(paimon) branch master updated: [doc] Fix remove duplicate descriptions about data file index. (#4124)

[lz...@apache.org]

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lzljs3620320 pushed a commit to branch master
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The following commit(s) were added to refs/heads/master by this push:
     new 14a1ef8a6 [doc] Fix remove duplicate descriptions about data file index. (#4124)
14a1ef8a6 is described below

commit 14a1ef8a62372842eeb91512b1de217a5cbc41cc
Author: HunterXHunter <cn...@gmail.com>
AuthorDate: Thu Sep 5 16:02:46 2024 +0800

    [doc] Fix remove duplicate descriptions about data file index. (#4124)
---
 docs/content/append-table/query.md | 8 ++------
 1 file changed, 2 insertions(+), 6 deletions(-)

diff --git a/docs/content/append-table/query.md b/docs/content/append-table/query.md
index 269bf6e76..4f7e0ab9f 100644
--- a/docs/content/append-table/query.md
+++ b/docs/content/append-table/query.md
@@ -49,15 +49,11 @@ CREATE TABLE <PAIMON_TABLE> (<COLUMN> <COLUMN_TYPE> , ...) WITH (
 );
 ```
 
-Define `file-index.bloom-filter.columns`, Paimon will create its corresponding index file for each file. If the index
-file is too small, it will be stored directly in the manifest, or in the directory of the data file. Each data file
+Define `file-index.bloom-filter.columns`, Data file index is an external index file and Paimon will create its corresponding index file for each file. If the index
+file is too small, it will be stored directly in the manifest, otherwise in the directory of the data file. Each data file
 corresponds to an index file, which has a separate file definition and can contain different types of indexes with
 multiple columns.
 
-Data file index is an external index file corresponding to a certain data file. If the index file is too small, it will
-be stored directly in the manifest, otherwise in the directory of the data file. Each data file corresponds to an index file,
-which has a separate file definition and can contain different types of indexes with multiple columns.
-
 Different file index may be efficient in different scenario. For example bloom filter may speed up query in point lookup
 scenario. Using a bitmap may consume more space but can result in greater accuracy.