InfluxDB data elements
InfluxDB 2.7 includes the following data elements:
The following sample data represents time series records stored in InfluxDB and is used to illustrate data elements concepts. Hover over highlighted terms to get acquainted with InfluxDB terminology and layout.
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 UTC associated with data. Timestamp precision is important when you write data.
_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
A field includes a field key stored in the
_field column and a field value stored in the
A field key is a string that represents the name of the field. In the preceding sample data,
ants are field keys.
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:
28 and the number of
ants at a specified time:
A field set is a collection of field key-value pairs associated with a timestamp. The sample data includes the following field sets:
census bees=23i,ants=30i 1566086400000000000
census bees=28i,ants=32i 1566086760000000000
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.
See how to use tags and fields to make your schema easier to query.
The columns in the sample data,
scientist, are tags.
Tags include tag keys and tag values that are stored as strings and metadata.
The tag keys in the sample data are
For information about tag key requirements, see Line protocol – Tag set.
The tag key
location has two tag values:
The tag key
scientist also has two tag values:
For information about tag value requirements, see Line protocol – Tag set.
The collection of tag key-value pairs make up a tag set. The sample data includes the following four tag sets:
location = klamath, scientist = anderson
location = portland, scientist = anderson
location = klamath, scientist = mullen
location = portland, scientist = mullen
Tags are indexed
Tags are optional. You don’t need tags in your data structure, but it’s typically a good idea to include them. Because InfluxDB indexes tags, the query engine doesn’t need to scan every record in a bucket to locate a tag value. See how to use tags to improve query performance.
Why your schema matters
How you structure measurements, fields, and tags in your data can make queries easier to write and more performant. Good schema design can prevent high series cardinality, resulting in better performing queries.
Now that you’re familiar with measurements, field sets, and tag sets, it’s time to discuss series keys and series.
In InfluxDB OSS (TSM), a series key is a unique combination of measurement and tag set.
For example, the sample data includes two unique series keys:
A series includes timestamps and field values for a given series key–for example, the sample data contains the following series key and corresponding series:
Sample data series
- 2019-08-18T00:00:00Z 23
- 2019-08-18T00:06:00Z 28
Understanding the concept of a series is essential when designing your schema and working with your data in InfluxDB.
A point includes the series key, a field value, and a timestamp–for example, a single point from the sample data:
2019-08-18T00:00:00Z census ants 30 portland mullen
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.
If you’re new to using InfluxDB, see how to get started writing and querying data.
For an overview of how these elements interconnect within InfluxDB’s data model, watch the following video:
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