Quickstart Guide
Welcome to Orca - the orchestration framework for running analytics on realtime data, at scale.
This guide will walk you through setting up Orca locally so you can start building and running custom algorithms on telemetry streams own algorithms in minutes.
1 - Install the Orca CLI
The Orca CLI helps you manage the life cycle of Orca services locally. Orca is built on top of open source resources, and containerisation is used to manage them when developing locally.
So, before installing the CLI, make sure Docker is installed on your machine.
docker --version
For Linux / macOS / Windows via WSL
Run:
curl -fsSL https://raw.githubusercontent.com/orc-analytics/orca/main/install-cli.sh | bash
2 - Start the Orca Stack
With the CLI installed, launch the Orca-Core stack:
orca start
Verify the system is running and retrieve connection details:
orca status
You should see something like this:
--noCopy
PostgreSQL: running
Connection string: postgresql://orca:orca@localhost:32768/orca?sslmode=disable
Redis: running
Connection string: redis://localhost:32769
Orca: running
Connection string: grpc://localhost:32770
→ Set these environment variables in your Orca SDKs to connect them to Orca:
→ ORCASERVER=grpc://localhost:32770
→ HOST=172.18.0.1
If Orca is not running, start the stack with the command orca start.
3 - Start Developing
Once the orca stack is running, you're ready to register your first algorithm using one of the SDKs:
For this example we will use python, but the pattern is similar language to language.
Defining a Window
The triggering element to all Orca analytics are Windows. Windows define
a period of time where an event of a particular type has occurred.
Building a Trigger
Analytics built within the Orca framework need to be triggered. They are triggered
by Windows via the EmitWindow function.