Data alignment feature does not seem to be fully working

Describe the bug @alexanderzobnin explained the feature with screenshots here: https://github.com/alexanderzobnin/grafana-zabbix/issues/1109#issuecomment-749543471

However after upgrading to v4.1.1 still the graphs look like this (there are no gaps for missing points):

Expected behavior I expected graph to look like this:

Screenshots See above. Although I realized when I select null as zero only the left part of the graph is connected to zero but not the right part.

Network data

Software versions

Grafana	Zabbix	Grafana-Zabbix Plugin
7.3.6	5.0.7	4.1.1

[mjtrangoni]

@alexanderzobnin +1 I see the same effect with Zabbix 4.2.8, Grafana 7.3.7 and Plugins 4.1.2 on some graphs, not all of them!. It looses one or 2 points point in the middle of the graph, no matter which range you take. I can see it for 30m, 1h, 3h until 1d ranges.

[goreck]

Similar problem: Grafana 8.1.2 Plugin 4.1.5 Zabbix 5.4.4

Data polled at 1m interval. Consider what happens when an item is polled late and recorded during a subsequent interval, and the next one is polled without delay.

Response data:

{
  "result": [{
      "clock": "1631706954",
      "itemid": "62593",
      "ns": "471772119",
      "value": "786044632"
    }, {
      "clock": "1631707013",
      "itemid": "62593",
      "ns": "900752623",
      "value": "813032080"
    }, {
      "clock": "1631707074",
      "itemid": "62593",
      "ns": "91841664",
      "value": "808997872"
    }, {
      "clock": "1631707136",
      "itemid": "62593",
      "ns": "792429903",
      "value": "811282464"
    }, {
      "clock": "1631707194",
      "itemid": "62593",
      "ns": "562055013",
      "value": "802328296"
    }, {
      "clock": "1631707260",
      "itemid": "62593",
      "ns": "640012802",
      "value": "818647920"
    }, {
      "clock": "1631707316",
      "itemid": "62593",
      "ns": "128444864",
      "value": "775530264"
    }, {
      "clock": "1631707376",
      "itemid": "62593",
      "ns": "579132108",
      "value": "808677400"
    }, {
      "clock": "1631707434",
      "itemid": "62593",
      "ns": "233046613",
      "value": "822326360"
    }, {
      "clock": "1631707495",
      "itemid": "62593",
      "ns": "969142375",
      "value": "776823024"
    }, {
      "clock": "1631707560",
      "itemid": "62593",
      "ns": "407341753",
      "value": "778134936"
    }, {
      "clock": "1631707616",
      "itemid": "62593",
      "ns": "839243307",
      "value": "757824760"
    }, {
      "clock": "1631707676",
      "itemid": "62593",
      "ns": "21640834",
      "value": "834242352"
    }, {
      "clock": "1631707735",
      "itemid": "62593",
      "ns": "701393332",
      "value": "815660384"
    }, {
      "clock": "1631707796",
      "itemid": "62593",
      "ns": "765022133",
      "value": "859856400"
    }
  ]
}

Unaliagned timestamp	Value		Aligned timestamp:	Value
2021-09-15 13:55:54	786 044 632		2021-09-15 13:55:00	786 044 632
2021-09-15 13:56:53	813 032 080		2021-09-15 13:56:00	813 032 080
2021-09-15 13:57:54	808 997 872		2021-09-15 13:57:00	808 997 872
2021-09-15 13:58:56	811 282 464		2021-09-15 13:58:00	811 282 464
2021-09-15 13:59:54	802 328 296		2021-09-15 13:59:00	802 328 296
2021-09-15 14:01:00	818 647 920		2021-09-15 14:00:00	810 488 108
2021-09-15 14:01:56	775 530 264		2021-09-15 14:01:00	818 647 920
2021-09-15 14:02:56	808 677 400		2021-09-15 14:02:00	775 530 264
2021-09-15 14:03:54	822 326 360		2021-09-15 14:03:00	808 677 400
2021-09-15 14:04:55	776 823 024		2021-09-15 14:04:00	822 326 360
2021-09-15 14:06:00	778 134 936		2021-09-15 14:06:00	776 823 024
2021-09-15 14:06:56	757 824 760		2021-09-15 14:07:00	778 134 936
2021-09-15 14:07:56	834 242 352		2021-09-15 14:08:00	757 824 760
2021-09-15 14:08:55	815 660 384		2021-09-15 14:09:00	834 242 352
2021-09-15 14:09:56	859 856 400

You’ll notice two “late” samples at minutes 14:00 and 14:05 When data alignment is enabled a “missing” value for 14:00 is an average of raw samples at 13:59:54 and 14:01:00. Then 14:01 shows value for raw 14:01 but subsequent samples are offset by one, i.e. 14:02 shows value for 14:01:56, 14:02:00 for 14:01:56. Then something strage happens at 14:05 - this sample is ommited in an aligned dataset. The following datapoints are offset by 2: 14:06:00 for 776 823 024, 14:07:00 for 14:06:00, 14:08:00 for 14:06:56 etc. You’ll also notice that an aligned dataset is one row shorter than an unaligned one. In fact for number of “late” samples n>1 the number of missing rows in aligned dataset is n-1.

The resulting graph shows values at wrong timestamps. If there are multiple stacked datasets where some of them contain such “late” data points the resulting values are misaligned as well.