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Comment by watty | Hacker News Reader

I don't get the snark - what's the glaring problem with the format? I work with sensor data at my job and very rarely is it uniformly distributed so we use a similar format.

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Because it's horribly inefficient. It's using 49 bytes to encode 8 bytes worth of data. If your data set is a few hundred observations this likely doesn't matter. But most users of timeseries data have millions or billions. (I come from a computational finance background.)

Even if they were wedded to JSON for some reason, they could have just used a list of observations, like:

[1458000000,63.422235],

That would have cut their data costs in half.

Or just use one of the many existing formats for transmitting time series data. It's not a new topic. https://github.com/mobileink/data.frame/wiki/What-is-a-Data-...

This is an API for very small datasets (daily time series data). The goal should be accessibility and readability over saving a few bytes.

I'm not saying it's ideal, I just think the snark is unwarranted considering how common it is. I just checked InfluxDB and they follow a similar model (even more verbose). https://docs.influxdata.com/influxdb/v1.2/guides/querying_da...

Checked a few more and I believe they're the same - Microsoft IoT, Predix (GE), etc.

that example you give does not follow a similar model. it defines the columns once (not repeated w/ every observation):

                    "columns": [
                        "time",
                        "value"
                    ],

and then the observations as a list of lists:

                    "values": [
                        [
                            "2015-01-29T21:55:43.702900257Z",
                            2
                        ],
                        [
                            "2015-01-29T21:55:43.702900257Z",
                            0.55
                        ],

exactly as i suggested in the "even if they were wedded to JSON for some reason" section of my original explanation.

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