For similar capabilities to Amazon Timestream for LiveAnalytics, consider Amazon Timestream for InfluxDB. It offers simplified data ingestion and single-digit millisecond query response times for real-time analytics. Learn more here.
Schema design best practices for Timestream for InfluxDB 3
By following these guidelines, you can design InfluxDB schemas that enable simpler and more performant queries while optimizing resource utilization.
Performance optimization guidelines
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Sort tags by query priority:
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The first write to a table determines physical column order in storage.
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Place most frequently queried tags first for better performance.
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Column order cannot be changed after initial write.
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Avoid wide schemas:
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Limit the number of columns (tags and fields) per table.
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Too many columns can increase resource usage and reduce performance.
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Consider segmenting fields into separate tables if needed.
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Avoid sparse schemas:
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Sparse schemas contain many null values across rows.
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Caused by non-homogenous table schemas or writing individual fields with different timestamps.
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Adds unnecessary overhead to storing and querying data.
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Maintain homogenous table schemas:
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Each row should have the same tag and field keys.
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Avoid tables with many null values.
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Use appropriate data types:
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Use the most appropriate data type for your data.
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Integer and boolean fields outperform string fields in queries.
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Use last-value
and distinct-value cache for repetitive queries where possible.
Query simplicity guidelines
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Keep N=names simple:
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Use one tag or field for each data attribute.
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Choose descriptive, simple names for tables, tags, and fields.
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Avoid embedding multiple data attributes in a single name.
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Avoid keywords and special characters:
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Do not use SQL or InfluxQL reserved keywords.
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Avoid special characters in table names, tag keys, and field keys.
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Using keywords or special characters requires additional quoting in queries.
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