analytics/configuration/xqe/presto.properties

#Licensed Materials - Property of IBM
# 
#OCO Source Materials
# 
#BI and PM: rdbmscert
# 
#(C) Copyright IBM Corp. 2009,2020
# 
#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.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=1
# limits.maxTableNameLength=
# limits.maxTablesInSelect=
# limits.maxUserNameLength=
# limits.defaultTransactionIsolation=
# limits.maxLengthInClause

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

# 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.literalsInSelectList=true
supports.booleanExpressionsInSelectList=true
supports.fieldsOfComplexTypeInSelectList=false
supports.likeEscapeClause=true
supports.outerJoins=true
supports.fullOuterJoins=true
supports.subqueriesInComparisons=true
supports.subqueriesInExists=true
# Due to presto throwing errors re NULL values are not allowed on the probe side of SemiJoin operator
supports.subqueriesInIns=false
# Presto computes incorrect results
supports.subqueriesInQuantifieds=false
supports.subqueriesInOnClause=true
supports.subqueriesInCase=true
supports.subqueriesInAggregate=true
supports.correlatedSubqueries=false
supports.correlatedSubqueriesInSelectList=false
supports.correlatedSubqueriesInIns=false
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
supports.transactions=false
# Results for other scalar, aggregate and set operations will differ from DQM/ISO-SQL.
supports.emptyStringIsNull=false
supports.expressionsInGroupBy=true
supports.expressionsInOrderBy=true
supports.expressionsInLikePattern=true
supports.aliasInOrderByExpression=false
supports.orderByUnrelated=true
supports.groupByUnrelated=false
supports.thetaJoins=true
supports.equiJoins=true
supports.crossProducts=true
supports.multipleDistinctAggregates=false
supports.recursiveWithClause=false
supports.rowNumberNoOrderBy=true
supports.parameterMarkers=false
supports.constantsInWindows=true
supports.orderByInDerivedTable=true
supports.callProcedureInDerivedTable=false
# Cannot use parameter markers in subquery
supports.orderByInDerivedTable=true
#join condition
supports.join.betweenInOnClause=true
supports.join.inPredicateInOnClause=true
supports.join.isNullInOnClause=true
supports.join.likeInOnClause=true
supports.join.notInOnClause=true
supports.join.orInOnClause=true
supports.join.subqueriesInOnClause=true
supports.join.onlyEquiWithAnd=false
supports.join.inner.limitedThetaJoins=false
supports.join.outer.thetaJoins=true
supports.join.full.thetaJoins=true
supports.join.full.distinctJoins=true
#isolation level support
supports.isolationLevelReadUncommitted=false
supports.isolationLevelReadCommitted=false
supports.isolationLevelRepeatableRead=false
supports.isolationLevelSerializable=false
supports.constantsInCount=true
supports.columnFiltersOnly=false
supports.hanaInputParameters=false
supports.mixedCaseIdentifiers=false
supports.mixedCaseQuotedIdentifiers=false

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=true
constructors.row=false
constructors.array=true
constructors.period=false
constructors.map=false

# 
# Constructors - context overrides.
# 
constructors.row.simpleCase=true
constructors.row.between=false
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$s
clauses.At=
clauses.Window= 
clauses.TableSampleSystem=TABLESAMPLE SYSTEM (%1$s)
clauses.TableSampleBernoulli=TABLESAMPLE BERNOULLI (%1$s)
clauses.ForSystemTimeAsOf=
clauses.ForSystemTimeFrom=
clauses.ForSystemTimeBetween=


# 
# Joins
# 
joins.Cross=%1$s CROSS JOIN %2$s
joins.Inner=%1$s INNER JOIN %2$s ON %3$s
joins.LeftOuter=%1$s LEFT OUTER JOIN %2$s ON %3$s
joins.RightOuter=%1$s RIGHT OUTER JOIN %2$s ON %3$s
joins.FullOuter=%1$s FULL OUTER JOIN %2$s ON %3$s
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=
operators.logical.IsNot=
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,datetime]= 
operators.arithmetic.Subtract[datetime,any]=
operators.arithmetic.Subtract[any,numeric]=%1$s - %2$s
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


# 
# Grouping Operators
# 
operators.groupBy.Rollup=ROLLUP
operators.groupBy.Cube=CUBE
operators.groupBy.GroupingSets= 

# 
# Comparison Predicates
# 


# due to Presto not correctly supporting character types nor describing them via JDBC consistently wrap expression to variable
# length with no blank padding

predicates.comparison.Equals[any,any]=%1$s = %2$s
predicates.comparison.Equals[text,text]=rtrim(cast(%1$s as varchar)) = rtrim(cast(%2$s as varchar))

predicates.comparison.GreaterThan[any,any]=%1$s > %2$s
predicates.comparison.GreaterThan[text,text]=rtrim(cast(%1$s as varchar)) > rtrim(cast(%2$s as varchar))

predicates.comparison.GreaterThanOrEquals[any,any]=%1$s >= %2$s
predicates.comparison.GreaterThanOrEquals[text,text]=rtrim(cast(%1$s as varchar)) >= rtrim(cast(%2$s as varchar))

predicates.comparison.LessThan[any,any]=%1$s < %2$s
predicates.comparison.LessThan[text,text]=rtrim(cast(%1$s as varchar)) < rtrim(cast(%2$s as varchar))

predicates.comparison.LessThanOrEquals[any,any]=%1$s <= %2$s
predicates.comparison.LessThanOrEquals[text,text]=rtrim(cast(%1$s as varchar)) <= rtrim(cast(%2$s as varchar))

predicates.comparison.NotEquals[any,any]=%1$s <> %2$s
predicates.comparison.NotEquals[text,text]=rtrim(cast(%1$s as varchar)) <> rtrim(cast(%2$s as varchar))


# 
# Predicates
# 

predicates.Between[any,any,any]=%1$s BETWEEN %2$s AND %3$s
predicates.Between[text,text,text]=rtrim(cast(%1$s as varchar)) BETWEEN rtrim(cast(%2$s as varchar)) AND rtrim(cast(%3$s as varchar))

predicates.In[any,any]=%1$s IN ( %2$s )
predicates.In[text,text]=rtrim(cast(%1$s as varchar)) IN ( %2$s )

predicates.Overlaps[any,any,any,any]= 
predicates.IsNull[any]=%1$s IS NULL
predicates.IsNotNull[any]=%1$s IS NOT NULL
predicates.Like=%1$s LIKE %2$s
predicates.Like.escape=%1$s LIKE %2$s ESCAPE %3$s
predicates.LikeRegex=%1$s LIKE_REGEX %2$s
predicates.LikeRegex.flag=
predicates.Similar= 
predicates.Similar.escape= 
predicates.Exists=EXISTS %1$s
# Presto computes incorrect results with quantified predicate and subquery
predicates.All= 
predicates.Any= 
predicates.Some=
predicates.IsDistinctFrom[any,any]=%1$s IS DISTINCT FROM %2$s
# due to Presto presto throwing type error
predicates.IsDistinctFrom[text,text]=(%1$s IS NULL AND %2$s IS NOT NULL) OR (%1$s IS NOT NULL AND %2$s IS NULL) OR rtrim(cast(%1$s as varchar)) <> rtrim(cast(%2$s as varchar))


predicates.IsNotDistinctFrom[any,any]=%1$s IS NOT DISTINCT FROM %2$s
predicates.IsNotDistinctFrom[text,text]=rtrim(cast(%1$s as varchar)) = rtrim(cast(%2$s as varchar)) 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

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

expressions.Coalesce=COALESCE(%1$s)

expressions.NullIf=NULLIF(%1$s, %2$s)




# 
# Cast
# 
# Cannot cast from null value to Float
# Cannot cast from null value to TimestampWithTZ
# presto 0.160 not accepting char source types
# presto 0.160 not accepting char as a target type
# presto 0.160 not accepting time zone bearing types as target type
# presto 0.160 not accepting intervals as a target type
# presto 0.170 not supporting cast to integer types
expressions.Cast[any,any]=CAST(%1$s AS %2$s)
expressions.Cast[any,float]=
expressions.Cast[float,any]=
expressions.Cast[any,char]=
expressions.Cast[text,smallint]=cast(rtrim(cast(%1$s as varchar)) as smallint)
expressions.Cast[text,integer]=cast(rtrim(cast(%1$s as varchar)) as integer)
expressions.Cast[text,long]=cast(rtrim(cast(%1$s as varchar)) as bigint)
expressions.Cast[double,varchar]=
expressions.Cast[time,timestamp]=
expressions.Cast[any,interval_day_time]=
expressions.Cast[any,interval_year_month]=
expressions.Cast[any,time_with_time_zone]=
expressions.Cast[any,timestamp_with_time_zone]=


# 
# 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]= 
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).
# 
# presto 0.160 does not support sum or avg of interval types
olap.Max[any]=MAX(%1$s)
olap.Min[any]=MIN(%1$s)
olap.Sum[any]=SUM(%1$s)
olap.Sum[interval_year_month]=
olap.Sum[interval_day_time]=
olap.Avg[any]=AVG(%1$s)
olap.Avg[interval_year_month]=
olap.Avg[interval_day_time]=
olap.Count[any]=COUNT(%1$s)
olap.CountStar[]=COUNT(*)
olap.StdDevPop[any]= 
olap.StdDevSamp[any]= 
olap.VarPop[any]= 
olap.VarSamp[any]= 
olap.Rank[]=RANK()
olap.DenseRank[]=DENSE_RANK()
olap.CumeDist[]=CUME_DIST()
olap.PercentileCont[any,any]=
olap.PercentileDisc[any,any]=
olap.PercentRank[]=PERCENT_RANK()
olap.RatioToReport[any]=
olap.Median[any]=
olap.RowNumber[]=ROW_NUMBER()
olap.Difference[any]=
olap.FirstValue[any]=FIRST_VALUE(%1$s)
olap.LastValue[any]=LAST_VALUE(%1$s)
# Olap Ntile without an order by will sort nulls first and not last.
olap.NTile[any]=NTILE(%1$s)
olap.Tertile[]=
olap.Lag[any]=LAG(%1$s)
olap.Lag[any,any]=LAG(%1$s,  cast( floor(%2$s) as integer))
olap.Lag[any,any,any]=LAG(%1$s,  cast( floor(%2$s) as integer), %3$s)
olap.Lag[any,any,any,any]=
olap.Lead[any]=LEAD(%1$s)
olap.Lead[any,any]=LEAD(%1$s,  cast( floor(%2$s) as integer))
olap.Lead[any,any,any]=LEAD(%1$s,  cast( floor(%2$s) as integer), %3$s)
olap.Lead[any,any,any,any]=
olap.NthValue[any,any]=NTH_VALUE(%1$s, cast( floor(%2$s) as integer))
olap.NthValue[any,any,any]=
olap.NthValue[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]=true
olap.Window.Specification[OF]=true
olap.Window.Specification[PO]=true
olap.Window.Specification[P]=true
olap.Window.Specification[O]=true
olap.Window.Specification[F]=true
olap.Window.Specification[]=true

# 
# Olap Distinct
# 
olap.Min.distinct[any]= 
olap.Max.distinct[any]= 
olap.Sum.distinct[any]= 
olap.Avg.distinct[any]= 
olap.Count.distinct[any]= 

# 
# Aggregates
# 
# presto 0.160 does not support sum or avg of interval types
aggregates.Max[any]=MAX(%1$s)
# due to presto not returning max time
aggregates.Max[time]=
aggregates.Min[any]=MIN(%1$s)
# due to presto not returning correct min time
aggregates.Min[time]=
aggregates.Count[any]=COUNT(%1$s)
aggregates.CountStar[]=COUNT(*)
aggregates.Sum[any]=SUM(%1$s)
aggregates.Sum[interval_day_time]=
aggregates.Sum[interval_year_month]=
aggregates.Avg[any]=AVG(%1$s)
aggregates.Avg[interval_day_time]=
aggregates.Avg[interval_year_month]=
aggregates.StdDevPop[any]=STDDEV_POP(%1$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$s)
aggregates.CumeDistH[any,any]=
aggregates.PercentileDisc[any,any]= 
aggregates.PercentileCont[any,any]=
aggregates.Median[any]=
aggregates.Grouping[any]=
aggregates.XMLAgg[any]=
aggregates.ArrayAgg[any]= 
aggregates.ArrayAgg[any,any]= 
aggregates.Collect[any]=
aggregates.ApproxCountDistinct[any]=APPROX_DISTINCT(%1$s)

# 
# Distinct aggregates
# 
# presto 0.160 does not support sum or avg of interval types
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)
# due to presto returning wrong min value for time
aggregates.Min.distinct[time]=
aggregates.Max.distinct[any]=MAX(DISTINCT %1$s)
# due to presto returning wrong max value for time
aggregates.Max.distinct[time]=
aggregates.Count.distinct[any]=COUNT(DISTINCT %1$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]=
aggregates.CovarPop[any,any]=
aggregates.CovarSamp[any,any]=
aggregates.RegrAvgX[any,any]=
aggregates.RegrAvgY[any,any]=
aggregates.RegrCount[any,any]=
aggregates.RegrIntercept[any,any]=
aggregates.RegrR2[any,any]=
aggregates.RegrSlope[any,any]=
aggregates.RegrSXX[any,any]=
aggregates.RegrSXY[any,any]=
aggregates.RegrSYY[any,any]=

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

# 
# Character scalar functions
# 
functions.CharLength[any]=LENGTH(%1$s)
functions.OctetLength[any]=
functions.BitLength[any]=
functions.Upper[any]=UPPER(%1$s)
functions.Lower[any]=LOWER(%1$s)
functions.Substring[any,any]=SUBSTR(%1$s, cast( floor(%2$s) as integer))
functions.Substring[any,any,any]=SUBSTR(%1$s, cast( floor(%2$s) as integer), cast( floor(%3$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]=NORMALIZE(%1$s)
functions.Normalize[any,any]=NORMALIZE(%1$s,%2$s) 
functions.Normalize[any,any,any]=


#
# 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)
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$s)
functions.WidthBucket[any,any,any,any]=WIDTH_BUCKET(%1$s, %2$s, %3$s, %4$s)

# 
# 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$s)
functions.Sinhyp[any]= 
functions.Arctan[any]=ATAN(%1$s)
functions.Tan[any]=TAN(%1$s)
functions.Tanhyp[any]=TANH(%1$s)

# 
# Temporal value expressions
# 
# Note: JDBC does not define fractional seconds for TIME data type.
functions.CurrentDate[]=CURRENT_DATE
functions.CurrentTime[]=CURRENT_TIME
functions.CurrentTime[numeric]=
functions.CurrentTimestamp[]=CURRENT_TIMESTAMP
functions.CurrentTimestamp[numeric]= 
functions.LocalTime[]=LOCALTIME
functions.LocalTime[numeric]= 
functions.LocalTimestamp[]=LOCALTIMESTAMP
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=true
functions.XMLQuery.EmptyHandlingOption.EMPTY_ON_EMPTY=true
functions.XMLSerialize.DeclarationOption.INCLUDING_XMLDECLARATION=false
functions.XMLSerialize.DeclarationOption.EXCLUDING_XMLDECLARATION=false

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


# 
# Business functions.
# 
functions.AddHours[any,any]=DATE_ADD('hour',cast( floor(%2$s) as integer), %1$s)
functions.AddMinutes[any,any]=DATE_ADD('minute',cast( floor(%2$s) as integer), %1$s)
functions.AddSeconds[any,any]=DATE_ADD('second',cast( floor(%2$s) as integer), %1$s)
functions.AddFractionalSeconds[any,any]=DATE_ADD('millisecond',cast( floor(%2$s) as integer), %1$s)
functions.AddDays[any,any]=DATE_ADD('day',cast( floor(%2$s) as integer), %1$s)
functions.AddWeeks[any,any]=DATE_ADD('week',cast( floor(%2$s) as integer), %1$s)
functions.AddMonths[any,any]=
functions.AddQuarters[any,any]=
functions.AddYears[any,any]=DATE_ADD('year',cast( floor(%2$s) as integer), %1$s)
functions.Age[any]=
functions.FractionalSecondsBetween[any,any]=DATE_DIFF('millisecond', %1$s, %2$s)
functions.SecondsBetween[any,any]=DATE_DIFF('second', %1$s, %2$s)
functions.MinutesBetween[any,any]=DATE_DIFF('minute', %1$s, %2$s)
functions.HoursBetween[any,any]=DATE_DIFF('hour', %1$s, %2$s)
functions.DaysBetween[any,any]=DATE_DIFF('day', %1$s, %2$s)
functions.WeeksBetween[any,any]=DATE_DIFF('week', %1$s, %2$s)
functions.MonthsBetween[any,any]=
functions.QuartersBetween[any,any]=
functions.YearsBetween[any,any]=DATE_DIFF('year', %1$s, %2$s)
functions.DayOfWeek[any,any]=
functions.DayOfYear[any]=extract(day_of_year from  %1$s)
functions.DaysToEndOfMonth[any]=DATE_DIFF('day', %1$s, ((DATE_TRUNC('month', %1$s) + interval '1' month) - interval '1' day))
functions.FirstOfMonth[any]=DATE_TRUNC('month', %1$s)
functions.LastOfMonth[any]=((DATE_TRUNC('month', %1$s) + interval '1' month) - interval '1' day)
functions.MakeTimestamp[any,any,any]=DATE_PARSE(cast(cast(%1$s as integer) as varchar) || '/' || cast(cast(%2$s as integer) as varchar) || '/' || cast(cast(%3$s as integer) as varchar), '%Y/%m/%d')
functions.WeekOfYear[any]=WEEK_OF_YEAR(%1$s)
functions.YMDIntBetween[any,any]=


# 
# Table functions
# 
functions.Unnest=

# 
# Literals
# 
literals.smallint=true
literals.decimal=true
literals.float=true
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=true
literals.time_with_time_zone=false
literals.timestamp=true
literals.timestamp_with_time_zone=true
# while Presto accepts interval literals it only describes two types y-m and d-s
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.date=true
literals.time=true
literals.time_with_time_zone=true
literals.timestamp=true
literals.timestamp_with_time_zone=true
literals.binary=false
literals.boolean=false
literals.xml=false
literals.datalink=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=cast('%1$s' as varchar)
literals.format.clob='%1$s'
literals.format.date=DATE '%1$04d-%2$02d-%3$02d'
literals.format.interval_day=
literals.format.interval_day_to_hour=
literals.format.interval_day_to_minute=
# presto only accepts 8 fractional places
literals.format.interval_day_to_second=INTERVAL %8$s'%1$d %2$02d:%3$02d:%4$02d%5$.8s' 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=INTERVAL %4$s'%1$d-%2$d' YEAR TO MONTH
literals.format.nchar='%1$s'
literals.format.nvarchar='%1$s'
literals.format.time=TIME '%1$02d:%2$02d:%3$02d%4$.3s'
literals.format.time_with_time_zone=TIME '%1$02d:%2$02d:%3$02d%4$.3s%7$c%5$02d:%6$02d'
literals.format.timestamp=TIMESTAMP '%1$04d-%2$02d-%3$02d %4$02d:%5$02d:%6$02d%7$.3s'
literals.format.timestamp_with_time_zone=TIMESTAMP '%1$04d-%2$02d-%3$02d %4$02d:%5$02d:%6$02d%7$.3s%10$c%8$02d:%9$02d'
literals.format.varchar='%s'
# otherwise presto treat x.y as double type
literals.format.decimal=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=true
dataType.time=true
dataType.time_with_time_zone=false
dataType.timestamp=true
dataType.timestamp_with_time_zone=true
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=true
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