I am wondering if there is size limit for S3 bucket? or I can store object limitless?
I need this information to assure I need to write a cleaner tool or not.
You can store unlimited objects in your S3 bucket. However, there are limits on individual objects stored -
An object can be 0 bytes to 5TB.
The largest object that can be uploaded in a single PUT is 5 gigabytes
For objects larger than 100 megabytes, customers should consider using the Multipart Upload capability.
If you have too many objects then consider randomizing some prefix to your object names.
When your workload is a mix of request types, introduce some randomness to key names by adding a hash string as a prefix to the key name. By introducing randomness to your key names the I/O load will be distributed across multiple index partitions. For example, you can compute an MD5 hash of the character sequence that you plan to assign as the key and add 3 or 4 characters from the hash as a prefix to the key name.
https://aws.amazon.com/premiumsupport/knowledge-center/s3-bucket-performance-improve/
Yes, you can store object limitless.
BTW, if your consideration is performance, see
http://aws.typepad.com/aws/2012/03/amazon-s3-performance-tips-tricks-seattle-hiring-event.html
Related
Suppose I upload a zero length object to S3 and then walk away.
Will I be charged (monthly) for storing it, besides the initial put? Or is the metadata for that object "free".
You will likely be changed for one PUT/POST request which is about $0.000005
The Amazon documentation is not clear on this, but there are a few things that lead me to assume that zero-length objects accrue storage costs.
The S3 Billing FAQs mention that metadata is included in storage costs.
The volume of storage billed in a month is based on the average storage used throughout the month. This includes all object data and metadata stored in buckets that you created under your AWS account.
https://aws.amazon.com/s3/faqs/#Billing
Metadata includes mandatory system-defined fields, such as ETag and the LastModified timestamp.
There are two kinds of metadata in Amazon S3: system-defined metadata and user-defined metadata.
https://docs.aws.amazon.com/AmazonS3/latest/userguide/UsingMetadata.html
Even delete markers, which are comparable to zero-length objects, are charged for the storage of their key name.
Delete markers accrue a nominal charge for storage in Amazon S3. The storage size of a delete marker is equal to the size of the key name of the delete marker. A key name is a sequence of Unicode characters. The UTF-8 encoding adds 1–4 bytes of storage to your bucket for each character in the name.
https://docs.aws.amazon.com/AmazonS3/latest/userguide/DeleteMarker.html
Problem set : I am looking to store 6 billion SHA256 hashes. I want to check if a hash exist and if so, an action will be performed. When it comes to storing the SHA256 hash (64 byte string) just to check the if the key exist, I've come across two functions to use
HSET/HEXIST and GETBIT/SETBIT
I want to make sure I take the least amount of memory, but also want to make sure lookups are quick.
The Use case will be "check if sha256 hash exist"
The problem,
I want to understand how to store this data as currently I have a 200% increase from text -> redis. I want to understand what would the best shard options using ziplist entries and ziplist value would be. How to split the hash to be effective so the ziplist is maximised.
I've tried setting the ziplist entries to 16 ^ 4 (65536) and the value to 60 based on splitting 4:60
Any help to help me understand options, and techniques to make this as small of a footprint but quick enough to run lookups.
Thanks
A bit late to the party but you can just use plain Redis keys for this:
# Store a given SHA256 hash
> SET 9f86d081884c7d659a2feaa0c55ad015a3bf4f1b2b0b822cd15d6c15b0f00a08 ""
OK
# Check whether a specific hash exists
> EXISTS 2c26b46b68ffc68ff99b453c1d30413413422d706483bfa0f98a5e886266e7ae
0
Where both SET and EXISTS have a time complexity of O(1) for single keys.
As Redis can handle a maximum of 2^32 keys, you should split your dataset into two or more Redis servers / clusters, also depending on the number of nodes and the total combined memory available to your servers / clusters.
I would also suggest to use the binary sequence of your hashes instead of their textual representation - as that would allow to save ~50% of memory while storing your keys in Redis.
I have a file with 13 million floats each of them have a associated index as integer. The original size of file is 80MB.
We want to pass multiple indexes to get float data. The only reason, I needed hashmap field and value as List does not support passing multiple indexes to get.
Stored them as hashmap in redis, with index being field and float as value. On checking memory usage it was about 970MB.
Storing 13 million as list is using 280MB.
Is there any optimization I can use.
Thanks in advance
running on elastic cache
You can do a real good optimization by creating buckets of index vs float values.
Hashes are very memory optimized internally.
So assume your data in original file looks like this:
index, float_value
2,3.44
5,6.55
6,7.33
8,34.55
And you have currently stored them one index to one float value in hash or a list.
You can do this optimization of bucketing the values:
Hash key would be index%1000, sub-key would be index, and value would be float value.
More details here as well :
At first, we decided to use Redis in the simplest way possible: for
each ID, the key would be the media ID, and the value would be the
user ID:
SET media:1155315 939 GET media:1155315
939 While prototyping this solution, however, we found that Redis needed about 70 MB to store 1,000,000 keys this way. Extrapolating to
the 300,000,000 we would eventually need, it was looking to be around
21GB worth of data — already bigger than the 17GB instance type on
Amazon EC2.
We asked the always-helpful Pieter Noordhuis, one of Redis’ core
developers, for input, and he suggested we use Redis hashes. Hashes in
Redis are dictionaries that are can be encoded in memory very
efficiently; the Redis setting ‘hash-zipmap-max-entries’ configures
the maximum number of entries a hash can have while still being
encoded efficiently. We found this setting was best around 1000; any
higher and the HSET commands would cause noticeable CPU activity. For
more details, you can check out the zipmap source file.
To take advantage of the hash type, we bucket all our Media IDs into
buckets of 1000 (we just take the ID, divide by 1000 and discard the
remainder). That determines which key we fall into; next, within the
hash that lives at that key, the Media ID is the lookup key within
the hash, and the user ID is the value. An example, given a Media ID
of 1155315, which means it falls into bucket 1155 (1155315 / 1000 =
1155):
HSET "mediabucket:1155" "1155315" "939" HGET "mediabucket:1155"
"1155315"
"939" The size difference was pretty striking; with our 1,000,000 key prototype (encoded into 1,000 hashes of 1,000 sub-keys each),
Redis only needs 16MB to store the information. Expanding to 300
million keys, the total is just under 5GB — which in fact, even fits
in the much cheaper m1.large instance type on Amazon, about 1/3 of the
cost of the larger instance we would have needed otherwise. Best of
all, lookups in hashes are still O(1), making them very quick.
If you’re interested in trying these combinations out, the script we
used to run these tests is available as a Gist on GitHub (we also
included Memcached in the script, for comparison — it took about 52MB
for the million keys)
I have total six type of keys, say a,b,..,f each having around a million subkeys, like a1,a2,...a99999(different in each bucket). What is the faster way to access?
Having separate keys by combining bucket name and key like: a_a1,b_b1 etc.
Use hash for 6 keys to have buckets and then have 1 million keys in each?
I search stack-overflow and couldn't find such comparison when I have few buckets with huge number of keys!
Edit1: Every key and value is string only at maximum 100 characters. I would access it using Jedis library of Java making transactions
Your question remind me this article. It doesn't contains performance benchmarks but seems like your second case (with buckets of keys) will have appropriate performance and small memory footprint.
Does anyone know if there is a limit to the number of objects I can put in an S3 bucket? can I put a million, 10 million etc.. all in a single bucket?
According to Amazon:
Write, read, and delete objects containing from 0 bytes to 5 terabytes of data each. The number of objects you can store is unlimited.
Source: http://aws.amazon.com/s3/details/ as of Sep 3, 2015.
It looks like the limit has changed. You can store 5TB for a single object.
The total volume of data and number of objects you can store are unlimited. Individual Amazon S3 objects can range in size from a minimum of 0 bytes to a maximum of 5 terabytes. The largest object that can be uploaded in a single PUT is 5 gigabytes. For objects larger than 100 megabytes, customers should consider using the Multipart Upload capability.
http://aws.amazon.com/s3/faqs/#How_much_data_can_I_store
There is no limit on objects per bucket.
There is a limit of 100 buckets per account (you need to request amazon if you need more).
There is no performance drop even if you store millions of objects in a
single bucket.
From docs,
There is no limit to the number of objects that can be stored in a
bucket and no difference in performance whether you use many buckets
or just a few. You can store all of your objects in a single bucket,
or you can organize them across several buckets.
as of Aug 2016
While you can store an unlimited number of files/objects in a single bucket, when you go to list a "directory" in a bucket, it will only give you the first 1000 files/objects in that bucket by default. To access all the files in a large "directory" like this, you need to make multiple calls to their API.
There are no limits to the number of objects you can store in your S3 bucket. AWS claims it to have unlimited storage. However, there are some limitations -
By default, customers can provision up to 100 buckets per AWS account. However, you can increase your Amazon S3 bucket limit by visiting AWS Service Limits.
An object can be 0 bytes to 5TB.
The largest object that can be uploaded in a single PUT is 5 gigabytes
For objects larger than 100 megabytes, customers should consider using the Multipart Upload capability.
That being said if you really have a lot of objects to be stored in S3 bucket consider randomizing your object name prefix to improve performance.
When your workload is a mix of request types, introduce some randomness to key names by adding a hash string as a prefix to the key name. By introducing randomness to your key names the I/O load will be distributed across multiple index partitions. For example, you can compute an MD5 hash of the character sequence that you plan to assign as the key and add 3 or 4 characters from the hash as a prefix to the key name.
More details - https://aws.amazon.com/premiumsupport/knowledge-center/s3-bucket-performance-improve/
-- As of June 2018
"You can store as many objects as you want within a bucket, and write,
read, and delete objects in your bucket. Objects can be up to 5
terabytes in size."
from http://aws.amazon.com/s3/details/ (as of Mar 4th 2015)
#Acyra- performance of object delivery from a single bucket would depend greatly on the names of the objects in it.
If the file names were distanced by random characters then their physical locations would be spread further on the AWS hardware, but if you named everything 'common-x.jpg', 'common-y.jpg' then those objects will be stored together.
This may slow delivery of the files if you request them simultaneously but not by enough to worry you, the greater risk is from data-loss or an outage, since these objects are stored together they will be lost or unavailable together.