#Licensed Materials - Property of IBM # #OCO Source Materials # #BI and PM: rdbmscert # #(C) Copyright IBM Corp. 2009,2016 # #US Government Users Restricted Rights - Use, duplication or disclosure restricted by GSA ADP Schedule Contract with IBM corp. #This properties file contains default configuration attributes for all # #relational data sources. Any data source that is different must override # #the value in their own properties file. # # # Delimiters # # While a vendor may parse a statement with comments it may strip them out and the server not see them delimiters.commentBegin=/* delimiters.commentEnd=*/ # delimiters.catalogDelimiter= delimiters.literalQuoteEscape=\\' # delimiters.schemaDelimiter= # delimiters.tableDelimiter= # delimiters.columnDelimiter= # delimiters.identifierQuoteString= # delimiters.searchStringEscape= # delimiters.catalogSeparator= # # Keywords # keywords.columnAlias=AS # # Limits # # Normally these values would be derived from the JDBC driver DatabaseMetadata limits.defaultTransactionIsolation= # # General # # # Null ordering # # Impala 1.2.x does not allow an order by statement without a LIMIT clause so cannot # push an ORDER BY down without a LIMIT clause general.nullsAreSortedHigh=false general.nullsAreSortedLow=false general.nullsAreSortedAtStart=false general.nullsAreSortedAtEnd=false general.nullsOrdering=false general.nullsOrderingInWindowSpecification=false # supports.hints=false # # Various # supports.duplicateColumnsInSelectList=true supports.columnAliasing=true supports.tableCorrelationNames=true # Does not allow ordering on non project column supports.expressionsInSelectList=true supports.expressionsInINPredicate=true supports.expressionsInOrderBy=false supports.booleanExpressionsInSelectList=true supports.likeEscapeClause=false supports.outerJoins=false supports.fullOuterJoins=true # Subqueries not supported in Having # Subqueries not supported in Group-by # Subquery column alias not supported supports.subqueriesInComparisons=false supports.subqueriesInExists=false supports.subqueriesInIns=false supports.subqueriesInQuantifieds=false supports.subqueriesInCase=false supports.correlatedSubqueries=false supports.correlatedSubqueriesInSelectList=false supports.correlatedSubqueriesInIns=false supports.scalarSubqueries=false supports.withClauseInDerivedTable=false supports.nestedWithClause=false supports.integerDivision=false supports.nestedOlap=false supports.derivedColumnLists=false supports.orderByAlias=false supports.orderByName=false supports.orderByOrdinal=false supports.blobsInGroupBy=false supports.blobsInOrderBy=false supports.emptyStringIsNull=false supports.expressionsInGroupBy=true supports.expressionsInOrderBy=false supports.aliasInOrderByExpression=true supports.orderByUnrelated=false supports.groupByUnrelated=false supports.multipleDistinctAggregates=false supports.recursiveWithClause=false supports.crossProducts=false supports.callProcedureInDerivedTable=false # because Impala does not support A = B or (A is null and B is null) and # current limited theta join transformation is only for inner joins we will prevent # pushing this pattern supports.join.full.distinctJoins=false supports.join.subqueriesInOnClause=false # Indicates whether inner joins require at least one equijoin predicate. # A INNER JOIN B ON A.C1 = B.C1 AND A.C2 > B.C2 is fine, but A INNER JOIN B ON A.C2 > B.C2 is not. supports.join.inner.limitedThetaJoins=true # # Grouping query optimization # # If the RDBMS has costing/execution issues with group by or distinct consider these transforms performance.convertGroupByToDistinct=false performance.convertDistinctToGroupBy=false # V5 master-detail optimization when allRows optimization is specified v5.master-detail.transform=false performance.convertHavingToWhere=false performance.transitiveClosure=false performance.predicatePushdown=false performance.semiJoin=false # # Commands # commands.Select=SELECT commands.Call= # # Tables # tables.joined=false tables.derived=true tables.lateral.derived=false # # Constructors # constructors.table=false constructors.row=false constructors.array=true constructors.period=false constructors.map=false # # Constructors - context overrides. # constructors.row.simpleCase=false constructors.row.between=false # # Clauses # clauses.From=FROM clauses.Where=WHERE clauses.GroupBy=GROUP BY clauses.Having=HAVING # Does not allow column list in common table expression # Recursive form of common table expression not supported clauses.WithRecursive= clauses.With= # Ordering of data will be performed locally which may impact performance clauses.OrderBy= clauses.Distinct=DISTINCT # Limit and Fetch First supported in 10.2 RP1 or higher clauses.Top= clauses.At= clauses.Window= clauses.TableSampleSystem= clauses.TableSampleBernoulli= clauses.ForSystemTimeAsOf= clauses.ForSystemTimeFrom= clauses.ForSystemTimeBetween= # # Joins # # Impala requires that joins include at least one equi-join. There is no transformation # to dynamically determine if a join predicate does not include an equi-join and decompose # accordingly etc. joins.Cross=%1$s CROSS JOIN %2$s joins.Inner=%1$s INNER JOIN %2$s ON %3$s joins.LeftOuter=%1$s LEFT OUTER JOIN %2$s ON %3$s joins.RightOuter=%1$s RIGHT OUTER JOIN %2$s ON %3$s joins.FullOuter=%1$s FULL OUTER JOIN %2$s ON %3$s joins.RightNested=%1$s # # Set Operators # operators.set.Union=%1$s UNION %2$s operators.set.Union.all=%1$s UNION ALL %2$s operators.set.Intersect= operators.set.Intersect.all= operators.set.Except= operators.set.Except.all= # # Logical Operators # operators.logical.And=%1$s AND %2$s operators.logical.Or=%1$s OR %2$s operators.logical.Not=NOT ( %1$s ) operators.logical.Is= operators.logical.IsNot= operators.logical.IsJson= operators.logical.IsNotJson= # # Arithmetic and Character operators # operators.arithmetic.Add[any,any]=%1$s + %2$s operators.arithmetic.Add[timestamp,any]= operators.arithmetic.Add[any,timestamp]= operators.arithmetic.Subtract[any, any]=%1$s - %2$s operators.arithmetic.Subtract[timestamp,any]= operators.arithmetic.Subtract[any,timestamp]= operators.arithmetic.Multiply[any,any]=%1$s * %2$s operators.arithmetic.UnaryPlus[any]=+%1$s operators.arithmetic.Negate[any]=-%1$s operators.arithmetic.Concat[any,any]=concat(%1$s, %2$s) # # Grouping Operators # operators.groupBy.Rollup= operators.groupBy.Cube= operators.groupBy.GroupingSets= # # Comparison Predicates # predicates.comparison.Equals[any,any]=%1$s = %2$s predicates.comparison.GreaterThan[any,any]=%1$s > %2$s predicates.comparison.GreaterThanOrEquals[any,any]=%1$s >= %2$s predicates.comparison.LessThan[any,any]=%1$s < %2$s predicates.comparison.LessThanOrEquals[any,any]=%1$s <= %2$s predicates.comparison.NotEquals[any,any]=%1$s <> %2$s # # Predicates # predicates.Between[any,any,any]= predicates.In[any,any]=%1$s IN ( %2$s ) predicates.Overlaps[any,any,any,any]= predicates.IsNull=%1$s IS NULL predicates.IsNotNull=%1$s IS NOT NULL predicates.Like=%1$s LIKE %2$s predicates.Like.escape= predicates.LikeRegex= predicates.LikeRegex.flag= predicates.Similar= predicates.Similar.escape= predicates.Exists= predicates.All= predicates.Any= predicates.Some= predicates.IsDistinctFrom[any,any]=(%1$s IS NULL AND %2$s IS NOT NULL) OR (%1$s IS NOT NULL AND %2$s IS NULL) OR %1$s <> %2$s predicates.IsNotDistinctFrom[any,any]=(%1$s = %2$s OR (%1$s IS NULL AND %2$s IS NULL)) # # Period predicates. # predicates.PeriodOverlaps[any,any]= predicates.PeriodEquals[any,any]= predicates.PeriodContains[any,any]= predicates.PeriodPrecedes[any,any]= predicates.PeriodSucceeds[any,any]= predicates.PeriodImmediatelyPrecedes[any,any]= predicates.PeriodImmediatelySucceeds[any,any]= # # Expressions # expressions.ArrayElementRef.zeroBased=false # # Conditional expressions # expressions.SimpleCase=CASE expressions.SearchedCase=CASE # note impala does not align to ISO SQL Result of data type combinations in terms of the returned data type # and will disallow combinations of types unless explicitly cast as an input parameter expressions.Coalesce= expressions.NullIf=CASE WHEN %1$s = %2$s THEN NULL ELSE %1$s END # # Cast # expressions.Cast[any,any]=CAST(%1$s AS %2$s) # Impala does not support CHAR and VARCHAR as an explicit type with enforced precision so cannot # push such a CAST down expressions.Cast[any,varchar]= expressions.Cast[any,char]= # Impala does not accept double precision it requires double as the type expressions.Cast[any,double]=CAST(%1$s AS double) # # Extract # expressions.Extract.YEAR[any]=YEAR(%1$s) expressions.Extract.MONTH[any]=MONTH(%1$s) expressions.Extract.DAY[any]=DAY(%1$s) expressions.Extract.HOUR[any]=HOUR(%1$s) expressions.Extract.MINUTE[any]=MINUTE(%1$s) # Impala second scalar does not return fractional seconds expressions.Extract.SECOND[any]= expressions.Extract.TIMEZONE_HOUR[any]= expressions.Extract.TIMEZONE_MINUTE[any]= expressions.Extract.EPOCH[any]= # # Trim # expressions.Trim.BOTH[any]=TRIM(%1$s) expressions.Trim.BOTH[any,any]= expressions.Trim.LEADING[any]=LTRIM(%1$s) expressions.Trim.LEADING[any,any]= expressions.Trim.TRAILING[any]=RTRIM(%1$s) expressions.Trim.TRAILING[any,any]= # # Windowed aggregates (SQL/OLAP). # olap.Max[any]= olap.Min[any]= olap.Sum[any]= olap.Avg[any]= olap.Count[any]= olap.CountStar[]= olap.StdDevPop[any]= olap.StdDevSamp[any]= olap.VarPop[any]= olap.VarSamp[any]= olap.Rank[]= olap.DenseRank[]= olap.PercentRank[]= olap.CumeDist[]= olap.PercentileCont[any,any]= olap.PercentileDisc[any,any]= olap.Median[any]= olap.RatioToReport[any]= olap.RowNumber[]= olap.Difference[any]= olap.FirstValue[any]= olap.LastValue[any]= olap.NTile[any]= olap.NthValue[any,any]= olap.NthValue[any,any,any]= olap.NthValue[any,any,any,any]= olap.Tertile[]= olap.Lag[any]= olap.Lag[any,any]= olap.Lag[any,any,any]= olap.Lag[any,any,any,any]= olap.Lead[any]= olap.Lead[any,any]= olap.Lead[any,any,any]= olap.Lead[any,any,any,any]= olap.Collect[any]= # # Window clause # olap.Window= olap.PartitionBy= olap.OrderBy= # # Window specification # olap.Window.Specification[POF]=false olap.Window.Specification[PF]=false olap.Window.Specification[OF]=false olap.Window.Specification[PO]=false olap.Window.Specification[P]=false olap.Window.Specification[O]=false olap.Window.Specification[F]=false olap.Window.Specification[]=false # # Olap Distinct # olap.Min.distinct[any]= olap.Max.distinct[any]= olap.Sum.distinct[any]= olap.Avg.distinct[any]= olap.Count.distinct[any]= # # Aggregates # aggregates.Max[any]=MAX(%1$s) aggregates.Min[any]=MIN(%1$s) aggregates.Count[any]=COUNT(%1$s) aggregates.CountStar[]=COUNT(*) aggregates.Sum[any]=SUM(%1$s) aggregates.Avg[any]=AVG(%1$s) aggregates.StdDevPop[any]= aggregates.StdDevSamp[any]= aggregates.VarPop[any]= aggregates.VarSamp[any]= aggregates.Rank[any,any]= aggregates.DenseRank[any,any]= aggregates.PercentRank[any,any]= aggregates.CumeDistH[any,any]= aggregates.PercentileDisc[any,any]= aggregates.PercentileCont[any,any]= aggregates.Median[any]= aggregates.Grouping[any]= aggregates.XMLAgg= aggregates.ArrayAgg[any]= aggregates.ArrayAgg[any,any]= aggregates.Collect[any]= # # Distinct aggregates # aggregates.Avg.distinct[any]=AVG(DISTINCT %1$s) aggregates.Min.distinct[any]=MIN(DISTINCT %1$s) aggregates.Max.distinct[any]=MAX(DISTINCT %1$s) aggregates.Count.distinct[any]=COUNT(DISTINCT %1$s) aggregates.Sum.distinct[any]=SUM(DISTINCT %1$s) # # Linear regression aggregates # aggregates.Corr[any,any]= aggregates.CovarPop[any,any]= aggregates.CovarSamp[any,any]= aggregates.RegrAvgX[any,any]= aggregates.RegrAvgY[any,any]= aggregates.RegrCount[any,any]= aggregates.RegrIntercept[any,any]= aggregates.RegrR2[any,any]= aggregates.RegrSlope[any,any]= aggregates.RegrSXX[any,any]= aggregates.RegrSXY[any,any]= aggregates.RegrSYY[any,any]= # # JSON aggregates. # aggregates.JSONArrayAgg= aggregates.JSONObjectAgg= # # Character scalar functions # functions.CharLength[any]=LENGTH(%1$s) functions.OctetLength[any]= functions.BitLength[any]= functions.Upper[any]=UPPER(%1$s) functions.Lower[any]=LOWER(%1$s) functions.Substring[any,any]=SUBSTR(%1$s, CAST(%2$s AS INTEGER)) functions.Substring[any,any,any]=SUBSTR(%1$s, CAST(%2$s AS INTEGER), CAST(%3$s AS INTEGER)) functions.SubstringR[any,any]= functions.SubstringR[any,any,any]= functions.Position[any,any]=INSTR(%1$s, %2$s) functions.Index[any,any]= functions.Ascii[any]= functions.Translate[any,any]= functions.Normalize[any]= functions.Normalize[any,any]= functions.Normalize[any,any,any]= # # Regular expression functions. # functions.SubstringRegex[any,any,any,any,any]= functions.OccurrencesRegex[any,any,any,any]= functions.PositionRegex[any,any,any,any,any,any]= # # Numeric scalar functions # functions.Abs[any]=ABS(%1$s) functions.Ceiling[any]=CEILING(%1$s) functions.Exp[any]=EXP(%1$s) functions.Floor[any]=FLOOR(%1$s) # Ln failed exception cases functions.Ln[any]=LN(%1$s) functions.Log10[any]=LOG10(%1$s) functions.Mod[any,any]= # Power failed exception cases functions.Power[any,any]=POWER(%1$s, %2$s) functions.Round[any]=ROUND(%1$s) functions.Round[any,any]= functions.Round[any,any,any]= functions.Sign[any]=SIGN(%1$s) # Sqrt failed exception cases functions.Sqrt[any]=SQRT(%1$s) functions.WidthBucket[any,any,any,any]= # # Array scalar functions # functions.Cardinality[any]= functions.TrimArray[any,any]= # # Trig Functions # functions.Arccos[any]=ACOS(%1$s) functions.Cos[any]=COS(%1$s) functions.Coshyp[any]= functions.Arcsin[any]=ASIN(%1$s) functions.Sin[any]=SIN(%1$s) functions.Sinhyp[any]= functions.Arctan[any]=ATAN(%1$s) functions.Tan[any]=TAN(%1$s) functions.Tanhyp[any]= # # Temporal value expressions # # Note: JDBC does not define fractional seconds for TIME data type. functions.CurrentDate[]= functions.CurrentTime[]= functions.CurrentTimestamp[]= functions.CurrentTime[numeric]= functions.CurrentTimestamp[numeric]= functions.LocalTime[]= functions.LocalTime[numeric]= functions.LocalTimestamp[]=now() functions.LocalTimestamp[numeric]= # # XML Functions # functions.XMLAttributes= functions.XMLComment= functions.XMLConcat= functions.XMLDocument= functions.XMLElement= functions.XMLExists= functions.XMLForest= functions.XMLNamespaces= functions.XMLParse= functions.XMLPI= functions.XMLQuery= functions.XMLSerialize= functions.XMLTable= functions.XMLText= functions.XMLTransform= functions.XMLValidate= functions.XMLElement.ContentOption.NULL_ON_NULL=false functions.XMLElement.ContentOption.EMPTY_ON_NULL=false functions.XMLForest.ContentOption.NULL_ON_NULL=false functions.XMLForest.ContentOption.EMPTY_ON_NULL=false functions.XMLParse.DocumentOrContent.DOCUMENT=false functions.XMLParse.DocumentOrContent.CONTENT=false functions.XMLParse.WhitespaceOption.STRIP_WHITESPACE=false functions.XMLParse.WhitespaceOption.PRESERVE_WHITESPACE=false functions.XMLQuery.EmptyHandlingOption.NULL_ON_EMPTY=false functions.XMLQuery.EmptyHandlingOption.EMPTY_ON_EMPTY=false functions.XMLSerialize.DeclarationOption.INCLUDING_XMLDECLARATION=false functions.XMLSerialize.DeclarationOption.EXCLUDING_XMLDECLARATION=false # # JSON functions. # functions.JSONArray= functions.JSONExists= functions.JSONObject= functions.JSONQuery= functions.JSONTable= functions.JSONValue= # # Business functions. # functions.AddHours[any,any]=(%1$s + interval cast(%2$s as integer) hours) functions.AddMinutes[any,any]=(%1$s + interval cast(%2$s as integer) minutes) functions.AddSeconds[any,any]=(%1$s + interval cast(%2$s as integer) seconds) functions.AddFractionalSeconds[any,any]= functions.AddDays[any,any]=(%1$s + interval cast(%2$s as integer) day) functions.AddWeeks[any,any]=(%1$s + interval ( cast(%2$s as integer) * 7 ) day) functions.AddMonths[any,any]=(%1$s + interval cast(%2$s as integer) month) functions.AddQuarters[any,any]=(%1$s + interval( cast(%2$s as integer) * 3 ) month) functions.AddYears[any,any]=(%1$s + interval cast(%2$s as integer) year) functions.Age[any]= functions.FractionalSecondsBetween[any,any]= functions.SecondsBetween[any,any]= functions.MinutesBetween[any,any]= functions.HoursBetween[any,any]= functions.DaysBetween[any,any]=datediff(%1$s, %2$s) functions.WeeksBetween[any,any]= functions.MonthsBetween[any,any]= functions.QuartersBetween[any,any]= functions.YearsBetween[any,any]= functions.DayOfWeek[any,any]= functions.DayOfYear[any]= functions.DaysToEndOfMonth[any]= functions.FirstOfMonth[any]=( %1$s - interval (dayofmonth(%1$s) -1) day) functions.LastOfMonth[any]=(( %1$s - interval (dayofmonth(%1$s) -1) day) + interval 1 month - interval 1 day) functions.MakeTimestamp[any,any,any]= functions.WeekOfYear[any]=weekofyear(%1$s) functions.YMDIntBetween[any,any]= # Multiple 'vendor' mappings were found first found is active. Select the preferred entry and delete the others. # functions.AddDays[any,any]=(%1$s + interval floor(%2$s) day) # functions.AddMonths[any,any]=(%1$s + interval floor(%2$s) month) # functions.AddYears[any,any]=(%1$s + interval floor(%2$s) year) # functions.FirstOfMonth[any,any]=( %1$s - interval(dayofmonth(%1$s) -1) day) # # Table functions # functions.Unnest= # # Literals # literals.integer=true literals.smallint=true literals.long=true literals.decimal=false literals.float=true literals.double=true literals.char=false literals.nchar=false literals.varchar=true literals.nvarchar=false literals.blob=false literals.clob=false literals.nclob=false literals.date=false literals.time=false literals.time_with_time_zone=false literals.timestamp=true literals.timestamp_with_time_zone=false literals.interval_year=false literals.interval_month=false literals.interval_year_to_month=false literals.interval_day=false literals.interval_hour=false literals.interval_minute=false literals.interval_second=false literals.interval_day_to_hour=false literals.interval_day_to_minute=false literals.interval_day_to_second=false literals.interval_hour_to_minute=false literals.interval_hour_to_second=false literals.interval_minute_to_second=false literals.date=false literals.time=false literals.time_with_time_zone=false literals.timestamp=true literals.timestamp_with_time_zone=false literals.binary=false literals.boolean=false literals.xml=false literals.distinct=false literals.array=false literals.perioddate=false # Literal format specifications. Formats are compatible with String.format(). # Values for default behaviour are listed. # Only char, temporal and string types can be overridden. # Fractional seconds are presented as a string of up to 10 characters: '.' followed by 9 character # 0-padded string representing nanoseconds or empty. literals.format.binary=X'%s' literals.format.boolean=TRUE:FALSE:UNKNOWN literals.format.char='%s' literals.format.clob='%s' literals.format.date=DATE '%1$04d-%2$02d-%3$02d' literals.format.interval_day=INTERVAL '%3$s%1$d' DAY@2[(%2$s)] literals.format.interval_day_to_hour=INTERVAL '%4$s%1$d %2$02d' DAY@3[(%3$s)] TO HOUR literals.format.interval_day_to_minute=INTERVAL '%5$s%1$d %2$02d:%3$02d' DAY@4[(%4$s)] TO MINUTE literals.format.interval_day_to_second=INTERVAL '%8$s%1$d %2$02d:%3$02d:%4$02d%5$.10s' DAY@6[(%6$s)] TO SECOND@7[(%7$s)] literals.format.interval_hour=INTERVAL '%3$s%1$d' HOUR@2[(%2$s)] literals.format.interval_hour_to_minute=INTERVAL '%4$s%1$d:%2$02d' HOUR@3[(%3$s)] TO MINUTE literals.format.interval_hour_to_second=INTERVAL '%7$s%1$d:%2$02d:%3$02d%4$.10s' HOUR@5[(%5$s)] TO SECOND@6[(%6$s)] literals.format.interval_minute=INTERVAL '%3$s%1$d' MINUTE@2[(%2$s)] literals.format.interval_minute_to_second=INTERVAL '%6$s%1$d:%2$02d%3$.10s' MINUTE@4[(%4$s)] TO SECOND@5[(%5$s)] literals.format.interval_second=INTERVAL '%5$s%1$02d%2$.10s' SECOND@3[(%3$s]@4[, %4$s)] literals.format.interval_year=INTERVAL '%3$s%1$d' YEAR@2[(%2$s)] literals.format.interval_year_to_month=INTERVAL '%4$s%1$d-%2$02d' YEAR@3[(%3$s)] TO MONTH literals.format.interval_month=INTERVAL '%3$s%1$d' MONTH@2[(%2$s)] literals.format.nchar='%s' literals.format.nvarchar='%s' literals.format.time=TIME '%1$02d:%2$02d:%3$02d%4$.10s' literals.format.time_with_time_zone=TIME '%1$02d:%2$02d:%3$02d%4$.10s%7$c%5$02d:%6$02d' literals.format.timestamp=cast( '%1$04d-%2$02d-%3$02d %4$02d:%5$02d:%6$02d%7$.10s' as timestamp ) literals.format.timestamp_with_time_zone=TIMESTAMP '%1$04d-%2$02d-%3$02d %4$02d:%5$02d:%6$02d%7$.10s%10$c%8$02d:%9$02d' literals.format.varchar='%s' literals.format.decimal=%s # DataTypes # dataType.smallint=true dataType.integer=true dataType.long=true dataType.decimal=false dataType.float=true dataType.double=true dataType.char=false dataType.nchar=false dataType.varchar=true dataType.nvarchar=false dataType.clob=false dataType.blob=false dataType.date=false dataType.time=false dataType.time_with_time_zone=false dataType.timestamp=true dataType.timestamp_with_time_zone=false dataType.interval_year=false dataType.interval_month=false dataType.interval_year_to_month=false dataType.interval_day=false dataType.interval_hour=false dataType.interval_minute=false dataType.interval_second=false dataType.interval_day_to_hour=false dataType.interval_day_to_minute=false dataType.interval_day_to_second=false dataType.interval_hour_to_minute=false dataType.interval_hour_to_second=false dataType.interval_minute_to_second=false dataType.boolean=true dataType.binary=false dataType.xml=false dataType.period=false dataType.array=false dataType.struct=false # # Collation # # Collation Sequence SQL (SQL statement for retrieving the collation sequence) # This statement returns a single row and single column containing the collation sequence collation.sequence.sql= # Datbase Encoding SQL. This statement retrieves the charset name for the non-unicode character data. # This statement returns a single row and single column with the charset name for use in a java.nio.CharsetEncoder. database.charset.sql= # # dataType.comparable # # Used to indicate that some data types that are comparable locally may not by the database # e.g. dataType.comparable[varchar,nvarchar]=false # # dataType.promotion # # Used to indicate what direction the promotion needs to occur # -> these properties are not symetrical # e.g. dataType.promotion[char,nvarchar]=true