analytics/configuration/xqe/impala2.9.properties

#Licensed Materials - Property of IBM
# 
#OCO Source Materials
# 
#BI and PM: rdbmscert
# 
#(C) Copyright IBM Corp. 2009,2019
# 
#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

# 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
# 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=false
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=
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)
# 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]=
functions.MinutesBetween[any,any]=
functions.HoursBetween[any,any]=
functions.DaysBetween[any,any]=datediff(%1$s, %2$s)
functions.WeeksBetween[any,any]=
functions.MonthsBetween[any,any]=
functions.QuartersBetween[any,any]=
functions.YearsBetween[any,any]=
functions.DayOfWeek[any,any]=
functions.DayOfYear[any]=
functions.DaysToEndOfMonth[any]=
functions.FirstOfMonth[any]=( %1$s - interval (dayofmonth(%1$s) -1) day)
functions.LastOfMonth[any]=(( %1$s - interval (dayofmonth(%1$s) -1) day) + interval 1 month - interval 1 day)
functions.MakeTimestamp[any,any,any]=
functions.WeekOfYear[any]=weekofyear(%1$s)
functions.YMDIntBetween[any,any]=




# Multiple 'vendor' mappings were found first found is active. Select the preferred entry and delete the others.
# functions.AddDays[any,any]=(%1$s + interval floor(%2$s) day)
# functions.AddMonths[any,any]=(%1$s + interval floor(%2$s) month)
# functions.AddYears[any,any]=(%1$s + interval floor(%2$s) year)
# functions.FirstOfMonth[any,any]=( %1$s - interval(dayofmonth(%1$s) -1) day)

# 
# Table functions
# 
functions.Unnest=

# 
# Literals
# 
literals.integer=true
literals.smallint=true
literals.long=true
literals.decimal=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
# <lhs> -> <rhs> these properties are not symetrical
# e.g. dataType.promotion[char,nvarchar]=true