Transaction Scheduling

Sawtooth supports both serial and parallel scheduling of transactions. The scheduler type is specified via a command line argument or as an option in the validator’s configuration file when the validator process is started. Both schedulers result in the same deterministic results and are completely interchangeable.

The parallel processing of transactions provides a performance improvement for even fast transaction workloads by reducing overall latency effects which occur when transaction execution is performed serially. When transactions are of non-uniform duration (such as may happen with more realistic complex workloads), the performance benefit is especially magnified when faster transactions outnumber slower transactions.

Transaction scheduling and execution in Sawtooth correctly and efficiently handles transactions which modify the same state addresses, including transactions within the same block. Even at the batch level, transactions can modify the same state addresses. Naive distributed ledger implementations may not allow overlapping state modifications within a block, severely limiting performance of such transactions to one-transaction-per-block, but Sawtooth has no such block-level restriction. Instead, state is incremental per transaction execution. This prevents double spends while allowing multiple transactions which alter the same state values to appear in a single block. Of course, in cases where these types of block-level restrictions are desired, transaction families may implement the appropriate business logic.

Scheduling within the Validator

The validator has two major components that use schedulers to calculate state changes and the resulting Merkle hashes based on transaction processing: the Chain Controller and the Block Publisher. These two components pass a scheduler to the Executor. While the validator contains only a single Chain Controller, a single Block Publisher, and a single Executor, there are numerous instances of schedulers that are dynamically created as needed.

Chain Controller

The Chain Controller is responsible for maintaining the current chain head (a pointer to the last block in the current chain). Processing is block-based; when it receives a candidate block (over the network or from the Block Publisher), it determines whether the chain head should be updated to point to that candidate block. The Chain Controller creates a scheduler to calculate new state with a related Merkle hash for the block being published. The Merkle hash is compared to the state root contained in the block header. If they match, the block is valid from a transaction execution and state standpoint. The Chain Controller uses this information in combination with consensus information to determine whether to update the current chain head.

Block Publisher

The Block Publisher is responsible for creating new candidate blocks. As batches are received by the validator (from clients or other Sawtooth nodes), they are added to the Block Publisher’s pending queue. Only valid transactions will be added to the next candidate block. For timeliness, batches are added to a scheduler as they are added to the pending queue; thus, transactions are processed incrementally as they are received.

When the pending queue changes significantly, such as when the chain head has been updated by the Chain Controller, the Block Publisher cancels the current scheduler and creates a new scheduler.


The Executor is responsible for the execution of transactions by sending them to transaction processors. The overall flow for each transaction is:

  1. The Executor obtains the next transaction and initial context from the scheduler.
  2. The Executor obtains a new context for the transaction from the Context Manager by providing the initial context (contexts are chained together).
  3. The Executor sends the transaction and a context reference to the transaction processor.
  4. The transaction processor updates the context’s state via context manager calls.
  5. The transaction processor notifies the Executor that the transaction is complete.
  6. The Executor updates the scheduler with the transaction’s result with the updated context.

In the case of serial scheduling, step 1 simply blocks until the previous transaction’s step 6 has completed. For the parallel scheduler, step 1 blocks until a transaction exists which can be executed because its dependencies have been satisfied, with steps 2 through 6 happening in parallel for each transaction being executed.

Iterative Scheduling

Each time the executor requests the next transaction, the scheduler calculates the next transaction dynamically based on knowledge of the transaction dependency graph and previously executed transactions within this schedule.

Serial Scheduler

For the serial scheduler, the dependency graph is straightforward; each transaction is dependent on the one before it. The next transaction is released only when the scheduler has received the execution results from the transaction before it.

Parallel Scheduler

As batches are added to the parallel scheduler, predecessor transactions are calculated for each transaction in the batch. A predecessor transaction is a transaction which must be fully executed prior to executing the transaction for which it is a predecessor.

Each transaction has a list of inputs and outputs; these are address declarations fields in the transaction’s header and are filled in by the client when the transaction is created. Inputs and outputs specify which locations in state are accessed or modified by the transaction. Predecessor transactions are determined using these inputs/outputs declarations.


It is possible for poorly written clients to impact parallelism by providing overly broad inputs/outputs declarations. Transaction processor implementations can enforce specific inputs/outputs requirements to provide an incentive for correct client behavior.

The parallel scheduler calculates predecessors using a Merkle-Radix tree with nodes that are addressable by state addresses or namespaces. This tree is called the "predecessor tree". Input declarations are considered reads, with output declarations considered writes. By keeping track of readers and writers within nodes of the tree, predecessors for a transaction can be quickly determined.

Unlike the serial scheduler, the order in which transactions will be returned to the Executor is not predetermined. The parallel scheduler is careful about which transactions are returned; only transactions with do not have state conflicts will be executed in parallel. When the Executor asks for the next transaction, the scheduler inspects the list of unscheduled transactions; the first in the list for which all predecessors have finished executed will be be returned. If none are found, the scheduler will block and re-check after a transaction has finished being executed.