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https://stackoverflow.com/questions/64180470/spark-structured-streaming-job-stuck-for-hours-without-getting-killed
I have a structured streaming job which reads from kafka, perform aggregations and write to hdfs. The job is running in cluster mode in yarn. I am using spark2.4. Every 2-3 days this job gets stuck. It doesn't fail but gets stuck at some microbatch microbatch. The microbatch doesn't even tend to start. The driver keeps printing following log ...
https://spark.apache.org/docs/latest/configuration.html
Properties that specify some time duration should be configured with a unit of time. The following format is accepted: 25ms (milliseconds) 5s (seconds) 10m or 10min (minutes) 3h (hours) 5d (days) 1y (years) Properties that specify a byte size should be configured with a unit of size.
https://www.mikulskibartosz.name/how-to-speed-up-pyspark/
On a typical day, Spark needed around one hour to finish it, but sometimes it required over four hours. The first problem was quite easy to spot. There was one task that needed more time to finish than others. That one task was running for over three hours, all of the others finished in under five minutes.
https://stackoverflow.com/questions/48348624/spark-tasks-stuck-at-running
I'm trying to run a Spark ML pipeline (load some data from JDBC, run some transformers, train a model) on my Yarn cluster but each time I run it, a couple - sometimes one, sometimes 3 or 4 - of my executors get stuck running their first task set (that'd be 3 tasks for each of their 3 cores), while the rest run normally, checking off 3 at a time.
https://dzone.com/articles/common-reasons-your-spark-applications-are-slow-or
A look at common reasons why an application based on Apache Spark is running slow or failing to run at all, with special attention to memory management issues.
https://www.unraveldata.com/common-reasons-spark-applications-slow-fail-part-1/
Sometimes an application which was running well so far, starts behaving badly due to resource starvation. The list goes on and on. It’s not only important to understand a Spark application, but also its underlying runtime components like disk usage, network usage, contention, etc., so that we can make an informed decision when things go bad.
https://spark.apache.org/docs/latest/web-ui.html
For stages belonging to Spark DataFrame or SQL execution, this allows to cross-reference Stage execution details to the relevant details in the Web-UI SQL Tab page where SQL plan graphs and execution plans are reported. Summary metrics for all task are represented in a table and in a timeline. Tasks deserialization time.
https://towardsdatascience.com/how-to-get-started-with-pyspark-1adc142456ec
Start a new Conda environment. You can install Anaconda and if you already have it, start a …
https://www.jetdrift.com/how-to-maintain-a-sea-doo-spark/
To flush a Sea-Doo Spark, make sure to first attach the garden hose to the connector while the hose off. Always start the engine before opening the water tap. Run your engine at idle for around 30-40 seconds. Then close the water tap before draining the water from the exhaust. And finally, shut the engine off.
https://www.quora.com/What-are-the-recommended-running-hours-of-a-generator-before-service
Larger industrial diesel engines usually have around 250 hours between oil changes and reciprocating gas engines can operate for about 1,500 hours to 2,500 hours between oil changes. Some industrial engines can be configured for extended sumps or even for hot-swap oil changes.
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