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new 85e67b04e2 fix-doc3 (#11587)
85e67b04e2 is described below
commit 85e67b04e2657a4a97de2d3063c5e58d10e0247c
Author: Liqf <109049295+lemonlit...@users.noreply.github.com>
AuthorDate: Tue Aug 9 13:35:32 2022 +0800
fix-doc3 (#11587)
bloomFilter fix-doc
---
docs/en/docs/data-table/index/bloomfilter.md | 9 +++------
docs/zh-CN/docs/data-table/index/bloomfilter.md | 12 ++++++------
2 files changed, 9 insertions(+), 12 deletions(-)
diff --git a/docs/en/docs/data-table/index/bloomfilter.md
b/docs/en/docs/data-table/index/bloomfilter.md
index c51450cc9d..e63fc5fd41 100644
--- a/docs/en/docs/data-table/index/bloomfilter.md
+++ b/docs/en/docs/data-table/index/bloomfilter.md
@@ -96,7 +96,7 @@ PROPERTIES (
Check that the BloomFilter index we built on the table is to use:
```sql
-SHOW CREATE TABLE <table_name>
+SHOW CREATE TABLE <table_name>;
```
### Delete BloomFilter index
@@ -120,14 +120,11 @@ ALTER TABLE <db.table_name> SET ("bloom_filter_columns" =
"k1,k3");
You can consider establishing a Bloom Filter index for a column when the
following conditions are met:
1. First, BloomFilter is suitable for non-prefix filtering.
-
2. The query will be filtered according to the high frequency of the column,
and most of the query conditions are in and = filtering.
-
-3. Unlike Bitmap, BloomFilter is suitable for high cardinality columns. Such
as UserID. Because if it is created on a low-cardinality column, such as a
"gender" column, each Block will almost contain all values, causing the
BloomFilter index to lose its meaning
+3. Unlike Bitmap, BloomFilter is suitable for high cardinality columns. Such
as UserID. Because if it is created on a low-cardinality column, such as a
"gender" column, each Block will almost contain all values, causing the
BloomFilter index to lose its meaning.
### **Doris BloomFilter use precautions**
1. It does not support the creation of Bloom Filter indexes for Tinyint,
Float, and Double columns.
-
2. The Bloom Filter index only has an acceleration effect on in and =
filtering queries.
-3. If you want to check whether a query hits the Bloom Filter index, you can
check the profile information of the query
+3. If you want to check whether a query hits the Bloom Filter index, you can
check the profile information of the query.
diff --git a/docs/zh-CN/docs/data-table/index/bloomfilter.md
b/docs/zh-CN/docs/data-table/index/bloomfilter.md
index cf18df47f9..3d15676616 100644
--- a/docs/zh-CN/docs/data-table/index/bloomfilter.md
+++ b/docs/zh-CN/docs/data-table/index/bloomfilter.md
@@ -93,7 +93,7 @@ PROPERTIES (
查看我们在表上建立的BloomFilter索引是使用:
```sql
-SHOW CREATE TABLE <table_name>
+SHOW CREATE TABLE <table_name>;
```
## 删除BloomFilter索引
@@ -116,12 +116,12 @@ ALTER TABLE <db.table_name> SET ("bloom_filter_columns" =
"k1,k3");
满足以下几个条件时可以考虑对某列建立Bloom Filter 索引：
-1. 首先BloomFilter适用于非前缀过滤.
-2. 查询会根据该列高频过滤，而且查询条件大多是in和 = 过滤.
-3. 不同于Bitmap,
BloomFilter适用于高基数列。比如UserID。因为如果创建在低基数的列上，比如”性别“列，则每个Block几乎都会包含所有取值，导致BloomFilter索引失去意义
+1. 首先BloomFilter适用于非前缀过滤。
+2. 查询会根据该列高频过滤，而且查询条件大多是 in 和 = 过滤。
+3. 不同于Bitmap, BloomFilter适用于高基数列。比如UserID。因为如果创建在低基数的列上，比如 “性别”
列，则每个Block几乎都会包含所有取值，导致BloomFilter索引失去意义。
## **Doris BloomFilter使用注意事项**
1. 不支持对Tinyint、Float、Double 类型的列建Bloom Filter索引。
-2. Bloom Filter索引只对in和 = 过滤查询有加速效果。
-3. 如果要查看某个查询是否命中了Bloom Filter索引，可以通过查询的Profile信息查看
+2. Bloom Filter索引只对 in 和 = 过滤查询有加速效果。
+3. 如果要查看某个查询是否命中了Bloom Filter索引，可以通过查询的Profile信息查看。
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