Example Dataset Bundle

Preloaded sample data so new users never face an empty database.

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Overview

The --demo server flag loads realistic sample datasets on startup. This eliminates the “blank canvas” problem — users can immediately run meaningful queries.

./zepto_server --port 8123 --demo
# or
docker run -p 8123:8123 zeptodb/zeptodb:latest --demo

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Datasets

trades — Equity tick data

5 symbols × ~20K ticks each = 100K rows

Column	Type	Example
symbol	STRING	AAPL
price	INT64	18750 (= $187.50)
volume	INT64	100
timestamp	INT64	1711234567000000000

Symbols: AAPL, GOOG, MSFT, TSLA, AMZN

Price distribution: realistic random walk around each symbol’s base price. Volume follows a U-shaped intraday pattern (high at open/close).

quotes — Bid/ask book

5 symbols × ~40K quotes each = 200K rows

Column	Type	Example
symbol	STRING	AAPL
bid	INT64	18748
ask	INT64	18752
bid_size	INT64	500
ask_size	INT64	300
timestamp	INT64	1711234567000000000

Quotes arrive ~2× the rate of trades. Spread varies by symbol volatility.

sensors — IoT telemetry

10 devices × ~5K readings each = 50K rows

Column	Type	Example
device_id	STRING	sensor_01
temperature	INT64	2350 (= 23.50°C)
humidity	INT64	6520 (= 65.20%)
timestamp	INT64	1711234567000000000

Devices: sensor_01 through sensor_10. Temperature follows a sinusoidal daily pattern with noise. sensor_07 has injected anomalies for detection demos.

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Starter Queries

Printed to stdout on --demo startup:

═══════════════════════════════════════════════════════
  ZeptoDB Demo Mode — Sample data loaded
  trades: 100K rows | quotes: 200K rows | sensors: 50K rows
═══════════════════════════════════════════════════════

  Try these queries:

  1. SELECT symbol, vwap(price, volume) AS vwap, count(*) AS n
     FROM trades GROUP BY symbol

  2. SELECT xbar(timestamp, 300000000000) AS bar,
            first(price) AS open, max(price) AS high,
            min(price) AS low, last(price) AS close
     FROM trades WHERE symbol = 'AAPL'
     GROUP BY xbar(timestamp, 300000000000) ORDER BY bar

  3. SELECT t.price, q.bid, q.ask
     FROM trades t ASOF JOIN quotes q
     ON t.symbol = q.symbol AND t.timestamp >= q.timestamp
     WHERE t.symbol = 'TSLA' LIMIT 10

  Web UI: http://localhost:8123
═══════════════════════════════════════════════════════

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Data Generation

Demo data is generated deterministically (seeded PRNG) so results are reproducible across runs.

Generation parameters

demo:
  seed: 42
  trading_day: "2024-03-23"
  market_hours: "09:30-16:00 ET"
  symbols:
    AAPL: { base_price: 18750, volatility: 0.001 }
    GOOG: { base_price: 17800, volatility: 0.0012 }
    MSFT: { base_price: 42500, volatility: 0.0008 }
    TSLA: { base_price: 17200, volatility: 0.002 }
    AMZN: { base_price: 18100, volatility: 0.0011 }
  sensors:
    count: 10
    interval_sec: 15
    anomaly_device: "sensor_07"
    anomaly_count: 5

Implementation location

• Data generator: src/demo/demo_data_generator.cpp
• Server flag handling: tools/zepto_http_server.cpp (--demo)
• Config schema: include/zeptodb/demo/demo_config.h

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Memory Footprint

Dataset	Rows	Columns	Approx. Memory
trades	100K	4	~3.2 MB
quotes	200K	6	~9.6 MB
sensors	50K	4	~1.6 MB
Total	350K		~14.4 MB

Negligible overhead — safe to enable even on constrained environments.