otlp2parquet

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convert OpenTelemetry metrics, logs, and traces to parquet files

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# otlp2parquet

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> What if your observability data was just a bunch of Parquet files?

Receive OpenTelemetry logs, metrics, and traces and write them as Parquet files to local disk or S3-compatible storage. Query with duckdb, Spark, pandas, or anything that reads Parquet.

**otlp2parquet deprecation notice:** As of June 2026, using [duckdb-otlp](https://github.com/smithclay/duckdb-otlp) is preferred for writing parquet files. See the ["Stream to Parquet" guide on the duckdb-otlp docs](https://smithclay.github.io/duckdb-otlp/guides/stream-to-parquet/) for more information.

```mermaid
flowchart TB
    subgraph Sources["OpenTelemetry Sources"]
        Traces
        Metrics
        Logs
    end

subgraph otlp2parquet["otlp2parquet"]
        Decode["Decode"] --> Arrow["Arrow"] --> Write["Parquet"]
    end

subgraph Storage["Storage"]
        Local["Local File"]
        S3["S3-Compatible"]
    end

Query["Query Engines"]

Sources --> otlp2parquet
    otlp2parquet --> Storage
    Query --> Storage
```

## Quick Start

```bash
# requires rust toolchain: `curl https://sh.rustup.rs -sSf | sh`
cargo install otlp2parquet

otlp2parquet
```

Server starts on `http://localhost:4318`. Send a simple OTLP HTTP log:

```bash
# otlp2parquet batches writes to disk every BATCH_AGE_MAX_SECONDS by default
curl -X POST http://localhost:4318/v1/logs \
  -H "Content-Type: application/json" \
  -d '{"resourceLogs":[{"scopeLogs":[{"logRecords":[{"body":{"stringValue":"hello world"}}]}]}]}'
```

Query it:

```bash
# see https://duckdb.org/install
duckdb -c "SELECT * FROM './data/logs/**/*.parquet'"
```

Print configuration to receive OTLP from a collector, Claude Code, or Codex:

```bash
otlp2parquet connect otel-collector
otlp2parquet connect claude-Code
otlp2parquet connect codex
```

## Why?

- **Keep monitoring data around a long time** Parquet on S3 can be 90% cheaper than large monitoring vendors for long-term analytics.
- **Query with good tools** — duckDB, Spark, Trino, Pandas
- **Deploy anywhere** — Local binary, containers, or your own servers.

## Run with Docker

```bash
docker-compose up
```

## Supported Signals

Logs, Metrics, Traces via OTLP/HTTP (protobuf or JSON, gzip compression supported). No gRPC support for now.

## APIs, schemas, and partition layout
- OTLP/HTTP endpoints: `/v1/logs`, `/v1/metrics`, `/v1/traces` (protobuf or JSON; gzip supported)
- Partition layout: `logs/{service}/year=.../hour=.../{ts}-{uuid}.parquet`, `metrics/{type}/{service}/...`, `traces/{service}/...`
- Storage: filesystem or S3-compatible object storage
- Schemas: ClickHouse-compatible, PascalCase columns; five metric schemas (Gauge, Sum, Histogram, ExponentialHistogram, Summary)
- Error model: HTTP 400 on invalid input/too large; 5xx on conversion/storage

## Future work (contributions welcome)
- OpenTelemetry Arrow alignment
- Additional platforms: Azure Functions; Kubernetes manifests

## Learn More

- [Sending data from your app](https://smithclay.github.io/otlp2parquet/sending-data/)
- [Querying Parquet files](https://smithclay.github.io/otlp2parquet/querying/)
- [Configuration reference](https://smithclay.github.io/otlp2parquet/reference/)
- [Full deployment guide](https://smithclay.github.io/otlp2parquet/deploying/)

---

<details>
<summary>Caveats</summary>

- **Batching**: Use an OTel Collector upstream to batch and reduce request overhead.
- **Schema**: Uses ClickHouse-compatible column names. Will converge with OTel Arrow (OTAP) when it stabilizes.
- **Status**: Functional but evolving. API may change.

</details>

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