Swim is an Apache 2.0 licensed platform designed for building applications that deliver continuous intelligence from streaming data at scale, automating the operation of distributed applications. It utilizes stateful, real-time stream processing through a graph of concurrent web agents, allowing real-time predictions and analytics on live data with reduced infrastructure needs. The platform supports applications that analyze complex relationships and continuously update insights, making it efficient for real-time traffic predictions and similar use cases.

1. Simon Crosby, CTO
swim.ai
simon@swim.ai
1

2. Swim is an Apache 2.0 licensed platform that makes it
easy to build applications that deliver continuous
intelligence from streaming data, at massive scale
swimos.org

3. Swim is a Stateful, Real-time Stream Processor
• Builds and scales apps from event data, creating a graph of stateful,
concurrent actors that continuously compute – driven by data
• Automates distributed application infrastructure operation
• Load balances, secures, persists and auto-scales the application
 App: Distributed, stateful, graph
of concurrent Web Agents (actors),
streaming APIs & real-time UIs
 Infrastructure: Distributed, p2p mesh of
instances on k8s using WebSockets
Fabric

4. >
Jacksonville, FL

5. Predict real-time traffic flow
traffic.swim.ai
Deliver a continuous stream of predictions

6. Continuous Intelligence

7. Analyze, learn & predict, driven by streaming data
Respond in real-time – in sync with the real-world
Continuously evaluate parametric functions and causal
relationships eg: “near to” or “predicted to be within”
Build the app from streaming data by linking sources into
a fluid graph of real-world relationships
Continuous Intelligence

8. Continuously evaluate complex parametric relationships
• Find trucks that are in or approaching a banned area
• Find trucks with bad braking behavior and alert the nearest inspector

9. 9
Benefits
Use 90% less infrastructure
Apps are easy to develop and operate
Do data science on live data
Deliver answers a million times faster…

10. Client
Client
Client
Client
Streaming analytics (solved!)
☞ polling  not real-time
(a, b) < 290
Ω(b, t) > 188
Ψ(c,t-1) < ¾
Complements Kafka Streaming & kSQL
Entity relationships are often
parametric in time and space
Evaluations are at huge scale,
stateful and interdependent
Application

11. Swim – Continuous Intelligence
Developer builds an object-
oriented Java application &
deploys with standard tools
Web Agents link to represent
relationships and share state
Swim uses streaming data to build a
stateful graph of concurrent actors –
Web Agents- one per object (source)
.. to respond in sync with the real
world, & deliver continuously
updated insights to UIs / apps
They continuously analyze, learn
& predict from their state and the
states of linked Agents
… and stream their analysis
and predictions in real-time
over their links …

12. Swim is like for the real world
• Creates a stateful, concurrent Web Agent for each data
source, that continuously and statefully analyzes data from its
real-world sibling (actors – like Akka without messaging)
• Each Agent dynamically links to related Web Agents, builiding
a fluid in-memory graph that tracks complex relationships eg:
• Containment, proximity - “neighbor” or ”is approaching”
• Computed - “correlated to” or ”predicted to be within”
• Linked Agents use each others’ state to continuously analyze,
learn and predict
• And stream their insights to apps, real-time UIs, storage

13. Web Agents are stateful, concurrent, distributed
(Java) actors
• Lanes are object members
– Properties and methods eg: “average”
– Hold state, have logic eg “reduce”
– Streamed as updates over links
• Links are relationships between web agents
• Schema derived: contains, average
• Computed: maps, joins, correlations,
membership, “predicted to be”, “near”,
“correlated”
– Build a graph
– A link to a lane lets the linker continuously
observe lane state
Data Lanes Links
(data, old_state)  new_state
Developer View: Web Agents

14. Streamed
Inputs
λ(i1, i2,.. in, ) → (o1,…oj, )
St-1 St
Streamed
Outputs
Continuously Current Materialized Views
λ(ỉ, ●) → (ỏ, ◙)
• A Web Agent can analyze millions of
(concurrent) updates to Agents it is linked to
• “Debounced” to allow bursts of state changes
• Changed input invalidates dependent outputs
• Does not immediately trigger recomputation
• Use timers to enforce latency bounds
• Informed of partitions
millions
of links

15. 1
5
Agents Continuously Analyze, Learn & Predict
MapReduce
Graph
Analysis
Learn &
Predict
Spark / Flink / Jupyter etc
Relational
Queries

16. A Swim Application is an Active Graph

17. 1000m
A Graph of Linked Web Agents

18. A Computational Graph
That Computes as Data
Flows

19. Edge Fog Cloud
k8s
P2p Mesh
Where?

20. Edge Fog Cloud
k8s
P2p Mesh
Web Agents Distribute Over Instances

21.  React in real-time
Easy to develop, deploy & run
Enable data science on live data
Continuous Intelligence for Apache Kafka
Use 90% less infrastructure
Always have the answer
Analyze, learn & predict on-the-fly
Deliver answers a million times faster…