Overview and Assumptions

In this section, we provide an overview of the Canton architecture, illustrate the high-level flows, entities (defining trust domains) and components. We then state the trust assumptions we make on the different entities, and the assumptions on communication links.

Canton is designed to fulfill its high-level requirements. We assume that the reader is familiar with the smart contract language Daml and the hierarchical transactions of the DA ledger model.

Canton 101

A Basic Example

We will use a simple delivery-versus-payment (DvP) example to provide some background on how Canton works. Alice and Bob want to exchange an IOU given to Alice by a bank for some shares that Bob owns. We have four parties: Alice (aka A), Bob (aka B), a Bank and a share registry SR. There are also three types of contracts:

  1. an Iou contract, always with Bank as the backer

  2. a Share contract, always with SR as the registry

  3. a DvP contract between Alice and Bob

Assume that Alice has a “swap” choice on a DvP contract instance that exchanges an Iou she owns for a Share that Bob has. We assume that the Iou and Share contract instances have already been allocated in the DvP. Alice wishes to commit a transaction executing this swap choice; the transaction has the following structure:


Transaction Processing in Canton

In Canton, committing the example transaction consists of two steps:

  1. Alice’s participant prepares a confirmation request for the transaction. The request provides different views on the transaction; participants see only the subtransactions exercising, fetching or creating contracts on which their parties are stakeholders (more precisely, the subtransactions where these parties are informees). The views for the DvP, and their recipients, are shown in the figure below. Alice’s participant submits the request to a sequencer, who orders all confirmation requests on a Canton domain; whenever two participants see the same two requests, they will see them according to this sequencer order. The sequencer has only two functions: ordering messages and delivering them to their stated recipients. The message contents are encrypted and not visible to the sequencer.


    Views in the transaction; each box represents a transaction part visible to the participants in its bottom-right corner. A participant might receive several views, some of which can be nested.

  1. The recipients then check the validity of the views that they receive. The validity checks cover three aspects:

    1. validity as defined in the DA ledger model: consistency, (mainly: no double spends), conformance (the view is a result of a valid Daml interpretation) and authorization (guaranteeing that the actors and submitters are allowed to perform the view’s action)

    2. authenticity (guaranteeing that the actors and submitters are who they claim to be).

    3. transparency (guaranteeing that participants who should be notified get notified).

    Conformance, authorization, authenticity and transparency problems only arise due to submitter malice. Consistency problems can arise with no malice. For example, the Iou that is to be transferred to Bob might simply have already been spent (assuming that we do not use the “locking” technique in Daml). Based on the check’s result, a subset of recipients, called confirmers then prepares a (positive or negative) confirmation response for each view separately. A confirmation policy associated with the request specifies which participants are confirmers, given the transaction’s informees.

    The confirmers send their responses to a mediator, another special entity that aggregates the responses into a single decision for the entire confirmation request. The mediator serves to hide the participants’ identities from each other (so that Bank and SR do not need to know that they are part of the same transaction). Like the sequencer, the mediator does not learn the transactions’ contents. Instead, Alice’s participant, in addition to sending the request, also simultaneously notifies the mediator about the informees of each view. The mediator receives a version of the transaction where only the informees of a view are visible and the contents blinded, as conceptually visualized in the diagram below.


    In the informee tree for the mediator, all transaction contents are blinded.

    From this, the mediator derives which (positive) confirmation responses are necessary in order to decide the confirmation request as approved.

    Requests submitted by malicious participants can contain bogus views. As participants can see only parts of requests (due to privacy reasons), upon receiving an approval for a request, each participant locally filters out the bogus views that are visible to it, and accepts all remaining valid views of an approved confirmation request. Under the confirmation policy’s trust assumptions, the protocol ensures that the local decisions of honest participants match for all views that they jointly see. The protocol thus provides a virtual shared ledger between the participants, whose transactions consist of such valid views. Once approved, the accepted views are final, i.e., they will never be removed from the participants’ records or the virtual ledger.

We can represent the confirmation workflow described above by the following message sequence diagram, assuming that each party in the example runs their own participant node.


The sequencer and the mediator, together with a so-called topology manager (described shortly), constitute a Canton domain. All messages within the domain are exchanged over the sequencer, which ensures a total order between all messages exchanged within a domain.

The total ordering ensures that participants see all confirmation requests and responses in the same order. The Canton protocol additionally ensures that all non-Byzantine (i.e. not malicious or compromised) participants see their shared views (such as the exercise of the Iou transfer, shared between the participants of Bank and A) in the same order, even with Byzantine submitters. This has the following implications:

  1. The correct confirmation response for each view is always uniquely determined, because Daml is deterministic. However, for performance reasons, we allow occasional incorrect negative responses, when participants start behaving in a Byzantine fashion or under contention. The system provides the honest participants with evidence of either the correctness of their responses or the reason for the incorrect rejections.

  2. The global ordering creates a (virtual) global time within a domain, measured at the sequencer; participants learn that time has progressed whenever they receive a message from the sequencer. This global time is used for detecting and resolving conflicts and determining when timeouts occur. Conceptually, we can therefore speak of a step happening at several participants simultaneously with respect to this global time, although each participant performs this step at a different physical time. For example, in the above message sequence diagram, Alice, Bob, the Bank, and the share registry’s participants receive the confirmation request at different physical times, but conceptually this happens at the timestamp ts1 of the global time, and similarly for the result message at timestamp ts6.

In this document, we focus on the basic version of Canton, with just a single domain. Canton also supports connecting a participant to multiple domains and transferring contracts between domains (see composability).

As mentioned in the introduction, the main challenges for Canton are reconciling integrity and privacy concerns while ensuring progress with the confirmation-based design, given that parties might be overloaded, offline, or simply refusing to respond. The main ways we cope with this problem are as follows:

  • We use timeouts: if a transaction’s validity cannot be determined after a timeout (which is a domain-wide constant), the transaction is rejected.

  • If a confirmation request times out, the system informs the participant submitting the request on which participants have failed to send a confirmation response. This allows the submitting participant to take out of band actions against misbehaviour.

  • Flexible confirmation policies: To offer a trade-off between trust, integrity, and liveness, we allow Canton domains to choose their confirmation policies. Confirmation policies specify which participants need to confirm which views. This enables the mediator to determine the sufficient conditions to declare a request approved. Of particular interest is the VIP confirmation policy, applicable to transactions which involve a trusted (VIP) party as an informee on every action. An example of a VIP party is a market operator. The policy ensures ledger validity assuming the VIP party’s participants behave correctly; incorrect behavior can still be detected and proven in this case, but the fallout must be handled outside of the system. Another important policy is the signatory confirmation policy, in which all signatories and actors are required to confirm. This requires a lower level of trust compared to the VIP confirmation policy sacrificing liveness when participants hosting signatories or actors are unresponsive. Another policy (being deprecated) is the full confirmation policy, in which all informees are required to confirm. This requires the lowest level of trust, but sacrifices liveness when some of the involved participants are unresponsive.

  • In the future, we will support attestators, which can be thought of as on-demand VIP participants. Instead of constructing Daml models so that VIP parties are informees on every action, attestators are only used on-demand. The participants who wish to have the transaction committed must disclose sufficient amount of history to provide the attestator with unequivocal evidence of a subtransaction’s validity. The attestator’s statement then substitutes the confirmations of the unresponsive participants.

The following image shows the state transition diagram of a confirmation request; all states except for Submitted are final.


A confirmation request can be rejected for several reasons:

Multiple domains

The transaction tried to use contracts created on different Canton domains. Multi-domain transactions are currently not supported.


Insufficient confirmations have been received within the timeout window to declare the transaction as accepted according to the confirmation policy. This happens due to one of the involved participants being unresponsive. The request then times out and is aborted. In the future, we will add a feature where aborts can be triggered by the submitting party, or anyone else who controls a contract in the submitted transaction. The aborts still have to happen after the timeout, but are not mandatory. Additionally, attestators can be used to supplant the confirmations from the unresponsive participants.


It conflicts with an earlier pending request, i.e., a request that has neither been approved nor rejected yet. Canton currently implements a simple pessimistic conflict resolution policy, which always fails the later request, even if the earlier request itself gets rejected at some later point.

Conflicting responses

Conflicting responses were received. In Canton, this only happens when one of the participants is Byzantine.

Conflict Detection

Participants detect conflicts between concurrent transactions by locking the contracts that a transaction consumes. The participant locks a contract when it receives the confirmation request of a transaction that archives the contract. The lock indicates that the contract is possibly archived. When the mediator’s decision arrives later, the contract is unlocked again - and archived if the transaction was approved. When a transaction wants to use a possibly archived contract, then this transaction will be rejected in the current version of Canton. This design decision is based on the optimistic assumption that transactions are typically accepted; the later conflicting transaction can therefore be pessimistically rejected.

The next three diagrams illustrate locking and pessimistic rejections using the counteroffer example from the DA ledger model. There are two transactions and three parties and every party runs their own participant node.

  • The painter P accepts A’s Counteroffer in transaction tx1. This transaction consumes two contracts:

    • The Iou between A and the Bank, referred to as c1.

    • The Counteroffer with stakeholders A and P, referred to as c2.

    The created contracts (the new Iou and the PaintAgreement) are irrelevant for this example.

  • Suppose that the Counteroffer contains an additional consuming choice controlled by A, e.g., Alice can retract her Counteroffer. In transaction tx2, A exercises this choice to consume the Counteroffer c2.

Since the messages from the sequencer synchronize all participants on the (virtual) global time, we may think of all participants performing the locking, unlocking, and archiving simultaneously.

In the first diagram, the sequencer sequences tx1 before tx2. Consequently, A and the Bank lock c1 when they receive the confirmation request, and so do A and P for c2. So when tx2 later arrives at A and P, the contract c2 is locked. Thus, A and P respond with a rejection and the mediator follows suit. In contrast, all stakeholders approve tx1; when the mediator’s approval arrives at the participants, each participant archives the appropriate contracts: A archives c1 and c2, the Bank archives c1, and P archives c2.


When two transactions conflict while they are in flight, the later transaction is always rejected.

The second diagram shows the scenario where A’s retraction is sequenced before P’s acceptance of the Counteroffer. So A and P lock c2 when they receive the confirmation request for tx2 from the sequencer and later approve it. For tx1, A and P notice that c2 is possibly archived and therefore reject tx1, whereas everything looks fine for the Bank. Consequently, the Bank and, for consistency, A lock c1 until the mediator sends the rejection for tx1.


Transaction tx2 is now submitted before tx1. The consumed contract c1 remains locked by the rejected transaction until the mediator sends the result message.


In reality, participants approve each view individually rather than the transaction as a whole. So A sends two responses for tx1: An approval for c1’s archival and a rejection for c2’s archival. The diagrams omit this technicality.

The third diagram shows how locking and pessimistic rejections can lead to incorrect negative responses. Now, the painter’s acceptance of tx1 is sequenced before Alice’s retraction like in the first diagram, but the Iou between A and the Bank has already been archived earlier. The painter receives only the view for c2, since P is not a stakeholder of the Iou c1. Since everything looks fine, P locks c2 when the confirmation request for tx1 arrives. For consistency, A does the same, although A already knows that the transaction will fail because c1 is archived. Hence, both P and A reject tx2 because it tries to consume the locked contract c2. Later, when tx1’s rejection arrives, c2 becomes active again, but the transaction tx2 remains rejected.


Even if the earlier transaction tx1 is rejected later, the later conflicting transaction tx2 remains rejected and the contract remains locked until the result message.

Time in Canton

The connection between time in Daml transactions and the time defined in Canton is explained in the respective ledger model section on time.

The respective section introduces ledger time and record time. The ledger time is the time the participant (or the application) chooses when computing the transaction prior to submission. We need the participant to choose this time as the transaction is pre-computed by the submitting participant and this transaction depends on the chosen time. The record time is assigned by the sequencer when registering the confirmation request (initial submission of the transaction).

There is only a bounded relationship between these times, ensuring that the ledger time must be in a pre-defined bound around the record time. The tolerance (max_skew) is defined on the domain as a domain parameter, known to all participants


The bounds are symmetric in Canton, so min_skew equals max_skew, equal to above parameter.


Canton does not support querying the time model parameters via the ledger API, as the time model is a per domain property and this can not be properly exposed on the respective ledger API endpoint.

Checking that the record time is within the required bounds is done by the validating participants and is visible to everyone. The sequencer does not know what was timestamped and therefore doesn’t perform this validation.

Therefore, a submitting participant can not control the output of a transaction depending on record time, as the submitting participant does not know exactly the point in time when the transaction will be timestamped by the sequencer. But the participant can guarantee that a transaction will either be registered before a certain record time, or the transaction will fail.

Domain Entities

A Canton domain consists of three entities:

  • the sequencer

  • the mediator

  • and topology manager, providing a PKI infrastructure, and party to participant mappings.

We call these the domain entities. The high-level communication channels between the domain entities are depicted below.


In general, every domain entity can run in a separate trust domain (i.e., can be operated by an independent organization). In practice, we assume that all domain entities are run by a single organization, and that the domain entities belong to a single trust domain.

Furthermore, each participant node runs in its own trust domain. Additionally, the participant may outsource a part of its identity management infrastructure, for example to a certificate authority. We assume that the participant trusts this infrastructure, that is, that the participant and its identity management belong to the same trust domain. Some participant nodes can be designated as VIP nodes, meaning that they are operated by trusted parties. Such nodes are important for the VIP confirmation policy.

The generic term member will refer to either a domain entity or a participant node.


We now list the high-level requirements on the sequencer.

Ordering: The sequencer provides a global total-order multicast where messages are uniquely time-stamped and the global ordering is derived from the timestamps. Instead of delivering a single message, the sequencer provides message batching, that is, a list of individual messages are submitted. All these messages get the timestamp of the batch they are contained in. Each message may have a different set of recipients; the messages in each recipient’s batch are in the same order as in the sent batch.

Evidence: The sequencer provides the recipients with a cryptographic proof of authenticity for every message batch it delivers, including evidence on the order of batches.

Sender and Recipient Privacy: The recipients do not learn the identity of the submitting participant. A recipient only learns the identities of recipients on a particular message from a batch if it is itself a recipient of that message.


The mediator’s purpose is to compute the final result for a confirmation request and distribute it to the participants, ensuring that transactions are atomically committed across participants, while preserving the participants’ privacy, by not revealing their identities to each other. At a high level, the mediator:

  • collects confirmation responses from participants,

  • validates them according to the Canton protocol,

  • computes the conclusions (approve / reject / timed out) according to the confirmation policy, and

  • sends the result message.

Additionally, for auditability, the mediator persists every received message (containing informee information or confirmation responses) in long term storage and allows an auditor to retrieve messages from this storage.

Topology Manager

The topology manager allows participants to join and leave the Canton domain, and to register, revoke and rotate public keys. It knows the parties hosted by a given participant. It defines the trust level of each participant. The trust level is either ordinary or VIP. A VIP trust level indicates that the participant is trusted to act honestly. A canonical example is a participant run by a trusted market operator.

Participant-internal Canton Components

Canton uses the Daml-on-X architecture, to promote code reuse. In this architecture, the participant node is broken down into a set of services, all but one of which are reused among ledger implementations. This ledger-specific service is called the Ledger Synchronization Service (LSS), which Canton implements using its protocol. This implementation is further broken down further into multiple components. We now describe the interface and properties of each component. The following figure shows the interaction between the different components and the relation to the existing Ledger API’s command and event services.


We next explain each component in turn.


This is the central component of LSS within Canton. We describe the main tasks below.

Submission and Segregation: A Daml transaction has a tree-like structure. The ledger privacy model defines which parts of a transaction are visible to which party, and thus participant. Each recipient obtains only the subtransaction (projection) it is entitled to see; other parts of the transaction are never shared with the participant, not even in an encrypted form. Furthermore, depending on the confirmation policy, some informees are marked as confirmers. In addition to distributing the transaction projections among participants, the submitter informs the mediator about the informees and confirmers of the transaction.

Validity and Confirmations Responses: Each informee of a requested transaction performs local checks on the validity of its visible subtransaction. The informees check that their provided projection conforms to the Daml semantics, and the ledger authorization model. Additionally, they check whether the request conflicts with an earlier request that is accepted or is not yet decided. Based on this, they send their responses (one for each of their views), together with the informee information for their projection, to the mediator. When the other participants or domain entities do not behave according to the protocol (for example, not sending timely confirmation responses, or sending malformed requests), the transaction processing component raises alarms.

Confirmation Result Processing. Based on the result message from the mediator, the transaction component commits or aborts the requested transaction.

Sequencer Client

The sequencer client handles the connection to the sequencer, ensures in-order delivery and stores the cryptographic proofs of authenticity for the messages from the sequencer.

Identity Client

The identity client handles the messages coming from the domain topology manager, and verifies the validity of the received identity information changes (for example, the validity of public key delegations).

System Model And Trust Assumptions

The different sets of rules that Canton domains specify affect the security and liveness properties in different ways. In this section, we summarize the system model that we assume, as well as the trust assumptions. Some trust assumptions are dependent on the domain rules, which we indicate in the text. As specified in the high-level requirements, the system provides guarantees only to honestly represented parties. Hence, every party must fully trust its participant (but no other participants) to execute the protocol correctly. In particular, signatures by participant nodes may be deemed as evidence of the party’s action in the transaction protocol.

System Model

We assume that pairwise communication is possible between any two system members. The links connecting the participant nodes to the sequencers and the referees are assumed to be mostly timely: there exists a known bound 𝛅 on the delay such that the overwhelming majority of messages exchanged between the participant and the sequencer are delivered within 𝛅. Domain entities are assumed to have clocks that are closely synchronized (up to some known bound) for an overwhelming majority of time. Finally, we assume that the participants know a probability distribution over the message latencies within the system.

General Trust Assumptions

These assumptions are relevant for all system properties, except for privacy.

  • The sequencer is trusted to correctly provide a global total-order multicast service, with evidence and ensuring the sender and recipient privacy. .

  • The mediator is trusted to produce and distribute all results correctly.

  • The topology managers of honest participants (including the underlying public key infrastructure, if any) are operating correctly.

When a transaction is submitted with the VIP confirmation policy (in which case every action in the transaction must have at least one VIP informee), there exist an additional integrity assumption:

  • All VIP stakeholders must be hosted by honest participants, i.e., participants that run the transaction protocol correctly.

We note that the assumptions can be weakened by replicating the trusted entities among multiple organization with a Byzantine fault tolerant replication protocol, if the assumptions are deemed too strong. Furthermore, we believe that with some extensions to the protocol we can make the violations of one of the above assumptions detectable by at least one participant in most cases, and often also provable to other participants or external entities. This would require direct communication between the participants, which we leave as future work.

Assumptions Relevant for Privacy

The following common assumptions are relevant for privacy:

  • The private keys of honest participants are not compromised, and all certificate authorities that the honest participants use are trusted.

  • The sequencer is privy to:

    1. the submitters and recipients of all messages

    2. the view structure of a transaction in a confirmation request, including informees and confirming parties

    3. the confirmation responses (approve / reject / ill-formed) of confirmers.

    4. encrypted transaction views

    5. timestamps of all messages

  • The sequencer is trusted with not storing messages for longer than necessary for operational procedures (e.g., delivering messages to offline parties or for crash recovery).

  • The mediator is privy to:

    1. the view structure of a transaction including informees and confirming parties, and the submitting party

    2. the confirmation responses (approve / reject / ill-formed) of confirmers

    3. timestamps of messages

  • The informees of a part of a transaction are trusted with not violating the privacy of the other stakeholders in that same part. In particular, the submitter is trusted with choosing strong randomness for transaction and contract IDs. Note that this assumption is not relevant for integrity, as Canton ensures the uniqueness of these IDs.

When a transaction is submitted with the VIP confirmation policy, every action in the transaction must have at least one VIP informee. Thus, the VIP informee is automatically privy to the entire contents of the transaction, according to the ledger privacy model.

Assumptions Relevant for Liveness

In addition to the general trust assumptions, the following additional assumptions are relevant for liveness and bounded liveness functional requirements on the system: bounded decision time, and no unnecessary rejections:

  • All the domain entities in Canton (the sequencer, the mediator, and the topology manager) are highly available.

  • The sequencer is trusted to deliver the messages timely and fairly (as measured by the probability distribution over the latencies).

  • The domain topology manager forwards all identity updates correctly.

  • Participants hosting confirming parties according to the confirmation policy are assumed to be highly available and responding correctly. For example in the VIP confirmation policy, only the VIP participant needs to be available whereas in the signatory policy, liveness depends on the availability of all participants that host signatories and actors.

Scaling and Performance

Network Scaling

The scaling and performance characteristics of a Canton based system is determined by the scaling characteristics of the entire system and each individual node. Under a Canton based system we understand the set of participant and domain nodes (hereafter, “participant” or “participants” and “domain” or “domains” respectively), the parties and the deployed Daml workflows. Such a system is not rigid, but flexible, as new participants can connect to multiple domains (preview feature) and new domain nodes and new participant nodes can be added to the network at any point in time. Also participants can be split up by migrating parties off to a new participant node (currently supported as a delicate manual preview feature). Therefore, new computational nodes can be added to the network when needed, allowing the entire system to scale horizontally.

The ledger state in Canton does not exist globally. There is no node that by design hosts all contracts. Instead, participants are involved in transactions that operate on the ledger state on a strict need to know basis (data minimization), only exchanging (encrypted) information on the domains used as coordination point for the given input contracts. As an example, if participant Alice and Bank transact on an i-owe-you contract on domain A, another participant Bob or another domain B will not even receive a single bit related to this transaction. This is contrast to Blockchains, where each node has to process each block, independently of how active or directly affected they are by a certain transaction.

A Canton based system can therefore scale horizontally by adding more domains and participants, as long as the workflows are independent of each other. As an example, if there are 100 parties performing multi-lateral transactions with each other, then the system can sharded by running 10 participants with 10 parties each, or say 100 participants with 1 party each. As most of the computation occurs on the participants, a domain can sustain some substantial load from multiple participants, entirely bound by disk and network io of the domain node. But in a large and active network where a domain reaches the capacity limit, additional domains can be rolled out, such that the workflows can be sharded over the available domains (preview).

If the workflows are made dependent, either by design or by accident, then this scaling property does not hold anymore. Therefore, the modelling of Daml contracts has an impact on the scaling properties and therefore requires some care. As an example, adding an auditor party to each contract in a large solution will require the participant hosting the said party to process every change, and therefore the vertical scaling limits of an individual node (network and disc io, cpu) will prevent horizontal scaling of the network. This does not mean that workflows must be entirely independent. They can converge and there can be steps that involve coordination across domains (preview), albeit every such (automatic) synchronization comes with additional overhead.

The standard way to recover horizontal scaling is by sharding the workflow across multiple participants. If a workflow is involving some large operator (i.e. an exchange), then an option would be to shard the operator by creating two operator parties and distribute the workflows evenly over the two operators, and by adding some intermediate steps for the few cases where the workflows would span across the two shards.

The bottom-line is that a Canton system can scale out horizontally if commands involve only a small number of participants and domains.


This feature is only available in Canton Enterprise

Node Scaling

The Canton Enterprise edition supports the following scaling of nodes:

  • The database backed domain integrations (Postgres and Oracle) can run in an active-active setup with parallel processing, supporting multiple writer and reader processes. Thus, such nodes can scale horizontally.

  • The enterprise participant node processes transactions in parallel (except the process of conflict detection which by definition must be sequential), allowing much higher throughput than the community version. The community version is processing each transaction sequentially. Canton processes make use of multiple cpus and will detect the number of available cpus automatically. The number of parallel threads can be controlled by setting the JVM properties scala.concurrent.context.numThreads to the desired value.

Generally, the performance of Canton nodes is currently storage I/O bound. Therefore, their performance depends on the scaling behaviour and throughput performance of the underlying storage layer, which can be a database, or a distributed ledger for some domain integrations. Therefore, appropriately sizing the database is key to achieve the necessary performance.

On a related note: the Daml interpretation is a pure operation, without side-effects. Therefore, the interpretation of each transaction can run in parallel, and only the conflict-detection between transactions must run sequentially.

Performance and Sizing

A Daml workflow can be computationally arbitrarily complex, performing lots of computation (cpu!) or fetching many contracts (io!), and involve different numbers of parties, participants and domains. Canton nodes store their entire data in the storage layer (database), with additional indexes. Every workflow and topology is different, and therefore, sizing requirements depends on the Daml application that is going to run and the resource requirements of the storage layer. Therefore, in order to obtain sizing estimates, you must measure the resource usage of dominant workflows using a representative topology and setup of your use-case.


As every transaction comes with an overhead (signatures, symmetric encryption keys, serialization and wrapping into messages for transport, http headers etc), we recommend to design the applications submitting commands in a way that batches smaller requests together into a single transaction.

Optimal batch sizes are workflow and topology dependent and need to be determined experimentally.