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| <div class="section" id="arrow-compute-ir-intermediate-representation"> |
| <h1>Arrow Compute IR (Intermediate Representation)<a class="headerlink" href="#arrow-compute-ir-intermediate-representation" title="Permalink to this headline">¶</a></h1> |
| <p>In the same way that the Arrow format provides a powerful tool |
| for communicating data, Compute IR is intended to provide a |
| consistent format for representing analytical operations against |
| that data. As an arrow-native expression of computation it includes |
| information such as explicit types and schemas and arrow formatted |
| literal data. It is also optimized for low runtime overhead in both |
| serialization and deserialization.</p> |
| <p>Built-in definitions are included to enable representation of |
| relational algebraic operations- the contents of a “logical query plan”. |
| Compute IR also has first class support for representing operations |
| which are not members of a minimal relational algebra, including |
| implementation and optimization details- the contents of a “physical |
| query plan”. This approach is taken in emulation of <a class="reference external" href="https://mlir.llvm.org">MLIR</a> (Multi-Level |
| Intermediate Representation), a system which has had strong successes in |
| spaces of comparable complexity to representation of analytic operations. |
| To borrow terms from that project, there are two mutations of interest:</p> |
| <ul class="simple"> |
| <li><p>Replacement of representations with semantically equivalent representations |
| which will yield better performance for consumers- an optimization pass.</p></li> |
| <li><p>Replacement of abstract or generic representations with more specific |
| and potentially consumer-specific representations- a lowering pass. |
| This modification corresponds to the translation of a logical plan |
| to a physical plan.</p></li> |
| </ul> |
| <p>Allowing representation of physical plans (and plans which are between |
| logical and physical) in Compute IR enables systems to define incremental |
| optimization and lowering passes which operate on and produce valid |
| Compute IR. This in turn enables communication, manipulation, and inspection |
| at every stage of lowering/optimization by the same tools |
| used for logical-plan-equivalent-IR. This is especially useful for systems |
| where such passes may depend on information only available on every node |
| of a distributed consumer (for example statistics unique to that node’s |
| local data) or may not be universal to all backends in a heterogeneous |
| consumer (for example which optimizations nodes are capable of for |
| non equi joins).</p> |
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