Client-side chunks 4: integrations #6441
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Started a full build: https://github.com/rerun-io/rerun/actions/runs/9282238447 |
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This new and improved `re_format_arrow` ™️ brings two major improvements: - It is now designed to format standard Arrow dataframes (aka chunks or batches), i.e. a `Schema` and a `Chunk`. In particular: chunk-level and field-level schema metadata will now be rendered properly with the rest of the table. - Tables larger than your terminal will now do their best to fit in, while making sure to still show just enough data. E.g. here's an excerpt of a real-world Rerun dataframe from our `helix` example: ``` cargo r -p rerun-cli --no-default-features --features native_viewer -- print helix.rrd --verbose ``` before (`main`):  and after:  --- Part of a PR series to implement our new chunk-based data model on the client-side (SDKs): - #6437 - #6438 - #6439 - #6440 - #6441
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…6438) Introduces the new `re_chunk` crate: > A chunk of Rerun data, encoded using Arrow. Used for logging, transport, storage and compute. Specifically, it introduces the `Chunk` type itself, and all methods and helpers related to sorting. A `Chunk` is self-describing: it contains all the data _and_ metadata needed to index it into storage. There are a lot of things that need to be sorted within a `Chunk`, and as such we must make sure to keep track of what is or isn't sorted at all times, to avoid needlessly re-sorting things everytime a chunk changes hands. This necessitates a bunch of sanity checking all over the place to make sure we never end up in undefined states. `Chunk` is not about transport, it's about providing a nice-to-work with representation when manipulating a chunk in memory. Transporting a `Chunk` happens in the next PR. - Fixes #1981 --- Part of a PR series to implement our new chunk-based data model on the client-side (SDKs): - #6437 - #6438 - #6439 - #6440 - #6441
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A `TransportChunk` is a `Chunk` that is ready for transport and/or storage. It is very cheap to go from `Chunk` to a `TransportChunk` and vice-versa. A `TransportChunk` maps 1:1 to a native Arrow `RecordBatch`. It has a stable ABI, and can be cheaply send across process boundaries. `arrow2` has no `RecordBatch` type; we will get one once we migrate to `arrow-rs`. A `TransportChunk` is self-describing: it contains all the data _and_ metadata needed to index it into storage. We rely heavily on chunk-level and field-level metadata to communicate Rerun-specific semantics over the wire, e.g. whether some columns are already properly sorted. The Arrow metadata system is fairly limited -- it's all untyped strings --, but for now that seems good enough. It will be trivial to switch to something else later, if need be. - Fixes #1760 - Fixes #1692 - Fixes #3360 - Fixes #1696 --- Part of a PR series to implement our new chunk-based data model on the client-side (SDKs): - #6437 - #6438 - #6439 - #6440 - #6441
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This is a fork of the old `DataTable` batcher, and works very similarly. Like before, this batcher will micro-batch using both space and time thresholds. There are two main differences: - This batcher maintains a dataframe per-entity, as opposed to the old one which worked globally. - Once a threshold is reached, this batcher further splits the incoming batch in order to fulfill these invariants: ```rust /// In particular, a [`Chunk`] cannot: /// * contain data for more than one entity path /// * contain rows with different sets of timelines /// * use more than one datatype for a given component /// * contain more rows than a pre-configured threshold if one or more timelines are unsorted ``` Most of the code is the same, the real interesting piece is `PendingRow::many_into_chunks`, as well as the newly added tests. - Fixes #4431 --- Part of a PR series to implement our new chunk-based data model on the client-side (SDKs): - #6437 - #6438 - #6439 - #6440 - #6441
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Integrate the new chunk batcher in all SDKs, and get rid of the old one.
On the backend, we make sure to deserialize incoming chunks into the old
DataTables, so business can continue as usual.Although the new batcher has a much more complicated task with all these sub-splits to manage, it is somehow already more performant than the old one 🤷♂️:
Notice the massive difference in user time.
Part of a PR series to implement our new chunk-based data model on the client-side (SDKs):
Chunkand its suffle/sort routines #6438TransportChunk#6439Checklist
mainbuild: rerun.io/viewernightlybuild: rerun.io/viewerTo run all checks from
main, comment on the PR with@rerun-bot full-check.