---

name: substreams-sql description: Expert knowledge for building SQL database sinks from Substreams. Covers database changes (CDC), relational mappings, PostgreSQL, ClickHouse, and materialized views. license: Apache-2.0 compatibility: platforms: [claude-code, cursor, vscode, windsurf] metadata: version: 1.0.0 author: StreamingFast documentation: https://substreams.streamingfast.io

Substreams SQL Expert

Expert assistant for building SQL database sinks from Substreams data - transforming blockchain data into relational databases.

Prerequisites

• substreams-sink-sql: Required CLI tool for database sink workflows. Install from releases.

Core Concepts

What is Substreams SQL?

Substreams SQL enables you to:

• Transform blockchain data into structured SQL tables
• Stream changes to PostgreSQL, ClickHouse, and other databases
• Build materialized views with real-time updates
• Handle reorgs with proper cursor-based streaming
• Scale horizontally with parallel processing

Two Main Approaches

1. Database Changes (CDC) - Stream individual row changes
2. Relational Mappings - Transform data into normalized tables

Database Changes (CDC) Approach

Overview

The CDC approach streams individual database operations (INSERT, UPDATE, DELETE) to maintain real-time consistency.

Key Components

Protobuf Schema: The DatabaseChanges protobuf type is provided by the official substreams-sink-database-changes spkg — you do NOT need to define your own proto. Import it in your manifest (see Manifest Configuration below).

Rust Implementation:

use substreams::prelude::*;
use substreams_database_change::pb::sf::substreams::sink::database::v1::DatabaseChanges;
use substreams_database_change::tables::Tables;

#[substreams::handlers::map]
pub fn db_out(events: Events) -> Result<DatabaseChanges, Error> {
    let mut tables = Tables::new();

    for transfer in events.transfers {
        tables
            .create_row("transfers", format!("{}-{}", transfer.tx_hash, transfer.log_index))
            .set("tx_hash", transfer.tx_hash)
            .set("from_addr", transfer.from)
            .set("to_addr", transfer.to)
            .set("amount", transfer.amount)
            .set("block_num", transfer.block_number)
            .set("timestamp", transfer.timestamp);
    }

    for balance in events.balance_changes {
        tables
            .update_row("balances", balance.address)
            .set("balance", balance.new_balance)
            .set("updated_at", balance.timestamp);
    }

    Ok(tables.to_database_changes())
}

Manifest Configuration

The manifest requires importing the database changes and sink-sql protodefs spkgs, and a sink: section:

specVersion: v0.1.0
package:
  name: my-substreams-sql
  version: v0.1.0

imports:
    database: https://github.com/streamingfast/substreams-sink-database-changes/releases/download/v3.0.0/substreams-sink-database-changes-v3.0.0.spkg
    sql: https://github.com/streamingfast/substreams-sink-sql/releases/download/protodefs-v1.0.7/substreams-sink-sql-protodefs-v1.0.7.spkg

protobuf:
  excludePaths:
    - sf/substreams
    - google

binaries:
  default:
    type: wasm/rust-v1
    file: ./target/wasm32-unknown-unknown/release/my_substreams_sql.wasm

network: mainnet

modules:
  - name: db_out
    kind: map
    inputs:
      - map: map_events
    output:
      type: proto:sf.substreams.sink.database.v1.DatabaseChanges

sink:
  module: db_out
  type: sf.substreams.sink.sql.v1.Service
  config:
    schema: ./schema.sql
    engine: postgres

Cargo.toml dependency (v4 with delta updates support):

[dependencies]
substreams-database-change = "4"

Running the sink (DSN is passed on the command line, not in the manifest):

# 1. Build
substreams build

# 2. Setup system tables + apply schema.sql
substreams-sink-sql setup "psql://user:pass@localhost:5432/db?sslmode=disable" my-substreams-sql-v0.1.0.spkg

# 3. Run the sink
substreams-sink-sql run "psql://user:pass@localhost:5432/db?sslmode=disable" my-substreams-sql-v0.1.0.spkg

Advantages

• ✅ Real-time consistency - Changes applied immediately
• ✅ Handles reorgs - Can reverse/replay operations
• ✅ Efficient updates - Only changed data is transmitted
• ✅ ACID compliance - Database transactions ensure consistency

Use Cases

• Real-time dashboards
• Trading applications
• Balance tracking
• Event sourcing

Delta Updates for Aggregations

Delta updates enable atomic modifications to database rows without read-modify-write cycles. This is essential for building aggregation patterns like candles, counters, and real-time analytics — pushing chain-wide computation directly into the database instead of using Substreams store modules.

Note: Delta updates require PostgreSQL and substreams-sink-sql >= v4.12.0.

use substreams_database_change::tables::Tables;
use substreams_database_change::pb::sf::substreams::sink::database::v1::DatabaseChanges;

#[substreams::handlers::map]
fn db_out(events: Events) -> Result<DatabaseChanges, substreams::errors::Error> {
    let mut tables = Tables::new();

    for event in &events.items {
        // Composite primary keys use an array of tuples
        tables.upsert_row("aggregates", [
            ("key1", value1),
            ("key2", value2),
        ])
            .set_if_null("first_seen", &timestamp)  // First write wins
            .set("last_seen", &timestamp)            // Always overwrite
            .max("highest", value)                   // Track maximum
            .min("lowest", value)                    // Track minimum
            .add("total", amount)                    // Accumulate
            .add("count", 1i64);                     // Count
    }

    Ok(tables.to_database_changes())
}

Supported Delta Operations:

Operation	SQL Equivalent	Use Case
set_if_null	COALESCE(column, value)	First-write-wins
set	column = value	Always overwrite
max	GREATEST(column, value)	Track maximum
min	LEAST(column, value)	Track minimum
add	COALESCE(column, 0) + value	Accumulate
sub	COALESCE(column, 0) - value	Decrement

Important Notes:

• The add() operation requires values implementing NumericAddable — pass owned values (volume.clone() or volume) rather than &String references
• Ordinals are automatically managed by the Tables struct — no manual management needed

Relational Mappings Approach

Overview

Transform Substreams data into normalized relational tables with proper foreign keys and indexes.

Schema Design

Example Schema (schema.sql):

-- Blocks table
CREATE TABLE blocks (
    number BIGINT PRIMARY KEY,
    hash VARCHAR(66) NOT NULL,
    timestamp TIMESTAMP NOT NULL,
    parent_hash VARCHAR(66),
    size_bytes BIGINT
);

-- Transactions table  
CREATE TABLE transactions (
    hash VARCHAR(66) PRIMARY KEY,
    block_number BIGINT REFERENCES blocks(number),
    from_addr VARCHAR(42),
    to_addr VARCHAR(42),
    value NUMERIC(78,0),
    gas_limit BIGINT,
    gas_used BIGINT,
    status INTEGER
);

-- ERC20 Transfers
CREATE TABLE erc20_transfers (
    id SERIAL PRIMARY KEY,
    tx_hash VARCHAR(66) REFERENCES transactions(hash),
    log_index INTEGER,
    contract_address VARCHAR(42),
    from_addr VARCHAR(42),
    to_addr VARCHAR(42),
    amount NUMERIC(78,0),
    block_number BIGINT
);

-- Token balances (aggregated)
CREATE TABLE token_balances (
    address VARCHAR(42),
    token_address VARCHAR(42),
    balance NUMERIC(78,0),
    last_updated BIGINT,
    PRIMARY KEY (address, token_address)
);

-- Indexes for performance
CREATE INDEX idx_transfers_contract ON erc20_transfers(contract_address);
CREATE INDEX idx_transfers_from ON erc20_transfers(from_addr);
CREATE INDEX idx_transfers_to ON erc20_transfers(to_addr);
CREATE INDEX idx_transfers_block ON erc20_transfers(block_number);

Rust Implementation:

#[substreams::handlers::map]
pub fn db_out(block: Block) -> Result<DatabaseChanges, Error> {
    let mut tables = Tables::new();

    // Insert block data
    tables
        .create_row("blocks", block.number.to_string())
        .set("number", block.number)
        .set("hash", Hex::encode(&block.hash))
        .set("timestamp", block.timestamp_seconds())
        .set("parent_hash", Hex::encode(&block.parent_hash))
        .set("size_bytes", block.size);

    for (tx_idx, tx) in block.transaction_traces.iter().enumerate() {
        // Insert transaction
        let tx_hash = Hex::encode(&tx.hash);
        tables
            .create_row("transactions", &tx_hash)
            .set("hash", &tx_hash)
            .set("block_number", block.number)
            .set("from_addr", Hex::encode(&tx.from))
            .set("to_addr", Hex::encode(&tx.to))
            .set("value", tx.value.to_string())
            .set("gas_limit", tx.gas_limit)
            .set("gas_used", tx.gas_used)
            .set("status", tx.status);

        // Process ERC20 transfers
        for (log_idx, log) in tx.receipt.logs.iter().enumerate() {
            if is_erc20_transfer(log) {
                let transfer = decode_transfer(log);
                let transfer_id = format!("{}-{}", tx_hash, log_idx);
                
                tables
                    .create_row("erc20_transfers", transfer_id)
                    .set("tx_hash", &tx_hash)
                    .set("log_index", log_idx as i32)
                    .set("contract_address", Hex::encode(&log.address))
                    .set("from_addr", transfer.from)
                    .set("to_addr", transfer.to)
                    .set("amount", transfer.amount)
                    .set("block_number", block.number);
            }
        }
    }

    Ok(tables.to_database_changes())
}

Advantages

• ✅ Normalized data - Proper relational structure
• ✅ Complex queries - JOIN operations, aggregations
• ✅ Data integrity - Foreign keys, constraints
• ✅ Analytics friendly - Optimized for reporting

PostgreSQL Implementation

Setup and Configuration

Docker Setup:

# docker-compose.yml
version: '3.8'
services:
  postgres:
    image: postgres:15
    environment:
      POSTGRES_DB: substreams
      POSTGRES_USER: substreams
      POSTGRES_PASSWORD: password
    ports:
      - "5432:5432"
    volumes:
      - postgres_data:/var/lib/postgresql/data
      - ./schema.sql:/docker-entrypoint-initdb.d/schema.sql

volumes:
  postgres_data:

Running the Sink:

# The DSN is passed as a CLI argument, not in the manifest
substreams-sink-sql setup "psql://substreams:password@localhost:5432/substreams?sslmode=disable" ./my-substreams.spkg
substreams-sink-sql run "psql://substreams:password@localhost:5432/substreams?sslmode=disable" ./my-substreams.spkg

# Development mode (allows re-processing):
substreams-sink-sql run --development-mode "psql://substreams:password@localhost:5432/substreams?sslmode=disable" ./my-substreams.spkg

PostgreSQL-Specific Features

JSONB Support:

-- Store complex data as JSONB
CREATE TABLE transaction_logs (
    tx_hash VARCHAR(66),
    log_index INTEGER,
    data JSONB,
    topics JSONB
);

-- Index JSONB fields
CREATE INDEX idx_logs_data ON transaction_logs USING GIN (data);

Materialized Views:

-- Daily transfer volumes
CREATE MATERIALIZED VIEW daily_transfer_volumes AS
SELECT 
    DATE(to_timestamp(timestamp)) as date,
    contract_address,
    COUNT(*) as transfer_count,
    SUM(amount) as total_volume
FROM erc20_transfers t
JOIN transactions tx ON t.tx_hash = tx.hash
GROUP BY DATE(to_timestamp(timestamp)), contract_address;

-- Refresh strategy (handled by Substreams)
CREATE UNIQUE INDEX ON daily_transfer_volumes (date, contract_address);

Performance Tuning:

-- Partitioning by block number
CREATE TABLE erc20_transfers (
    -- columns...
    block_number BIGINT
) PARTITION BY RANGE (block_number);

CREATE TABLE erc20_transfers_y2024 PARTITION OF erc20_transfers
    FOR VALUES FROM (18000000) TO (20000000);

-- Parallel processing
SET max_parallel_workers_per_gather = 4;
SET max_parallel_workers = 8;

Best Practices for PostgreSQL

1. Use appropriate data types:

   -- Use NUMERIC for big integers (token amounts)
   amount NUMERIC(78,0)  -- Not BIGINT
   
   -- Use proper VARCHAR sizes
   address VARCHAR(42)   -- Ethereum addresses
   hash VARCHAR(66)      -- Transaction hashes
   

2. Index strategically:

   -- Query-specific indexes
   CREATE INDEX idx_transfers_token_date ON erc20_transfers(contract_address, block_number);
   
   -- Partial indexes for active data
   CREATE INDEX idx_active_balances ON token_balances(address) WHERE balance > 0;
   

3. Use constraints:

   -- Data validation
   ALTER TABLE erc20_transfers ADD CONSTRAINT check_positive_amount 
       CHECK (amount >= 0);
   
   -- Foreign keys for referential integrity
   ALTER TABLE erc20_transfers ADD CONSTRAINT fk_transaction
       FOREIGN KEY (tx_hash) REFERENCES transactions(hash);
   

ClickHouse Implementation

Setup and Configuration

Docker Setup:

# docker-compose.yml
version: '3.8'
services:
  clickhouse:
    image: clickhouse/clickhouse-server:latest
    ports:
      - "9000:9000"
      - "8123:8123"
    environment:
      CLICKHOUSE_DEFAULT_ACCESS_MANAGEMENT: 1
    volumes:
      - clickhouse_data:/var/lib/clickhouse
      - ./clickhouse-schema.sql:/docker-entrypoint-initdb.d/schema.sql

volumes:
  clickhouse_data:

Manifest Configuration (ClickHouse):

# In substreams.yaml, set engine to clickhouse
sink:
  module: db_out
  type: sf.substreams.sink.sql.v1.Service
  config:
    schema: ./clickhouse-schema.sql
    engine: clickhouse

Running the Sink (DSN passed on command line):

substreams-sink-sql setup "clickhouse://default:@localhost:9000/default" ./my-substreams.spkg
substreams-sink-sql run "clickhouse://default:@localhost:9000/default" ./my-substreams.spkg

ClickHouse Schema Design

Optimized for Analytics:

-- Blocks table with MergeTree engine
CREATE TABLE blocks (
    number UInt64,
    hash String,
    timestamp DateTime,
    parent_hash String,
    size_bytes UInt64,
    tx_count UInt32
) ENGINE = MergeTree()
ORDER BY number
SETTINGS index_granularity = 8192;

-- ERC20 transfers optimized for time-series queries
CREATE TABLE erc20_transfers (
    block_number UInt64,
    tx_hash String,
    log_index UInt32,
    contract_address String,
    from_addr String,
    to_addr String,
    amount UInt256,
    timestamp DateTime,
    
    -- Pre-computed fields for analytics
    hour DateTime,
    date Date
) ENGINE = MergeTree()
PARTITION BY toYYYYMM(date)
ORDER BY (contract_address, timestamp, block_number)
SETTINGS index_granularity = 8192;

-- Materialized view for real-time aggregations
CREATE MATERIALIZED VIEW hourly_transfer_stats
ENGINE = SummingMergeTree()
PARTITION BY toYYYYMM(hour)
ORDER BY (contract_address, hour)
AS SELECT
    contract_address,
    toStartOfHour(timestamp) AS hour,
    count() AS transfer_count,
    sum(amount) AS total_volume
FROM erc20_transfers
GROUP BY contract_address, hour;

ClickHouse-Specific Features:

-- Compression and optimization
ALTER TABLE erc20_transfers MODIFY COLUMN amount Codec(Delta, ZSTD);

-- TTL for data lifecycle management
ALTER TABLE erc20_transfers MODIFY TTL date + INTERVAL 2 YEAR;

-- Dictionaries for dimension data
CREATE DICTIONARY token_metadata (
    address String,
    symbol String,
    decimals UInt8,
    name String
) PRIMARY KEY address
SOURCE(POSTGRESQL(
    host 'postgres'
    port 5432
    user 'substreams'
    password 'password'
    db 'substreams'
    table 'token_metadata'
))
LIFETIME(300)
LAYOUT(HASHED());

Performance Optimization

Partitioning Strategy:

-- Monthly partitioning for time-series data
CREATE TABLE erc20_transfers_distributed AS erc20_transfers
ENGINE = Distributed('cluster', 'default', 'erc20_transfers', rand());

-- Partition pruning
SELECT * FROM erc20_transfers 
WHERE date >= '2024-01-01' AND date < '2024-02-01';

Query Optimization:

-- Use projection for common query patterns
ALTER TABLE erc20_transfers ADD PROJECTION daily_stats (
    SELECT 
        date,
        contract_address,
        sum(amount),
        count()
    GROUP BY date, contract_address
);

-- Pre-aggregated tables
CREATE TABLE daily_token_stats
ENGINE = AggregatingMergeTree()
PARTITION BY toYYYYMM(date)
ORDER BY (date, contract_address)
AS SELECT
    date,
    contract_address,
    sumState(amount) as total_volume,
    countState() as transfer_count
FROM erc20_transfers
GROUP BY date, contract_address;

Materialized Views and Real-time Updates

Concept

Materialized views provide pre-computed query results that update automatically as new data arrives.

Implementation Patterns

PostgreSQL Materialized Views:

-- Token holder counts
CREATE MATERIALIZED VIEW token_holder_counts AS
SELECT 
    token_address,
    COUNT(DISTINCT address) as holder_count,
    SUM(balance) as total_supply
FROM token_balances 
WHERE balance > 0
GROUP BY token_address;

-- Refresh trigger (automated by Substreams)
CREATE OR REPLACE FUNCTION refresh_token_holder_counts()
RETURNS TRIGGER AS $$
BEGIN
    REFRESH MATERIALIZED VIEW CONCURRENTLY token_holder_counts;
    RETURN NULL;
END;
$$ LANGUAGE plpgsql;

CREATE TRIGGER token_balance_change_trigger
    AFTER INSERT OR UPDATE OR DELETE ON token_balances
    FOR EACH STATEMENT
    EXECUTE FUNCTION refresh_token_holder_counts();

ClickHouse Materialized Views:

-- Real-time price calculations
CREATE MATERIALIZED VIEW token_prices_mv
ENGINE = ReplacingMergeTree(timestamp)
ORDER BY (token_address, timestamp)
AS SELECT
    token_address,
    timestamp,
    price_usd,
    volume_24h,
    market_cap
FROM (
    -- Complex price calculation logic
    SELECT 
        t.contract_address as token_address,
        toStartOfMinute(t.timestamp) as timestamp,
        calculatePrice(t.amount, p.eth_price) as price_usd,
        sum(t.amount) OVER (
            PARTITION BY t.contract_address 
            ORDER BY t.timestamp 
            RANGE BETWEEN INTERVAL 24 HOUR PRECEDING AND CURRENT ROW
        ) as volume_24h
    FROM erc20_transfers t
    JOIN eth_prices p ON toStartOfMinute(t.timestamp) = p.timestamp
    WHERE t.contract_address = '0x...' -- USDC
);

Update Strategies

Incremental Updates:

// Track last processed block for incremental updates
#[substreams::handlers::store]
pub fn store_last_block(block: Block, store: StoreSetInt64) {
    store.set(0, "last_processed_block", &(block.number as i64));
}

// Only process new data
#[substreams::handlers::map]
pub fn incremental_db_out(
    block: Block,
    last_block_store: StoreGetInt64
) -> Result<DatabaseChanges, Error> {
    let last_processed = last_block_store.get_last("last_processed_block")
        .unwrap_or(0);

    if block.number <= last_processed as u64 {
        return Ok(DatabaseChanges::default()); // Skip already processed
    }

    // Process only new block data
    process_block_data(block)
}

Cursor-based Consistency:

Cursor management is handled automatically by substreams-sink-sql. The sink stores cursor state in the cursors system table, enabling automatic resumption and reorg handling. No special manifest configuration is needed — just run substreams-sink-sql setup followed by run.

Advanced Patterns

Multi-Database Sinks

To stream to multiple databases, create separate substreams packages (or separate sink runs) with different sink: configurations:

# operational-substreams.yaml - PostgreSQL for operational data
sink:
  module: db_out_operational
  type: sf.substreams.sink.sql.v1.Service
  config:
    schema: ./operational-schema.sql
    engine: postgres

# analytics-substreams.yaml - ClickHouse for analytics
sink:
  module: db_out_analytics
  type: sf.substreams.sink.sql.v1.Service
  config:
    schema: ./analytics-schema.sql
    engine: clickhouse

# Run each sink separately with its own DSN
substreams-sink-sql run "psql://user:pass@postgres:5432/operational" operational.spkg &
substreams-sink-sql run "clickhouse://default:@clickhouse:9000/analytics" analytics.spkg &

Data Transformation Pipelines

// Multi-stage data processing
#[substreams::handlers::map]
pub fn extract_events(block: Block) -> Result<RawEvents, Error> {
    // Stage 1: Extract raw events
    extract_raw_blockchain_events(block)
}

#[substreams::handlers::map]  
pub fn enrich_events(raw_events: RawEvents) -> Result<EnrichedEvents, Error> {
    // Stage 2: Enrich with metadata, decode parameters
    enrich_with_token_metadata(raw_events)
}

#[substreams::handlers::map]
pub fn db_out_operational(enriched: EnrichedEvents) -> Result<DatabaseChanges, Error> {
    // Stage 3: Transform for operational database (normalized)
    create_operational_tables(enriched)
}

#[substreams::handlers::map]
pub fn db_out_analytics(enriched: EnrichedEvents) -> Result<DatabaseChanges, Error> {
    // Stage 4: Transform for analytics database (denormalized)
    create_analytics_tables(enriched)
}

Troubleshooting

Common Issues

Connection Problems:

# Test database connectivity
psql "postgresql://user:pass@localhost:5432/db" -c "SELECT version();"
clickhouse-client --host localhost --port 9000 --query "SELECT version()"

# Check sink logs
substreams run -s 1000000 -t +1000 db_out --debug

Schema Mismatches:

# Compare expected vs actual schema
pg_dump --schema-only dbname > current_schema.sql
diff schema.sql current_schema.sql

# Re-run setup to apply schema changes (drops and recreates in dev mode)
substreams-sink-sql setup "psql://..." my-substreams.spkg

Performance Issues:

-- Monitor query performance
EXPLAIN ANALYZE SELECT * FROM erc20_transfers WHERE contract_address = '0x...';

-- Check index usage
SELECT schemaname, tablename, indexname, idx_scan, idx_tup_read, idx_tup_fetch
FROM pg_stat_user_indexes 
ORDER BY idx_scan DESC;

-- ClickHouse query profiling
SELECT * FROM system.query_log 
WHERE type = 'QueryFinish' 
ORDER BY event_time DESC 
LIMIT 10;

Data Consistency

Reorg Handling:

// Proper reorg handling in stores
#[substreams::handlers::store]
pub fn store_balances(events: Events, store: StoreSetBigInt) {
    for transfer in events.transfers {
        // Use ordinal for correct ordering within a block
        let key = format!("{}:{}", transfer.contract, transfer.from);
        store.set(transfer.ordinal, &key, &transfer.amount);
    }
}

Cursor Management:

Cursor state is automatically persisted in the cursors table created by substreams-sink-sql setup. On restart, the sink resumes from the last committed cursor. In development mode (--development-mode), undos are handled automatically for reorg safety.

Monitoring and Alerting

Key Metrics:

-- PostgreSQL monitoring
SELECT 
    schemaname,
    tablename,
    n_tup_ins as inserts,
    n_tup_upd as updates,
    n_tup_del as deletes
FROM pg_stat_user_tables 
ORDER BY n_tup_ins DESC;

-- ClickHouse monitoring  
SELECT 
    table,
    sum(rows) as total_rows,
    sum(bytes_on_disk) as size_bytes
FROM system.parts 
WHERE active = 1
GROUP BY table
ORDER BY size_bytes DESC;

Health Checks:

#!/bin/bash
# Database health monitoring script

# Check latest block processed
LATEST_BLOCK=$(psql $DSN -t -c "SELECT MAX(block_number) FROM transactions;")
CHAIN_HEAD=$(curl -s https://api.etherscan.io/api?module=proxy&action=eth_blockNumber | jq -r .result)

LAG=$((CHAIN_HEAD - LATEST_BLOCK))
if [ $LAG -gt 100 ]; then
    echo "WARNING: Database is $LAG blocks behind chain head"
    exit 1
fi

echo "Database is healthy, lag: $LAG blocks"

Resources

• Database Changes Documentation
• PostgreSQL Best Practices
• ClickHouse Optimization Guide
• Schema Design Patterns

Getting Help

• Substreams Discord
• SQL Sink Documentation
• GitHub Issues