# Reactive Programming in RxInfer
## Overview
[[reactive_programming|Reactive Programming]] in RxInfer enables dynamic, streaming-based probabilistic inference through the integration with [[rocket_jl|Rocket.jl]]. This paradigm allows models to automatically update as new data arrives.
```mermaid
graph TD
subgraph Data Sources
D1[Sensors]
D2[Time Series]
D3[Events]
end
subgraph Reactive Layer
R1[Streams]
R2[Operators]
R3[Subscriptions]
end
subgraph Inference
I1[Updates]
I2[Messages]
I3[Posteriors]
end
D1 --> R1
D2 --> R1
D3 --> R1
R1 --> R2
R2 --> R3
R3 --> I1
I1 --> I2
I2 --> I3
style D1 fill:#f9f
style D2 fill:#f9f
style D3 fill:#f9f
style R1 fill:#bbf
style R2 fill:#bbf
style R3 fill:#bbf
style I1 fill:#bfb
style I2 fill:#bfb
style I3 fill:#bfb
```
## Core Concepts
### 1. Streams
Data streams are the fundamental building blocks:
```julia
using RxInfer, Rocket
# Create a basic stream
stream = Subject(Float64)
# Create a labeled stream for RxInfer
observations = labeled(Val((:y,)), stream)
```
### 2. Operators
Transform and combine streams:
```julia
# Filter and map operations
filtered = observations |>
filter(x -> !isnan(x)) |>
map(x -> (y = float(x),))
# Windowing operations
windowed = observations |>
buffer(size = 10, stride = 5) |>
map(window -> (ys = collect(window),))
```
### Stream Processing Patterns
```mermaid
graph LR
subgraph Input
I1[Raw Data]
I2[Events]
end
subgraph Processing
P1[Filter]
P2[Transform]
P3[Combine]
end
subgraph Output
O1[Model Input]
O2[Updates]
end
I1 --> P1
I2 --> P1
P1 --> P2
P2 --> P3
P3 --> O1
P3 --> O2
style I1 fill:#f9f
style I2 fill:#f9f
style P1 fill:#bbf
style P2 fill:#bbf
style P3 fill:#bbf
style O1 fill:#bfb
style O2 fill:#bfb
```
## Integration with Models
### 1. Reactive Models
Create models that respond to streaming data:
```julia
@model function reactive_model(y)
# State variables
x ~ Normal(0, 1)
# Streaming observations
y ~ Normal(x, 1)
end
# Auto-updates for online learning
updates = @autoupdates begin
x_mean, x_prec = params(q(x))
end
```
### 2. Stream Handling
```julia
# Run streaming inference
result = infer(
model = reactive_model(),
datastream = observations,
autoupdates = updates
)
# Subscribe to posterior updates
subscribe!(result.posteriors[:x]) do posterior
println("Updated state: ", mean(posterior))
end
```
## Advanced Features
### 1. Backpressure Management
Handle fast data streams:
```julia
# Add rate limiting
controlled_stream = observations |>
throttle(0.1) |> # Limit to 10 updates per second
buffer(size = 100, stride = 50)
```
### 2. Error Handling
Robust stream processing:
```julia
subscription = subscribe!(stream,
# OnNext handler
data -> try_update(model, data),
# OnError handler
err -> handle_error(err),
# OnCompleted handler
() -> cleanup_resources()
)
```
### Error Handling Flow
```mermaid
graph TD
subgraph Stream
S1[Data] --> S2{Valid?}
S2 -->|Yes| S3[Process]
S2 -->|No| S4[Handle Error]
end
subgraph Recovery
R1[Retry]
R2[Skip]
R3[Terminate]
end
S4 --> R1
S4 --> R2
S4 --> R3
style S1 fill:#f9f
style S2 fill:#bbf
style S3 fill:#bfb
style S4 fill:#fbb
style R1 fill:#bfb
style R2 fill:#bfb
style R3 fill:#fbb
```
## Best Practices
### 1. Resource Management
```julia
# Proper cleanup
function setup_stream()
stream = Subject(Float64)
subscription = subscribe!(stream, handler)
return stream, subscription
end
function cleanup(subscription)
unsubscribe!(subscription)
end
```
### 2. Performance Optimization
- Buffer sizes for memory efficiency
- Appropriate update frequencies
- Resource cleanup
### Performance Considerations
```mermaid
mindmap
root((Performance))
Buffer Management
Size
Stride
Cleanup
Update Frequency
Throttling
Batching
Scheduling
Timing
Resource Usage
Memory
CPU
Network
```
## Common Patterns
### 1. Event-Based Updates
```julia
# Create event stream
events = Subject(Symbol)
# Handle different event types
processed = events |>
filter(evt -> evt in [:update, :reset]) |>
map(evt -> handle_event(evt))
```
### 2. Time-Based Processing
```julia
# Time-windowed processing
windowed_data = observations |>
buffer_time(1.0) |> # 1-second windows
filter(window -> !isempty(window)) |>
map(process_window)
```
### 3. State Management
```julia
# Stateful stream processing
function create_stateful_stream()
state = Ref(initial_state())
return stream |>
map(data -> update_state!(state, data)) |>
filter(valid_state)
end
```
## Debugging and Testing
### 1. Stream Debugging
```julia
# Add debug points
debugged_stream = stream |>
tap(x -> println("Raw: ", x)) |>
map(process_data) |>
tap(x -> println("Processed: ", x))
```
### 2. Testing Streams
```julia
# Test stream processing
function test_stream()
test_data = [1.0, 2.0, 3.0]
results = []
stream = from(test_data) |>
your_processing_pipeline() |>
subscribe!(x -> push!(results, x))
@test results == expected_results
end
```
## References
- [[rocket_jl|Rocket.jl Documentation]]
- [[reactive_programming_concepts|Reactive Programming Concepts]]
- [[streaming_inference|Streaming Inference Guide]]
- [[message_passing|Message Passing in RxInfer]]