Anatomy of a distributed data structure
Although each distributed data structure (DDS) has its own unique functionality, they all share some broad traits. Understanding these traits is the first step to understanding how DDSes work. They are:
- Local representation
- Op vocabulary
- Data serialization format (op)
- Data serialization format (summary operations)
- Reaction to remote changes
- Conflict resolution strategies
As the in-memory representation is modified on one client, we need to notify other clients of the updates. Most DDSes will have multiple operations that can be performed, so we’ll need to differentiate the types of notifications (ops) we’re sending. For example, a SharedMap might be modified through “set”, “delete”, or “clear”.
These ops will probably correspond loosely with specific APIs on the DDS that cause data modification with the expectation that there is a 1:1:1 correspondence between that API call on client A, the op that is sent, and the corresponding update being applied on client B. However, this correspondence is not mandatory.
Data serialization format (op)
Frequently, ops will need to carry a data payload. For example, when performing a “set” on a SharedMap, the new key:value pair needs to be communicated to other clients. As a result, DDSes will have some serialization format for op data payloads that can be reconstituted on the receiving end. This is why SharedMap requires its keys to be strings and values to be serializable - non-serializable keys or values can’t be transmitted to other clients.
Data serialization format (summary operations)
Although the state of a DDS can be reconstructed by playing back every op that has ever been applied to it, this becomes inefficient as the number of ops grows. Instead, DDSes should be able to serialize their entire contents into a format that clients can use to reconstruct the DDS without processing the entire op history. There may be some overlap with the serialization format used in ops, but it isn’t strictly necessary. For instance, the SharedMap uses the same serialization format for key/value pairs in its summary as it does in its set ops, but the Ink DDS serializes individual coordinate updates in its ops while serializing entire ink strokes in its summary.
Reaction to remote changes
As compared to their non-distributed counterparts, DDSes can change state without the developer’s awareness as remote ops are received. A standard JS Map will never change values without the local client calling a method on it, but a SharedMap will, as remote clients modify data. To make the local client aware of the update, DDSes must expose a means for the local client to observe and respond to these changes. This is typically done through eventing, like the “valueChanged” event on SharedMap.
Conflict resolution strategies
Data structures must be aware that multiple clients can act on the structure remotely, and the propagation of those changes take time. It’s possible then for a client to make a change to a data structure while unaware of its most-recent state. The data structure must incorporate strategies for handling these scenarios such that any two clients which have received the same set of ops will agree on the state. This property is referred to as “eventual consistency” or “convergence”. These strategies may be varied depending on the specific operation even within a single DDS. Some (non-exhaustive) examples of valid strategies:
Some data structures may not need to worry about conflict because their nature makes it impossible. For instance, the Counter DDS increment operations can be applied in any order, since end result of the addition will be the same. Characteristics of data structures that can take this approach:
- The data structure somehow ensures no data can be acted upon simultaneously by multiple users (purely additive, designated owner, etc.)
- The order in which actions are taken is either guaranteed (single actor, locking, etc.) or is irrelevant to the scenario (incrementing a counter, etc.)
If it’s possible to cause conflicts in the data, then a last-wins strategy may be appropriate. This strategy is used by SharedMap, for example, in the case that multiple clients attempt to set the same key. In this case, clients need to be aware that their locally applied operations may actually be chronologically before or after unprocessed remote operations. As remote updates come in, each client needs to update the value to reflect the last (chronologically) set operation.
Operational Transform and Intention Preservation
More-advanced DDSes require a more-sophisticated conflict resolution strategy to meet user expectations. The general principle is referred to as Intention Preservation. For example, the text I insert at position 23 of a SharedString while a friend deletes at position 12 needs to be transformed to insert at the location that matches my intention (that is, remains in the same location relative to the surrounding text, not the numerical index).
Consensus and quorum
Some resolution strategies may not be satisfied with eventual consistency, and instead require stronger guarantees about the global state of the data. The consensus data structures achieve this by accepting a delay of a roundtrip to the server before applying any changes locally (thus allowing them to confirm their operation was applied on a known data state). The quorum offers an even stronger guarantee (with a correspondingly greater delay), that the changes will not be applied until all connected clients have accepted the modification. These delays generally aren’t acceptable for real-time interactivity, but can be useful for scenarios with more lenient performance demands.
- Strictly speaking, summarization isn’t a mandatory requirement of a DDS. If the ops are retained, the DDS can be reconstructed from those. However, in practice it is not practical to load from ops alone, as this will degrade load time over the lifetime of the DDS.
- The requirement of “eventual consistency” has some flexibility to it. Discrepancies between clients are allowed as
long as they don’t result in disagreements between clients on the observable state of the data. For example:
- SharedString can be represented differently across clients in internal in-memory representation depending on op order, but this discrepancy is invisible to the user of the SharedString DDS.
- SharedMap will raise a different number of valueChanged events across clients when simultaneous sets occur. the client that set last will get a single valueChanged event, while earlier setters will get an additional event for each set after their own.