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86 lines
1.6 KiB
86 lines
1.6 KiB
package simulation |
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import ( |
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"math/rand" |
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) |
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type DataPipelineNode struct { |
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ID string |
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Label string |
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BatchSize int |
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Transformation string |
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CurrentLoad int |
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Queue []*Request |
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Alive bool |
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Targets []string |
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output []*Request |
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} |
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func (n *DataPipelineNode) GetID() string { return n.ID } |
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func (n *DataPipelineNode) Tick(tick int) { |
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if len(n.Queue) == 0 { |
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return |
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} |
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if len(n.Queue) < n.BatchSize { |
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if tick%50 == 0 { |
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n.processBatch(len(n.Queue)) |
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} |
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return |
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} |
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batchesToProcess := len(n.Queue) / n.BatchSize |
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maxBatchesPerTick := 2 |
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actualBatches := min(batchesToProcess, maxBatchesPerTick) |
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for i := 0; i < actualBatches; i++ { |
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n.processBatch(n.BatchSize) |
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} |
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} |
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func (n *DataPipelineNode) processBatch(size int) { |
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if size == 0 { |
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return |
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} |
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batch := n.Queue[:size] |
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n.Queue = n.Queue[size:] |
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batchLatency := 100 + (size * 5) |
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for _, req := range batch { |
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req.LatencyMS += batchLatency |
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req.Path = append(req.Path, n.ID+"(batch)") |
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switch n.Transformation { |
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case "aggregation": |
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req.Type = "AGGREGATED" |
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case "filtering": |
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if rand.Float64() < 0.9 { |
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n.output = append(n.output, req) |
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} |
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continue |
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case "enrichment": |
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req.Type = "ENRICHED" |
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} |
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n.output = append(n.output, req) |
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} |
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} |
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func (n *DataPipelineNode) Receive(req *Request) { |
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if req == nil { |
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return |
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} |
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req.LatencyMS += 1 |
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n.Queue = append(n.Queue, req) |
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} |
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func (n *DataPipelineNode) Emit() []*Request { |
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out := append([]*Request(nil), n.output...) |
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n.output = n.output[:0] |
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return out |
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} |
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func (n *DataPipelineNode) Type() string { return "datapipeline" } |
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func (n *DataPipelineNode) IsAlive() bool { return n.Alive }
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