Embedded Log Compression & Indexing for Elixir

"I found it ironic that the first thing you do to time series data is squash the timestamp. That's how the name Timeless was born." --Mark Cotner

Embedded log compression and indexing for Elixir applications. Add one dependency, configure a data directory, and your app gets compressed, searchable logs with zero external infrastructure.

Logs are written to raw blocks, automatically compacted with OpenZL (~12.8x compression ratio), and indexed in SQLite for crash-safe persistence. The index keeps level terms plus a curated set of low-cardinality metadata terms, while message substring search still scans message text and metadata values inside matching blocks. Includes optional real-time subscriptions and a VictoriaLogs-compatible HTTP API.

Documentation

• Getting Started
• Configuration Reference
• Architecture
• Querying
• HTTP API
• Real-Time Subscriptions
• Storage & Compression
• Operations
• Telemetry

Installation

def deps do
  [
    {:timeless_logs, "~> 1.0"}
  ]
end

Setup

# config/config.exs
config :timeless_logs,
  data_dir: "priv/timeless_logs"

That's it. TimelessLogs installs itself as a :logger handler on application start. All Logger calls are automatically captured, compressed, and indexed.

Querying

# Recent errors
TimelessLogs.query(level: :error, since: DateTime.add(DateTime.utc_now(), -3600))

# Search by indexed metadata
TimelessLogs.query(level: :info, metadata: %{service: "payments"})

# Substring match on message
TimelessLogs.query(message: "timeout")

# Pagination
TimelessLogs.query(level: :warning, limit: 50, offset: 100, order: :asc)

Returns a TimelessLogs.Result struct:

{:ok, %TimelessLogs.Result{
  entries: [%TimelessLogs.Entry{timestamp: ..., level: :error, message: "...", metadata: %{}}],
  total: 42,
  limit: 100,
  offset: 0
}}

Semantic Host and Service Filtering

TimelessLogs supports semantic host/service filtering so callers do not need to hardcode one raw metadata key for every producer.

When you query with:

TimelessLogs.query(metadata: %{host: "web-01"})
TimelessLogs.query(metadata: %{service: "payments"})

TimelessLogs expands those filters across common aliases:

• host matches host.name, host, hostname, and node
• service matches service.name, service, and application

The same semantic expansion applies to real-time subscriptions:

TimelessLogs.subscribe(metadata: %{host: "web-01"})
TimelessLogs.subscribe(metadata: %{service: "payments"})

Query Filters

Streaming

For memory-efficient access to large result sets, use stream/1. Blocks are decompressed on demand as the stream is consumed:

TimelessLogs.stream(level: :error)
|> Enum.take(10)

TimelessLogs.stream(since: DateTime.add(DateTime.utc_now(), -86400))
|> Stream.filter(fn entry -> String.contains?(entry.message, "timeout") end)
|> Enum.to_list()

Real-Time Subscriptions

Subscribe to log entries as they arrive:

TimelessLogs.subscribe(level: :error)

# Entries arrive as messages
receive do
  {:timeless_logs, :entry, %TimelessLogs.Entry{} = entry} ->
    IO.puts("Got error: #{entry.message}")
end

# Stop subscribing
TimelessLogs.unsubscribe()

You can filter subscriptions by level and metadata:

TimelessLogs.subscribe(level: :warning, metadata: %{service: "payments"})

Statistics

Get aggregate storage statistics without reading blocks:

{:ok, stats} = TimelessLogs.stats()

# %TimelessLogs.Stats{
#   total_blocks: 48,
#   total_entries: 125_000,
#   total_bytes: 24_000_000,
#   disk_size: 24_000_000,
#   index_size: 3_200_000,
#   oldest_timestamp: 1700000000000000,
#   newest_timestamp: 1700086400000000,
#   raw_blocks: 2,
#   raw_bytes: 50_000,
#   raw_entries: 500,
#   openzl_blocks: 46,
#   openzl_bytes: 23_950_000,
#   openzl_entries: 124_500
# }

Backup

Create a consistent online backup without stopping the application:

{:ok, result} = TimelessLogs.backup("/tmp/logs_backup")

# %{path: "/tmp/logs_backup", files: [...], total_bytes: 24_000_000}

Creates a consistent SQLite backup (VACUUM INTO) and copies block files.

Retention

Configure automatic cleanup to prevent unbounded disk growth:

config :timeless_logs,
  data_dir: "priv/timeless_logs",
  retention_max_age: 7 * 24 * 3600,       # Delete logs older than 7 days
  retention_max_size: 512 * 1024 * 1024,   # Keep total blocks under 512 MB
  retention_check_interval: 300_000         # Check every 5 minutes (default)

You can also trigger cleanup manually:

TimelessLogs.Retention.run_now()

Compaction

New log entries are first written as uncompressed raw blocks for low-latency ingestion. A background compactor periodically merges raw blocks into compressed blocks:

config :timeless_logs,
  compaction_threshold: 500,       # Min raw entries to trigger compaction
  compaction_interval: 30_000,     # Check every 30 seconds
  compaction_max_raw_age: 60,      # Force compact raw blocks older than 60s
  compaction_format: :openzl,      # :openzl (default) or :zstd
  openzl_compression_level: 9      # OpenZL level 1-22 (default 9)

Trigger manually:

TimelessLogs.Compactor.compact_now()

HTTP API

TimelessLogs includes an optional HTTP API compatible with VictoriaLogs. Enable it in config:

config :timeless_logs,
  http: [port: 9428, bearer_token: "secret"]

Or simply http: true to use defaults (port 9428, no auth).

Endpoints

Health check (always accessible, no auth required):

GET /health
→ {"status": "ok", "blocks": 48, "entries": 125000, "disk_size": 24000000}

Ingest (NDJSON, one JSON object per line):

POST /insert/jsonline?_msg_field=_msg&_time_field=_time

{"_msg": "Request completed", "_time": "2024-01-15T10:30:00Z", "level": "info", "request_id": "abc123"}
{"_msg": "Connection timeout", "level": "error", "service": "api"}

Query:

GET /select/logsql/query?level=error&start=2024-01-15T00:00:00Z&limit=50
→ NDJSON response, one entry per line

Stats:

GET /select/logsql/stats
→ {"total_blocks": 48, "total_entries": 125000, ...}

Flush buffer:

GET /api/v1/flush
→ {"status": "ok"}

Backup:

POST /api/v1/backup
Content-Type: application/json
{"path": "/tmp/backup"}

→ {"status": "ok", "path": "/tmp/backup", "files": [...], "total_bytes": 24000000}

Authentication

When bearer_token is configured, all endpoints except /health require either:

• Header: Authorization: Bearer <token>
• Query param: ?token=<token>

Reducing Overhead

The biggest source of logging overhead in most Elixir apps is stdout/console output, not the log capture itself. For production or embedded use, disable the default console handler and let TimelessLogs be the sole destination:

# config/prod.exs (or config/config.exs for all environments)
config :logger,
  backends: [],
  handle_otp_reports: true,
  handle_sasl_reports: false

# Remove the default handler on boot
config :logger, :default_handler, false

This eliminates the cost of formatting and writing every log line to stdout while TimelessLogs captures everything at the level you choose:

# Only capture :info and above (skip :debug in production)
config :logger, level: :info

If you still want console output during development:

# config/dev.exs
config :logger, :default_handler, %{level: :debug}

Configuration

Telemetry

TimelessLogs emits telemetry events for monitoring:

How It Works

1. Your app logs normally via Logger
2. TimelessLogs captures log events via an OTP :logger handler
3. Events buffer in a GenServer, flushing every 1s or 1000 entries
4. Each flush writes a raw (uncompressed) block file
5. A background compactor merges raw blocks into OpenZL-compressed blocks (~12.8x ratio)
6. Block metadata and an inverted term index are stored in SQLite (WAL mode, single writer + reader pool) for crash-safe persistence
7. Queries use the SQLite reader pool to find relevant blocks, decompress only those in parallel, and filter entries
8. Real-time subscribers receive matching entries as they're buffered

Benchmarks

Run on M5 Pro (18 cores). Reproduce with mix timeless_logs.ingest_benchmark, mix timeless_logs.benchmark, and mix timeless_logs.search_benchmark.

Ingestion (1.1M simulated Phoenix log entries, 1 week, 1000-entry blocks):

Compression (1.1M entries, 1000-entry blocks):

Head-to-head (default levels: zstd=3, OpenZL=9):

OpenZL columnar wins on filtered queries (86.8% faster) because it can skip irrelevant columns during decompression. Decompression (the read hot path) is 12.4% faster than zstd.

License

MIT - see LICENSE for details.