Vega Array to Data Table

[comijo (Joseph)]

Hi there,

Thanks for being on this topic. I hope it will help others

Here is what I'm trying to achieve:
The data source of my data is from elasticsearch and the data format is like that:
[timestamp: data, metric11:number, metric12: number, metric21:number, metric22: number]

metric11 and metric12 are associated: value11(0) is asociated with value12(0)
Same for metric21 and metric22

I'd like to create a table like that, showing only the latest value (last timestamp) and refreshing everytime it updates

metric11 (sorted) | metric 12 || metric 21 (sorted) | metric22
value11(0) | value12(0)|| value21(0) | value22(0)
value11(1) | value12(1)|| value21(1) | value22(1)
value11(2) | value12(2)|| value21(2) | value22(2)

I'm really struggle to understand how I can do that.

Have you already done something similar ? Any idea ?

thx vm for reading and for your help

[lukas (Lukas Olson)]

It might help if you actually showed an example document you have in Elasticsearch.

It sounds like you might be wanting to use the Top Hits aggregation, have you given that a try?

[comijo (Joseph)]

Hi Lukas, thank you very much for responding !
Here is an example of the data (simplified):
{
"_index": "mydata",
"_type": "_doc",
"_id": "-JRJ220BzFM5gRVLQt0q",
"_version": 1,
"_score": null,
"_source": {
"timestamp": 1571342073684,
"name": "sample1",
"metric1": [
{
"rank": 1,
"value1": "0.021934",
"value2": "0.06",
"source": "google"
},]
"metric2": [
{
"rank": 1,
"value1": "0.02193913",
"value2": "0.43918947",
"source": "twitter"
}]
},
"sort": [
1571342073684
]
}

And I'd like to have:
metric1 .................................. | metric2...............................................|
value1 .... | value2 .| source |value1 ........ | value2 ........| source |
0.021934 | 0.06..... | google |0.02193913 | 0.43918947 | twitter |

Or even if the table metric1 and metric2 are separated that would do the job

Top Hits would be to get only the last value, but I don't understand how I can get this result (probably with the flatten transformation).

Does this sample is of any help ?