Skip to main content

Documentation Index

Fetch the complete documentation index at: https://goldrush.dev/docs/llms.txt

Use this file to discover all available pages before exploring further.

​
Use Case

You want to build a real-time DEX analytics dashboard by streaming trade data across multiple Solana DEX protocols (Raydium, Orca, Meteora, PumpFun, and others) into ClickHouse. The dex_trades entity aggregates trades from all supported protocols into a single, unified schema.

​
Pipeline Configuration

1

Create a new pipeline

In the GoldRush Platform, navigate to Manage Pipelines and click Create Pipeline. Name it dex-analytics.
2

Configure the ClickHouse destination

Select ClickHouse as the destination type:
destination:
  type: "clickhouse"
  url: "clickhouse://your-host:8123/dex_data"
  user: "${CH_USER}"
  password: "${CH_PASSWORD}"
  batch_size: 10000
  flush_interval_ms: 3000
3

Select your source

Choose Solana as the chain and Swaps as the data type. This streams every decoded DEX trade across all supported Solana protocols.
4

Add a SQL transform (optional)

Filter out dust trades and select the most useful columns:
transforms:
  swaps: >
    SELECT block_slot, block_date, tx_id, pool_address, base_mint, quote_mint,
           base_amount, quote_amount, price_usd, volume_usd, protocol_name,
           trader, txn_fee_lamports
    FROM swaps
    WHERE volume_usd > 10
5

Deploy

Review and deploy. DEX trades begin streaming to ClickHouse.

​
Verify Data

SELECT protocol_name, count(*) AS trades, sum(volume_usd) AS total_volume
FROM dex_analytics.sol_dex_trades
WHERE block_date = today()
GROUP BY protocol_name
ORDER BY total_volume DESC;

​
Sample Dashboard Queries

Volume by protocol over the last 24 hours: 
SELECT protocol_name,
       count(*) AS trade_count,
       sum(volume_usd) AS volume_usd,
       avg(volume_usd) AS avg_trade_size
FROM dex_analytics.sol_dex_trades
WHERE block_date >= toString(today() - 1)
GROUP BY protocol_name
ORDER BY volume_usd DESC;
Top pools by volume: 
SELECT pool_address, base_mint, quote_mint, protocol_name,
       count(*) AS trades,
       sum(volume_usd) AS volume_usd
FROM dex_analytics.sol_dex_trades
WHERE block_date = today()
GROUP BY pool_address, base_mint, quote_mint, protocol_name
ORDER BY volume_usd DESC
LIMIT 20;
OHLCV aggregation for a token (5-minute candles): 
SELECT toStartOfFiveMinutes(parseDateTimeBestEffort(block_date)) AS candle_time,
       min(price_usd) AS low,
       max(price_usd) AS high,
       argMin(price_usd, block_slot) AS open,
       argMax(price_usd, block_slot) AS close,
       sum(volume_usd) AS volume
FROM dex_analytics.sol_dex_trades
WHERE base_mint = 'your-token-mint'
  AND block_date >= toString(today())
GROUP BY candle_time
ORDER BY candle_time;
Most active traders: 
SELECT trader,
       count(*) AS trade_count,
       sum(volume_usd) AS total_volume,
       countDistinct(base_mint) AS unique_tokens
FROM dex_analytics.sol_dex_trades
WHERE block_date = today()
GROUP BY trader
ORDER BY total_volume DESC
LIMIT 20;

​
Production Tips

  • Flush interval: A 3-second flush interval keeps dashboard data fresh. Increase to 5-10 seconds if write throughput becomes a bottleneck.
  • volume_usd > 10 filter: Removes dust trades that add noise to analytics. Adjust the threshold based on your use case.
  • Protocol coverage: The sol_dex_trades entity aggregates trades from all supported Solana protocols. The protocol_name column identifies the source (Raydium, Orca, Meteora, etc.).
  • Materialized views: For high-frequency dashboard queries, consider ClickHouse materialized views to pre-aggregate data by time bucket and protocol.