Does Mimir Compare Other Submissions? Downsampling for Enhanced Query Performance

Mimir allows for efficient querying of large time-series datasets through downsampling techniques. This article explores how Mimir handles downsampling and how it can be configured to optimize query performance and storage costs.

Understanding the Need for Downsampling

When querying extensive time-series data, retrieving every single data point can significantly impact response times and user experience. Downsampling reduces the number of data points queried by applying aggregation functions like min, max, average, count, or sum over specified time intervals. This results in faster query responses while still providing a representative view of the data. In addition to these standard aggregations, Mimir could leverage algorithms like Largest Triangle Three Sides (LTTB) to preserve the visual characteristics of the original data even after downsampling.

Configuring Downsampling in Mimir

Mimir offers flexible downsampling configurations at both the cluster and tenant levels. This allows for tailoring downsampling strategies to specific needs and use cases.

Cluster-Level Configuration

Downsampling rules can be set at the cluster level via the configuration file. This sets default downsampling behavior for all tenants. An example configuration might look like this:

compactor:
  downsampling:
  - 1d:1m # After 1 day, keep 1 sample per minute
  - 2d:5m # After 2 days, keep 1 sample per 5 minutes
  - 2w:1h # After 2 weeks, keep 1 sample per hour

This configuration specifies that after one day, data should be downsampled to one sample per minute, after two days to one sample per five minutes, and after two weeks to one sample per hour.

Tenant-Level Configuration

Individual tenants can override the cluster-level defaults with their own specific downsampling rules. This allows for fine-tuning based on individual data characteristics and query patterns. For example:

overrides:
  tenant1:
    downsampling:
    - 1d:1m # After 1 day, keep 1 sample per minute
    - 5d:5m # After 5 days, keep 1 sample per 5 minutes
    - 4w:1h # After 4 weeks, keep 1 sample per hour

Regex-Based Downsampling

Mimir could potentially offer more granular control by allowing downsampling rules to be applied to specific series based on regular expressions. This enables customized downsampling for different data types or sources within a tenant. This could be implemented as follows:

overrides:
  tenant1:
    downsampling:
    ".*": # Apply to all series
    - 1d:1m 
    - 5d:5m
    - 4w:1h
    "cpu.*": # Apply to series starting with "cpu"
    - 1d:1m 
    - 4w:1h

Retention Policies and Downsampling

Combining downsampling with retention policies allows for further optimization of storage costs. After a defined period, the full-resolution data might no longer be necessary, allowing for its deletion while retaining the downsampled data for long-term analysis.

Use Cases for Downsampling

Several use cases benefit from downsampling:

High-Frequency Sampling: Retain high-resolution data for a short period and downsample older data.
Capacity Planning: Keep low-resolution, long-term data for trend analysis and forecasting.
Data Pruning: Delete full-resolution data after a certain time, keeping only the downsampled data.

Conclusion

Downsampling in Mimir offers a powerful mechanism for optimizing query performance and managing storage costs for large time-series datasets. The flexible configuration options allow for customization at both the cluster and tenant levels, enabling fine-grained control over downsampling strategies. This functionality addresses crucial needs in various scenarios, from handling high-frequency data to long-term capacity planning. This makes Mimir a compelling solution for organizations dealing with the challenges of managing and analyzing extensive time-series data.