How do I log from my Python Spark script

log analysis using spark python
spark logging best practices
python logging
how to create a log file in spark scala
spark logging java
spark set log level programmatically
spark dataframe logging
logging python fatal

I have a Python Spark program which I run with spark-submit. I want to put logging statements in it."This is an informative message.")
logging.debug("This is a debug message.")

I want to use the same logger that Spark is using so that the log messages come out in the same format and the level is controlled by the same configuration files. How do I do this?

I've tried putting the logging statements in the code and starting out with a logging.getLogger(). In both cases I see Spark's log messages but not mine. I've been looking at the Python logging documentation, but haven't been able to figure it out from there.

Not sure if this is something specific to scripts submitted to Spark or just me not understanding how logging works.

You can get the logger from the SparkContext object:

log4jLogger =
LOGGER = log4jLogger.LogManager.getLogger(__name__)"pyspark script logger initialized")

How do I log from my Python Spark script, 1 Answer. You should try to get the logger for spark itself, by default getLogger(), it will return the logger for you own module. Here, you can also replace 'py4j' with 'pyspark'.. I have a Python Spark program which I run with spark-submit. I want to put logging statements in it."This is an informative message.") logging.debug("This is a debug message.") I want to use the same logger that Spark is using so that the log messages come out in the same format and the level is controlled by the same

You need to get the logger for spark itself, by default getLogger() will return the logger for you own module. Try something like:

logger = logging.getLogger('py4j')"My test info statement")

It might also be 'pyspark' instead of 'py4j'.

In case the function that you use in your spark program (and which does some logging) is defined in the same module as the main function it will give some serialization error.

This is explained here and an example by the same person is given here

I also tested this on spark 1.3.1


To change logging from STDERR to STDOUT you will have to remove the current StreamHandler and add a new one.

Find the existing Stream Handler (This line can be removed when finished)

# will look like [<logging.StreamHandler object at 0x7fd8f4b00208>]

There will probably only be a single one, but if not you will have to update position.


Add new handler for sys.stdout

import sys # Put at top if not already there
sh = logging.StreamHandler(sys.stdout)

Logging in PySpark - shantanu alshi, Your spark script is ready to log to console and log file. I personally set the logger level to WARN and log messages inside my script as log.warn. 1 Answer 1. One of the ways is to have a main driver program for your Spark application as a python file (.py) that has to be passed to spark-submit. This primary script has the main method to help the Driver identify the entry point. This file will customise configuration properties initialise the SparkContext.

We needed to log from the executors, not from the driver node. So we did the following:

  1. We created a /etc/rsyslog.d/spark.conf on all of the nodes (using a Bootstrap method with Amazon Elastic Map Reduceso that the Core nodes forwarded sysloglocal1` messages to the master node.

  2. On the Master node, we enabled the UDP and TCP syslog listeners, and we set it up so that all local messages got logged to /var/log/local1.log.

  3. We created a Python logging module Syslog logger in our map function.

  4. Now we can log with ...

One of the things we discovered is that the same partition is being processed simultaneously on multiple executors. Apparently Spark does this all the time, when it has extra resources. This handles the case when an executor is mysteriously delayed or fails.

Logging in the map functions has taught us a lot about how Spark works.

How to wrangle log data with Python and Apache Spark , In the real world, you are of course free to choose your own toolbox when analyzing your log data. Let's get started! Main objective: NASA log  Taming Big Data with Apache Spark and Python Apache Spark is written in Scala programming language that compiles the program code into byte code for the JVM for spark big data processing. The open source community has developed a wonderful utility for spark python big data processing known as PySpark.

In my case, I am just happy to get my log messages added to the workers stderr, along with the usual spark log messages.

If that suits your needs, then the trick is to redirect the particular Python logger to stderr.

For example, the following, inspired from this answer, works fine for me:

def getlogger(name, level=logging.INFO):
    import logging
    import sys

    logger = logging.getLogger(name)
    if logger.handlers:
        # or else, as I found out, we keep adding handlers and duplicate messages
        ch = logging.StreamHandler(sys.stderr)
        formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
    return logger


def tst_log():
    logger = getlogger('my-worker')

Output (plus a few surrounding lines for context):

17/05/03 03:25:32 INFO MemoryStore: Block broadcast_24 stored as values in memory (estimated size 5.8 KB, free 319.2 MB)
2017-05-03 03:25:32,849 - my-worker - INFO - b
2017-05-03 03:25:32,849 - my-worker - WARNING - c
2017-05-03 03:25:32,849 - my-worker - ERROR - d
2017-05-03 03:25:32,849 - my-worker - CRITICAL - e
17/05/03 03:25:32 INFO PythonRunner: Times: total = 2, boot = -40969, init = 40971, finish = 0
17/05/03 03:25:32 INFO Executor: Finished task 7.0 in stage 20.0 (TID 213). 2109 bytes result sent to driver

How to analyze log data with Python and Apache Spark , Case study with NASA logs to show how Spark can be leveraged for most accessed assets are GIFs, the home page, and some CGI scripts. Note that, even though the Spark, Python and R data frames can be very similar, there are also a lot of differences: as you have read above, Spark DataFrames carry the specific optimalization under the hood and can use distributed memory to handle big data, while Pandas DataFrames and R data frames can only run on one computer.

The key of interacting pyspark and java log4j is the jvm. This below is python code, the conf is missing the url, but this is about logging.

from pyspark.conf import SparkConf
from pyspark.sql import SparkSession

my_jars = os.environ.get("SPARK_HOME")
myconf = SparkConf()
myconf.set("spark.jars","%s/jars/log4j-1.2.17.jar" % my_jars)
spark = SparkSession\
 .config(conf = myconf) \

mylogger = Logger.getLogger(__name__)
mylogger.error("some error trace")"some info trace")

[100% Working Code], With the SparkContext object, we can get the logger. log4jLogger =​apache.log4j LOGGER = log4jLogger.LogManager. 1 Answer 1. Printing or logging inside of a transform will end up in the Spark executor logs, which can be accessed through your Application's AppMaster or HistoryServer via the YARN ResourceManager Web UI. You could alternatively collect the information you are printing alongside your output (e.g. in a dict or tuple).

How to redirect the pyspark log from console to my, (ps: the message can not write to file by > or >> , such as pyspark 3 can only add message to spark log, I dont know how to direct to other file Python Programming Guide. The Spark Python API (PySpark) exposes the Spark programming model to Python. To learn the basics of Spark, we recommend reading through the Scala programming guide first; it should be easy to follow even if you don’t know Scala. This guide will show how to use the Spark features described there in Python.

PySpark Tutorial-Learn to use Apache Spark with Python, At the end of the PySpark tutorial, you will learn to use spark python together to Holds the scripts to launch a cluster on amazon cloud space with multiple ec2 is the code to do log analysis in the python file named as “”: The Python packaging for Spark is not intended to replace all of the other use cases. This Python packaged version of Spark is suitable for interacting with an existing cluster (be it Spark standalone, YARN, or Mesos) — but does not contain the tools required to setup your own standalone Spark cluster.

Spark Programming Guide, Scala; Python. In the Spark shell, a special interpreter-aware SparkContext is already created for you, in the variable called sc . Behind the scenes, spark-​shell invokes the more general spark-submit script. If you want to simply type python.exe C:\Users\Username\Desktop\ you must add python.exe to your PATH environmental variable. To do this, checkout the adding Python to the PATH environment article.. Window's python.exe vs pythonw.exe. Note that Windows comes with two Python executables - python.exe and pythonw.exe.

  • You probably don't see your logging statements because default logging level is WARNING, so when you are trying to INFO or DEBUG you are filtered out.
  • I get the issue: logger = logging.getLogger('py4j') TypeError: 'JavaPackage' object is not callable
  • This is definitely allowing me to log like Spark does (thanks!). Is there a way to get this logger other than from the SparkContext? I have some logs that have to be printed before my SparkContext is created
  • @marlieg Before spark context is created you don't have access to spark logging.
  • I got an error trying to use this idea in PySpark. What I did was try to store the logger as a global, then when that didn't work try to store the context itself as a global. My use case is being able to do logging calls on my executors inside a foreach function (where it doesn't have the spark context). "Exception: It appears that you are attempting to reference SparkContext from a broadcast variable, action, or transformation. SparkContext can only be used on the driver, not in code that it run on workers. For more information, see SPARK-5063."
  • Do I have to pass that logger object as a parameter to all my components that use it? Is there some way to set it globally?
  • As long as you are not doing threading or multiprocessing you should be able to just set it at the top of your module and use it wherever. Just change logging. to logger. anytime you are about to log something.
  • Thanks. It works this way. But the message always goes to stderr. How can we direct to stdout or stderr?
  • I updated my answer to address that for you. There may be a way to update an existing StreamHandler, I am not sure, but above is how I know how to do it.
  • I'm tempted to downvote this answer because it doesn't work for me. Looking through the pyspark source, pyspark never configures the py4j logger, and py4j uses java.utils.logging instead of the log4j logger that spark uses, so I'm skeptical that this would work at all. I think it's possible that this would work for code on the master node, but not anything running on the workers.