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# How original is Miller?

It isn't. Miller is just one of many participants in the online analytical processing culture. Other key participants include `awk`, SQL, spreadsheets, etc. etc.  etc.  Far from being an original concept, Miller explicitly strives to imitate several existing tools:

**The Unix toolkit**: Intentional similarities as described in [Unix-toolkit Context](unix-toolkit-context.md).

Recipes abound for command-line data analysis using the Unix toolkit. Here are just a couple of my favorites:

* [http://en.wikibooks.org/wiki/Ad_Hoc_Data_Analysis_From_The_Unix_Command_Line](http://en.wikibooks.org/wiki/Ad_Hoc_Data_Analysis_From_The_Unix_Command_Line)
* [http://www.gregreda.com/2013/07/15/unix-commands-for-data-science](http://www.gregreda.com/2013/07/15/unix-commands-for-data-science)
* [https://github.com/dbohdan/structured-text-tools](https://github.com/dbohdan/structured-text-tools)

**RecordStream**: Miller owes particular inspiration to [RecordStream](https://github.com/benbernard/RecordStream). The key difference is that RecordStream is a Perl-based tool for manipulating JSON (including requiring it to separately manipulate other formats such as CSV into and out of JSON), while Miller is a fast Go tool that handles its formats natively.  The similarities include the `sort`, `stats1` (analogous to RecordStream's `collate`), and `delta` operations, as well as `filter` and `put`, and the use of pretty-print formatting.

**stats_m**: A third source of lineage is my Python [stats_m](https://github.com/johnkerl/scripts-math/tree/master/stats) module.  This includes simple single-pass algorithms which form Miller's `stats1` and `stats2` subcommands.

**SQL**: Fourthly, Miller's `group-by` command name is from SQL, as is the term `aggregate`.

**Added value**: Miller's added values include:

* Name-indexing, compared to the Unix toolkit's positional indexing.
* Raw speed, compared to `awk`, RecordStream, `stats_m`, or various other kinds of Python/Ruby/etc. scripts that one can easily create.
* Compact keystroking for many common tasks, with a decent amount of flexibility.
* Ability to handle text files on the Unix pipe, without the need for creating database tables, compared to SQL databases.
* Various file formats, and on-the-fly format conversion.

**jq**: Miller does for name-indexed text what [jq](https://stedolan.github.io/jq/) does for JSON. If you're not already familiar with `jq`, please check it out!.

**What about similar tools?**

Here's a comprehensive list: [https://github.com/dbohdan/structured-text-tools](https://github.com/dbohdan/structured-text-tools).  Last I knew, it doesn't mention [rows](https://github.com/turicas/rows) so here's a plug for that as well.  As it turns out, I learned about most of these after writing Miller.

**What about DOTADIW?** One of the key points of the [Unix philosophy](http://en.wikipedia.org/wiki/Unix_philosophy) is that a tool should do one thing and do it well.  Hence, `sort` and `cut` do just one thing. Why does Miller put `awk`-like processing, a few SQL-like operations, and statistical reduction all into one tool?  This is a fair question. First, note that many standard tools, such as `awk` and `perl`, do quite a few things -- as does `jq`.  But I could have pushed for putting format awareness and name-indexing options into `cut`, `awk`, and so on (so you could do `cut -f hostname,uptime` or `awk '{sum += $x*$y}END{print sum}'`).  Patching `cut`, `sort`, etc., on multiple operating systems is a non-starter in terms of uptake.  Moreover, it makes sense for me to have Miller be a tool that collects together format-aware record-stream processing into one place, with good reuse of Miller's internal library code for its various features.

**Why not use Perl/Python/Ruby, etc.?** Maybe you should. With those tools, you'll gain significantly more expressive power and a sufficiently quick turnaround time for small to medium-sized datasets.  Using Miller, you'll get something less than a complete programming language, but which is fast, with moderate amounts of flexibility and much less keystroking.

When I was first developing Miller, I made a survey of several languages. Using low-level implementation languages like C, Go, Rust, and Nim, I'd need to create my own domain-specific language (DSL), which would always be less featured than a full programming language, but I'd get better performance.  Using high-level interpreted languages such as Perl/Python/Ruby, I'd get the language's `eval` for free and I wouldn't need a DSL; Miller would have mainly been a set of format-specific I/O hooks. If I'd gotten good enough performance from the latter, I'd have done it without question, and Miller would be far more flexible.  But low-level languages win the performance criteria by a landslide, so we have Miller in Go with a custom DSL.

**No, really, why one more command-line data-manipulation tool?** I wrote Miller because I was frustrated with tools like `grep`, `sed`, and so on being *line-aware* without being *format-aware*. The single most poignant example I can think of is seeing people grep data lines from their CSV files and sadly losing their header lines.  While some lighter-than-SQL processing is very nice to have, at core I wanted the format-awareness of [RecordStream](https://github.com/benbernard/RecordStream) combined with the raw speed of the Unix toolkit. Miller does precisely that.
