
## Review of the initial version (from October 2025) 

```text
As you can see in this directory I want to have a Raku package that connects to H2O Java library/ platform. 
Examine the code and make recommendations. 
My biggest question is should I have a Raku proxy class for H2O's data frames, what would be its attributes and methods. 
If you need documentation of H2O says, so. 
The GitHub repository https://github.com/h2oai/h2o-3 has relevant code.
```

### Answer:

I’ll inspect the package structure and the connector’s Java interop surface first. Then I’ll compare it with H2O’s frame model and give a concrete proxy API recommendation; I’ll call out any points that genuinely require upstream documentation or source verification.

The current connector is a REST client/process manager, not direct JVM interop. That distinction matters: the useful Raku object should proxy H2O REST resources (frame keys and endpoints), while preserving H2O’s server-side/lazy semantics instead of attempting to mirror Java `Frame`/`Vec` objects locally. I’m checking the examples and package contract now, including a few connector correctness issues that affect such a proxy.

A concrete constructor bug is already visible: `H2O::Client.new("http://example.test:1234")` silently resets the URL to `127.0.0.1:54321`, and the embedded connector is never constructed/configured. I’m also validating H2O’s actual REST schemas (`FrameV3`, `FramesV3`) so the proxy recommendation follows stable wire-level fields rather than the much larger Python `H2OFrame` convenience API.

Guardian Review

Yes—add a Raku `H2O::Client::Frame` proxy. It should represent a remote H2O frame key, not copy Java’s `Frame` object or eagerly download data.

The package is currently best described as an H2O-3 REST client rather than direct Java-library interop. That is the right architectural direction.

### Recommended frame proxy

```raku
class H2O::Client::Frame {
    has H2O::Client:D $.client is required;
    has Str:D        $.id     is required;

    has %!metadata;
    has Bool $!deleted = False;

    method refresh(:$light = True --> H2O::Client::Frame) { ... }
    method exists(--> Bool)                               { ... }

    method nrows(--> UInt)   { ... }
    method ncols(--> UInt)   { ... }
    method shape(--> List)   { ... }
    method names(--> Array)  { ... }
    method types(--> Map)    { ... }
    method checksum()        { ... }
    method byte-size()       { ... }

    method column(Str:D $name --> H2O::Client::Column) { ... }

    method preview(
        UInt :$rows = 10,
        UInt :$offset = 0,
             :@columns
        --> Array
    ) { ... }

    method head(UInt $rows = 10 --> Array) { ... }
    method tail(UInt $rows = 10 --> Array) { ... }
    method summary(:$column)               { ... }

    method download(IO::Path:D $path, *%options) { ... }
    method export(Str:D $server-path, *%options --> H2O::Client::Job) { ... }
    method delete() { ... }
    method raw()    { ... }
}
```

Only `client` and

`id` should be authoritative attributes. Rows, columns, names, types, checksum, and statistics should be cached metadata refreshed from `/3/Frames/{id}`. They should not be public `rw` attributes because the server owns that state.

A companion `Column` proxy is worthwhile:

```raku
class H2O::Client::Column {
    has H2O::Client::Frame:D $.frame;
    has Str:D $.name;

    method type()          { ... }
    method domain()        { ... }
    method summary()       { ... }
    method missing-count() { ... }
    method min()           { ... }
    method max()           { ... }
    method mean()          { ... }
    method sigma()         { ... }
    method percentiles()   { ... }
}
```

These properties correspond closely to H2O’s upstream [`FrameV3.ColV3`](https://github.com/h2oai/h2o-3/blob/master/h2o-core/src/main/java/water/api/schemas3/FrameV3.java).

I would allow `$frame<Age>` to return a column proxy eventually, but avoid positional indexing and overloaded arithmetic initially: hidden network operations and ambiguous row/column semantics become surprising.

### A job proxy is equally important

Parsing, model building, prediction, and export are asynchronous in H2O. The current example starts operations and immediately queries frames/models.

Add `H2O::Client::Job` with:

- `id`, `destination-id`, `destination-type`
- `status`, `progress`, `error`
- `refresh`, `wait`, `cancel`
- `result`, returning a `Frame` or `Model` proxy when appropriate

Then importing can naturally become:

```raku
my $frame = $h2o.upload(@records, :id<titanic.hex>).wait.result;
```

### Important current problems

1. [`H2O::Client.new`](../lib/H2O/Client.rakumod:17) discards the supplied base URL. I verified that:

```raku
H2O::Client.new("http://example.test:1234").base-url
```

returns `http://127.0.0.1:54321`.

2. `$!conn` is never initialized in [`H2O::Client`](../lib/H2O/Client.rakumod), so delegated lifecycle methods and `init` are unreliable.

3. [`Connector.!request`](../lib/H2O/Client/Connector.rakumod) reads `$res<body>`. This `HTTP::Tiny` implementation returns response bytes as `$res<content>`, so successful JSON requests return `Nil`.

4. There are two incompatible HTTP layers. `Connector` sends JSON, while `H2O::Client` sends form data. Consolidate all HTTP behavior into one transport owned by `H2O::Client`; make `Connector` only responsible for launching/stopping a local H2O process.

5. [`data-import(@data)`](../lib/H2O/Client.rakumod:190) writes a local CSV and calls `/3/ImportFiles`. That works only when the H2O server shares the same filesystem. Remote clients need `/3/PostFile` multipart upload. The temporary file is also not removed.

6. Query parameters and frame/model IDs are not URL-encoded.

7. HTTP failures should throw a typed exception containing status, URL, and H2O’s error body instead of returning `Nil` or an undecoded string.

8. `shutdown` currently shuts down an externally managed cluster too. Separate `stop-local-process` from an explicit `shutdown-cluster`.

9. `HTTP::Tiny` is used directly but missing from `META6.json`; several declared LLM/Text dependencies appear unused. There are no tests.

### Suggested implementation order

1. Unify and test the HTTP transport.
2. Fix construction and connector lifecycle.
3. Add `Job`.
4. Add the minimal `Frame` and `Column` proxies.
5. Make `frames()` return proxies, with `:$raw` for the current hashes.
6. Make upload/parse/prediction return jobs or proxies.
7. Add Rapids-backed transformations later.

Do not try to reproduce all 5,000 lines of Python `H2OFrame`. Start with identity, metadata, preview, summary, download/export, and deletion. For transformations, build a smaller explicit Rapids expression layer once the basic resource model is stable.

I did not need additional documentation for this design: the upstream `FrameV3`, `FramesV3`, `FramesHandler`, and Python `H2OFrame` sources were sufficient. Before implementation, I would choose a supported H2O baseline—probably the 3.46.x version already used by the example—and capture its `/3/Metadata/endpoints` and schema output for compatibility tests.

---

## Implementation (core)

```text
Ok. Do the suggested implementation steps. Add corresponding tests in the directories "./t" and "./xt". 
In "./t" put "easy" or sanity tests; in "./xt" tests that involve actual Java H2O execution.
```

----

## Implementation extension (data frames transformations, Rapids)

```text
I started the H2O cluster -- can be found at: http://localhost:54321 .
Proceed with implementing data frame(s)  identity, metadata, preview, summary, download/export, and deletion. 
For transformations, build a smaller explicit Rapids expression layer once the basic resource model is stable.
```

*After I got the answer below.*

```text
Ok. Implement the lazy conversion.
```

### Answer

I have the Rapids layer scaffolded and am resolving its Raku type signature before live execution. I’ll preserve your newly added credits while completing the proxy and integration coverage.

The Rapids API now compiles and its fast tests confirm AST construction, safe identifiers, expression-client ownership, and explicit `tmp=` materialization. I’m running the attached integration suite next; it creates only uniquely prefixed frames and temporary CSVs, then deletes them in cleanup.

Guardian Review

Guardian Review

The first attached run validated all basic frame endpoints and even completed one Rapids transformation. A second run exposed H2O’s Rapids session requirement: submitting without a `/4/sessions` key can leave server-side vector references behind. I’m adding lazy session creation and explicit client closure, matching H2O’s own Python client protocol.

Guardian Review

Implemented and live-validated the frame resource API against your running H2O cluster.

- `Frame` now has stable cluster/key identity, lazy metadata, preview/head/tail, summaries, download, export jobs, deletion, and explicit Rapids entry points: [Frame.rakumod](../lib/H2O/Client/Frame.rakumod)
- Added a small, safe, lazy Rapids expression layer: [Expr.rakumod](../lib/H2O/Client/Rapids/Expr.rakumod)
- Rapids now creates and uses an H2O session lazily, preventing server-side reference leaks: [Client.rakumod](../lib/H2O/Client.rakumod)
- Documented the transformation API in [README.md](../README.md).

Example:

```raku
my $adults = $frame
    .where($frame<age>.expression.greater-than(18))
    .select(<age income>)
    .materialize('adults.hex');
```

All fast tests pass. The attached-cluster integration test also passed against `http://localhost:54321`, covering metadata, preview, summary, download, export, Rapids filtering/selection, and cleanup: [xt/02-frame-and-rapids-live.rakutest](../xt/02-frame-and-rapids-live.rakutest).

Your `resources/` directory was left untouched.

---

## Implementation of AT-POS for data frames

```text
For `H2O::Client::Frame` implement the method `AT-POS` that can take as argument integer index, list of indexes, range, `*`, or `Whatever`. 
It can be based on the method `preview`.
```

---

## Transform column values to factors/enums

```text
I want to have column conversions. 
For example, a column of integers to be converted to factors. 
Where it is best to do that? In the frame class or in the column class?
```

*After I got the answer below.*

```text
Ok. Implement the lazy conversion.
```

### Answer 

Put column conversions on `H2O::Client::Column`.
A conversion such as integer → factor is intrinsically a single-column transformation, so an API like this keeps the intent clear:

```raku
my $factor-column = $frame<age>.as-factor;
```

The method can return a new `Frame` (H2O transformations normally materialize a new server-side frame) or a Rapids expression that the caller explicitly materializes. I’d favor a lazy expression initially:

```raku
my $converted = $frame<age>.as-factor.materialize;
```

`Frame` should own frame-wide operations—selection, filtering, joins, adding/removing columns, or bulk conversion mappings such as `convert-columns(%(age => 'factor', score => 'real'))`.

That gives a clean division:

- `Column`: `as-factor`, `as-int`, `as-real`, `as-string`, possibly `replace-missing`.
- `Frame`: operations involving multiple columns or the frame schema.

A useful detail: conversion should not mutate the source frame implicitly. Return a derived frame/expression, with an optional explicit replacement operation later if desired.


----

## Grouping

```text
In order to implement data grouping over the values of one or several columns -- 
should there be a separate `GroupBy` class or just a method in `H2O::Client::Rapids::Expr` would be sufficient? 
If H2O has a separate class of grouped data frames, I assume the client also should have corresponding proxy. 
```

### Answer

Use a separate `H2O::Client::Rapids::GroupBy` builder, returned by an `Expr.group-by` method.
H2O’s grouped-frame object is best treated as a client-side, lazy query plan—not a persistent server resource like `Frame`. Grouping only becomes a frame once an aggregation is requested and materialized.

```raku
my $grouped = $frame.expression.group-by(<region year>);

my $result = $grouped
    .count
    .mean('revenue')
    .materialize('revenue-by-region-year.hex');
```
`GroupBy` would retain:

- the source `Rapids::Expr`
- grouping column selectors
- requested aggregations

Its aggregation methods return another lazy expression (or keep returning the builder until `.expression`/`.materialize`). This prevents `Expr` from accumulating stateful grouping configuration and leaves room for H2O’s `GB` Rapids operation and aggregation-specific validation.

So: separate class, yes—but a lightweight lazy builder/proxy, not a separately fetched H2O server object.

### Implementation finished

I’ll add a lightweight lazy `Rapids::GroupBy` builder, expose it from expressions, and make its `count` and `mean` aggregations emit the H2O Rapids `GB` plan without creating a server-side object.

The builder now resolves column names to H2O’s required column indexes when the aggregation plan is compiled; integer selectors remain fully metadata-free. I’m running the complete test set and checking the final diff.

Implemented lazy grouping with [GroupBy.rakumod](../lib/H2O/Client/Rapids/GroupBy.rakumod).

```raku
my $result = $frame.expression
    .group-by(<region year>)
    .count
    .mean('revenue')
    .materialize('revenue-by-region-year.hex');
```

`group-by` returns a mutable lazy builder supporting `count`, `mean`, `sum`, `min`, and `max`; `.expression`, `.evaluate`, or `.materialize` compiles it into H2O’s `GB` Rapids expression. Named columns are resolved to H2O column indexes at compilation.

Also updated [Expr.rakumod](../lib/H2O/Client/Rapids/Expr.rakumod), package metadata, and grouping tests. All tests pass.

----

## Import files (from URLs)

```text
Implement the method import-file to produce a data frame job if the argument "job" is true. 
Call self.data-parse-setup and self.data-parse as it is done in upload-file. 
Do the appropriate refactoring between the two functions. 
Is there a better, more direct way using H2O.ai's REST API ?
```

### Answer

[...]

H2O-3 has no one-call REST endpoint that both imports server-visible files and creates a parsed frame. 
The standard REST flow is exactly `GET /3/ImportFiles` → `POST /3/ParseSetup` → `POST /3/Parse`; 
skipping setup is only practical when every parse setting is already known.

----

## Fixing extra row addition when uploading data with specified column names

```text
When I use `H20::Client::upload` with column names I get data frames that have one additional row. 
See my change on row 31 of "./xt/02-frame-and-rapids-live.rakutest'. 
With that change 6 of the tests below it fail.
```

### Answer

[...] I reproduced the six failures against the live cluster. The generated CSV is headed group,x, while the explicit names are x,group; that explains the downstream predicate failure, but I’m checking the exact H2O parse request semantics to address the extra header row too.

The cause is confirmed: H2O treats supplied `column_names` as an instruction to parse the first CSV line as data, regardless of `check_header`. 
I’ll make `upload` serialize records in the requested column order with those names as the CSV’s actual header, 
then let `ParseSetup` provide the matching parsed names instead of sending `column_names` again to `Parse`.

Fixed the extra-row issue in "Client.rakumod".

When explicit `column-names` are supplied, `upload` now:

- Writes those names as the actual CSV header, in the requested order.
- Orders associative record values to match those names.
- Does not resend `column_names` to H2O’s `Parse` endpoint, since H2O otherwise treats the header as a data row.

This also fixes the mismatched `x`/`group` types that caused the Rapids filter failures.

Verified with:

- `prove6 -Ilib xt/02-frame-and-rapids-live.rakutest` — pass, 16 tests
- `prove6 -Ilib t` — pass, 57 tests