airflow-adapter
Airflow adapter pattern for v2/v3 API compatibility. Use when working with adapters, version detection, or adding new API methods that need to work across…
Configures Airflow to run language SDK tasks (Java, Go, and future native SDKs) — register a coordinator, map a queue to it, ensure the runtime/artifact on workers, and tune coordinator options. Use when the user wants Airflow to route a queue to a native-language coordinator,
$ npx -y skills add astronomer/agents --skill configuring-airflow-language-sdks --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/configuring-airflow-language-sdksContext preview
The summary Claude sees to decide when to auto-load this skill.
Configures Airflow to run language SDK tasks (Java, Go, and future native SDKs) — register a coordinator, map a queue to it, ensure the runtime/artifact on workers, and tune coordinator options. Use when the user wants Airflow to route a queue to a native-language coordinator,
name: configuring-airflow-language-sdks description: Configures Airflow to run language SDK tasks (Java, Go, and future native SDKs) — register a coordinator, map a queue to it, ensure the runtime/artifact on workers, and tune coordinator options. Use when the user wants Airflow to route a queue to a native-language coordinator, asks about the `[sdk]` `coordinators`/`queue_to_coordinator` settings, `AIRFLOW__SDK__COORDINATORS`, `jars_root`, `executables_root` or other coordinator `kwargs`, `task_startup_timeout`, or why their native tasks aren't being picked up. Covers the shared routing mechanism plus per-coordinator options (e.g. JavaCoordinator, ExecutableCoordinator).
To run language SDK tasks, Airflow needs to know two things: which **coordinator** launches the native subprocess, and which **queue** routes to that coordinator. The mechanism is identical across every language SDK — only each coordinator's `classpath` and `kwargs` differ. This skill documents the shared wiring once, then the per-coordinator options. It is platform-neutral: the same settings apply on open-source Airflow and on managed platforms like Astro.
> **Experimental.** The language SDKs are in preview; configuration keys may change.
> For the task code, see **authoring-language-sdk-tasks** (and the per-language authoring skill, e.g. **authoring-java-sdk-tasks**, **authoring-go-sdk-tasks**). For building and shipping the artifact, see the per-language deploy skill (e.g. **deploying-java-sdk-bundles**, **deploying-go-sdk-bundles**).
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Both live in the `[sdk]` configuration section and apply to every language SDK:
1. **`coordinators`** — a JSON object mapping a coordinator *name* you choose to its implementation (`classpath`) and constructor `kwargs`. 2. **`queue_to_coordinator`** — a JSON object mapping a task *queue* to a coordinator name.
A task whose stub sets `queue="..."` is handed to the named coordinator, which launches the native subprocess. The coordinator name is arbitrary — it just has to be the same string in both settings. The queue name must match the `queue=` set on the Python `@task.stub`.
[sdk]
coordinators = {
"java-jdk17": {
"classpath": "airflow.sdk.coordinators.java.JavaCoordinator",
"kwargs": {"jars_root": ["/opt/airflow/jars"]}
},
"go": {
"classpath": "airflow.sdk.coordinators.executable.ExecutableCoordinator",
"kwargs": {"executables_root": ["/opt/airflow/executable-bundles"]}
}
}
queue_to_coordinator = {"java": "java-jdk17", "golang": "go"}Each value must be **valid one-line JSON**. This form is convenient for containers, `.env` files, Docker Compose, and Helm.
export AIRFLOW__SDK__COORDINATORS='{"java-jdk17": {"classpath": "airflow.sdk.coordinators.java.JavaCoordinator", "kwargs": {"jars_root": ["/opt/airflow/jars"]}}, "go": {"classpath": "airflow.sdk.coordinators.executable.ExecutableCoordinator", "kwargs": {"executables_root": ["/opt/airflow/executable-bundles"]}}}'
export AIRFLOW__SDK__QUEUE_TO_COORDINATOR='{"java": "java-jdk17", "golang": "go"}'The examples above register **multiple coordinators** at once (one per language) and map a different queue to each — register only the ones you use.
---
The `classpath` and `kwargs` are specific to each coordinator. Add a subsection here as new language SDKs land.
| Parameter | Default | Description | |-----------|---------|-------------| | `jars_root` | *(required)* | One or more directories scanned **recursively** for `.jar` files. Accepts a string or a list of strings/paths. The classpath is assembled automatically. | | `java_executable` | `"java"` | Path to the `java` binary. Defaults to `java` on `$PATH`. | | `jvm_args` | `[]` | Extra JVM arguments, e.g. `["-Xmx1g", "-Dsome.property=value"]`. | | `main_class` | *(auto-detect)* | Explicit entry-point class. If omitted, the coordinator scans `jars_root` for a JAR whose manifest declares `Main-Class`. **Set this explicitly if multiple executable JARs are present** — otherwise the choice is non-deterministic. | | `task_startup_timeout` | `10.0` | Seconds to wait for the subprocess to connect after launch. Increase it if JVM startup is slow (constrained hardware, large classpath, first cold start). |
**Java logging via `java.util.logging`.** Of the SDK logging integrations, only JPL and SLF4J are zero-config build dependencies; Log4j 2 and JUL need extra setup — see the logging integration section in **deploying-java-sdk-bundles**. JUL's documented alternative to calling `AirflowJulHandler.setup()` in `main()` is a `logging.properties` file, wired through `jvm_args`:
[sdk]
coordinators = {
"java-jdk17": {
"classpath": "airflow.sdk.coordinators.java.JavaCoordinator",
"kwargs": {
"jars_root": ["/opt/airflow/jars"],
"jvm_args": ["-Djava.util.logging.config.file=/opt/airflow/logging.properties"]
}
}
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