How to stop INFO messages displaying on spark console?

I'd like to stop various messages that are coming on spark shell.

I tried to edit the log4j.properties file in order to stop these message.

Here are the contents of log4j.properties

# Define the root logger with appender file
log4j.rootCategory=WARN, console
log4j.appender.console=org.apache.log4j.ConsoleAppender
log4j.appender.console.target=System.err
log4j.appender.console.layout=org.apache.log4j.PatternLayout
log4j.appender.console.layout.ConversionPattern=%d{yy/MM/dd HH:mm:ss} %p %c{1}: %m%n

# Settings to quiet third party logs that are too verbose
log4j.logger.org.eclipse.jetty=WARN
log4j.logger.org.eclipse.jetty.util.component.AbstractLifeCycle=ERROR
log4j.logger.org.apache.spark.repl.SparkIMain$exprTyper=INFO
log4j.logger.org.apache.spark.repl.SparkILoop$SparkILoopInterpreter=INFO

But messages are still getting displayed on the console.

Here are some example messages

15/01/05 15:11:45 INFO SparkEnv: Registering BlockManagerMaster
15/01/05 15:11:45 INFO DiskBlockManager: Created local directory at /tmp/spark-local-20150105151145-b1ba
15/01/05 15:11:45 INFO MemoryStore: MemoryStore started with capacity 0.0 B.
15/01/05 15:11:45 INFO ConnectionManager: Bound socket to port 44728 with id = ConnectionManagerId(192.168.100.85,44728)
15/01/05 15:11:45 INFO BlockManagerMaster: Trying to register BlockManager
15/01/05 15:11:45 INFO BlockManagerMasterActor$BlockManagerInfo: Registering block manager 192.168.100.85:44728 with 0.0 B RAM
15/01/05 15:11:45 INFO BlockManagerMaster: Registered BlockManager
15/01/05 15:11:45 INFO HttpServer: Starting HTTP Server
15/01/05 15:11:45 INFO HttpBroadcast: Broadcast server star

How do I stop these?

Solution 1:

Edit your conf/log4j.properties file and change the following line:

log4j.rootCategory=INFO, console

to

log4j.rootCategory=ERROR, console

Another approach would be to :

Start spark-shell and type in the following:

import org.apache.log4j.Logger
import org.apache.log4j.Level

Logger.getLogger("org").setLevel(Level.OFF)
Logger.getLogger("akka").setLevel(Level.OFF)

You won't see any logs after that.

Other options for Level include: all, debug, error, fatal, info, off, trace, trace_int, warn

Details about each can be found in the documentation.

Solution 2:

Right after starting spark-shell type ;

sc.setLogLevel("ERROR")

In Spark 2.0 (Scala):

spark = SparkSession.builder.getOrCreate()
spark.sparkContext.setLogLevel("ERROR")

API Docs : https://spark.apache.org/docs/2.2.0/api/scala/index.html#org.apache.spark.sql.SparkSession

For Java:

spark = SparkSession.builder.getOrCreate();
spark.sparkContext().setLogLevel("ERROR");

Solution 3:

All the methods collected with examples

Intro

Actually, there are many ways to do it. Some are harder from others, but it is up to you which one suits you best. I will try to showcase them all.

#1 Programatically in your app

Seems to be the easiest, but you will need to recompile your app to change those settings. Personally, I don't like it but it works fine.

Example:

import org.apache.log4j.{Level, Logger}

val rootLogger = Logger.getRootLogger()
rootLogger.setLevel(Level.ERROR)

Logger.getLogger("org.apache.spark").setLevel(Level.WARN)
Logger.getLogger("org.spark-project").setLevel(Level.WARN)

You can achieve much more just using log4j API.
Source: [Log4J Configuration Docs, Configuration section]

#2 Pass log4j.properties during spark-submit

This one is very tricky, but not impossible. And my favorite.

Log4J during app startup is always looking for and loading log4j.properties file from classpath.

However, when using spark-submit Spark Cluster's classpath has precedence over app's classpath! This is why putting this file in your fat-jar will not override the cluster's settings!

Add -Dlog4j.configuration=<location of configuration file> to spark.driver.extraJavaOptions (for the driver) or
spark.executor.extraJavaOptions (for executors).

Note that if using a file, the file: protocol should be explicitly provided, and the file needs to exist locally on all the nodes.

To satisfy the last condition, you can either upload the file to the location available for the nodes (like hdfs) or access it locally with driver if using deploy-mode client. Otherwise:

upload a custom log4j.properties using spark-submit, by adding it to the --files list of files to be uploaded with the application.

Source: Spark docs, Debugging

Steps:

Example log4j.properties:

# Blacklist all to warn level
log4j.rootCategory=WARN, console

log4j.appender.console=org.apache.log4j.ConsoleAppender
log4j.appender.console.target=System.err
log4j.appender.console.layout=org.apache.log4j.PatternLayout
log4j.appender.console.layout.ConversionPattern=%d{yy/MM/dd HH:mm:ss} %p %c{1}: %m%n

# Whitelist our app to info :)
log4j.logger.com.github.atais=INFO

Executing spark-submit, for cluster mode:

spark-submit \
    --master yarn \
    --deploy-mode cluster \
    --conf "spark.driver.extraJavaOptions=-Dlog4j.configuration=file:log4j.properties" \
    --conf "spark.executor.extraJavaOptions=-Dlog4j.configuration=file:log4j.properties" \
    --files "/absolute/path/to/your/log4j.properties" \
    --class com.github.atais.Main \
    "SparkApp.jar"

Note that you must use --driver-java-options if using client mode. Spark docs, Runtime env

Executing spark-submit, for client mode:

spark-submit \
    --master yarn \
    --deploy-mode client \
    --driver-java-options "-Dlog4j.configuration=file:/absolute/path/to/your/log4j.properties" \
    --conf "spark.executor.extraJavaOptions=-Dlog4j.configuration=file:log4j.properties" \
    --files "/absolute/path/to/your/log4j.properties" \
    --class com.github.atais.Main \
    "SparkApp.jar"

Notes:

1. Files uploaded to spark-cluster with --files will be available at root dir, so there is no need to add any path in file:log4j.properties.
2. Files listed in --files must be provided with absolute path!
3. file: prefix in configuration URI is mandatory.

#3 Edit cluster's conf/log4j.properties

This changes global logging configuration file.

update the $SPARK_CONF_DIR/log4j.properties file and it will be automatically uploaded along with the other configurations.

Source: Spark docs, Debugging

To find your SPARK_CONF_DIR you can use spark-shell:

atais@cluster:~$ spark-shell 
Welcome to
      ____              __
     / __/__  ___ _____/ /__
    _\ \/ _ \/ _ `/ __/  '_/
   /___/ .__/\_,_/_/ /_/\_\   version 2.1.1
      /_/   

scala> System.getenv("SPARK_CONF_DIR")
res0: String = /var/lib/spark/latest/conf

Now just edit /var/lib/spark/latest/conf/log4j.properties (with example from method #2) and all your apps will share this configuration.

#4 Override configuration directory

If you like the solution #3, but want to customize it per application, you can actually copy conf folder, edit it contents and specify as the root configuration during spark-submit.

To specify a different configuration directory other than the default “SPARK_HOME/conf”, you can set SPARK_CONF_DIR. Spark will use the configuration files (spark-defaults.conf, spark-env.sh, log4j.properties, etc) from this directory.

Source: Spark docs, Configuration

Steps:

1. Copy cluster's conf folder (more info, method #3)

2. Edit log4j.properties in that folder (example in method #2)

3. Set SPARK_CONF_DIR to this folder, before executing spark-submit,
   example:

   export SPARK_CONF_DIR=/absolute/path/to/custom/conf
   
   spark-submit \
       --master yarn \
       --deploy-mode cluster \
       --class com.github.atais.Main \
       "SparkApp.jar"
   

Conclusion

I am not sure if there is any other method, but I hope this covers the topic from A to Z. If not, feel free to ping me in the comments!

Enjoy your way!