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EIP-7594 -- Data Availability Sampling Core

Notice: This document is a work-in-progress for researchers and implementers.

Constants
- Misc
Custom types
Configuration
Helper functions
Custody
- Custody requirement
- Public, deterministic selection
Subnet sampling
Extended data
Column gossip
- Parameters
Reconstruction and cross-seeding
FAQs
- Row (blob) custody
- Subnet stability

Constants

The following values are (non-configurable) constants used throughout the specification.

Misc

Name	Value
`UINT256_MAX`	`uint256(2**256 - 1)`

Custom types

Name	SSZ equivalent	Description
`RowIndex`	`uint64`	Row identifier in the matrix of cells
`ColumnIndex`	`uint64`	Column identifier in the matrix of cells

Configuration

Data size

Name	Value	Description
`NUMBER_OF_COLUMNS`	`uint64(CELLS_PER_EXT_BLOB)` (= 128)	Number of columns in the extended data matrix

Networking

Name	Value	Description
`DATA_COLUMN_SIDECAR_SUBNET_COUNT`	`128`	The number of data column sidecar subnets used in the gossipsub protocol

Custody setting

Name	Value	Description
`SAMPLES_PER_SLOT`	`8`	Number of `DataColumnSidecar` random samples a node queries per slot
`CUSTODY_REQUIREMENT`	`4`	Minimum number of subnets an honest node custodies and serves samples from

Containers

`DataColumnSidecar`

class DataColumnSidecar(Container):
    index: ColumnIndex  # Index of column in extended matrix
    column: List[Cell, MAX_BLOB_COMMITMENTS_PER_BLOCK]
    kzg_commitments: List[KZGCommitment, MAX_BLOB_COMMITMENTS_PER_BLOCK]
    kzg_proofs: List[KZGProof, MAX_BLOB_COMMITMENTS_PER_BLOCK]
    signed_block_header: SignedBeaconBlockHeader
    kzg_commitments_inclusion_proof: Vector[Bytes32, KZG_COMMITMENTS_INCLUSION_PROOF_DEPTH]

`MatrixEntry`

class MatrixEntry(Container):
    cell: Cell
    kzg_proof: KZGProof
    column_index: ColumnIndex
    row_index: RowIndex

Helper functions

`get_custody_columns`

def get_custody_columns(node_id: NodeID, custody_subnet_count: uint64) -> Sequence[ColumnIndex]:
    assert custody_subnet_count <= DATA_COLUMN_SIDECAR_SUBNET_COUNT

    subnet_ids: List[uint64] = []
    current_id = uint256(node_id)
    while len(subnet_ids) < custody_subnet_count:
        subnet_id = (
            bytes_to_uint64(hash(uint_to_bytes(uint256(current_id)))[0:8])
            % DATA_COLUMN_SIDECAR_SUBNET_COUNT
        )
        if subnet_id not in subnet_ids:
            subnet_ids.append(subnet_id)
        if current_id == UINT256_MAX:
            # Overflow prevention
            current_id = NodeID(0)
        current_id += 1

    assert len(subnet_ids) == len(set(subnet_ids))

    columns_per_subnet = NUMBER_OF_COLUMNS // DATA_COLUMN_SIDECAR_SUBNET_COUNT
    return sorted([
        ColumnIndex(DATA_COLUMN_SIDECAR_SUBNET_COUNT * i + subnet_id)
        for i in range(columns_per_subnet)
        for subnet_id in subnet_ids
    ])

`compute_matrix`

def compute_matrix(blobs: Sequence[Blob]) -> Sequence[MatrixEntry]:
    """
    Return the full, flattened sequence of matrix entries.

    This helper demonstrates the relationship between blobs and the matrix of cells/proofs.
    The data structure for storing cells/proofs is implementation-dependent.
    """
    matrix = []
    for blob_index, blob in enumerate(blobs):
        cells, proofs = compute_cells_and_kzg_proofs(blob)
        for cell_index, (cell, proof) in enumerate(zip(cells, proofs)):
            matrix.append(MatrixEntry(
                cell=cell,
                kzg_proof=proof,
                row_index=blob_index,
                column_index=cell_index,
            ))
    return matrix

`recover_matrix`

def recover_matrix(partial_matrix: Sequence[MatrixEntry], blob_count: uint64) -> Sequence[MatrixEntry]:
    """
    Recover the full, flattened sequence of matrix entries.

    This helper demonstrates how to apply ``recover_cells_and_kzg_proofs``.
    The data structure for storing cells/proofs is implementation-dependent.
    """
    matrix = []
    for blob_index in range(blob_count):
        cell_indices = [e.column_index for e in partial_matrix if e.row_index == blob_index]
        cells = [e.cell for e in partial_matrix if e.row_index == blob_index]
        recovered_cells, recovered_proofs = recover_cells_and_kzg_proofs(cell_indices, cells)
        for cell_index, (cell, proof) in enumerate(zip(recovered_cells, recovered_proofs)):
            matrix.append(MatrixEntry(
                cell=cell,
                kzg_proof=proof,
                row_index=blob_index,
                column_index=cell_index,
            ))
    return matrix

`get_data_column_sidecars`

def get_data_column_sidecars(signed_block: SignedBeaconBlock,
                             cells_and_kzg_proofs: Sequence[Tuple[
        Vector[Cell, CELLS_PER_EXT_BLOB],
        Vector[KZGProof, CELLS_PER_EXT_BLOB]]]) -> Sequence[DataColumnSidecar]:
    """
    Given a signed block and the cells/proofs associated with each blob in the
    block, assemble the sidecars which can be distributed to peers.
    """
    blob_kzg_commitments = signed_block.message.body.blob_kzg_commitments
    assert len(cells_and_kzg_proofs) == len(blob_kzg_commitments)
    signed_block_header = compute_signed_block_header(signed_block)
    kzg_commitments_inclusion_proof = compute_merkle_proof(
        signed_block.message.body,
        get_generalized_index(BeaconBlockBody, 'blob_kzg_commitments'),
    )

    sidecars = []
    for column_index in range(NUMBER_OF_COLUMNS):
        column_cells, column_proofs = [], []
        for cells, proofs in cells_and_kzg_proofs:
            column_cells.append(cells[column_index])
            column_proofs.append(proofs[column_index])
        sidecars.append(DataColumnSidecar(
            index=column_index,
            column=column_cells,
            kzg_commitments=blob_kzg_commitments,
            kzg_proofs=column_proofs,
            signed_block_header=signed_block_header,
            kzg_commitments_inclusion_proof=kzg_commitments_inclusion_proof,
        ))
    return sidecars

Custody

Custody requirement

Each node downloads and custodies a minimum of CUSTODY_REQUIREMENT subnets per slot. The particular subnets that the node is required to custody are selected pseudo-randomly (more on this below).

A node may choose to custody and serve more than the minimum honesty requirement. Such a node explicitly advertises a number greater than CUSTODY_REQUIREMENT via the peer discovery mechanism -- for example, in their ENR (e.g. custody_subnet_count: 4 if the node custodies 4 subnets each slot) -- up to a DATA_COLUMN_SIDECAR_SUBNET_COUNT (i.e. a super-full node).

A node stores the custodied columns for the duration of the pruning period and responds to peer requests for samples on those columns.

Public, deterministic selection

The particular columns that a node custodies are selected pseudo-randomly as a function (get_custody_columns) of the node-id and custody size -- importantly this function can be run by any party as the inputs are all public.

Note: increasing the custody_size parameter for a given node_id extends the returned list (rather than being an entirely new shuffle) such that if custody_size is unknown, the default CUSTODY_REQUIREMENT will be correct for a subset of the node's custody.

Subnet sampling

At each slot, a node advertising custody_subnet_count downloads a minimum of subnet_sampling_size = max(SAMPLES_PER_SLOT, custody_subnet_count) total subnets. The corresponding set of columns is selected by get_custody_columns(node_id, subnet_sampling_size), so that in particular the subset of columns to custody is consistent with the output of get_custody_columns(node_id, custody_subnet_count). Sampling is considered successful if the node manages to retrieve all selected columns.

Extended data

In this construction, we extend the blobs using a one-dimensional erasure coding extension. The matrix comprises maximum MAX_BLOBS_PER_BLOCK rows and fixed NUMBER_OF_COLUMNS columns, with each row containing a Blob and its corresponding extension. compute_matrix demonstrates the relationship between blobs and the matrix, a potential method of storing cells/proofs.

Column gossip

Parameters

For each column -- use data_column_sidecar_{subnet_id} subnets, where subnet_id can be computed with the compute_subnet_for_data_column_sidecar(column_index: ColumnIndex) helper. The sidecars can be computed with the get_data_column_sidecars(signed_block: SignedBeaconBlock, blobs: Sequence[Blob]) helper.

Verifiable samples from their respective column are distributed on the assigned subnet. To custody a particular column, a node joins the respective gossipsub subnet. If a node fails to get a column on the column subnet, a node can also utilize the Req/Resp protocol to query the missing column from other peers.

Reconstruction and cross-seeding

If the node obtains 50%+ of all the columns, it SHOULD reconstruct the full data matrix via recover_matrix helper. Nodes MAY delay this reconstruction allowing time for other columns to arrive over the network. If delaying reconstruction, nodes may use a random delay in order to desynchronize reconstruction among nodes, thus reducing overall CPU load.

Once the node obtains a column through reconstruction, the node MUST expose the new column as if it had received it over the network. If the node is subscribed to the subnet corresponding to the column, it MUST send the reconstructed DataColumnSidecar to its topic mesh neighbors. If instead the node is not subscribed to the corresponding subnet, it SHOULD still expose the availability of the DataColumnSidecar as part of the gossip emission process.

Note: A node always maintains a matrix view of the rows and columns they are following, able to cross-reference and cross-seed in either direction.

Note: There are timing considerations to analyze -- at what point does a node consider samples missing and choose to reconstruct and cross-seed.

Note: There may be anti-DoS and quality-of-service considerations around how to send samples and consider samples -- is each individual sample a message or are they sent in aggregate forms.

FAQs

Row (blob) custody

In the one-dimension construction, a node samples the peers by requesting the whole DataColumnSidecar. In reconstruction, a node can reconstruct all the blobs by 50% of the columns. Note that nodes can still download the row via blob_sidecar_{subnet_id} subnets.

The potential benefits of having row custody could include:

Allow for more "natural" distribution of data to consumers -- e.g., roll-ups -- but honestly, they won't know a priori which row their blob is going to be included in in the block, so they would either need to listen to all rows or download a particular row after seeing the block. The former looks just like listening to column [0, N) and the latter is req/resp instead of gossiping.
Help with some sort of distributed reconstruction. Those with full rows can compute extensions and seed missing samples to the network. This would either need to be able to send individual points on the gossip or would need some sort of req/resp faculty, potentially similar to an IHAVEPOINTBITFIELD and IWANTSAMPLE.

However, for simplicity, we don't assign row custody assignments to nodes in the current design.

Subnet stability

To start with a simple, stable backbone, for now, we don't shuffle the subnet assignments via the deterministic custody selection helper get_custody_columns. However, staggered rotation likely needs to happen on the order of the pruning period to ensure subnets can be utilized for recovery. For example, introducing an epoch argument allows the function to maintain stability over many epochs.

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