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RDO hangout on gnocchi

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Overview of OpenStack Ceilometer plans for Gnocchi integration

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RDO hangout on gnocchi

  1. 1. Rebasing Ceilometer storage on Gnocchi OpenStack Telemetry 1
  2. 2. 2 What’s the problem we’re trying to solve here? ● Flexible but heavyweight Ceilometer samples model with free-form metadata ● This legacy issue has led to many of the problems impacting Ceilometer adoption: ● massive storage footprint ● suboptimal data ingestion ● non-scaling query API ● Also gives us a weakly-typed API representation
  3. 3. ● Track strongly-typed resource attributes ● Rely on events to reconstruct resource state timeline ● Eagerly pre-aggregate metric data ● Support restricted cross-metric aggregation 3 Key approaches taken by Gnocchi
  4. 4. 4 Compare and contrast ... “classic” Ceilometer Gnocchi Heavy-weight samples with embedded metadata Light-weight time-series shorn of metadata Global data expiry policy set across the board Per time-series configurable retention policies
  5. 5. 5 Compare and contrast ... “classic” Ceilometer Gnocchi On-demand aggregation Eager pre-aggregation Intertwined storage of resources and samples Separated storage and data models for resources & time-series data
  6. 6. ● Resource = cloud resource (instance, volume, etc.) ● Metric = anything you’d like to collect data about ● identified by UUID, or by name combined with resource ID ● Measure = (timestamp, value) time-series datapoint 6 Gnocchi basics
  7. 7. ● Archive policy = data storage policy defined by admin ● 1 second resolution over a day, 1 hour resolution over a year, or even both ● Consists of granularity (in seconds) and retention time-span ● Aggregation = function used to roll up data ● Retention = do not store fine grained data forever, instead store aggregated data according to the per- metric archive policies Gnocchi basics 7
  8. 8. 8 Gnocchi aggregation mechanism Time 1s 1m 1h Now Data to be calculated in run-time Most recent and small- granularity aggregated data Original non-aggregated measures to calculate aggregations from Additional data - 1 h Aggregated data
  9. 9. ● Capturing measurements for different metrics is the main concept of Gnocchi ● Although, metrics have no actual use without some- resource association ● Resources have strongly-typed attributes ● Metric association is by name (e.g. “cpu_util”) ● Metrics for loosely associated resources can be cross- aggregated 9 Gnocchi Indexer concept
  10. 10. ● Gnocchi indexer is responsible for indexing entities, resources, and linking them together ● Resources and their attributes are well-defined, typed, and indexed ● The generic type can be used if the resource type is unknown to Gnocchi 10 Gnocchi Indexer concept
  11. 11. ● Alarming to drive Heat autoscaling, based on aggregating samples across all instances with matching metadata ● use cross-metric aggregation based on strongly-typed resource attributes, as opposed to free-from metadata ● Reconstructing the resource state timeline, from per-sample resource metadata ● use queries over relatively infrequent events capturing state transitions 11 Covering existing ceilometer use-cases
  12. 12. ● Existing specialized metrics-oriented DBs can be leveraged by Gnocchi’s pluggable driver model ● actively working on drivers for InfluxDB and OpenTSDB ● Gnocchi itself provides a canonical storage driver based on Pandas and Swift ● In the specialized TSBD use-case, Gnocchi manages the resource-metric association & abstract archive policy concepts 12 No, we’re not re-inventing TSDB here

Overview of OpenStack Ceilometer plans for Gnocchi integration

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