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The Parquet Format and Performance Optimization Opportunities Boudewijn Braams (Databricks)
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- Опубліковано 15 сер 2024
- The Parquet format is one of the most widely used columnar storage formats in the Spark ecosystem. Given that I/O is expensive and that the storage layer is the entry point for any query execution, understanding the intricacies of your storage format is important for optimizing your workloads. As an introduction, we will provide context around the format, covering the basics of structured data formats and the underlying physical data storage model alternatives (row-wise, columnar and hybrid). Given this context, we will dive deeper into specifics of the Parquet format: representation on disk, physical data organization (row-groups, column-chunks and pages) and encoding schemes. Now equipped with sufficient background knowledge, we will discuss several performance optimization opportunities with respect to the format: dictionary encoding, page compression, predicate pushdown (min/max skipping), dictionary filtering and partitioning schemes. We will learn how to combat the evil that is 'many small files', and will discuss the open-source Delta Lake format in relation to this and Parquet in general. This talk serves both as an approachable refresher on columnar storage as well as a guide on how to leverage the Parquet format for speeding up analytical workloads in Spark using tangible tips and tricks.
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The best representation of Parquet file structure!! Simply Awesome!!
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@Iker Crew Instablaster :)
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This content explained most of the thing and It is really amazing .
Great talk, Great Teach, Excellent Tutor! One of the best presentation I have ever viewed and listened.
Great overview to address performance issues with storage layer design 👍
Best Parquet File presentation I watch
Great, this makes me know more about Parquet. Thanks for the pre!
Great presentation 👏 👌
Best presentation in Parquet.
great talk with simple explanations.
Awesome video with great content and explanation. Very very useful.
Awesome video - not too much extraneous or labored points. Thank you!
Amazing. All concepts really well explained.
Superb
Impressive presentation well structured explanations.
Thank you for this very useful video!
Great Presentation. Thank you
@databricks - what is the best practice to use or not use nested columns. For Example, I have struct of customer with Age, Gender, Name, etc attributes. Is it better to keep it as struct or separate into its own columns?
Great talk. Thank you!
Thanks for the content!
Great! Well explained!
Just excellent 👍
Finally understood what parquet format, thanks.
So I have one small doubt, does it mean that footer metadata is nothing but schema details, like underlying table details? Like way to mention table name, column names? etc.
I'll also dig from my side, but just meanwhile ....
too good video. Excellent
Thanks for great explanation
Very helpful, thank you 😊
great talk
Great!
Seems the time and i/o needed before use the data in doing the sort first is not considered?
I haven't watched this yet but for the sake of prioritizing when I do, how well does this topic apply to platforms and systems other than Spark?
Thank you!
interesting how Parquet (columnar analytical focused) data can be optimized using dictionary-based compression and partitioning
Thankyou :)
anyone who says parquet is columnar format is having just bookish knowledge
Its a mixture of both Horizontal and Vertical partitioning and combines best of both worlds
i like the way it compresses the data into dictionaries per file. reminds me a bit of an EAV database stored as a file
Thanks for posting this presentation. Could you clarify something? How does performance improve when you compress pages only to decompress it again to read it? I'm sure I'm not understanding something, but not sure what.
He mentions around @19:30 that you only see a benefit when the I/O savings outweigh the cost of decompressing.
I/O is more expensive compared to the time taken by CPU to decompress the data, thus ColumnStore is faster compared to RowStore.
Snappy provides good compression with a low CPU overhead during compression/decompression. The real win in performance comes from reduced I/O cost when reading a column chunk's page. The overall cost (CPU+I/O) is generally lower for reading snappy compressed as compared to uncompressed.
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@harsh savla Good for you. Ecoli enters the body on vegetables.
How is storing json/xml (not parquet) more efficient than csv? You literally store the "column names" in each "row" in xml/json (at least when stored in a text file) . Also, there is definitely the notion of a "record" in csv.
Example 1. If you wanted to add a new properties to records overtime, you only need to add it to the new records (no need to back date blanks for legacy records for example). So think scale and change at scale.
Example 2. You can leverage hash/dictionary data structures in programming, these can find records at a much better scaling, look up hash functions and big o. Again think scaling related to data access, hashing vs at best search trees.
Example 3. You can more easily partition records via collections paradigm. Again storage and access at scale.
Example 4. You will more easily access and operate xml / json - like data from applications via APIs. Systems and interoperability at scale.
@@happywednesday6741 i asked how it was more efficient to store it. if i have 500 million "entries" in a text file, I'm definitely storing it in a delimited format or parquet to take advantage of said dictionaries and not json/xml. you can parse either into objects directly from the file, or bulk insert into a db table. the json/xlm formats would be 10x slower to parse/read in based on sheer disk/network i/o alone... if we're talking about efficiency in processing it. no one is going to load csv into memory and start trying to scan row by row for data, it's going to get converted into objects or a db anyways. my concern is when people store json formatted files to disk to be read into objects later. what does that buy you?
Bucketing explanation was not great. Rest was fantabulous.
Or whatever.... 😂
Worst lecture ever