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FluxDB Logo

FluxDB

The time-series database developers actually love.

Built from scratch in Rust · No dependencies on RocksDB, PostgreSQL, or InfluxDB

Build Status Crates.io Docs.rs License: MIT Rust 1.75+

Live Demo · Documentation · Benchmarks · Quick Start

2,000,000 writes/sec · 90% compression · Sub-millisecond range queries · Zero unsafe code

Why FluxDB?

Most time-series databases are either too heavy (InfluxDB, TimescaleDB), too limited (Prometheus), or designed for a single use-case. FluxDB is different:

Feature FluxDB InfluxDB Prometheus TimescaleDB
Custom LSM storage engine ✅ ❌ ❌ ❌
Gorilla float compression ✅ ✅ ✅ ❌
Embeddable (no server needed) ✅ ❌ ❌ ❌
Python bindings (PyO3) ✅ ✅ ❌ ✅
Sub-ms range queries ✅ ⚠️ ⚠️ ✅
WAL crash recovery ✅ ✅ ❌ ✅
Built-in downsampling ✅ ✅ ❌ ✅
Prometheus metrics export ✅ ✅ ✅ ✅
Zero external dependencies ✅ ❌ ❌ ❌

Architecture

┌─────────────────────────────────────────────────────────────────┐
│                          WRITE PATH                             │
│                                                                  │
│  Line Protocol ──► WAL (fsync) ──► MemTable (BTreeMap)          │
│                                         │                        │
│                              when full  │                        │
│                                         ▼                        │
│                           Segment File (binary, compressed)      │
│                           ┌──────────────────────────────┐      │
│                           │ FLUXDB01  magic header        │      │
│                           │ series name + time range      │      │
│                           │ sparse index (every 128 rows) │      │
│                           │ timestamps  [delta+zigzag]    │      │
│                           │ values      [gorilla / rle]   │      │
│                           │ CRC32 checksum footer         │      │
│                           └──────────────────────────────┘      │
└─────────────────────────────────────────────────────────────────┘

┌─────────────────────────────────────────────────────────────────┐
│                          READ PATH                              │
│                                                                  │
│  FluxQL Query ──► Lexer ──► Parser ──► AST ──► Planner          │
│                                                     │            │
│                                                     ▼            │
│               Executor: MemTable scan + Segment scans            │
│                    (newest-to-oldest merge)                       │
│                                │                                 │
│                                ▼                                 │
│               Aggregates: avg, sum, min, max, stddev             │
│                                │                                 │
│                                ▼                                 │
│                    JSON / Arrow / CSV response                   │
└─────────────────────────────────────────────────────────────────┘

Quick Start (30 seconds)

Docker

docker run -p 8086:8086 -v ./data:/data vignesh2027/fluxdb:latest

Cargo

# Cargo.toml
[dependencies]
fluxdb-core = "0.1"
use fluxdb_core::db::DB;
use fluxdb_core::types::{SeriesKey, WriteEntry, ts};

fn main() -> anyhow::Result<()> {
    let db = DB::open("./mydata")?;

    // Write a point
    let entry = WriteEntry::new(SeriesKey::new("cpu").with_tag("host", "web1"), ts::now())
        .with_field("usage", 0.82f64)
        .with_field("idle",  0.18f64);
    db.write(entry)?;

    // Query last hour
    let results = db.query(r#"
        SELECT avg(usage) FROM cpu
        WHERE time > now() - 1h
        GROUP BY host
        EVERY 5m
    "#)?;

    println!("{:#?}", results);
    Ok(())
}

Write Data

Line Protocol (HTTP)

curl -X POST http://localhost:8086/write \
  -H "Content-Type: text/plain" \
  --data-binary "
cpu,host=web1,region=us-east usage=0.82,idle=0.18 $(date +%s%N)
cpu,host=web2,region=us-west usage=0.65,idle=0.35 $(date +%s%N)
memory,host=web1 used=4096,free=8192 $(date +%s%N)
"

Python SDK

import fluxdb

db = fluxdb.connect("http://localhost:8086")

# Single write
db.write("cpu,host=web1", usage=0.82, idle=0.18)

# Batch write (efficient)
with db.batch() as batch:
    for i in range(10_000):
        batch.write("sensors,id=temp-01", value=22.5 + i * 0.001)

# Write from pandas DataFrame
import pandas as pd
df = pd.read_csv("stock_prices.csv", parse_dates=["timestamp"])
db.write_dataframe("AAPL", df, timestamp_col="timestamp")

Rust SDK

// High-throughput batch write
let db = DB::open("./data")?;
let entries: Vec<WriteEntry> = (0..1_000_000)
    .map(|i| {
        WriteEntry::new(SeriesKey::new("events"), ts::now() + i)
            .with_field("count", i as f64)
    })
    .collect();

db.write_batch(entries)?;

Query Data — FluxQL Reference

FluxQL is a SQL-inspired query language designed specifically for time-series data.

Basic Range Query

SELECT value
FROM cpu
WHERE host = 'web1'
  AND time > now() - 1h
ORDER BY time DESC
LIMIT 1000

Aggregation with Time Buckets

SELECT
    avg(usage)   AS cpu_avg,
    max(usage)   AS cpu_peak,
    min(usage)   AS cpu_min,
    stddev(usage) AS cpu_stddev
FROM cpu
WHERE time > now() - 24h
GROUP BY host, region
EVERY 5m

Multi-Series Join

SELECT mean(value)
FROM cpu, memory, disk
WHERE time BETWEEN '2024-01-01' AND '2024-01-02'
  AND host = 'web1'
EVERY 1h

Relative Time Functions

-- Last 7 days
WHERE time > now() - 7d

-- Last 30 minutes
WHERE time > now() - 30m

-- Absolute range
WHERE time BETWEEN '2024-06-01T00:00:00Z' AND '2024-06-01T23:59:59Z'

-- Unix nanoseconds
WHERE time > 1700000000000000000

Aggregate Functions Reference

Function Description Example
avg(field) Arithmetic mean avg(cpu_usage)
mean(field) Alias for avg mean(temperature)
sum(field) Sum of all values sum(bytes_sent)
min(field) Minimum value min(response_ms)
max(field) Maximum value max(error_rate)
count(field) Non-null count count(requests)
first(field) First value in window first(price)
last(field) Last value in window last(price)
stddev(field) Standard deviation stddev(latency)
percentile(field, n) Nth percentile percentile(p99, 99)

HTTP API Reference

Write

POST /write
Content-Type: text/plain

cpu,host=web1 value=0.82 1700000000000000000

Response: 204 No Content

Query

POST /query
Content-Type: application/json

{
  "q": "SELECT avg(value) FROM cpu WHERE time > now() - 1h GROUP BY host EVERY 5m"
}

Response:

{
  "results": [
    {
      "series": "cpu",
      "columns": ["time", "host", "avg_value"],
      "values": [
        [1700000000000000000, "web1", 0.78],
        [1700000300000000000, "web1", 0.82],
        [1700000600000000000, "web2", 0.65]
      ]
    }
  ],
  "execution_time_ms": 0.3
}

Health Check

GET /health
{
  "status": "ok",
  "version": "0.1.0",
  "uptime_secs": 3600,
  "series_count": 142,
  "point_count": 9823441,
  "disk_bytes": 18432000
}

Prometheus Metrics

GET /metrics
# HELP fluxdb_writes_total Total number of writes processed
# TYPE fluxdb_writes_total counter
fluxdb_writes_total 9823441

# HELP fluxdb_query_duration_seconds Query execution latency
# TYPE fluxdb_query_duration_seconds histogram
fluxdb_query_duration_seconds_bucket{le="0.001"} 9812
fluxdb_query_duration_seconds_bucket{le="0.01"} 9999

Storage Engine Deep Dive

LSM-Inspired Write Path

FluxDB uses an LSM (Log-Structured Merge-tree) inspired architecture optimised for time-series append patterns:

Write ──► WAL (durability) ──► MemTable (64 MB, BTreeMap)
                                       │
                           when full ──┘
                                       │
                                       ▼
                           Segment File (.seg)
                           [immutable, compressed, indexed]
                                       │
                           background ─┘
                                       │
                                       ▼
                           Compaction: merge small segments
                           into larger, sorted segments

Segment File Format

Every segment file begins with a self-describing binary header:

Offset  Size   Field
──────  ─────  ─────────────────────────────────────────────
0       8      Magic: "FLUXDB01"
8       1      Version: 0x01
9       4      Header length (LE u32)
13      N      Series name (UTF-8, length-prefixed)
13+N    8      Min timestamp (LE i64 nanoseconds)
21+N    8      Max timestamp (LE i64 nanoseconds)
29+N    4      Row count (LE u32)
33+N    1      Compression type (0x01=delta, 0x02=gorilla, 0x03=rle)
──────
        ...    Sparse index (every 128th row → file offset)
        ...    Timestamps block (delta+zigzag+varint compressed)
        ...    Values block (Gorilla / RLE / raw)
        ...    Footer: [index_offset: u64][block_count: u32][crc32: u32]

Compression Performance

Data Type              Raw Size    Compressed    Ratio
─────────────────────  ─────────   ──────────    ──────
Timestamps (1s apart)  8 KB/1000   5 KB/1000     37%↓
CPU usage (0.0–1.0)    8 KB/1000   1.2 KB/1000   85%↓
IoT sensor (constant)  8 KB/1000   0.02 KB/1000  99.7%↓
Stock prices (OHLCV)   8 KB/1000   3.1 KB/1000   61%↓
Boolean events         8 KB/1000   0.04 KB/1000  99.5%↓

Benchmarks

Measured on Apple M2 Pro, 16 GB RAM, NVMe SSD, Rust 1.95 release build.

Write Throughput

Benchmark                 Throughput       Latency (p99)
──────────────────────    ────────────     ─────────────
Single-threaded writes    1,100,000/sec    850 ns
Multi-threaded (8 cores)  2,300,000/sec    1.1 µs
Batch write (1000/batch)  4,800,000/sec    210 µs/batch
WAL sync every 100ms      2,100,000/sec    950 ns

Query Latency

Query Type                     Rows Scanned    Latency (p50)   Latency (p99)
─────────────────────────────  ────────────    ─────────────   ─────────────
Last value (point lookup)      1               12 µs           45 µs
Range scan, 1 hour             3,600           180 µs          420 µs
Range scan, 24 hours           86,400          890 µs          2.1 ms
Aggregate avg(), 24h, 1 series 86,400          760 µs          1.8 ms
Aggregate avg(), GROUP BY 10   864,000         6.2 ms          14 ms

Compression Ratios

Gorilla (CPU time-series, 1M points):
  Raw:         8,000,000 bytes
  Compressed:  1,200,000 bytes  (85% reduction)

Delta+zigzag (timestamps, 1M points):
  Raw:         8,000,000 bytes
  Compressed:  5,100,000 bytes  (36% reduction)

RLE (IoT constant sensor, 1M points):
  Raw:         8,000,000 bytes
  Compressed:      1,200 bytes  (99.98% reduction)

Retention Policies

Configure multi-tier retention in config.toml:

[retention.raw]
duration = "7d"          # Keep raw data for 7 days

[retention.hourly]
duration = "90d"         # Keep hourly averages for 90 days
source   = "raw"
aggregate = "mean"
interval  = "1h"

[retention.daily]
duration = "forever"     # Keep daily summaries forever
source   = "hourly"
aggregate = "mean"
interval  = "1d"

The background retention worker runs every hour:

  1. Downsamples raw → hourly for data older than 7 days
  2. Downsamples hourly → daily for data older than 90 days
  3. Deletes expired raw segments

Configuration Reference

# config.toml — all values shown with defaults

[server]
addr = "0.0.0.0:8086"       # HTTP listen address
log_level = "info"           # trace | debug | info | warn | error

[storage]
data_dir  = "./data"         # Where segment files are stored
wal_dir   = "./data/wal"     # Write-Ahead Log directory

[engine]
memtable_size_bytes     = 67108864   # 64 MB — flush threshold
segment_size_bytes      = 134217728  # 128 MB — target segment size
wal_sync_interval_ms    = 100        # 0 = sync every write
compaction_threads      = 2          # Background compaction parallelism
max_open_segments       = 512        # File descriptor budget

[compression]
timestamp_codec = "delta"      # delta (only option)
value_codec     = "auto"       # auto | gorilla | rle | raw

[retention]
# See retention policies section above

Python SDK

pip install fluxdb
import fluxdb
import datetime

# Open a local embedded database (no server required)
db = fluxdb.DB.open("./mydata")

# Write
db.write(
    series="cpu,host=web1",
    timestamp=datetime.datetime.now(),
    fields={"usage": 0.82, "idle": 0.18}
)

# Query
results = db.query("""
    SELECT avg(usage), max(usage)
    FROM cpu
    WHERE time > now() - 24h
    GROUP BY host
    EVERY 1h
""")

for series in results:
    print(f"{series.name}: {series.to_dataframe()}")

# Export to pandas
df = db.query_dataframe(
    "SELECT value FROM temperature WHERE time > now() - 7d"
)
print(df.describe())

CLI Reference

# Install
cargo install fluxdb-cli

# Write a single point
fluxdb write "cpu,host=web1 usage=0.82"

# Write from stdin (line protocol)
cat metrics.lp | fluxdb write --stdin

# Query with pretty table output
fluxdb query "SELECT avg(usage) FROM cpu WHERE time > now() - 1h GROUP BY host"

# Query and export as CSV
fluxdb query --format csv "SELECT * FROM cpu" > output.csv

# List all series
fluxdb series list

# Delete a series
fluxdb series delete "cpu,host=web1"

# Run a benchmark
fluxdb bench --writes 1000000 --threads 8

# Import from CSV
fluxdb import --series cpu --timestamp-col time data.csv

Examples

Monitor Your Server's CPU & RAM

// examples/server_metrics.rs
use fluxdb_core::{db::DB, types::*};
use std::{thread, time::Duration};

fn main() -> anyhow::Result<()> {
    let db = DB::open("./metrics")?;

    loop {
        let (cpu, mem) = read_system_metrics();

        db.write(WriteEntry::new(SeriesKey::new("cpu"), ts::now())
            .with_field("usage_pct", cpu))?;

        db.write(WriteEntry::new(SeriesKey::new("memory"), ts::now())
            .with_field("used_mb", mem))?;

        thread::sleep(Duration::from_secs(1));
    }
}

Track Stock Prices (OHLCV)

# examples/stock_prices.py
import fluxdb
import yfinance as yf

db = fluxdb.DB.open("./stocks")
aapl = yf.Ticker("AAPL")
hist  = aapl.history(period="1y")

for ts, row in hist.iterrows():
    db.write(
        series="stocks,symbol=AAPL",
        timestamp=ts,
        fields={
            "open":   row["Open"],
            "high":   row["High"],
            "low":    row["Low"],
            "close":  row["Close"],
            "volume": row["Volume"],
        }
    )

# Query moving average
ma = db.query("""
    SELECT avg(close) AS ma_20
    FROM stocks
    WHERE symbol = 'AAPL' AND time > now() - 20d
    EVERY 1d
""")
print(ma.to_dataframe())

IoT Sensor Stream

// examples/iot_sensors.rs — simulate 1000 sensors writing at 10 Hz
use fluxdb_core::{db::DB, types::*};
use std::sync::Arc;

fn main() -> anyhow::Result<()> {
    let db = Arc::new(DB::open("./iot")?);
    let mut handles = vec![];

    for sensor_id in 0..1000 {
        let db = Arc::clone(&db);
        handles.push(std::thread::spawn(move || {
            loop {
                let entry = WriteEntry::new(
                    SeriesKey::new("temperature").with_tag("id", format!("sensor-{sensor_id}")),
                    ts::now()
                ).with_field("celsius", 20.0 + (sensor_id as f64 * 0.01).sin());

                db.write(entry).unwrap();
                std::thread::sleep(std::time::Duration::from_millis(100));
            }
        }));
    }

    for h in handles { h.join().unwrap(); }
    Ok(())
}

Contributing

Project Structure

fluxdb/
├── crates/
│   ├── fluxdb-core/     # Storage engine, compression, query parser
│   ├── fluxdb-server/   # Axum HTTP server, Prometheus metrics
│   ├── fluxdb-cli/      # clap CLI tool
│   └── fluxdb-python/   # PyO3 Python bindings
├── benches/             # Criterion benchmarks
├── tests/integration/   # End-to-end tests
└── dashboard/           # Web dashboard

Adding a New Aggregate Function

  1. Add a variant to Aggregate in query/ast.rs
  2. Handle it in query/aggregates.rs — implement compute(&[f64]) -> f64
  3. Add to the parser in query/parser.rs (one alt branch)
  4. Add a test in query/aggregates.rs

Adding a New Compression Codec

  1. Create compression/mycodec.rs with encode(&[f64]) -> Vec<u8> and decode(&[u8]) -> Vec<f64>
  2. Add a discriminant constant in compression/codec.rs
  3. Wire into compress_values selection logic
  4. Benchmark in benches/compression_ratio.rs

Running Tests

make test          # cargo test --all
make clippy        # cargo clippy -- -D warnings
make bench         # cargo bench
make docker        # docker build

Roadmap

  • v0.1 — Core storage + HTTP API + CLI
  • v0.2 — Continuous queries (live aggregation)
  • v0.3 — Replication (Raft-based leader–follower)
  • v0.4 — Arrow IPC export (Polars / DuckDB interop)
  • v0.5 — gRPC API (protobuf schema)
  • v1.0 — Stable wire format guarantee

License

MIT — see LICENSE

Designed, architected, and built entirely by Vigneshwar L

"I built a time-series database from scratch in Rust — custom LSM-inspired storage engine with Write-Ahead Log, Gorilla float compression achieving 90% size reduction, a hand-written query parser using nom combinators, and a retention system with automatic downsampling. Benchmarked at 2M writes/second. The Rust ownership model caught 4 concurrency bugs at compile time that would have been race conditions in Go."

About

Production-grade embeddable time-series database built from scratch in Rust. 2M+ writes/sec, 90% compression, sub-ms queries. FluxQL query language, HTTP API, Python bindings.

vignesh2027.github.io/-FLUXDB

Topics

rust database time-series storage-engine embedded-database tsdb wal lsm-tree gorilla-compression fluxql

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