removed cruft from key concepts edit
Scott Anderson
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Before working with InfluxDB 2.0, it's helpful to learn a few key concepts, including:
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Before working with InfluxDB 2.0, it's helpful to learn a few key concepts, including:
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{{< children >}}
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{{< children >}}
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<!-- - [InfluxDB data elements](#influxdb-data-elements)
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- [InfluxDB table structure](#influxdb-layout)
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- [InfluxDB design principles](/v2.0/reference/design-principles)
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<!--- [InfluxDB platform](/v2.0/reference/) --> -->
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### InfluxDB data elements
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InfluxDB 2.0 includes the following data elements:
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- [timestamp](#timestamp)
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- [field key](#field-key)
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- [field value](#field-value)
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- [field set](#field-set)
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- [tag key](#tag-key)
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- [tag value](#tag-value)
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- [tag set](#tag-set)
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- [measurement](#measurement)
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- [series](#series)
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- [point](#point)
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- [bucket](#bucket)
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- [organization](#organization)
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The [sample data](#sample-data) below is used to illustrate data elements concepts.
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### InfluxDB table structure
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InfluxDB 2.0 uses the following table structure to store data:
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- **Annotation rows:** include the following rows: #group, #datatype, and #default.
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- **Header row:** describes the data labels for each column in a row.
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- **Data columns:** include the following columns: annotation, result, and table.
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- **Data rows:** all rows that contain time series data. See [sample data](#sample-data) below.
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For specifications on the InfluxDB 2.0 table structure, see [Tables](/v2.0/reference/annotated-csv/#tables).
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**_Tip:_** To visualize your table structure in the InfluxDB user interface, click the **Data Explorer** icon
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in the sidebar, create a query, click **Submit**, and then select **View Raw Data**.
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### Sample data
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The sample data below shows a number of bees counted by two scientists (`anderson` and `mullen`) in two locations (`klamath` and `portland`) from 12 AM to 6 AM on August 18, 2019. The sample data is stored in the bucket, `my_bucket`, and retained for the duration of the retention policy specified in the [bucket](#bucket).
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**_Tip:_** Hover over purple terms to get acquainted with InfluxDB terminology and layout.
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**bucket:** `my_bucket`
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| _time | _measurement | <span class ="tooltip" data-tooltip-text ="Tag key">location</span> | <span class ="tooltip" data-tooltip-text ="Tag key">scientist</span> | _field | _value |
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|:------------------- |:------------ |:------- |:------ |:-- |:------ |
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| 2019-08-18T00:00:00Z | census | klamath | anderson | bees | 23 |
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| 2019-08-18T00:00:00Z | census | portland | mullen | ants | 30 |
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| 2019-08-18T00:06:00Z | census | klamath | anderson | bees | 28 |
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| <span class="tooltip" data-tooltip-text="Timestamp">2019-08-18T00:06:00Z</span> | <span class="tooltip" data-tooltip-text="measurement"> census</span> | <span class ="tooltip" data-tooltip-text ="Tag value">portland</span> | <span class ="tooltip" data-tooltip-text ="Tag value">mullen</span> | <span class ="tooltip" data-tooltip-text ="Field key">ants</span> | <span class ="tooltip" data-tooltip-text ="Field value">32</span> |
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#### Timestamp
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All data stored in InfluxDB has a `_time` column that stores timestamps. On disk, timestamps are stored in epoch nanosecond format. InfluxDB formats timestamps show the date and time in [RFC3339](https://www.ietf.org/rfc/rfc3339.txt) UTC associated with data. Timestamp precision is important when you write data.
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#### Measurement
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The `_measurement` column shows the name of the measurement `census`. Measurement names are strings. A measurement acts as a container for tags, fields, and timestamps. Use a measurement name that describes your data. The name `census` tells us that the field values record the number of `bees` and `ants`.
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#### Fields
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A field includes a field key stored in the `_field` column and a field value stored in the `_value` column.
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##### Field key
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A field key is a string that represents the name of the field. In the sample data above, `bees` and `ants` are field keys.
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##### Field values
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A field value represents the value of an associated field. Field values can be strings, floats, integers, or booleans. The field values in the sample data show the number of `bees` at specified times: `23`, and `28` and the number of `ants` at a specified time: `30` and `32`.
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##### Field sets
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A field set is a collection of field key-value pairs associated with a timestamp. The sample data includes the following field sets:
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```bash
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census bees=23i,ants=30i 1566086400000000000
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census bees=28i,ants=32i 1566086760000000000
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-----------------
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Field set
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```
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{{% note %}}
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**Fields aren't indexed:** Fields are required in InfluxDB data and are not indexed. Queries that filter field values must scan all field values to match query conditions. As a result, queries on tags > are more performant than queries on fields. **Store commonly queried metadata in tags.**
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{{% /note %}}
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#### Tags
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The columns in the sample data, `location` and `scientist`, are tags.
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Tags include tag keys and tag values that are stored as strings and metadata.
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##### Tag keys
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The tag keys in the sample data are `location` and `scientist`.
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##### Tag values
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The tag key `location` has two tag values: `klamath` and `portland`.
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The tag key `scientist` also has two tag values: `anderson` and `mullen`.
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##### Tag sets
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The collection of tag key-value pairs make up a tag set. The sample data includes the following four tag sets:
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```bash
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location = klamath, scientist = anderson
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location = portland, scientist = anderson
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location = klamath, scientist = mullen
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location = portland, scientist = mullen
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```
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{{% note %}}
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**Tags are indexed:** Tags are optional. You don't need tags in your data structure, but it's typically a good idea to include tags.
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Because tags are indexed, queries on tags are faster than queries on fields. This makes tags ideal for storing commonly-queried metadata.
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{{% /note %}}
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#### Why your schema matters
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If most of your queries focus on values in the fields, for example, a query to find when 23 bees were counted:
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```bash
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from(bucket: "bucket-name")
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range(start: 2019-08-17T00:00:00Z, stop: 2019-08-19T00:00:00Z)
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filter(fn: (r) => r._field == "bees" and r._value == 23)
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```
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InfluxDB scans every field value in the dataset for `bees` before the query returns a response. If our sample `census` data grew to millions of rows, to optimize your query, you could rearrange your [schema](/v2.0/reference/glossary/#schema) so the fields (`bees` and `ants`) becomes tags and the tags (`location` and `scientist`) become fields:
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| _time | _measurement | <span class ="tooltip" data-tooltip-text ="Tag key">bees</span> | _field | _value |
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|:------------------- |:------------ |:------- |:-- |:------ |
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| 2019-08-18T00:00:00Z | census | 23 | location | klamath |
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| 2019-08-18T00:00:00Z | census | 23 | scientist | anderson |
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| 2019-08-18T00:06:00Z | census | <span class ="tooltip" data-tooltip-text ="Tag value">28</span> | <span class ="tooltip" data-tooltip-text ="Field key">location</span> | <span class ="tooltip" data-tooltip-text ="Field value">klamath</span> |
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| 2019-08-18T00:06:00Z | census | 28 | scientist | anderson |
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| _time | _measurement | <span class ="tooltip" data-tooltip-text ="Tag key">ants</span> | _field | _value |
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|:------------------- |:------------ |:------- |:-- |:------ |
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| 2019-08-18T00:00:00Z | census | 30 | location | portland |
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| 2019-08-18T00:00:00Z | census | 30 | scientist | mullen |
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| 2019-08-18T00:06:00Z | census | <span class ="tooltip" data-tooltip-text ="Tag value">32</span> | <span class ="tooltip" data-tooltip-text ="Field key">location</span> | <span class ="tooltip" data-tooltip-text ="Field value">portland</span>|
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| 2019-08-18T00:06:00Z | census | 32 | scientist | mullen |
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Now that `bees` and `ants` are tags, InfluxDB doesn't have to scan all `_field` and `_value` columns. This makes your queries faster.
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#### Series
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Now that you're familiar with measurements, field sets, and tag sets, it's time to discuss series keys and series. A **series key** is a collection of points that share a measurement, tag set, and field key. For example, the [sample data](#sample-data) includes two unique series keys:
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| _measurement | tag set | _field |
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|:------------- |:------------------------------- |:------ |
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| census | <span class="tooltip" data-tooltip-text="Tag set">location=klamath,scientist=anderson</span> |<span class="tooltip" data-tooltip-text="Field key">bees</span>|
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| census | location=portland,scientist=mullen | ants |
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A **series** includes timestamps and field values for a given series key. From the sample data, here's a **series key** and the corresponding **series**:
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```bash
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# series key
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census,location=klamath,scientist=anderson bees
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# series
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2019-08-18T00:00:00Z 23
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2019-08-18T00:06:00Z 28
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```
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Understanding the concept of a series is essential when designing your [schema](v2.0/reference/glossary/#schema) and working with your data in InfluxDB.
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#### Point
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A **point** includes the series key, a field value, and a timestamp. For example, a single point from the [sample data](#sample-data) looks like this:
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`2019-08-18T00:00:00Z census ants 30 portland mullen`
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#### Bucket
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All InfluxDB data is stored in a bucket. A **bucket** combines the concept of a database and a retention period (the duration of time that each data point persists). A bucket belongs to an organization. For more information about buckets, see [Manage buckets](https://v2.docs.influxdata.com/v2.0/organizations/buckets/).
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#### Organization
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An InfluxDB **organization** is a workspace for a group of [users](/v2.0/users/). All [dashboards](/v2.0/visualize-data/dashboards/), [tasks](/v2.0/process-data/), buckets, and users belong to an organization. For more information about organizations, see [Manage organizations](https://v2.docs.influxdata.com/v2.0/organizations/).
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If you're just starting out, we recommend taking a look at the following guides:
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- [Getting Started](/influxdb/v0.10/introduction/getting_started/)
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- [Writing Data](/influxdb/v0.10/guides/writing_data/)
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- [Querying Data](/influxdb/v0.10/guides/querying_data/)
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