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Use Case

You want to build a real-time trading analytics dashboard by streaming Hyperliquid fill events into ClickHouse. This gives you sub-second query performance over high-volume trading data for PnL tracking, volume analysis, and market monitoring.

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Pipeline Configuration

1

Create a new pipeline

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

Select your source

Choose Hyperliquid as the chain and Fills as the data type. This streams every order fill event from Hyperliquid.
3

Configure the ClickHouse destination

Select ClickHouse as the destination type and enter your connection details:
destination:
  type: "clickhouse"
  url: "clickhouse://your-host:8123/analytics"
  user: "${CH_USER}"
  password: "${CH_PASSWORD}"
  batch_size: 10000
4

Add a SQL transform (optional)

Add a transform to filter for specific trading pairs and map side codes to human-readable labels:
transforms:
  hl_fills: >
    SELECT block_number, block_time, user_address, coin, px, sz, fee,
           CASE WHEN side = 'B' THEN 'buy' ELSE 'sell' END AS trade_side,
           closed_pnl, tid
    FROM hl_fills
    WHERE coin IN ('ETH', 'BTC', 'SOL')
5

Deploy

Review and deploy. Fill events begin flowing to ClickHouse within seconds.

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Verify Data

SELECT coin, trade_side, count(*) AS fills,
       sum(toFloat64OrZero(sz)) AS total_size
FROM hl_analytics.hl_fills
WHERE block_time >= now() - INTERVAL 1 HOUR
GROUP BY coin, trade_side
ORDER BY total_size DESC;

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Sample Analytical Queries

Volume by coin over the last 24 hours: 
SELECT coin,
       count(*) AS trade_count,
       sum(toFloat64OrZero(sz) * toFloat64OrZero(px)) AS volume_usd
FROM hl_analytics.hl_fills
WHERE block_time >= toString(now() - INTERVAL 24 HOUR)
GROUP BY coin
ORDER BY volume_usd DESC;
Top traders by realized PnL: 
SELECT user_address,
       sum(toFloat64OrZero(closed_pnl)) AS total_pnl,
       count(*) AS fill_count
FROM hl_analytics.hl_fills
WHERE closed_pnl != '0' AND closed_pnl != ''
GROUP BY user_address
ORDER BY total_pnl DESC
LIMIT 20;

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Production Tips

  • Batch size: ClickHouse performs best with large inserts. The default of 10,000 rows per batch is a good starting point. Increase to 50,000+ for high-throughput streams.
  • ORDER BY key: Choose columns that match your most common query patterns. (coin, block_number) works well for time-series analytics by trading pair.
  • Trades vs Fills: Use the fills entity for per-user fill data (includes PnL, fees). Use the trades entity for aggregate market trades (includes buyer/seller details).