#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 # -> these properties are not symetrical # e.g. dataType.promotion[char,nvarchar]=true