[typo](docs)Modify some document label errors (apache#16866)

* [typo](docs)Modify some document label errors * fix
yujun777 · Feb 17, 2023 · adc4260 · adc4260
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diff --git a/docs/en/docs/advanced/best-practice/compaction.md b/docs/en/docs/advanced/best-practice/compaction.md
@@ -34,7 +34,10 @@ Appropriately adjusting the compaction strategy can greatly improve load and que
 
 ## Vertical compaction
 
-Vertical compaction is a new compaction algorithm implemented in Doris 2.0, which is used to optimize compaction execution efficiency and resource overhead in large-scale and wide table scenarios. It can effectively reduce the memory overhead of compaction and improve the execution speed of compaction. The test results show that the memory consumption by vertical compaction is only 1/10 of the original compaction algorithm, and the compaction rate is increased by 15%.
+<version since="1.2.2">
+</version>
+
+Vertical compaction is a new compaction algorithm implemented in Doris 1.2.2, which is used to optimize compaction execution efficiency and resource overhead in large-scale and wide table scenarios. It can effectively reduce the memory overhead of compaction and improve the execution speed of compaction. The test results show that the memory consumption by vertical compaction is only 1/10 of the original compaction algorithm, and the compaction rate is increased by 15%.
 
 In vertical compaction, merging by row is changed to merging by column group. The granularity of each merge is changed to column group, which reduces the amount of data involved in single compaction and reduces the memory usage during compaction.
 

diff --git a/docs/en/docs/advanced/hight-concurrent-point-query.md b/docs/en/docs/advanced/hight-concurrent-point-query.md
@@ -51,9 +51,9 @@ CREATE TABLE `tbl_point_query` (
   `v6` float NULL,
   `v7` datev2 NULL
 ) ENGINE=OLAP
-UNIQUE KEY(key)
+UNIQUE KEY(`key`)
 COMMENT 'OLAP'
-DISTRIBUTED BY HASH(key) BUCKETS 1
+DISTRIBUTED BY HASH(`key)` BUCKETS 1
 PROPERTIES (
 "replication_allocation" = "tag.location.default: 1",
 "enable_unique_key_merge_on_write" = "true",
@@ -71,7 +71,7 @@ In order to reduce CPU cost for parsing query SQL and SQL expressions, we provid
 
 1. Setup JDBC url and enable server side prepared statement
 ```
-url = jdbc:mysql://127.0.0.1:9137/ycsb?useServerPrepStmts=true
+url = jdbc:mysql://127.0.0.1:9030/ycsb?useServerPrepStmts=true
 ``
 
 2. Using `PreparedStatement`

diff --git a/docs/en/docs/data-table/dynamic-schema-table.md b/docs/en/docs/data-table/dynamic-schema-table.md
@@ -1,7 +1,7 @@
 ---
 {
     "title": "dynamie schema table",
-    "language": "zh-CN"
+    "language": "en"
 }
 ---
 
@@ -25,9 +25,11 @@ under the License.
 -->
 
 # Dynamic Table
-A dynamic schema table is a special kind of table which schema expands automatically with the import procedure. Currently, this feature is mainly used for importing semi-structured data such as JSON. Because JSON is self-describing, we can extract the schema information from the original document and infer the final type information. This special table can reduce manual schema change operations and easily import semi-structured data and automatically expand its schema.
 
+<version since="2.0.0">
+</version>
 
+A dynamic schema table is a special kind of table which schema expands automatically with the import procedure. Currently, this feature is mainly used for importing semi-structured data such as JSON. Because JSON is self-describing, we can extract the schema information from the original document and infer the final type information. This special table can reduce manual schema change operations and easily import semi-structured data and automatically expand its schema.
 
 ## Terminology
 - Schema change, changing the structure of the table, such as adding columns, reducing columns, changing column types

diff --git a/docs/en/docs/data-table/index/inverted-index.md b/docs/en/docs/data-table/index/inverted-index.md
@@ -26,7 +26,7 @@ under the License.
 
 # Inverted Index
 
-<version since="1.2.0">
+<version since="2.0.0">
 
 </version>
 

diff --git a/docs/en/docs/data-table/index/ngram-bloomfilter-index.md b/docs/en/docs/data-table/index/ngram-bloomfilter-index.md
@@ -26,6 +26,9 @@ under the License.
 
 # Doris NGram BloomFilter Index
 
+<version since="2.0.0">
+</version>
+
 In order to improve the like query performance, the NGram BloomFilter index was implemented, which referenced to the ClickHouse's ngrambf skip indices;
 
 ## Create Column With NGram BloomFilter Index

diff --git a/docs/sidebars.json b/docs/sidebars.json
@@ -56,6 +56,7 @@
                     "items": [
                         "data-table/index/inverted-index",
                         "data-table/index/bloomfilter",
+                        "data-table/index/ngram-bloomfilter-index",
                         "data-table/index/prefix-index",
                         "data-table/index/bitmap-index"
                     ]

diff --git a/docs/zh-CN/docs/advanced/best-practice/compaction.md b/docs/zh-CN/docs/advanced/best-practice/compaction.md
@@ -31,7 +31,11 @@ Doris 提供如下2种compaction方式进行调优：
 
 
 ## Vertical compaction
-Vertical compaction 是 Doris 2.0 版本中实现的新的 Compaction 算法，用于解决大宽表场景下的 Compaction 执行效率和资源开销问题。可以有效降低Compaction的内存开销，并提升 Compaction 的执行速度。
+
+<version since="1.2.2">
+</version>
+
+Vertical compaction 是 Doris 1.2.2 版本中实现的新的 Compaction 算法，用于解决大宽表场景下的 Compaction 执行效率和资源开销问题。可以有效降低Compaction的内存开销，并提升 Compaction 的执行速度。
 实际测试中，Vertical compaction 使用内存仅为原有compaction算法的1/10，同时compaction速率提升15%。
 
 Vertical compaction中将按行合并的方式改变为按列组合并，每次参与合并的粒度变成列组，降低单次compaction内部参与的数据量，减少compaction期间的内存使用。

diff --git a/docs/zh-CN/docs/advanced/hight-concurrent-point-query.md b/docs/zh-CN/docs/advanced/hight-concurrent-point-query.md
@@ -26,7 +26,7 @@ under the License.
 
 # 基于主键的高并发点查询
 
-<version since="1.2.1">
+<version since="2.0.0">
 </version>
 
 ## 背景 
@@ -51,9 +51,9 @@ CREATE TABLE `tbl_point_query` (
   `v6` float NULL,
   `v7` datev2 NULL
 ) ENGINE=OLAP
-UNIQUE KEY(key)
+UNIQUE KEY(`key`)
 COMMENT 'OLAP'
-DISTRIBUTED BY HASH(key) BUCKETS 1
+DISTRIBUTED BY HASH(`key`) BUCKETS 1
 PROPERTIES (
 "replication_allocation" = "tag.location.default: 1",
 "enable_unique_key_merge_on_write" = "true",
@@ -70,7 +70,7 @@ PROPERTIES (
 为了减少SQL解析和表达式计算的开销， 我们在FE端提供了与mysql协议完全兼容的`PreparedStatement`特性（目前只支持主键点查）。当`PreparedStatement`在FE开启，SQL和其表达式将被提前计算并缓存到session级别的内存缓存中，后续的查询直接使用缓存对象即可。当CPU成为主键点查的瓶颈， 在开启`PreparedStatement`后，将会有4倍+的性能提升。下面是在JDBC中使用`PreparedStatement`的例子
 1. 设置JDB url并在server端开启prepared statement
 ```
-url = jdbc:mysql://127.0.0.1:9137/ycsb?useServerPrepStmts=true
+url = jdbc:mysql://127.0.0.1:9030/ycsb?useServerPrepStmts=true
 ``
 
 2. 使用 `PreparedStatement`

diff --git a/docs/zh-CN/docs/data-table/dynamic-schema-table.md b/docs/zh-CN/docs/data-table/dynamic-schema-table.md
@@ -26,6 +26,9 @@ under the License.
 
 # 动态表
 
+<version since="2.0.0">
+</version>
+
 动态schema表是一种特殊的表，其schema随着导入自动进行扩展。目前该功能，主要用于半结构数据，例如JSON等的导入、自动列生成。因为JSON是类型自描述的，所以我们可以从原始文档中提取schema信息，推断最终类型信息。这种特殊的表可以减少人工schema change的操作，并轻松导入半结构数据并自动扩展其schema。
 
 ## 名词解释

diff --git a/docs/zh-CN/docs/data-table/index/inverted-index.md b/docs/zh-CN/docs/data-table/index/inverted-index.md
@@ -26,7 +26,7 @@ under the License.
 
 # 倒排索引
 
-<version since="1.2.0">
+<version since="2.0.0">
 
 </version>
 

diff --git a/docs/zh-CN/docs/data-table/index/ngram-bloomfilter-index.md b/docs/zh-CN/docs/data-table/index/ngram-bloomfilter-index.md
@@ -26,6 +26,9 @@ under the License.
 
 # Doris NGram BloomFilter索引及使用使用场景
 
+<version since="2.0.0">
+</version>
+
 为了提升like的查询性能，增加了NGram BloomFilter索引，其实现主要参照了ClickHouse的ngrambf。
 
 ## NGram BloomFilter创建