| func dumpResponseToFile(resp *http.Response, filename string) error { | |
| dump, err := httputil.DumpResponse(resp, true) | |
| if err != nil { | |
| return err | |
| } | |
| f, err := os.OpenFile(filename, os.O_APPEND|os.O_WRONLY, 0600) | |
| if err != nil { | |
| return err | |
| } |
| { | |
| "Frames": [ | |
| { | |
| "fields": [ | |
| { | |
| "name": "Time", | |
| "type": "time", | |
| "values": [ | |
| "1568652080000000000", | |
| "1568652085000000000", |
| diff --git a/pkg/dataframe/csv.go b/pkg/dataframe/csv.go | |
| new file mode 100644 | |
| index 0000000000..0838a7c413 | |
| --- /dev/null | |
| +++ b/pkg/dataframe/csv.go | |
| @@ -0,0 +1,48 @@ | |
| +package dataframe | |
| + | |
| +import ( | |
| + "encoding/csv" |
| // Code generated by protoc-gen-go. DO NOT EDIT. | |
| // source: Flight.proto | |
| package arrow_flight_protocol | |
| import ( | |
| fmt "fmt" | |
| proto "github.com/golang/protobuf/proto" |
| package dataframe | |
| //go:generate genny -in=$GOFILE -out=vector_vector.gen.go gen "gen=byte" | |
| type genVectorVector [][]gen | |
| func newgenVectorVector(n int) *genVectorVector { | |
| v := genVectorVector(make([][]gen, n)) | |
| return &v | |
| } |
| $ ./run.sh -d 5m -v 150 -c annotations_by_tag_test -u http://localhost:3000 | |
| /\ |‾‾| /‾‾/ /‾/ | |
| /\ / \ | |_/ / / / | |
| / \/ \ | | / ‾‾\ | |
| / \ | |‾\ \ | (_) | | |
| / __________ \ |__| \__\ \___/ .io | |
| execution: local | |
| output: - |
Currently our SQL plugins derive things from the special names of the columns. Instead we can use more conventional Long and Wide formats and derive the format from the schema. This will result in a more conventional SQL experience for users. The amount of code plugin authors need to create will be reduced.
Taking a MySQL Example:
SELECT
| === RUN TestLongToWide/sparse:_one_value,_two_factor | |
| Name: long_to_wide_test | |
| Dimensions: 4 Fields by 6 Rows | |
| +-------------------------------+---------------------+---------------------+-------------------------+ | |
| | Name: Time | Name: Values Floats | Name: Animal Factor | Name: Location | | |
| | Labels: | Labels: | Labels: | Labels: | | |
| | Type: []time.Time | Type: []float64 | Type: []string | Type: []string | | |
| +-------------------------------+---------------------+---------------------+-------------------------+ | |
| | 2020-01-02 03:04:00 +0000 UTC | 1 | cat | Florida | | |
| | 2020-01-02 03:04:00 +0000 UTC | 2 | sloth | Central & South America | |
Grafana's "wide tent" philosophy means we want Grafana to work with our users' data, and not make our users work to use Grafana. The data frame structure is a concept borrowed from data analysis like R and Pandas to allow us to do this.
Data frames allow users' data to be more accurately represented and with less effort.
Grafana's "wide tent" philosophy means we want Grafana to work with our users' data, and not make our users work to use Grafana. The data frame structure is a concept borrowed from data analysis like R and Pandas to allow us to do this.
Data frames allow users' data to be more accurately represented and with less effort.