analytics/configuration/xqe/redshift.properties

# Licensed Materials - Property of IBM
# IBM Cognos Products: OQP
# (C) Copyright IBM Corp. 2005, 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.
#
# The #define values in comments are the values used by UDA to initialize
# their dbInfo data structure.  We are using this as a starting point for
# the XQE default values.  At some point these can be removed from this 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.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=
# due to issues in Redshift that causes statements to hang override the response from the driver.
limits.maxStatements=1
# limits.maxTableNameLength=
# limits.maxTablesInSelect=
# limits.maxUserNameLength=
# limits.defaultTransactionIsolation=
# limits.maxLengthInClause

# 
# General
# 
# 
# Null ordering
# 
general.nullsAreSortedHigh=true
general.nullsAreSortedLow=false
general.nullsAreSortedAtStart=false
general.nullsAreSortedAtEnd=false
general.nullsOrdering=false
general.nullsOrderingInWindowSpecification=false

#
# 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.expressionsInSelectList=true
supports.expressionsInINPredicate=true
supports.expressionsInOrderBy=true
supports.booleanExpressionsInSelectList=true
supports.fieldsOfComplexTypeInSelectList=false
supports.likeEscapeClause=true
supports.outerJoins=true
supports.fullOuterJoins=true
supports.subqueriesInComparisons=true
supports.subqueriesInExists=true
supports.subqueriesInIns=true
supports.subqueriesInQuantifieds=false
supports.subqueriesInCase=true
supports.correlatedSubqueries=true
supports.correlatedSubqueriesInSelectList=true
supports.correlatedSubqueriesInIns=true
supports.scalarSubqueries=true
supports.withClauseInDerivedTable=true
supports.nestedWithClause=true
supports.integerDivision=true
supports.nestedOlap=false
supports.derivedColumnLists=true
supports.orderByAlias=true
supports.orderByName=true
supports.orderByOrdinal=true
supports.blobsInGroupBy=false
supports.blobsInOrderBy=false
# Results for other scalar, aggregate and set operations will differ from DQM/ISO-SQL.
supports.emptyStringIsNull=false
supports.mixedCaseIdentifiers=false
supports.mixedCaseQuotedIdentifiers=false
supports.expressionsInGroupBy=true
supports.expressionsInOrderBy=true
supports.aliasInOrderByExpression=false
supports.orderByUnrelated=true
supports.groupByUnrelated=false
supports.thetaJoins=true
supports.equiJoins=true
supports.crossProducts=true
supports.multipleDistinctAggregates=true
supports.recursiveWithClause=false
supports.orderByInDerivedTable=true
supports.rowNumberNoOrderBy=True
supports.parameterMarkers=False
supports.constantsInWindows=true
supports.callProcedureInDerivedTable=false
supports.join.full.thetaJoins=false
supports.join.full.distinctJoins=false
supports.join.subqueriesInOnClause=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
# RTC 377496
# Set this entry to F to avoid generation of predicates of the form
# T1.C1 = T2.C1 OR ( T1.C1 IS NULL AND T2.C1 IS NULL ). Care must be
# taken, however, since doing so may cause data integrity problems if
# data contains null values.
performance.generateEqualOrNull=true

# 
# Commands
# 
commands.Select=SELECT
commands.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.map=false

# 
# Constructors - context overrides.
# 
constructors.row.simpleCase=true
constructors.row.between=true
constructors.row.isDistinctFrom=false
constructors.row.inListToTable=false


# 
# Clauses
# 
clauses.From=FROM
clauses.Where=WHERE
clauses.GroupBy=GROUP BY
clauses.Having=HAVING
clauses.WithRecursive=
clauses.With=WITH
clauses.OrderBy=ORDER BY
clauses.Distinct=DISTINCT
clauses.Top=LIMIT %1$s
clauses.Top.Position=
clauses.Limit= 
clauses.FetchFirst= 
clauses.At=
clauses.Window= 
clauses.TableSampleSystem=
clauses.TableSampleBernoulli=
clauses.ForSystemTimeAsOf=
clauses.ForSystemTimeFrom=
clauses.ForSystemTimeBetween=

# 
# Joins
# 
# Does not allow non equi joins in full outer join
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
operators.logical.IsJson=
operators.logical.IsNotJson=

# 
# Arithmetic and Character operators
# 
# Division may return a precise type if expression not coerced to imprecise type
operators.arithmetic.Add[any,any]=%1$s + %2$s
operators.arithmetic.Subtract[any,numeric]=%1$s - %2$s
operators.arithmetic.Subtract[any,datetime]= 
operators.arithmetic.Subtract[datetime,any]= 
operators.arithmetic.Multiply[any,any]=%1$s * %2$s
operators.arithmetic.Divide[any,any]=%1$s / %2$s
operators.arithmetic.UnaryPlus[any]=+%1$s
operators.arithmetic.Negate[any]=-%1$s
operators.arithmetic.Concat[any,any]=(%1$s || %2$s)
operators.arithmetic.Concat[string,any]= 
operators.arithmetic.Concat[any,string]= 



# 
# 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.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$s )
predicates.Overlaps[any,any,any,any]=(%1$s, %2$s) OVERLAPS (%3$s, %4$s)
predicates.IsNull=%1$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$s)
expressions.NullIf=NULLIF(%1$s, %2$s)
# Minimum number of arguments for Coalesce function.
expressions.Coalesce.minArgs=2

# 
# Cast
# 
expressions.Cast[any,any]=CAST(%1$s AS %2$s)
# ensure they do not round up to next highest value on cast
expressions.Cast[decimal,integer]=CAST(TRUNC(%1$s) as INTEGER)
expressions.Cast[double,integer]=CAST(TRUNC(%1$s) as INTEGER)
expressions.Cast[float,integer]=CAST(TRUNC(%1$s) as INTEGER)
expressions.Cast[timestamp,char]=
expressions.Cast[timestamp,varchar]=

# 
# Extract
# 
expressions.Extract.YEAR[any]=CAST(EXTRACT(YEAR FROM %1$s) as INTEGER)
expressions.Extract.MONTH[any]=CAST(EXTRACT(MONTH FROM %1$s) as INTEGER)
expressions.Extract.DAY[any]=CAST(EXTRACT(DAY FROM %1$s) as INTEGER)
expressions.Extract.HOUR[any]=CAST(EXTRACT(HOUR FROM %1$s) as INTEGER)
expressions.Extract.MINUTE[any]=CAST(EXTRACT(MINUTE FROM %1$s) as INTEGER)
expressions.Extract.SECOND[any]=EXTRACT(SECOND FROM %1$s) + (EXTRACT (MILLISECONDS FROM %1$s) / 1000.0)
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)

# 
# Windowed aggregates (SQL/OLAP).
# 

olap.Min[any]=MIN(%1$s)
olap.Max[any]=MAX(%1$s)
olap.Sum[any]=SUM(%1$s)
olap.Avg[any]=AVG(%1$s)
olap.Count[any]=COUNT(%1$s)
olap.CountStar[]=COUNT(*)
olap.StdDevSamp[any]=STDDEV_SAMP(%1$s)
olap.StdDevPop[any]=STDDEV_POP(%1$s)
olap.VarSamp[any]=VAR_SAMP(%1$s)
olap.VarPop[any]=VAR_POP(%1$s)
olap.PercentileCont[any,any]=PERCENTILE_CONT(%1$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)
# Redshift forces functions which use an ORDER BY to include a frame clause
olap.FirstValue[any]=FIRST_VALUE(%1$s)
olap.Lag[any]=LAG(%1$s)
olap.Lag[any,any]=LAG(%1$s, CAST(TRUNC(%2$s) as INTEGER))
olap.Lag[any,any,any]=
olap.Lag[any,any,any,any]=
olap.LastValue[any]=LAST_VALUE(%1$s)
olap.Lead[any]=LEAD(%1$s)
olap.Lead[any,any]=LEAD(%1$s, CAST(TRUNC(%2$s) as INTEGER))
olap.Lead[any,any,any]=
olap.Lead[any,any,any,any]=
olap.NthValue[any,any]=NTH_VALUE(%1$s, %2$s)
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]=NTILE(%1$s)
olap.Tertile[]=
olap.Rank[]=RANK()
olap.DenseRank[]=DENSE_RANK()
olap.PercentRank[]=PERCENT_RANK()
olap.CumeDist[]=CUME_DIST()
olap.RatioToReport[any]=
olap.RowNumber[]=ROW_NUMBER()
olap.Difference[any]=
olap.Collect[any]=



# 
# Window clause
# 
olap.Window=OVER(%1$s)
olap.PartitionBy=PARTITION BY %1$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]=false
olap.Window.Specification[P]=true
olap.Window.Specification[O]=false
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)
# Avg may return a precise type if expression not coerced to imprecise type
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$s)
aggregates.Rank[any,any]=
aggregates.DenseRank[any,any]=
aggregates.PercentRank[any,any]=PERCENT_RANK(%1$s) WITHIN GROUP (ORDER BY %2$s)
aggregates.CumeDistH[any,any]=
aggregates.PercentileDisc[any,any]=
aggregates.PercentileCont[any,any]=PERCENTILE_CONT(%1$s) WITHIN GROUP (ORDER BY %2$s)
aggregates.Median[any]=MEDIAN(%1$s)
aggregates.Grouping[any]=
aggregates.XMLAgg=
aggregates.ArrayAgg[any]= 
aggregates.ArrayAgg[any,any]= 
aggregates.Collect[any]=
aggregates.ApproxCountDistinct[any]=APPROXIMATE COUNT(DISTINCT(%1$s))

# 
# 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]=CHAR_LENGTH(%1$s)
functions.OctetLength[any]=
functions.BitLength[any]=
functions.Upper[string]= 
functions.Upper[varchar]=UPPER(%1$s)
functions.Lower[string]= 
functions.Lower[varchar]=LOWER(%1$s)
functions.Substring[any,any]=SUBSTRING(%1$s FROM CAST(TRUNC(%2$s) as INTEGER))
functions.Substring[any,any,any]=SUBSTRING(%1$s FROM CAST(TRUNC(%2$s) as INTEGER) FOR CAST(TRUNC(%3$s) as INTEGER))
functions.Position[any,any]=POSITION(%1$s IN %2$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,nullArg,nullArg,nullArg]=REGEXP_SUBSTR(%2$s, %1$s)
functions.SubstringRegex[any,any,any,nullArg,nullArg]=REGEXP_SUBSTR(%2$s, %1$s, %3$s)
functions.SubstringRegex[any,any,any,any,any]=
functions.OccurrencesRegex[any,any,nullArg,nullArg]=REGEXP_COUNT(%2$s, %1$s)
functions.OccurrencesRegex[any,any,any,nullArg]=REGEXP_COUNT(%2$s, %1$s, %3$s)
functions.OccurrencesRegex[any,any,any,any]=
functions.PositionRegex[nullArg,any,any,nullArg,nullArg,nullArg]=REGEXP_INSTR(%3$s, %2$s)
functions.PositionRegex[nullArg,any,any,any,nullArg,nullArg]=REGEXP_INSTR(%3$s, %2$s, %4$s)
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)
functions.Ln[any]=LN(%1$s)
# not supported on Redshift tables
functions.Ln[decimal]=
functions.Log10[any]=LOG(%1$s)
# not supported on Redshift tables 
functions.Log10[decimal]=
functions.Mod[any,any]=MOD(%1$s, %2$s)
functions.Mod[double,any]=
functions.Mod[float,any]=
functions.Mod[decimal,any]=
functions.Mod[any,double]=
functions.Mod[any,float]=
functions.Mod[any,decimal]=
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]=

# 
# Table functions
# 
functions.Unnest=

# 
# 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.
# Redshift constrains the usage of LOCAL functions to the leader node which can prevent queries from using non-leader tables
# and those functions are marked for deprecation
functions.CurrentDate[]=CURRENT_DATE
functions.CurrentTime[]=
functions.CurrentTimestamp[]=
functions.CurrentTimestamp[numeric]= 
functions.LocalTime[]=
functions.LocalTime[numeric]= 
functions.LocalTimestamp[]=SYSDATE
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=
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=true
functions.XMLQuery.EmptyHandlingOption.EMPTY_ON_EMPTY=true
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]=dateadd(hours, cast(floor(%2$s) as int), %1$s)
functions.AddMinutes[any,any]=dateadd(minutes, cast(floor(%2$s) as int), %1$s)
functions.AddSeconds[any,any]=dateadd(seconds, cast(floor(%2$s) as int), %1$s)

functions.AddFractionalSeconds[any,any]=

functions.AddDays[timestamp,any]=dateadd(days, cast(floor(%2$s) as int), %1$s)
functions.AddDays[date,any]=cast(dateadd(days, cast(floor(%2$s) as int), %1$s) as date)
functions.AddDays[any,any]=dateadd(days, cast(floor(%2$s) as int), %1$s)

functions.AddWeeks[any,any]=

functions.AddMonths[timestamp,any]=dateadd(months, cast(floor(%2$s) as int), %1$s)
functions.AddMonths[date,any]=cast(dateadd(months, cast(floor(%2$s) as int), %1$s) as date)
functions.AddMonths[any,any]=dateadd(months, cast(floor(%2$s) as int), %1$s)

functions.AddQuarters[any,any]=

functions.AddYears[timestamp,any]=dateadd(years, cast(floor(%2$s) as int), %1$s)
functions.AddYears[date,any]=cast(dateadd(years, cast(floor(%2$s) as int), %1$s) as date)
functions.AddYears[any,any]=dateadd(years, cast(floor(%2$s) as int), %1$s)

functions.Age[any]=
functions.FractionalSecondsBetween[any,any]=
functions.SecondsBetween[any,any]=datediff(second, %2$s, %1$s)
functions.MinutesBetween[any,any]=datediff(minute, %2$s, %1$s)
functions.HoursBetween[any,any]=datediff(hour, %2$s, %1$s)
functions.DaysBetween[any,any]=datediff(days, %2$s, %1$s)
functions.WeeksBetween[any,any]=
functions.MonthsBetween[any,any]=datediff(months, %2$s, %1$s)
functions.QuartersBetween[any,any]=
functions.YearsBetween[any,any]=datediff(years, %2$s, %1$s)

functions.DayOfWeek[any,any]=(MOD(cast(TO_CHAR( %1$s, 'D' ) as integer ) + 6 - %2$s, 7 ) + 1)
functions.DayOfYear[any]=DATE_PART('doy', %1$s)
functions.DaysToEndOfMonth[any]=datediff(days, %1$s, cast(dateadd(months, 1, %1$s - (cast(extract( day from %1$s) as integer))) as timestamp))
functions.FirstOfMonth[timestamp]=(%1$s - (EXTRACT( DAY FROM %1$s )-1) * INTERVAL '1 DAY')
functions.FirstOfMonth[date]=cast((%1$s - (EXTRACT( DAY FROM %1$s )-1) * INTERVAL '1 DAY') as date)
functions.FirstOfMonth[any]=(%1$s - (EXTRACT( DAY FROM %1$s )-1) * INTERVAL '1 DAY')
functions.LastOfMonth[timestamp]=dateadd(days, -1, dateadd(months, 1, (%1$s - (extract(day from %1$s) - 1))))
functions.LastOfMonth[date]=cast(dateadd(days, -1, dateadd(months, 1, (%1$s - (extract(day from %1$s) - 1)))) as date)
functions.LastOfMonth[any]=dateadd(days, -1, dateadd(months, 1, (%1$s - (extract(day from %1$s) - 1))))
functions.MakeTimestamp[any,any,any]=
functions.WeekOfYear[any]=
functions.YMDIntBetween[any,any]=


# 
# Literals
# 
literals.smallint=true
literals.decimal=true
literals.float=false
literals.char=true
literals.nchar=false
literals.varchar=true
literals.nvarchar=false
literals.blob=false
literals.clob=false
literals.nclob=false
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=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=true
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.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.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=
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='%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'
literals.format.double=cast(%s as double precision)
# 
# DataTypes
# 
dataType.smallint=true
dataType.integer=true
dataType.long=true
dataType.decimal=true
dataType.float=false
dataType.double=true
dataType.char=true
dataType.nchar=false
dataType.varchar=true
dataType.nvarchar=false
dataType.clob=false
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=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=false
dataType.perioddate=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