def evaluate_interactive(self,env = None):
## use a persistent environment for interactive interpreter
if ( not env ):
env = Environment( self.call_stack, self.function_map, \
self.builtin_map, self.debug )
env.call_function("__toplevel__") ##some context
## use this from the interactive-interpreter
rval = self.ast.evaluate(env)
return [ rval, env ]
def evaluate(self):
env = Environment( self.call_stack, self.function_map, \
self.builtin_map, self.debug, int(self.MAX_REC_DEPTH/10) )
env.call_function("__toplevel__") ##some context
return self.ast.evaluate(env)
java_import(gateway.jvm, "org.apache.spark.SparkConf")
java_import(gateway.jvm, "org.apache.spark.api.java.*")
java_import(gateway.jvm, "org.apache.spark.api.python.*")
java_import(gateway.jvm, "org.apache.spark.mllib.api.python.*")
java_import(gateway.jvm, "org.apache.spark.sql.*")
java_import(gateway.jvm, "org.apache.spark.sql.hive.*")
java_import(gateway.jvm, "scala.Tuple2")
jconf = entry_point.getSparkConf()
jsc = entry_point.getJavaSparkContext()
job_id = entry_point.getJobId()
javaEnv = entry_point.getEnv()
working_dir = javaEnv.workingDir() or '/tmp/amaterasu'
env = Environment(javaEnv.name(), javaEnv.master(), javaEnv.inputRootPath(),
javaEnv.outputRootPath(), working_dir,
javaEnv.configuration())
conf = SparkConf(_jvm=gateway.jvm, _jconf=jconf)
sc = SparkContext(jsc=jsc, gateway=gateway, conf=conf)
spark = SparkSession(sc, entry_point.getSparkSession())
ama_context = AmaContext(sc, spark, job_id, env)
while True:
actionData = queue.getNext()
resultQueue = entry_point.getResultQueue(actionData._2())
actionSource = actionData._1()
tree = ast.parse(actionSource)
exports = actionData._3()
