I have flat files in which I store data and retrieve it instead of storing to database. This is temporary and may last for couple of months.I was wondering If I should be using EBS or S3. EBS is mainly used for I/O , S3 for content delivery , but S3 is on use you go model and EBS is you have to pay for the volume purchased ?
Pls guide, which one is better ?
S3 sounds like it's more appropriate for your use case.
S3 is object storage. Think of it as an Amazon-run file server. (Objects are not exactly equal to files, but it's close enough here.) You tell S3 to put a file, it'll store it. You tell S3 to get a file, it'll get return it. You tell S3 to delete it, it's gone. This is easy to work with and very scalable.
EBS is block storage. Think of it as an Amazon-run external hard drive. You can plug an EBS volume into an EC2 virtual machine, or you access it over the Internet via AWS Storage Gateway. Like an external hard drive, you can only plug it into one computer at a time. The size is set up front, and while there are ways to grow and shrink it, you're paying for all the bits all the time. It's also much more complex than S3, since it has to provide strong consistency guarantees for the entire volume, instead of just on a file-by-file basis.
To build on the good answer from willglynn. If you are interacting with the data regularly, or need more file-system-like access you might consider EBS more strongly.
If the amount of data is relatively small and you read and write to the data store regularly, you might consider something like elasticache for in-memory storage which would likely be superior performance-wise then using s3 or EBS.
Similarly, you might look at DynamoDb for document type storage, especially if you need to be able to search/filter across your data objects.
Point 1) You can use both S3 and EBS for this option. If you want reduced latency and file sizes are bigger then EBS is better option.
Point 2) If you want lower costs, then S3 is a better option.
From what you describe, S3 will be the most cost-effective and likely easiest solution.
Pros to S3:
1. You can access the data from anywhere. You don't need to spin up an EC2 instance.
2. Crazy data durability numbers.
3. Nice versioning story around buckets.
4. Cheaper than EBS
Pros to EBS
1. Handy to have the data on a file system in EC2. That let you do normal processing with the Unix pipeline.
2. Random Access patterns work as you would expect.
3. It's a drive. Everyone knows how to deal with files on drives.
If you want to get away from a flat file, DynamoDB provides a nice set of interfaces for putting lots and lots of rows into a table, then running operations against those rows.
Related
I will be doing a crawl of several million URLs from EC2 over a few months and I am thinking about where I ought to store this data. My eventual goal is to analyze it, but the analysis might not be immediate (even though I would like to crawl it now for other reasons) and I may want to eventually transfer a copy of the data out for storage on a local device I have. I estimate the data will be around 5TB.
My question: I am considering using Glacier for this, with the idea that I will run a multithreaded crawler that stores the crawled pages locally (on EB) and then use a separate thread that combines, compresses, and shuttles that data to Glacier. I know transfer speeds on Glacier are not necessarily good, but since there is no online element of this process, it would seem feasible (esp since I could always increase the size of my local EBS volume in case I'm crawling faster than I can store to Glacier).
Is there a flaw in my approach or can anyone suggest a more cost-effective, reliable way to do this?
Thanks!
Redshift seems more relevant than Glacier. Glacier is all about freeze / thaw and you'll have to move the data prior to doing any analysis.
Redshift is more about adding the data into a large, inexpensive, data warehouse and running queries over it.
Another option is to store the data in EBS and leave it there. When you're done with your crawling take a Snapshot to push the volume into S3 and decomission the volume and EC2 instance. Then when you're ready to do the analysis just create a volume from the snapshot.
The upside of this approach is that it's all file access (no formal data store) which may be easier for you.
Personally, I would probably push the data into Redshift. :-)
--
Chris
If your analysis will not be immediate then you can adopt one of the following 2 approaches
Approach 1) Amazon EC2 crawler -> store in EBS disks - Move them frequently to Amazon S3-> archive regularly to glacier. You can store your last X days data in Amazon S3 and use it for adhoc processing as well.
Approach 2) Amazon EC2 crawler -> store in EBS disks - Move them frequently to Amazon Glacier. Retrieve when needed and do the processing on EMR or other processing tools
If you need frequent analysis:
Approach 3) Amazon EC2 crawler -> store in EBS disks - Move them frequently to Amazon S3-> Analysis through EMR or other tools and store the processed results in S3/DB/MPP and move the raw files to glacier
Approach 4) if your data is structured, then Amazon EC2 crawler -> store in EBS disks and move them to Amazon RedShift and move the raw files to glacier
Additional tips:
If you can retrieve the data again(from source)then you can use ephemeral disks for your crawlers instead of EBS
Amazon has introduced Data pipeline service, check whether it fits your needs on data movement.
Does anyone know of any real-world analysis on data loss using these two AWS s3 storage options? I know from the AWS docs (via Quora) that one is 99.9999999% guarenteed and the other is only 99.99% gaurenteed but I'm looking for data from a non-AWS source.
Anecdotes or something more thorough would both be great. I apologize if this isn't the right SE site for this question. Feel free to suggest a place to migrate it.
I guess it depends on the data you're storing if you really need 99.999999999% level of durability …
If you keep copies of your data locally and are just using S3 as a convenient place to store data that is actively being accessed by services within the AWS infrastructure, RRS might be the right choice for you :)
In my case, I keep fresh files on the normal durability level till I created a local backup and then move them to RRS, which saves you quite a bit a money.
I am migrating my Java,Tomcat, Mysql server to AWS EC2.
I have already attached EBS volume for storing MySql data. In my web application people may upload images. So I should persist them. There are 2 alternatives in my mind:
Save uploaded images to EBS volume.
Use the S3 service.
The followings are my notes, please be skeptic about them, as my expertise is not on servers, but software development.
EBS plus: S3 storage is more expensive. (0.15 $/Gb > 0.1$/Gb)
S3 plus: Serving statics from EBS may influence my web server's performance negatively. Is this true? Does Serving images affect server performance notably? For S3 my server will not be responsible for serving statics.
S3 plus: Serving statics from EBS may result I/O cost, probably it will be minor.
EBS plus: People say EBS is faster.
S3 plus: People say S3 is more safe for persistence.
EBS plus: No need to learn API, it is straight forward to save the images to EBS volume.
Namely I can not decide, will be happy if you guide.
Thanks
The price comparison is not quite right:
S3 charges are $0.14 per GB USED, whereas EBS charges are $0.10 per GB PROVISIONED (the size of your EBS volume), whether you use it or not. As a result, S3 may or may not be cheaper than EBS.
I'm currently using S3 for a project and it's working extremely well.
EBS means you need to manage a volume + machines to attach it to. You need to add space as it's filling up and perform backups (not saying you shouldn't back up your S3 data, just that it's not as critical).
It also makes it harder to scale: when you want to add additional machines, you either need to pull off the images to a separate machine or clone the images across all. This also means you're adding a bottleneck: you'll have to manage your own upload process that will either upload to all machines or have a single machine managing it.
I recommend S3: it's set and forget. Any number of machines can be performing uploads in parallel and you don't really need to notify other machines about the upload.
In addition, you can use Amazon Cloudfront as a cheap CDN in front of the images instead of directly downloading from S3.
I have architected solutions on AWS for Stock photography sites which stores millions of images spanning TB's of data, I would like to share some of the best practice in AWS for your requirement:
P1) Store the Original Image file in S3 Standard option
P2) Store the reproducible images like thumbs etc in the S3 Reduced Redundancy option (RRS) to save costs
P3) Meta data about images including the S3 URL can be stored in Amazon RDS or Amazon DynamoDB depending upon the query complexity. Query the entries from Amazon RDS. If your query is complex it is also common practice to Store the meta data in Amazon CloudSearch or Apache Solr.
P4) Deliver your thumbs to users with low latency using Amazon CloudFront.
P5) Queue your image conversion either thru SQS or RabbitMQ on Amazon EC2
P6) If you are planning to use EBS, then they are not scalable with your EC2. So ideally you can use GlusterFS as your common storage pool for all your images. Multiple Amazon EC2 in Auto Scaled mode can still connect to it and access/write images.
You already outlined the advantages and disadvantages of both.
If you are planning to store terabytes of images, with storage requirements increasing day after day, S3 will probably be your best bet as it is built especially for these kinds of situations. You get unlimited storage space, without having to worry about sharding your data over many EBS volumes.
The recurrent cost of S3 is that it comes 50% more expensive than EBS. You will also have to learn the API and implement it in your application, but that is a one-off expense which I think you should be able to absorb very quickly.
Do you expect the images to last indefinitely?
The Amazon EBS FAQ is pretty clear; the annual failure rate is not "essentially zero"; they quote 0.1% to 0.5%. It's better than the disk under your desk, but it would need some kind of backup.
I have a large collection of data chunks sized 1kB (in the order of several hundred million), and need a way to store and query these data chunks. The data chunks are added, but never deleted or updated. Our service is deployed on the S3, EC2 platform.
I know Amazon SimpleDB exists, but I want a solution that is platform agnostic (in case we need to move out of AWS for example).
So my question is, what are the pro's and con's of these two options for storing and retrieving data chunks. How would the performance compare?
Store the data chunks as files on S3 and GET them when needed
Store the data chunks on a MySQL Server cluster
Would there be that much of a performance difference?
I tried using S3 as a sort of "database" using tiny XML files to hold my structured data objects, and relying on the S3 "keys" to look up these objects.
The performance was unacceptable, even from EC2 - the latency to S3 is just too high.
Running MySQL on an EBS device will be an order of magnitude faster, even with so many records.
Do you need to provide access to these data chunks directly to the users of your application? If not, then S3 and HTTP GET requests are an overkill. Having also in mind that S3 is a secured service, the overhead for every GET request (for just 1KB of data) will be considerably large.
MySQL server cluster would be a better idea, but to run in EC2 you need to employ Elastic Block Storage. Finally, do not rule out SimpleDB. It is perhaps the best solution for your problem. Design your system carefully and you would be able to easily migrate in other database systems (distributed or relational) in the future.
What is the easiest way to duplicate an entire Amazon S3 bucket to a bucket in a different account?
Ideally, we'd like to duplicate the bucket nightly to a different account in Amazon's European data center for backup purposes.
One thing to consider is that you might want to have whatever is doing this running in an Amazon EC2 VM. If you have your backup running outside of Amazon's cloud then you pay for the data transfer both ways. If you run in an EC2 VM, you pay no bandwidth fees (although I'm not sure if this is true when going between the North American and European stores) - only for the wall time that the EC2 instance is running (and whatever it costs to store the EC2 VM, which should be minimal I think).
Cool, I may look into writing a script to host on Ec2. The main purpose of the backup is to guard against human error on our side -- if a user accidentally deletes a bucket or something like that.
If you're worried about deletion, you should probably look at S3's new Versioning feature.
I suspect there is no "automatic" way to do this. You'll just have to write a simple app that moves the files over. Depending on how you track the files in S3 you could move just the "changes" as well.
On a related note, I'm pretty sure Amazon does a darn good job backup up the data so I don't think you necessarily need to worry about data loss, unless your back up for archival purposes, or you want to safeguard against accidentally deleting files.
You can make an application or service that responsible to create two instances of AmazonS3Client one for the source and the other for the destination, then the source AmazonS3Client start looping in the source bucket and streaming objects in, and the destination AmazonS3Client streaming them out to the destination bucket.
Note: this doesn't work for cross-account syncing, but this works for cross-region on the same account.
For simply copying everything from one bucket to another, you can use the AWS CLI (https://aws.amazon.com/premiumsupport/knowledge-center/move-objects-s3-bucket/): aws s3 sync s3://SOURCE_BUCKET_NAME s3://NEW_BUCKET_NAME
In your case, you'll need the --source-region flag: https://docs.aws.amazon.com/cli/latest/reference/s3/sync.html
If you are moving an enormous amount of data, you can optimize how quickly it happens by finding ways to split the transfers into different groups: https://aws.amazon.com/premiumsupport/knowledge-center/s3-large-transfer-between-buckets/
There are a variety of ways to run this nightly. One is example is the AWS instance-schedule (personally unverified) https://docs.aws.amazon.com/solutions/latest/instance-scheduler/appendix-a.html