#Licensed Materials - Property of IBM # #OCO Source Materials # #BI and PM: rdbmscert # #(C) Copyright IBM Corp. 2009,2022 # #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.schemaDelimiter= # delimiters.tableDelimiter= # delimiters.columnDelimiter= delimiters.literalQuoteEscape=\\' # delimiters.identifierQuoteString= # delimiters.searchStringEscape= # delimiters.catalogSeparator= # # Keywords # keywords.columnAlias=AS # # Limits # # Normally these values would be derived from the JDBC driver DatabaseMetadata limits.defaultTransactionIsolation= # avoid a potential hang when multiple statements attempted on the same connection limits.maxStatements=1 # # 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=true general.nullsAreSortedLow=false general.nullsAreSortedAtStart=false general.nullsAreSortedAtEnd=false general.nullsOrdering=true general.nullsOrderingInWindowSpecification=true # # Override sampling policy with a different one. # 1. tablesample accepting values such as BERNOULLI or SYSTEM # 2. rowsample accepting values such as NTH or RANDOM # sampling.tablesample= sampling.rowsample=RANDOM # supports.hints=false supports.constantsInWindows=false # Cursor options - appended to end of generated SELECT statement. general.cursorOptions= # # Various # supports.duplicateColumnsInSelectList=true supports.columnAliasing=true supports.tableCorrelationNames=true supports.distinct.WindowAggregation=false # Does not allow ordering on non project column supports.expressionsInSelectList=true supports.expressionsInINPredicate=true supports.expressionsInOrderBy=true supports.booleanExpressionsInSelectList=true supports.fieldsOfComplexTypeInSelectList=false supports.likeEscapeClause=false supports.outerJoins=true supports.fullOuterJoins=true # Subqueries not supported in Having # Subqueries not supported in Group-by # Subquery column alias not supported # Imposes requirement to use correlated subqueries. 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=true supports.orderByOrdinal=true supports.blobsInGroupBy=false supports.blobsInOrderBy=false supports.emptyStringIsNull=false supports.expressionsInGroupBy=true supports.expressionsInOrderBy=true supports.aliasInOrderByExpression=false supports.orderByUnrelated=false supports.groupByUnrelated=false supports.multipleDistinctAggregates=true supports.recursiveWithClause=false supports.crossProducts=false supports.callProcedureInDerivedTable=false supports.rowNumberNoOrderBy=false # pre Impala 2.1 imposed many restrictions on join predicate in the join on and where # clauses supports.join.full.distinctJoins=false supports.join.subqueriesInOnClause=false supports.join.full.thetaJoins=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 #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 performance.convertHavingToWhere=false performance.transitiveClosure=false performance.predicatePushdown=false performance.semiJoin=false # # Commands # commands.Select=SELECT commands.Call= # # Tables # tables.joined=true 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 constructors.row.in=false constructors.row.isDistinctFrom=false constructors.row.simpleCase=false #DB2 supports table value constructor in IN clause, but not row expression list. constructors.row.inListToTable=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=WITH clauses.OrderBy=ORDER BY clauses.Distinct=DISTINCT clauses.Top=LIMIT %1$s clauses.At= clauses.Window= clauses.TableSampleSystem=TABLESAMPLE SYSTEM (%1$s)@2[ REPEATABLE (%2$s)] clauses.TableSampleBernoulli= clauses.ForSystemTimeAsOf= clauses.ForSystemTimeFrom= clauses.ForSystemTimeBetween= # # Joins # # Impala requires that joins include at least one equi-join. There is no transformation # to dynamically determine if a join predicate does not include an equi-join and decompose # accordingly etc. joins.Cross=%1$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$s joins.RightNested= # # Set Operators # operators.set.Union=%1$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 # Impala removes trailing spaces from char types operators.arithmetic.Concat[char,any]= operators.arithmetic.Concat[any,char]= 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]=%1$s BETWEEN %2$s AND %3$s predicates.In[any,any]=%1$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$s predicates.Like.escape= predicates.LikeRegex=%1$s REGEXP %2$s predicates.LikeRegex.flag= predicates.Similar= predicates.Similar.escape= predicates.Exists= predicates.All= predicates.Any= predicates.Some= predicates.IsDistinctFrom[any,any]=NOT (%1$s <=> %2$s) predicates.IsNotDistinctFrom[any,any]=%1$s <=> %2$s # # Period predicates. # predicates.PeriodOverlaps[any,any]= predicates.PeriodEquals[any,any]= predicates.PeriodContains[any,any]= predicates.PeriodPrecedes[any,any]= predicates.PeriodSucceeds[any,any]= predicates.PeriodImmediatelyPrecedes[any,any]= predicates.PeriodImmediatelySucceeds[any,any]= # # Expressions # expressions.ArrayElementRef.zeroBased=false # Minimum number of arguments for Coalesce function. expressions.Coalesce.minArgs=1 # # Conditional expressions # # note impala does not align to ISO SQL Result of data type combinations in terms of the returned data type # and will disallow combinations of types unless explicitly cast as an input parameter expressions.SimpleCase=CASE expressions.SearchedCase=CASE expressions.Coalesce=COALESCE(%1$s) expressions.NullIf=CASE WHEN %1$s = %2$s THEN NULL ELSE %1$s END # # Cast # expressions.Cast[any,any]=CAST(%1$s AS %2$s) # Impala does not convert the source types into an expected string literal representation # Length appears to not function for cases such as # ( cast ( 'a' as char(32)) return 1 and not 32 but length( concat(cast( 'a' as char(32)),'')) returns 32 expressions.Cast[decimal,text]= expressions.Cast[float,text]= expressions.Cast[double,text]= expressions.Cast[timestamp,text]= expressions.Cast[boolean,text]= # Impala does not accept double precision it requires double as the type expressions.Cast[any,double]=CAST(%1$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]=MAX(%1$s) olap.Min[any]=MIN(%1$s) olap.Sum[any]=SUM(%1$s) olap.Avg[any]=AVG(%1$s) olap.Count[any]=COUNT(%1$s) olap.CountStar[]=COUNT(*) olap.StdDevPop[any]= olap.StdDevSamp[any]= olap.VarPop[any]= olap.VarSamp[any]= olap.Rank[]=RANK() olap.DenseRank[]=DENSE_RANK() olap.PercentRank[]=PERCENT_RANK() olap.CumeDist[]=CUME_DIST() olap.PercentileCont[any,any]= olap.PercentileDisc[any,any]= olap.Median[any]= olap.RatioToReport[any]= olap.RowNumber[]=ROW_NUMBER() olap.Difference[any]= # forces ORDER BY to be used in first last which ISO does not require etc. olap.FirstValue[any]=FIRST_VALUE(%1$s) olap.LastValue[any]=LAST_VALUE(%1$s) olap.NTile[any]=NTILE(%1$s) olap.NthValue[any,any]= olap.NthValue[any,any,any]= olap.NthValue[any,any,any,any]= olap.Tertile[]= olap.Lag[any]=LAG(%1$s) olap.Lag[any,any]=LAG(%1$s, cast(%2$s as integer)) olap.Lag[any,any,any]= olap.Lag[any,any,any,any]= olap.Lead[any]=LEAD(%1$s) olap.Lead[any,any]=LEAD(%1$s, cast(%2$s as integer)) olap.Lead[any,any,any]= olap.Lead[any,any,any,any]= olap.Collect[any]= # # Window clause # olap.Window= olap.PartitionBy=PARTITION BY %1$s olap.OrderBy=ORDER BY %1$s # # Window specification # olap.Window.Specification[POF]=true olap.Window.Specification[PF]=false olap.Window.Specification[OF]=true 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.Window.Frame.Moving=true # # 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$s) aggregates.StdDevPop[any]=STDDEV_POP(%1$s) aggregates.StdDevSamp[any]=STDDEV_SAMP(%1$s) aggregates.VarPop[any]=VAR_POP(%1$s) aggregates.VarSamp[any]=VAR_SAMP(%1$s) aggregates.Rank[any,any]= aggregates.DenseRank[any,any]= aggregates.PercentRank[any,any]= aggregates.CumeDistH[any,any]= aggregates.PercentileDisc[any,any]= aggregates.PercentileCont[any,any]= aggregates.Median[any]= aggregates.Grouping[any]= aggregates.XMLAgg= aggregates.ArrayAgg[any]= aggregates.ArrayAgg[any,any]= aggregates.Collect[any]= aggregates.ApproxCountDistinct[any]= # # Distinct aggregates # aggregates.Avg.distinct[any]=AVG(DISTINCT %1$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 # # Impala removes trailing spaces from char types functions.CharLength[char]= functions.CharLength[any]=LENGTH(%1$s) functions.OctetLength[any]= functions.BitLength[any]= # Impala removes trailing spaces from char types functions.Upper[char]= functions.Upper[any]=UPPER(%1$s) # Impala removes trailing spaces from char types functions.Lower[char]= 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. # # # 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.Random[]=RAND() functions.Random[any]=RAND(%1$s) # Values ending in .5 are rounded up for positive numbers, down for negative numbers (that is, away from zero). # Rounded up for negative numbers using case statement. functions.Round[double]=CASE WHEN %1$s > 0 THEN ROUND(%1$s) ELSE ROUND((%1$s)*(-1))*(-1) END functions.Round[any]=ROUND(%1$s) functions.Round[double,any]=CASE WHEN %1$s > 0 THEN ROUND(%1$s, %2$s) ELSE ROUND((%1$s)*(-1), %2$s)*(-1) END functions.Round[any,any]=ROUND(%1$s, %2$s) 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]=(UNIX_TIMESTAMP(%1$s) - UNIX_TIMESTAMP(%2$s)) functions.MinutesBetween[any,any]=((UNIX_TIMESTAMP(%1$s) - UNIX_TIMESTAMP(%2$s)) / 60) functions.HoursBetween[any,any]=((UNIX_TIMESTAMP(%1$s) - UNIX_TIMESTAMP(%2$s)) / (60 * 60)) functions.DaysBetween[any,any]=datediff(%1$s, %2$s) functions.WeeksBetween[any,any]=(datediff(%1$s, %2$s) / 7) functions.MonthsBetween[any,any]=int_months_between(%1$s, %2$s) functions.QuartersBetween[any,any]= functions.YearsBetween[any,any]=cast((int_months_between(%1$s, %2$s) / 12) as integer) functions.DayOfWeek[any,any]=(mod(dayofweek(%1$s) + 6 - %2$s, 7) + 1) functions.DayOfYear[any]=dayofyear(%1$s) functions.DaysToEndOfMonth[any]=datediff(((trunc(%1$s, "month") + interval 1 month) - interval 1 day), (trunc(%1$s, 'DDD'))) functions.FirstOfMonth[any]=(%1$s - interval (dayofmonth(%1$s)-1) day) functions.LastOfMonth[any]=(%1$s - interval (dayofmonth(%1$s)-1) day + interval 1 month - interval 1 day) functions.MakeTimestamp[any,any,any]=to_timestamp(unix_timestamp(cast( ( cast(%1$s as integer) * 10000 ) + ( cast(%2$s as integer) * 100 ) + cast(%3$s as integer) as string), "yyyyMMdd")) 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=true literals.float=true literals.double=true literals.char=true 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=true dataType.float=true dataType.double=true dataType.char=true 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