today we return the column names but not the data types. Pinot knows about the data type either from the segment metadata or from the schema. Knowing the data types will make it easier to write connectors to Pinot.
{ "selectionResults":{ "columns":[ "Cancelled", "Carrier", "DaysSinceEpoch", "Delayed", "Dest", "DivAirports", "Diverted", "Month", "Origin", "Year" ], "results":[ [ "0", "AA", "16130", "0", "SFO", [], "0", "3", "LAX", "2014" ], [ "0", "AA", "16130", "0", "LAX", [], "0", "3", "SFO", "2014" ], [ "0", "AA", "16130", "0", "SFO", [], "0", "3", "LAX", "2014" ] ] }, "traceInfo":{}, "numDocsScanned":3, "aggregationResults":[], "timeUsedMs":10, "segmentStatistics":[], "exceptions":[], "totalDocs":102 }
today we return the column names but not the data types. Pinot knows about the data type either from the segment metadata or from the schema. Knowing the data types will make it easier to write connectors to Pinot.
{ "selectionResults":{ "columns":[ "Cancelled", "Carrier", "DaysSinceEpoch", "Delayed", "Dest", "DivAirports", "Diverted", "Month", "Origin", "Year" ], "results":[ [ "0", "AA", "16130", "0", "SFO", [], "0", "3", "LAX", "2014" ], [ "0", "AA", "16130", "0", "LAX", [], "0", "3", "SFO", "2014" ], [ "0", "AA", "16130", "0", "SFO", [], "0", "3", "LAX", "2014" ] ] }, "traceInfo":{}, "numDocsScanned":3, "aggregationResults":[], "timeUsedMs":10, "segmentStatistics":[], "exceptions":[], "totalDocs":102 }