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Otto

Meet Otto, your friendly continuous delivery companion.

Otto is a robust distributed system for scalable continuous integration and delivery. To accomplish this Otto is multi-process oriented and distributed by default; all system interactions must occur over process boundaries even if executing in the same logical host. This document outlines the high level architecture but omits specific inter-process communication formats, protocols, and dependencies.

Otto does not aim to be the center of the entire continuous delivery process, but rather seeks to interoperate seamlessly with all the various components which make CD work for you.

Status

Otto is currently not usable.

There are design documents in the rfc/ directory which can help describe the state of development for Otto.

The components are in different states of development. Please consult the README document in the subfolders for their current purpose and status.

Development

Much of Otto is built in Rust. The project is a "cargo workspace" which means that there are multiple binaries and libraries defined in the source tree, which can all be built together via the root Cargo.toml. Much can be accomplished with cargo build and cargo test, which is easily scoped to a single project via the -p flag, for example cargo test -p otto-parser. There is also a Makefile which drives some higher-level build system behavior.

Otto is composed of many different services, which communicate via JSON over HTTP.

Make targets

Running make in the root directory will list some brief help output, but some useful make targets to be aware of are documented below:

release

Build and strip release binaries to prepare for packaging. Most developers won’t need to execute this target.

run

Launch the services defined in the Procfile. This requires a cargo build ahead of time, and is really only useful for manual integration testing.

steps

This target will build and package all the steps defined in stdlib/

test

Runs all the acceptance tests, typically implemented with shunit2. This target will not run cargo build or cargo test. From a fresh clone, running the acceptance tests will require an initial build, for example cargo build && make test.

Subdirectories

Each subdirectory should have its own README with a little more information, but at a glance:

cli/

This directory contains all the command-line interfaces for Otto.

crates/

This directory contains the various pieces of shared code

rfcs/

RFCs (Request for Comment) are design documents for different patterns or subcomponents within Otto.

services/

Projects in this directory are Otto’s mesh of services, which speak HTTP to provide different aspects of functionality for the Otto project.

stdlib/

The Otto step "Standard Library." In essence, these are all the step libraries that are assumed to be installed by default with Otto.

Problems to Solve

Below is an incomplete listing of the problems Otto aims to solve for:

  • Existing tools do not model the entire continuous delivery process. Using an external tool such as Puppet, or relying on an external provider such as AWS ECS, there can be a "black hole" in the deployment. A point where control is delegated to an external system, and the orchestration tool (Otto), loses sight of what is happening.

  • Expecting "one single instance" to be the hub is unrealistic. Many deployment processes have "development" operated components, and "ops" operated components, with little to no automated hand-off of control between the two.

  • Mixing of management and execution contexts causes a myriad of issues. Many tools allow the management/orchestrator process to run user-defined workloads. This allows breaches of isolation between user-defined workloads and administrator configuration and data.

  • Non-deterministic runtime behavior adds instability. Without being able to "explain" a set of operations which should occur before runtime, it is impossible to determine whether or not a given delivery pipeline is correctly constructed.

  • Blending runtime data and logic with process definition confuses users. Related to the problem above, Providing runtime data about the process in a manner which is only accessible in the delivery process itself, overly complicates the parsing and execution of a defined continuous delivery process.

  • Modeling of the delivery process is blurred with build tooling. Without a clear separation of concerns between the responsibility of build tools like GNU/Make, Maven, Gradle, etc and the continuous delivery process definition, logic invariably bleeds between the two.

  • Opinionated platform requirements prevent easy usage across different environments. Forcing a reliance on containers, or a runtime like the Java Virtual Machine results in burdensome system configuration before starting to do the real work of defining a continuous delivery process. Without gracefully degrading in functionality depending on the system requirements which are present, users are forced to hack around the platform requirements, or spent significant worrying about and maintaining pre-requisites.

  • Many tools are difficult to configure by default. For most application stacks, there are common conventions which can be easily prescribed for the 80% use-case. Ruby on Rails applications will almost all look identical, and should require zero additional configuration.

  • Secrets and credentials can be inadvertently leaked. Many tools provide some ability to configure secrets for the continuous delivery process, but expose them to the process itself in insecure ways, which allow for leakage.

  • Extensibility must not come at the expense of system integrity. Systems which allow for administrator, or user-injected code at runtime cannot avoid system reliability and security problems. Extensibility is an important characteristic to support, but secondary to system integrity.

  • Usage cannot grow across an organization without user-defined extension. The operators of the system will not be able to provide for every eventual requirement from users. Some mechanism for extending or consolidating aspects of a continuous delivery process must exist.

Modeling Continuous Delivery

Some characteristics of a continuous delivery process model which Otto must ensure:

  • Deterministic ahead-of-time. Without executing the full process, it must be possible to "explain" what will happen.

  • External interactions must be model-able. Deferring control to an external system must be accounted for in a user-defined model. For example, submitting a deployment request, and then waiting for some external condition to be made to indicate that the deployment has completed and the service is now online. This should support both an evented model, wherein the external service "calls back" and a polling model, where the process waits until some external condition can be verified.

  • Branching logic, a user must be able to easily define branching logic. For example, a web application’s delivery may be different depending on whether this is a production or a staging deployment.

  • Describe, though not fully, environments. All applications have at least some concept of environments, whether it is a web application’s concept of staging/production, or a compiled asset’s concept of debug/release builds.

  • Safe credentials access, credentials should not be exposed to in a way which might allow the user-defined code to inadvertently leak the credential.

  • Caching data between runs must be describable in some form or fashion. Taking Maven projects as an example, where successive runs of mvn on a cold-cache will result in significant downloads of data, whereas caching ~/.m2 will result in more acceptable performance.

  • Refactor/extensibility support in-repo or externally. Depending on whether the source repository is a monorepo, or something more modular. Common aspects of the process must be able to be templatized/parameterized in some form, and shared within the repository or via an external repository.

  • Scale down to near zero-configuration. the simplest model possible should simply define what platform’s conventions to use. With Rails applications, many applications are functionally in the same with their use of Bundler, Rake, and RSpec.