Low-abstraction robust orchestration for scalable event-driven systems.
Danger: very aspirational README-driven development.
Teaser trailer (not there yet):
(TODO hello world example, operations flow)
See API reference.
Spīve's primary goal is to boost productivity in software engineering, ranging from near-real time data processing pipelines to microservices, websites and enterprise internal tools:
- Must minimize CRUD boilerplate (achieve more with less code), encourage decoupled event-driven architectures, assist with versioning to make refactoring and modernizing of components a breeze (shorten life of legacy code), embrace determinism, and remove difficulties around statekeeping
- Intuitive operations and robust observability must come by default (should be hard to deploy your software in such a way that you miss an error stacktrace or miss the onset of degraded service)
Second to the above, we should also not neglect performance:
- Should scale without contention on a group centralized nodes or a single point of failure
- Should leverage existing durable store implementations (there are already plenty), and let you as an application owner pick optimal price/throughput among the most suitable storage and compute providers
Spīve, as a platform, implements a few relatively non-leaky abstractions for managing consistently ordered, partitioned streams of events, and orchestrates event-sourced distributed applications that reliably consume and produce such streams.
(Event-sourcing, as a paradigm, capitalizes on history retention and thus repeatable behaviors. It uncomplicates effective use of in-memory data structures, to marry simple and easy development work with predictable processing performance. Spīve API is designed to directly assist application developers in successfully leveraging these principles.)
- automated KTLO operations, such as instance restoration to healthy and live state by replaying historical events in case of crashes
- assistance in debugging and rollout of code changes
- proactive scaling (by sharding) in anticipation of excessive number of events hitting any single instance
- always up-to-date visualization of data flow between applications
(Think of a nightly scheduled job producing statistical model updates into a service, then maybe a web-based administration tool allowing account managers to enter data in another service, both of them being depended on by a user-facing app server, and so on.)
Spīve stays on top of the entire lifecycles of event streams and the applications which communicate through these streams. Tens and hundreds of interdependent applications, distributed across thousands of instances, can be easily operated and understood on a "single pane of glass" interface. The rich interactive visualization comes in especially handy when a software system grows to require more than one team of developers, and the system's architecture evolves through years.
Spīve API strives to abstract away storage and encoding of events — the application developer/owner should be enabled to work "in language" without extraneous concern about where technically the events came from and where they went. Spīve guarantees that deployed application code will keep executing in a consistent and timelyTBD fashion to react to incoming events, or otherwise application owners will get alerted.
Under the hood, the Spīve abstractions around event handling unlock a slew of blanket optimizations that can be applied transparently. Examples include microbatching to trade off latency against throughput, moving the persisted event streams between differently priced storages, moving the computation between different machine types, etc.
Another upside is that the persisted events are easily available for offline analytical processing in notebooks and batch pipelines, with minimal effort to extract accurate data. No more "data collection as an afterthought".
(TODO gallery style:
- Fast transactions, arbitrary read volumes, thanks to in-memory state
- State machines, workflows and sagas
- Near-real time trading decisions
- Streaming and batch processes that ship data in and out of backend services are also made efficient to write and test, and deploy alongside the services
- The long, fat tail of custom, smallish and business critical systems
- Many Big Data problems can just as well benefit from event-sourcing model, especially if the problem is embarrassingly parallel)
Spīve can be thought of as a workflow engine, akin to e.g. AWS Simple Workflow Service or Temporal, or Infinitic, yet it centers on a different, lower level of abstraction. Spīve runs relatively "close to metal", in terms of concepts supported by its SDK.
Spīve UI, on the other hand, is centered on serving the high level picture with ease, while also allowing to seamlessly zoom in and replicate lower level details, such as event stream contents and instance states at any given point in event time.
DevOps-friendly and microservices-friendly, this platform as a whole is heavily optimized for flow, meaning that development and maintenance operations on interconnected live applications can be conducted rapidly and coherently, even at extremely large production scale. A knock-on effect is joy-sparking reliability and manageability of your codebase — absent of many maintenance-focused concerns, codebases become easier to evolve in a modular fashion, and less prone to deteriorate in clarity (accrue suboptimal hacks) when touched by any number of autonomous contributing teams.
To look at Spīve as yet another streaming data analysis engine, would be misleading. Spīve does act as a framework to handle large amounts of data (streams can be seen as large datasets consisting of events) in parallel, but the API makes different tradeoffs than those commonplace in Beam or Hadoop ecosystems:
- Serializability of event handler code to transfer it to distributed workers is not required — on the contrary, the application code (image, resp. jar) is deployed and executed as-built, usually on a set of virtual machine instances.
- Control of per-instance in-memory state using the native constructs of the programming language is facilitated, avoiding superfluous indirection.
- Event handling logic is designed to be single threaded: among simplicity benefits, it should be particularly noted that stacktraces in case of errors tend to be informative and pointing to the relevant places in business logic. Meanwhile, the necessary parallelism for performance is achieved via partitioning of streams, resp. by sharding of processes into multiple instances.
- Complementing synchronous event handling, Spīve sports asynchronous workloads that run concurrently with event handlers. Workloads are perfectly suitable for serving requests off the in-memory state with low latency and high availability, among other things.
Spīve ultimately provides safeguards so that out-of-order events are never observed by the business logic, which is of great help to easily reason about the correctness of application behavior. This needs to be reconciled with the real world circumstances where information may happen to arrive out-of-order though. Instead of exposing late arrival of events as a complicating condition in business logic, Spīve chooses to primarily address this problem through first class versioning of streams, resp. processes, along with convenience automation and orchestration to migrate the world (the dependency graph downstream of the corrupt stream, and their hitherto created side effects) to a corrected version of the event order. (Cf. n-temporal models, backdating.) This comes with recomputation costs that may be formidable yet feasible, a tradeoff for overall productivity gain.
Depending on how suitable your application is for the event-sourcing paradigm, Spīve provides predictably low overhead; "you don't pay for what you don't use".
Last but not least, on the flipside of Spīve's conceptual simplicity lies its ubiquity. Wide variety of supported types of event stores, and adaptation into diverse execution environments allow Spīve to prove itself early, and use your cloud or on-premise resources efficiently as your appetite grows.
Backend and application examples are currently developed as a Maven project using Java 19. Set up like:
curl -s "https://get.sdkman.io" | bash
source ~/.sdkman/bin/sdkman-init.sh
sdk install java 19.0.2-open
sdk install mvnd 1.0-m6-m40
mvnd clean verify
Frontend uses Qwik, D3 and Material UI. See the respective README.md file for instructions.
A basic "walking skeleton" can be launched in a docker compose environment:
./tools/dev-0/start.sh
(TODO screenshots and more detail on development flow)
Code generation when .st templates have been updated:
./code-gen/run.sh
(TODO code, architecture, UX conventions and opinions)
InterruptedException for (indefinitely) blocking methods... Hardly seeing need for other checked exceptions.
Strongly opinionated that common development iteration UX should be blazingly fast, to not break flow. Aspire to gamify common chores, such as deployment, scaling, and verification of code changes. Do precompute views, throw hardware at the problem, get UI to update in split seconds. Key durations guidance, applicable throughout ecosystem: < 400 ms for roundtrip from UI interaction to backend, 5 seconds between status updates, 5 minutes for replay on each instance...
Hermeticity. Spive app is self-hosting, its code seems self-referential to an extent. Astonishingly self-manipulating as a quine it should be not. We do not want to contort Spīve into a platform that readily mangles itself out in the field — nor one that is prone to mangling itself if a user happens to click the wrong button.
A TODO list full of wishes.
"Spīve" (/spiːve/) is a very underused Latvian word that means roughly "fierceness" or "incisiveness".
Herein it will be spelled as Spīve when referring to the platform at a conceptual level, and as Spive when it is the literal name of an implementation artifact, such as Spive class.