Hi,
We've been using InfluxDB for some time now, although there are some
differences in how we are structuring data.
One approach is to take each measurement as a top-level entity, so then the
series would be:
{
"results": [
{
"series": [
{
"columns": [
"key"
],
"values": [
[
"cache_hit,host=foo,region=eu-west-1"
],
[
"cache_miss,host=foo,region=eu-west-1"
],
[
"response_time_x,host=foo,region=eu-west-1"
]
]
}
]
}
]
}
...so each measurement is at the top-level.
However, another approach is to group them in a top-level series and have
multiple keys for each measurement. So, the series would be:
{
"results": [
{
"series": [
{
"columns": [
"key"
],
"values": [
[
"cache"
],
[
"timing"
]
]
}
]
}
]
}
...and the measurements for cache operations would be within 'cache' and all
for timings would be in 'timing'. Therefore the field keys would be:
{
"results": [
{
"series": [
{
"name": "cache",
"columns": [
"fieldKey"
],
"values": [
[
"hit"
],
[
"miss"
]
]
},
{
"name": "timing",
"columns": [
"fieldKey"
],
"values": [
[
"response_time_x"
]
]
}
]
}
]
}
I know how we'd approach it if we were saving this data into a relational
database, but which is the most 'InfluxDB' way of doing it? Are there any
performance trade-offs to having things organised in different ways?
Thanks,
Beth
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