# Licensed Materials - Property of IBM # IBM Cognos Products: OQP # (C) Copyright IBM Corp. 2005, 2019 # US Government Users Restricted Rights - Use, duplication or disclosure restricted by GSA ADP Schedule Contract with IBM corp. # # 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=*/ # # Keywords # keywords.columnAlias=AS # # General # # # Null ordering # # No support is provided for vendors who change how nulls sort based on data type. general.nullsAreSortedHigh=false general.nullsAreSortedLow=true # # Various # supports.columnAliasing=true supports.tableCorrelationNames=true supports.expressionsInOrderBy=true supports.aliasInOrderByExpression=false supports.orderByName=true supports.orderByOrdinal=true supports.expressionsInINPredicate=true supports.likeEscapeClause=true supports.fullOuterJoins=true supports.outerJoins=true # Subqueries not supported in Group-by # Subquery column alias not supported supports.subqueriesInComparisons=true supports.subqueriesInExists=true supports.subqueriesInIns=true supports.subqueriesInQuantifieds=true supports.subqueriesInCase=true supports.correlatedSubqueries=true supports.scalarSubqueries=true supports.withClauseInDerivedTable=false supports.nestedWithClause=false supports.recursiveWithClause=false # Currently, SAP hana returns 1/2 as decimal(34,0) which is same as integer. Therefore, the switch should set to "true" supports.integerDivision=true supports.nestedOlap=false supports.derivedColumnLists=false # Does not allow grouping on non project column supports.blobsInGroupBy=false supports.blobsInOrderBy=false supports.emptyStringIsNull=true supports.expressionsInGroupBy=true supports.constantsInWindows=false supports.callProcedureInDerivedTable=false supports.join.subqueriesInOnClause=false supports.hanaInputParameters=true #casting with formatting pattern support supports.formatters.string_to_date=false supports.formatters.string_to_time=false supports.formatters.string_to_time_with_time_zone=false supports.formatters.string_to_timestamp=false supports.formatters.string_to_timestamp_with_time_zone=false # # 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 # # Commands # commands.Select=SELECT commands.Call=CALL # # Tables # tables.joined=true tables.derived=true tables.lateral.derived=false # # Constructors # constructors.table=false constructors.row=true constructors.array=false constructors.period=false # # Constructors - context overrides. # constructors.row.between=false constructors.row.comparison=true constructors.row.in=true constructors.row.isDistinctFrom=false constructors.row.simpleCase=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 # Common table expression cannot be used within a derived table clauses.With= clauses.WithRecursive= clauses.OrderBy=ORDER BY clauses.Distinct=DISTINCT clauses.Top= TOP %1$s clauses.Top.Position=top.distinct clauses.ForSystemTimeAsOf= clauses.ForSystemTimeFrom= clauses.ForSystemTimeBetween= # # Joins # joins.Cross=%1$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$s # # Set Operators # # One or more set operations does not follow ISO data type combination rules. Can effect set operations, CASE, COALESCE... operators.set.Union=%1$s UNION %2$s operators.set.Union.all=%1$s UNION ALL %2$s operators.set.Intersect=%1$s INTERSECT %2$s operators.set.Intersect.all= operators.set.Except=%1$s EXCEPT %2$s operators.set.Except.all= # # Logical Operators # operators.logical.And=%1$s AND %2$s operators.logical.Or=%1$s OR %2$s operators.logical.Not=NOT ( %1$s ) operators.logical.Is= operators.logical.IsNot= # # Arithmetic and Character operators # #SAP HANA connot support date - date operators.arithmetic.Subtract[date,date]=DAYS_BETWEEN(%2$s, %1$s) operators.arithmetic.Subtract[date,any]= operators.arithmetic.Subtract[timestamp,any]= operators.arithmetic.Subtract[time,any]= #OSS 0001101325 2012 operators.arithmetic.Divide[integer,integer]= operators.arithmetic.Divide[integer,smallint]= operators.arithmetic.Divide[integer,long]= operators.arithmetic.Divide[smallint,smallint]= operators.arithmetic.Divide[smallint,integer]= operators.arithmetic.Divide[smallint,long]= operators.arithmetic.Divide[long,smallint]= operators.arithmetic.Divide[long,integer]= operators.arithmetic.Divide[long,long]= #String functions return wrong datatype with wrong precision, therefore, turning off some string functions operators.arithmetic.Concat[char,any]= operators.arithmetic.Concat[nchar,any]= operators.arithmetic.Concat[any,char]= operators.arithmetic.Concat[any,nchar]= operators.arithmetic.Concat[blob,any]= operators.arithmetic.Concat[any,blob]= operators.arithmetic.Concat[any,any]=%1$s || %2$s # # Grouping Operators # operators.groupBy.Rollup=ROLLUP operators.groupBy.Cube=CUBE operators.groupBy.GroupingSets=GROUPING SETS # # Comparison Predicates # predicates.comparison.Equals[clob,any]= predicates.comparison.Equals[any,clob]= predicates.comparison.Equals[blob,any]= predicates.comparison.Equals[any,blob]= predicates.comparison.GreaterThan[clob,any]= predicates.comparison.GreaterThan[any,clob]= predicates.comparison.GreaterThan[blob,any]= predicates.comparison.GreaterThan[any,blob]= predicates.comparison.GreaterThanOrEquals[clob,any]= predicates.comparison.GreaterThanOrEquals[any,clob]= predicates.comparison.GreaterThanOrEquals[blob,any]= predicates.comparison.GreaterThanOrEquals[any,blob]= predicates.comparison.LessThan[clob,any]= predicates.comparison.LessThan[any,clob]= predicates.comparison.LessThan[blob,any]= predicates.comparison.LessThan[any,blob]= predicates.comparison.LessThanOrEquals[clob,any]= predicates.comparison.LessThanOrEquals[any,clob]= predicates.comparison.LessThanOrEquals[blob,any]= predicates.comparison.LessThanOrEquals[any,blob]= predicates.comparison.NotEquals[clob,any]= predicates.comparison.NotEquals[any,clob]= predicates.comparison.NotEquals[blob,any]= predicates.comparison.NotEquals[any,blob]= # # Predicates # predicates.In[any,any]=%1$s IN ( %2$s ) predicates.In[clob,any]= predicates.In[any,clob]= predicates.Overlaps[any,any,any,any]= predicates.LikeRegex= predicates.LikeRegex.flag= predicates.Similar.escape= predicates.Similar= predicates.Similar.escape= 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]= # # Conditional expressions # # One or more case operations does not follow ISO type rules. expressions.SimpleCase=CASE #expressions.SearchedCase.compatibleResults=false expressions.Coalesce=COALESCE(%1$s) expressions.NullIf=NULLIF(%1$s, %2$s) # # Cast # # OSS: 0000491475 2012 expressions.Cast[any,float]=TO_REAL(%1$s) #OSS: 0000491530 2012 #SQL standard page 213 using respective values in an execution of CURRENT_DATE expressions.Cast[time,timestamp]=TO_TIMESTAMP(CONCAT(CONCAT(current_date,' '), %1$s) ) expressions.Cast[date,timestamp]=TO_TIMESTAMP(%1$s,'YYYY-MM-DD') # turn off cast to char/nchar, because it return varchar/nvarchar, when concat for date+time generated from transfermation ConvertDatePlusTimeToCompatibleSQL, it will be failed # failed testcase testConvertDatePlusTimeToCompatibleSQL expressions.Cast[any,nchar]= expressions.Cast[any,char]= expressions.Cast[xml,any]= expressions.Cast[any,xml]= expressions.Cast[decimal,nvarchar]= expressions.Cast[decimal,varchar]= # Minimum number of arguments for Coalesce function. expressions.Coalesce.minArgs=2 # # Extract # expressions.Extract.YEAR[any]=EXTRACT(YEAR FROM %1$s) expressions.Extract.MONTH[any]=EXTRACT(MONTH FROM %1$s) expressions.Extract.DAY[any]=EXTRACT(DAY FROM %1$s) expressions.Extract.HOUR[any]=EXTRACT(HOUR FROM %1$s) expressions.Extract.MINUTE[any]=EXTRACT(MINUTE FROM %1$s) #OSS: 0001070313 2012 #SAP HANA return decimal(34,0) and our RQE regard it as integer and automatically round it or cast it to integer. expressions.Extract.SECOND[timestamp]=TO_DECIMAL(EXTRACT(SECOND FROM %1$s),11,9) expressions.Extract.SECOND[any]=EXTRACT(SECOND FROM %1$s) expressions.Extract.TIMEZONE_HOUR[any]= expressions.Extract.TIMEZONE_MINUTE[any]= expressions.Extract.EPOCH[any]= # # Trim # #functional support; expressions.Trim.BOTH[char]= expressions.Trim.LEADING[char]= expressions.Trim.TRAILING[char]= expressions.Trim.BOTH[any,char]= expressions.Trim.LEADING[any,char]= expressions.Trim.TRAILING[any,char]= xpressions.Trim.BOTH[nchar]= expressions.Trim.LEADING[nchar]= expressions.Trim.TRAILING[nchar]= expressions.Trim.BOTH[any,nchar]= expressions.Trim.LEADING[any,nchar]= expressions.Trim.TRAILING[any,nchar]= expressions.Trim.BOTH[blob]= expressions.Trim.LEADING[blob]= expressions.Trim.TRAILING[blob]= expressions.Trim.BOTH[any,blob]= expressions.Trim.LEADING[any,blob]= expressions.Trim.TRAILING[any,blob]= expressions.Trim.BOTH[any]=TRIM(%1$s) expressions.Trim.LEADING[any]=LTRIM(%1$s) expressions.Trim.TRAILING[any]=RTRIM(%1$s) expressions.Trim.BOTH[any,any]=RTRIM(LTRIM(%2$s,%1$s),%1$s) expressions.Trim.LEADING[any,any]=LTRIM(%2$s,%1$s) expressions.Trim.TRAILING[any,any]=RTRIM(%2$s,%1$s) # # Window clause # # Lack of window ordering impacts many aggregates being pushed # Unable to specify a literal in window ordering # Unable to specify ordering in a window general.nullsOrderingInWindowSpecification=true # # Window specification # olap.Window.Specification[POF]=false olap.Window.Specification[PF]=false olap.Window.Specification[OF]=false olap.Window.Specification[PO]=true olap.Window.Specification[P]=true olap.Window.Specification[O]=true olap.Window.Specification[F]=false olap.Window.Specification[]=true # # Olap Distinct # olap.Min.distinct[any]= olap.Max.distinct[any]= olap.Sum.distinct[any]= olap.Avg.distinct[any]= olap.Count.distinct[any]= # # Aggregates # # OSS 0000481944 AVG returns precsion=34 SCALE=0. Cast the result to double. aggregates.Avg[any]=CAST(AVG(%1$s) as DOUBLE) aggregates.Max[blob]= aggregates.Max[any]=MAX(%1$s) aggregates.Min[blob]= aggregates.Min[any]=MIN(%1$s) aggregates.Count[any]=COUNT(%1$s) aggregates.Count[clob]=sum( case when %1$s is not null then 1 else 0 end) aggregates.Count[blob]=sum( case when %1$s is not null then 1 else 0 end) aggregates.CountStar[]=COUNT(*) aggregates.Sum[any]=SUM(%1$s) aggregates.StdDevPop[any]= aggregates.StdDevSamp[any]=STDDEV(%1$s) aggregates.VarPop[any]= aggregates.VarSamp[any]=VAR(%1$s) aggregates.Grouping[any]= aggregates.XMLAgg[any]= aggregates.Rank[any,any]= aggregates.DenseRank[any,any]= aggregates.PercentRank[any,any]= aggregates.CumeDistH[any,any]= aggregates.PercentileCont[any,any]= aggregates.PercentileDisc[any,any]= aggregates.ArrayAgg[any]= aggregates.ArrayAgg[any,any]= aggregates.Collect[any]= aggregates.PercentileCont[any,any]= aggregates.PercentileDisc[any,any]= aggregates.Median[any]= # # Distinct aggregates # aggregates.Avg.distinct[any]= aggregates.Count.distinct[blob]= # # 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$s) functions.BitLength[any]= functions.OctetLength[any]= #String functions return wrong datatype with wrong precision, therefore, turning off some string functions functions.Upper[char]= functions.Upper[nchar]= functions.Upper[blob]= #String functions return wrong datatype with wrong precision, therefore, turning off some string functions functions.Lower[char]= functions.Lower[nchar]= functions.Lower[blob]= #Substring function to negative START value to parse the input string from its rightmost end. functions.SubstringR[any,any]=CASE WHEN (%2$s) < 0 THEN (SUBSTRING( %1$s, (LENGTH(%1$s ) - ABS(%2$s) + 1))) ELSE (SUBSTRING(%1$s, %2$s)) END functions.SubstringR[any,any,any]=CASE WHEN (%2$s) < 0 THEN (SUBSTRING( %1$s, (LENGTH(%1$s ) - ABS(%2$s) + 1), %3$s)) ELSE (SUBSTRING(%1$s, %2$s, %3$s)) END functions.Position[any,any]= LOCATE(%2$s,%1$s ) functions.Index[blob,any]= functions.Index[any,blob]= functions.Index[any,any]= LOCATE(%1$s ,%2$s) functions.Ascii[any]= functions.Translate[any,any]= functions.Normalize[any]= functions.Normalize[any,any]= functions.Normalize[any,any,any]= # # Regular expression functions. # functions.SubstringRegex[any,any,any,any,any]=SUBSTR_REGEXPR(%1$s@5[ FLAG %5$s] IN %2$s@3[ FROM %3$s]@4[ OCCURRENCE %4$s]) functions.OccurrencesRegex[any,any,any,any]=OCCURRENCES_REGEXPR(%1$s@4[ FLAG %4$s] IN %2$s@3[ FROM %3$s]) functions.PositionRegex[any,any,any,any,any,any]=LOCATE_REGEXPR(@1[%1$s ]%2$s@6[ FLAG %6$s] IN %3$s@4[ FROM %4$s]@5[ OCCURRENCE %5$s]) # # Numeric scalar functions # functions.Abs[interval_day_time]= functions.Abs[interval_year_month]= functions.Ceiling[any]=CEIL(%1$s) #EXP returns double without decimal(34,0) issue functions.Exp[any]=EXP(%1$s) # OSS 0000481944 return precsion=34 functions.Floor[decimal]= functions.Floor[any]=FLOOR(%1$s) functions.Ln[any]=LN(%1$s) functions.Log10[any]= LOG(10,%1$s) # Mod failed exception cases # OSS 0000481944 return precsion=34 functions.Mod[decimal,any]= functions.Mod[any,any]=MOD(%1$s, %2$s) functions.Sign[any]=SIGN(%1$s) #Sqrt returns double without decimal(34,0) issue functions.Sqrt[any]=SQRT(%1$s) functions.WidthBucket[any,any,any,any]= #power returns double without decimal(34,0) issue functions.Power[any,any]=POWER(%1$s, %2$s) functions.Round[any]=ROUND(%1$s) # # Array scalar functions. # functions.Cardinality[any]= functions.TrimArray[any,any]= # OSS 0000481944 return precsion=34 without scaler functions.Round[decimal,any]= functions.Round[any,any]=ROUND(%1$s, %2$s) functions.Round[any,any,any]= # # Trig Functions # # # Windowed aggregates (SQL/OLAP). # olap.Max[any]=MAX(%1$s) olap.Min[any]=MIN(%1$s) olap.Sum[any]=SUM(%1$s) olap.Avg[any]= olap.Count[any]=COUNT(%1$s) olap.Count[blob]= olap.CountStar[]=COUNT(*) olap.StdDevSamp[any]= olap.StdDevPop[any]= olap.VarSamp[any]= olap.VarPop[any]= olap.Rank[]=RANK() olap.DenseRank[]=DENSE_RANK() olap.PercentRank[]=PERCENT_RANK() olap.CumeDist[]=CUME_DIST() olap.PercentileCont[any,any]= olap.PercentileDisc[any,any]= olap.Median[any]= olap.RowNumber[]=ROW_NUMBER() olap.FirstValue[any]=FIRST_VALUE(%1$s) olap.LastValue[any]=LAST_VALUE(%1$s) olap.NTile[any]=NTILE(%1$s) olap.Tertile[]= olap.RatioToReport[any]= olap.Difference[any]= olap.Lag[any]=LAG(%1$s) olap.Lag[any,any]=LAG(%1$s, %2$s) olap.Lag[any,any,any]=LAG(%1$s, %2$s, %3$s) olap.Lag[any,any,any,any]= olap.Lead[any]=LEAD(%1$s) olap.Lead[any,any]=LEAD(%1$s, %2$s) olap.Lead[any,any,any]=LEAD(%1$s, %2$s, %3$s) olap.Lead[any,any,any,any]= olap.NthValue[any,any]= olap.NthValue[any,any,any]= olap.NthValue[any,any,any,any]= olap.Collect[any]= # # Temporal value expressions # # Note: JDBC does not define fractional seconds for TIME data type. functions.CurrentDate[]=CURRENT_DATE functions.CurrentTime[]= functions.CurrentTime[numeric]= functions.CurrentTimestamp[]= functions.CurrentTimestamp[numeric]= functions.LocalTime[]=CURRENT_TIME functions.LocalTimestamp[]=CURRENT_TIMESTAMP functions.LocalTime[numeric]= functions.LocalTimestamp[numeric]= # # XML Functions # functions.XMLAttributes= functions.XMLComment= functions.XMLConcat= functions.XMLDocument= functions.XMLElement= functions.XMLExists= functions.XMLForest= functions.XMLParse= functions.XMLPI= functions.XMLNamespaces= functions.XMLQuery= functions.XMLSerialize= functions.XMLTable= functions.XMLText= functions.XMLTransform= functions.XMLValidate= # # JSON functions. # functions.JSONArray= functions.JSONExists= functions.JSONObject= functions.JSONQuery= functions.JSONTable= functions.JSONValue= # # Business functions. # functions.AddFractionalSeconds[any,any]= functions.AddSeconds[time,any]= functions.AddSeconds[any,any]=add_seconds(%1$s,%2$s) functions.AddMinutes[time,any]= functions.AddMinutes[any,any]=add_seconds(%1$s,floor(%2$s) * 60) functions.AddHours[time,any]= functions.AddHours[any,any]=add_seconds(%1$s,floor(%2$s) * 3600) functions.AddDays[timestamp,any]=(add_days(%1$s, FLOOR(%2$s))) functions.AddDays[date,any]=(add_days(%1$s, FLOOR(%2$s))) functions.AddDays[any,any]= functions.AddWeeks[timestamp,any]=(add_days(%1$s, FLOOR(%2$s * 7))) functions.AddWeeks[date,any]=(add_days(%1$s, FLOOR(%2$s * 7))) functions.AddWeeks[any,any]= functions.AddMonths[timestamp,any]=(add_months(%1$s, FLOOR(%2$s))) functions.AddMonths[date,any]=(add_months(%1$s , floor(%2$s))) functions.AddMonths[any,any]= functions.AddQuarters[timestamp,any]=(add_months(%1$s, FLOOR(%2$s * 3))) functions.AddQuarters[date,any]=(add_months(%1$s , floor(%2$s * 3))) functions.AddQuarters[any,any]= functions.AddYears[timestamp,any]=(add_years(%1$s , floor(%2$s))) functions.AddYears[date,any]=(add_years(%1$s , floor(%2$s))) functions.AddYears[any,any]= functions.FractionalSecondsBetween[any,any]= functions.SecondsBetween[any,any]= functions.SecondsBetween[date,date]=SECONDS_BETWEEN(%1$s,%2$s) functions.MinutesBetween[any,any]= functions.HoursBetween[any,any]= functions.DaysBetween[timestamp,timestamp]=DAYS_BETWEEN(TO_DATE(%2$s),TO_DATE(%1$s)) functions.DaysBetween[date,timestamp]=DAYS_BETWEEN(TO_DATE(%2$s),%1$s) functions.DaysBetween[timestamp,date]=DAYS_BETWEEN(%2$s,TO_DATE(%1$s)) functions.DaysBetween[date,date]=DAYS_BETWEEN(%2$s,%1$s) functions.DaysBetween[any,any]= functions.WeeksBetween[any,any]= functions.MonthsBetween[any,any]= functions.QuartersBetween[any,any]= functions.YearsBetween[any,any]= functions.Age[any]= functions.DayOfWeek[any,any]= functions.DayOfYear[date]=DAYOFYEAR(%1$s) functions.DayOfYear[timestamp]=DAYOFYEAR(%1$s) functions.DayOfYear[any]= functions.DaysToEndOfMonth[date]=DAYS_BETWEEN(%1$s,LAST_DAY(%1$s)) functions.DaysToEndOfMonth[any]= functions.FirstOfMonth[any]=add_days(%1$s, -dayofmonth(%1$s)+1) functions.LastOfMonth[timestamp]=CONCAT(CONCAT(LAST_DAY(%1$s),' '), TO_TIME(%1$s)) functions.LastOfMonth[any]=LAST_DAY(%1$s) functions.MakeTimestamp[any,any,any]=TO_TIMESTAMP( ( LPAD( %1$d, 4, '0' ) || '-' || LPAD( %2$d, 2, '0' ) || '-' || LPAD( %3$d, 2, '0' ) ), 'YYYY-MM-DD' ) functions.YMDIntBetween[any,any]= functions.WeekOfYear[date]=TO_INT(SUBSTRING(ISOWEEK(%1$s),7,2)) functions.WeekOfYear[timestamp]=TO_INT(SUBSTRING(ISOWEEK(%1$s),7,2)) functions.WeekOfYear[time]=TO_INT(SUBSTRING(ISOWEEK(CURRENT_DATE),7,2)) functions.WeekOfYear[any]= # Multiple 'vendor' mappings were found first found is active. Select the preferred entry and delete the others. # functions.AddDays[any,any]=(add_days(%1s, FLOOR(%2s))) # functions.AddMonths[timestamp,any]=(add_months(%1s, FLOOR(%2s))) # functions.AddMonths[date,double]=(add_months(%1s , floor(%2s))) # functions.AddMonths[date,decimal]=(add_months(%1s, FLOOR(%2s))) # functions.AddMonths[date,smallint]=(add_months(%1s , floor(%2s))) # functions.AddMonths[date,long]=(add_months(%1s , floor(%2s))) # functions.AddMonths[date,float]=(add_months(%1s, FLOOR(%2s))) # functions.AddMonths[date,integer]=(add_months(%1s , floor(%2s))) # functions.AddYears[timestamp,decimal]=(add_months(%1s , floor(%2s) * 12)) # functions.AddYears[timestamp,double]=(add_months(%1s , floor(%2s) * 12)) # functions.AddYears[timestamp,smallint]=(add_months(%1s , floor(%2s) * 12)) # functions.AddYears[timestamp,long]=(add_months(%1s , floor(%2s) * 12)) # functions.AddYears[timestamp,float]=(add_years(%1s, FLOOR(%2s))) # functions.AddYears[timestamp,integer]=(add_months(%1s , floor(%2s) * 12)) # functions.AddYears[date,double]=(add_months(%1s , floor(%2s) * 12)) # functions.AddYears[date,decimal]=(add_years(%1s, FLOOR(%2s))) # functions.AddYears[date,smallint]=(add_months(%1s , floor(%2s) * 12)) # functions.AddYears[date,long]=(add_months(%1s , floor(%2s) * 12)) # functions.AddYears[date,float]=(add_years(%1s, FLOOR(%2s))) # functions.AddYears[date,integer]=(add_months(%1s , floor(%2s) * 12)) # functions.FirstOfMonth[any,any]=(add_days(add_days(%1s, - dayofmonth(%1s)),1)) # functions.LastOfMonth[any,any]=(add_days(add_days(%1s, - dayofmonth(%1s)),1)) # functions.MakeTimestamp[any,any,any]=cast(TO_TIMESTAMP('%1s-%2s-%3s','YYYY-MM-DD') as timestamp) # functions.AddHours[any,any]=add_seconds(%1s,floor(%2s) * 3600) # functions.AddMinutes[any,any]=add_seconds(%1s,floor(%2s) * 60) # functions.AddSeconds[any,any]=add_seconds(%1s,floor(%2s)) # # Literals # literals.integer=true literals.smallint=true literals.long=true literals.decimal=true literals.float=true literals.double=true literals.char=true literals.nchar=true literals.varchar=true literals.nvarchar=true literals.clob=true literals.date=true literals.time=true 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.binary=true literals.boolean=true literals.xml=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.boolean=TRUE:FALSE:UNKNOWN literals.format.char='%s' literals.format.clob='%s' #cannot use this date format, because when running add_days(DATE '2000-12-31', 1), SAP HANA throws Error: SAP DBTech JDBC: [266] (at 7): inconsistent datatype: line 1 col 8 (at pos 7) #SQLState: 07006 #ErrorCode: 266 #DATE '%1$04d-%2$02d-%3$02d' literals.format.date=TO_DATE('%1$04d-%2$02d-%3$02d','YYYY-MM-DD') literals.format.interval_day= literals.format.interval_day_to_hour= literals.format.interval_day_to_minute= literals.format.interval_day_to_second= literals.format.interval_hour= literals.format.interval_hour_to_minute= literals.format.interval_hour_to_second= literals.format.interval_minute= literals.format.interval_minute_to_second= literals.format.interval_month= literals.format.interval_second= literals.format.interval_year= literals.format.interval_year_to_month= literals.format.nchar=TO_NCHAR('%s') literals.format.nvarchar=TO_NVARCHAR('%s') literals.format.time=TO_TIME('%1$02d:%2$02d:%3$02d','HH:MI:SS') literals.format.time_with_time_zone= literals.format.timestamp=TO_TIMESTAMP('%1$04d-%2$02d-%3$02d %4$02d:%5$02d:%6$02d%7$.4s') literals.format.timestamp_with_time_zone= literals.format.varchar='%s' literals.format.double=TO_DOUBLE('%s') # # DataTypes # dataType.smallint=true dataType.integer=true dataType.long=true dataType.decimal=true dataType.float=true dataType.double=true dataType.char=false dataType.nchar=false dataType.varchar=true dataType.nvarchar=true dataType.clob=true dataType.blob=true dataType.date=true dataType.time=true 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=false dataType.binary=false dataType.xml=true dataType.period=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