CSV incorrectly parses multiple results with multiple tables #61
Description
CSV parsing fails to properly parse the following into 4 tables:
#group,false,false,true,true,true,true,false,false,true
#datatype,string,long,string,string,dateTime:RFC3339,dateTime:RFC3339,dateTime:RFC3339,double,string
#default,t1,,,,,,,,
,result,table,_field,_measurement,_start,_stop,_time,_value,tag
,,0,value,python_client_test,2010-02-27T04:48:32.752600083Z,2020-02-27T16:48:32.752600083Z,2020-02-27T16:20:00Z,2,test1
,,0,value,python_client_test,2010-02-27T04:48:32.752600083Z,2020-02-27T16:48:32.752600083Z,2020-02-27T16:21:40Z,2,test1
,,0,value,python_client_test,2010-02-27T04:48:32.752600083Z,2020-02-27T16:48:32.752600083Z,2020-02-27T16:23:20Z,2,test1
,,0,value,python_client_test,2010-02-27T04:48:32.752600083Z,2020-02-27T16:48:32.752600083Z,2020-02-27T16:25:00Z,2,test1
,,0,value,python_client_test,2010-02-27T04:48:32.752600083Z,2020-02-27T16:48:32.752600083Z,2020-02-27T16:26:40Z,2,test1
,,0,value,python_client_test,2010-02-27T04:48:32.752600083Z,2020-02-27T16:48:32.752600083Z,2020-02-27T16:28:20Z,2,test1
,,0,value,python_client_test,2010-02-27T04:48:32.752600083Z,2020-02-27T16:48:32.752600083Z,2020-02-27T16:30:00Z,2,test1
,,1,value,python_client_test,2010-02-27T04:48:32.752600083Z,2020-02-27T16:48:32.752600083Z,2020-02-27T16:20:00Z,2,test2
,,1,value,python_client_test,2010-02-27T04:48:32.752600083Z,2020-02-27T16:48:32.752600083Z,2020-02-27T16:21:40Z,2,test2
,,1,value,python_client_test,2010-02-27T04:48:32.752600083Z,2020-02-27T16:48:32.752600083Z,2020-02-27T16:23:20Z,2,test2
,,1,value,python_client_test,2010-02-27T04:48:32.752600083Z,2020-02-27T16:48:32.752600083Z,2020-02-27T16:25:00Z,2,test2
,,1,value,python_client_test,2010-02-27T04:48:32.752600083Z,2020-02-27T16:48:32.752600083Z,2020-02-27T16:26:40Z,2,test2
,,1,value,python_client_test,2010-02-27T04:48:32.752600083Z,2020-02-27T16:48:32.752600083Z,2020-02-27T16:28:20Z,2,test2
,,1,value,python_client_test,2010-02-27T04:48:32.752600083Z,2020-02-27T16:48:32.752600083Z,2020-02-27T16:30:00Z,2,test2
#group,false,false,true,true,true,true,false,false,true
#datatype,string,long,string,string,dateTime:RFC3339,dateTime:RFC3339,dateTime:RFC3339,double,string
#default,t2,,,,,,,,
,result,table,_field,_measurement,_start,_stop,_time,_value,tag
,,0,value,python_client_test,2010-02-27T04:48:32.752600083Z,2020-02-27T16:48:32.752600083Z,2020-02-27T16:20:00Z,2,test1
,,0,value,python_client_test,2010-02-27T04:48:32.752600083Z,2020-02-27T16:48:32.752600083Z,2020-02-27T16:21:40Z,2,test1
,,0,value,python_client_test,2010-02-27T04:48:32.752600083Z,2020-02-27T16:48:32.752600083Z,2020-02-27T16:23:20Z,2,test1
,,0,value,python_client_test,2010-02-27T04:48:32.752600083Z,2020-02-27T16:48:32.752600083Z,2020-02-27T16:25:00Z,2,test1
,,0,value,python_client_test,2010-02-27T04:48:32.752600083Z,2020-02-27T16:48:32.752600083Z,2020-02-27T16:26:40Z,2,test1
,,0,value,python_client_test,2010-02-27T04:48:32.752600083Z,2020-02-27T16:48:32.752600083Z,2020-02-27T16:28:20Z,2,test1
,,0,value,python_client_test,2010-02-27T04:48:32.752600083Z,2020-02-27T16:48:32.752600083Z,2020-02-27T16:30:00Z,2,test1
,,1,value,python_client_test,2010-02-27T04:48:32.752600083Z,2020-02-27T16:48:32.752600083Z,2020-02-27T16:20:00Z,2,test2
,,1,value,python_client_test,2010-02-27T04:48:32.752600083Z,2020-02-27T16:48:32.752600083Z,2020-02-27T16:21:40Z,2,test2
,,1,value,python_client_test,2010-02-27T04:48:32.752600083Z,2020-02-27T16:48:32.752600083Z,2020-02-27T16:23:20Z,2,test2
,,1,value,python_client_test,2010-02-27T04:48:32.752600083Z,2020-02-27T16:48:32.752600083Z,2020-02-27T16:25:00Z,2,test2
,,1,value,python_client_test,2010-02-27T04:48:32.752600083Z,2020-02-27T16:48:32.752600083Z,2020-02-27T16:26:40Z,2,test2
,,1,value,python_client_test,2010-02-27T04:48:32.752600083Z,2020-02-27T16:48:32.752600083Z,2020-02-27T16:28:20Z,2,test2
,,1,value,python_client_test,2010-02-27T04:48:32.752600083Z,2020-02-27T16:48:32.752600083Z,2020-02-27T16:30:00Z,2,test2
To reproduce:
- Create "tests" bucket and load the following into it:
python_client_test,tag=test1 value=2 1582820400000000000
python_client_test,tag=test1 value=2 1582820500000000000
python_client_test,tag=test1 value=2 1582820600000000000
python_client_test,tag=test1 value=2 1582820700000000000
python_client_test,tag=test1 value=2 1582820800000000000
python_client_test,tag=test1 value=2 1582820900000000000
python_client_test,tag=test1 value=2 1582821000000000000
python_client_test,tag=test2 value=2 1582820400000000000
python_client_test,tag=test2 value=2 1582820500000000000
python_client_test,tag=test2 value=2 1582820600000000000
python_client_test,tag=test2 value=2 1582820700000000000
python_client_test,tag=test2 value=2 1582820800000000000
python_client_test,tag=test2 value=2 1582820900000000000
python_client_test,tag=test2 value=2 1582821000000000000
- Run the following code:
c = InfluxDBClient(...)
query = "%s\n%s" % (
'from(bucket: "tests") |> range(start: -10y) |> filter(fn: (r) => r._measurement == "python_client_test") |> map(fn: (r) => ({ r with _value: r._value })) |> yield(name: "t1")',
'from(bucket: "tests") |> range(start: -10y) |> filter(fn: (r) => r._measurement == "python_client_test") |> map(fn: (r) => ({ r with _value: r._value })) |> yield(name: "t2")',
)
result = list(c.query_api().query(query))
print(len(result))
This should print out 4, but prints out 3.
The third table contains data from both 3rd and 4th table.