python - 星火/PySpark : An error occurred while trying to connect to the Java server (127. 0.0.1:39543)

Question

下午好

在过去的两天里，Java 服务器出现了许多连接问题。这有点不常见，因为错误并不总是发生，只是有时......

我正在结合使用 PySpark 和 Jupyter Notebook。一切都在谷歌云中的虚拟机实例上运行。我在 Google Cloud 中使用这个:

custom (8 vCPUs, 200 GB) 

这些是其他设置:

conf = pyspark.SparkConf().setAppName("App")
conf = (conf.setMaster('local[*]')
        .set('spark.executor.memory', '180G')
        .set('spark.driver.memory', '180G')
        .set('spark.driver.maxResultSize', '180G'))

sc = pyspark.SparkContext(conf=conf)
sq = pyspark.sql.SQLContext(sc)

我训练了一个随机森林模型并做出了预测:

model = rf.fit(train)
predictions = model.transform(test)

然后我创建了 ROC 曲线并计算了 AUC 值。

然后我想看看混淆矩阵:

confusion_mat = metrics.confusionMatrix().toArray()
print(confusion_mat_train_rf)

现在错误发生了:

    Traceback (most recent call last):
  File "/usr/lib/python2.7/SocketServer.py", line 290, in _handle_request_noblock
    self.process_request(request, client_address)
  File "/usr/lib/python2.7/SocketServer.py", line 318, in process_request
    self.finish_request(request, client_address)
  File "/usr/lib/python2.7/SocketServer.py", line 331, in finish_request
    self.RequestHandlerClass(request, client_address, self)
  File "/usr/lib/python2.7/SocketServer.py", line 652, in __init__
    self.handle()
  File "/usr/local/lib/python2.7/dist-packages/pyspark/accumulators.py", line 235, in handle
    num_updates = read_int(self.rfile)
  File "/usr/local/lib/python2.7/dist-packages/pyspark/serializers.py", line 577, in read_int
    raise EOFError
EOFError
ERROR:root:Exception while sending command.
Traceback (most recent call last):
  File "/usr/local/lib/python2.7/dist-packages/py4j/java_gateway.py", line 883, in send_command
    response = connection.send_command(command)
  File "/usr/local/lib/python2.7/dist-packages/py4j/java_gateway.py", line 1040, in send_command
    "Error while receiving", e, proto.ERROR_ON_RECEIVE)
Py4JNetworkError: Error while receiving
ERROR:py4j.java_gateway:An error occurred while trying to connect to the Java server (127.0.0.1:39543)
Traceback (most recent call last):
  File "/usr/local/lib/python2.7/dist-packages/py4j/java_gateway.py", line 963, in start
    self.socket.connect((self.address, self.port))
  File "/usr/lib/python2.7/socket.py", line 228, in meth
    return getattr(self._sock,name)(*args)
error: [Errno 111] Connection refused

这是控制台的输出:

OpenJDK 64-Bit Server VM warning
: INFO: os::commit_memory(0x00007f4998300000, 603979776, 0) failed; error='Cannot allocate memory' (errno=12)
#
# There is insufficient memory for the Java Runtime Environment to continue.
# Native memory allocation (mmap) failed to map 603979776 bytes for committing reserved memory.

日志文件:

#
# There is insufficient memory for the Java Runtime Environment to continue.
# Native memory allocation (mmap) failed to map 603979776 bytes for committing reserved memory.
# Possible reasons:
#   The system is out of physical RAM or swap space
#   In 32 bit mode, the process size limit was hit
# Possible solutions:
#   Reduce memory load on the system
#   Increase physical memory or swap space
#   Check if swap backing store is full
#   Use 64 bit Java on a 64 bit OS
#   Decrease Java heap size (-Xmx/-Xms)
#   Decrease number of Java threads
#   Decrease Java thread stack sizes (-Xss)
#   Set larger code cache with -XX:ReservedCodeCacheSize=
# This output file may be truncated or incomplete.
#
#  Out of Memory Error (os_linux.cpp:2643), pid=2377, tid=0x00007f1c94fac700
#
# JRE version: OpenJDK Runtime Environment (8.0_151-b12) (build 1.8.0_151-8u151-b12-0ubuntu0.16.04.2-b12)
# Java VM: OpenJDK 64-Bit Server VM (25.151-b12 mixed mode linux-amd64 )
# Failed to write core dump. Core dumps have been disabled. To enable core dumping, try "ulimit -c unlimited" before starting Java again
#

---------------  S Y S T E M  ---------------

OS:DISTRIB_ID=Ubuntu
DISTRIB_RELEASE=16.04
DISTRIB_CODENAME=xenial
DISTRIB_DESCRIPTION="Ubuntu 16.04.3 LTS"

uname:Linux 4.13.0-1008-gcp #11-Ubuntu SMP Thu Jan 25 11:08:44 UTC 2018 x86_64
libc:glibc 2.23 NPTL 2.23 
rlimit: STACK 8192k, CORE 0k, NPROC 805983, NOFILE 1048576, AS infinity
load average:7.69 4.51 3.57

/proc/meminfo:
MemTotal:       206348252 kB
MemFree:         1298460 kB
MemAvailable:     250308 kB
Buffers:            6812 kB
Cached:           438232 kB
SwapCached:            0 kB
Active:         203906416 kB
Inactive:         339540 kB
Active(anon):   203804300 kB
Inactive(anon):     8392 kB
Active(file):     102116 kB
Inactive(file):   331148 kB
Unevictable:        3652 kB
Mlocked:            3652 kB
SwapTotal:             0 kB
SwapFree:              0 kB
Dirty:              4688 kB
Writeback:             0 kB
AnonPages:      203805168 kB
Mapped:            23076 kB
Shmem:              8776 kB
Slab:             114476 kB
SReclaimable:      50640 kB
SUnreclaim:        63836 kB
KernelStack:        4752 kB
PageTables:       404292 kB
NFS_Unstable:          0 kB
Bounce:                0 kB
WritebackTmp:          0 kB
CommitLimit:    103174124 kB
Committed_AS:   205956256 kB
VmallocTotal:   34359738367 kB
VmallocUsed:           0 kB
VmallocChunk:          0 kB
HardwareCorrupted:     0 kB
AnonHugePages:         0 kB
ShmemHugePages:        0 kB
ShmemPmdMapped:        0 kB
CmaTotal:              0 kB
CmaFree:               0 kB
HugePages_Total:       0
HugePages_Free:        0
HugePages_Rsvd:        0
HugePages_Surp:        0
Hugepagesize:       2048 kB
DirectMap4k:       71628 kB
DirectMap2M:     4122624 kB
DirectMap1G:    207618048 kB


CPU:total 8 (initial active 8) (4 cores per cpu, 2 threads per core) family 6 model 85 stepping 3, cmov, cx8, fxsr, mmx, sse, sse2, sse3, ssse3, sse4.1, sse4.2, popcnt, avx, avx2, aes, clmul, erms, rtm, 3dnowpref, lzcnt, ht, tsc, tscinvbit, bmi1, bmi2, adx

有没有人知道问题可能是什么以及我该如何解决？我很绝望。 :(

//我认为 Java Runtime Environment 没有足够的内存来继续......但是我该怎么办？

非常感谢!

Answer 1

如果你是

using this one in Google Cloud:

custom (8 vCPUs, 200 GB)

然后你显着超额订阅了内存。忽略 spark.executor.memory 在 local 模式下没有效果。

spark.executor.memory 仅占 JVM 堆，不包括:

• PySpark worker 内存。
• PySpark 驱动程序内存。

即使使用 JVM，也只有一部分可用于数据处理(参见 Memory Management Overview)，因此 spark.driver.maxResultSize 等于分配的总内存没有意义。