Redundant Array of Independent Clouds: Share To Cloud Mapping
This document describes a proposed design for the mapping of LAFS shares to objects in a cloud storage service. It also analyzes the costs for each of the functional requirements, including network, disk, storage and API usage costs.
- LAFS share
A Tahoe-LAFS share representing part of a file after encryption and erasure encoding.
- LAFS shareset
The set of shares stored by a LAFS storage server for a given storage index. The shares within a shareset are numbered by a small integer.
- Cloud storage service
- Cloud storage interface
A protocol interface supported by a cloud storage service, such as the S3 interface ⁶, the OpenStack Object Storage interface ⁷, the Google Cloud Storage interface ⁸, or the Azure interface ⁹. There may be multiple services implementing a given cloud storage interface. In this design, only REST-based APIs ¹⁰ over HTTP will be used as interfaces.
- Store object
A file-like abstraction provided by a cloud storage service, storing a sequence of bytes. Store objects are mutable in the sense that the contents and metadata of the store object with a given name in a given backend store can be replaced. Store objects are called “blobs” in the Azure interface, and “objects” in the other interfaces.
- Cloud backend store
A container for store objects provided by a cloud service. Cloud backend stores are called “buckets” in the S3 and Google Cloud Storage interfaces, and “containers” in the Azure and OpenStack Storage interfaces.
Upload: a LAFS share can be uploaded to an appropriately configured Tahoe-LAFS storage server and the data is stored to the cloud storage service.
Scalable shares: there is no hard limit on the size of LAFS share that can be uploaded.
If the cloud storage interface offers scalable files, then this could be implemented by using that feature of the specific cloud storage interface. Alternately, it could be implemented by mapping from the LAFS abstraction of an unlimited-size immutable share to a set of size-limited store objects.
Streaming upload: the size of the LAFS share that is uploaded can exceed the amount of RAM and even the amount of direct attached storage on the storage server. I.e., the storage server is required to stream the data directly to the ultimate cloud storage service while processing it, instead of to buffer the data until the client is finished uploading and then transfer the data to the cloud storage service.
Download: a LAFS share can be downloaded from an appropriately configured Tahoe-LAFS storage server, and the data is loaded from the cloud storage service.
Streaming download: the size of the LAFS share that is downloaded can exceed the amount of RAM and even the amount of direct attached storage on the storage server. I.e. the storage server is required to stream the data directly to the client while processing it, instead of to buffer the data until the cloud storage service is finished serving and then transfer the data to the client.
Modify: a LAFS share can have part of its contents modified.
If the cloud storage interface offers scalable mutable files, then this could be implemented by using that feature of the specific cloud storage interface. Alternately, it could be implemented by mapping from the LAFS abstraction of an unlimited-size mutable share to a set of size-limited store objects.
Efficient modify: the size of the LAFS share being modified can exceed the amount of RAM and even the amount of direct attached storage on the storage server. I.e. the storage server is required to download, patch, and upload only the segment(s) of the share that are being modified, instead of to download, patch, and upload the entire share.
Tracking leases: The Tahoe-LAFS storage server is required to track when each share has its lease renewed so that unused shares (shares whose lease has not been renewed within a time limit, e.g. 30 days) can be garbage collected. This does not necessarily require code specific to each cloud storage interface, because the lease tracking can be performed in the storage server’s generic component rather than in the component supporting each interface.
This section describes the mapping between LAFS shares and store objects.
A LAFS share will be split into one or more “chunks” that are each stored in a store object. A LAFS share of size C bytes will be stored as ceiling(C / chunksize) chunks. The last chunk has a size between 1 and chunksize bytes inclusive. (It is not possible for C to be zero, because valid shares always have a header, so, there is at least one chunk for each share.)
For an existing share, the chunk size is determined by the size of the first chunk. For a new share, it is a parameter that may depend on the storage interface. It is an error for any chunk to be larger than the first chunk, or for any chunk other than the last to be smaller than the first chunk. If a mutable share with total size less than the default chunk size for the storage interface is being modified, the new contents are split using the default chunk size.
Rationale: this design allows the chunksize parameter to be changed for new shares written via a particular storage interface, without breaking compatibility with existing stored shares. All cloud storage interfaces return the sizes of store objects with requests to list objects, and so the size of the first chunk can be determined without an additional request.
The name of the store object for chunk i > 0 of a LAFS share with storage index STORAGEINDEX and share number SHNUM, will be
where ST is the first two characters of STORAGEINDEX. When i is 0, the .0 is omitted.
Rationale: this layout maintains compatibility with data stored by the prototype S3 backend, for which Least Authority Enterprises has existing customers. This prototype always used a single store object to store each share, with name
By using the same prefix “shares/ST/STORAGEINDEX/” for old and new layouts, the storage server can obtain a list of store objects associated with a given shareset without having to know the layout in advance, and without having to make multiple API requests. This also simplifies sharing of test code between the disk and cloud backends.
Mutable and immutable shares will be “chunked” in the same way.
Rationale for Chunking
Limiting the amount of data received or sent in a single request has the following advantages:
It is unnecessary to write separate code to take advantage of the “large object” features of each cloud storage interface, which differ significantly in their design.
Data needed for each PUT request can be discarded after it completes. If a PUT request fails, it can be retried while only holding the data for that request in memory.
In this section we analyze the costs of the proposed design in terms of network, disk, memory, cloud storage, and API usage.
Network usage—bandwidth and number-of-round-trips
When a Tahoe-LAFS storage client allocates a new share on a storage server, the backend will request a list of the existing store objects with the appropriate prefix. This takes one HTTP request in the common case, but may take more for the S3 interface, which has a limit of 1000 objects returned in a single “GET Bucket” request.
If the share is to be read, the client will make a number of calls each specifying the offset and length of the required span of bytes. On the first request that overlaps a given chunk of the share, the server will make an HTTP GET request for that store object. The server may also speculatively make GET requests for store objects that are likely to be needed soon (which can be predicted since reads are normally sequential), in order to reduce latency.
Each read will be satisfied as soon as the corresponding data is available, without waiting for the rest of the chunk, in order to minimize read latency.
All four cloud storage interfaces support GET requests using the Range HTTP header. This could be used to optimize reads where the Tahoe-LAFS storage client requires only part of a share.
If the share is to be written, the server will make an HTTP PUT request for each chunk that has been completed. Tahoe-LAFS clients only write immutable shares sequentially, and so we can rely on that property to simplify the implementation.
When modifying shares of an existing mutable file, the storage server will be able to make PUT requests only for chunks that have changed. (Current Tahoe-LAFS v1.9 clients will not take advantage of this ability, but future versions will probably do so for MDMF files.)
In some cases, it may be necessary to retry a request (see the Structure of Implementation section below). In the case of a PUT request, at the point at which a retry is needed, the new chunk contents to be stored will still be in memory and so this is not problematic.
In the absence of retries, the maximum number of GET requests that will be made when downloading a file, or the maximum number of PUT requests when uploading or modifying a file, will be equal to the number of chunks in the file.
If the new mutable share content has fewer chunks than the old content, then the remaining store objects for old chunks must be deleted (using one HTTP request each). When reading a share, the backend must tolerate the case where these store objects have not been deleted successfully.
The last write to a share will be reported as successful only when all corresponding HTTP PUTs and DELETEs have completed successfully.
Disk usage (local to the storage server)
It is never necessary for the storage server to write the content of share chunks to local disk, either when they are read or when they are written. Each chunk is held only in memory.
A proposed change to the Tahoe-LAFS storage server implementation uses a sqlite database to store metadata about shares. In that case the same database would be used for the cloud backend. This would enable lease tracking to be implemented in the same way for disk and cloud backends.
The use of chunking simplifies bounding the memory usage of the storage server when handling files that may be larger than memory. However, this depends on limiting the number of chunks that are simultaneously held in memory. Multiple chunks can be held in memory either because of pipelining of requests for a single share, or because multiple shares are being read or written (possibly by multiple clients).
For immutable shares, the Tahoe-LAFS storage protocol requires the client to specify in advance the maximum amount of data it will write. Also, a cooperative client (including all existing released versions of the Tahoe-LAFS code) will limit the amount of data that is pipelined, currently to 50 KiB. Since the chunk size will be greater than that, it is possible to ensure that for each allocation, the maximum chunk data memory usage is the lesser of two chunks, and the allocation size. (There is some additional overhead but it is small compared to the chunk data.) If the maximum memory usage of a new allocation would exceed the memory available, the allocation can be delayed or possibly denied, so that the total memory usage is bounded.
It is not clear that the existing protocol allows allocations for mutable shares to be bounded in general; this may be addressed in a future protocol change.
The above discussion assumes that clients do not maliciously send large messages as a denial-of-service attack. Foolscap (the protocol layer underlying the Tahoe-LAFS storage protocol) does not attempt to resist denial of service.
The storage requirements, including not-yet-collected garbage shares, are the same as for the Tahoe-LAFS disk backend. That is, the total size of cloud objects stored is equal to the total size of shares that the disk backend would store.
Erasure coding causes the size of shares for each file to be a factor shares.total / shares.needed times the file size, plus overhead that is logarithmic in the file size ¹¹.
Cloud storage backends typically charge a small fee per API request. The number of requests to the cloud storage service for various operations is discussed under “network usage” above.
Structure of Implementation
A generic “cloud backend”, based on the prototype S3 backend but with support for chunking as described above, will be written.
An instance of the cloud backend can be attached to one of several “cloud interface adapters”, one for each cloud storage interface. These adapters will operate only on chunks, and need not distinguish between mutable and immutable shares. They will be a relatively “thin” abstraction layer over the HTTP APIs of each cloud storage interface, similar to the S3Bucket abstraction in the prototype.
For some cloud storage services it may be necessary to transparently retry requests in order to recover from transient failures. (Although the erasure coding may enable a file to be retrieved even when shares are not stored by or not readable from all cloud storage services used in a Tahoe-LAFS grid, it may be desirable to retry cloud storage service requests in order to improve overall reliability.) Support for this will be implemented in the generic cloud backend, and used whenever a cloud storage adaptor reports a transient failure. Our experience with the prototype suggests that it is necessary to retry on transient failures for Amazon’s S3 service.
There will also be a “mock” cloud interface adaptor, based on the prototype’s MockS3Bucket. This allows tests of the generic cloud backend to be run without a connection to a real cloud service. The mock adaptor will be able to simulate transient and non-transient failures.
This design worsens a known “write hole” issue in Tahoe-LAFS when updating the contents of mutable files. An update to a mutable file can require changing the contents of multiple chunks, and if the client fails or is disconnected during the operation the resulting state of the store objects for that share may be inconsistent—no longer containing all of the old version, but not yet containing all of the new version. A mutable share can be left in an inconsistent state even by the existing Tahoe-LAFS disk backend if it fails during a write, but that has a smaller chance of occurrence because the current client behavior leads to mutable shares being written to disk in a single system call.
The best fix for this issue probably requires changing the Tahoe-LAFS storage protocol, perhaps by extending it to use a two-phase or three-phase commit (ticket #1755).
² “Amazon S3” Amazon (2012)
³ “Rackspace Cloud Files” Rackspace (2012)
⁴ “Google Cloud Storage” Google (2012)
⁵ “Windows Azure Storage” Microsoft (2012)
⁶ “Amazon Simple Storage Service (Amazon S3) API Reference: REST API” Amazon (2012)
⁷ “OpenStack Object Storage” openstack.org (2012)
⁸ “Google Cloud Storage Reference Guide” Google (2012)
⁹ “Windows Azure Storage Services REST API Reference” Microsoft (2012)
¹⁰ “Representational state transfer” English Wikipedia (2012)
¹¹ “Performance costs for some common operations” tahoe-lafs.org (2012)