Design your schema

Schema design can have a significant impact on both write and query performance in your InfluxDB cluster. The items below cover high-level considerations and recommendation. For detailed recommendations, see Schema design recommendations.

Understand the difference between tags and fields

In the InfluxDB data structure, there are three main "categories" of information--timestamps, tags, and fields. Understanding the difference between what should be a tag and what should be a field is important when designing your schema.

Use the following guidelines to determine what should be tags versus fields:

• Use tags to store metadata that provides information about the source or context of the data.
• Use fields to store measured values.
• Field values typically change over time. Tag values do not.
• Tag values can only be strings.
• Field values can be any of the following data types:
  • Integer
  • Unsigned integer
  • Float
  • String
  • Boolean

For more information, see Tags versus fields.

Schema restrictions

InfluxDB enforces the following schema restrictions:

• You cannot use the same name for a tag and a field in the same table.
• By default, a table can have up to 250 columns.

For more information, see InfluxDB schema restrictions.

Design for performance

The following guidelines help to ensure write and query performance:

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• Avoid wide schemas: A wide schema is one with a large number of columns (tags and fields).
• Avoid sparse schemas: A sparse schema is one where, for many rows, columns contain null values.
• Keep table schemas homogenous: A homogenous table schema is one where every row has values for all tags and fields.
• Use the best data type for your data: Write integers as integers, decimals as floats, and booleans as booleans. Queries against a field that stores integers outperforms a query against string data.

Design for query simplicity

The following guidelines help to ensure that, when querying data, the schema makes it easy to write queries:

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• Keep table names, tags, and fields simple: Use one tag or one field for each data attribute. If your source data contains multiple data attributes in a single parameter, split each attribute into its own tag or field.
• Avoid keywords and special characters: Reserved keywords or special characters in table names, tag keys, and field keys makes writing queries more complex.

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