本文整理汇总了Python中pyspark.conf.SparkConf.setMaster方法的典型用法代码示例。如果您正苦于以下问题:Python SparkConf.setMaster方法的具体用法?Python SparkConf.setMaster怎么用?Python SparkConf.setMaster使用的例子?那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类pyspark.conf.SparkConf
的用法示例。
在下文中一共展示了SparkConf.setMaster方法的6个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: __init__
# 需要导入模块: from pyspark.conf import SparkConf [as 别名]
# 或者: from pyspark.conf.SparkConf import setMaster [as 别名]
def __init__(self):
# Setup PySpark. This is needed until PySpark becomes available on PyPI,
# after which we can simply add it to requirements.txt.
_setup_pyspark()
from pyspark.conf import SparkConf
from pyspark.context import SparkContext
from pyspark.serializers import MarshalSerializer
# Create a temporary .zip lib file for Metis, which will be copied over to
# Spark workers so they can unpickle Metis functions and objects.
metis_lib_file = tempfile.NamedTemporaryFile(suffix='.zip', delete=False)
metis_lib_file.close()
_copy_lib_for_spark_workers(metis_lib_file.name)
# Also ship the Metis lib file so worker nodes can deserialize Metis
# internal data structures.
conf = SparkConf()
conf.setMaster(app.config['SPARK_MASTER'])
conf.setAppName('chronology:metis')
parallelism = int(app.config.get('SPARK_PARALLELISM', 0))
if parallelism:
conf.set('spark.default.parallelism', parallelism)
self.context = SparkContext(conf=conf,
pyFiles=[metis_lib_file.name],
serializer=MarshalSerializer())
# Delete temporary Metis lib file.
os.unlink(metis_lib_file.name)
# We'll use this to parallelize fetching events in KronosSource.
# The default of 8 is from:
# https://spark.apache.org/docs/latest/configuration.html
self.parallelism = parallelism or 8
示例2: test_from_conf_with_settings
# 需要导入模块: from pyspark.conf import SparkConf [as 别名]
# 或者: from pyspark.conf.SparkConf import setMaster [as 别名]
def test_from_conf_with_settings(self):
conf = SparkConf()
conf.set("spark.cleaner.ttl", "10")
conf.setMaster(self.master)
conf.setAppName(self.appName)
self.ssc = StreamingContext(conf=conf, duration=self.batachDuration)
self.assertEqual(int(self.ssc.sparkContext._conf.get("spark.cleaner.ttl")), 10)
示例3: SparkContext
# 需要导入模块: from pyspark.conf import SparkConf [as 别名]
# 或者: from pyspark.conf.SparkConf import setMaster [as 别名]
class SparkContext(object):
"""
Main entry point for Spark functionality. A SparkContext represents the
connection to a Spark cluster, and can be used to create L{RDD} and
broadcast variables on that cluster.
"""
_gateway = None
_jvm = None
_next_accum_id = 0
_active_spark_context = None
_lock = RLock()
_python_includes = None # zip and egg files that need to be added to PYTHONPATH
PACKAGE_EXTENSIONS = ('.zip', '.egg', '.jar')
def __init__(self, master=None, appName=None, sparkHome=None, pyFiles=None,
environment=None, batchSize=0, serializer=PickleSerializer(), conf=None,
gateway=None, jsc=None, profiler_cls=BasicProfiler):
"""
Create a new SparkContext. At least the master and app name should be set,
either through the named parameters here or through C{conf}.
:param master: Cluster URL to connect to
(e.g. mesos://host:port, spark://host:port, local[4]).
:param appName: A name for your job, to display on the cluster web UI.
:param sparkHome: Location where Spark is installed on cluster nodes.
:param pyFiles: Collection of .zip or .py files to send to the cluster
and add to PYTHONPATH. These can be paths on the local file
system or HDFS, HTTP, HTTPS, or FTP URLs.
:param environment: A dictionary of environment variables to set on
worker nodes.
:param batchSize: The number of Python objects represented as a single
Java object. Set 1 to disable batching, 0 to automatically choose
the batch size based on object sizes, or -1 to use an unlimited
batch size
:param serializer: The serializer for RDDs.
:param conf: A L{SparkConf} object setting Spark properties.
:param gateway: Use an existing gateway and JVM, otherwise a new JVM
will be instantiated.
:param jsc: The JavaSparkContext instance (optional).
:param profiler_cls: A class of custom Profiler used to do profiling
(default is pyspark.profiler.BasicProfiler).
>>> from pyspark.context import SparkContext
>>> sc = SparkContext('local', 'test')
>>> sc2 = SparkContext('local', 'test2') # doctest: +IGNORE_EXCEPTION_DETAIL
Traceback (most recent call last):
...
ValueError:...
"""
self._callsite = first_spark_call() or CallSite(None, None, None)
SparkContext._ensure_initialized(self, gateway=gateway, conf=conf)
try:
self._do_init(master, appName, sparkHome, pyFiles, environment, batchSize, serializer,
conf, jsc, profiler_cls)
except:
# If an error occurs, clean up in order to allow future SparkContext creation:
self.stop()
raise
def _do_init(self, master, appName, sparkHome, pyFiles, environment, batchSize, serializer,
conf, jsc, profiler_cls):
self.environment = environment or {}
# java gateway must have been launched at this point.
if conf is not None and conf._jconf is not None:
# conf has been initialized in JVM properly, so use conf directly. This represent the
# scenario that JVM has been launched before SparkConf is created (e.g. SparkContext is
# created and then stopped, and we create a new SparkConf and new SparkContext again)
self._conf = conf
else:
self._conf = SparkConf(_jvm=SparkContext._jvm)
if conf is not None:
for k, v in conf.getAll():
self._conf.set(k, v)
self._batchSize = batchSize # -1 represents an unlimited batch size
self._unbatched_serializer = serializer
if batchSize == 0:
self.serializer = AutoBatchedSerializer(self._unbatched_serializer)
else:
self.serializer = BatchedSerializer(self._unbatched_serializer,
batchSize)
# Set any parameters passed directly to us on the conf
if master:
self._conf.setMaster(master)
if appName:
self._conf.setAppName(appName)
if sparkHome:
self._conf.setSparkHome(sparkHome)
if environment:
for key, value in environment.items():
self._conf.setExecutorEnv(key, value)
for key, value in DEFAULT_CONFIGS.items():
self._conf.setIfMissing(key, value)
#.........这里部分代码省略.........
示例4: SparkConf
# 需要导入模块: from pyspark.conf import SparkConf [as 别名]
# 或者: from pyspark.conf.SparkConf import setMaster [as 别名]
'''
Created on Oct 30, 2015
@author: dyerke
'''
from pyspark.context import SparkContext
from pyspark.conf import SparkConf
if __name__ == '__main__':
m_hostname= "dyerke-Inspiron-7537"
#
conf= SparkConf()
conf.setAppName("MyTestApp")
conf.setMaster("spark://" + m_hostname + ":7077")
conf.setSparkHome("/usr/local/spark")
conf.set("spark.driver.host", m_hostname)
logFile = "/usr/local/spark/README.md" # Should be some file on your system
#
sc= SparkContext(conf=conf)
logData= sc.textFile(logFile).cache()
#
countAs= logData.filter(lambda x: 'a' in x).count()
countBs= logData.filter(lambda x: 'b' in x).count()
#
print("Lines with a: %i, lines with b: %i" % (countAs, countBs))
sc.stop()
示例5: main
# 需要导入模块: from pyspark.conf import SparkConf [as 别名]
# 或者: from pyspark.conf.SparkConf import setMaster [as 别名]
#.........这里部分代码省略.........
# {computeStatistic.id -> list[step_conf_tuple]}, 其中 step_conf_tuple = (step_id, step_conf_dict)
compute_prepares_config_active = dict(map(
lambda computeStatistic_conf: (computeStatistic_conf[0],
sorted(list_dict_merge(
map(lambda step_conf: map_conf_properties(step_conf[1], 'step.id'),
filter(
lambda step_conf: step_conf[1].get('step.enabled', False),
computeStatistic_conf[1].get('prepares.steps', {}).iteritems())
)).iteritems())
), compute_computeStatistics_config_active))
# print('= = ' * 30, compute_prepares_config_active2 == compute_prepares_config_active)
print('= = ' * 20, type(compute_prepares_config_active), 'compute_prepares_config_active = ')
pprint(compute_prepares_config_active)
compute_computes_config_active = dict(map(
lambda computeStatistic_conf: (computeStatistic_conf[0],
sorted(list_dict_merge(
map(lambda step_conf: map_conf_properties(step_conf[1], 'step.id'),
filter(lambda step_conf: step_conf[1].get('step.enabled', False),
computeStatistic_conf[1].get('computes.steps', {}).iteritems())
)).iteritems())
), compute_computeStatistics_config_active))
print('= = ' * 20, type(compute_computes_config_active), 'compute_computes_config_active = ')
pprint(compute_computes_config_active)
test_flag = False
if not test_flag:
# 初始化
# 测试 serializer
# serializer 默认取值 PickleSerializer() #UnpicklingError: invalid load key, '{'.
# serializer=MarshalSerializer() # ValueError: bad marshal data
# serializer=AutoSerializer() # ValueError: invalid sevialization type: {
# serializer=CompressedSerializer(PickleSerializer()) # error: Error -3 while decompressing data: incorrect header check
# sc = SparkContext(master, app_name, sparkHome = spark_home, pyFiles=pyFiles)
# sc = SparkContext(master, app_name, sparkHome = sparkHome, pyFiles=pyFiles, serializer=MarshalSerializer())
# sc = SparkContext(master, app_name, sparkHome = sparkHome, pyFiles=pyFiles, serializer=AutoSerializer())
# sc = SparkContext(master, app_name, sparkHome = sparkHome, pyFiles=pyFiles, serializer=CompressedSerializer(PickleSerializer()))
spark_conf = SparkConf()
spark_conf.setMaster(master).setAppName(app_name).setSparkHome(spark_home)
# spark streaming 调优配置
spark_streaming_blockInterval = str(app_conf.get('spark.streaming.blockInterval', '')).strip()
if spark_streaming_blockInterval:
spark_conf.set('spark.streaming.blockInterval', spark_streaming_blockInterval)
spark_streaming_kafka_maxRatePerPartition = str(
app_conf.get('spark.streaming.kafka.maxRatePerPartition', '')).strip()
if spark_streaming_kafka_maxRatePerPartition:
spark_conf.set('spark.streaming.kafka.maxRatePerPartition', spark_streaming_kafka_maxRatePerPartition)
spark_streaming_receiver_maxRate = str(app_conf.get('spark.streaming.receiver.maxRate', '')).strip()
if spark_streaming_receiver_maxRate:
spark_conf.set('spark.streaming.receiver.maxRate', spark_streaming_receiver_maxRate)
spark_streaming_concurrentJobs = str(app_conf.get('spark.streaming.concurrentJobs', '')).strip()
if spark_streaming_concurrentJobs:
spark_conf.set('spark.streaming.concurrentJobs', spark_streaming_concurrentJobs)
# spark sql 调优配置
spark_sql_shuffle_partitions = str(app_conf.get('spark.sql.shuffle.partitions', '')).strip()
if spark_sql_shuffle_partitions:
spark_conf.set('spark.sql.shuffle.partitions', spark_sql_shuffle_partitions)
sc = SparkContext(conf=spark_conf)
for path in (pyFiles or []):
sc.addPyFile(path)
# 外部缓存优化,broadcast 分发
cache_manager = CacheManager()
cache_broadcast_list = \
[(cache_id, cache_manager.cache_dataset(sc, cache_conf))
for cache_id, cache_conf in cache_confs_with_ds_conf.iteritems()
if cache_conf.get('broadcast.enabled', False)]
for cache_id, cache_broadcast in cache_broadcast_list:
cache_confs_with_ds_conf[cache_id]['broadcast'] = cache_broadcast
batchDruationSeconds = app_conf['batchDuration.seconds']
ssc = StreamingContext(sc, batchDruationSeconds)
sqlc = SQLContext(sc)
# 读取数据源
stream = StreamingReader.readSource(ssc, di_in_conf_with_ds_conf, app_conf)
# 流处理: 1 根据配置初始化处理指定数据接口的类的实例, 2 调用指定处理类实例的流数据处理方法
# 测试 kafka_wordcount
# counts = stream.flatMap(lambda line: line.split(" ")) \
# .map(lambda word: (word, 1)) \
# .reduceByKey(lambda a, b: a+b)
# counts.pprint()
StreamingApp.process(
stream, sc, sqlc,
di_in_conf_with_ds_conf, di_out_confs_with_ds_conf, cache_confs_with_ds_conf,
prepares_config_active_steps, compute_prepares_config_active, compute_computes_config_active)
ssc.start()
ssc.awaitTermination()
示例6: addPysparkPath
# 需要导入模块: from pyspark.conf import SparkConf [as 别名]
# 或者: from pyspark.conf.SparkConf import setMaster [as 别名]
spark_pylib = os.path.join(spark_home, "python", "lib")
py4jlib = [ziplib
for ziplib in os.listdir(spark_pylib)
if ziplib.startswith('py4j') and ziplib.endswith('.zip')][0]
py4jlib = os.path.join(spark_pylib, py4jlib)
sys.path.append(os.path.join(spark_home, "python"))
sys.path.append(py4jlib)
addPysparkPath()
from pyspark.conf import SparkConf
from pyspark.context import SparkContext
from pyspark.storagelevel import StorageLevel
conf = SparkConf()
conf.setMaster('local[*]').setAppName('SparkLit test')
sc = SparkContext(conf=conf)
logger = sc._jvm.org.apache.log4j
logger.LogManager.getLogger("org"). setLevel( logger.Level.ERROR )
logger.LogManager.getLogger("akka").setLevel( logger.Level.ERROR )
import sparklit
suite = {}
def setUp():
PATH = 'tests/grace_dubliners_james_joyce.txt'
data = sc.textFile(PATH, 4)
data.persist(StorageLevel.MEMORY_ONLY)
suite['data'] = data