nealrichardson commented on a change in pull request #19: ARROW-6419: [Website]
Blog post about Parquet C++ read performance improvements in Arrow 0.15
URL: https://github.com/apache/arrow-site/pull/19#discussion_r320432915
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File path: _posts/2019-09-03-faster-strings-cpp-parquet.md
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+---
+layout: post
+title: "Faster C++ Apache Parquet performance on string-heavy data coming in
Apache Arrow 0.15"
+date: "2019-09-01 00:00:00 -0600"
+author: Wes McKinney
+categories: [application]
+---
+<!--
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+
+We have been implementing a series of optimizations in the Apache Parquet C++
+internals to improve read and write efficiency (both performance and memory
+use) for Arrow columnar binary and string types, including native support for
+Arrow's dictionary types. This should have a big impact on users of the C++,
+MATLAB, Python, R, and Ruby interfaces to Parquet files.
+
+We discuss the work that was done and show benchmarks comparing Arrow 0.11.0
+(released in October, 2018) with the current development version (to be
+released soon as Arrow 0.15.0).
+
+# Summary of work
+
+One of the challenges of developing the Parquet C++ library is that we
+maintain low-level read and write APIs that do not involve the Arrow columnar
+data structures. So we have had to take care to do Arrow-related optimizations
+without impacting non-Arrow Parquet users, which includes database systems like
+Clickhouse and Vertica.
+
+One of the largest and most complex optimizations involves encoding and
+decoding Parquet files' internal dictionary-encoded data streams to and from
+Arrow's in-memory dictionary-encoded `DictionaryArray`
+representation. Dictionary encoding is a compression strategy in Parquet, and
+there is no formal "dictionary" or "categorical" type. I will go into more
+detail about this below.
+
+Some of the particular JIRA issues related to this work include:
+
+- Vectorize comparators for computing statistics ([PARQUET-1523][1])
+- Read binary directly data directly into DictionaryBuilder<T>
+ ([ARROW-3769][2])
+- Writing Parquet's dictionary indices directly into DictionaryBuilder<T>
+ ([ARROW-3772][3])
+- Write dense (non-dictionary) Arrow arrays directly into Parquet data encoders
+ ([ARROW-6152][4])
+- Direct writing of arrow::DictionaryArray to Parquet column writers
([ARROW-3246][5])
+- Supporting changing dictionaries ([ARROW-3144][6])
+- Internal IO optimizations and improved raw `BYTE_ARRAY` encoding performance
+ ([ARROW-4398][7])
+
+# Background: how Parquet files do dictionary encoding
+
+Many direct and indirect users of Apache Arrow use dictionary encoding to
+improve performance and memory use on binary or string data types that include
+many repeated values. pandas users will know this as the [Categorical type][8]
+while in R such encoding is known as [`factor`][9]. In the Arrow C++ library
+and various bindings we have the `DictionaryArray` object for representing such
+data in memory.
+
+For example, an array such as
+
+```
+['apple', 'orange', 'apple', NULL, 'orange', 'orange']
+```
+
+has dictionary-encoded form
+
+```
+dictionary: ['apple', 'orange']
+indices: [0, 1, 0, NULL, 1, 1]
+```
+
+The [Parquet format uses dictionary encoding][10] to compress data, and it is
+used for all Parquet data types, not just binary or string data. Parquet
+further uses bit-packing and run-length encoding (RLE) to compress the
+dictionary indices, so if you had data like
+
+```
+['apple', 'apple', 'apple', 'apple', 'apple', 'apple', 'orange']
+```
+
+the indices would be encoded like
+
+```
+[rle-run=(6, 0),
+ bit-packed-run=[1]]
+```
+
+The full details of the rle-bitpacking encoding are found in the Parquet
+specification.
+
+When writing a Parquet file, most implementations will use dictionary encoding
+to compress a column until the dictionary itself reaches a certain size
+threshold, usually around 1 megabyte. At this point, the column writer will
+"fall back" to `PLAIN` encoding where values are written end-to-end in "data
+pages" and then usually compressed with Snappy or Gzip. See the following rough
+diagram:
+
+<div align="center">
+<img src="{{ site.base-url }}/img/20190903-parquet-dictionary-column-chunk.png"
+ alt="Internal ColumnChunk structure"
+ width="80%" class="img-responsive">
+</div>
+
+When reading a Parquet file, the dictionary-encoded portions are usually
Review comment:
This seems to start a new section, where you've moved on from "background"
and are starting to discuss your optimizations.
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