analytics/configuration/xqe/exasolution.properties

# Licensed Materials - Property of IBM
# IBM Cognos Products: OQP
# (C) Copyright IBM Corp. 2005, 2020
# 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=*/
# delimiters.catalogDelimiter=
# 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.maxBinaryLiteralLength=
# limits.maxCharLiteralLength=
# limits.maxColumnNameLength=
# limits.maxColumnsInGroupby=
# limits.maxColumnsInIndex=
# limits.maxColumnsInOrderBy=
# limits.maxColumnsInSelect=
# limits.maxColumnsInTable=
# limits.maxConnections=
# limits.maxCursorNameLength=
# limits.maxIndexLength=
# limits.maxSchemaNameLength=
# limits.maxProcedureNameLength=
# limits.maxCatalogNameLength=
# limits.maxRowSize=
# limits.maxStatementLength=
# limits.maxStatements=
# limits.maxTableNameLength=
# limits.maxTablesInSelect=
# limits.maxUserNameLength=
# limits.defaultTransactionIsolation=
# limits.maxLengthInClause
limits.castClobToVarcharMaxSize=2000000

# 
# General
# 
# 
# Null ordering
# 
# No support is provided for vendors who change how nulls sort based on data type.
general.nullsAreSortedAtEnd=false
general.nullsAreSortedAtStart=false
general.nullsAreSortedHigh=true
general.nullsAreSortedLow=false
general.nullsOrdering=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 RAND
#
sampling.tablesample=
sampling.rowsample=RANDOM

# 
# Various
# 
supports.columnAliasing=true
supports.tableCorrelationNames=true
supports.expressionsInOrderBy=true
supports.orderByAlias=true
supports.orderByName=true
supports.orderByOrdinal=true
supports.expressionsInINPredicate=true
supports.likeEscapeClause=true
supports.fullOuterJoins=true
supports.outerJoins=true
# Subquery column alias not supported
supports.subqueriesInComparisons=true
supports.subqueriesInExists=true
supports.subqueriesInIns=true
supports.subqueriesInQuantifieds=false
supports.subqueriesInCase=false
supports.correlatedSubqueries=true
supports.scalarSubqueries=true
supports.withClauseInDerivedTable=false
supports.nestedWithClause=true
supports.integerDivision=false
supports.nestedOlap=false
supports.derivedColumnLists=true
supports.blobsInGroupBy=false
supports.blobsInOrderBy=false
supports.emptyStringIsNull=true
supports.concatNullIsNull=false
supports.expressionsInGroupBy=true
supports.duplicateColumnsInSelectList=true
supports.expressionsInOrderBy=true
supports.aliasInOrderByExpression=false
supports.orderByUnrelated=true
supports.groupByUnrelated=false
supports.nonStandardDatetimeComparison=false
supports.multipleDistinctAggregates=true
supports.hints=false
supports.join.subqueriesInOnClause=false
#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=true
constructors.row=false
constructors.array=false
constructors.period=false
constructors.row.simpleCase=false
constructors.row.between=false
constructors.map=false

# 
# Clauses
# 
clauses.From=FROM
clauses.Where=WHERE
clauses.GroupBy=GROUP BY
clauses.Having=HAVING
# Recursive form of common table expression not supported
clauses.WithRecursive=
# Common table expression cannot be used within a derived table
clauses.With=WITH
clauses.OrderBy=ORDER BY
clauses.Distinct=DISTINCT
#LIMIT with aggregated query is not supported
clauses.Top=LIMIT %1$s
clauses.At=
clauses.Window= 
clauses.TableSampleSystem=
clauses.TableSampleBernoulli=
#Temporal tables not supported 
clauses.ForSystemTimeAsOf=
clauses.ForSystemTimeFrom=
clauses.ForSystemTimeBetween=

# 
# Joins
# 
# Does not allow on condition to use set functions
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
# 
# 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$s )
operators.logical.Is=%1$s IS %2$s
operators.logical.IsNot=%1$s IS NOT %2$s

# 
# Arithmetic and Character operators
# 
operators.arithmetic.Add[any,any]=%1$s + %2$s
operators.arithmetic.Subtract[any,any]=%1$s - %2$s
operators.arithmetic.Subtract[datetime,datetime]=
operators.arithmetic.Multiply[any,any]=%1$s * %2$s
operators.arithmetic.Divide[any,any]=%1$s / %2$s
operators.arithmetic.UnaryPlus[numeric]=+%1$s
operators.arithmetic.Negate[numeric]=-%1$s
operators.arithmetic.Negate[interval_day_time]=
operators.arithmetic.Negate[interval_year_month]= 
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[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$s
predicates.Like.escape=%1$s LIKE %2$s ESCAPE %3$s
predicates.LikeRegex=
predicates.LikeRegex.flag=
predicates.Similar=
predicates.Similar.escape= 
predicates.Exists=EXISTS %1$s
predicates.All= 
predicates.Any= 
predicates.Some= 
predicates.IsDistinctFrom[any,any]=(%1$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
expressions.Coalesce=COALESCE(%1$s)
#expressions.Coalesce[any,text]= 
expressions.NullIf=NULLIF(%1$s, %2$s)
#expressions.NullIf[string,varchar]= 
#expressions.NullIf[datetime,varchar]= 
#expressions.NullIf[varchar,string]= 
#expressions.NullIf[numeric,varchar]= 
# NULLIF(%1$s, %2$s) is equivalent to CASE WHEN %1$s = %2$s THEN NULL ELSE %1$s END

# Minimum number of arguments for Coalesce function.
expressions.Coalesce.minArgs=2

# 
# Cast
# 
expressions.Cast[any,any]=CAST(%1$s AS %2$s)
expressions.Cast[decimal,integer]=CAST(TRUNCATE(%1$s) AS INTEGER)
expressions.Cast[double,integer]=CAST(TRUNCATE(%1$s) AS INTEGER)
expressions.Cast[float,integer]=CAST(TRUNCATE(%1$s) AS INTEGER)
expressions.Cast[smallint,clob]=
expressions.Cast[integer,clob]=
expressions.Cast[long,clob]=
expressions.Cast[decimal,char]=
expressions.Cast[decimal,varchar]=
expressions.Cast[decimal,clob]=
expressions.Cast[float,char]=
expressions.Cast[float,varchar]=
expressions.Cast[float,clob]=
expressions.Cast[double,char]=
expressions.Cast[double,varchar]=
expressions.Cast[double,clob]=
expressions.Cast[char,char]=
expressions.Cast[char,varchar]=
expressions.Cast[char,clob]=
expressions.Cast[varchar,varchar]=
expressions.Cast[varchar,clob]=
expressions.Cast[clob,smallint]=
expressions.Cast[clob,integer]=
expressions.Cast[clob,long]=
expressions.Cast[clob,decimal]=
expressions.Cast[clob,float]=
expressions.Cast[clob,double]=
expressions.Cast[clob,char]=
expressions.Cast[clob,varchar]=
expressions.Cast[clob,clob]=
expressions.Cast[clob,date]=
expressions.Cast[clob,timestamp]=
expressions.Cast[date,clob]=
expressions.Cast[timestamp,char]=
expressions.Cast[timestamp,varchar]=
expressions.Cast[timestamp,clob]=
expressions.Cast[interval_day_to_second,varchar]=
# 
# Extract
# 
expressions.Extract.YEAR[any]=EXTRACT(YEAR FROM %1$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)
expressions.Extract.SECOND[any]=EXTRACT(SECOND FROM %1$s)
expressions.Extract.TIMEZONE_HOUR[any]=
expressions.Extract.TIMEZONE_MINUTE[any]=

# 
# Trim
# 
expressions.Trim.BOTH[any]=TRIM(BOTH FROM %1$s)
expressions.Trim.BOTH[any,any]=TRIM(BOTH %1$s FROM %2$s)
expressions.Trim.LEADING[any]=TRIM(LEADING FROM %1$s)
expressions.Trim.LEADING[any,any]=TRIM(LEADING %1$s FROM %2$s)
expressions.Trim.TRAILING[any]=TRIM(TRAILING FROM %1$s)
expressions.Trim.TRAILING[any,any]=TRIM(TRAILING %1$s FROM %2$s)

# 
# Window clause
# 
# Olap functions cannot be used in Subquery but no method to disable it.
olap.PartitionBy=PARTITION BY %1$s
general.nullsOrderingInWindowSpecification=true
olap.OrderBy=ORDER BY %1$s

# 
# Window specification
# 
olap.Window.Specification[POF]=true
olap.Window.Specification[PO]=true
olap.Window.Specification[OF]=true
olap.Window.Specification[PF]=false
olap.Window.Specification[P]=true
olap.Window.Specification[O]=true
olap.Window.Specification[F]=false
olap.Window.Specification[]=true
olap.Window.Frame.Moving=false


# 
# Olap Distinct
# 
olap.Min.distinct[any]=MIN(DISTINCT %1$s)
olap.Max.distinct[any]=MAX(DISTINCT %1$s)
olap.Sum.distinct[any]=SUM(DISTINCT %1$s)
olap.Sum.distinct[interval_day_time]=
olap.Sum.distinct[interval_year_month]=
olap.Avg.distinct[any]=AVG(DISTINCT %1$s)
olap.Avg.distinct[interval_day_time]=
olap.Avg.distinct[interval_year_month]=
olap.Count.distinct[any]=COUNT(DISTINCT %1$s)

# 
# Aggregates
# 

aggregates.Max[any]=MAX(%1$s)
aggregates.Max[interval_day_time]=
aggregates.Min[any]=MIN(%1$s)
aggregates.Count[any]=COUNT(%1$s)
aggregates.Count[blob]= 
aggregates.CountStar[]=COUNT(*)
aggregates.Sum[any]=SUM(%1$s)
aggregates.Sum[interval_day_time]=
aggregates.Sum[interval_year_month]=
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$s)
aggregates.Grouping[any]=
aggregates.Rank[any,any]=
aggregates.DenseRank[any,any]=
aggregates.PercentRank[any,any]=
aggregates.CumeDistH[any,any]=
aggregates.Grouping[any]=
aggregates.XMLAgg=
aggregates.PercentileCont[any,any]=PERCENTILE_CONT(%1$s) WITHIN GROUP (ORDER BY %2$s)
aggregates.PercentileDisc[any,any]=PERCENTILE_DISC(%1$s) WITHIN GROUP (ORDER BY %2$s)
aggregates.Median[any]=MEDIAN(%1$s)
aggregates.ArrayAgg[any]=
aggregates.ArrayAgg[any,any]=
aggregates.Collect[any]=
aggregates.ApproxCountDistinct[any]=

# 
# Distinct aggregates
# 
aggregates.Avg.distinct[any]=AVG(DISTINCT %1$s)
aggregates.Avg.distinct[interval_day_time]=
aggregates.Avg.distinct[interval_year_month]=
aggregates.Min.distinct[any]=MIN(DISTINCT %1$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)
aggregates.Sum.distinct[interval_day_time]=
aggregates.Sum.distinct[interval_year_month]=

# 
# Linear regression aggregates
# 
aggregates.Corr[any,any]=CORR(%1$s,%2$s)
aggregates.CovarPop[any,any]=COVAR_POP(%1$s,%2$s)
aggregates.CovarSamp[any,any]=COVAR_SAMP(%1$s,%2$s)
aggregates.RegrAvgX[any,any]=REGR_AVGX(%1$s, %2$s)
aggregates.RegrAvgY[any,any]=REGR_AVGY(%1$s, %2$s)
aggregates.RegrCount[any,any]=REGR_COUNT(%1$s, %2$s)
aggregates.RegrIntercept[any,any]=REGR_INTERCEPT(%1$s, %2$s)
aggregates.RegrR2[any,any]=REGR_R2(%1$s, %2$s)
aggregates.RegrSlope[any,any]=REGR_SLOPE(%1$s, %2$s)
aggregates.RegrSXX[any,any]=REGR_SXX(%1$s, %2$s)
aggregates.RegrSXY[any,any]=REGR_SXY(%1$s, %2$s)
aggregates.RegrSYY[any,any]=REGR_SYY(%1$s, %2$s)

#
# JSON aggregates.
#
aggregates.JSONArrayAgg=
aggregates.JSONObjectAgg=

# 
# Table functions
# 
functions.Unnest=

# 
# Character scalar functions
# 
functions.CharLength[any]=CHAR_LENGTH(%1$s)
functions.OctetLength[any]=OCTET_LENGTH(%1$s)
functions.BitLength[any]=BIT_LENGTH(%1$s)
functions.Upper[any]=UPPER(%1$s)
functions.Lower[any]=LOWER(%1$s)
functions.Substring[any,any]=SUBSTRING(%1$s FROM truncate(%2$s))
functions.Substring[any,any,any]=SUBSTRING(%1$s FROM truncate(%2$s) FOR truncate(%3$s))
functions.Position[any,any]=POSITION(%1$s IN %2$s)
functions.Index[any,any]=
functions.Translate[any,any]=
functions.Normalize[any]=
functions.Normalize[any,any]=
functions.Normalize[any,any,any]=


# 
# Numeric scalar functions
# 
functions.Abs[any]=ABS(%1$s)
functions.Abs[interval_day_time]=
functions.Abs[interval_year_month]=
functions.Ceiling[any]=CEILING(%1$s)
functions.Exp[any]=EXP(%1$s)
functions.Floor[any]=FLOOR(%1$s)
functions.Ln[any]=LN(%1$s)
functions.Log10[any]=LOG10(%1$s)
functions.Mod[any,any]=MOD(%1$s, %2$s)
functions.Power[any,any]=POWER(%1$s, %2$s)
functions.Random[]=RANDOM()
functions.Random[any]=
functions.Round[any]=ROUND(%1$s)
functions.Round[any,any]=ROUND(%1$s, %2$s)
functions.Round[any,any,any]=
functions.Sign[any]=SIGN(%1$s)
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]=COSH(%1$s)
functions.Arcsin[any]=ASIN(%1$s)
functions.Sin[any]=SIN(%1$s)
functions.Sinhyp[any]=SINH(%1$s)
functions.Arctan[any]=ATAN(%1$s)
functions.Tan[any]=TAN(%1$s)
functions.Tanhyp[any]=TANH(%1$s)

#
# Regular expression functions.
#
functions.SubstringRegex[any,any,nullArg,nullArg,nullArg]=REGEXP_SUBSTR(%2$s,%1$s)
functions.SubstringRegex[any,any,any,nullArg,nullArg]=REGEXP_SUBSTR(%2$s,%1$s,%3$s)
functions.SubstringRegex[any,any,any,any,nullArg]=REGEXP_SUBSTR(%2$s,%1$s,%3$s,%4$s)
functions.SubstringRegex[any,any,nullArg,any,nullArg]=
functions.SubstringRegex[any,any,any,any,any]=
functions.OccurrencesRegex[any,any,any,any]=
functions.PositionRegex[null,any,any,nullArg,nullArg,nullArg]=REGEXP_INSTR(%3$s,%2$s)
functions.PositionRegex[null,any,any,any,nullArg,nullArg]=REGEXP_INSTR(%3$s,%2$s,%4$s)
functions.PositionRegex[null,any,any,any,any,nullArg]=REGEXP_INSTR(%3$s,%2$s,%4$s,%5$s)
functions.PositionRegex[any,any,any,any,any,any]=

# 
# Olap Functions
# 
olap.Min[any]=MIN(%1$s)
olap.Max[any]=MAX(%1$s)
olap.Sum[any]=SUM(%1$s)
olap.Sum[interval_day_time]= 
olap.Sum[interval_year_month]= 
olap.Avg[any]=AVG(%1$s)
olap.Avg[interval_day_time]=
olap.Avg[interval_year_month]= 
olap.Count[any]=COUNT(%1$s)
olap.CountStar[]=COUNT(*)
olap.CumeDist[]=
olap.FirstValue[any]=FIRST_VALUE(%1$s)
# Olap lag did not throw expected exceptions
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.LastValue[any]=LAST_VALUE(%1$s)
# Olap lead did not throw expected exceptions
olap.Lead[any]=LEAD(%1$s)
olap.Lead[any,any]=LEAD(%1$s, %2$s)
olap.Lead[any,any,any]=LEAD(%1$s, %2$s, %3$s)
olap.Lead[any,any,any,any]=
# Olap Nth value did not throw expected exceptions
olap.NthValue[any,any]=
olap.NthValue[any,any,any]=
olap.NthValue[any,any,any,any]=
# Olap Ntile without an order by will sort nulls first and not last.
olap.NTile[any]=
olap.PercentileCont[any,any]=PERCENTILE_CONT(%1$s) WITHIN GROUP (ORDER BY %2$s)
olap.PercentileDisc[any,any]=PERCENTILE_DISC(%1$s) WITHIN GROUP (ORDER BY %2$s)
olap.Median[any]=MEDIAN(%1$s)
olap.Tertile[]=
olap.DenseRank[]=DENSE_RANK()
olap.PercentRank[]=
olap.Rank[]=RANK()
olap.RatioToReport[any]=RATIO_TO_REPORT(%1$s)
#olap.RowNumber without order by not supported
olap.RowNumber[]=
olap.Difference[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[]=
functions.LocalTime[numeric]= 
functions.LocalTimestamp[]=LOCALTIMESTAMP
functions.LocalTimestamp[numeric]=LOCALTIMESTAMP(%1$s)

# 
# XML Functions
# 
functions.XMLAttributes=
functions.XMLElement= 
functions.XMLComment=
functions.XMLConcat=
functions.XMLTable=
functions.XMLSerialize= 
functions.XMLQuery= 
functions.XMLExists=
functions.XMLForest=
functions.XMLPI=
functions.XMLValidate= 
functions.XMLTransform= 
functions.XMLNamespaces= 
functions.XMLText= 

# 
# Business functions.
# 
functions.AddDays[timestamp,any]=(%1$s + (interval '1' day * floor(%2$s)))
functions.AddDays[date,any]=(%1$s + interval '1' day * floor(%2$s))
functions.AddHours[any,any]=add_seconds(%1$s,floor(%2$s) * 3600)
functions.AddMinutes[any,any]=add_seconds(%1$s,floor(%2$s) * 60)
functions.AddSeconds[any,any]=add_seconds(%1$s,floor(%2$s))
functions.AddFractionalSeconds[any,any]=
functions.AddMonths[timestamp,any]=case when extract( day from %1$s - numtodsinterval( extract( day from %1$s ), 'day' ) + interval '1' day + numtoyminterval( %2$s, 'month' ) + interval '1' month - interval '1' day ) < extract( day from %1$s ) then %1$s - numtodsinterval( extract( day from %1$s ), 'day' ) + interval '1' day + numtoyminterval( %2$s, 'month' ) + interval '1' month - interval '1' day else %1$s + numtoyminterval( %2$s, 'month' ) end
functions.AddMonths[date,any]=(add_months(%1$s , floor(%2$s)))
functions.AddWeeks[any,any]=
functions.AddMonths[any,any]=
functions.AddQuarters[any,any]=
functions.AddYears[any,any]=add_months(%1$s , floor(%2$s) * 12)
functions.Age[any]=
functions.DayOfWeek[any,any]=(MOD( MOD( TO_NUMBER( TO_CHAR( %1$s, 'D' ) ) - TO_NUMBER( TO_CHAR( TO_DATE( '2003-01-06', 'YYYY-MM-DD' ), 'D' ) ) + 7, 7 ) + 1 - %2$s + 7, 7 ) + 1)
functions.DayOfYear[any]=
functions.DaysBetween[any,any]=DAYS_BETWEEN(%1$s, %2$s)
functions.DaysToEndOfMonth[any]=
functions.FirstOfMonth[any]=trunc(%1$s,'MM')
functions.FirstOfMonth[timestamp]=
functions.LastOfMonth[any]=
functions.MakeTimestamp[any,any,any]=TO_TIMESTAMP( ( LPAD( %1$d, 4, '0' ) || '-' || LPAD( %2$d, 2, '0' ) || '-' || LPAD( %3$d, 2, '0' ) ), 'YYYY-MM-DD' )
functions.MonthsBetween[any,any]=
functions.WeekOfYear[any]=TO_NUMBER( TO_CHAR( %1$s, 'IW' ) )
functions.YMDIntBetween[any,any]=
functions.HoursBetween[any,any]=
functions.MinutesBetween[any,any]=
functions.SecondsBetween[any,any]=
functions.FractionalSecondsBetween[any,any]=
functions.YearsBetween[any,any]=


#
# JSON functions.
#
functions.JSONArray=
functions.JSONExists=
functions.JSONObject=
functions.JSONQuery=
functions.JSONTable=
functions.JSONValue=


# note that pre-10.2.1RP1FP1 there were macro entries
#functions.macro.AddHours[any,any]=add_seconds(%1$s,floor(%2$s) * 3600)
#functions.macro.AddMinutes[any,any]=add_seconds(%1$s,floor(%2$s) * 60)
#functions.macro.AddSeconds[any,any]=add_seconds(%1$s,floor(%2$s))
#functions.macro.HoursBetween[any,any]=
#functions.macro.MinutesBetween[any,any]=
#functions.macro.SecondsBetween[any,any]=


# 
# 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=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=true
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=true
literals.interval_hour_to_minute=false
literals.interval_hour_to_second=false
literals.interval_minute_to_second=false
literals.binary=false
literals.blob=false
literals.boolean=false
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'
literals.format.date=DATE '%1$04d-%2$02d-%3$02d'
literals.format.interval_day=
literals.format.interval_day_to_hour=
literals.format.interval_day_to_minute=
literals.format.interval_day_to_second=INTERVAL '%8$s%1$d %2$02d:%3$02d:%4$02d%5$.10s' DAY(9) TO SECOND(9)
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=INTERVAL '%4$s%1$d-%2$02d' YEAR(9) TO MONTH
literals.format.nchar='%s'
literals.format.nvarchar='%s'
literals.format.time=
literals.format.time_with_time_zone=
literals.format.timestamp=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'
# 
# 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=true
dataType.blob=false
dataType.date=true
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=true
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=true
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=false
dataType.period=false
dataType.array=false
dataType.struct=false
dataType.map=false
dataType.json=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
# <lhs> -> <rhs> these properties are not symetrical
# e.g. dataType.promotion[char,nvarchar]=true