orchestera-lib

Spark runtime configuration

OrchesteraSparkSession creates a client-mode Spark session from inside an Orchestera notebook pod. It never selects an image, registry secret, service account, or node placement on its own:

• ORCH_SPARK_K8S_CONTAINER_IMAGE is required and must be the digest-pinned driver/executor image supplied by the notebook provisioner.
• ORCH_SPARK_K8S_NAMESPACE is required; ORCH_SPARK_K8S_SERVICE_ACCOUNT defaults to workload and can be overridden for another tenant contract.
• ORCH_SPARK_K8S_NODE_SELECTOR and ORCH_SPARK_K8S_TOLERATIONS are optional JSON values copied into Spark's executor pod template.
• ORCH_SPARK_EVENT_LOG_DIR is optional. Event logging is disabled unless a directory is explicitly supplied.

The image must already contain matching Spark/PySpark and S3A/JDBC requirements. The library does not install packages at runtime or inject image pull credentials.

External connections

The control plane mounts the workspace's S3 and database connections into the pod as a read-only connections.json, pointed at by ORCH_CONNECTIONS_FILE (default /var/run/orchestera/connections/connections.json). The file holds only the connections of the workspace its namespace is bound to.

OrchesteraSparkSession reads it when the session is created and applies each S3 connection's per-bucket spark.hadoop.fs.s3a.bucket.<bucket>.* config, so s3a:// paths work with no setup in the notebook:

with OrchesteraSparkSession(app_name="etl", executor_instances=2,
                            executor_cores=1, executor_memory="2g") as spark:
    df = spark.read.parquet("s3a://analytics/events/")

Those keys are per bucket, so they take precedence for their own bucket while leaving the global Pod Identity credentials provider in place for every other bucket. An explicit additional_spark_conf entry still overrides both. Because the file is read at session creation, a connection added after the notebook started is picked up by the next session, once the kubelet has refreshed the mounted Secret (about a minute).

Database connections are not applied automatically -- read one by name:

from orchestera.connections import get_connection

conn = get_connection("warehouse")
df = spark.read.format("jdbc").options(**conn.jdbc).option("dbtable", "public.orders").load()

A missing file means the workspace has no connections and is not an error. A file that exists but cannot be parsed raises ConnectionsError rather than starting a session that would fail later with a confusing permission error.

Minimal TUI

Run from a local checkout:

uv run python -m orchestera.tui.cli

Run after install (console script):

pip install orchestera-lib
orchestera-tui

Run with uvx:

uvx --from orchestera-lib orchestera-tui

Run with uvx from the current local repo:

uvx --from . orchestera-tui

Inside the TUI, run:

/start-proxy

Stop it with:

/stop-proxy

Update kube context with:

/update-cluster-context <cluster-name>

Upload IAM policy JSON to Parameter Store with:

/upload-iam-policy <cluster-name> <namespace> <alias>

Upload secrets JSON to Parameter Store with:

/upload-secrets <cluster-name> <namespace> <alias>

Exit the TUI with:

/exit

Commands support fuzzy matching, so close variants like /strt-proxy also work.

/update-cluster-context <cluster-name> runs:

aws eks update-kubeconfig --name <cluster-name> --region us-east-1 --profile orchestera-dev

/upload-iam-policy <cluster-name> <namespace> <alias> writes:

/orchestera/sparklith/<cluster-name>/<namespace>/iams/<alias>

/upload-secrets <cluster-name> <namespace> <alias> writes:

/orchestera/sparklith/<cluster-name>/<namespace>/secrets/<alias>

For both upload commands:

• TUI opens a multiline JSON editor.
• Ctrl+S validates and uploads.
• Esc cancels.
• Uses AWS profile orchestera-useradmin, region us-east-1, and overwrites existing values.

This starts:

kubectl port-forward svc/traefik -n traefik 8080:80 3080:3080 9001:8080

Forwarded local ports:

• 8080
• 3080
• 9001