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added description and modified intro of duplicate data doc

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Scott Anderson 2019-06-03 16:40:03 -06:00
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content/v2.0/write-data/best-practices/duplicate-points.md
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@ -2,19 +2,19 @@
title: Handle duplicate data points title: Handle duplicate data points
seotitle: Handle duplicate data points when writing to InfluxDB seotitle: Handle duplicate data points when writing to InfluxDB
description: > description: >
placeholder InfluxDB identifies unique data points by their measurement, tag set, and timestamp.
This article discusses methods for preserving data from two points with a common
measurement, tag set, and timestamp but a different field set.
weight: 202 weight: 202
menu: menu:
v2_0: v2_0:
name: Handle duplicate points name: Handle duplicate points
parent: write-best-practices parent: write-best-practices
v2.0/tags: [best practices, write]
--- ---
<!-- Intro here --> InfluxDB identifies unique data points by their measurement, tag set, and timestamp
(each a part of [Line protocol](/v2.0/reference/line-protocol) used to write data to InfluxDB).
## Identifying unique data points
Data points are written to InfluxDB using [Line protocol](/v2.0/reference/line-protocol).
InfluxDB identifies unique data points by their measurement name, tag set, and timestamp.
```txt ```txt
web,host=host2,region=us_west firstByte=15.0 1559260800000000000 web,host=host2,region=us_west firstByte=15.0 1559260800000000000
@ -40,12 +40,6 @@ web,host=host2,region=us_west firstByte=15.0 1559260800000000000
After you submit the new point, InfluxDB overwrites `firstByte` with the new field After you submit the new point, InfluxDB overwrites `firstByte` with the new field
value and leaves the field `dnsLookup` alone: value and leaves the field `dnsLookup` alone:
{{% note %}}
The output of examples queries in this article has been modified to clearly show
the different approaches to handling duplicate data.
The
{{% /note %}}
```sh ```sh
from(bucket: "example-bucket") from(bucket: "example-bucket")
|> range(start: 2019-05-31T00:00:00Z, stop: 2019-05-31T12:00:00Z) |> range(start: 2019-05-31T00:00:00Z, stop: 2019-05-31T12:00:00Z)
@ -120,3 +114,8 @@ Table: keys: [_measurement, host, region]
2019-05-31T00:00:00.000000000Z web host2 us_west 24 7 2019-05-31T00:00:00.000000000Z web host2 us_west 24 7
2019-05-31T00:00:00.000000001Z web host2 us_west 15 2019-05-31T00:00:00.000000001Z web host2 us_west 15
``` ```
{{% note %}}
The output of examples queries in this article has been modified to clearly show
the different approaches and results for handling duplicate data.
{{% /note %}}