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Merged
SylvainCorlay merged 1 commit into from
Apr 23, 2026
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Fix typo fixes #331

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2 changes: 1 addition & 1 deletion src/components/fundable/descriptions/Decimal32InArrowCpp.md
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Apache Arrow is the universal columnar format and multi-language toolbox for fast data interchange and in-memory analytics.

Fixed-width decimal data in Arrow is usually represented the Decimal128 data type.
Fixed-width decimal data in Arrow is usually represented by the Decimal128 data type.
This data type has non-trivial memory costs (16 bytes per value) and computational costs (operations on 128-bit integers must be emulated on most if not all architectures).

Arrow recently gained Decimal32 and Decimal64 data types which, as their names suggest, encode fixed-width decimal data more compactly.
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6 changes: 3 additions & 3 deletions src/components/fundable/descriptions/ParquetNullOptimizations.md
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Apache Parquet is an open source, column-oriented data file format designed for
efficient data storage and retrieval. Together with Apache Arrow for in-memory data,
it has become for the *de facto* standard for efficient columnar analytics.
it has become the *de facto* standard for efficient columnar analytics.

While Parquet and Arrow are most often used together, they have incompatible physical
representations of data with optional values: data where some values can be
Expand All @@ -18,7 +18,7 @@ the data is declared nullable (optional) at the schema level.
We propose to optimize the conversion of null values from Parquet in Arrow C++
for flat (non-nested) data:

1. decoding Parquet definition levels directly into a Arrow validity bitmap, rather than using an
1. decoding Parquet definition levels directly into an Arrow validity bitmap, rather than using an
intermediate representation as 16-bit integers;

2. avoiding decoding definition levels entirely when a data page's statistics shows
Expand All @@ -27,7 +27,7 @@ for flat (non-nested) data:
As a subsequent task, these optimizations may be extended so as to apply to schemas
with moderate amounts of nesting.

This work will benefit to applications using Arrow C++ or any of its language
This work will benefit applications using Arrow C++ or any of its language
bindings (such as PyArrow, R-Arrow...).

Depending on the typology of Parquet data, this could make Parquet reading 2x
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