[jira] [Created] (DRILL-5376) Rationalize Drill's row structure for simpler code, better performance

["Paul Rogers (JIRA)" <ji...@apache.org>]

Paul Rogers created DRILL-5376:
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             Summary: Rationalize Drill's row structure for simpler code, better performance
                 Key: DRILL-5376
                 URL: https://issues.apache.org/jira/browse/DRILL-5376
             Project: Apache Drill
          Issue Type: Improvement
    Affects Versions: 1.10.0
            Reporter: Paul Rogers


Drill is a columnar system, but data is ultimately represented as rows (AKA records or tuples.) The way that Drill represents rows leads to excessive code complexity and runtime cost.

Data in Drill is stored in vectors: one (or more) per column. Vectors do not stand alone, however, they are "bundled" into various forms of grouping: the {{VectorContainer}}, {{RecordBatch}}, {{VectorAccessible}}, {{VectorAccessibleSerializable}}, and more. Each has slightly different semantics, requiring large amounts of code to bridge between the representations.

Consider only a simple row: one with only scalar columns. In classic relational theory, such a row is a tuple:

{code}
R = (a, b, c, d, ...)
{code}

A tuple is defined as an ordered list of column values. Unlike a list or array, the column values also have names and may have varying data types.

In SQL, columns are referenced by either position or name. In most execution engines, columns are referenced by position (since positions, in most systems, cannot change.) A 1:1 mapping is provided between names and positions. (See the JDBC {{RecordSet}} interface.)

This allows code to be very fast: code references columns by index, not by name, avoiding name lookups for each column reference.

Drill provides a murky, hybrid approach. Some structures ({{BatchSchema}}, for example) appear to provide a fixed column ordering, allowing indexed column access. But, other abstractions provide only an iterator. Others (such as {{VectorContainer}}) provides name-based access or, by clever programming, indexed access.

As a result, it is never clear exactly how to quickly access a column: by name, by name to multi-part index to vector?

Of course, Drill also supports maps, which add to the complexity. First, we must understand that a "map" in Drill is not a "map" in the classic sense: it is not a collection of (name, value) pairs in the JSON sense: a collection in which each instance may have a different set of pairs.

Instead, in Drill, a "map" is really a nested tuple: a map has the same structure as a Drill record: a collection of names and values in which all rows have the same structure. (This is so because maps are really a collection of value vectors, and the vectors cut across all rows.)

Drill, however, does not reflect this symmetry: that a row and a map are both tuples. There are no common abstractions for the two. Instead, maps are represented as a {{MapVector}} that contains a (name, vector) map for its children.

Because of this name-based mapping, high-speed indexed access to vectors is not provided "out of the box." Certainly each consumer of a map can build its own indexing mechanism. But, this leads to code complexity and redundancy.

This ticket asks to rationalize Drill's row, map and schema abstractions around the tuple concept. A schema is a description of a tuple and should (as in JDBC) provide both name and index based access. That is, provide methods of the form:

{code}
MaterializedField getField(int index);
MaterializedField getField(String name);
...
ValueVector getVector(int index);
ValueVector getVector(String name);
{code}

Provide a common abstraction for rows and maps, recognizing their structural similarity.

There is an obvious issue with indexing columns in a row when the row contains maps. Should indexing be multi-part (index into row, then into map) as today? A better alternative is to provide a flattened interface:

{code}
0: a, 1: b.x, 2: b.y, 3: c, ...
{code}

Use this change to simplify client code, over time, to use a simple indexed-based column access.



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