How Partitioning Works In Apache Spark?

Welcome back to our comprehensive series on Apache Spark performance optimization techniques! In today's episode, we dive deep into the world of partitioning in Spark - a crucial concept for anyone looking to master Apache Spark for big data processing.

🔥 What's Inside:
1. Partitioning Basics in Spark: Understand the fundamental principles of partitioning in Apache Spark and why it's essential for performance tuning.
2. Coding Partitioning in Spark: Step-by-step guide on implementing partitioning in your Spark applications using Python. Perfect for both beginners and experienced developers.
3. How Partitioning Enhances Performance: Discover how strategic partitioning leads to faster and easier access to data, improving overall application performance.
4. Smart Resource Allocation: Learn how partitioning in Spark allocates resources for optimised execution.
5. Choosing the Right Partition Key: A comprehensive guide to selecting the most effective partition key for your Spark application.

🌟 Whether you're preparing for Spark interview questions, starting your journey with our Apache Spark beginner tutorial, or looking to enhance your skills in Apache Spark, this video is for you.

📚 Keep Learning:

Chapters:
00:00 Introduction
02:22 Code for understanding partitioning
05:44 Problems that partitioning solves
09:48 Factors to consider when choosing a partition column
13:36 Code to show single/multi level partitioning
22:09 Thank you

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[VenkatakrishnaGangavarapu]

super super super detailed way thanks for uploading. i was unable understand it before but now could understand thanks a lot. .... please do this kind indeapth topic videos when ever you are free to do. (u may not get view and money like other entertainment vidoes. but you are helping people to grow in this field surly there are so many people benifitting from you're content. please continue to do this kind of videos)

[sayedsamimahamed]

Till now the best explaination in youtube. Thank you very much.

[sagarteli]

Thank you brother..! 🙏 I am new to this, but the way you explained was made me understand these concepts to the depth.

[kartikjaiswal]

I love you bro for such crisp explanation, the way to experiment and teach helps a lot!

[Fullon]

tranks for share your knowlegde, your videos are amazing.

[RaviSingh-dpxc]

again!! its a great content, very much clearly explained. 🙏

[iamexplorer]

very great detailed way understandable way so... Great ...

[AviE-cj]

Thanks for the detailed video. I have few questions here on partitioning. 1. How does it decide the number of partitions if we dont specify the properties and is it good to do repartition(some 400) after read. Is it good practice? 2. How does we decide the number for repartition value before writing to disk? If we put large number to repartition method, will that be optimal?

[utsavchanda]

Good video. In fact, all of your videos are. One thing, in this video majorly you were talking about actual physical partitions on the disk. But towards the end, when you were talking about "maxpartitionbytes" and doing only a READ operation, you were talking about shuffle partitions which is in-memory and not disk partitions. I had found that hard to grasp for a very long time, so wanted to confirm if my understanding is right here.

[lunatyck]

great video as always - when can we get a video to set up our IDE like yours? really nice UI - visual studio I believe?

[purushottamkumar]

Thank you for sharing knowledge in detail . i have become big fan of you and shared spark video to couple of my friends and seniors .
Just curious to know which tool you are using to run spark code. If possible please upload one video related to spark installation on mac.

[voxdiary]

Really great explanation. Hard to find someone talking about what matters without fluff. I have more implementation related question. If I have many tables ranging from 100mb to 10gb. should i dynamically change my maxparititon bytes before i process this data. if i have 100 tables, does it make sense to adjust this parameter in a loop or something before i start transforming the dataframe

[Ali-qdc]

Very good explaination! One question, what do you recommend for big fact table CLUSTER BY or PARTITION BY based on months and year columns? CLUSTER BY is a new concept and you don't have to run OPTIMIZE command for maintance but one thing it doesn't create a separate directory as PARITITION BY which can lead read multiple files for the same months or years instead just go one directory folder. Please advise. Thank you!

[vamsikrishnabhadragiri]

Thank for the informative videos. I have a question regarding repatiton(4).partitionby(key)

Does it mean 4 part files in each of the partition will be a separate partition while reading ?

Or it considers the maxpartitonbytes specified and depending upon the size it creates partition (combining two or more part files) if the both size is within the maxpartitionbytes limit

[retenim]

I am a little bit confused: at minute 15:17 in a specific folder relating to a specific value of listen_date you say that there is only 1 file that corresponds to 1 partition. But I thought that partitions are created depending on the values of listen_date, so as far as I can see, I would say there are more than 30 partitions (each one corresponding to a specific value of listen_date). After that you used repartition function to change the number of partitions inside each folder. So the question is: the number of partitions is the number of listen_date folder or the number of file inside each folder?

[anandchandrashekhar]

Thank you so much again! I have one follow up question about partition during writes. If I use a df.write but specify no partitioning column or use repartition, could you pls let me know how many partitions does spark write to by default?
Does it simply take the number of input partitions (total input size / 128m) or assuming if shuffling was involved and the default shuffle partitions being used were 200, does it use that shuffled partition number ?
Thank you

[tusharannam.]

Hi bro, thanks for such valuable content. I have a doubt, What is the need of repartition before partitionBy? I understand that it helps to create files within partition but how can it help to optimize? Please clarify.

[danieldigital]

Hello, thanks for this video and for the whole course. I have a question about high cardinality columns: Say you have table A and table B with customer_id on both. You want to perform a join on this column, how do you alleviate the performance issue that occurs?

[kvin]

Thanks for the content Afaque. Question regarding I was thinking about this would be beneficial when reading a file that you know the size of upfront. What about files you don’t know the size. Do you recommend repartition or coalesce in those cases to adjust the number of partitions for the Dataframe?

[Amarjeet-fblk]

At 16.39, when u use repartition(3), why there are 6 files?