A small, embedded time-series database designed for resource-constrained hosts (Raspberry Pi, edge nodes, IoT gateways). No external dependencies at runtime. All data lives in plain files under a single root directory.

Engine
 ├── "prod"    Database  → WAL (prod.wal) + Catalog (catalog.json) + partitioned .dat files
 ├── "sensors" Database  → WAL + Catalog + partitioned .dat files
 └── "internal"          → engine self-metrics (same layout, never exposed to users)

The Engine is the single entry point. It owns a collection of named databases and routes ingested samples to the right one based on the line-protocol prefix.

Each Database has three storage layers:

AddLine("prod/room.temp 21.5 1715000000000000000")
  │
  ├─ parse line protocol  →  dbName="prod"  metric="room.temp"  ts=…  value=21.5
  ├─ getOrCreateDB        →  open or reuse prod Database
  ├─ WAL append           →  write compact record to prod.wal  (crash-safe)
  ├─ addToOpenDay         →  append to in-memory Page for today's bucket
  └─ if page full         →  compress + write page frame to data-<partition>.dat
                              reset WAL (replay no longer needed)

Timestamps must be monotonically non-decreasing per metric across the entire write stream. Out-of-order or stale samples are rejected.

When a database is opened, the WAL is replayed into the in-memory page if the data file is behind. The catalog is used to resolve ValueTypes for metrics that omit them (compact format optimization). After a full replay the engine is ready to accept new writes.

QueryRange("prod", "room.temp", fromTS, toTS, stride, callback)
  │
  ├─ iterate UTC days in [fromTS, toTS]
  │    ├─ open data-<partition>.dat  →  scan page frame headers
  │    │    skip frames outside time window (no decompression)
  │    │    decompress + scan matching frames
  │    └─ check in-memory page for today's data
  └─ call callback for each sample (every Nth if stride > 1)

DB/metric.name value [ts]

• DB — database name (created automatically on first write)
• metric.name — arbitrary metric identifier (slash-separated namespaces work well)
• value — integer (42, -7) or float (3.14, 1e-3). An integer literal always creates an int32 metric; a float literal creates a float32 metric. Type is fixed on first write; mixing types for the same metric is an error.
• ts — Unix nanosecond timestamp (optional; defaults to time.Now())

Examples:

prod/room.temp 21.5 1715000000000000000
sensors/pressure.hpa 1013
internal/batch.size 256i

The i suffix forces integer interpretation for values that look like floats.

Each record is a uvarint length prefix followed by a fixed-layout payload:

[uvarint: payload_len] [payload]

Payload layout:

Offset  Size  Field
  0      2    MetricID          uint16 LE
  2      3    TS delta          uint24 LE nanoseconds from baseline
  5      1    CompactTL flags   bit 7 = new baseline, bit 6 = new metric
  6      8    Baseline TS       int64 LE  (only when bit 7 set)
  —      var  name_len+name+vtype         (only when bit 6 set)
  —      4    Value             int32 or float32 LE, always present

• Hot path (known metric, same baseline): 2+3+1+4 = 10 bytes + 1 varint = 11 bytes.
• A new baseline is emitted on the first record of each WAL and whenever the timestamp gap exceeds ~16.7 ms (2²⁴ ns). Typical sensor streams fit hundreds of seconds between baseline resets.
• Known metrics (previously seen in the session) omit the name and value type; those fields are recovered from the catalog during replay.

<root>/
  engine.toml          — engine configuration (auto-created on first start)
  <db>/
    catalog.json       — metric registry: name → id + type
    manifest.toml      — per-database settings (retention, WAL, page limits)
    <db>.wal           — write-ahead log (single reusable file)
    data-<partition>.dat — compressed page frames for completed partitions

Data files are append-only sequences of page frames:

Frame = PageHeader(18 bytes) + compressed_len(uvarint) + S2-compressed payload + CRC32(4 bytes)

The payload is a flat array of interleaved (MetricID, Timestamp, Value) triples, sorted by timestamp. S2 compression typically achieves 3–4× on realistic sensor data.

Created automatically at <root>/engine.toml on first start. Key settings:

Durability profiles:

Per-database settings (retention, partitioning, WAL skip window, page flush thresholds, rollups) live in <db>/manifest.toml and default values can be set in engine.toml under [manifest_defaults].

Partition options in [retention]:

• partition = "day" (default): data-YYYY-MM-DD.dat
• partition = "month": data-YYYY-MM.dat
• partition = "year": data-YYYY.dat
• partition = "forever": data-forever.dat

Rollup jobs are defined in the source database manifest under [rollups].

Example:

[rollups]
enabled = true
checkpoint_file = "rollup.checkpoints.log"
default_grace = "5m"

[[rollups.jobs]]
id = "outside_temp_1h"
source_metric = "temp.out_dry"
interval = "1h"
aggregates = ["min", "max", "sum", "avg", "count"]
destination_db = "sensors_rollup_1h"
destination_metric_prefix = "temp.out_dry"

Rollup config reference:

Notes:

• Checkpoints are stored in the source DB (default rollup.checkpoints.log).
• Destination DBs can also define their own rollup jobs to create cascades (for example 1h -> 1d).
• For low-frequency rollup outputs, use coarser partitions on destination DBs (for example month or year) to avoid many tiny day files.

nanotdb --config <path>      start server using given engine.toml
nanotdb --init --config <path>   write default engine.toml and exit

Exposes a small HTTP API compatible with the VictoriaMetrics instant/range query wire format (/api/v1/query, /api/v1/query_range, /api/v1/import/prometheus).

Operates directly on the data directory without a running server.

nanocli inspect db  --root <dir> [--db <name>] [--json]  — overview of all/one database
nanocli inspect dat --root <dir>  --db <name>  [--json]  — page frame headers in .dat files
nanocli inspect wal --root <dir>  --db <name>  [--json]  — WAL record dump

nanocli import --root <dir> --in <file.lp>  [--json]     — bulk import line-protocol file
nanocli export --root <dir> --db <name> [--out <file.lp>] — export database to line protocol (stdout when --out is omitted)

nanocli query  --root <dir> --db <name> --metric <regex>
               [--start <time>] [--end <time>] [--format table|json]

LP timestamps (import and exported files) accept / use: YYYY-MM-DD HH:MM:SS.nnnnnnnnn (UTC) and also accept raw Unix nanoseconds on import.

--start / --end accept RFC3339 strings, YYYY-MM-DD [HH[:MM[:SS[.nnnnnnnnn]]]], or Unix timestamps (seconds or nanoseconds).

For deterministic end-to-end verification (generate LP -> import -> rollups -> export -> compare expected), run:

./scripts/rollup_full_cycle_check.sh

Optional arguments:

• ./scripts/rollup_full_cycle_check.sh <root-dir> <duration-hours> <metrics> <cadence-seconds> <gap-metrics>
• Defaults: root-dir=test-data/full-cycle-check, duration-hours=30, metrics=10, cadence-seconds=10, gap-metrics=2

Generated artifacts are placed in <root-dir>/work for easy discovery:

• scenario_summary.json (duration, rates, counts, per-metric stats)
• known_gaps.csv (deterministic missing windows for temp.gap_probeXX metrics)
• SCENARIO.md (quick human-readable summary)

e, err := engine.OpenEngine("/data", 0)   // 0 = default WAL segment size
defer e.Close()

// Ingest
err = e.AddLine("sensors/temp 22.1 " + strconv.FormatInt(time.Now().UnixNano(), 10))

// Range query
err = e.QueryRange("sensors", "temp", fromTS, toTS, 1, func(s engine.Sample) error {
    fmt.Println(s.TS, s.Float32)
    return nil
})

// Last value (from in-memory catalog cache)
sample, ok, err := e.QueryLast("sensors", "temp")

// Bulk import / export
err = e.ImportFile("backup.lp")
err = e.ExportFile("sensors", "backup.lp")

Key types: