The following are selected stories about my work on distributed systems, event sourcing, system integration and machine learning. They primarily cover work that has been open-sourced but also some proprietary work.
From 2014 to 2017, I mainly worked on the global distribution of an international customer’s in-house digital asset management platform. State replication across multiple datacenters, low-latency write access to replicated state and write-availability during inter-datacenter network partitions were important requirements from the very beginning. We decided to follow an event sourcing approach for persistence and developed an event replication mechanism that preserves the causal ordering of events in event streams. For replicated state, we used a causal consistency model which is the strongest form of consistency that is still compatible with AP of CAP. The implementation was based on both generic and domain-specific operation-based CRDTs. I was responsible for the complete distributed systems architecture and the development of the generic platform components. These components evolved into the Eventuate open source project of which I’m the founder and lead developer. Eventuate has several production deployments today.
We also used Eventuate to build distributed systems composed of microservices that collaborate via events. With Eventuate, services can rely on consuming events from local event logs in correct causal order, without duplicates. They can also rely on the write-availability of local event logs during network partitions and the reliable delivery of written events to collaborators. Eventuate-based microservice systems are conceptually similar to those that can be built with Apache Kafka and Kafka Streams but Eventuate additionally implements a causal consistency model for systems that are distributed across multiple datacenters.
I already worked with globally distributed systems in 2004 where I developed a distributed computing solution for an international pharmaceutical company. In their drug discovery pipeline they used several computing services, deployed at different locations around the globe, for analyzing chemical structures regarding their biological activity. The developed solution integrated these computing services so that researchers could run them with a single mouse click from a chemical spreadsheet. The solution managed the reliable execution of compute jobs, persisted the results and delivered them back to the user. It ran in production for many years and was an integral part of the drug discovery pipeline of that company.
I use event sourcing since 2011 in my projects. I started to apply that approach during the development of an electronic health record for an international customer. Event sourcing proved to be the right choice in this project given the demanding read and write throughput requirements and the needed flexibility to integrate with other healthcare IT systems. I later generalized that work in the Eventsourced open source project that I developed in collaboration with Eligotech. Eventsourced adds persistence to stateful Akka actors by writing inbound messages to a journal and replaying them on actor restart. Eventsourced was used as persistence solution in Eligotech products.
In 2013, Eventsourced attracted the interest of Lightbend (formerly Typesafe) and we decided to start a collaboration to build Akka Persistence which is now the official successor of Eventsourced. I was responsible for the complete development of Akka Persistence, from initial idea to production quality code. Akka Persistence has numerous production deployments today and is used as persistence mechanism in the Lagom microservices framework. I also developed the Cassandra storage plugin for Akka Persistence which is now the officially recommended plugin for using Akka Persistence in production.
In 2014, I started to further develop the idea of Akka Persistence in the Eventuate open source project. Among other features, Eventuate additionally supports the replication of persistent actors, up to global scale. The replication mechanism supports a causal consistency model which is the strongest form of consistency that is still compatible with AP of CAP. The concepts of Eventuate are closely related to those of operation-based CRDTs which is further described in this blog post (see also section Distributed Systems).
In 2007, I started to work on a project at ICW in which we integrated the hospital information systems of several customers using IHE standards. Technical basis for the integration solutions was the Apache Camel integration framework for which I developed integration components that implement actor interfaces of several IHE profiles and a DSL for processing HL7 messages and CDA documents (see also this article for an introduction). In 2009, these extensions have been open sourced as Open eHealth Integration Platform (IPF) of which I’m the founder and initial lead developer. IPF has many production deployments in international customer projects today and is still actively developed by ICW, the sponsor of the open source project. IPF is a central component of ICW’s eHealth Suite and provides connectivity to a wide range of healthcare information systems. Its standard compliance has been certified in several IHE Connectathons. During my work on IPF I also became an Apache Camel committer.
To meet the increasing scalability requirements in some IPF projects I started to investigate alternatives to Apache Camel’s routing engine. I decided to use Akka actors for message routing and processing which proved to be a better basis for scaling IPF applications under load. The result of these efforts was the akka-camel module that I contributed to Akka in 2011. It implements a generic integration layer between Akka actors and Apache Camel components, including the IHE components of IPF. The akka-camel module is still part of Akka today and has many production deployments.
I also developed other routing engine alternatives that follow a pure functional programming approach. A first attempt was scalaz-camel which is now superseded by the Streamz project which I’m still actively developing today. It allows application developers to integrate Apache Camel components into FS2 applications with a high-level integration DSL. It also implements that DSL on top of Akka Streams. Streamz is meanwhile the official replacement for akka-camel and part of the Alpakka ecosystem.
I’m currently working as freelance machine learning engineer for MerlinOne where I’m responsible for the development of a multimodal neural search engine. The responsibility covers both scientific and engineering work. The search engine supports text-text, text-image, text-video and image-image search modes, integrated by a metasearch engine. A further feature is face recognition in image and video searches. The search engine can be also trained on custom datasets to learn new concepts.
The neural networks that are used by the search engine to encode assets (documents, images, videos, …) can be trained at different scales depending on the model type, training algorithm and dataset. Large scale training is done in the cloud on multiple nodes with AWS SageMaker. Batch-encoding of large asset databases is also cloud-based and can be scaled horizontally.
Encoded assets are managed by a distributed search index. Distribution of the search index is required for fault-tolerance and for scaling with data volume and concurrent users. Asset management (creation, update, versioning, deletion, …) is done with causal consistency, the strongest form of consistency that is still compatible with AP (availability and partition-tolerance) of CAP. The distributed search index is updated in near-realtime as new assets are added to an asset database.
Another solution I developed is an image quality assessment service for scoring the aesthetic and technical quality of images. The underlying model is trained from scratch with a high-quality in-house dataset. All solutions I developed for MerlinOne have productions deployments at several customer sites which must be maintained at an ever-increasing scale. Keeping myself up-to-date with state-of-the-art representation learning approaches and training models on challenging new datasets is part of my daily work.
I got into machine learning in 2014 with Andrew Ng’s machine learning course on Coursera. After having applied machine learning in several smaller projects I decided for a sabbatical year in 2017 to go deeper into mathematics, statistics, “traditional” machine learning and deep learning. I attended several online courses and gained further experience from reading numerous books and papers. Of special interest for me are Bayesian methods for machine learning including Bayesian deep learning. I implemented what I learned with scikit-learn, Tensorflow, PyTorch and PyMC3 and published part of my exercise work as open source projects and blog posts.