PySpark工作原理
原文作者：李海强，来自平安银行零售大数据团队
前言
Spark是一个开源的通用分布式计算框架，支持海量离线数据处理、实时计算、机器学习、图计算，结合大数据场景，在各个领域都有广泛的应用。Spark支持多种开发语言，包括Python、Java、Scala、R，上手容易。其中，Python因为入门简单、开发效率高（人生苦短，我用Python），广受大数据工程师喜欢，本文主要探讨Pyspark的工作原理。
环境准备
因为我的环境是Mac，所以本文一切以Mac环境为前提，不过其它环境过车过都是差不多的。
Spark环境
首先下载安装Anaconda
https://www.jetbrains.com/idea/download/#section=mac，
选择Python 3.7。
Anaconda安装完之后，开一个终端，执行如下命令安装Pyspark和Openjdk，然后启动Jupyterlab。
- 创建一个虚拟Python环境，名字是test，避免影响Anaconda原始环境
% conda create --clone base -n test
% source activate test
- 安装Pyspark和Openjdk
% conda install pyspark=2.4.4
% conda install openjdk
- 安装并启动Jupyterlab
% conda install jupyterlab
% jupyter-lab
到此会启动一个基于浏览器的开发环境，可用于编写、调试Python代码。
阅读Spark代码环境
Spark本身是用Scala、Java、Python开发的，建议安装IntelliJ IDEA
https://www.jetbrains.com/idea/download/#section=mac
安装完IDEA，通过下面的命令下载Spark-2.4.4的代码。
% git clone https://github.com/apache/spark.git
% cd spark
% git checkout v2.4.4
代码下载完之后，打开IEDA，选择New->Project from existing sources，新建一个项目，IDEA会扫描整个项目、下载依赖，完成之后就可以阅读代码了。
深入Pyspark
Pyspark用法
在学习Pyspark的工作原理之前，我们先看看Pyspark是怎么用的，先看一段代码。代码很简单，首先创建spark session，然后从csv文件创建dataframe，最后通过rdd的map算子转换数据形式。中间利用了自定义函数test来转换输入数据，test函数的输入数据是一行数据。
from pyspark.sql import SparkSession
from pyspark.sql import Row
# 创建spark session
spark = SparkSession \
.builder \
.appName("pyspark demo") \
.getOrCreate()
# 从csv文件创建dataframe
df = spark.read.csv("stock.csv", header=True)
# 自定义分布式函数，将输入行转成另外一种形式
def test(r):
return repr(r)
# dataframe转成RDD，通过map转换数据形式，最后获取10条数据
df.rdd.map(lambda r: test(r)).take(10)
通过在Jupyterlab里面启动spark session之后，我们来看一下相关的进程父子关系。05920是Jupyterlab进程，我启动一个Python kernel，进程05964。然后启动spark session，这是一个Java进程，ID是06450。同时Spark java进程启动了一个Python守护进程，这个进程是处理PythonRDD数据的。因为我起的Spark是local模式，所以只有一个Spark进程和一个Python进程。如果是yarn模式，每一个executor都会启动一个Python进程，PythonRDD在Python守护进程里处理然后返回结果给Spark Task线程。
| | \-+= 05920 haiqiangli /Users/haiqiangli/anaconda3/envs/ml/bin/python3.7 /Users/haiqiangli/anaconda3/envs/ml/bin/jupyter-lab
| | \-+= 05964 haiqiangli /Users/haiqiangli/anaconda3/envs/ml/bin/python -m ipykernel_launcher -f /Users/haiqiangli/Library/Jupyter/runtime/kernel-62a08e01-a4c7-4fe6-b92f-621e9967197e.json
| | \-+- 06450 haiqiangli /Users/haiqiangli/anaconda3/envs/ml/jre/bin/java -cp /Users/haiqiangli/anaconda3/envs/ml/lib/python3.7/site-packages/pyspark/conf:/Users/haiqiangli/anaconda3/envs/ml/lib/python3.7/site-packages/pyspark/jars/* -Xmx1g org.
| | \--= 06750 haiqiangli python -m pyspark.daemon
PythonRDD实现
我们从这段代码开始分析，先看df.rdd，代码在pyspark/sql/dataframe.py。
df.rdd.map(lambda r: test(r)).take(10)
jrdd是通过py4j调用Java代码将Spark driver内部当前这个dataframe转成Python rdd，类RDD是Python rdd的封装，我们看一下Python rdd的定义，代码在pyspark/rdd.py。
@property
@since(1.3)
def rdd(self):
"""Returns the content as an :class:`pyspark.RDD` of :class:`Row`.
"""
if self._lazy_rdd is None:
jrdd = self._jdf.javaToPython()
self._lazy_rdd = RDD(jrdd, self.sql_ctx._sc, BatchedSerializer(PickleSerializer()))
return self._lazy_rdd
class RDD(object):
"""
A Resilient Distributed Dataset (RDD), the basic abstraction in Spark.
Represents an immutable, partitioned collection of elements that can be
operated on in parallel.
"""
def __init__(self, jrdd, ctx, jrdd_deserializer=AutoBatchedSerializer(PickleSerializer())):
self._jrdd = jrdd
self.is_cached = False
self.is_checkpointed = False
self.ctx = ctx
self._jrdd_deserializer = jrdd_deserializer
self._id = jrdd.id()
self.partitioner = None
...
现在来看一下rdd.map的实现，代码如下。map接口先定义一个闭包函数func（引用lambda r: test(r)），然后再调用mapPartitionsWithIndex。mapPartitionsWithIndex只返回了新的对象PipelinedRDD，也就是说map会返回一个新的RDD对象（PipelinedRDD），我们来看一下PipelinedRDD的定义，self.func就是map里面定义的闭包函数func，这个很重要，后面会再次用到。
def map(self, f, preservesPartitioning=False):
def func(_, iterator):
return map(fail_on_stopiteration(f), iterator)
return self.mapPartitionsWithIndex(func, preservesPartitioning)
def mapPartitionsWithIndex(self, f, preservesPartitioning=False):
return PipelinedRDD(self, f, preservesPartitioning)
class PipelinedRDD(RDD):
def __init__(self, prev, func, preservesPartitioning=False, isFromBarrier=False):
if not isinstance(prev, PipelinedRDD) or not prev._is_pipelinable():
# This transformation is the first in its stage:
self.func = func
self.preservesPartitioning = preservesPartitioning
self._prev_jrdd = prev._jrdd
self._prev_jrdd_deserializer = prev._jrdd_deserializer
else:
prev_func = prev.func
def pipeline_func(split, iterator):
return func(split, prev_func(split, iterator))
self.func = pipeline_func
self.preservesPartitioning = \
prev.preservesPartitioning and preservesPartitioning
self._prev_jrdd = prev._prev_jrdd # maintain the pipeline
self._prev_jrdd_deserializer = prev._prev_jrdd_deserializer
self.is_cached = False
self.is_checkpointed = False
self.ctx = prev.ctx
self.prev = prev
self._jrdd_val = None
self._id = None
self._jrdd_deserializer = self.ctx.serializer
self._bypass_serializer = False
self.partitioner = prev.partitioner if self.preservesPartitioning else None
self.is_barrier = prev._is_barrier() or isFromBarrier
现在我们看一下df.rdd.map(lambda r: test(r)).take(10)里面的take，提醒一下map操作只是一个transform，不会触发真正的计算任务，只有action会，这里的take就是一个action。take会触发当前RDD的transform，包括它的父RDD，最终返回num条数据。我们看一下take函数，有点长，最关键的是self.context.runJob。
def take(self, num):
items = []
totalParts = self.getNumPartitions()
partsScanned = 0
while len(items) < num and partsScanned < totalParts:
# The number of partitions to try in this iteration.
# It is ok for this number to be greater than totalParts because
# we actually cap it at totalParts in runJob.
numPartsToTry = 1
if partsScanned > 0:
# If we didn't find any rows after the previous iteration,
# quadruple and retry. Otherwise, interpolate the number of
# partitions we need to try, but overestimate it by 50%.
# We also cap the estimation in the end.
if len(items) == 0:
numPartsToTry = partsScanned * 4
else:
# the first parameter of max is >=1 whenever partsScanned >= 2
numPartsToTry = int(1.5 * num * partsScanned / len(items)) - partsScanned
numPartsToTry = min(max(numPartsToTry, 1), partsScanned * 4)
left = num - len(items)
def takeUpToNumLeft(iterator):
iterator = iter(iterator)
taken = 0
while taken < left:
try:
yield next(iterator)
except StopIteration:
return
taken += 1
p = range(partsScanned, min(partsScanned + numPartsToTry, totalParts))
res = self.context.runJob(self, takeUpToNumLeft, p)
items += res
partsScanned += numPartsToTry
return items[:num]
接着我们看self.context.runJob代码（pyspark/context.py）,主要看self._jvm.PythonRDD.runJob(self._jsc.sc(), mappedRDD._jrdd, partitions)这行，其中_jrdd是在PipelinedRDD里面定义，看代码。
def runJob(self, rdd, partitionFunc, partitions=None, allowLocal=False):
if partitions is None:
partitions = range(rdd._jrdd.partitions().size())
# Implementation note: This is implemented as a mapPartitions followed
# by runJob() in order to avoid having to pass a Python lambda into
# SparkContext#runJob.
mappedRDD = rdd.mapPartitions(partitionFunc)
sock_info = self._jvm.PythonRDD.runJob(self._jsc.sc(), mappedRDD._jrdd, partitions)
return list(_load_from_socket(sock_info, mappedRDD._jrdd_deserializer))
_jrdd代码是Spark支持Python API的关键，_wrap_function这里是序列化上面定义的闭包函数func以及它的所有依赖，我们知道这个函数是被分布式算子map调用的函数，这个函数会在executor上执行，确切的说是executor上启动的Python守护进程里执行。因此这里Python必须序列化并打包这个func函数和它的执行环境，随后会在executor的Python进程里加载，这样就完成了分布式函数的自动广播操作。有点烧脑，关于Python函数序列化的内容我们放到单独一篇文章里再讲。
@property
def _jrdd(self):
if self._jrdd_val:
return self._jrdd_val
if self._bypass_serializer:
self._jrdd_deserializer = NoOpSerializer()
if self.ctx.profiler_collector:
profiler = self.ctx.profiler_collector.new_profiler(self.ctx)
else:
profiler = None
wrapped_func = _wrap_function(self.ctx, self.func, self._prev_jrdd_deserializer,
self._jrdd_deserializer, profiler)
python_rdd = self.ctx._jvm.PythonRDD(self._prev_jrdd.rdd(), wrapped_func,
self.preservesPartitioning, self.is_barrier)
self._jrdd_val = python_rdd.asJavaRDD()
if profiler:
self._id = self._jrdd_val.id()
self.ctx.profiler_collector.add_profiler(self._id, profiler)
return self._jrdd_val
继续看_wrap_function，_prepare_for_python_RDD就是序列化Python函数的过程，细节下一篇再讲。再看一下sc._jvm.PythonFunction，这个是scala写的类，代码在core/src/main/scala/org/apache/spark/api/pythn/PythonRDD.scala，这个类封装了一些Python必需的数据和环境。
def _wrap_function(sc, func, deserializer, serializer, profiler=None):
assert deserializer, "deserializer should not be empty"
assert serializer, "serializer should not be empty"
command = (func, profiler, deserializer, serializer)
pickled_command, broadcast_vars, env, includes = _prepare_for_python_RDD(sc, command)
return sc._jvm.PythonFunction(bytearray(pickled_command), env, includes, sc.pythonExec,
sc.pythonVer, broadcast_vars, sc._javaAccumulator)
/**
* A wrapper for a Python function, contains all necessary context to run the function in Python
* runner.
*/
private[spark] case class PythonFunction(
command: Array[Byte],
envVars: JMap[String, String],
pythonIncludes: JList[String],
pythonExec: String,
pythonVer: String,
broadcastVars: JList[Broadcast[PythonBroadcast]],
accumulator: PythonAccumulatorV2)
继续看_jrdd代码，sock_info = self._jvm.PythonRDD.runJob(self._jsc.sc(), mappedRDD._jrdd, partitions)这里创建了一个新对象，来看一下runJob的定义。
def runJob(
sc: SparkContext,
rdd: JavaRDD[Array[Byte]],
partitions: JArrayList[Int]): Array[Any] = {
type ByteArray = Array[Byte]
type UnrolledPartition = Array[ByteArray]
val allPartitions: Array[UnrolledPartition] =
sc.runJob(rdd, (x: Iterator[ByteArray]) => x.toArray, partitions.asScala)
val flattenedPartition: UnrolledPartition = Array.concat(allPartitions: _*)
serveIterator(flattenedPartition.iterator,
s"serve RDD ${rdd.id} with partitions ${partitions.asScala.mkString(",")}")
}
PythonRDD.runJob调用了SparkContext.runJob，再来看看这个runJob的定义。看到我们熟悉的dagScheduler，它是Spark的核心，dag将RDD依赖划分到不同的Stage，构建这些Stage的父子关系，最后将Stage按照Partition切分成多个Task。这些细节不是本文的重点，后面再讲。
def runJob[T, U: ClassTag](
rdd: RDD[T],
func: (TaskContext, Iterator[T]) => U,
partitions: Seq[Int],
resultHandler: (Int, U) => Unit): Unit = {
if (stopped.get()) {
throw new IllegalStateException("SparkContext has been shutdown")
}
val callSite = getCallSite
val cleanedFunc = clean(func)
logInfo("Starting job: " + callSite.shortForm)
if (conf.getBoolean("spark.logLineage", false)) {
logInfo("RDD's recursive dependencies:\n" + rdd.toDebugString)
}
dagScheduler.runJob(rdd, cleanedFunc, partitions, callSite, resultHandler, localProperties.get)
progressBar.foreach(_.finishAll())
rdd.doCheckpoint()
}
最后我们看一下，在什么地方会用到上面定义的Python函数func。还记得之前给的Pyspark的进程父子关系，其中06750 haiqiangli python -m pyspark.daemon这个进程是Spark java的子进程，我们来看一下它的实现（pysark/daemon.py）。一开始创建sock，随机分配一个监听端口。然后通过write_int(listen_port, stdout_bin)写标准输出，把自己的监听端口号告诉父进程。接着通过epoll的方式监听连接，一旦有连接就会创建一个子进程来处理这个连接的请求，为了提高性能。
def manager():
# Create a new process group to corral our children
os.setpgid(0, 0)
# Create a listening socket on the AF_INET loopback interface
listen_sock = socket.socket(AF_INET, SOCK_STREAM)
listen_sock.bind(('127.0.0.1', 0))
listen_sock.listen(max(1024, SOMAXCONN))
listen_host, listen_port = listen_sock.getsockname()
# re-open stdin/stdout in 'wb' mode
stdin_bin = os.fdopen(sys.stdin.fileno(), 'rb', 4)
stdout_bin = os.fdopen(sys.stdout.fileno(), 'wb', 4)
write_int(listen_port, stdout_bin)
stdout_bin.flush()
reuse = os.environ.get("SPARK_REUSE_WORKER")
# Initialization complete
try:
while True:
try:
ready_fds = select.select([0, listen_sock], [], [], 1)[0]
except select.error as ex:
if ex[0] == EINTR:
continue
else:
raise
if 0 in ready_fds:
try:
worker_pid = read_int(stdin_bin)
except EOFError:
# Spark told us to exit by closing stdin
shutdown(0)
try:
os.kill(worker_pid, signal.SIGKILL)
except OSError:
pass # process already died
if listen_sock in ready_fds:
try:
sock, _ = listen_sock.accept()
except OSError as e:
if e.errno == EINTR:
continue
raise
# Launch a worker process
try:
pid = os.fork()
except OSError as e:
if e.errno in (EAGAIN, EINTR):
time.sleep(1)
pid = os.fork() # error here will shutdown daemon
else:
outfile = sock.makefile(mode='wb')
write_int(e.errno, outfile) # Signal that the fork failed
outfile.flush()
outfile.close()
sock.close()
continue
if pid == 0:
# in child process
listen_sock.close()
try:
# Acknowledge that the fork was successful
outfile = sock.makefile(mode="wb")
write_int(os.getpid(), outfile)
outfile.flush()
outfile.close()
authenticated = False
while True:
code = worker(sock, authenticated)
if code == 0:
authenticated = True
if not reuse or code:
# wait for closing
try:
while sock.recv(1024):
pass
except Exception:
pass
break
gc.collect()
except:
traceback.print_exc()
os._exit(1)
else:
os._exit(0)
else:
sock.close()
finally:
shutdown(1)
子进程创建两个文件句柄，infile和outfile，都是在那个sock基础上，然后调用worker_main(infile, outfile)。
def worker(sock, authenticated):
"""
Called by a worker process after the fork().
"""
signal.signal(SIGHUP, SIG_DFL)
signal.signal(SIGCHLD, SIG_DFL)
signal.signal(SIGTERM, SIG_DFL)
# restore the handler for SIGINT,
# it's useful for debugging (show the stacktrace before exit)
signal.signal(SIGINT, signal.default_int_handler)
# Read the socket using fdopen instead of socket.makefile() because the latter
# seems to be very slow; note that we need to dup() the file descriptor because
# otherwise writes also cause a seek that makes us miss data on the read side.
infile = os.fdopen(os.dup(sock.fileno()), "rb", 65536)
outfile = os.fdopen(os.dup(sock.fileno()), "wb", 65536)
if not authenticated:
client_secret = UTF8Deserializer().loads(infile)
if os.environ["PYTHON_WORKER_FACTORY_SECRET"] == client_secret:
write_with_length("ok".encode("utf-8"), outfile)
outfile.flush()
else:
write_with_length("err".encode("utf-8"), outfile)
outfile.flush()
sock.close()
return 1
exit_code = 0
try:
worker_main(infile, outfile)
except SystemExit as exc:
exit_code = compute_real_exit_code(exc.code)
finally:
try:
outfile.flush()
except Exception:
pass
return exit_code
worker_main代码比较长，我们只看核心的部分，func, profiler, deserializer, serializer = read_command(pickleSer, infile)这行是不是很熟悉，反序列化出函数func，然后在process里面处理sock输入的数据，再写回sock。
if eval_type == PythonEvalType.NON_UDF:
func, profiler, deserializer, serializer = read_command(pickleSer, infile)
else:
func, profiler, deserializer, serializer = read_udfs(pickleSer, infile, eval_type)
init_time = time.time()
def process():
iterator = deserializer.load_stream(infile)
serializer.dump_stream(func(split_index, iterator), outfile)
到此已经基本讲清楚PythonRDD的实现，正是因为Spark contributer的贡献，我们才能非常方便地通过Python开发Spark程序，让更多的数据分析师、机器学习工程师受益，在此对开源contributer们致以最崇高的敬意！
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