I have a azure databricks job and it's triggered via ADF using a api call. I want see why the job has been taking n minutes to complete the tasks. When the job execution results, The job execution time says 15 mins and the individual cells/commands doesn't add up to even 4-5 mins
The interactive cluster is already up and running while this got triggered. Please tell me why this sum of individual cell execution time doesn't match with the overall job execution time ? Where can i see what has taken the additional time here ?
Please follow below reference it has detail explanation about:
Execution time taken for the command cell in data bricks notebook.
Measuring Apache Spark Workload Metrics for Performance.
Reference:
How to measure the execution time of a query on Spark
https://db-blog.web.cern.ch/blog/luca-canali/2017-03-measuring-apache-spark-workload-metrics-performance-troubleshooting
https://spark.apache.org/docs/latest/monitoring.html
Currently using PySpark on Databricks Interactive Cluster (with Databricks-connect to submit jobs) and Snowflake as Input/Output data.
My Spark application is supposed to read data from Snowflake, apply some simple SQL transformations (mainly F.when.otherwise, narrow transformation) , then load it back to Snowflake. (FYI, schema are passed to Snowflake reader & writer)
EDIT : There's also an sort transformation at the end of the process, before writing.
For testing purpose, I named my job like this: (Writer, and Reader are supposed to be named)
sc.setJobDescription("Step Snowflake Reader")
I have trouble understanding what the Spark UI is showing me :
So, I get 3 jobs, with all same jobs name (Writer).
I can understand that I have only one Spark Action, so suppose to have one job, so Spark did name the jobs the last value set by sc.setJobDescription (Reader, which trigg spark compute).
I did also tag my "ReaderClass"
sc = spark.sparkContext
sc.setJobDescription("Step Snowflake Reader")
Why it doesn't show ?
Is the first job is like "Downloading Data from Snowflake", the second "Apply SQL transformation", then the third "Upload data to Snowflake" ?
Why all my jobs are related to same SQL Query ?
What is Query 0 which is related to ... zero jobs ?
Thanks for help.
There are a few things in this.
First thing, is that a job is triggered for an action, and tranformations are not really part of it (they're computed during an action, but a single action can do multiple transformations).
In your case, reading, tranformation and sorting, all these steps would take place when the action is triggered
Please note that reading from snowflake doesn't trigger a job (this is an assumption as the same behaviour is exhibited by Hive) because snowflake already has the metadata which spark needs by traversing the files.
If you'll read a parquet file directly, it'll trigger a different job, and you'll be able to see the job description.
Now comes the part of you naming your job
sc.setJobDescription("Step Snowflake Reader")
This will name the job that was triggered by your write action. And this action in turn is calling multiple jobs (but are still part of the last action you're performing, refer here for more details see this post
Similarly, the last configuration that you make before calling an action is picked up (Same thing happens for setting shufflePartition for instance, you may wanna have a perticular step with more or less shuffle, but for 1 complete action, it'll be set to a single value)
Hope this answers your question.
I am trying to process my near real time batch csv files using spark streaming. I am reading these files in batches of 100 files and doing some operation and writing to output files. I am using spark.readstream and spark.writestream functions to read and write streaming files.
I am trying to find out how I can stop the spark streaming?
stream_df = spark.readstream.csv(filepath_directory).
.option("maxFilesPerTrigger", 10)
query = spark.writeStream.format("parquet")..outputMode("append").option("output filepath")
I faced the issue while streaming job is running, due to some reason
my job is failing or some exceptions are.
I tried try, except.
I am planning to stop the query in case of any exceptions and process the same code again.
I used query.stop(), is this the right way to stop streaming job?
I read one post, I am not sure whether it is for Dstream and how to execute this code in my pyspark code.
https://www.linkedin.com/pulse/how-shutdown-spark-streaming-job-gracefully-lan-jiang
Below is the scenario I would need suggestions on,
Scenario:
Data ingestion is done through Nifi into Hive tables.
Spark program would have to perform ETL operations and complex joins on the data in Hive.
Since the data ingested from Nifi is continuous streaming, I would like the Spark jobs to run every 1 or 2 mins on the ingested data.
Which is the best option to use?
Trigger spark-submit jobs every 1 min using a scheduler?
How do we reduce the over head and time lag in submitting the job recursively to the spark cluster? Is there a better way to run a single program recursively?
Run a spark streaming job?
Can spark-streaming job get triggered automatically every 1 min and process the data from hive? [Can Spark-Streaming be triggered only time based?]
Is there any other efficient mechanism to handle such scenario?
Thanks in Advance
If you need something that runs every minute you better use spark-streaming and not batch.
You may want to get the data directly from kafka and not from hive table, since it is faster.
As for your questions what is better batch / stream. You can think of spark streaming as micro batch process that runs every "batch interval".
Read this : https://spark.apache.org/docs/latest/streaming-programming-guide.html
I have a spark batch job that talks with cassandra. After the batch job gets completed , I need to verify few entries in cassandra and the cycle continues for 2-3 times. How do I know when the batch job ends ? I don't want to track the status of batch job by adding entry in db.
How to write functional test in spark ?