How to Read the File From Aws S3 Process Using Spark Sql
Leveraging Apache Spark to execute billions of operations on AWS S3
It is only moving files, not rocket science, right? Until yous have to do information technology for over a one thousand thousand files and tens of terabytes…
Sometimes you lot can discover yourself in a situation that requires to execute AWS S3 operations in a huge corporeality of files, such as DELETE, Re-create or fifty-fifty store them in another bucket irresolute the storage tier (i.e. Glacier). Maybe the rules you demand to follow to select these files inside the buckets are likewise circuitous to use prefix and fourth dimension-based lifecycle rules.
Forget virtually the AWS panel. It is a south ign that yous should do information technology programmatically, but the simplest approach of getting a listing of files and using Python boto3 (or whatsoever other AWS SDK) to apply the operations sequentially would have an entire life because the number of files yous take.
If you recall a little further on the problem, you could decide to use a pool of threads to make the extra cores of your machine to work for you lot, but you lot are still restricted to the number of cores that fits into ane single machine.
At iFood we use Spark to tackle our data problems on a daily basis. We are used to apply user-defined functions (UDFs) to billions of rows, transforming data in plenty of ways. Considering this context, and the size of the problems that we normally deal with, it seems natural to relate both situations. In other words, if I can encapsulate my AWS S3 operation inside a role, and then apply it to a dataframe of S3 file paths, than I can leverage the distributed surroundings of Spark to horizontally calibration my task. Our Spark infrastructure has more than than 700 machines running at the peak hours, and an average that is probably higher up a half of this number, so there is always a cluster available to take a ride and run these side tasks.
The strategy itself has issues (that we volition discuss in a moment), but the thought behind it tin can, indeed, solve the problem. The rate limits of S3 Become , PUT , DELETE and COPY operations are insanely loftier, meaning that you can go wild on parallelization of those kind of requests. But the Devil is in the details. In this case, it lies on the nature of the boto3 session object (that I will refer to just as session from now on) and on how Spark UDFs work behind the scenes.
A session object cannot be broadcasted to several different machines, and that will make sense if think well-nigh it for a moment. One tin can not just start one global session with AWS at the driver and share information technology amidst different nodes to be used from different IP addresses, this is a security issue. If you do not believe me, feel free to attempt. Yous will accept exceptions being thrown at you.
Some other choice you have is to instantiate the session inside the UDF that will be applied to your dataframe. UDF variables are instantiated from inside the node the function was called, and so every node would start their own sessions. Just the trouble is that would result in A LOT of sessions. Exactly 1 session per row of your dataframe. And we are talking nearly billions of rows, and then, think about the overhead of starting xx billion sessions with AWS.
Dude, it seems to be a expressionless end
… but you told me that the idea behind it tin can solve the problem. I'm starting to think that you are a liar.
50et me tell you a story. Before at that place were dataframes, in that location existed some other creatures roaming the Spark earth, called Resilient Distributed Datasets (RDDs), the Spark most simple abstraction. In fact, dataframes are implemented on peak of RDDs and y'all tin can get those RDDs from the dataframe entrails calling its .rdd() method. Well, RDDs are unpolished creatures, just they can solve this kind of problem with a method called .mapPartitions() . It is a little more than circuitous that that, but you lot tin can think for a moment that .mapPartitions() is a manner to apply UDFs not in a row-wise, but in a partitioning-wise mode, and frequently the partitions of your dataframe/RDD are much less numerous than their rows. Yes, it solves the problem of those billion sessions correct away! With this approach, the number of sessions is exactly the number of partitions (that you lot can ascertain yourself, using the dataframe .repartition() method).
Of course there is a trade-off there (in that location always has): less partitions means less sessions, just also less parallelism. Spark clusters tin can tackle a lot of partitions at once, depending on its current parallelism state (that is related to the number of cores the cluster has every bit a whole — the sum of the cores of its nodes/machines — plus how much of its resources are already in utilize by other jobs running on information technology, and can be accessed by the Spark context attribute sc.defaultParallelism). If y'all restrict the number of partitions nether your cluster maximum capacity, your job will probably take more time to finish. And then, now, your problem is reduced to find this balance.
The code below shows how to get a lot of AWS S3 COPY operations done using the strategy higher up, assuming the rows of your dataframe are structured like ['bucket', 'key', 'file_number', 'total_files'] . Actually, file_number and total_files information are dispensable, but they tin can be used afterwards to sanity check your process (if you know the total number of files, you can cantankerous check it against a row count of your resulting dataframe).
This is a pretty simple example, with no filters being applied to exclude files from the Copy procedure, merely it should exist enough to illustrate the idea. Maybe you could just exclude files with hot in the proper noun, since Glacier is a cold storage… (very funny), or something more than elaborate like checking them against another list et coetera.
Let'due south become back to the AWS S3 documentation for a scrap, focusing on that part that says:
You tin can send iii,500 PUT/Copy/Post/DELETE or five,500 Get/HEAD requests per second **per prefix** in an S3 bucket
And that is another precious hint to this approach. If y'all can segmentation your dataframe based on the prefixes of your files (bold there is not much skew in your file data, giving you relatively counterbalanced partitions), you will never reach the asking limits, even if you lot have a lot of parallelism, because these limits apply per prefix (well, assuming your cluster has non blazingly fast cores). Each node core will be dealing with a different prefix, fugitive simultaneous requests for the aforementioned prefix.
The beauty of this strategy is that information technology can be used for any API requests scenario, equally long as yous pay attention to the request rate of the API you are using. If you are reaching the limits, y'all can always put more than weight on the segmentation side of the scale, and even tweak a little with sleeps. Maybe there are cheaper ways to solve this trouble, and mayhap lifecycle rules or the relatively new S3 batch operations solves your trouble nicely, merely you take to value the elegance of this one.
Source: https://towardsdatascience.com/leveraging-apache-spark-to-execute-billions-of-operations-on-aws-s3-2f62930d19fd
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