Hosting a Foundational Store

This guide explains how to host a Foundational Store for Substreams data processing pipelines. Foundational Stores provide persistent, fork-aware key-value storage that can be populated by Substreams modules and served to other modules.

What is a Foundational Store?

A Foundational Store is a high-performance, multi-backend key-value storage system designed for Substreams ingestion and serving. It provides:

• Fork-aware storage: Handles blockchain reorganizations automatically

• Multiple backends: Supports Badger (embedded) and PostgreSQL

• Block-level versioning: Every entry tagged with block number for historical queries

• High-performance serving: gRPC API for data retrieval

• Streaming ingestion: Continuous processing via Substreams sink

Foundational Stores are typically populated by Substreams modules that extract and transform blockchain data, then serve that data to other Substreams modules for efficient lookups.

Hosting a Foundational Store

To host a Foundational Store, you need:

1. A Substreams module that produces the data to store

2. The foundational-store binary

3. A storage backend (Badger or PostgreSQL)

Step 1: Prepare Your Data Source

Your Substreams module should output data in the format expected by the Foundational Store. The data is stored as key-value pairs with block-level versioning.

Example: The SPL Initialized Account module tracks Solana SPL token account initializations and stores account-to-owner mappings.

Step 2: Build the Foundational Store Binary

git clone https://github.com/streamingfast/substreams-foundational-store
cd substreams-foundational-store
go build -o foundational-store ./cmd/foundational-store

Step 3: Run the Server

Start the server with your Substreams module as the data source:

./foundational-store server \
  --dsn "badger:///path/to/data" \
  --type-url "your.module.Type" \
  --manifest-path "../your-substreams-module/substreams.yaml" \
  --output-module-name "your_output_module" \
  --endpoint "your-blockchain.streamingfast.io:443" \
  --start-block 1000000

Configuration Options:

• --dsn: Storage backend (Badger: badger:///path, PostgreSQL: postgres://user:pass@host/db)

• --type-url: Protobuf type URL for stored values

• --manifest-path: Path to your Substreams manifest

• --output-module-name: Name of the output module in your Substreams

• --endpoint: StreamingFast endpoint for blockchain data

• --start-block: Block to start ingestion from

Step 4: Monitor and Maintain

The server exposes:

• gRPC API on port 50051 (default)

• Prometheus metrics on port 9102 (default)

• Health checks via gRPC reflection

Monitor ingestion progress through cursor files and Prometheus metrics.

Using SinkEntries for Data Ingestion

Foundational Stores ingest data through SinkEntries messages, which allow batch operations with conditional insertion.

SinkEntries Structure

message SinkEntries {
  repeated Entry entries = 1;
  bool if_not_exist = 2;
}

• entries: Array of key-value pairs to store

• if_not_exist: Controls insertion behavior

Conditional Insertion with if_not_exist

The if_not_exist flag determines how entries are inserted:

• When false (default): Entries are inserted regardless of existing keys, potentially overwriting previous values

• When true: Entries are only inserted if the key doesn't already exist in the store

The check is performed against both the in-memory cache and persistent storage to ensure consistency.

Example: Producing Data for a Foundational Store

The spl-initialized-account module shows how to produce data that populates a Foundational Store.

It processes Solana transactions and outputs account-owner mappings as SinkEntries:

#[substreams::handlers::map]
fn map_initialize_account(txn: Transaction) -> Result<SinkEntries, Error> {
    let mut entries = vec![];

    // Extract account initialization data
    let account = txn.accounts[0];
    let owner = extract_owner_from_instruction(&txn)?;

    // Create entry with conditional insertion to prevent duplicates
    entries.push(Entry {
        key: Key { bytes: account.to_bytes().to_vec() },
        value: Any::pack(&AccountOwner {
            account: account.clone(),
            owner,
        })?,
    });

    Ok(SinkEntries {
        entries,
        if_not_exist: true,  // Only store if account not already initialized
    })
}

This data is then ingested by the Foundational Store sink for later querying.

Best Practices

Hosting

• Configure appropriate batch sizes and flush intervals for your throughput needs

• Implement proper backup strategies for your storage backend

Performance Tuning

• Adjust --batch-size and --max-batch-time based on your data volume

• Use SSD storage for Badger backend