Troubleshoot MongoDB Altas To PySpark

Hi there, want to read from MongoDB Atlas data to Pyspark.

The read script is from this forum:

from Trademe_MongoDB.Credentials.Credentials import uri
from datetime import datetime
# from motor.motor_asyncio import AsyncIOMotorClient
from Trademe_MongoDB.logger_config import Logger_config
from pyspark.sql import SparkSession

logger = Logger_config().get_logger()

spark = SparkSession.\
config("spark.executor.memory", "1g").\
config("", uri).\
config("spark.jars.packages", "org.mongodb.spark:mongo-spark-connector:10.0.3").\

df ="mongodb").option('database', '1').option('collection', '2').load()

Throwing errors:

The system cannot find the path specified.
Error: Missing application resource.

Usage: spark-submit [options] <app jar | python file | R file> [app arguments]
Usage: spark-submit --kill [submission ID] --master [spark://...]
Usage: spark-submit --status [submission ID] --master [spark://...]
Usage: spark-submit run-example [options] example-class [example args]

  --master MASTER_URL         spark://host:port, mesos://host:port, yarn,
                              k8s://https://host:port, or local (Default: local[*]).
  --deploy-mode DEPLOY_MODE   Whether to launch the driver program locally ("client") or
                              on one of the worker machines inside the cluster ("cluster")
                              (Default: client).
  --class CLASS_NAME          Your application's main class (for Java / Scala apps).
  --name NAME                 A name of your application.
  --jars JARS                 Comma-separated list of jars to include on the driver
                              and executor classpaths.
  --packages                  Comma-separated list of maven coordinates of jars to include
                              on the driver and executor classpaths. Will search the local
                              maven repo, then maven central and any additional remote
                              repositories given by --repositories. The format for the
                              coordinates should be groupId:artifactId:version.
  --exclude-packages          Comma-separated list of groupId:artifactId, to exclude while
                              resolving the dependencies provided in --packages to avoid
                              dependency conflicts.
  --repositories              Comma-separated list of additional remote repositories to
                              search for the maven coordinates given with --packages.
  --py-files PY_FILES         Comma-separated list of .zip, .egg, or .py files to place
                              on the PYTHONPATH for Python apps.
  --files FILES               Comma-separated list of files to be placed in the working
                              directory of each executor. File paths of these files
                              in executors can be accessed via SparkFiles.get(fileName).
  --archives ARCHIVES         Comma-separated list of archives to be extracted into the
                              working directory of each executor.

  --conf, -c PROP=VALUE       Arbitrary Spark configuration property.
  --properties-file FILE      Path to a file from which to load extra properties. If not
                              specified, this will look for conf/spark-defaults.conf.

  --driver-memory MEM         Memory for driver (e.g. 1000M, 2G) (Default: 1024M).
  --driver-java-options       Extra Java options to pass to the driver.
  --driver-library-path       Extra library path entries to pass to the driver.
  --driver-class-path         Extra class path entries to pass to the driver. Note that
                              jars added with --jars are automatically included in the

  --executor-memory MEM       Memory per executor (e.g. 1000M, 2G) (Default: 1G).

  --proxy-user NAME           User to impersonate when submitting the application.
                              This argument does not work with --principal / --keytab.

  --help, -h                  Show this help message and exit.
  --verbose, -v               Print additional debug output.
  --version,                  Print the version of current Spark.

 Spark Connect only:
   --remote CONNECT_URL       URL to connect to the server for Spark Connect, e.g.,
                              sc://host:port. --master and --deploy-mode cannot be set
                              together with this option. This option is experimental, and
                              might change between minor releases.

 Cluster deploy mode only:
  --driver-cores NUM          Number of cores used by the driver, only in cluster mode
                              (Default: 1).

 Spark standalone or Mesos with cluster deploy mode only:
  --supervise                 If given, restarts the driver on failure.

 Spark standalone, Mesos or K8s with cluster deploy mode only:
  --kill SUBMISSION_ID        If given, kills the driver specified.
  --status SUBMISSION_ID      If given, requests the status of the driver specified.

 Spark standalone, Mesos and Kubernetes only:
  --total-executor-cores NUM  Total cores for all executors.

 Spark standalone, YARN and Kubernetes only:
  --executor-cores NUM        Number of cores used by each executor. (Default: 1 in
                              YARN and K8S modes, or all available cores on the worker
                              in standalone mode).

 Spark on YARN and Kubernetes only:
  --num-executors NUM         Number of executors to launch (Default: 2).
                              If dynamic allocation is enabled, the initial number of
                              executors will be at least NUM.
  --principal PRINCIPAL       Principal to be used to login to KDC.
  --keytab KEYTAB             The full path to the file that contains the keytab for the
                              principal specified above.

 Spark on YARN only:
  --queue QUEUE_NAME          The YARN queue to submit to (Default: "default").
'w' is not recognized as an internal or external command,
operable program or batch file.
Traceback (most recent call last):
  File "C:\Projects\Web projects\Trademe_MongoDB\Data analysis\Load From", line 11, in <module>
    spark = SparkSession.\
  File "C:\Projects\Web projects\venv\lib\site-packages\pyspark\sql\", line 477, in getOrCreate
    sc = SparkContext.getOrCreate(sparkConf)
  File "C:\Projects\Web projects\venv\lib\site-packages\pyspark\", line 512, in getOrCreate
    SparkContext(conf=conf or SparkConf())
  File "C:\Projects\Web projects\venv\lib\site-packages\pyspark\", line 198, in __init__
    SparkContext._ensure_initialized(self, gateway=gateway, conf=conf)
  File "C:\Projects\Web projects\venv\lib\site-packages\pyspark\", line 432, in _ensure_initialized
    SparkContext._gateway = gateway or launch_gateway(conf)
  File "C:\Projects\Web projects\venv\lib\site-packages\pyspark\", line 106, in launch_gateway
    raise RuntimeError("Java gateway process exited before sending its port number")
RuntimeError: Java gateway process exited before sending its port number

Process finished with exit code 1

Background: Download Pyspark not hadoop. Spark_home envrioments variabls set up, Java envrionment variables set up. Both can read from system path.

Hi @JJ_J ,

How is the Spark cluster Hosted. Is this a self hosted cluster or something like databricks?
“Java gateway process exited before sending its port number” - makes me think of a possible configuration with Spark env. Can you verify that network access exists via connecting to Atlas cluster directly from the Spark worker node?
Also can you verify that you have the pymongo installed in the environment?