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Downsampling is the term used when reducing the rate of a signal. When you downsample data, the size of the data on disk is reduced, but so is the resolution of the data. This means that you have fewer entries in your source data, to take up less space on disk. The main reasons for downsampling are to reduce cost and increase performance. Querying downsampled data gets faster as the size of the data decreases.
The two downsampling methods are:
There are a few things to take into account when deciding on a downsampling method:
- Access to recent data: if this materialization is being used in operational or real-time dashboards, choose continuous aggregates.
- Size of the time bucket: continuous aggregates materialize the intermediate, not the final form, so querying the data is a bit more expensive than with recording rules. Choose continuous aggregates if you are aggregating more data points together, such as one hour or more of data. Choose recording rules for small buckets.
- Number of metrics in materialization: continuous aggregates can only be defined on a single metric. If you need to materialize queries on more than one metric, choose recording rules. However, if you want to use continuous aggregates, you could consider joining the materialized metrics and querying the result of the materialization instead of the raw input.
- Query flexibility: if you know the exact queries that you want to run on the materialization, recording rules can be more efficient. However, if you want flexibility in your queries, continuous aggregates can answer more queries based on the materialized data.
- Access to old data: if you need old data points to also be aggregated as soon as downsampling for a metric is configured, choose continuous aggregates. This is especially important if it is something you do often, because backfilling old data with recording rules requires additional steps.