1.
Technology Division
Case study: Goldman Sachs
An enterprise-scale Workflow and Decision platform
September 20, 2018
Disclaimer:
Views and opinions expressed are for informational purposes only. They do not constitute a recommendation by Goldman Sachs to buy, sell or hold any security. No part of this material may,
without Goldman Sachs’s prior written consent, be (i) copied, photocopied or duplicated in any form, by any means, or (ii) distributed to any person that is an employee, officer, director, or
authorized agent of the recipient
Copyright Note: 2018 Goldman Sachs. All rights reserved.

2.
2Technology Division
Agenda
• Workflow Elements
• Building an enterprise-wide Workflow Engine platform on Camunda
• Decentralizing the platform through Workflow Elements
• Decision Modeling
• Orchestrating a Decision Services Platform using Camunda
• New developments in Decision Modeling
Agenda
Case study: Goldman Sachs

3.
3
Workflow Elements
Enterprise-scale Workflow Engine capabilities

4.
4Technology Division
Workflow at an enterprise scale
Case study: Goldman Sachs
40,000 5,000 2,000,000 1,000 14,000
TOTAL USERS
(100% OF
THE FIRM)
DAILY USERS NEW ACTIVITIES
DAILY
PLATFORM
DEVELOPERS
UNIQUE
ACTIVE
WORKFLOWS

5.
5Technology Division
Evolution of workflow at GS
Case study: Goldman Sachs
1. Operations task service
Central
Task Service
GS Operations
Division
External
System
External
System
…
Create
tasks
Create
tasks
External
BPM
execution
server
Workflow
jBPM
library
…
2. jBPM library for deploying BPM execution servers
…
External
BPM
execution
server
Workflow
jBPM
library
Central
Task Service
GS Operations
Division
 Central task service for Operations
 Challenges:
 Volume
 Management
 Support
• Core team built jBPM Library
 BPMN used to standardize workflows
• Appdev teams run own BPM server
 must understand jBPM services

6.
6Technology Division
Evolution of workflow at GS
Case study: Goldman Sachs
Workflow Engine
(Business 1)
Camunda process
engine
Workflow Engine
(Business 2)
Camunda process
engine
3. Hosted Workflow Engine service built upon Camunda
…
Aggregation Service
Web UI
… …
Workflow Engine
(Business 1)
Camunda process
engine
Central Task Service
(Business 1)
Central Task
Service
(Business 2)
Workflow Engine
(Business N)
Camunda process
engine
Central Task
Service
(Business N)
…

7.
7Technology Division
Model Driven Development Focus
Case study: Goldman Sachs
We Promote Model-Driven Development
Why?
• Scalable Orchestration
• Self Documenting
• Promotes Business Partnerships
• Available to whole Firm
• Leverages Camunda capabilities
Built an Integrated Design Tool
• Workflow Designer Built on BPMN.IO
• Integrates with SDLC
• Bespoke Forms Designer / UI Builder
• Coming: DMN Integration
Above: Workflow Designer for BPMN modelling,
BPMN.IO courtesy of Courtesy of Camunda Services GmbH
Above: Custom forms design integrated to the Workflow Designer

8.
8Technology Division
Workflow Elements
• Containerized version of Workflow Engine
• Requested by appdev teams
• Automated deployment
Scaling to the cloud with Camunda
Case study: Goldman Sachs
Workflow Control Centre
• UI to support engine provisioning
• Metrics, logs, configuration
• Appdev teams can self-manage engines
Above: Self-service order for a new Workflow Elements instance
Above: Teams are able to configure and manage their Elements instance independently

9.
9Technology Division
Evolution of workflow at GS
Case study: Goldman Sachs
Workflow Engine
(Business 1)
Camunda
process engine
Workflow Engine
Camunda process
engine and
embedded task
service
4. Workflow Elements Engine running with embedded Task Service
Aggregation Service
Web UI
… …
Workflow Engine
(Business 1)
Camunda
process engine
…
Workflow Engine
Camunda process
engine and
embedded task
service
Central Task Service
(Business 1)

10.
10
Decision Modelling
Orchestration of decisions using Camunda, and new developments

11.
11Technology Division
A firm-wide Decision Services Platform
Case study: Goldman Sachs
REST
endpoint
Regional
HAProxy
routing
Asia
CN …CN CN
… … …
CN …CN CN
EMEA
CN …CN CN
… … …
CN …CN CN
Americas
CN …CN
… … …
CN …CN CN
Apache Zookeeper
Common decision repository
Elasticsearch
Audit Store
Modelling
Application
CN = Compute Node
DSP Compute Node
Rules Engine 1
…
Rules Engine N
Orchestration
External Data
Integration
Local repository
REST
I/face
Summary
• The GS Decision Services Platform (DSP) is a firm-wide execution
platform for Decision Models
• Models are stateless & declarative representations of business logic
• Globally-distributed, horizontally-scalable and highly resilient
• Approx. 160 million executions across ~2600 decision models per day
• Rapid growth from 100k (2015)  2m (2016)  50m (2017)  160m
(2018) daily executions
• Leveraging Camunda process execution engine for orchestration
CN

12.
12Technology Division
Decision orchestration with Camunda
Case study: Goldman Sachs
Decision Orchestration with Camunda
• Driven by increasing complexity of solutions and limitations of a purely declarative representation
• E.g. Calculate X  Then use X to retrieve data Y from external source  Then continue execution using Y
• But we have no concept of “before” or “after”
• Manual coordination of these steps by the user would be unreliable and introduce back-and-forth latency
• We required a solution to orchestrate these scenarios and chose to adopt the Camunda process engine
Key considerations
• Highly performant
• Ease of integration
• Intuitive design environment
• Lightweight implementation
• Doesn’t require the full expressive power of BPMN
• Can be stateless; no persistence required
• All executions can be considered atomic
• Potential for customisation

13.
13Technology Division
Decision orchestration with Camunda
Case study: Goldman Sachs
The GS “Decision Flow” implementation
• We implemented a lightweight and restricted version of the Camunda process engine for orchestrating these decisions
• Customisation was achieved through Camunda configuration and implementation against standard interfaces
• Limited components – restricted to a subset of BPMN, all stateless components
• In-memory H2 database – no stateful components so no need for communication with shared database
• Custom stubbed components – for example, no object serialization required given lack of persistence
• Integrated with existing Camunda-based design environment
BPMN.IO courtesy of Courtesy of Camunda Services GmbH

14.
14Technology Division
An open-source Java execution library for DMN
Case study: Goldman Sachs
Camunda DMN design environment
• We have adopted the Camunda DMN.js library to allow (initial, basic)
rendering of these DMN models inside our own designer
• Integrated with the execution platform; users can open their decision
models and execute in bulk against the platform directly
• Decision modelling still takes place within an external application
• However we are exploring the capabilities of the DMN.js library and
considering extensions to modelling support in the future
BPMN.IO courtesy of Courtesy of Camunda Services GmbH

15.
15Technology Division
An open-source Java execution library for DMN
Case study: Goldman Sachs
Native Java execution for DMN
• We have been executing decisions within powerful & general-purpose rules engines
• Scope of decision modelling expanding to other use cases, in particular very high-volume, low-latency scenarios
• This drove the development of a purpose-built library for native Java code-generation and execution of DMN decision models
• We are releasing this as an open-source library under the name “jDMN”
Decision Modelling1 DMN XML2 Code-generated Java3

16.
16Technology Division
An open-source Java execution library for DMN
Case study: Goldman Sachs
Performance of jDMN-generated Java
• Runtime of these generated Java models is 10-40x faster than our existing (and optimised) rules engine implementation
Open-sourcing
• We have released jDMN as an open-source library: https://github.com/goldmansachs/jdmn
• jDMN has full support for DMN 1.1 and the soon-to-be-finalised DMN 1.2 standard, and we fully intend to support DMN 2.0 as it
develops
• Please feel free to use or contribute, and get in touch if you’d like to understand more about the project
Existing rules engine
jDMN
Averageexecutiontime(ms)