parser[LogicalPlan] = cache | uncache | set | show | others  private lazy val cache: Parser[LogicalPlan] =  CACHE ~> LAZY.? ~ (TABLE ~> ident) ~ (AS ~> restInput).? ^^ {    case isLazy ~ tableName ~ plan =>      CacheTableCommand(tableName, plan.map(fallback), isLazy.isDefined)  }private lazy val uncache: Parser[LogicalPlan] =  ( UNCACHE ~ TABLE ~> ident ^^ {      case tableName => UncacheTableCommand(tableName)    }  | CLEAR ~ CACHE ^^^ ClearCacheCommand  )private lazy val set: Parser[LogicalPlan] =  SET ~> restInput ^^ {    case input => SetCommandParser(input)  }private lazy val show: Parser[LogicalPlan] =  SHOW ~> TABLES ~ (IN ~> ident).? ^^ {    case _ ~ dbName => ShowTablesCommand(dbName)  }private lazy val others: Parser[LogicalPlan] =  wholeInput ^^ {    case input => fallback(input)//select语句利用fallback解析  }}//继续往下追踪getSQLDialect().parse(_)就是DefaultParserDialect.parse(_)private[spark] class DefaultParserDialect extends ParserDialect {  @transient  protected val sqlParser = new SqlParser  override def parse(sqlText: String): LogicalPlan = {    sqlParser.parse(sqlText)  }}

关键就是SqlParser

class SqlParser extends AbstractSparkSQLParser with DataTypeParser {protected lazy val select: Parser[LogicalPlan] =  SELECT ~> DISTINCT.? ~    repsep(projection, ",") ~    (FROM   ~> relations).? ~    (WHERE  ~> expression).? ~    (GROUP  ~  BY ~> rep1sep(expression, ",")).? ~    (HAVING ~> expression).? ~    sortType.? ~    (LIMIT  ~> expression).? ^^ {      case d ~ p ~ r ~ f ~ g ~ h ~ o ~ l =>//解析顺序为r，f，g，p，d，h，o，l        val base = r.getOrElse(OneRowRelation)        val withFilter = f.map(Filter(_, base)).getOrElse(base)        val withProjection = g          .map(Aggregate(_, assignAliases(p), withFilter))          .getOrElse(Project(assignAliases(p), withFilter))        val withDistinct = d.map(_ => Distinct(withProjection)).getOrElse(withProjection)        val withHaving = h.map(Filter(_, withDistinct)).getOrElse(withDistinct)        val withOrder = o.map(_(withHaving)).getOrElse(withHaving)        val withLimit = l.map(Limit(_, withOrder)).getOrElse(withOrder)        withLimit    }}

比方说：

Sql语句为：

SELECT id,dev_chnid,dev_chnname,car_num,car_speed,car_direct fromtest where id > 1 group by dev_chnid sort by car_num

unresolvedlogical plan为：

['Sort ['car_num ASC], false//最后是o

 'Aggregate ['dev_chnid],['id,'dev_chnid,'dev_chnname,'car_num,'car_speed,'car_direct]//接着是g

  'Filter ('id > 1)//然后f

   'UnresolvedRelation [test],None//先解析r

]

可见其unresolvedlogical plan的语法树是根据select语句的解析顺序生成的。