26thSep1_Notes.txt

external.conf
It's in s3 but you're not in s3 this is not running in s3 your script error doesn't run in s3 it runs on it runs in a pod.
you want to get that thing from s3 to another to it yet another pod, where is what is actually reading it? What is reading it from s3 Putting the values into memory and then passing it.
a file exists on an s3 you can't just say the name of the file and think something's going to load it for you.
If you want a configuration file, you're either going to have to pass a full JSON payload right there, or you you're
going to have to mount an external
source in your driver pod or your executer pod, I don't think you want to do that. So that's going to be that's going to be down at the infrastructure level. 
What that means is we would have to actually mount a virtual drive in the pods to your s3 location. 
That's That's one legitimate way to get an s3 file into your thing where you can then address it as dot slash external config. That's the only way that that would actually work what you wrote.
You're gonna have to mount. You're gonna have to mount that configuration file as an external mount.
infrastructure layer, and that, that driver and executer we're going to have to figure out how to mount an s3 file as a directory inside our containers. 
ChatGPT said:
Thought for 42s

You’re right: nothing in Kubernetes “auto-loads” a file from S3. You have a few solid patterns—pick the one that fits your ops constraints.

0) Quick decision guide

Just a small config file? Put it in a ConfigMap/Secret and mount it. Easiest, no S3 needed. 
Kubernetes
+1

Must source directly from S3 but only at start-up? Use an initContainer to aws s3 cp into an emptyDir, then mount that path to your driver/executor. (Grant S3 read with IRSA.)

App truly needs a POSIX-ish path to a bucket (e.g., /mnt/config/external.conf) at runtime? Use the Mountpoint for Amazon S3 CSI driver (EKS add-on) to mount the bucket/prefix into the pod. Best “S3-as-filesystem” option today; understand POSIX caveats. 
AWS Docs
+2
AWS Docs
+2

For Spark specifically, another option is spark-submit --files s3a://bucket/path/external.conf and then reference it by filename in your app. That avoids mounts entirely if your image has hadoop-aws and credentials set. 
Apache Spark
+1

1) ConfigMap/Secret (simplest)

If external.conf is non-secret:

apiVersion: v1
kind: ConfigMap
metadata:
  name: external-conf
data:
  external.conf: |
    key1=val1
    key2=val2
---
apiVersion: apps/v1
kind: Deployment
spec:
  template:
    spec:
      containers:
      - name: app
        image: yourimage
        volumeMounts:
        - name: conf
          mountPath: /app/config
      volumes:
      - name: conf
        configMap:
          name: external-conf
          items:
          - key: external.conf
            path: external.conf


Now your code reads /app/config/external.conf. (Use a Secret instead if the file is sensitive.) 
Kubernetes
+1

2) Init container pulls from S3 (no special CSI driver)

Good when you want the truth to live in S3 but don’t need a live mount.

Prereqs

IRSA role with s3:GetObject for s3://your-bucket/path/external.conf.

Image with awscli in the init container.

apiVersion: apps/v1
kind: Deployment
spec:
  template:
    spec:
      serviceAccountName: spark # bound to an IAM role via IRSA
      initContainers:
      - name: fetch-conf
        image: amazon/aws-cli
        command: ["sh","-c"]
        args:
          - aws s3 cp s3://your-bucket/path/external.conf /work/config/external.conf
        volumeMounts:
        - name: work
          mountPath: /work
      containers:
      - name: app
        image: yourimage
        volumeMounts:
        - name: work
          mountPath: /app/config
      volumes:
      - name: work
        emptyDir: {}


Your app reads /app/config/external.conf. (This pattern is common and avoids the POSIX caveats of S3 filesystems.) For Spark on EKS, make sure you use IRSA for the driver/executors. 
Amazon Web Services, Inc.

3) Mount an S3 prefix into the pod (CSI driver)

If you must treat S3 like a local directory, use Mountpoint for Amazon S3 CSI (the EKS add-on).

Install (once per cluster): follow the EKS docs or add-on in the console. 
AWS Docs
+1

Define a PV/PVC to your bucket/prefix (read-only for configs):

apiVersion: v1
kind: PersistentVolume
metadata:
  name: s3-conf-pv
spec:
  capacity:
    storage: 5Gi
  accessModes: ["ReadOnlyMany"]
  csi:
    driver: s3.csi.aws.com
    volumeHandle: s3-conf-handle
    volumeAttributes:
      bucketName: your-bucket
      # Optional prefix inside the bucket:
      prefix: path/
      mounter: mountpoint  # uses Mountpoint for S3
---
apiVersion: v1
kind: PersistentVolumeClaim
metadata:
  name: s3-conf-pvc
spec:
  accessModes: ["ReadOnlyMany"]
  resources:
    requests:
      storage: 5Gi
  volumeName: s3-conf-pv


Mount it in your pod:

apiVersion: apps/v1
kind: Deployment
spec:
  template:
    spec:
      serviceAccountName: spark  # IRSA with s3:GetObject
      containers:
      - name: app
        image: yourimage
        volumeMounts:
        - name: s3conf
          mountPath: /app/config
          readOnly: true
      volumes:
      - name: s3conf
        persistentVolumeClaim:
          claimName: s3-conf-pvc


Now /app/config/external.conf is just there. Be aware: S3≠POSIX; some semantics differ. This driver targets high throughput and is the AWS-supported path; older FUSE options (s3fs/goofys) exist but come with performance/compatibility tradeoffs. 
Pure Storage Blog
+4
AWS Docs
+4
GitHub
+4

4) Spark-native alternative (no mounts at all)

If your use case is Spark (driver/executors on EKS), you can push the config file with --files:

spark-submit \
  --master k8s://https://$K8S_APISERVER \
  --deploy-mode cluster \
  --conf spark.kubernetes.authenticate.driver.serviceAccountName=spark \
  --files s3a://your-bucket/path/external.conf \
  local:///opt/spark/app.jar \
  --your-args ...


Inside your code, read it by filename only (external.conf) from the working dir; Spark distributes it to driver and executors. Ensure your image includes hadoop-aws and credentials via IRSA or Spark conf. 
Apache Spark
+1

Which one should you use?

For a single external.conf that changes occasionally, I’d start with (2) initContainer pull (clean, predictable, easy to cache/version).

If you need the file to look like a regular file path without modifying the app, and possibly many files, use (3) S3 CSI (Mountpoint).

If it’s Spark-only, consider (4) --files—it’s built for exactly this.

If you want, tell me your exact runner (plain K8s vs Spark on K8s via MWAA KPO), and I’ll drop in the Airflow KubernetesPodOperator snippet with the appropriate volumes/volume_mounts or an init_containers block wired for IRSA.

You said:
Yeah, I just wanted to understand like how my jar which is stored inside S3 location is reading through the kubernetes cluster EKS pods because in arguments I'm passing with the flags like hyphen hyphen jar then jar location while external configuration files are not able to read but jar how is taking up without mounting it
ChatGPT said:
Thought for 11s

Great question — the “why does my JAR work from S3 but my config file doesn’t?” puzzle is super common on Spark-on-K8s.

What’s happening under the hood

Your application JAR at s3a://.../app.jar works because Spark knows how to fetch remote URIs (Hadoop-compatible schemes like s3a://, hdfs://, wasbs://, etc.) for application code and dependencies.

When you pass --jars s3a://.../lib1.jar or the app itself as local:///... or s3a://.../app.jar, Spark’s launcher downloads those artifacts to the driver and propagates them to executors automatically (distributed cache).

This requires your image to include hadoop-aws + AWS SDK and credentials (ideally via IRSA).

Your external config file doesn’t work because Spark only auto-fetches items it knows about: app JAR, --jars, --packages, --files, --archives, --py-files.

A plain code path like new File("/app/config/external.conf") expects a local file in the container filesystem. Spark won’t pull it unless you explicitly tell it to (e.g., --files) or you mount/copy it into the container.

Three clean ways to make the config available (no Kubernetes volume mounts needed)

Use Spark’s distributed files
Add the config with --files (or spark.files=...), then read it via SparkFiles:

spark-submit \
 --master k8s://https://$K8S_APISERVER \
 --deploy-mode cluster \
 --class com.your.App \
 --conf spark.kubernetes.authenticate.driver.serviceAccountName=spark \
 --files s3a://your-bucket/path/external.conf \
 local:///opt/spark/app.jar


In Scala/Java:

import org.apache.spark.SparkFiles
val confPath = SparkFiles.get("external.conf")  // now a local path on driver & executors


(You can keep your code reading a relative filename if you prefer; Spark copies it into the working dir.)

Read it directly from S3 using Hadoop FS (no local file at all)
If your code can accept an S3A URL, skip local files:

val spark = SparkSession.builder.getOrCreate()
val confUri = "s3a://your-bucket/path/external.conf"
val lines = spark.sparkContext.textFile(confUri).collect()
// or use Hadoop FS API:
val fs = org.apache.hadoop.fs.FileSystem.get(spark.sparkContext.hadoopConfiguration)
val in = fs.open(new org.apache.hadoop.fs.Path(confUri))


This keeps the “source of truth” in S3 and avoids extra flags, at the cost of tweaking your code to accept S3 URIs.

Bake or fetch at container start (still no K8s volume mount)

Bake it into the image (simple but less dynamic), or

Init-container pull: have an init container aws s3 cp ... /work/external.conf into an emptyDir, then your app reads /work/external.conf. (This is infra-lite and works for any JVM app, not just Spark.)

Minimal Airflow KPO example (using Spark’s --files)

If you’re launching via KubernetesPodOperator:

from airflow.providers.cncf.kubernetes.operators.kubernetes_pod import KubernetesPodOperator

submit = KubernetesPodOperator(
    task_id="spark-job",
    name="spark-job",
    namespace="default",
    image="your-spark-image:latest",  # must include hadoop-aws + aws sdk
    service_account_name="spark",     # IRSA-enabled SA
    cmds=["/opt/spark/bin/spark-submit"],
    arguments=[
        "--master", f"k8s://https://{K8S_APISERVER}",
        "--deploy-mode", "cluster",
        "--class", "com.your.App",
        "--conf", "spark.kubernetes.container.image=your-spark-image:latest",
        "--conf", "spark.kubernetes.authenticate.driver.serviceAccountName=spark",
        "--files", "s3a://your-bucket/path/external.conf",
        "--jars",  "s3a://your-bucket/jars/dep1.jar,s3a://your-bucket/jars/dep2.jar",
        "local:///opt/spark/app.jar",
        # app args...
    ],
)

Sanity checklist (applies to all approaches)

Image contents: hadoop-aws jar + matching AWS SDK version for your Spark/Hadoop build.

Auth: IRSA role with s3:GetObject (and kms:Decrypt if objects are KMS-encrypted).

Spark conf: (often optional) spark.hadoop.fs.s3a.aws.credentials.provider if you’re not using IRSA defaults; ensure fs.s3a settings match your environment.

Don’t use s3://; use s3a:// for Hadoop/Spark.

TL;DR

JARs “just work from S3” because Spark knows to fetch them as code dependencies.

Your config is not a dependency unless you declare it. Use --files (or read via s3a:// in code), or pre-stage it (init-container or baked image).

No mount is required unless you specifically want a POSIX path exposed by the OS.

You said:
JARs “just work from S3” because Spark knows to fetch them as code dependencies.

Your config is not a dependency unless you declare it. please elaborate

A network error occurred. Please check your connection and try again.

Retry
ChatGPT said:

Got it 👍 — let’s dive deeper into that difference.

1. Why JARs “just work”

When you run spark-submit and pass your application JAR or extra dependencies with --jars s3a://bucket/app.jar:

Spark knows these are code artifacts.

The launcher uses the Hadoop FileSystem layer (fs.s3a) to download those JARs into the driver container.

Spark then ships them to executors automatically (through its distributed cache mechanism).

That means your app code is present on every executor without you needing to manually mount or copy anything.

👉 JARs are in a special category: Spark treats them as “application dependencies” and has built-in logic to fetch them from remote stores (S3, HDFS, WASB, GCS, etc.).

2. Why configs don’t work by default

A file like external.conf is not a code artifact in Spark’s eyes. Unless you tell Spark otherwise:

Your app tries new File("/path/to/external.conf") → the container’s filesystem is checked.

There’s no auto-fetch step, because Spark doesn’t assume every arbitrary file path might be in S3.

Result: file not found unless you manually mount, bake, or download it.

3. How to make config behave like a JAR

You have to explicitly promote your config into one of Spark’s distributable resources:

--files flag → tells Spark “this is a small side file to distribute.”

spark-submit \
  --files s3a://bucket/external.conf \
  --class com.example.App \
  s3a://bucket/app.jar


Then inside your code:

import org.apache.spark.SparkFiles
val confPath = SparkFiles.get("external.conf")


Spark ensures external.conf is downloaded into the driver and executors’ working directories.

Alternative: change your code to read directly from S3 (s3a://bucket/external.conf). That bypasses Spark’s distribution logic but ties your code to S3.

4. Quick analogy

JARs = Spark treats them like libraries → auto-fetch, auto-distribute.

Configs = Spark treats them like data → you must explicitly tell Spark how to fetch/distribute (--files, --archives, or read via s3a://).

✅ Bottom line:
JARs are “first-class citizens” in Spark’s submission process. Config files are not. If you want your config file to be handled the same way, you must either use --files (so Spark distributes it just like JARs) or mount/copy it yourself into the pod.

Do you want me to show you a KubernetesPodOperator YAML/JSON snippet where I add --files s3a://.../external.conf so the config ends up mounted in the driver and executors automatically?