Handle duplicate data points

    For points that have the same measurement name, tag set, and timestamp, InfluxDB creates a union of the old and new field sets. For any matching field keys, InfluxDB uses the field value of the new point. For example:

    2. web,host=host2,region=us_west firstByte=24.0,dnsLookup=7.0 1559260800000000000
    4. # New data point
    5. web,host=host2,region=us_west firstByte=15.0 1559260800000000000

    After you submit the new data point, InfluxDB overwrites firstByte with the new field value and leaves the field dnsLookup alone:

    1. from(bucket: "example-bucket")
    2.   |> range(start: 2019-05-31T00:00:00Z, stop: 2019-05-31T12:00:00Z)
    3.   |> filter(fn: (r) => r._measurement == "web")
    5.                _time  _measurement   host   region  dnsLookup  firstByte
    7. 2019-05-31T00:00:00Z           web  host2  us_west          7         15

    Preserve duplicate points

    Add an arbitrary tag with unique values so InfluxDB reads the duplicate points as unique.

    For example, add a uniq tag to each data point:

    After writing the new point to InfluxDB:

    1. from(bucket: "example-bucket")
    2.   |> range(start: 2019-05-31T00:00:00Z, stop: 2019-05-31T12:00:00Z)
    3.   |> filter(fn: (r) => r._measurement == "web")
    5. Table: keys: [_measurement, host, region, uniq]
    6.                _time  _measurement   host   region  uniq  firstByte  dnsLookup
    7. --------------------  ------------  -----  -------  ----  ---------  ---------
    8. 2019-05-31T00:00:00Z           web  host2  us_west     1         24          7
    10.                _time  _measurement   host   region  uniq  firstByte
    11. --------------------  ------------  -----  -------  ----  ---------
    12. 2019-05-31T00:00:00Z           web  host2  us_west     2         15

    Increment the timestamp

    Increment the timestamp by a nanosecond to enforce the uniqueness of each point.

    1. from(bucket: "example-bucket")
    2.   |> range(start: 2019-05-31T00:00:00Z, stop: 2019-05-31T12:00:00Z)
    3.   |> filter(fn: (r) => r._measurement == "web")
    5. Table: keys: [_measurement, host, region]
    6.                          _time  _measurement   host   region  firstByte  dnsLookup
    7. ------------------------------  ------------  -----  -------  ---------  ---------
    8. 2019-05-31T00:00:00.000000000Z           web  host2  us_west         24          7
    9. 2019-05-31T00:00:00.000000001Z           web  host2  us_west         15

    The output of examples queries in this article has been modified to clearly show the different approaches and results for handling duplicate data.