10 PySpark Logging Best Practices
Logging is an important part of any PySpark application. Here are 10 best practices for logging in PySpark.
Logging is an important part of any application, and PySpark is no exception. Logging can be used to track events, debug issues, and monitor performance.
There are a few best practices to keep in mind when configuring logging for PySpark applications. In this article, we will discuss 10 of those best practices.
1. Logging Levels
Logging levels help you control the amount of information that gets logged. For example, you might want to log everything when you’re first developing your PySpark application, but once it’s running in production, you might only want to log warnings and errors.
The six logging levels, from most verbose to least verbose, are:
1. DEBUG
2. INFO
3. WARNING
4. ERROR
5. CRITICAL
6. FATAL
You can set the logging level in your code using the following syntax:
logger.setLevel(level)
Where logger is an instance of the Python logging class and level is one of the logging levels listed above.
For example, to only log warnings and errors, you would set the logging level to WARNING:
logger.setLevel(logging.WARNING)
2. Logging Format
When you’re troubleshooting an issue, the first thing you need to do is identify what caused the problem. With PySpark, this means looking at the logs to see what happened before the error occurred.
If your logs are in the right format, it will be much easier to find the information you’re looking for. The right logging format includes the timestamp, log level, logger name, and message.
Here’s an example of a well-formatted PySpark log:
2020-01-01 00:00:00 INFO MyLogger my_message
3. Logging to File and Console
Logging to file gives you a permanent record of what happened in your PySpark application. This can be useful for debugging purposes or for auditing. Logging to console, on the other hand, allows you to see what’s happening in real-time. This is especially important when you’re running long-running PySpark jobs, as it can help you spot issues early on.
It’s easy to set up logging to both file and console in PySpark. Simply add the following lines to your code:
log4j.rootCategory=INFO, file, console
log4j.appender.file=org.apache.log4j.FileAppender
log4j.appender.file.File=
log4j.appender.console=org.apache.log4j.ConsoleAppender
log4j.appender.console.target=System.out
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
Replace
That’s all there is to it! Now you’ll have a complete record of everything that happens in your PySpark application, which can be invaluable for debugging or auditing purposes.
4. Logging to Syslog
Syslog is a standard for message logging that allows for centralization of all your logs in one place. This is important because it can be difficult to troubleshoot issues when you have to search through multiple log files on different servers.
When logging to Syslog from PySpark, make sure to use the proper facility code so that your logs are routed to the correct location. For example, if you’re using the default syslog port (514), you would use the local0 facility code.
It’s also a good idea to include relevant metadata in your logs, such as the Spark application ID and job ID. This will help you quickly identify which logs belong to which application or job when you’re searching through them later.
5. Logging to Kafka
Kafka is a distributed streaming platform that’s often used for building data pipelines and streaming apps. It’s well suited for PySpark logging because it’s highly available and scalable.
When you log to Kafka, your logs are stored in a central location where they can be easily monitored and analyzed. This is important because it allows you to quickly identify and debug issues in your PySpark applications.
To set up logging to Kafka, you’ll need to create a Kafka topic and configure your PySpark application to log to that topic. You can find more information about how to do this in the Apache Kafka documentation.
6. Logging to Elasticsearch
Elasticsearch is a search engine that’s designed for fast and powerful searches. It’s also scalable, so it can handle large amounts of data. And it’s easy to set up and use.
When you’re working with PySpark, logging to Elasticsearch will give you the ability to quickly and easily search through your logs to find the information you need. You can also set up alerts so that you’re notified when something important happens in your logs.
To get started, all you need is an Elasticsearch cluster and a PySpark application. Then you can configure your PySpark application to log to Elasticsearch.
7. Logging to Splunk
Splunk is a platform that enables you to search, monitor, and analyze data from any source, in any format, all in one place. This is incredibly valuable when trying to debug PySpark applications because it gives you the ability to see all of your application’s logs in one place.
To log to Splunk from your PySpark application, you’ll need to use the splunk-sdk-python library. You can install this library using pip:
pip install splunk-sdk
Once you have the library installed, you can configure your PySpark application to log to Splunk by adding the following lines of code:
from splunk_sdk import connect
service = connect(token=”your-token-here”)
logger = service.logs()
logger.info(“This is a test message!”)
Replace “your-token-here” with the token for your Splunk account.
Now, every time you call logger.info(), the message will be logged to Splunk.
8. Logging to Graylog
Graylog is a centralized logging platform that can be used to collect, index, and analyze logs from multiple sources. It’s especially useful for Spark because it provides an easy way to view all of your Spark logs in one place. This can be helpful for troubleshooting purposes, as you can quickly see what’s going on with your Spark cluster at any given time.
Additionally, Graylog offers a number of features that make it easier to work with Spark logs, such as the ability to search and filter logs, set up alerts, and create dashboards.
9. Logging to HDFS
HDFS is a distributed file system that is well suited for storing large amounts of data. When you are working with PySpark, you will often be dealing with large datasets. Therefore, it makes sense to store your logs in HDFS so that they can be easily accessed and processed.
There are two main benefits of logging to HDFS. Firstly, it is easy to set up and configure. Secondly, it is highly scalable and can handle large volumes of data.
To set up logging to HDFS, you first need to create a directory in HDFS where the log files will be stored. You can do this using the hdfs dfs -mkdir command.
Once the directory has been created, you need to edit the spark-defaults.conf file to specify the location of the directory. The property that you need to set is called “spark.hadoop.validateOutputSpecs” and you should set it to “true”.
Finally, you need to restart your PySpark application for the changes to take effect.
10. Logging to Amazon S3
S3 is a highly scalable, reliable, and inexpensive data storage service. It’s perfect for storing your PySpark logs because it can handle any amount of data, and you don’t have to worry about losing any log data if your cluster goes down.
Plus, S3 is easy to set up and use. All you need to do is create an Amazon S3 bucket and configure your PySpark application to write logs to that bucket.
Once your logs are in S3, you can use Amazon Athena to query them. Athena is a serverless, interactive query service that makes it easy to analyze data in S3 using standard SQL.