hardwood-hq/hardwood

Description and use

**Summary**
Hardwood is a lightweight Java library that provides a fast, minimal‑dependency implementation of the Apache Parquet format, letting teams store and retrieve columnar data without the overhead of full‑blown data‑warehouse frameworks. It is suited for quickly prototyping database‑backed applications, accelerating data access, and handling persistence in internal tools, though its integration points are not well documented.

**Value**
By handling Parquet serialization/deserialization with a tiny footprint, Hardwood reduces the amount of custom plumbing required to move data between services, enabling developers to persist large datasets efficiently while keeping the runtime and dependency surface small. This makes it attractive for teams that need high‑performance columnar storage but cannot afford the complexity of heavier ecosystems like Spark or Hive.

**Practical adoption path**

1. **Prototype** – Add the Maven/Gradle artifact to a sandbox project and run the provided examples to verify that read/write operations meet your latency and size expectations.
2. **Integration review** – Examine the source (the API is small) to understand how it expects file paths, compression codecs, and schema definitions; adjust your build pipeline to include the required Java version and any optional codecs.
3. **Validation** – Write a small integration test that reads/writes a representative Parquet file from your existing data source (e.g., S3, HDFS, local FS) to confirm compatibility.
4. **Wrap** – If the test passes, encapsulate the library behind a thin service or DAO layer in your application, making future swaps easier.

**Production readiness**
Hardwood sits at a “medium” readiness level. It has a modest community (≈273 ★, 36 forks) and recent activity (last commit 2026‑05‑13), indicating active maintenance, but the lack of detailed integration documentation means you should perform a thorough integration test and monitor for edge‑case bugs before deploying to production. For internal workflows or prototypes it is a solid choice; for mission‑critical services, pair it with additional testing, version pinning, and a fallback strategy (e.g., fallback to a more mature Parquet library) to mitigate the integration risk.

Use in a workflow

hardwood-hq/hardwood helps teams persist, query, and move data with less custom plumbing.

• manage persistence
• speed up data access
• prototype database-backed apps

Limitations and review notes

Medium: useful for prototypes or internal workflows, with dependency and maintenance checks before production.