Python conf.SparkConf方法代碼示例

# 需要導入模塊: from pyspark import conf [as 別名]
# 或者: from pyspark.conf import SparkConf [as 別名]
def _create_shell_session():
        """
        Initialize a SparkSession for a pyspark shell session. This is called from shell.py
        to make error handling simpler without needing to declare local variables in that
        script, which would expose those to users.
        """
        import py4j
        from pyspark.conf import SparkConf
        from pyspark.context import SparkContext
        try:
            # Try to access HiveConf, it will raise exception if Hive is not added
            conf = SparkConf()
            if conf.get('spark.sql.catalogImplementation', 'hive').lower() == 'hive':
                SparkContext._jvm.org.apache.hadoop.hive.conf.HiveConf()
                return SparkSession.builder\
                    .enableHiveSupport()\
                    .getOrCreate()
            else:
                return SparkSession.builder.getOrCreate()
        except (py4j.protocol.Py4JError, TypeError):
            if conf.get('spark.sql.catalogImplementation', '').lower() == 'hive':
                warnings.warn("Fall back to non-hive support because failing to access HiveConf, "
                              "please make sure you build spark with hive")

        return SparkSession.builder.getOrCreate() 

# 需要導入模塊: from pyspark import conf [as 別名]
# 或者: from pyspark.conf import SparkConf [as 別名]
def test_user_configuration(self):
        """Make sure user configuration is respected (SPARK-19307)"""
        script = self.createTempFile("test.py", """
            |from pyspark import SparkConf, SparkContext
            |
            |conf = SparkConf().set("spark.test_config", "1")
            |sc = SparkContext(conf = conf)
            |try:
            |    if sc._conf.get("spark.test_config") != "1":
            |        raise Exception("Cannot find spark.test_config in SparkContext's conf.")
            |finally:
            |    sc.stop()
            """)
        proc = subprocess.Popen(
            self.sparkSubmit + ["--master", "local", script],
            stdout=subprocess.PIPE,
            stderr=subprocess.STDOUT)
        out, err = proc.communicate()
        self.assertEqual(0, proc.returncode, msg="Process failed with error:\n {0}".format(out)) 

# 需要導入模塊: from pyspark import conf [as 別名]
# 或者: from pyspark.conf import SparkConf [as 別名]
def main():
    if len(sys.argv) != 3:
        print >> sys.stderr, "Usage: example <keyspace_name> <column_family_name>"
        sys.exit(-1)

    keyspace_name = sys.argv[1]
    column_family_name = sys.argv[2]

    # Valid config options here https://github.com/datastax/spark-cassandra-connector/blob/master/doc/1_connecting.md
    conf = SparkConf().set("spark.cassandra.connection.host", "127.0.0.1")

    sc = SparkContext(appName="Spark + Cassandra Example",
                      conf=conf)

    # import time; time.sleep(30)
    java_import(sc._gateway.jvm, "com.datastax.spark.connector.CassandraJavaUtil")
    print sc._jvm.CassandraJavaUtil

    users = (
        ["Mike", "Sukmanowsky"],
        ["Andrew", "Montalenti"],
        ["Keith", "Bourgoin"],
    )
    rdd = sc.parallelize(users)
    print rdd.collect() 

# 需要導入模塊: from pyspark import conf [as 別名]
# 或者: from pyspark.conf import SparkConf [as 別名]
def config(self, key=None, value=None, conf=None):
            """Sets a config option. Options set using this method are automatically propagated to
            both :class:`SparkConf` and :class:`SparkSession`'s own configuration.

            For an existing SparkConf, use `conf` parameter.

            >>> from pyspark.conf import SparkConf
            >>> SparkSession.builder.config(conf=SparkConf())
            <pyspark.sql.session...

            For a (key, value) pair, you can omit parameter names.

            >>> SparkSession.builder.config("spark.some.config.option", "some-value")
            <pyspark.sql.session...

            :param key: a key name string for configuration property
            :param value: a value for configuration property
            :param conf: an instance of :class:`SparkConf`
            """
            with self._lock:
                if conf is None:
                    self._options[key] = str(value)
                else:
                    for (k, v) in conf.getAll():
                        self._options[k] = v
                return self 

# 需要導入模塊: from pyspark import conf [as 別名]
# 或者: from pyspark.conf import SparkConf [as 別名]
def init_spark_session(self, application_name, spark_master=None):
        """Setup a spark session.

        :param spark_master: A master parameter used by spark session builder.
          Use default value (None) to use system
          environment configured spark cluster.
          Use 'local[*]' to run on a local box.

        :return: spark_session: A spark session
        """
        eva_spark_conf = SparkConf()
        eva_spark_conf.set('spark.logConf', 'true')

        session_builder = SparkSession \
            .builder \
            .appName(application_name) \
            .config(conf=eva_spark_conf)

        if spark_master:
            session_builder.master(spark_master)

        # Gets an existing SparkSession or,
        # if there is no existing one, creates a new one based
        # on the options set in this builder.
        self._session = session_builder.getOrCreate()

        # Configure logging
        log4j_level = LoggingManager().getLog4JLevel()
        spark_context = self._session.sparkContext
        spark_context.setLogLevel(log4j_level) 

# 需要導入模塊: from pyspark import conf [as 別名]
# 或者: from pyspark.conf import SparkConf [as 別名]
def test_external_sort_in_rdd(self):
        conf = SparkConf().set("spark.python.worker.memory", "1m")
        sc = SparkContext(conf=conf)
        l = list(range(10240))
        random.shuffle(l)
        rdd = sc.parallelize(l, 4)
        self.assertEqual(sorted(l), rdd.sortBy(lambda x: x).collect())
        sc.stop() 

# 需要導入模塊: from pyspark import conf [as 別名]
# 或者: from pyspark.conf import SparkConf [as 別名]
def conf(cls):
        """
        Override this in subclasses to supply a more specific conf
        """
        return SparkConf() 

# 需要導入模塊: from pyspark import conf [as 別名]
# 或者: from pyspark.conf import SparkConf [as 別名]
def setUp(self):
        self._old_sys_path = list(sys.path)
        class_name = self.__class__.__name__
        conf = SparkConf().set("spark.python.profile", "true")
        self.sc = SparkContext('local[4]', class_name, conf=conf) 

# 需要導入模塊: from pyspark import conf [as 別名]
# 或者: from pyspark.conf import SparkConf [as 別名]
def test_profiler_disabled(self):
        sc = SparkContext(conf=SparkConf().set("spark.python.profile", "false"))
        try:
            self.assertRaisesRegexp(
                RuntimeError,
                "'spark.python.profile' configuration must be set",
                lambda: sc.show_profiles())
            self.assertRaisesRegexp(
                RuntimeError,
                "'spark.python.profile' configuration must be set",
                lambda: sc.dump_profiles("/tmp/abc"))
        finally:
            sc.stop() 

# 需要導入模塊: from pyspark import conf [as 別名]
# 或者: from pyspark.conf import SparkConf [as 別名]
def getOrCreate(cls, conf=None):
        """
        Get or instantiate a SparkContext and register it as a singleton object.

        :param conf: SparkConf (optional)
        """
        with SparkContext._lock:
            if SparkContext._active_spark_context is None:
                SparkContext(conf=conf or SparkConf())
            return SparkContext._active_spark_context 

# 需要導入模塊: from pyspark import conf [as 別名]
# 或者: from pyspark.conf import SparkConf [as 別名]
def getConf(self):
        conf = SparkConf()
        conf.setAll(self._conf.getAll())
        return conf 

# 需要導入模塊: from pyspark import conf [as 別名]
# 或者: from pyspark.conf import SparkConf [as 別名]
def _test_multiple_broadcasts(self, *extra_confs):
        """
        Test broadcast variables make it OK to the executors.  Tests multiple broadcast variables,
        and also multiple jobs.
        """
        conf = SparkConf()
        for key, value in extra_confs:
            conf.set(key, value)
        conf.setMaster("local-cluster[2,1,1024]")
        self.sc = SparkContext(conf=conf)
        self._test_encryption_helper([5])
        self._test_encryption_helper([5, 10, 20]) 

# 需要導入模塊: from pyspark import conf [as 別名]
# 或者: from pyspark.conf import SparkConf [as 別名]
def setUpClass(cls):
        gateway = launch_gateway(SparkConf())
        cls._jvm = gateway.jvm
        cls.longMessage = True
        random.seed(42) 

# 需要導入模塊: from pyspark import conf [as 別名]
# 或者: from pyspark.conf import SparkConf [as 別名]
def registerFunction(self, ssc, jsession, function_name, params):
        jvm = self.gateway.jvm
        # If we don't have a reference to a running SparkContext
        # Get the SparkContext from the provided SparkSession.
        if not self._sc:
            master = ssc.master()
            jsc = jvm.org.apache.spark.api.java.JavaSparkContext(ssc)
            jsparkConf = ssc.conf()
            sparkConf = SparkConf(_jconf=jsparkConf)
            self._sc = SparkContext(
                master=master,
                conf=sparkConf,
                gateway=self.gateway,
                jsc=jsc)
            self._session = SparkSession.builder.getOrCreate()
        if function_name in functions_info:
            function_info = functions_info[function_name]
            if params:
                evaledParams = ast.literal_eval(params)
            else:
                evaledParams = []
            func = function_info.func(*evaledParams)
            ret_type = function_info.returnType()
            self._count = self._count + 1
            registration_name = function_name + str(self._count)
            udf = UserDefinedFunction(func, ret_type, registration_name)
            # Used to allow non-default (e.g. Arrow) UDFS
            udf.evalType = function_info.evalType()
            judf = udf._judf
            return judf
        else:
            print("Could not find function")
            # We do this rather than raising an exception since Py4J debugging
            # is rough and we can check it.
            return None 

# 需要導入模塊: from pyspark import conf [as 別名]
# 或者: from pyspark.conf import SparkConf [as 別名]
def spark_jvm_imports(jvm):
        # Import the classes used by PySpark
        java_import(jvm, "org.apache.spark.SparkConf")
        java_import(jvm, "org.apache.spark.api.java.*")
        java_import(jvm, "org.apache.spark.api.python.*")
        java_import(jvm, "org.apache.spark.ml.python.*")
        java_import(jvm, "org.apache.spark.mllib.api.python.*")
        # TODO(davies): move into sql
        java_import(jvm, "org.apache.spark.sql.*")
        java_import(jvm, "org.apache.spark.sql.hive.*")
        java_import(jvm, "scala.Tuple2")