Iceberg Schema Definitions

We use Apache Iceberg for historical data storage. The metadata server is a PostgreSQL database.

This directory stores schema files and database setup.

Tables

trading.ohlc

Historical OHLC (Open, High, Low, Close, Volume) candle data for all periods in a single table.

Schema: ohlc_schema.sql

Natural Key: (ticker, period_seconds, timestamp) - uniqueness enforced by application

Partitioning: (ticker, days(timestamp))

Partition by ticker to isolate different markets
Partition by days for efficient time-range queries
Hidden partitioning - not exposed in queries

Iceberg Version: Format v1 (1.10.1)

Uses equality delete files for deduplication
Flink upsert mode generates equality deletes
Last-write-wins semantics for duplicates
Copy-on-write mode for better query performance

Deduplication:

Flink Iceberg sink with upsert mode
Equality delete files on (ticker, period_seconds, timestamp)
PyIceberg automatically filters deleted rows during queries

Storage Format: Parquet with Snappy compression

Supported Periods: Any period in seconds (60, 300, 900, 3600, 14400, 86400, 604800, etc.)

Usage:

-- Query 1-hour candles for specific ticker and time range
SELECT * FROM trading.ohlc
WHERE ticker = 'BINANCE:BTC/USDT'
  AND period_seconds = 3600
  AND timestamp BETWEEN 1735689600000000 AND 1736294399000000
ORDER BY timestamp;

-- Query most recent 1-minute candles
SELECT * FROM trading.ohlc
WHERE ticker = 'BINANCE:BTC/USDT'
  AND period_seconds = 60
  AND timestamp > (UNIX_MICROS(CURRENT_TIMESTAMP()) - 3600000000)
ORDER BY timestamp DESC
LIMIT 60;

-- Query all periods for a ticker
SELECT period_seconds, COUNT(*) as candle_count
FROM trading.ohlc
WHERE ticker = 'BINANCE:BTC/USDT'
GROUP BY period_seconds;

Access Patterns

Flink (Write)

Reads OHLCBatch from Kafka
Writes rows to Iceberg table
Uses Iceberg Flink connector
Upsert mode to handle duplicates

Client-Py (Read)

Queries historical data after receiving HistoryReadyNotification
Uses PyIceberg or Iceberg REST API
Read-only access via Iceberg catalog

Web UI (Read)

Queries for chart display
Time-series queries with partition pruning
Read-only access

Catalog Configuration

The Iceberg catalog is accessed via REST API:

catalog:
  type: rest
  uri: http://iceberg-catalog:8181
  warehouse: s3://trading-warehouse/
  s3:
    endpoint: http://minio:9000
    access-key-id: ${S3_ACCESS_KEY}
    secret-access-key: ${S3_SECRET_KEY}

Table Naming Convention

{namespace}.ohlc where:

namespace: Trading namespace (default: "trading")
All OHLC data is stored in a single table
Partitioned by ticker and date for efficient queries

Integration Examples

Flink Write

TableLoader tableLoader = TableLoader.fromCatalog(
    CatalogLoader.rest("trading", catalogUri),
    TableIdentifier.of("trading", "ohlc")
);

DataStream<Row> ohlcRows = // ... from OHLCBatch

FlinkSink.forRow(ohlcRows, schema)
    .tableLoader(tableLoader)
    .upsert(true)
    .build();

Python Read

from pyiceberg.catalog import load_catalog

catalog = load_catalog("trading", uri="http://iceberg-catalog:8181")
table = catalog.load_table("trading.ohlc")

# Query with filters
df = table.scan(
    row_filter=(
        (col("ticker") == "BINANCE:BTC/USDT") &
        (col("period_seconds") == 3600) &
        (col("timestamp") >= 1735689600000000)
    )
).to_pandas()

3.6 KiB Raw Permalink Blame History