Show HN: MorletDB is a Postgres extension for waveforms

MorletDB, a PostgreSQL extension released by Merkhet, stores waveform recordings as intact channels with sample rate, unit and calibration metadata rather than scattering samples across rows, and answers similarity queries over a bounded range of time shifts. The extension ships a Grizzbit codec that codes raw samples bit-exactly and is often several times smaller than general-purpose time-series compression on the same data, though it is weaker on floats without a grid. MorletDB exposes plain SQL functions such as morlet_search_envelope_probes and morlet_read_interval so AI agents can retrieve matching intervals and original samples, and a demo loading four days of public seismic recordings through a read-only MCP server found that four of the five closest matches were other catalogued earthquakes.

ἁρμονίη ἀφανὴς φανερῆς κρείττων. <sub — Heraclitus, fragment B54</sub MorletDB is a PostgreSQL extension for waveform archives. Select an interval and ask “Have we seen a signal like this before?” — every answer is a recording and a sample interval you can read back, for an engineer or for an AI agent. AI agents for-ai-agents · Install install · Use use · Research research · Explore explore · Evidence what-has-been-measured · Website https://merkhet-io.github.io/morletdb/ Recordings, not rows. A recording is one channel with a sample rate, a unit, a calibration and its acquisition metadata. MorletDB keeps that unit intact inside PostgreSQL, instead of scattering samples over rows or vectors. - Lossless and compact. Raw samples are coded bit-exactly, often several times smaller than general-purpose time-series compression on the same data. The codec, Grizzbit https://github.com/merkhet-io/morletdb/blob/main/docs/grizzbit.md , stores each chunk on the value grid it detects, with no precision parameter, and is weaker on floats without a grid. - Search that tolerates time shifts. Two occurrences of the same pattern rarely line up with a window grid. MorletDB scores similarity over a bounded range of shifts, exactly or through a standard vector index. - Every hit traces back. Results are intervals of recordings, with their metadata, that read back the original samples. - Safe to keep ingesting. Appends, search representations and index rows commit in one transaction; retries are verified, gaps and overlaps are rejected. - Plain PostgreSQL. SQL functions, your roles and row-level security, your backups. pgvector is optional. Give your agents signal evidence to reason with. An investigation agent can search your recording archive before it answers: have we seen a signal like this before? MorletDB supplies the matching intervals and their original samples; your application connects them to analysis tools, maintenance notes or experiment records. The tool is plain SQL, so it runs with the agent's database role, row-level security and limits: -- a bounded tool an agent can call: similar intervals for a source interval and an asset CREATE FUNCTION agent similar intervals recording text, from sample bigint, to sample bigint, asset text, k integer DEFAULT 5 RETURNS TABLE recording id text, start sample bigint, end sample bigint, distance float8 LANGUAGE sql STABLE AS $$ SELECT recording id, start sample, end sample, distance FROM morlet search envelope probes 'signals', recording, from sample, to sample, jsonb build object 'asset', asset , least k, 20 $$; -- and to bring evidence into analysis: morlet read interval 'signals', recording id, start, end | Use case | What the agent does | |---|---| | Anomaly investigation | an external detector flags an interval; the agent checks whether it resembles earlier events and under which operating conditions | | Autonomous factory workflows | gathers signal evidence across machine runs before proposing maintenance, a quality check or an escalation; your application decides which actions need approval | | Engineering investigation | retrieves earlier test runs to explain a changed transient, with the experiment metadata | | Research and labelling assistants | collects similar intervals for human review, annotation or model evaluation | Try it with Claude: the agent demo https://github.com/merkhet-io/morletdb/blob/main/examples/agent-demo/README.md loads four days of public seismic recordings with Docker and lets Claude Code or Claude Desktop investigate an earthquake through the read-only MCP server https://github.com/merkhet-io/morletdb/blob/main/docs/mcp.md . Four of the five closest matches it finds are other catalogued earthquakes. The machine demo https://github.com/merkhet-io/morletdb/blob/main/examples/machine-demo/README.md does the same for a bearing run to failure NASA/IMS : Claude finds when the failing bearing first changed and whether the others show it. Important The SQL function above is an integration pattern; the bundled MCP server only reads. Detection models, agent orchestration and machine controls come from your application. On seismic data, this search returns catalogued earthquakes about twice as often as zero-lag similarity E5 https://github.com/merkhet-io/morletdb/blob/main/evaluation/task relevance/RESULTS.md , E5-H https://github.com/merkhet-io/morletdb/blob/main/evaluation/task relevance/RESULTS HELDOUT.md ; anomaly workflows have not been evaluated, and similarity alone does not establish normality, a fault or its cause. 1. Append chunks of a recording at their global sample offset. 2. Build search representations, such as amplitude envelopes of 4096-sample windows, in the same transaction, so new windows are searchable at commit. 3. Search with a query interval: exact scoring of every eligible window, or approximate candidates from a vector index followed by exact re-ranking. 4. Read back each match as the original samples, with its recording metadata. Grizzbit is MorletDB's lossless chunk codec: what goes in comes back bit for bit . Each chunk of up to 32,768 float64 samples is stored in the smallest of three exact forms: integer steps on the value grid the encoder detects predicted and Rice-coded as in FLAC, with last-place corrections where needed , the float64 bit patterns coded the same way, or plain float64. Every chunk carries a CRC-32C checksum. - No precision parameter. Nothing can be configured to drop detail. Finite values up to ±10⁹ are stored exactly; anything else is refused at write time, never altered. - Checked when written. With morlet.verify appends on the default , every append decodes each chunk again and compares it bit for bit before storing it. - Strong on gridded signals, weaker without a grid. On ADC counts, fixed decimals and calibrated waveforms it is among the smallest codecs measured B4 ; on floats without a clean grid it is 1.6–4× larger than the best. Grizzbit 2, an attempt to close that gap, failed study B5 and was not shipped 326 . - Float32 codings, on by default chunk format version 6 : samples that are float32 values, such as PLC and CNC controller signals, are coded in float32 terms where that is smaller. It passed study B8: on IU4 force and controller channels 3.02 and 2.29 bytes per sample instead of 6.60 and 6.52, never a larger chunk results https://github.com/merkhet-io/morletdb/blob/main/evaluation/storage b8/RESULTS.md ; SET morlet.float32 chunks = off turns it off . - Grizzbit 2.1, opt-in morlet.chunk format = grizzbit21 : a per-chunk linear predictor and corrections per grid index, kept only where smaller. It passed study B6: waveforms 12.6% smaller in total, never larger how to use it https://github.com/merkhet-io/morletdb/blob/main/docs/grizzbit.md using-grizzbit-21 . - Nothing here is novel , and the docs say so: Grizzbit is a named, specified format built from established lossless techniques FLAC's fixed predictors and Rice coding, SEED and EDF style gain and offset . More: docs/grizzbit.md https://github.com/merkhet-io/morletdb/blob/main/docs/grizzbit.md · codecs at a glance https://github.com/merkhet-io/morletdb/blob/main/docs/grizzbit.md codecs-at-a-glance · format specification https://github.com/merkhet-io/morletdb/blob/main/docs/chunk-format.md · crates/grizzbit https://github.com/merkhet-io/morletdb/blob/main/crates/grizzbit Full setup and query examples: installation and query guide https://github.com/merkhet-io/morletdb/blob/main/docs/installation-and-queries.md . Source builds use Rust 1.99.0 and pgrx 0.19.3. Run the release image prebuilt: PostgreSQL 17 with pg morlet, Linux amd64 and arm64, with SBOM and build provenance; no pgvector, so no probe search; choose your own password : docker run -d --name morletdb -e POSTGRES USER=morlet -e POSTGRES PASSWORD=change-me \ -e POSTGRES DB=morlet -p 127.0.0.1:54329:5432 -v morletdb-data:/var/lib/postgresql/data \ ghcr.io/merkhet-io/morletdb:0.3.14-alpha.1 docker exec morletdb psql -U morlet -d morlet -c "CREATE EXTENSION pg morlet" Try the walkthrough builds a PostgreSQL 17 preview for your machine, amd64 or arm64; imports synthetic data, finds a known interval and reconstructs its matches : docker compose run --rm walkthrough Build from source with Rust and pgrx https://github.com/pgcentralfoundation/pgrx PostgreSQL 17, 18 or 19; on Debian/Ubuntu install build-essential bison flex libreadline-dev zlib1g-dev libclang-dev clang pkg-config libssl-dev first : cargo install cargo-pgrx --version 0.19.3 --locked cargo pgrx init --pg17 download or --pg18, --pg19 /path/to/pg config, or --pg17 /path/to/pg config cd extensions/pg morlet cargo pgrx install --release --pg-config "$ sed -n 's/^pg17 = "\ . \ "/\1/p' ~/.pgrx/config.toml " Then, in your database: CREATE EXTENSION pg morlet; CREATE EXTENSION vector; -- optional: only for probe search pgvector 0.8 or later Use it from Python : ingest a NumPy waveform, read exact intervals and search similar ones with the Python SDK https://github.com/merkhet-io/morletdb/blob/main/docs/python-sdk.md sdk/python , alpha , starting with a runnable quickstart https://github.com/merkhet-io/morletdb/blob/main/examples/python/quickstart.py . Let an AI agent investigate your recordings through a read-only MCP server Claude Code, Claude Desktop and other MCP clients : MorletDB for AI agents https://github.com/merkhet-io/morletdb/blob/main/docs/mcp.md . What MorletDB works with PostgreSQL, pgvector, Docker, MCP, Grafana, Python and Jupyter, file formats , with how each is verified: integrations https://github.com/merkhet-io/morletdb/blob/main/docs/integrations.md . Import your own files WAV, EDF, miniSEED, CSV and get a first search query: from files to a first search https://github.com/merkhet-io/morletdb/blob/main/docs/import.md . Supported versions, CI coverage and local test commands: PostgreSQL compatibility https://github.com/merkhet-io/morletdb/blob/main/docs/compatibility.md . Upgrading an existing installation: existing installations https://github.com/merkhet-io/morletdb/blob/main/extensions/pg morlet/README.md existing-installations . -- a collection holds recordings, raw chunks and search representations SELECT morlet create stream collection 'public', 'signals' ; SELECT morlet enable envelopes 'signals' ; -- one recording = one channel: id, sample rate, channel, unit, metadata SELECT morlet start recording 'signals', 'pump-7', 25600, 'accel x', 'g', '{"asset": "P7"}' ; -- append samples at their global offset; repeating an identical append is a no-op SELECT morlet append chunk 'signals', 'pump-7', 0, ARRAY SELECT sin i / 40.0 1 + i / 3000 % 3 FROM generate series 0, 32767 i ; -- build envelopes 4096-sample windows, stride 2048 ; call it in the append -- transaction to make new windows searchable at commit SELECT morlet build envelopes 'signals', 'pump-7' ; -- where else does samples 8192..12288 occur? shift-tolerant search SELECT recording id, start sample, end sample, distance FROM morlet search similar 'signals', 'pump-7', 8192, 12288, '{}', 5 ; -- read any match back as original samples SELECT samples FROM morlet read interval 'signals', 'pump-7', 26624, 30720 ; -- progress and backlog per window size SELECT FROM morlet collection status 'signals' ; For large archives, enable probe search needs pgvector : it finds candidates through an HNSW index and re-ranks them with the same exact score. It is approximate in which windows it considers, never in the distances it returns. SELECT morlet enable envelope probes 'signals', 4096 ; SELECT FROM morlet search similar 'signals', 'pump-7', 8192, 12288 ; -- now uses probes morlet search similar uses probe search when it is enabled for the window size and exact search otherwise; morlet search envelope interval and morlet search envelope probes choose a path explicitly. The fifth argument of the interval searches is a metadata filter '{"asset": "P7"}' . Small filtered galleries are routed to exact search automatically. Searches run with the caller's permissions, including row-level security. More: SQL reference https://github.com/merkhet-io/morletdb/blob/main/extensions/pg morlet/README.md · search contract https://github.com/merkhet-io/morletdb/blob/main/docs/search-contract.md · recording workflow https://github.com/merkhet-io/morletdb/blob/main/docs/recording-workflow.md · operations https://github.com/merkhet-io/morletdb/blob/main/docs/operations.md · access control https://github.com/merkhet-io/morletdb/blob/main/docs/access-control.md · Parquet import https://github.com/merkhet-io/morletdb/blob/main/tools/ingest/README.md | Task | Main functions | |---|---| | Create a collection and recording | morlet create stream collection , morlet start recording | | Append and inspect progress | morlet append chunk , morlet stream status , morlet collection status | | Read original samples | morlet read interval | | Search Morlet/statistical features | morlet search streams , morlet search interval | | Materialize envelopes | morlet enable envelopes , morlet build envelopes | | Search similar intervals probes if enabled, else exact | morlet search similar | | Search bounded-lag envelopes exactly | morlet search envelopes , morlet search envelope interval | | Enable and query optional indexed probes | morlet enable envelope probes , morlet search envelope probes | | Inspect storage | morlet recording storage , morlet collection storage | The SQL guide https://github.com/merkhet-io/morletdb/blob/main/docs/installation-and-queries.md store-search-read-back walks through storing two synthetic recordings, building envelopes, filtering a query, and reading its matches. Intervals are zero-based and half-open; feature and envelope search use different scorers. Envelopes require an explicit builder; raw ingestion alone does not make a recording searchable through that path. Probe candidate selection is approximate above the exact-routing threshold. See the search contract https://github.com/merkhet-io/morletdb/blob/main/docs/search-contract.md for budgets, permissions, source exclusion, overlap suppression and snapshot semantics. | MorletDB: Recordings, Not Rows — Lag-Tolerant Waveform Retrieval in PostgreSQL https://github.com/merkhet-io/morletdb/blob/main/research/paper/morletdb.pdf Version 0.25 Bounded-lag cross-correlation is the right score when matches may be shifted in time, but standard vector indexes rank by an unshifted metric. Because each lag term is an inner product with a shifted copy of the query, a sparse grid of shifted probes into an ordinary pgvector HNSW index, re-ranked with the exact score, recovers the shift-tolerant top ten. | On held-out seismic data 234,622 windows : | What it costs and where it is not shown: a probe table and index of about the size of the raw data, slower search under filters that exclude a query's neighbours, and extra write-ahead logging for index maintenance. The paper's retrieval results are seismic. Study N1 https://github.com/merkhet-io/morletdb/blob/main/evaluation/nilm n1/RESULTS.md added appliance identification from current transients PLAID : exact envelope search named the appliance about as well as a current-feature 1-NN H1 passed , but its distances did not flag unknown appliances H2 failed . Other signals, diagnostic relevance and long-running operation are not shown. Supplement https://github.com/merkhet-io/morletdb/blob/main/research/paper/supplement.pdf · study overview https://github.com/merkhet-io/morletdb/blob/main/research/paper/STUDIES.md · reproduce the numbers https://github.com/merkhet-io/morletdb/blob/main/research/paper/ARTIFACT.md · main results https://github.com/merkhet-io/morletdb/blob/main/research/paper/README.md main-numbers | Recording viewer. A local, read-only page for a recording, its context lanes program, block, tool, part , segments and labels; compare a segment with the same context on other parts, or find similar windows. tools/viewer https://github.com/merkhet-io/morletdb/blob/main/tools/viewer/README.md python -m pip install -r tools/viewer/requirements.txt DATABASE URL=postgresql://... python tools/viewer/demo.py DATABASE URL=postgresql://... python tools/viewer/viewer.py | | | Sleep staging by example. 153 Sleep-EDF nights stored as raw recordings; click an epoch of a held-out night and see the 40 most similar expert-scored epochs from other people, each read back from its interval. A research demo, not a medical device. examples/sleep https://github.com/merkhet-io/morletdb/blob/main/examples/sleep/README.md | | Also: compare a machine with its own history https://github.com/merkhet-io/morletdb/blob/main/docs/context-workflow.md under the same operating context · visual notebook https://github.com/merkhet-io/morletdb/blob/main/examples/notebooks/README.md · Parquet importer https://github.com/merkhet-io/morletdb/blob/main/tools/ingest/README.md Every evaluation fixes its protocol before reading the data, runs once, and reports the result, including negative ones. In short: - Held up: lossless storage 1.2–6× smaller than TimescaleDB compression on the same samples , exact and shift-tolerant search, probe search on standard indexes, retrieval of labelled examples as explanation, comparison under the same operating context, access control. - Did not hold up: Morlet features have not beaten simple FFT band powers on bearings, ECG or sleep EEG; no wavelet or filter type has beaten a plain band-pass filter for bearing faults; own-history comparison has not warned earlier than a plain RMS alarm on run-to-failure bearings. Every evaluation with its data, baselines, confidence intervals and verdict: docs/evaluations.md https://github.com/merkhet-io/morletdb/blob/main/docs/evaluations.md . Append-only recordings; 1–32,768 samples per chunk; explicit channel and unit; no resampling, gap interpolation, background extraction worker or cross-channel search. Exact search scans eligible windows; probe search is approximate and opt-in. Alpha: no stable API or schema guarantee yet. Details: support boundaries https://github.com/merkhet-io/morletdb/blob/main/SUPPORT.md · compatibility https://github.com/merkhet-io/morletdb/blob/main/docs/compatibility.md · numerical contract https://github.com/merkhet-io/morletdb/blob/main/docs/morlet-extractor.md . The name. MorletDB honours Jean Morlet and the project's starting hypothesis that Morlet wavelets would suit search in technical signal archives. The system is not tied to that transform, and our evaluations have not shown a general advantage for it. | Path | What | |---|---| | extensions/pg morlet https://github.com/merkhet-io/morletdb/blob/main/extensions/pg morlet | the PostgreSQL extension: storage, ingestion, interval reads, search | | crates/grizzbit https://github.com/merkhet-io/morletdb/blob/main/crates/grizzbit | Grizzbit https://github.com/merkhet-io/morletdb/blob/main/docs/grizzbit.md , the lossless chunk codec specification https://github.com/merkhet-io/morletdb/blob/main/docs/chunk-format.md | | crates/morlet-core https://github.com/merkhet-io/morletdb/blob/main/crates/morlet-core | portable Rust core: envelopes, features, checkpoints; re-exports the codec | | tools https://github.com/merkhet-io/morletdb/blob/main/tools | Parquet import, Python client, recording viewer, independent numerical reference | | examples https://github.com/merkhet-io/morletdb/blob/main/examples | sleep staging, notebooks and other worked examples | | evaluation https://github.com/merkhet-io/morletdb/blob/main/evaluation · benchmarks https://github.com/merkhet-io/morletdb/blob/main/benchmarks | frozen evaluations and workload benchmarks | | research/paper https://github.com/merkhet-io/morletdb/blob/main/research/paper | the paper, supplement and reproduction package | make test Rust core and formatting make pg-test needs a pgrx-managed PostgreSQL Contributing https://github.com/merkhet-io/morletdb/blob/main/CONTRIBUTING.md · Development https://github.com/merkhet-io/morletdb/blob/main/docs/development.md · Support https://github.com/merkhet-io/morletdb/blob/main/SUPPORT.md · Security https://github.com/merkhet-io/morletdb/blob/main/SECURITY.md · Roadmap https://github.com/merkhet-io/morletdb/blob/main/docs/product-roadmap.md · Brand assets https://github.com/merkhet-io/morletdb/blob/main/assets/brand/README.md Copyright 2026 MorletDB contributors. Code, documentation and original brand artwork are licensed under the Apache License, Version 2.0 https://github.com/merkhet-io/morletdb/blob/main/LICENSE . Every part of MorletDB, integrations included, is open source under that licence; there is no enterprise edition licensing https://github.com/merkhet-io/morletdb/blob/main/docs/licensing.md . The technical report's text and figures are licensed under CC BY 4.0 https://github.com/merkhet-io/morletdb/blob/main/research/paper/LICENSE.md . A Merkhet https://github.com/merkhet-io project.