Summa Aggregation

Summa Aggregation is a scalable solution specifically designed to accelerate the process of building Merkle sum tree. It addresses the time-intensive challenge of constructing these trees by enabling efficient scaling through parallelization and distributed computation across multiple machines.

Running test

Tests can be run using the following command:

cargo test --release

Note: The Worker will run locally and uses port 4000 as the default for its server. Please ensure that this port is not already in use to avoid errors.

Running Additional Tests Involving Docker and Docker Swarm

For additional tests involving Docker and Docker Swarm mode, the presence of the "summadev/summa-aggregation-mini-tree" image in the local Docker registry is required. Please refer to the Mini Tree Server for more information about the mini tree.

Building the docker image

Build the image using the following command:

docker build . -t summadev/summa-aggregation-mini-tree

Downloading the Docker Image

Alternatively, the image can be downloaded from Docker Hub:

docker pull summadev/summa-aggregation-mini-tree

Testing with LocalSpawner

The following command runs an additional test case using the LocalSpawner, which spawns worker containers in the local Docker environment. This extra test case involves running two containers during the testing process:

cargo test --features docker

Testing with CloudSpawner

For Summa-Aggregation, it's necessary to prepare a distributed environment where Workers can operate on remote machines, referred to as 'Nodes'. For guidance on setting up swarm nodes, please see Getting Started with swarm mode

When the Docker environment is running successfully in Swarm mode, an additional test case that spawns workers on Swarm nodes using the CloudSpawner can be run:

cargo test --features docker-swarm

It is critical to ensure that the Docker Swarm includes at least one node connected to the manager node. Additionally, each worker node in the swarm must have the "summadev/summa-aggregation-mini-tree" image in its Docker registry. Without this image on nodes connected to the manager node, spawning workers on that node is not possible.

Summa Aggregation Example

This example demonstrates the setup and operation of a distributed environment using Summa Aggregation, including the initialization of round and generating inclusion proof. A notable aspect of this demonstration is how the AggregationMerkleSumTree can produce the generation of inclusion proofs, similarly to the MerkleSumTree.

1. Setup Distributed Environment

Custodians can leverage any cloud infrastructure to establish worker nodes. In this example, we use two local servers running mini-tree services as workers, rather than deploying worker containers on remote nodes.

Key steps:

• Spawning Worker Nodes: Two local servers are spawned, each running a mini-tree service.

• Worker URLs: It is crucial to ensure the number of worker URLs matches the number of executors. In this example, we use 127.0.0.1:4000 and 127.0.0.1:4001.

2. Initialize the Round with Aggregation Merkle Sum Tree

Initiating the round with an AggregationMerkleSumTree is a key step after setting up the distributed environment with worker nodes. This process involves the Orchestrator and the Round.

• Orchestrator and AggregationMerkleSumTree: The Orchestrator is initialized with the CloudSpawner and paths to the CSV files containing entry data. It uses this information to generate the AggregationMerkleSumTree, which forms the basis for the round's operations.

• Round Initialization: Subsequently, the Round is initialized using the aggregation merkle sum tree. The Round is integral for interactions with the Summa contract and relies on the setup performed by the Orchestrator.

3. Interact with the Summa Contract and Generate Proof of Inclusion

The actual example only shows the creation of an inclusion proof.

For detailed information on interaction patterns similar to those in the summa-backend example, refer to the 'summa_solvency_flow'.

Example Execution

Run the example using the following command:

cargo run --release --example aggregation_flow