Deploy apps with dynamic dependencies.

CHANGELOG.md

@@ -15,14 +15,19 @@ All notable changes to this project will be documented in this file.
 - `operator-rs` `0.44.0` -> `0.48.0` ([#267], [#275]).
 - Removed usages of SPARK_DAEMON_JAVA_OPTS since it's not a reliable way to pass extra JVM options ([#272]).
 - [BREAKING] use product image selection instead of version ([#275]).
-- BREAKING refactored application roles to use `CommonConfiguration` structures from the operator framework ([#277]).
+- [BREAKING] refactored application roles to use `CommonConfiguration` structures from the operator framework ([#277]).
+
+### Fixed
+
+- Dynamic loading of Maven packages ([#281]).
 
 [#267]: https://github.com/stackabletech/spark-k8s-operator/pull/267
 [#268]: https://github.com/stackabletech/spark-k8s-operator/pull/268
 [#269]: https://github.com/stackabletech/spark-k8s-operator/pull/269
 [#272]: https://github.com/stackabletech/spark-k8s-operator/pull/272
 [#275]: https://github.com/stackabletech/spark-k8s-operator/pull/275
 [#277]: https://github.com/stackabletech/spark-k8s-operator/pull/277
+[#281]: https://github.com/stackabletech/spark-k8s-operator/pull/281
 
 ## [23.7.0] - 2023-07-14
 

docs/modules/spark-k8s/pages/usage-guide/job-dependencies.adoc

@@ -90,25 +90,28 @@ include::example$example-pvc.yaml[]
 
 === Spark native package coordinates and Python requirements
 
-IMPORTANT: With the platform release 23.4.1 (and all previous releases), dynamic provisioning of dependencies using the Spark `packages` field doesn't work. This is a known problem with Spark and is tracked https://github.com/stackabletech/spark-k8s-operator/issues/141[here].
-
 The last and most flexible way to provision dependencies is to use the built-in `spark-submit` support for Maven package coordinates.
 
-These can be specified by adding the following section to the `SparkApplication` manifest file:
+The snippet below showcases how to add Apache Iceberg support to a Spark (version 3.4.x) application.
 
 [source,yaml]
 ----
 spec:
+  sparkConf:
+    spark.sql.extensions: org.apache.iceberg.spark.extensions.IcebergSparkSessionExtensions
+    spark.sql.catalog.spark_catalog: org.apache.iceberg.spark.SparkSessionCatalog
+    spark.sql.catalog.spark_catalog.type: hive
+    spark.sql.catalog.local: org.apache.iceberg.spark.SparkCatalog
+    spark.sql.catalog.local.type: hadoop
+    spark.sql.catalog.local.warehouse: /tmp/warehouse
   deps:
-    repositories:
-      - https://repository.example.com/prod
     packages:
-      - com.example:some-package:1.0.0
-    excludePackages:
-      - com.example:other-package
+      - org.apache.iceberg:iceberg-spark-runtime-3.4_2.12:1.3.1
 ----
 
-These directly translate to the spark-submit parameters `--packages`, `--exclude-packages`, and `--repositories`.
+IMPORTANT: Currently it's not possible to provision dependencies that are loaded by the JVM's (system class loader)[https://docs.oracle.com/en/java/javase/11/docs/api/java.base/java/lang/ClassLoader.html#getSystemClassLoader()]. Such dependencies include JDBC drivers. If you need access to JDBC sources from your Spark application, consider building your own custom Spark image.
+
+IMPORTANT: Spark version 3.3.x has a https://issues.apache.org/jira/browse/SPARK-35084[known bug] that prevents this mechanism to work.
 
 When submitting PySpark jobs, users can specify `pip` requirements that are installed before the driver and executor pods are created.
 

rust/crd/src/constants.rs

@@ -2,6 +2,9 @@ use stackable_operator::memory::{BinaryMultiple, MemoryQuantity};
 
 pub const APP_NAME: &str = "spark-k8s";
 
+pub const VOLUME_MOUNT_NAME_IVY2: &str = "ivy2";
+pub const VOLUME_MOUNT_PATH_IVY2: &str = "/ivy2";
+
 pub const VOLUME_MOUNT_NAME_DRIVER_POD_TEMPLATES: &str = "driver-pod-template";
 pub const VOLUME_MOUNT_PATH_DRIVER_POD_TEMPLATES: &str = "/stackable/spark/driver-pod-templates";
 

rust/crd/src/lib.rs

@@ -182,6 +182,14 @@ impl SparkApplication {
             .map(|req| req.join(" "))
     }
 
+    pub fn packages(&self) -> Vec<String> {
+        self.spec
+            .deps
+            .as_ref()
+            .and_then(|deps| deps.packages.clone())
+            .unwrap_or_default()
+    }
+
     pub fn volumes(
         &self,
         s3conn: &Option<S3ConnectionSpec>,
@@ -242,6 +250,13 @@ impl SparkApplication {
                 .build(),
         );
 
+        if !self.packages().is_empty() {
+            result.push(
+                VolumeBuilder::new(VOLUME_MOUNT_NAME_IVY2)
+                    .empty_dir(EmptyDirVolumeSource::default())
+                    .build(),
+            );
+        }
         if let Some(cert_secrets) = tlscerts::tls_secret_names(s3conn, s3logdir) {
             result.push(
                 VolumeBuilder::new(STACKABLE_TRUST_STORE_NAME)
@@ -332,6 +347,13 @@ impl SparkApplication {
             ..VolumeMount::default()
         });
 
+        if !self.packages().is_empty() {
+            mounts.push(VolumeMount {
+                name: VOLUME_MOUNT_NAME_IVY2.into(),
+                mount_path: VOLUME_MOUNT_PATH_IVY2.into(),
+                ..VolumeMount::default()
+            });
+        }
         if let Some(cert_secrets) = tlscerts::tls_secret_names(s3conn, s3logdir) {
             mounts.push(VolumeMount {
                 name: STACKABLE_TRUST_STORE_NAME.into(),
@@ -458,7 +480,10 @@ impl SparkApplication {
                 deps.repositories
                     .map(|r| format!("--repositories {}", r.join(","))),
             );
-            submit_cmd.extend(deps.packages.map(|p| format!("--packages {}", p.join(","))));
+            submit_cmd.extend(
+                deps.packages
+                    .map(|p| format!("--conf spark.jars.packages={}", p.join(","))),
+            );
         }
 
         // some command elements need to be initially stored in a map (to allow overwrites) and
@@ -498,6 +523,10 @@ impl SparkApplication {
             submit_conf.extend(log_dir.application_spark_config());
         }
 
+        if !self.packages().is_empty() {
+            submit_cmd.push(format!("--conf spark.jars.ivy={VOLUME_MOUNT_PATH_IVY2}"))
+        }
+
         // conf arguments: these should follow - and thus override - values set from resource limits above
         if let Some(spark_conf) = self.spec.spark_conf.clone() {
             submit_conf.extend(spark_conf);

tests/templates/kuttl/iceberg/01-assert.yaml.j2

@@ -0,0 +1,10 @@
+---
+apiVersion: kuttl.dev/v1beta1
+kind: TestAssert
+{% if lookup('env', 'VECTOR_AGGREGATOR') %}
+---
+apiVersion: v1
+kind: ConfigMap
+metadata:
+  name: vector-aggregator-discovery
+{% endif %}

tests/templates/kuttl/iceberg/01-install-vector-aggregator-discovery-configmap.yaml.j2

@@ -0,0 +1,9 @@
+{% if lookup('env', 'VECTOR_AGGREGATOR') %}
+---
+apiVersion: v1
+kind: ConfigMap
+metadata:
+  name: vector-aggregator-discovery
+data:
+  ADDRESS: {{ lookup('env', 'VECTOR_AGGREGATOR') }}
+{% endif %}

tests/templates/kuttl/iceberg/10-assert.yaml.j2

@@ -0,0 +1,17 @@
+---
+apiVersion: kuttl.dev/v1beta1
+kind: TestAssert
+timeout: 300
+---
+# The Job starting the whole process
+apiVersion: spark.stackable.tech/v1alpha1
+kind: SparkApplication
+metadata:
+  name: pyspark-iceberg
+status:
+{% if test_scenario['values']['spark'].startswith("3.3") %}
+  # Spark 3.3 is expected to fail because of this https://issues.apache.org/jira/browse/SPARK-35084
+  phase: Failed
+{% else %}
+  phase: Succeeded
+{% endif %}

tests/templates/kuttl/iceberg/10-deploy-spark-app.yaml.j2

@@ -0,0 +1,84 @@
+---
+apiVersion: spark.stackable.tech/v1alpha1
+kind: SparkApplication
+metadata:
+  name: pyspark-iceberg
+spec:
+  version: "1.0"
+{% if lookup('env', 'VECTOR_AGGREGATOR') %}
+  vectorAggregatorConfigMapName: vector-aggregator-discovery
+{% endif %}
+  sparkImage:
+{% if test_scenario['values']['spark'].find(",") > 0 %}
+    custom: "{{ test_scenario['values']['spark'].split(',')[1] }}"
+    productVersion: "{{ test_scenario['values']['spark'].split(',')[0] }}"
+{% else %}
+    productVersion: "{{ test_scenario['values']['spark'] }}"
+{% endif %}
+    pullPolicy: IfNotPresent
+  mode: cluster
+  mainApplicationFile: "local:///stackable/spark/jobs/write-to-iceberg.py"
+  sparkConf:
+    spark.sql.extensions: org.apache.iceberg.spark.extensions.IcebergSparkSessionExtensions
+    spark.sql.catalog.spark_catalog: org.apache.iceberg.spark.SparkSessionCatalog
+    spark.sql.catalog.spark_catalog.type: hive
+    spark.sql.catalog.local: org.apache.iceberg.spark.SparkCatalog
+    spark.sql.catalog.local.type: hadoop
+    spark.sql.catalog.local.warehouse: /tmp/warehouse
+  job:
+    logging:
+      enableVectorAgent: {{ lookup('env', 'VECTOR_AGGREGATOR') | length > 0 }}
+  driver:
+    config:
+      logging:
+        enableVectorAgent: {{ lookup('env', 'VECTOR_AGGREGATOR') | length > 0 }}
+      volumeMounts:
+        - name: script
+          mountPath: /stackable/spark/jobs
+  executor:
+    replicas: 1
+    config:
+      logging:
+        enableVectorAgent: {{ lookup('env', 'VECTOR_AGGREGATOR') | length > 0 }}
+      volumeMounts:
+        - name: script
+          mountPath: /stackable/spark/jobs
+  deps:
+    packages:
+      - org.apache.iceberg:iceberg-spark-runtime-{{ test_scenario['values']['spark'].rstrip('.0') }}_2.12:1.3.1
+  volumes:
+    - name: script
+      configMap:
+        name: write-to-iceberg
+---
+apiVersion: v1
+kind: ConfigMap
+metadata:
+  name: write-to-iceberg
+data:
+  write-to-iceberg.py: |
+    from pyspark.sql import SparkSession
+    from pyspark.sql.types import *
+
+    spark = SparkSession.builder.appName("write-to-iceberg").getOrCreate()
+
+    schema = StructType([
+      StructField("id", LongType(), True),
+      StructField("data", StringType(), True)
+    ])
+
+
+    # create table
+    df = spark.createDataFrame([], schema)
+    df.writeTo("local.db.table").create()
+
+    # append to table
+    data = [
+        (1,"one"),
+        (2,"two"),
+        (3,"three"),
+        (4,"four")
+      ]
+
+    df = spark.createDataFrame(data, schema)
+    df.writeTo("local.db.table").append()

tests/test-definition.yaml

@@ -67,6 +67,9 @@ tests:
       - spark
       - ny-tlc-report
       - openshift
+  - name: iceberg
+    dimensions:
+      - spark
 suites:
   - name: nightly
     patch: