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。
原创文章,作者:ItWorker,如若转载,请注明出处:https://blog.ytso.com/tech/bigdata/9312.html