ZeptoDB Quick Start

From zero to first query in 5 minutes.

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1. Start the Server

Option A: Docker (recommended)

docker run -p 8123:8123 -p 8815:8815 zeptodb/zeptodb:0.0.1 --demo

The --demo flag preloads sample trade/quote data so you can query immediately.

Web UI available at http://localhost:8123/ui/ For Docker-specific options, see Docker Deployment Guide.

Option B: Build from source

git clone https://github.com/zeptodb/zeptodb.git && cd zeptodb
mkdir -p build && cd build
cmake .. -G Ninja -DCMAKE_BUILD_TYPE=Release \
  -DCMAKE_C_COMPILER=clang-19 -DCMAKE_CXX_COMPILER=clang++-19
ninja -j$(nproc)
./zepto_server --port 8123 --demo

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2. Verify It’s Running

curl http://localhost:8123/ping
# Ok

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3. Insert Data

Skip this if you used --demo — sample data is already loaded.

curl -s -X POST http://localhost:8123/ -d "
CREATE TABLE IF NOT EXISTS trades (
  symbol STRING, price INT64, volume INT64, timestamp INT64
)"

curl -s -X POST http://localhost:8123/ -d "
INSERT INTO trades VALUES
  ('AAPL', 18750, 100, 1711234567000000000),
  ('AAPL', 18760, 200, 1711234568000000000),
  ('AAPL', 18755, 150, 1711234569000000000),
  ('GOOG', 17800, 80,  1711234567000000000),
  ('GOOG', 17810, 120, 1711234568000000000)
"

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4. Query

# VWAP by symbol
curl -s -X POST http://localhost:8123/ \
  -d "SELECT symbol, vwap(price, volume) AS vwap, count(*) AS n FROM trades GROUP BY symbol"

# 5-minute OHLCV bars
curl -s -X POST http://localhost:8123/ -d "
SELECT xbar(timestamp, 300000000000) AS bar,
       first(price) AS open, max(price) AS high,
       min(price) AS low, last(price) AS close,
       sum(volume) AS vol
FROM trades WHERE symbol = 'AAPL'
GROUP BY xbar(timestamp, 300000000000)
"

# EMA with delta
curl -s -X POST http://localhost:8123/ -d "
SELECT symbol, price,
       EMA(price, 20) OVER (PARTITION BY symbol ORDER BY timestamp) AS ema20,
       DELTA(price) OVER (ORDER BY timestamp) AS change
FROM trades
"

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5. Connect from Python

pip install zeptodb pyarrow

Via Arrow Flight (recommended for remote)

import pyarrow.flight as fl

client = fl.connect("grpc://localhost:8815")
table = client.do_get(fl.Ticket(
    "SELECT symbol, vwap(price, volume) AS vwap FROM trades GROUP BY symbol"
)).read_all()
print(table.to_pandas())

Via HTTP client

import zepto_py as zp

db = zp.connect("localhost", 8123)
df = db.query_pandas("SELECT * FROM trades WHERE symbol = 'AAPL' ORDER BY timestamp")
print(df)

In-process (maximum performance)

import zeptodb
from zepto_py import from_polars, query_to_pandas
import polars as pl

pipeline = zeptodb.Pipeline()
pipeline.start()

df = pl.DataFrame({
    "symbol": [1, 1, 2], "price": [15000, 15010, 20000],
    "volume": [100, 150, 80],
})
from_polars(df, pipeline, symbol_col="symbol", price_col="price", volume_col="volume")
pipeline.drain()

result = query_to_pandas(pipeline, "SELECT symbol, vwap(price, volume) AS vwap FROM trades GROUP BY symbol")
print(result)
pipeline.stop()

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6. Open the Web UI

Navigate to http://localhost:8123 in your browser.

The built-in query editor supports:

• SQL autocomplete (schema-aware + ZeptoDB functions)
• Multi-tab editor with independent results
• Chart view (line, bar, candlestick)
• Export to CSV/JSON
• Query history

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Next Steps

What	Link
Full SQL syntax	SQL Reference
Python API	Python Reference
Arrow Flight	Flight Reference
HTTP endpoints	HTTP Reference
Configuration	Config Reference
Production deployment	Deployment Guide
Migrate from kdb+/ClickHouse	Migration