The Proxy Server is responsible for tying together the rest of the Swift architecture. For each request, it will look up the location of the account, container, or object in the ring (see below) and route the request accordingly. The public API is also exposed through the Proxy Server.
A large number of failures are also handled in the Proxy Server. For example, if a server is unavailable for an object PUT, it will ask the ring for a handoff server and route there instead.
When objects are streamed to or from an object server, they are streamed directly through the proxy server to or from the user – the proxy server does not spool them.
A ring represents a mapping between the names of entities stored on disk and their physical location. There are separate rings for accounts, containers, and one object ring per storage policy. When other components need to perform any operation on an object, container, or account, they need to interact with the appropriate ring to determine its location in the cluster.
The Ring maintains this mapping using zones, devices, partitions, and replicas. Each partition in the ring is replicated, by default, 3 times across the cluster, and the locations for a partition are stored in the mapping maintained by the ring. The ring is also responsible for determining which devices are used for handoff in failure scenarios.
Data can be isolated with the concept of zones in the ring. Each replica of a partition is guaranteed to reside in a different zone. A zone could represent a drive, a server, a cabinet, a switch, or even a datacenter.
The partitions of the ring are equally divided among all the devices in the Swift installation. When partitions need to be moved around (for example if a device is added to the cluster), the ring ensures that a minimum number of partitions are moved at a time, and only one replica of a partition is moved at a time.
Weights can be used to balance the distribution of partitions on drives across the cluster. This can be useful, for example, when different sized drives are used in a cluster.
The ring is used by the Proxy server and several background processes (like replication).
Storage Policies provide a way for object storage providers to differentiate service levels, features and behaviors of a Swift deployment. Each Storage Policy configured in Swift is exposed to the client via an abstract name. Each device in the system is assigned to one or more Storage Policies. This is accomplished through the use of multiple object rings, where each Storage Policy has an independent object ring, which may include a subset of hardware implementing a particular differentiation.
For example, one might have the default policy with 3x replication, and create a second policy which, when applied to new containers only uses 2x replication. Another might add SSDs to a set of storage nodes and create a performance tier storage policy for certain containers to have their objects stored there.
This mapping is then exposed on a per-container basis, where each container can be assigned a specific storage policy when it is created, which remains in effect for the lifetime of the container. Applications require minimal awareness of storage policies to use them; once a container has been created with a specific policy, all objects stored in it will be done so in accordance with that policy.
The Storage Policies feature is implemented throughout the entire code base so it is an important concept in understanding Swift architecture.
The Object Server is a very simple blob storage server that can store, retrieve and delete objects stored on local devices. Objects are stored as binary files on the filesystem with metadata stored in the file’s extended attributes (xattrs). This requires that the underlying filesystem choice for object servers support xattrs on files. Some filesystems, like ext3, have xattrs turned off by default.
Each object is stored using a path derived from the object name’s hash and the operation’s timestamp. Last write always wins, and ensures that the latest object version will be served. A deletion is also treated as a version of the file (a 0 byte file ending with ”.ts”, which stands for tombstone). This ensures that deleted files are replicated correctly and older versions don’t magically reappear due to failure scenarios.
The Container Server’s primary job is to handle listings of objects. It doesn’t know where those object’s are, just what objects are in a specific container. The listings are stored as sqlite database files, and replicated across the cluster similar to how objects are. Statistics are also tracked that include the total number of objects, and total storage usage for that container.
The Account Server is very similar to the Container Server, excepting that it is responsible for listings of containers rather than objects.
Replication is designed to keep the system in a consistent state in the face of temporary error conditions like network outages or drive failures.
The replication processes compare local data with each remote copy to ensure they all contain the latest version. Object replication uses a hash list to quickly compare subsections of each partition, and container and account replication use a combination of hashes and shared high water marks.
Replication updates are push based. For object replication, updating is just a matter of rsyncing files to the peer. Account and container replication push missing records over HTTP or rsync whole database files.
The replicator also ensures that data is removed from the system. When an item (object, container, or account) is deleted, a tombstone is set as the latest version of the item. The replicator will see the tombstone and ensure that the item is removed from the entire system.
There are times when container or account data can not be immediately updated. This usually occurs during failure scenarios or periods of high load. If an update fails, the update is queued locally on the filesystem, and the updater will process the failed updates. This is where an eventual consistency window will most likely come in to play. For example, suppose a container server is under load and a new object is put in to the system. The object will be immediately available for reads as soon as the proxy server responds to the client with success. However, the container server did not update the object listing, and so the update would be queued for a later update. Container listings, therefore, may not immediately contain the object.
In practice, the consistency window is only as large as the frequency at which the updater runs and may not even be noticed as the proxy server will route listing requests to the first container server which responds. The server under load may not be the one that serves subsequent listing requests – one of the other two replicas may handle the listing.
Auditors crawl the local server checking the integrity of the objects, containers, and accounts. If corruption is found (in the case of bit rot, for example), the file is quarantined, and replication will replace the bad file from another replica. If other errors are found they are logged (for example, an object’s listing can’t be found on any container server it should be).