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| # Introduction | ||
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| The Observability Operator is in charge of configuring the Prometheus Agent instances running in workload clusters like remote write configuration, [sharding](sharding.md) and so on. | ||
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| TODO(atlas): Add documentation here. |
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| # Prometheus Agent Sharding | ||
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| To be able to ingest metrics without disrupting the workload running in the clusters, the observability operator decides on the number of running __prometheus agent shards__ for each workload cluster based . The number of shards depends on the __total number of time series__ stored for a given cluster. | ||
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| __By default__, the operator configures a shard for every 1M time series present in Mimir for the workload cluster. To avoid scaling down too abruptly, we defined a scale down threshold of 20%. | ||
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| As this default value was not enough to avoid workload disruptions, we added 2 ways to be able to override the scale up series count target and the scale down percentage. | ||
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| 1. Those values can be configured at the installation level by overriding the following values: | ||
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| ```yaml | ||
| monitoring: | ||
| sharding: | ||
| scaleUpSeriesCount: 1000000 | ||
| scaleDownPercentage: 0.20 | ||
| ``` | ||
| 2. Those values can also be set per cluster using the following cluster annotations: | ||
| ```yaml | ||
| monitoring.giantswarm.io/prometheus-agent-scale-up-series-count: 1000000 | ||
| monitoring.giantswarm.io/prometheus-agent-scale-down-percentage: 0.20 | ||
| ``` |
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| package monitoring | ||
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| // Config represents the configuration used by the monitoring package. | ||
| type Config struct { | ||
| Enabled bool | ||
| ShardingScaleUpSeriesCount float64 | ||
| ShardingScaleDownPercentage float64 | ||
| // TODO(atlas): validate prometheus version | ||
| PrometheusVersion string | ||
| } |
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| package sharding | ||
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| import "math" | ||
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| type ShardingStrategy struct { | ||
| // Configures the number of series needed to add a new shard. Computation is number of series / ScaleUpSeriesCount | ||
| ScaleUpSeriesCount float64 | ||
| // Percentage of needed series based on ScaleUpSeriesCount to scale down agents | ||
| ScaleDownPercentage float64 | ||
| } | ||
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| func (pass1 ShardingStrategy) Merge(pass2 *ShardingStrategy) ShardingStrategy { | ||
| strategy := ShardingStrategy{ | ||
| pass1.ScaleUpSeriesCount, | ||
| pass1.ScaleDownPercentage, | ||
| } | ||
| if pass2 != nil { | ||
| if pass2.ScaleUpSeriesCount > 0 { | ||
| strategy.ScaleUpSeriesCount = pass2.ScaleUpSeriesCount | ||
| } | ||
| if pass2.ScaleDownPercentage > 0 { | ||
| strategy.ScaleDownPercentage = pass2.ScaleDownPercentage | ||
| } | ||
| } | ||
| return strategy | ||
| } | ||
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| // We want to start with 1 prometheus-agent for each 1M time series with a scale down 20% threshold. | ||
| func (pass ShardingStrategy) ComputeShards(currentShardCount int, timeSeries float64) int { | ||
| shardScaleDownThreshold := pass.ScaleDownPercentage * pass.ScaleUpSeriesCount | ||
| desiredShardCount := int(math.Ceil(timeSeries / pass.ScaleUpSeriesCount)) | ||
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| // Compute Scale Down | ||
| if currentShardCount > desiredShardCount { | ||
| // We get the rest of a division of timeSeries by shardStep and we compare it with the scale down threshold | ||
| if math.Mod(timeSeries, pass.ScaleUpSeriesCount) > pass.ScaleUpSeriesCount-shardScaleDownThreshold { | ||
| desiredShardCount = currentShardCount | ||
| } | ||
| } | ||
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| // We always have a minimum of 1 agent, even if there is no worker node | ||
| if desiredShardCount <= 0 { | ||
| return 1 | ||
| } | ||
| return desiredShardCount | ||
| } |
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| package sharding | ||
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| import ( | ||
| "testing" | ||
| ) | ||
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| func TestShardComputationScaleUp(t *testing.T) { | ||
| pass := ShardingStrategy{ScaleUpSeriesCount: float64(1_000_000), ScaleDownPercentage: float64(0.20)} | ||
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| expected := 1 | ||
| result := pass.ComputeShards(0, float64(1_000_000)) | ||
| if result != expected { | ||
| t.Errorf(`expected computeShards(0, 1_000_000) to be %d, got %d`, expected, result) | ||
| } | ||
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| expected = 2 | ||
| result = pass.ComputeShards(0, float64(1_000_001)) | ||
| if result != expected { | ||
| t.Errorf(`expected computeShards(0, 1_000_001) to be %d, got %d`, expected, result) | ||
| } | ||
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| expected = 3 | ||
| result = pass.ComputeShards(0, float64(2_000_001)) | ||
| if result != expected { | ||
| t.Errorf(`expected computeShards(0, 2_000_001) to be %d, got %d`, expected, result) | ||
| } | ||
| } | ||
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| func TestShardComputationReturnsAtLeast1Shart(t *testing.T) { | ||
| pass := ShardingStrategy{ScaleUpSeriesCount: float64(1_000_000), ScaleDownPercentage: float64(0.20)} | ||
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| expected := 1 | ||
| result := pass.ComputeShards(0, 0) | ||
| if result != expected { | ||
| t.Errorf(`expected computeShards(0, 0) to be %d, got %d`, expected, result) | ||
| } | ||
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| expected = 1 | ||
| result = pass.ComputeShards(0, -5) | ||
| if result != expected { | ||
| t.Errorf(`expected computeShards(0, -5) to be %d, got %d`, expected, result) | ||
| } | ||
| } | ||
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| func TestShardComputationScaleDown(t *testing.T) { | ||
| pass := ShardingStrategy{ScaleUpSeriesCount: float64(1_000_000), ScaleDownPercentage: float64(0.20)} | ||
| expected := 2 | ||
| result := pass.ComputeShards(1, 1_000_001) | ||
| if result != expected { | ||
| t.Errorf(`expected computeShards(1, 1_000_001) to be %d, got %d`, expected, result) | ||
| } | ||
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| expected = 2 | ||
| result = pass.ComputeShards(2, 999_999) | ||
| if result != expected { | ||
| t.Errorf(`expected computeShards(2, 999_999) to be %d, got %d`, expected, result) | ||
| } | ||
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| expected = 2 | ||
| result = pass.ComputeShards(2, 800_001) | ||
| if result != expected { | ||
| t.Errorf(`expected computeShards(2, 800_001) to be %d, got %d`, expected, result) | ||
| } | ||
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| // threshold hit | ||
| expected = 1 | ||
| result = pass.ComputeShards(2, 800_000) | ||
| if result != expected { | ||
| t.Errorf(`expected computeShards(2, 800_000) to be %d, got %d`, expected, result) | ||
| } | ||
| } |
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