# Implementing a Garbage-Collected Graph CRDT (Part 1 of 2)

by Austen Barker · edited by Natasha Mittal and Lindsey Kuper

## Introduction

Conflict-Free Replicated Data Types (CRDTs) are a class of specialized data structures designed to be replicated across a distributed system while providing eventual consistency and high availability. CRDTs can be modified concurrently without coordination while providing a means to reconcile conflicts between replicas.

While CRDTs are a promising solution to the problem of building an eventually consistent distributed system, numerous practical implementation challenges remain. To deal with issues that arise when processing concurrent operations such as, for example, conflicting additions and removals of elements in a set, many CRDT specifications rely on the use of tombstones, which are markers to represent deleted items. These tombstones can accumulate over time and necessitate the use of a garbage collection system in order to avoid unacceptably costly growth of underlying data structures. These garbage collection systems can prove to be difficult to implement in practice. This series of two posts will chronicle my exploration of garbage collection in the context of CRDTs, and my attempts to implement a non-trivial garbage-collected CRDT based on the specifications in Shapiro et al.’s A Comprehensive Study of Convergent and Commutative Replicated Data Types.

## Background

Shapiro et al. present two styles of specifying CRDTs: state-based and operation-based. The difference comes from how the replicas propagate updates to one another. In the state-based model, replicas transmit their entire local state to other replicas, which then reconcile inconsistencies through a commutative, associative, and idempotent merge operation. As seen later in this blog post, a merge operation can often be represented by a union between two sets.

Operation-based, or op-based, CRDTs transmit their state by sending only the update operations performed to other replicas, so each operation is individually replayed on the recipient replica. In this model, the operations must be commutative but not necessarily idempotent. Op-based CRDTs are more particular about the messaging protocol between replicas, but require less bandwidth than state-based CRDTs, which must transmit the entire local state instead of small operations. State-based CRDTs, on the other hand, provide an associative merge operation.

Shapiro et al. give specifications for a variety of CRDT data structures, including sets, counters, registers, and graphs. This blog post is primarily concerned with the implementation of sets and graphs. The two simplest sets specified are the Grow-only Set (G-Set) and the so-called Two-Phase Set (2P-Set). The G-Set is a set of elements that grows monotonically, with no removal operation. 2P-Sets, on the other hand, support removing items from the set. In the case of a state-based 2P-Set, conflicts between add and remove operations during a merge necessitate some record of which elements have been removed from the set; an additional G-Set, sometimes called the tombstone set, maintains markers or tombstones denoting removed elements.

Shapiro et al. then use 2P-Sets and G-Sets to represent sets of vertices and edges in a directed graph. Their Montonic Directed Acyclic Graph (Monotonic DAG) CRDT specification is simply two G-Sets, one for the vertices and one for the edges. In this data structure, there is no operation for removing vertices, and its contents are monotonically increasing.

Finally, Shapiro et al. introduce the Add-Remove Partial Order (ARPO) graph CRDT as a solution to the mess that arises when one attempts to include vertex removals in their Monotonic DAG specification. They define the ARPO using a 2P-Set to represent vertices and a G-Set to represent edges.

## The need for garbage collection

An example of a situation in which garbage collection is useful is when an update to an ARPO is applied and considered stable; at that point, one can discard the set of removed vertices. For any CRDT that maintains tombstones, such as the state-based 2P-Set and the ARPO, the tombstones might pile up and cause unnecessary bloat. The difficulty with implementing garbage collection is that it will often require synchronization. Shapiro et al. present two challenges related to garbage collection: stability and commitment.

Stability refers to whether an update has been received by all replicas. The purpose of tombstones is to help resolve conflicts between concurrent operations by having a record of removed elements. Eventually, a tombstone is no longer required when all concurrent updates have been “delivered” and an update can be considered stable. The paper applies a modified form of Wuu and Bernstein’s stability algorithm, which requires each replica to maintain a set of all the other replicas and for there to be a mechanism to detect when a replica crashes. The algorithm uses vector clocks to determine concurrency of updates.

Commitment issues arise when one needs to perform an operation with a need for synchronization, such as removing tombstones from a 2P-Set or resetting all the replicas of an object to their initial values. Shapiro et al.’s conclusion is to require atomic agreement between all replicas concerning the application of the desired operation.

## ARPO building blocks

To implement the ARPO CRDT specification, I had to first implement both 2P-Sets and G-Sets. I implemented both as state-based CRDTs rather easily from Shapiro et al.’s specifications.

After some initial experiments with Python, I did the implementation in Go so I could easily run garbage collection on its own thread and core, and the garbage collection thread would not compete with the actual CRDT for CPU resources.

The first set CRDT I implemented was the G-Set, which is straightforward and is reused for almost every other set or graph CRDT that Shapiro et al. describe. The current G-Set implementation is as follows:

//map interfaces (key) to interfaces (value) in our set
type baseSet map[interface{}]interface{}

//all our Gset has to contain is a single set that grows monotonically
type Gset struct {
BaseSet baseSet
}

func NewGset() *Gset {
return &Gset{BaseSet: baseSet{}}
}

//Adds a key-value pair to the map. Contents can be left null if desired.
func (g *Gset) Add(element, contents interface{}){
g.BaseSet[element] = contents
}

//Returns the value when given a key.
func (g *Gset) Fetch(element interface{}) interface{}{
contents := g.BaseSet[element]
return contents
}

//Does an element exist?
func (g *Gset) Query(element interface{}) bool{
_, isThere := g.BaseSet[element]
return isThere
}

//A list of all elements
func (g *Gset) List()  ([]interface{}, error){
elements := make([]interface{}, 0, len(g.BaseSet))
for element := range g.BaseSet{
elements = append(elements, element)
}
return elements, nil
}

//merge two sets by performing a Union
func Merge(s, t *Gset) (*Gset, error){
newGset := NewGset()
for k, v := range s.BaseSet{
newGset.BaseSet[k] = v
}
for k, v := range t.BaseSet{
newGset.BaseSet[k] = v
}
return newGset, nil
}


The 2P-Set implementation is then built with two G-Sets, one for added elements and one for removed elements. In these implementations, I used maps of key-value pairs to represent sets, where the key is of type interface and the value is another interface. The interface construct in Go is an easy way to achieve polymorphism and allows the implementation to use practically anything as a key or value. The drawback is that it is up to the programmer to make sure data from the interface is processed properly.

Interestingly, although my G-Set implementation was state-based, I was able to reuse the same code for an implementation in the op-based style. The only thing I added to the code for the op-based implementation was an ApplyOps function that will apply a list of operations in order to the G-Set (although these are only adds).

The same was not true with the 2P-Set, where the biggest difference between the op-based and state-basd styles existed when removing elements. As concurrent add and remove operations are commutative, the tombstone set is only necessary when implementing a state-based 2P-set, with the trade-off being a few additional checks. We can also re-use the ApplyOps function from the G-Set implementation to handle applying operations to another 2P-Set, as the op-based 2P-Set is a G-Set with a removal function.

A naïve implementation of the 2P-Set is not very space-efficient, as it can in the worst case require double the space of a G-Set with the same number of elements. This bloat could be curtailed by maintaining the removal set as a bitmap. Each entry in the bitmap would correspond to an entry in the add set. The merge function for a 2P-Set with a bitmap would use a bitwise OR operation between the bitmaps. This technique could also apply to other CRDTs that use tombstones.

The tombstones in the state-based 2P-Set implementation make it a good candidate for experimentation with CRDT garbage collection. This is also true if the 2P-Set is being used to build more sophisticated data structures, as we are doing here.

## Implementing the Add-Remove Partial Order CRDT

Shapiro et al.’s ARPO specification leaves out the addEdge and removeEdge operations. In my implementation, I attempted to add the missing operations. Many of the same challenges that exist for garbage collection of vertex tombstones also exist for edges. Edge addition is necessary to maintain a connected graph and avoid partitions. Edge removal also turns out to be necessary: in a naïve implementation, where edges are represented as a separate data structure (in my naïve ARPO implementation, for instance, edges are represented by their own G-Set), we have to remove edges along with their vertices to avoid cluttering the data structure with unneeded objects. In an implementation where edges are represented by a list of references in each vertex, one must clean up the relevant references during a vertex removal. If we allow edge addition and removal independent of vertices, we end up requiring another set of tombstones to avoid the same problems with concurrent operations that we had with vertex removal. The implementation we are left with resembles what Shapiro et al. call a 2P2P-Graph (that is, it has a tombstone set for both vertices and edges), but with a different initial state, a few new preconditions, and a QueryBefore function to compute transitive relations.

//an edge points from the origin to the destination
type Edge struct{
left *Node
right *Node
}

//vector clocks are used to determine causal relations and the current “version” of the ARPO
vectorClock *vclock.VClock
causalVectorClocks []vclock.VClock
V *Twopset.Twopset
E *Twopset.Twopset
}

//Initializes the ARPO with left and right sentinels and a single edge
vectorClock: vclock.New(),
V: Twopset.Newtwopset(),
E: Twopset.Newtwopset(),
}
leftSentinel := NewNode("leftSentinel")
rightSentinel := NewNode("rightSentinel")
return AR
}

//Checks if a vertex exists
func (a *AddRemove) Lookup (element interface{}) bool{
if a.V.Query(element){
return true
}
return false
}

//Depth-first search to establish transitive relationship
func (a *AddRemove) QueryBefore(u, v interface{}) bool{
isBefore := false
if a.V.Query(u) && a.V.Query(v){
if edgeExists := a.FetchEdge(u, v); edgeExists!= nil {
return true
}
if u.(*Node).ID == "leftSentinel" && v.(*Node).ID == "rightSentinel" {
return true
}
edges := a.GetEdges(u)
for k := range edges{
isBefore = a.QueryBeforeRecurse(edges[k].(*Edge).right, v)
}
}
return isBefore
}

//Recursive helper for the depth-first search
func (a *AddRemove) QueryBeforeRecurse(u, v interface{}) bool{
edges := a.GetEdges(u)
isBefore := false
//if we have hit the sentinel then we are done
if len(edges) == 1 && edges[0].(*Edge).right.ID == "rightSentinel" {
isBefore = false
}
for k := range edges{
if edgeExists := a.FetchEdge(u, v); edgeExists != nil {
isBefore = true
}else{
isBefore = a.QueryBeforeRecurse(edges[k].(*Edge).right, v)
}
}
return isBefore
}

//get all edges originating at a node
func (a *AddRemove) GetEdges(u interface{}) []interface{}{
edges := a.E.List()
returnEdges := make([]interface{}, 0)
for k := range edges{
if edges[k].(*Edge).left == u.(*Node){
returnEdges = append(returnEdges, edges[k])
}
}
return returnEdges
}

//Adds a vertex between two others.
if !a.Lookup(v) && a.QueryBefore(u, w){
}
}

//needs a check to remove dangling edges, could possibly be done during garbage collection
if a.Lookup(v) && (v != "left" || v != "right"){
a.V.Remove(v)
}
}


I also wrote a few functions to handle adding and removing edges that perform almost the same functions as their vertex counterparts.

func (a *AddRemove) LookupEdge (element interface{}) bool{
if a.E.Query(element){
return true
}
return false
}

if a.V.Query(u) && a.V.Query(v){
newEdge := &Edge{
left: a.FetchNode(u),
right: a.FetchNode(v),
}
}
}

if a.LookupEdge(v){
a.E.Remove(v)
}
}


The specification leaves out details on how to handle transmitting and applying operations. So I added a structure to represent a single operation and function to apply a linked list of these operations to the ARPO. Messaging is not currently implemented but it is assumed that the operations are added to the linked list in causal order.

//A struct for a single operation on an ARPO
//here we represent the operands as an array
//0 is the element to add or remove (v)
//1 is the first element in an addbetween (u)
//2 is the last element in an addbetween (w)
type OpList struct {
Operation string
Element   []interface{}
contents  struct{}
}

func (a *AddRemove) ApplyOps(opslist *list.List) error{
for e := opslist.Front(); e != nil; e = e.Next(){
oplistElement := e.Value.(*OpList)
}else if oplistElement.Operation == "Remove"{
a.Remove(oplistElement.Element[0])
}else{
return nil
}
}
return nil
}


The preconditions for edge addition and removal are concerned with the existence of an edge when removing one, prevention of duplicate edges, and the existence of both endpoint vertices when adding edges. Further testing and prodding the implementation could reveal a need for more preconditions.

## Implementing Garbage Collection

Having implemented an ARPO-like CRDT from the original specification, my next step was to investigate garbage collection. Implementing garbage collection for a CRDT like this one is a challenge. First, establishing the stability of an update as described in the paper assumes that the set of all replicas is known and that they do not crash permanently. Thus the implementation must include a way to detect crashed replicas (in practice, using a timeout) and a way to communicate the failure of a replica reliably to all other replicas.

Another issue is the metadata storage requirements for implementing garbage collection. Assuming causal delivery of updates requires the use of vector clocks or some similar mechanism to establish causality. Shapiro et al. specifically mention using vector clocks for determining stability in section 4.1 of their paper. As the definition of stability depends on causality, one can use the same vector clocks to establish both. However, the paper’s scheme for determining stability requires each replica to store a copy of the last received vector clock from every other known replica. Therefore, the space complexity required to store the vector clocks locally for $N$ replicas is $O(N^2)$, and total space consumption across the whole set of replicas, $O(N^3)$ — considerably worse than the usual $O(N)$ necessary to store a single vector clock at each replica for tracking causal relationships, and enough to make programmers uneasy.

When adding the class of commitment problems to the already mounting pile of dilemmas, the programmer loses hope for the availability and performance of their system. The solutions discussed by Shapiro et al. include the Paxos Commit and Two-Phase Commit protocols, which add considerably to the complexity of the implementation along with sacrificing availability. Shapiro et al. suggest performing operations requiring strong synchronization during periods when network partitions are rare; it may also help to limit such operations to when the availability of a system is not paramount. For example, one could run a garbage collection job during a scheduled server maintenance window.

To sum up, distributed garbage collection requires confronting some of the hardest problems in distributed systems. Perhaps the easiest solution to the unbounded growth of a CRDT via tombstones is to use the ostrich algorithm, or to avoid CRDTs that use tombstones entirely.

## Next steps

For a future blog post, I plan to investigate garbage collection approaches currently in use with CRDTs. Some interesting avenues to explore include pure operation-based CRDTs, Victor Grishchenko’s causal trees, and delta-state CRDTs. Also, methods for reducing the space costs of vector clocks could prove useful in lowering garbage collection metadata overhead. After investigating the options, I’ll hopefully be able to choose a garbage collection method to implement, integrate it with my existing ARPO implementation, and evaluate its performance.

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