execution

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Published: Sep 4, 2024 License: AGPL-3.0 Imports: 2 Imported by: 0

README

Execution

The Execution Node (EN) processes blocks and collections prepared by consensus and collection nodes. It executes the transaction in a Flow Virtual Machine, runtime environment of Flow protocol. EN also maintains Execution State, ledger containing user data which is manipulated by transactions. As a last step of processing block, it prepares and distributes Execution Receipt allowing verification nodes to verify correctness of computation, and later, consensus nodes to seal blocks.

##Table of Contents

Terminology

  • Computation - is a process of evaluating block content and yielding list of changes to the ledger.
  • Execution - complete, Flow protocol compliant operation of executing block. It manages environment for computation of block, stores execution states, creates execution receipt among others.

Architecture overview

Ingestion engine

Central component - input of the node - execution state owner, only component allowed to mutate it. It receives blocks, assembles them (gather collections) and forms execution queue (fork-aware ordering). Once a block is assembled and ready to be computed (full content of transactions is known and valid, previous execution state is available) it passes ExecutableBlock for computation (to ComputationManager) After receiving computation results ingestion engine generates Flow-compliant entities (ExecutionReceipt) and saves them along with other internal execution state data (block's StateCommitment).

ComputationManager

Abstraction of computing blocks, creates and manages Cadence runtime, exposes interface for computing blocks.

Provider engine

The output of the Execution Node. It's responsible for broadcasting ExecutionReceipts and answering requests for various states of protocol.

RPC Engine

It's gRPC endpoint exposing Observation API. This is a temporary solution and Observation Node is expected to take over this responsibility.

Ingestion operation

Mempool queues

Ingestion engine accepts incoming blocks and classifies them into two mempool map of queues:

  • execution queue - which contains subqueues of blocks executable in order. This allow forks to be executed in parallel. Head of each queue is either being executed or waiting for collections. It's starting state commitment is present. It will produce state commitment for it's children.
  • orphan queue - which contains subqueues of orphaned blocks. Those who cannot be executed immediately or are not known to be executable soon. It's kept separately, as it will be used to determine when the node should switch into synchronisation mode
Mempool cache

Additionally, EN keeps a simple mapping of collection IDs to block, for lookup when the collection is received.

Syncing

If EN cannot execute number of consecutive blocks (syncThreshold parameter) it enter synchronisation mode. The number of blocks required to trigger this condition is put in place to prevent triggering it in case of blocks arriving out of order, which can happen on unstable networks. EN keeps track of the highest block it has executed. This is not a Flow protocol feature, and only serves synchronisation needs.

Missing blocks

If no other EN are available, the block-level synchronisation is started. This requests blocks from consensus nodes, and incoming blocks are processed as if they were received during normal mode of operation

Operation

In order to execute a block, all collections must be requested and validated. A valid collection must be signed by an authorized collection node (i.e. with positive weight). The protocol state can be altered by executing transactions, hence the parent block must be executed to provide up-to-date copy of protocol state. This allows to validate collection nodes identities and in turn, validity of collection itself. Having all collections retrieved and Execution State of a parent known - a block execution can commence. Blocks are executed in separate Go routine to allow potential forks (sharing parent block) to be computed in parallel. After execution is finished, it passes newly created execution state to its children, and if they are now ready - they are, repeating the loop.

Documentation

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