S3 FAQ mentions that "Amazon S3 buckets in all Regions provide read-after-write consistency for PUTS of new objects and eventual consistency for overwrite PUTS and DELETES." However, I don't know how long it takes to get eventual consistency. I tried to search for this but couldn't find an answer in S3 documentation.
Situation:
We have a website consists of 7 steps. When user clicks on save in each step, we want to save a json document (contains information of all 7 steps) to Amazon S3. Currently we plan to:
Create a single S3 bucket to store all json documents.
When user saves step 1 we create a new item in S3.
When user saves step 2-7 we override the existing item.
After user saves a step and refresh the page, he should be able to see the information he just saved. i.e. We want to make sure that we always read after write.
The full json document (all 7 steps completed) is around 20 KB.
After users clicked on save button we can freeze the page for some time and they cannot make other changes until save is finished.
Question:
How long does it take for AWS S3 to save and load an item? (We can freeze our website when document is being saved to S3)
Is there a function to calculate save/load time based on item size?
Is the save/load time gonna be different if I choose another S3 region? If so which is the best region for Seattle?
I wanted to add to #error2007s answers.
How long does it take for AWS S3 to save and load an item? (We can freeze our website when document is being saved to S3)
It's not only that you will not find the exact time anywhere - there's actually no such thing exact time. That's just what "eventual consistency" is all about: consistency will be achieved eventually. You can't know when.
If somebody gave you an upper bound for how long a system would take to achieve consistency, then you wouldn't call it "eventually consistent" anymore. It would be "consistent within X amount of time".
The problem now becomes, "How do I deal with eventual consistency?" (instead of trying to "beat it")
To really find the answer to that question, you need to first understand what kind of consistency you truly need, and how exactly the eventual consistency of S3 could affect your workflow.
Based on your description, I understand that you would write a total of 7 times to S3, once for each step you have. For the first write, as you correctly cited the FAQs, you get strong consistency for any reads after that. For all the subsequent writes (which are really "replacing" the original object), you might observe eventual consistency - that is, if you try to read the overwritten object, you might get the most recent version, or you might get an older version. This is what is referred to as "eventual consistency" on S3 in this scenario.
A few alternatives for you to consider:
don't write to S3 on every single step; instead, keep the data for each step on the client side, and then only write 1 single object to S3 after the 7th step. This way, there's only 1 write, no "overwrites", so no "eventual consistency". This might or might not be possible for your specific scenario, you need to evaluate that.
alternatively, write to S3 objects with different names for each step. E.g., something like: after step 1, save that to bruno-preferences-step-1.json; then, after step 2, save the results to bruno-preferences-step-2.json; and so on, then save the final preferences file to bruno-preferences.json, or maybe even bruno-preferences-step-7.json, giving yourself the flexibility to add more steps in the future. Note that the idea here to avoid overwrites, which could cause eventual consistency issues. Using this approach, you only write new objects, you never overwrite them.
finally, you might want to consider Amazon DynamoDB. It's a NoSQL database, you can securely connect to it directly from the browser or from your server. It provides you with replication, automatic scaling, load distribution (just like S3). And you also have the option to tell DynamoDB that you want to perform strongly consistent reads (the default is eventually consistent reads; you have to change a parameter to get strongly consistent reads). DynamoDB is typically used for "small" records, 20kB is definitely within the range -- the maximum size of a record would be 400kB as of today. You might want to check this out: DynamoDB FAQs: What is the consistency model of Amazon DynamoDB?
How long does it take for AWS S3 to save and load an item? (We can freeze our website when document is being saved to S3)
You will not find the exact time anywhere. If you ask AWS they will give you approx timings. Your file is 20 KB so as per my experience from S3 usage the time will be more or less 60-90 Sec.
Is there a function to calculate save/load time based on item size?
No there is no any function using which you can calculate this.
Is the save/load time gonna be different if I choose another S3 region? If so which is the best region for Seattle?
For Seattle US West Oregon Will work with no problem.
You can also take a look at this experiment for comparison https://github.com/andrewgaul/are-we-consistent-yet
Related
I have the following requirements:
For every deleted record in RDS we need to archive it into somewhere cheaper on AWS.
Reduce storage cost
Not using Glacier
Context oriented (e.g. a file per table)
re-import is not a requirement
I'm not an experienced user with AWS, so I'm still a bit lost among the amount of options it has to offer and I'd like to know if you have more ideas to help me clear it out.
Initial thoughts:
The microservice that deletes the record, might send it to a broker (RabbitMQ for e.g.) and another microservice (let's call it archiver) will listen to it, write into a file, zip and send to S3. This approach has some technical challenges though: in order to make sense create big files, I need to wait the queue to growth a bit, wrap it into a stream and zip inside S3. The transaction control is very weak as well, since file writing and ack on messages are signal based i.e. I'll remove the messages from the broker just after the file is created.
Add a new column to the "archiveble" tables as "deleted (bool)" and run a separate job fetching only those records and saving them into S3. Discarded they don't want the new microservice with access to other's databases.
Following the same approach as in the first item, but instead of save into S3, save into a cheaper database. SimpleDB?
option 1, but instead of rabbitmq, write it to a kinesis firehose and direct that to an s3 location - it doesn't get much cheaper or easier than that.
I am trying to find possible orphans in an S3 bucket. What I mean is that we might delete something out of the DB, and for whatever reason, it doesn't get cleared from S3. This can be a bug in our system or something of that nature. I want to double check against our API that the object in S3 maps to something that exists - the naming convention let's us map things together like that.
Scraping an entire bucket every X days seems unscalable. I was thinking that for each object in the bucket, it can add itself to an SQS queue for the relevant checking to happen, every 30 days or so.
I've only found events around uploads and specific modifications over at https://docs.aws.amazon.com/AmazonS3/latest/dev/NotificationHowTo.html. Is there anything more generalized I can't find? Any creative solutions to this problem?
You should activate Amazon S3 Inventory, which can provide a regular CSV file (as often as daily) that contains a list of every object in the Amazon S3 bucket.
You could then trigger some code that compares the contents of the CSV file against the database to find 'orphan' objects.
I have a bucket in S3 (Infrequent access) containing 2 billion objects. It is too big to delete in the console or over the api without taking years.
I can create a lifecycle rule to expire and delete the objects but the calculator predicts this will cost me >$20,000. Is that correct? Is there a better way to delete a bucket?
I have a file effectively containing a list of all the objects in that bucket if that helps.
Update 2021:
An answer below from #MAP points out that there is now an "Empty" button. I haven't tested yet, but looks like the way to go (I'll accept that answer once tested):
If you have a list of all the objects available then you can certainly use Multi Delete Object action. Apparently this API is free. I would create AWS Step Functions state machine to loop through the file and delete 1000 objects at a time. 1000 appears to be the limit.
It will take around 2M step function transactions to delete all the objects in the bucket. As per the pricing for step function it will cost you around $50 + cost of Lambda invocations around $1 so total cost roughly $51.
Update
Using Lambda or Step Functions is probably not the most cost effective option because both ways you will need to read the file (that contains object keys) from some source such as S3. So I think running the script from local machine or any EC2 linux screen appears to be the best option.
In 2021, anyone who comes across this question may benefit to know that AWS console now provides an empty button.
Select the bucket and click on "empty" button and all objects versioned or not versioned would be emptied/deleted. Depending on the number of objects it can take minutes to days.
Expiration lifecycle rules are free. From the original feature announcement:
As with standard delete requests, Amazon S3 doesn’t charge you for using Object Expiration.
Delete operations are for free. You can create a lifecycle
Policy to automate a bulk delete.
I would start with a small number of objects first and check billing report to 100% confirm that the delete will not be charged, then go for the rest.
Building an index of S3 objects can be very useful to make them searchable quickly : the natural, most obvious way is to store additional data on the object meta-data and use a lambda to write in DynamoDB or RDS, as described here: https://aws.amazon.com/blogs/big-data/building-and-maintaining-an-amazon-s3-metadata-index-without-servers/
However, this strategy is limited by the amount of data one can store in the object metadata, which is 2KB, as described here: https://docs.aws.amazon.com/AmazonS3/latest/dev/UsingMetadata.html. Suppose you need to build a system where every time an object is uploaded on S3 you store need to add some information not contained in the file and the object name to a database and this data exceeds 2KB:you can't store it in the object metadata.
What are viable strategies to keep the bucket and the index updated?
Implement two chained API calls where each call is idempotent: if the second fails when the first succeed, one can retry until success. What happens if you perform a PUT of an identical object on S3, and you have versioning activated? Will S3 increase the version? In this case, implementing idempotency requires a single writer to be active at each time
Use some sort of workflow engine to keep track of this two-step behaviour, such as AWS Step. What are the gotchas with this solution?
I've inherited a project at work. Its essentially a niche content repository, and we use S3 to store the content. The project was severely outdated, and I'm in the process of a thorough update.
For some unknown and undocumented reason, the content is stored in an AWS S3 bucket with the pattern web_cl_000000$DB_ID$CONTENT_NAME So, one particular folder can be named web_cl_0000003458zyxwv. This makes no sense, and requires a bit of transformation logic to construct a URL to serve up the content!
I can write a Python script using the boto3 library to do an item-by-item rename, but would like to know if there's a faster way to do so. There are approximately 4M items in that bucket, which will take quite a long time.
That isn't possible, because the folders are an illusion derived from the strings between / delimiters in the object keys.
Amazon S3 has a flat structure with no hierarchy like you would see in a typical file system. However, for the sake of organizational simplicity, the Amazon S3 console supports the folder concept as a means of grouping objects. Amazon S3 does this by using key name prefixes for objects. (emphasis added)
http://docs.aws.amazon.com/AmazonS3/latest/UG/FolderOperations.html
The console contributes to the illusion by allowing you to "create" a folder, but all that actually does is create a 0-byte object with / as its last character, which the console will display as a folder whether there are other objects with that prefix or not, making it easier to upload objects manually with some organization.
But any tool or technique that allows renaming folders in S3 will in fact be making a copy of each object with the modified name, then deleting the old object, because S3 does not actually support rename or move, either -- objects in S3, including their key and metadata, are actually immutable. Any "change" is handled at the API level with a copy/overwrite or copy-then-delete.
Worth noting, S3 should be able to easily sustain 100 such requests per second, so with asynchronous requests or multi-threaded code, or even several processes each handling a shard of the keyspace, you should be able to do the whole thing in a few hours.
Note also that the less sorted (more random) the new keys are in the requests, the harder you can push S3 during a mass-write operation like this. Sending the requests so that the new keys are in lexical order will be the most likely scenario in which you might see 503 Slow Down errors... in which case, you just back off and retry... but if the new keys are not ordered, S3 can more easily accommodate a large number of requests.