How to use the py4j.protocol.Py4JJavaError function in py4j

def run_spark_action(action):
    import py4j
    try:
        results = action()
    except py4j.protocol.Py4JJavaError:
        logging.error("Spark job failed to run! Jenkins should probably restart this build.")
        raise

    return results

def get(self):
                if failure_reason:
                    return "failure-reason"
                else:
                    raise Py4JJavaError("msg", JavaException())

self.assertRaises(ValueError, Statistics.chiSqTest, observed3, expected3)

        # Negative counts in observed
        neg_obs = Vectors.dense([1.0, 2.0, 3.0, -4.0])
        self.assertRaises(Py4JJavaError, Statistics.chiSqTest, neg_obs, expected1)

        # Count = 0.0 in expected but not observed
        zero_expected = Vectors.dense([1.0, 0.0, 3.0])
        pearson_inf = Statistics.chiSqTest(observed, zero_expected)
        self.assertEqual(pearson_inf.statistic, inf)
        self.assertEqual(pearson_inf.degreesOfFreedom, 2)
        self.assertEqual(pearson_inf.pValue, 0.0)

        # 0.0 in expected and observed simultaneously
        zero_observed = Vectors.dense([2.0, 0.0, 1.0])
        self.assertRaises(Py4JJavaError, Statistics.chiSqTest, zero_observed, zero_expected)

def test_pipe_functions(self):
        data = ['1', '2', '3']
        rdd = self.sc.parallelize(data)
        with QuietTest(self.sc):
            self.assertEqual([], rdd.pipe('cc').collect())
            self.assertRaises(Py4JJavaError, rdd.pipe('cc', checkCode=True).collect)
        result = rdd.pipe('cat').collect()
        result.sort()
        for x, y in zip(data, result):
            self.assertEqual(x, y)
        self.assertRaises(Py4JJavaError, rdd.pipe('grep 4', checkCode=True).collect)
        self.assertEqual([], rdd.pipe('grep 4').collect())

def deco(*a, **kw):
        try:
            return f(*a, **kw)
        except py4j.protocol.Py4JJavaError as e:
            s = e.java_exception.toString()
            stackTrace = '\n\t at '.join(map(lambda x: x.toString(),
                                             e.java_exception.getStackTrace()))
            if s.startswith('org.apache.spark.sql.AnalysisException: '):
                raise AnalysisException(s.split(': ', 1)[1], stackTrace)
            if s.startswith('org.apache.spark.sql.catalyst.analysis'):
                raise AnalysisException(s.split(': ', 1)[1], stackTrace)
            if s.startswith('org.apache.spark.sql.catalyst.parser.ParseException: '):
                raise ParseException(s.split(': ', 1)[1], stackTrace)
            if s.startswith('org.apache.spark.sql.streaming.StreamingQueryException: '):
                raise StreamingQueryException(s.split(': ', 1)[1], stackTrace)
            if s.startswith('org.apache.spark.sql.execution.QueryExecutionException: '):
                raise QueryExecutionException(s.split(': ', 1)[1], stackTrace)
            if s.startswith('java.lang.IllegalArgumentException: '):
                raise IllegalArgumentException(s.split(': ', 1)[1], stackTrace)
            raise

mlhd_df.registerTempTable('mlhd')
    except AnalysisException as e:
        logging.critical("Error while registering dataframe mlhd: %s", str(e))
        raise

    for _ in range(5):
        try:
            print("Running SQL...")
            artist_popularity_df = listenbrainz_spark.sql_context.sql("""
                    SELECT artist_mbid, COUNT(artist_mbid) as cnt
                      FROM mlhd
                  GROUP BY artist_mbid
                  ORDER BY cnt DESC
            """)
            break
        except Py4JJavaError as e:
            logging.error("error while running the query: %s", str(e))
    else:
        logging.critical("Could not run query. Exiting...")
        sys.exit(-1)

    print("number of rows: ", artist_popularity_df.count())
    artist_popularity_df.show()
    print("Saving...")
    file_name = 'mlhd-artist-popularity-%s.csv' % datetime.now.strftime('%Y%m%d-%H%M%S')
    csv_path = config.HDFS_CLUSTER_URI + os.path.join(MLHD_DATA_PATH, 'csv', file_name)
    for _ in range(10):
        try:
            artist_popularity_df.write.csv(csv_path)
            break
        except Exception as e:
            logging.error("Couldn't write result to CSV, trying again, error: %s", str(e))

def init_dir(rm, recursive, create_dir):
    """ Create directories in HDFS to run the recommendation engine.
    """
    try:
        listenbrainz_spark.init_spark_session('Manage Directories')
    except Py4JJavaError as err:
        logging.error('{}\n{}\nAborting...'.format(str(err), err.java_exception))
        sys.exit(-1)

    hdfs_connection.init_hdfs(config.HDFS_HTTP_URI)
    if rm:
        try:
            utils.delete_dir(path.RECOMMENDATION_PARENT_DIR)
            utils.delete_dir(path.CHECKPOINT_DIR)
            logging.info('Successfully deleted directories.')
        except HdfsError as err:
            logging.error('{}: Some/all directories are non-empty. Try "--recursive" to delete recursively.'.format(
                type(err).__name__))
            logging.warning('Deleting directory recursively will delete all the recommendation data.')
            sys.exit(-1)

    if recursive:

# An action must be called for persist to evaluate.
    num_training = training_data.count()
    num_validation = validation_data.count()
    num_test = test_data.count()

    current_app.logger.info('Training models...')
    t0 = time()

    model, model_metadata, best_model_metadata = train(training_data, validation_data, num_validation, config.RANKS,
        config.LAMBDAS, config.ITERATIONS)
    models_training_time = '{:.2f}'.format((time() - t0) / 3600)

    try:
        best_model_test_rmse = compute_rmse(model.model, test_data, num_test)
    except Py4JJavaError as err:
        current_app.logger.error('Root mean squared error for best model for test data not computed\n{}\nAborting...'.format(
            str(err.java_exception)), exc_info=True)
        sys.exit(-1)

    # Cached data must be cleared to avoid OOM.
    training_data.unpersist()
    validation_data.unpersist()

    current_app.logger.info('Saving model...')
    t0 = time()
    metadata_file_path = os.path.join(path.DATA_DIR, best_model_metadata['model_id'])
    try:
        model.model.save(listenbrainz_spark.context, config.HDFS_CLUSTER_URI + metadata_file_path)
    except Py4JJavaError as err:
        current_app.logger.error('Unable to save best model "{}"\n{}. Aborting...'.format(best_model_metadata['model_id'],
            str(err.java_exception)), exc_info=True)

def fromFile(cls, name):
        """Load a model from PMML file with given pathname"""
        pc = PMMLContext.getOrCreate()
        try:
            java_model = pc._jvm.org.pmml4s.model.Model.fromFile(name)
            return cls(java_model)
        except Py4JJavaError as e:
            je = e.java_exception
            raise PmmlError(je.getClass().getSimpleName(), je.getMessage())

def transform(self, word):
        """
        Transforms a word to its vector representation

        .. note:: Local use only

        :param word: a word
        :return: vector representation of word(s)
        """
        try:
            return self.call("transform", word)
        except Py4JJavaError:
            raise ValueError("%s not found" % word)