Spark applications often need connectors or data-source modules that are not part of the runtime classpath. Supplying a Maven coordinate at submission keeps that dependency with one application instead of changing the shared Spark installation on every cluster node.

The --packages option resolves a group:artifact:version coordinate and its transitive dependencies before the driver starts. The artifact's Spark release and Scala binary suffix must match the runtime, because a package can download successfully and still fail later with incompatible classes.

The external spark-avro module makes the classpath change observable. A job that writes and reads Avro data proves that Spark loaded the submitted package, while the same format operation fails when the module is absent or incompatible.

Related: How to submit an Apache Spark job
Related: How to package Python dependencies for a PySpark job

Steps to add a package to an Apache Spark job:

  1. Choose the spark-avro coordinate that matches the Spark and Scala versions on the submission host. 
    Spark runtime: 4.1.2
Scala binary version: 2.13
Package: org.apache.spark:spark-avro_2.13:4.1.2

    The Spark release supplies the package version, while the runtime's Scala binary version supplies the artifact suffix. The coordinates shown here match the current Spark 4.1.2 distribution.

  2. Create spark_avro_package_check.py with the imports and Avro output path.
    spark_avro_package_check.py
    from pathlib import Path
 
from pyspark.sql import SparkSession
 
 
output_path = Path("/tmp/spark-package-check/orders-avro")
  3. Add the local Spark session below output_path.
    spark_avro_package_check.py
    spark = (
    SparkSession.builder.master("local[2]")
    .appName("spark-package-check")
    .getOrCreate()
)
spark.sparkContext.setLogLevel("ERROR")

    A cluster submission can supply its own master instead of the local[2] value used for this self-contained check.

  4. Define the sample order rows after the log-level setting.
    spark_avro_package_check.py
    orders = spark.createDataFrame(
    [
        ("ORD-1001", "APAC", 3),
        ("ORD-1002", "EMEA", 1),
    ],
    "order_id string, region string, item_count int",
)
  5. Append the Avro write below the orders DataFrame.
    spark_avro_package_check.py
    orders.write.format("avro").mode("overwrite").save(str(output_path))

    The avro provider lookup occurs at this point and fails when spark-avro is missing or incompatible.
    Related: How to read and write Avro files with Spark

  6. Load the written Avro dataset below the write operation.
    spark_avro_package_check.py
    loaded = spark.read.format("avro").load(str(output_path))
  7. Add the row and data-file checks below the Avro read.
    spark_avro_package_check.py
    expected = [
    ("ORD-1001", "APAC", 3),
    ("ORD-1002", "EMEA", 1),
]
actual = [tuple(row) for row in loaded.orderBy("order_id").collect()]
assert actual == expected, actual
 
avro_files = list(output_path.glob("part-*.avro"))
assert avro_files, "No Avro data files were written"
 
print("Package-backed Avro rows:")
loaded.orderBy("order_id").show(truncate=False)
print(f"Avro data files: {len(avro_files)}")
  8. Stop the Spark session after the result output.
    spark_avro_package_check.py
    spark.stop()
  9. Compare the assembled file with the complete package-check program.
    spark_avro_package_check.py
    from pathlib import Path
 
from pyspark.sql import SparkSession
 
 
output_path = Path("/tmp/spark-package-check/orders-avro")
 
spark = (
    SparkSession.builder.master("local[2]")
    .appName("spark-package-check")
    .getOrCreate()
)
spark.sparkContext.setLogLevel("ERROR")
 
orders = spark.createDataFrame(
    [
        ("ORD-1001", "APAC", 3),
        ("ORD-1002", "EMEA", 1),
    ],
    "order_id string, region string, item_count int",
)
 
orders.write.format("avro").mode("overwrite").save(str(output_path))
loaded = spark.read.format("avro").load(str(output_path))
 
expected = [
    ("ORD-1001", "APAC", 3),
    ("ORD-1002", "EMEA", 1),
]
actual = [tuple(row) for row in loaded.orderBy("order_id").collect()]
assert actual == expected, actual
 
avro_files = list(output_path.glob("part-*.avro"))
assert avro_files, "No Avro data files were written"
 
print("Package-backed Avro rows:")
loaded.orderBy("order_id").show(truncate=False)
print(f"Avro data files: {len(avro_files)}")
 
spark.stop()
  10. Submit the completed job with the matching Avro package. 
    $ spark-submit \
  --master local[2] \
  --name spark-package-check \
  --conf spark.ui.showConsoleProgress=false \
  --packages org.apache.spark:spark-avro_2.13:4.1.2 \
  spark_avro_package_check.py
org.apache.spark#spark-avro_2.13 added as a dependency
##### snipped #####
Package-backed Avro rows:
+--------+------+----------+
|order_id|region|item_count|
+--------+------+----------+
|ORD-1001|APAC  |3         |
|ORD-1002|EMEA  |1         |
+--------+------+----------+

Avro data files: 2

    A successful row comparison and a nonzero Avro file count prove that the submitted package supplied the external data source. --packages accepts comma-separated coordinates when one job needs several artifacts; use --repositories only for artifacts outside the default repositories, and never place repository credentials in a command that may enter shell history.

  11. Remove the generated Avro output from the first package-backed run. 
    $ rm -r /tmp/spark-package-check
  12. Re-submit spark_avro_package_check.py after cleanup to confirm the package-backed job remains runnable. 
    $ spark-submit \
  --master local[2] \
  --name spark-package-check \
  --conf spark.ui.showConsoleProgress=false \
  --packages org.apache.spark:spark-avro_2.13:4.1.2 \
  spark_avro_package_check.py
org.apache.spark#spark-avro_2.13 added as a dependency
##### snipped #####
Package-backed Avro rows:
+--------+------+----------+
|order_id|region|item_count|
+--------+------+----------+
|ORD-1001|APAC  |3         |
|ORD-1002|EMEA  |1         |
+--------+------+----------+

Avro data files: 2