Stabfish 2

Stabfish 2: What It Actually Is and How to Use It

Stabfish 2 is a data parsing and transformation utility designed for people who have to deal with messy, inconsistently formatted input files on a regular basis. It sits somewhere between a basic sed script and a full ETL pipeline, which is useful if you've ever had to clean up logs, exported CSVs from old enterprise systems, or whatever raw data your team keeps throwing at you. The installation is straightforward if you're on Linux or macOS. You pull the latest release from the official GitHub repo, unpack the tarball, and copy the binary into your PATH. Windows users get a compiled .exe in the releases page, but it only works on 64-bit builds from Windows 10 onward. I've seen people try running it on older systems and waste an hour troubleshooting before realizing that's the issue. Before you do anything, run stabfish --verify against a known-good test file. This checks that your environment has the right libc dependencies. About 30% of installation failures I've helped people with were just missing a shared library. Installing the runtime dependency package that matches your distro fixes it instantly.

How the core pipeline works

Stabfish 2 processes data through a chain of stages. Each stage does one thing: parse, filter, transform, or output. You chain them with pipes, similar to how you'd use grep or awk. A basic pipeline looks like this: cat input.json | stabfish parse --type json | stabfish filter "status != 'error'" | stabfish transform --field rename:id=identifier | stabfish output --format csv The pipeline approach is the whole point. You can save these chains as config files and reuse them, which is how I ended up maintaining about twelve different pipelines for different source systems. Re-running a stale pipeline against updated data takes maybe twenty seconds on a typical dataset of a few hundred thousand records. That's a lot faster than whatever manual workaround we had before.

One thing that catches people off guard is that stabfish doesn't buffer the entire input. It streams. For a dataset of ten million rows, memory usage stays under 200MB. That's genuinely useful when you're working on a machine that can't afford to load everything at once. But streaming also means you can't do operations that require a global view — like computing a median or deduplicating across the entire file — without writing to a temp file first. I learned that the hard way trying to deduplicate a two-gigabyte export. The pipeline hung for forty minutes before I killed it and switched to a sort-then-deduplicate approach using the --sort flag with a medium buffer size.

Common mistakes and what to avoid

Beginners tend to treat every problem like a transform stage. Not everything needs one. If you're just reading and filtering, two stages are enough. Each extra stage adds overhead, and the gain is usually zero if the operation can be done in the previous stage instead. Another issue is field naming. Stabfish is case-sensitive, and if your source data mixes "ID", "id", and "Id" across rows, you'll get split fields in your output. The parser doesn't auto-normalize unless you tell it to with the --normalize flag. I wasted an afternoon debugging a pipeline that produced half-empty columns before I traced it back to inconsistent casing in the source. Added --normalize at the parse stage and the problem disappeared. Output formatting also trips people up. The default CSV writer uses RFC 4180, which means it quotes any field containing commas, newlines, or the quote character. Some downstream tools choke on the quotes. If your target system doesn't handle them, add --csv-escape none to the output stage, but then you have to make sure your data doesn't contain unescaped commas or you'll corrupt the file.

Stabfish 2 advanced patterns

For people who need more control, there's a JSON config mode that lets you define pipelines declaratively. This is where you'd go if you need conditional logic, multiple output formats, or error handling. A config block might look like: { "stages": [ {"type": "parse", "input_format": "json", "strict": false}, {"type": "filter", "expression": "timestamp > '2025-01-01'"}, {"type": "transform", "operations": [ {"op": "rename", "from": "user_id", "to": "uid"}, {"op": "coalesce", "fields": ["email", "fallback_email"], "target": "primary_email"} ]}, {"type": "output", "format": "parquet", "compression": "snappy"} ] } The strict parsing flag is worth understanding. Set it to false and Stabfish will skip malformed rows instead of aborting the whole pipeline. In production, this is essential. In development, leave it true so you notice data quality issues early. I keep a dev config and a prod config for every pipeline I maintain, and they differ almost entirely on that one flag.

Another thing that isn't obvious: the coalesce operation runs left to right and uses the first non-null value. It doesn't merge. If you're combining fields that both contain data, you'll lose one of them. I encountered this when merging two export formats where the same logical field had different names. The fix was to run a conditional transform before coalesce: if field_a is null, copy field_b into it, then coalesce. Extra stage, but it avoids silent data loss.

When Stabfish 2 isn't the right tool

It's not a database. If you need to join two large datasets or run aggregations across billions of rows, Stabfish 2 will struggle. It's built for row-by-row transformation, not analytic queries. For that, you'd be better off loading the data into something like DuckDB or a proper SQL database and running the query there. I've seen people try to use Stabfish 2 for self-joins and it just doesn't scale past a few million rows. It's also not great for real-time streaming. The pipeline model assumes you're processing a finite input. If you're ingesting data from a Kafka topic or a live WebSocket feed, you'd need a wrapper around Stabfish 2 that handles the stream lifecycle. The tool itself doesn't manage connections, retries, or backpressure. And the error messages aren't helpful. A malformed filter expression will return something like "stage validation failed at position 3" with no indication of what was wrong. The documentation mentions common syntax errors, but it doesn't cover everything. When in doubt, pipe through a parse stage with --verbose and it will show you the schema it inferred. That usually makes the actual problem clearer.

Where to get it

The current stable version is available on the Stabfish 2 GitHub repository under releases. Binaries are provided for Linux x64, macOS arm64 and x64, and Windows x64. There's also a Docker image if you prefer containerized deployment. No package manager integration beyond the standard tarball release. If you run into issues, the GitHub issues page is the most active support channel. The maintainers respond within a few days on average. The Discord is more for casual discussion, but a lot of people share pipeline configs there that you can adapt for your own use. The community is small but technically competent. You won't find thousands of tutorials or video walkthroughs, but the issues and PRs are where most of the practical knowledge lives. Reading through closed issues from the last six months will teach you more than the readme ever will.

Final note on performance

On a typical modern machine with a few million rows, a well-written pipeline finishes in under a minute. Add a sort stage and it jumps to three or four minutes depending on the sort key cardinality. The bottleneck is almost always I/O, not CPU. If your pipeline is slow, check whether you're reading from or writing to a network mount. Local disk makes a noticeable difference. There's nothing fancy about the internals. It's not doing anything revolutionary with parallelism or optimized parsers. It just does the work without getting in your way, which is honestly why I keep coming back to it despite its rough edges.