I followed s3 documentation and example for uploading my large file as multipart to amazon s3 storages with using a boto3 library. However, the information in official documentation was not sufficient for me. In my case, I have multiple storages in different regions, and I should upload my parts (overall, 500 GB) to different storages. I would like to see an example, how to upload multiple parts to different S3 storages in different zones with using the boto3 library. Any information is valuable for me. Thank you for reading
boto3 documentaion
boto3 example
Okay, you seem to be confused about some of the terminology. Let's define some terms:
Region: A geographic location containing multiple Availability Zones, eg London, Ohio
Availability Zone: A data centre (or multiple). Each AZ in a region is geographically separated to ensure High Availability.
Regional service: A few services, including Amazon S3 is a regional service. This means it automatically copies data amongst the AZs.
Bucket: Storage location for objects in Amazon S3. A bucket exists in only one region.
Object: A file in a bucket.
Now, to answer your questions:
An object exists in one bucket only. An object cannot be split between buckets. (And since a bucket exists i na region, it cannot also be split between regions.)
If you wish to replicate objects between regions, use Cross-Region Replication. This will automatically copies objects from one bucket to another.
The maximum size of an object in Amazon S3 is 5TB, but you really, really don't want to get that large. Most big data applications use many, smaller files (eg 5MB in size). This allows parallel loading across multiple processes, which is commonly done in Hadoop. It also allows the additional of new data by simply adding files, rather than updating an existing file. (By the way, you cannot append to an S3 object, you can only replace it.)
The easiest way to upload data to S3 is by using the AWS Command-Line Interface (CLI).
Multi-part uploads are merely a means of uploading a single object by splitting it into multiple parts, uploading each part, then stitching them together. The method of upload is unrelated to the actual objects once they have been uploaded.
You should always store your data close to where it is being processed. So, only replicate data between regions if it needs to be processed in multiple regions.
Related
This is more of design and architecture question so please bare with me.
Requirement - Lets consider we have 2 type of flat files (csv, xls or txt) for below two db tables.
Doctor
name
degree
...
Patient
name
doctorId
age
...
each file contains data of each tables respectively. (volume 2-3millions each file).
we have to load these two files data into Doctor, Patient table of warehouse. after some of the validations like null value, foreign key, duplicates in doctor etc..
if any invalid data identifies, i will need to attach the reasons like null value, duplicate value. so that i can evaluate the invalid data.
Note that, expectations is to load 1 million records in ~1-2mins of span.
My Designed workflow (so far)
After several articles and blog reading i find it to go with AWS Glue & Databrew of my ETL for source to target along with custom validations.
Please find below design and architecture. Suggest & guide me on it.
Is there any scope of parallel or partition based processing for quick validation and loading the data? Your help is going to really help me and others (who gonna come to this type of case).
Thanks a ton.
I'm not sure if you're asking the same thing, but your architecture should follow these guidelines
File land to S3 raw bucket.
Your lambda trigger once file put on S3 bucket.
AWS lambda trigger will invoke Step function which contains following steps
3.1 ) Data governance (AWS Deeque) check all validations
3.2 ) perform transformation
Move process data to your process bucket where you have data for reconciliation and other process.
Finally your data move to your production bucket where you have only required process data not all
Note :
Partitioning is help to achieve parallels processing in Glue.
Your lambda and ETL logic should be stateless so rerun will not corrupts your data.
All Synchronous call should use proper retry Exponential Backoff and jitter.
Logs every steps into DynamoDB table so analysis logs and help in reconciliation.
Is there anyway to get the number of files(objects) in a specific folder in an s3 bucket, from a lambda function using python.
Amazon S3 Storage Lens "provides a single view of object storage usage and activity across your entire Amazon S3 storage. It includes drilldown options to generate insights at the organization, account, Region, bucket, or even prefix level." However, the ability to obtain metrics at a Prefix level requires Advanced metrics, which is priced at $0.20 per million objects monitored per month. There is a boto3 library that gives access to Storage Lens, but that seems to be about configuration rather than retrieving the actual metrics. (I haven't used it, so I'm not sure what's involved.)
Alternatively, you could call list_objects_v2() for the desired Prefix. However, it only returns a maximum of 1000 objects, so you would need to keep calling it while NextContinuationToken is not null. Each call returns a KeyCount, which is the number of keys returned with the request.
Alternatively, if you use the resource-based call of bucket.objects.all(), then boto3 will perform the loop for you and will present back a list of s3.ObjectSummary objects. You can simply use len() on the list to obtain the count.
Both methods will be quite slow for buckets/folders with a large number of objects. Therefore, another option is to use Amazon S3 Inventory, which can provide a daily or weekly CSV file listing all objects. It might not be 'current', but it is a very easy way to count the objects without having to loop through calls.
Is it possible in Django to store image files in different buckets/containers in external object storage OpenStack Swift?
I have an issue related with creating proper way to upload image files through REST service request. The user is able to POST 'Task' instance via first endpoint, containing name, description, owner_id etc. He is also able to POST images via another endpoint, which has a relation many to one with Task.
Images should be stored in OpenStack Swift in my case (server already exists, was set up etc.) in unique containers/buckets as follows: owner_id_task_id. It is related, that users can upload files with the same names and extensions, but different content.
Another part will be sending also to the same containers in OpenStack Swift a files from Celery worker tasks (some processing based on uploaded files).
My goal is to achieve dynamically-created/overrided at runtime container structure, for storing raw images, and also post-processed images.
Any ideas how this problem can be solved?
Thanks for the help!
Yes. This is possible. You can use FileField.storage to configure which storage to use in individual model fields.
My application would create .csv files temporarily for storing some data rows. What is the best strategy to manage this kind of temporary files creation and delete them after user logs out of the app?
I think creating temporary .csv files on the server isn't good idea.
Is there any simple way to manage temporary file creation at client machine (browser)?
These .csv files contains table records -> which would be used as source later for d3.js visualization charts/elements.
Please share your experience on real time applications for this scenario ?
I'm using DJango framework (Python) for doing this.
Why create on-disk files at all? For smaller files, use an in-memory file object like StringIO.
If your CSV file sizes can potentially get large, use a tempfile.SpooledTemporaryFile() object; these dynamically swap out data to a on-disk file if you write enough data to them. Once closed, the file is cleared from disk automatically.
If your preference is to store the data client side, "HTML local storage" could be an option for you. This lets you store string data in key value pairs in the user's browser subject to a same origin policy (so data for one origin (domain) is visible only to that origin). There is also a 5MB limit on data size which must be strings - OK for CSV.
Provided that your visualisation pages/code is served from the same domain, the data in local storage will be accessible to it.
Within AWS Glue how do I deal with files from S3 that will change every week.
Example:
Week 1: “filename01072018.csv”
Week 2: “filename01142018.csv”
These files are setup in the same format but I need Glue to be able to change per week to load this data into Redshift from S3. The code for Glue uses native Python as the backend.
AWS Glue crawlers should be able to just find your CSV files as they are named without any configuration on your part.
For instance, my Kinesis stream produces files that have paths and names that look like these:
my_events_folder/2018/02/13/20/my-prefix-3-2018-02-13-20-18-28-112ab3f0-5794-4f77-9a84-83efafeecabc
my_events_folder/2018/02/13/20/my-prefix-2-2018-02-13-20-12-00-7f2efb62-827b-46a6-83c4-b4c52dd87d60
...
AWS Glue just finds these files and classifies them automatically. Hope this helps.
AWS Glue should be able to process all the files in a folder irrespective of the name in a single job. If you don’t want the old file to be processed again move it using boto3 api for s3 to another location after each run.
If you have two different TYPES of files (with different internal formats), they must be in separate folder hierarchies. There is no way to tell a crawler to only look for redfile*.csv and ignore bluefile%.csv. Instead user separate hierarchies like:
s3://my-bucket/redfiles/
redfile01072018.csv
redfile01142018.csv
...
s3://my-bucket/bluefiles/
bluefile01072018.csv
bluefile01142018.csv
...
Setup two crawlers, one crawling s3://my-bucket/redfiles/ and the other crawling s3://my-bucket/bluefiles/