This is an automated email from the ASF dual-hosted git repository.
kassiez pushed a commit to branch master
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The following commit(s) were added to refs/heads/master by this push:
new c2bc50234ad [fix] Fix crondeploy by Java UDF Docs Format (#1418)
c2bc50234ad is described below
commit c2bc50234ad6e9b475da96c87201d756fd031cf4
Author: KassieZ <139741991+kass...@users.noreply.github.com>
AuthorDate: Thu Nov 28 12:11:23 2024 +0800
[fix] Fix crondeploy by Java UDF Docs Format (#1418)
## Versions
- [x] dev
- [ ] 3.0
- [ ] 2.1
- [ ] 2.0
## Languages
- [x] Chinese
- [x] English
## Docs Checklist
- [ ] Checked by AI
- [ ] Test Cases Built
## Version Mgt
https://github.com/apache/doris-website/pull/1377
---
.../gettingStarted/demo-block/page-hero-2.tsx | 8 +-
.../cluster-deployment/standard-deployment.md | 2 +-
.../high-concurrent-point-query.md | 2 +-
docs/query-data/udf/java-user-defined-function.md | 300 +++++++++---------
gettingStarted/demo-block/page-hero-2.tsx | 8 +-
.../cluster-deployment/standard-deployment.md | 2 +-
.../high-concurrent-point-query.md | 2 +-
.../query-data/udf/java-user-defined-function.md | 338 +++++++++++----------
.../version-1.2/install/standard-deployment.md | 2 +-
.../high-concurrent-point-query.md | 2 +-
.../cluster-deployment/standard-deployment.md | 2 +-
.../cluster-deployment/standard-deployment.md | 2 +-
.../high-concurrent-point-query.md | 2 +-
.../cluster-deployment/standard-deployment.md | 2 +-
.../high-concurrent-point-query.md | 2 +-
15 files changed, 342 insertions(+), 334 deletions(-)
diff --git a/common_docs_zh/gettingStarted/demo-block/page-hero-2.tsx
b/common_docs_zh/gettingStarted/demo-block/page-hero-2.tsx
index 8c6d822dffa..80d1a3567b9 100644
--- a/common_docs_zh/gettingStarted/demo-block/page-hero-2.tsx
+++ b/common_docs_zh/gettingStarted/demo-block/page-hero-2.tsx
@@ -22,8 +22,8 @@ export default function PageHero2() {
</div>
</div> */}
<div className="home-page-hero-right">
- <a className="latest-button-CN"
href={`/zh-CN/docs${currentVersion === '' ? '' :
`/${currentVersion}`}/query/nereids/nereids-new`}>
- <div
className="home-page-hero-button-label"><div>数据查询</div></div>
+ <a className="latest-button-CN"
href={`/zh-CN/docs${currentVersion === '' ? '' :
`/${currentVersion}`}/query-acceleration/tuning/overview`}>
+ <div
className="home-page-hero-button-label"><div>查询加速</div></div>
<div className="latest-button-title">
{/* <div className="home-page-hero-button-icon">
<svg width="24px" viewBox="0 0 24 24"
xmlns="http://www.w3.org/2000/svg";>
@@ -31,9 +31,9 @@ export default function PageHero2() {
<path fill="none" d="M0 0h24v24H0Z"></path>
</svg>
</div> */}
- <div style={{ marginBottom: 10 }}>全新优化器</div>
+ <div style={{ marginBottom: 10 }}>查询调优原理与实践</div>
</div>
- <div style={{ fontSize: 12, marginBottom: 20
}}>现代架构的全新查询优化器，能够更高效处理当前 Doris 场景的查询请求，同时提供更好的扩展性。</div>
+ <div style={{ fontSize: 12, marginBottom: 20
}}>查询性能调优是一个系统工程 Doris 为用户提供了各个维度的工具，方便从不同层面进行性能问题的诊断、定位、分析与解决。</div>
</a>
<a className="latest-button-CN"
href={`/zh-CN/docs${currentVersion === '' ? '' :
`/${currentVersion}`}/table-design/index/inverted-index`}>
<div className="latest-button-title">
diff --git a/docs/install/cluster-deployment/standard-deployment.md
b/docs/install/cluster-deployment/standard-deployment.md
index 7d79e06672b..6a7c4657051 100644
--- a/docs/install/cluster-deployment/standard-deployment.md
+++ b/docs/install/cluster-deployment/standard-deployment.md
@@ -221,7 +221,7 @@ Doris instances communicate directly over the network,
requiring the following p
| Instance | Port | Default Port | Communication Direction
| Description |
| -------- | ---------------------- | ------------
|-----------------------------|
------------------------------------------------------------ |
| BE | be_port | 9060 | FE --> BE
| thrift server port on BE, receiving requests from FE |
-| BE | webserver_port | 8040 | BE <--> BE
| http server port on BE |
+| BE | webserver_port | 8040 | BE <--> BE, Client <-->
FE | http server port on BE
|
| BE | heartbeat_service_port | 9050 | FE --> BE
| heartbeat service port (thrift) on BE, receiving heartbeats from FE |
| BE | brpc_port | 8060 | FE <--> BE，BE <--> BE
| brpc port on BE, used for communication between BEs |
| FE | http_port | 8030 | FE <--> FE，Client <--> FE
| http server port on FE |
diff --git a/docs/query-acceleration/high-concurrent-point-query.md
b/docs/query-acceleration/high-concurrent-point-query.md
index f0e417ffdf1..2d626436f5b 100644
--- a/docs/query-acceleration/high-concurrent-point-query.md
+++ b/docs/query-acceleration/high-concurrent-point-query.md
@@ -82,7 +82,7 @@ PROPERTIES (
5. Enabling rowstore may lead to space expansion and occupy more disk space.
For scenarios where querying only specific columns is needed, starting from
Doris 2.1, it is recommended to use `"row_store_columns"="key,v1,v2"` to
specify certain columns for rowstore storage. Queries can then selectively
access these columns, for example:
```sql
- SELECT key, v1, v2 FROM tbl_point_query WHERE key = 1
+ SELECT `key`, v1, v2 FROM tbl_point_query WHERE key = 1
```
## Using `PreparedStatement`
diff --git a/docs/query-data/udf/java-user-defined-function.md
b/docs/query-data/udf/java-user-defined-function.md
index 1887debd08e..2cafc39e60b 100644
--- a/docs/query-data/udf/java-user-defined-function.md
+++ b/docs/query-data/udf/java-user-defined-function.md
@@ -1,6 +1,6 @@
---
{
-"title": "Java UDF、UDAF、UDTF",
+"title": "Java UDF, UDAF, UDTF",
"language": "en"
}
---
@@ -144,182 +144,186 @@ When writing a `UDAF` using Java, there are some
functions that must be implemen
1. Write the corresponding Java UDAF code and package it into a JAR file.
- <details><summary> Example 1: SimpleDemo will implement a simple function
similar to sum, where the input parameter is INT and the output parameter is
INT.</summary>
+<details>
+<summary> Example 1: SimpleDemo will implement a simple function similar to
sum, where the input parameter is INT and the output parameter is
INT.</summary>
- ```java
- package org.apache.doris.udf;
-
- import java.io.DataInputStream;
- import java.io.DataOutputStream;
- import java.io.IOException;
- import java.util.logging.Logger;
-
- public class SimpleDemo {
+```java
+package org.apache.doris.udf;
- Logger log = Logger.getLogger("SimpleDemo");
+import java.io.DataInputStream;
+import java.io.DataOutputStream;
+import java.io.IOException;
+import java.util.logging.Logger;
- //Need an inner class to store data
- /*required*/
- public static class State {
- /*some variables if you need */
- public int sum = 0;
- }
+public class SimpleDemo {
- /*required*/
- public State create() {
- /* here could do some init work if needed */
- return new State();
- }
+ Logger log = Logger.getLogger("SimpleDemo");
- /*required*/
- public void destroy(State state) {
- /* here could do some destroy work if needed */
- }
-
- /*Not Required*/
- public void reset(State state) {
- /*if you want this udaf function can work with window function.*/
- /*Must impl this, it will be reset to init state after calculate
every window frame*/
- state.sum = 0;
- }
+ //Need an inner class to store data
+ /*required*/
+ public static class State {
+ /*some variables if you need */
+ public int sum = 0;
+ }
- /*required*/
- //first argument is State, then other types your input
- public void add(State state, Integer val) throws Exception {
- /* here doing update work when input data*/
- if (val != null) {
- state.sum += val;
- }
- }
+ /*required*/
+ public State create() {
+ /* here could do some init work if needed */
+ return new State();
+ }
- /*required*/
- public void serialize(State state, DataOutputStream out) throws
Exception {
- /* serialize some data into buffer */
- out.writeInt(state.sum);
- }
+ /*required*/
+ public void destroy(State state) {
+ /* here could do some destroy work if needed */
+ }
- /*required*/
- public void deserialize(State state, DataInputStream in) throws
Exception {
- /* deserialize get data from buffer before you put */
- int val = in.readInt();
- state.sum = val;
- }
+ /*Not Required*/
+ public void reset(State state) {
+ /*if you want this udaf function can work with window function.*/
+ /*Must impl this, it will be reset to init state after calculate every
window frame*/
+ state.sum = 0;
+ }
- /*required*/
- public void merge(State state, State rhs) throws Exception {
- /* merge data from state */
- state.sum += rhs.sum;
+ /*required*/
+ //first argument is State, then other types your input
+ public void add(State state, Integer val) throws Exception {
+ /* here doing update work when input data*/
+ if (val != null) {
+ state.sum += val;
}
+ }
- /*required*/
- //return Type you defined
- public Integer getValue(State state) throws Exception {
- /* return finally result */
- return state.sum;
- }
+ /*required*/
+ public void serialize(State state, DataOutputStream out) throws Exception {
+ /* serialize some data into buffer */
+ out.writeInt(state.sum);
}
- ```
- </details>
+ /*required*/
+ public void deserialize(State state, DataInputStream in) throws Exception {
+ /* deserialize get data from buffer before you put */
+ int val = in.readInt();
+ state.sum = val;
+ }
+ /*required*/
+ public void merge(State state, State rhs) throws Exception {
+ /* merge data from state */
+ state.sum += rhs.sum;
+ }
- <details><summary> Example 2: MedianUDAF is a function that calculates the
median. The input types are (DOUBLE, INT), and the output type is DOUBLE.
</summary>
+ /*required*/
+ //return Type you defined
+ public Integer getValue(State state) throws Exception {
+ /* return finally result */
+ return state.sum;
+ }
+}
+```
- ```java
- package org.apache.doris.udf.demo;
-
- import java.io.DataInputStream;
- import java.io.DataOutputStream;
- import java.io.IOException;
- import java.math.BigDecimal;
- import java.util.Arrays;
- import java.util.logging.Logger;
-
- /* UDAF to calculate the median */
- public class MedianUDAF {
- Logger log = Logger.getLogger("MedianUDAF");
-
- // State storage
- public static class State {
- // Precision of the return result
- int scale = 0;
- // Whether it is the first time to execute the add method for a
certain aggregation condition under a certain tablet
- boolean isFirst = true;
- // Data storage
- public StringBuilder stringBuilder;
- }
+</details>
- // Initialize the state
- public State create() {
- State state = new State();
- // Pre-initialize based on the amount of data that needs to be
aggregated under each aggregation condition of each tablet to increase
performance
- state.stringBuilder = new StringBuilder(1000);
- return state;
- }
- // Process each data under respective aggregation conditions for each
tablet
- public void add(State state, Double val, int scale) {
- if (val != null && state.isFirst) {
-
state.stringBuilder.append(scale).append(",").append(val).append(",");
- state.isFirst = false;
- } else if (val != null) {
- state.stringBuilder.append(val).append(",");
- }
- }
+<details>
+<summary> Example 2: MedianUDAF is a function that calculates the median. The
input types are (DOUBLE, INT), and the output type is DOUBLE. </summary>
- // Data needs to be output for aggregation after processing
- public void serialize(State state, DataOutputStream out) throws
IOException {
- // Currently, only DataOutputStream is provided. If serialization
of objects is required, methods such as concatenating strings, converting to
JSON, or serializing into byte arrays can be considered
- // If the State object needs to be serialized, it may be necessary
to implement a serialization interface for the State inner class
- // Ultimately, everything needs to be transmitted via
DataOutputStream
- out.writeUTF(state.stringBuilder.toString());
+```java
+package org.apache.doris.udf.demo;
+
+import java.io.DataInputStream;
+import java.io.DataOutputStream;
+import java.io.IOException;
+import java.math.BigDecimal;
+import java.util.Arrays;
+import java.util.logging.Logger;
+
+/* UDAF to calculate the median */
+public class MedianUDAF {
+ Logger log = Logger.getLogger("MedianUDAF");
+
+ // State storage
+ public static class State {
+ // Precision of the return result
+ int scale = 0;
+ // Whether it is the first time to execute the add method for a
certain aggregation condition under a certain tablet
+ boolean isFirst = true;
+ // Data storage
+ public StringBuilder stringBuilder;
+ }
+
+ // Initialize the state
+ public State create() {
+ State state = new State();
+ // Pre-initialize based on the amount of data that needs to be
aggregated under each aggregation condition of each tablet to increase
performance
+ state.stringBuilder = new StringBuilder(1000);
+ return state;
+ }
+
+ // Process each data under respective aggregation conditions for each
tablet
+ public void add(State state, Double val, int scale) {
+ if (val != null && state.isFirst) {
+
state.stringBuilder.append(scale).append(",").append(val).append(",");
+ state.isFirst = false;
+ } else if (val != null) {
+ state.stringBuilder.append(val).append(",");
}
-
- // Obtain the output data from the data processing execution unit
- public void deserialize(State state, DataInputStream in) throws
IOException {
- String string = in.readUTF();
- state.scale = Integer.parseInt(String.valueOf(string.charAt(0)));
- StringBuilder stringBuilder = new
StringBuilder(string.substring(2));
- state.stringBuilder = stringBuilder;
+ }
+
+ // Data needs to be output for aggregation after processing
+ public void serialize(State state, DataOutputStream out) throws
IOException {
+ // Currently, only DataOutputStream is provided. If serialization of
objects is required, methods such as concatenating strings, converting to JSON,
or serializing into byte arrays can be considered
+ // If the State object needs to be serialized, it may be necessary to
implement a serialization interface for the State inner class
+ // Ultimately, everything needs to be transmitted via DataOutputStream
+ out.writeUTF(state.stringBuilder.toString());
+ }
+
+ // Obtain the output data from the data processing execution unit
+ public void deserialize(State state, DataInputStream in) throws
IOException {
+ String string = in.readUTF();
+ state.scale = Integer.parseInt(String.valueOf(string.charAt(0)));
+ StringBuilder stringBuilder = new StringBuilder(string.substring(2));
+ state.stringBuilder = stringBuilder;
+ }
+
+ // The aggregation execution unit merges the processing results of data
under certain aggregation conditions for a given key. The state1 parameter is
the initialized instance during the first merge of each key
+ public void merge(State state1, State state2) {
+ state1.scale = state2.scale;
+ state1.stringBuilder.append(state2.stringBuilder.toString());
+ }
+
+ // Output the final result after merging the data for each key
+ public Double getValue(State state) {
+ String[] strings = state.stringBuilder.toString().split(",");
+ double[] doubles = new double[strings.length];
+ for (int i = 0; i < strings.length - 1; i++) {
+ doubles[i] = Double.parseDouble(strings[i + 1]);
}
- // The aggregation execution unit merges the processing results of
data under certain aggregation conditions for a given key. The state1 parameter
is the initialized instance during the first merge of each key
- public void merge(State state1, State state2) {
- state1.scale = state2.scale;
- state1.stringBuilder.append(state2.stringBuilder.toString());
+ Arrays.sort(doubles);
+ double n = doubles.length;
+ if (n == 0) {
+ return 0.0;
}
+ double index = (n - 1) / 2.0;
- // Output the final result after merging the data for each key
- public Double getValue(State state) {
- String[] strings = state.stringBuilder.toString().split(",");
- double[] doubles = new double[strings.length];
- for (int i = 0; i < strings.length - 1; i++) {
- doubles[i] = Double.parseDouble(strings[i + 1]);
- }
+ int low = (int) Math.floor(index);
+ int high = (int) Math.ceil(index);
- Arrays.sort(doubles);
- double n = doubles.length;
- if (n == 0) {
- return 0.0;
- }
- double index = (n - 1) / 2.0;
+ double value = low == high ? (doubles[low] + doubles[high]) / 2 :
doubles[high];
- int low = (int) Math.floor(index);
- int high = (int) Math.ceil(index);
+ BigDecimal decimal = new BigDecimal(value);
+ return decimal.setScale(state.scale,
BigDecimal.ROUND_HALF_UP).doubleValue();
+ }
- double value = low == high ? (doubles[low] + doubles[high]) / 2 :
doubles[high];
-
- BigDecimal decimal = new BigDecimal(value);
- return decimal.setScale(state.scale,
BigDecimal.ROUND_HALF_UP).doubleValue();
- }
-
- // Executed after each execution unit completes
- public void destroy(State state) {
- }
- }
- ```
+ // Executed after each execution unit completes
+ public void destroy(State state) {
+ }
+}
+```
+
</details>
+
2. Register and create the Java-UDAF function in Doris. For more syntax
details, please refer to [CREATE
FUNCTION](../../sql-manual/sql-statements/Data-Definition-Statements/Create/CREATE-FUNCTION.md).
```sql
diff --git a/gettingStarted/demo-block/page-hero-2.tsx
b/gettingStarted/demo-block/page-hero-2.tsx
index 54984612c72..06aa0eb3276 100644
--- a/gettingStarted/demo-block/page-hero-2.tsx
+++ b/gettingStarted/demo-block/page-hero-2.tsx
@@ -22,8 +22,8 @@ export default function PageHero1() {
</div>
</div> */}
<div className="home-page-hero-right">
- <a className="latest-button" href={`/docs${currentVersion
=== '' ? '' : `/${currentVersion}`}/query/nereids/nereids-new`}>
- <div className="home-page-hero-button-label"><div>Data
Query</div></div>
+ <a className="latest-button" href={`/docs${currentVersion
=== '' ? '' : `/${currentVersion}`}/query-acceleration/tuning/overview`}>
+ <div
className="home-page-hero-button-label"><div>Query Acceleration</div></div>
<div className="latest-button-title">
{/* <div className="home-page-hero-button-icon">
<svg width="24px" viewBox="0 0 24 24"
xmlns="http://www.w3.org/2000/svg";>
@@ -31,9 +31,9 @@ export default function PageHero1() {
<path fill="none" d="M0 0h24v24H0Z"></path>
</svg>
</div> */}
- <div style={{ marginBottom: 10 }}>Cost-Based
Optimizer </div>
+ <div style={{ marginBottom: 10 }}>Best Practice of
Tuning </div>
</div>
- <div style={{ fontSize: 12, marginBottom: 20 }}>To
build an open, high-performance, cost-effective and unified log storage and
analysis platform.</div>
+ <div style={{ fontSize: 12, marginBottom: 20 }}>Query
performance tuning is systematic engineering that requires optimization of the
database system from multiple levels and dimensions.</div>
</a>
<a className="latest-button" href={`/docs${currentVersion
=== '' ? '' : `/${currentVersion}`}/table-design/index/inverted-index`}>
<div className="latest-button-title">
diff --git
a/i18n/zh-CN/docusaurus-plugin-content-docs/current/install/cluster-deployment/standard-deployment.md
b/i18n/zh-CN/docusaurus-plugin-content-docs/current/install/cluster-deployment/standard-deployment.md
index 42d3549283c..ccb8104f56a 100644
---
a/i18n/zh-CN/docusaurus-plugin-content-docs/current/install/cluster-deployment/standard-deployment.md
+++
b/i18n/zh-CN/docusaurus-plugin-content-docs/current/install/cluster-deployment/standard-deployment.md
@@ -225,7 +225,7 @@ Doris 各个实例直接通过网络进行通讯，其正常运行需要网络
| 实例名称 | 端口名称 | 默认端口 | 通信方向 | 说明
|
| -------- | ---------------------- | -------- |----------------------------|
---------------------------------------------------- |
| BE | be_port | 9060 | FE --> BE |
BE 上 thrift server 的端口，用于接收来自 FE 的请求 |
-| BE | webserver_port | 8040 | BE <--> BE |
BE 上的 http server 的端口 |
+| BE | webserver_port | 8040 | BE <--> BE, Client <--> FE
| BE 上的 http server 的端口 |
| BE | heartbeat_service_port | 9050 | FE --> BE |
BE 上心跳服务端口（thrift），用于接收来自 FE 的心跳 |
| BE | brpc_port | 8060 | FE <--> BE，BE <--> BE |
BE 上的 brpc 端口，用于 BE 之间通讯 |
| FE | http_port | 8030 | FE <--> FE，Client <--> FE |
FE 上的 http server 端口 |
diff --git
a/i18n/zh-CN/docusaurus-plugin-content-docs/current/query-acceleration/high-concurrent-point-query.md
b/i18n/zh-CN/docusaurus-plugin-content-docs/current/query-acceleration/high-concurrent-point-query.md
index 8299a631b27..49e8ffa7db3 100644
---
a/i18n/zh-CN/docusaurus-plugin-content-docs/current/query-acceleration/high-concurrent-point-query.md
+++
b/i18n/zh-CN/docusaurus-plugin-content-docs/current/query-acceleration/high-concurrent-point-query.md
@@ -80,7 +80,7 @@ PROPERTIES (
5. 开启行存会导致空间膨胀，占用更多的磁盘空间，如果只需要查询部分列，在 Doris 2.1
后建议使用`"row_store_columns"="key,v1,v2"` 类似的方式指定部份列作为行存，查询的时候只查询这部份列，例如
```sql
- SELECT key, v1, v2 FROM tbl_point_query WHERE key = 1
+ SELECT `key`, v1, v2 FROM tbl_point_query WHERE key = 1
```
## 使用 PreparedStatement
diff --git
a/i18n/zh-CN/docusaurus-plugin-content-docs/current/query-data/udf/java-user-defined-function.md
b/i18n/zh-CN/docusaurus-plugin-content-docs/current/query-data/udf/java-user-defined-function.md
index f1ff590dcdb..803e8a399d4 100644
---
a/i18n/zh-CN/docusaurus-plugin-content-docs/current/query-data/udf/java-user-defined-function.md
+++
b/i18n/zh-CN/docusaurus-plugin-content-docs/current/query-data/udf/java-user-defined-function.md
@@ -1,6 +1,6 @@
---
{
-"title": "Java UDF、UDAF、UDTF",
+"title": "Java UDF, UDAF, UDTF",
"language": "zh-CN"
}
---
@@ -27,9 +27,9 @@ under the License.
## 概述
Java UDF 为用户提供 UDF 编写的 Java 接口，以方便用户使用 Java 语言进行自定义函数的执行。
Doris 支持使用 JAVA 编写 UDF、UDAF 和 UDTF。下文如无特殊说明，使用 UDF 统称所有用户自定义函数。
-1. Java UDF 是较为常见的自定义标量函数(Scalar
Function)，即每输入一行数据，就会有一行对应的结果输出，较为常见的有ABS，LENGTH等。值得一提的是对于用户来讲，Hive UDF
是可以直接迁移至 Doris 的。
-2. Java UDAF 即为自定义的聚合函数(Aggregate
Function)，即在输入多行数据进行聚合后，仅输出一行对应的结果，较为常见的有MIN，MAX，COUNT等。
-3. JAVA UDTF 即为自定义的表函数(Table Function)，即每输一行数据，可以产生一行或多行的结果，在 Doris
中需要结合Lateral View 使用可以达到行转列的效果，较为常见的有EXPLODE，EXPLODE_SPLIT等。
+1. Java UDF 是较为常见的自定义标量函数 (Scalar Function)，即每输入一行数据，就会有一行对应的结果输出，较为常见的有
ABS，LENGTH 等。值得一提的是对于用户来讲，Hive UDF 是可以直接迁移至 Doris 的。
+2. Java UDAF 即为自定义的聚合函数 (Aggregate Function)，即在输入多行数据进行聚合后，仅输出一行对应的结果，较为常见的有
MIN，MAX，COUNT 等。
+3. JAVA UDTF 即为自定义的表函数 (Table Function)，即每输一行数据，可以产生一行或多行的结果，在 Doris 中需要结合
Lateral View 使用可以达到行转列的效果，较为常见的有 EXPLODE，EXPLODE_SPLIT 等。
## 类型对应关系
@@ -63,7 +63,7 @@ Doris 支持使用 JAVA 编写 UDF、UDAF 和 UDTF。下文如无特殊说明，
## 使用限制
1. 不支持复杂数据类型（HLL，Bitmap）。
-2. 当前允许用户自己指定JVM最大堆大小，配置项是 be.conf 中的 `JAVA_OPTS` 的 -Xmx 部分。默认
1024m，如果需要聚合数据，建议调大一些，增加性能，减少内存溢出风险。
+2. 当前允许用户自己指定 JVM 最大堆大小，配置项是 be.conf 中的 `JAVA_OPTS` 的 -Xmx 部分。默认
1024m，如果需要聚合数据，建议调大一些，增加性能，减少内存溢出风险。
3. 由于 jvm 加载同名类的问题，不要同时使用多个同名类作为 udf 实现，如果想更新某个同名类的 udf，需要重启 be 重新加载 classpath。
@@ -71,9 +71,9 @@ Doris 支持使用 JAVA 编写 UDF、UDAF 和 UDTF。下文如无特殊说明，
本小节主要介绍如何开发一个 Java UDF。在 `samples/doris-demo/java-udf-demo/`
下提供了示例，可供参考，查看点击[这里](https://github.com/apache/doris/tree/master/samples/doris-demo/java-udf-demo)
UDF 的使用与普通的函数方式一致，唯一的区别在于，内置函数的作用域是全局的，而 UDF 的作用域是 DB 内部。
-所以如果当前链接 session 位于数据库DB 内部时，直接使用 UDF 名字会在当前 DB 内部查找对应的 UDF。否则用户需要显示的指定 UDF
的数据库名字，例如 `dbName.funcName`。
+所以如果当前链接 session 位于数据库 DB 内部时，直接使用 UDF 名字会在当前 DB 内部查找对应的 UDF。否则用户需要显示的指定 UDF
的数据库名字，例如 `dbName.funcName`。
-接下来的章节介绍实例，均会在`test_table` 上做测试，对应建表如下:
+接下来的章节介绍实例，均会在`test_table` 上做测试，对应建表如下：
```sql
CREATE TABLE `test_table` (
@@ -94,7 +94,7 @@ insert into test_table values (6, 666.66, "d,e");
使用 Java 代码编写 UDF，UDF 的主入口必须为 `evaluate` 函数。这一点与 Hive 等其他引擎保持一致。在本示例中，我们编写了
`AddOne` UDF 来完成对整型输入进行加一的操作。
-1. 首先编写对应的Java 代码，打包生成JAR 包。
+1. 首先编写对应的 Java 代码，打包生成 JAR 包。
```java
public class AddOne extends UDF {
@@ -104,7 +104,7 @@ insert into test_table values (6, 666.66, "d,e");
}
```
-2. 在Doris 中注册创建 Java-UDF 函数。 更多语法帮助可参阅 [CREATE
FUNCTION](../../sql-manual/sql-statements/Data-Definition-Statements/Create/CREATE-FUNCTION.md).
+2. 在 Doris 中注册创建 Java-UDF 函数。更多语法帮助可参阅 [CREATE
FUNCTION](../../sql-manual/sql-statements/Data-Definition-Statements/Create/CREATE-FUNCTION.md).
```sql
CREATE FUNCTION java_udf_add_one(int) RETURNS int PROPERTIES (
@@ -116,7 +116,7 @@ insert into test_table values (6, 666.66, "d,e");
```
3. 用户使用 UDF 必须拥有对应数据库的 `SELECT` 权限。
- 如果想查看注册成功的对应UDF 函数，可以使用[SHOW
FUNCTIONS](../../sql-manual/sql-statements/Show-Statements/SHOW-FUNCTIONS.md)
命令。
+ 如果想查看注册成功的对应 UDF 函数，可以使用[SHOW
FUNCTIONS](../../sql-manual/sql-statements/Show-Statements/SHOW-FUNCTIONS.md)
命令。
``` sql
select id,java_udf_add_one(id) from test_table;
@@ -136,182 +136,186 @@ insert into test_table values (6, 666.66, "d,e");
在使用 Java 代码编写 UDAF 时，有一些必须实现的函数 (标记 required) 和一个内部类 State，下面将以具体的实例来说明。
-1. 首先编写对应的Java UDAF 代码，打包生成JAR 包。
+1. 首先编写对应的 Java UDAF 代码，打包生成 JAR 包。
- <details><summary> 示例1: SimpleDemo 将实现一个类似的 sum 的简单函数，输入参数 INT，输出参数是
INT</summary>
+<details>
+<summary> 示例 1: SimpleDemo 将实现一个类似的 sum 的简单函数，输入参数 INT，输出参数是 INT</summary>
- ```java
- package org.apache.doris.udf;
+```java
+package org.apache.doris.udf;
- import java.io.DataInputStream;
- import java.io.DataOutputStream;
- import java.io.IOException;
- import java.util.logging.Logger;
+import java.io.DataInputStream;
+import java.io.DataOutputStream;
+import java.io.IOException;
+import java.util.logging.Logger;
- public class SimpleDemo {
+public class SimpleDemo {
- Logger log = Logger.getLogger("SimpleDemo");
+Logger log = Logger.getLogger("SimpleDemo");
- //Need an inner class to store data
- /*required*/
- public static class State {
- /*some variables if you need */
- public int sum = 0;
- }
+//Need an inner class to store data
+/*required*/
+public static class State {
+ /*some variables if you need */
+ public int sum = 0;
+}
- /*required*/
- public State create() {
- /* here could do some init work if needed */
- return new State();
- }
+/*required*/
+public State create() {
+ /* here could do some init work if needed */
+ return new State();
+}
- /*required*/
- public void destroy(State state) {
- /* here could do some destroy work if needed */
- }
+/*required*/
+public void destroy(State state) {
+ /* here could do some destroy work if needed */
+}
- /*Not Required*/
- public void reset(State state) {
- /*if you want this udaf function can work with window function.*/
- /*Must impl this, it will be reset to init state after calculate
every window frame*/
- state.sum = 0;
- }
+/*Not Required*/
+public void reset(State state) {
+ /*if you want this udaf function can work with window function.*/
+ /*Must impl this, it will be reset to init state after calculate every
window frame*/
+ state.sum = 0;
+}
- /*required*/
- //first argument is State, then other types your input
- public void add(State state, Integer val) throws Exception {
- /* here doing update work when input data*/
- if (val != null) {
- state.sum += val;
- }
- }
+/*required*/
+//first argument is State, then other types your input
+public void add(State state, Integer val) throws Exception {
+ /* here doing update work when input data*/
+ if (val != null) {
+ state.sum += val;
+ }
+}
- /*required*/
- public void serialize(State state, DataOutputStream out) throws
IOException {
- /* serialize some data into buffer */
- out.writeInt(state.sum);
- }
+/*required*/
+public void serialize(State state, DataOutputStream out) throws IOException {
+ /* serialize some data into buffer */
+ out.writeInt(state.sum);
+}
- /*required*/
- public void deserialize(State state, DataInputStream in) throws
IOException {
- /* deserialize get data from buffer before you put */
- int val = in.readInt();
- state.sum = val;
- }
+/*required*/
+public void deserialize(State state, DataInputStream in) throws IOException {
+ /* deserialize get data from buffer before you put */
+ int val = in.readInt();
+ state.sum = val;
+}
- /*required*/
- public void merge(State state, State rhs) throws Exception {
- /* merge data from state */
- state.sum += rhs.sum;
- }
+/*required*/
+public void merge(State state, State rhs) throws Exception {
+ /* merge data from state */
+ state.sum += rhs.sum;
+}
- /*required*/
- //return Type you defined
- public Integer getValue(State state) throws Exception {
- /* return finally result */
- return state.sum;
- }
- }
+/*required*/
+//return Type you defined
+public Integer getValue(State state) throws Exception {
+ /* return finally result */
+ return state.sum;
+}
+}
- ```
- </details>
+```
+</details>
- <details><summary> 示例2: MedianUDAF是一个计算中位数的功能，输入类型为(DOUBLE, INT),
输出为DOUBLE </summary>
- ```java
- package org.apache.doris.udf.demo;
-
- import java.io.DataInputStream;
- import java.io.DataOutputStream;
- import java.math.BigDecimal;
- import java.util.Arrays;
- import java.util.logging.Logger;
-
- /*UDAF 计算中位数*/
- public class MedianUDAF {
- Logger log = Logger.getLogger("MedianUDAF");
-
- //状态存储
- public static class State {
- //返回结果的精度
- int scale = 0;
- //是否是某一个 tablet 下的某个聚合条件下的数据第一次执行 add 方法
- boolean isFirst = true;
- //数据存储
- public StringBuilder stringBuilder;
- }
+<details>
+<summary> 示例 2: MedianUDAF 是一个计算中位数的功能，输入类型为 (DOUBLE, INT), 输出为 DOUBLE
</summary>
- //状态初始化
- public State create() {
- State state = new State();
- //根据每个 tablet 下的聚合条件需要聚合的数据量大小，预先初始化，增加性能
- state.stringBuilder = new StringBuilder(1000);
- return state;
- }
+```java
+package org.apache.doris.udf.demo;
+
+import java.io.DataInputStream;
+import java.io.DataOutputStream;
+import java.math.BigDecimal;
+import java.util.Arrays;
+import java.util.logging.Logger;
+
+/*UDAF 计算中位数*/
+public class MedianUDAF {
+Logger log = Logger.getLogger("MedianUDAF");
+
+//状态存储
+public static class State {
+ //返回结果的精度
+ int scale = 0;
+ //是否是某一个 tablet 下的某个聚合条件下的数据第一次执行 add 方法
+ boolean isFirst = true;
+ //数据存储
+ public StringBuilder stringBuilder;
+}
+//状态初始化
+public State create() {
+ State state = new State();
+ //根据每个 tablet 下的聚合条件需要聚合的数据量大小，预先初始化，增加性能
+ state.stringBuilder = new StringBuilder(1000);
+ return state;
+}
- //处理执行单位处理各自 tablet 下的各自聚合条件下的每个数据
- public void add(State state, Double val, int scale) throws IOException
{
- if (val != null && state.isFirst) {
-
state.stringBuilder.append(scale).append(",").append(val).append(",");
- state.isFirst = false;
- } else if (val != null) {
- state.stringBuilder.append(val).append(",");
- }
- }
- //处理数据完需要输出等待聚合
- public void serialize(State state, DataOutputStream out) throws
IOException {
- //目前暂时只提供 DataOutputStream，如果需要序列化对象可以考虑拼接字符串，转换 json，序列化成字节数组等方式
- //如果要序列化 State 对象，可能需要自己将 State 内部类实现序列化接口
- //最终都是要通过 DataOutputStream 传输
- out.writeUTF(state.stringBuilder.toString());
- }
+//处理执行单位处理各自 tablet 下的各自聚合条件下的每个数据
+public void add(State state, Double val, int scale) throws IOException {
+ if (val != null && state.isFirst) {
+ state.stringBuilder.append(scale).append(",").append(val).append(",");
+ state.isFirst = false;
+ } else if (val != null) {
+ state.stringBuilder.append(val).append(",");
+ }
+}
- //获取处理数据执行单位输出的数据
- public void deserialize(State state, DataInputStream in) throws
IOException {
- String string = in.readUTF();
- state.scale = Integer.parseInt(String.valueOf(string.charAt(0)));
- StringBuilder stringBuilder = new
StringBuilder(string.substring(2));
- state.stringBuilder = stringBuilder;
- }
+//处理数据完需要输出等待聚合
+public void serialize(State state, DataOutputStream out) throws IOException {
+ //目前暂时只提供 DataOutputStream，如果需要序列化对象可以考虑拼接字符串，转换 json，序列化成字节数组等方式
+ //如果要序列化 State 对象，可能需要自己将 State 内部类实现序列化接口
+ //最终都是要通过 DataOutputStream 传输
+ out.writeUTF(state.stringBuilder.toString());
+}
- //聚合执行单位按照聚合条件合并某一个键下数据的处理结果 ,每个键第一次合并时，state1 参数是初始化的实例
- public void merge(State state1, State state2) throws IOException {
- state1.scale = state2.scale;
- state1.stringBuilder.append(state2.stringBuilder.toString());
- }
+//获取处理数据执行单位输出的数据
+public void deserialize(State state, DataInputStream in) throws IOException {
+ String string = in.readUTF();
+ state.scale = Integer.parseInt(String.valueOf(string.charAt(0)));
+ StringBuilder stringBuilder = new StringBuilder(string.substring(2));
+ state.stringBuilder = stringBuilder;
+}
- //对每个键合并后的数据进行并输出最终结果
- public Double getValue(State state) throws IOException {
- String[] strings = state.stringBuilder.toString().split(",");
- double[] doubles = new double[strings.length + 1];
- doubles =
Arrays.stream(strings).mapToDouble(Double::parseDouble).toArray();
+//聚合执行单位按照聚合条件合并某一个键下数据的处理结果 ,每个键第一次合并时，state1 参数是初始化的实例
+public void merge(State state1, State state2) throws IOException {
+ state1.scale = state2.scale;
+ state1.stringBuilder.append(state2.stringBuilder.toString());
+}
- Arrays.sort(doubles);
- double n = doubles.length - 1;
- double index = n * 0.5;
+//对每个键合并后的数据进行并输出最终结果
+public Double getValue(State state) throws IOException {
+ String[] strings = state.stringBuilder.toString().split(",");
+ double[] doubles = new double[strings.length + 1];
+ doubles =
Arrays.stream(strings).mapToDouble(Double::parseDouble).toArray();
- int low = (int) Math.floor(index);
- int high = (int) Math.ceil(index);
+ Arrays.sort(doubles);
+ double n = doubles.length - 1;
+ double index = n * 0.5;
- double value = low == high ? (doubles[low] + doubles[high]) * 0.5
: doubles[high];
+ int low = (int) Math.floor(index);
+ int high = (int) Math.ceil(index);
- BigDecimal decimal = new BigDecimal(value);
- return decimal.setScale(state.scale,
BigDecimal.ROUND_HALF_UP).doubleValue();
- }
+ double value = low == high ? (doubles[low] + doubles[high]) * 0.5 :
doubles[high];
- //每个执行单位执行完都会执行
- public void destroy(State state) {
- }
+ BigDecimal decimal = new BigDecimal(value);
+ return decimal.setScale(state.scale,
BigDecimal.ROUND_HALF_UP).doubleValue();
+}
+
+//每个执行单位执行完都会执行
+public void destroy(State state) {
+}
+
+}
+```
- }
- ```
</details>
-2. 在Doris 中注册创建 Java-UADF 函数。 更多语法帮助可参阅 [CREATE
FUNCTION](../../sql-manual/sql-statements/Data-Definition-Statements/Create/CREATE-FUNCTION.md).
+2. 在 Doris 中注册创建 Java-UADF 函数。更多语法帮助可参阅 [CREATE
FUNCTION](../../sql-manual/sql-statements/Data-Definition-Statements/Create/CREATE-FUNCTION.md).
```sql
CREATE AGGREGATE FUNCTION simple_demo(INT) RETURNS INT PROPERTIES (
@@ -322,7 +326,7 @@ insert into test_table values (6, 666.66, "d,e");
);
```
-3. 使用 Java-UDAF, 可以分组聚合或者聚合全部结果:
+3. 使用 Java-UDAF, 可以分组聚合或者聚合全部结果：
```sql
select simple_demo(id) from test_table group by id;
@@ -348,8 +352,8 @@ insert into test_table values (6, 666.66, "d,e");
UDTF 自 Doris 3.0 版本开始支持
:::
-1. 首先编写对应的Java UDTF 代码，打包生成JAR 包。
-UDTF 和 UDF 函数一样，需要用户自主实现一个 `evaluate` 方法， 但是 UDTF 函数的返回值必须是 Array 类型。
+1. 首先编写对应的 Java UDTF 代码，打包生成 JAR 包。
+UDTF 和 UDF 函数一样，需要用户自主实现一个 `evaluate` 方法，但是 UDTF 函数的返回值必须是 Array 类型。
```JAVA
public class UDTFStringTest {
@@ -363,7 +367,7 @@ UDTF 和 UDF 函数一样，需要用户自主实现一个 `evaluate` 方法，
}
```
-2. 在Doris 中注册创建 Java-UDTF 函数。 此时会注册两个UTDF 函数，另外一个是在函数名后面加上`_outer`后缀，
其中带后缀`_outer` 的是针对结果为0行时的特殊处理，具体可查看[OUTER
组合器](../../sql-manual/sql-functions/table-functions/explode-numbers-outer.md)。
+2. 在 Doris 中注册创建 Java-UDTF 函数。此时会注册两个 UTDF
函数，另外一个是在函数名后面加上`_outer`后缀，其中带后缀`_outer` 的是针对结果为 0 行时的特殊处理，具体可查看[OUTER
组合器](../../sql-manual/sql-functions/table-functions/explode-numbers-outer.md)。
更多语法帮助可参阅 [CREATE
FUNCTION](../../sql-manual/sql-statements/Data-Definition-Statements/Create/CREATE-FUNCTION.md).
```sql
@@ -375,7 +379,7 @@ UDTF 和 UDF 函数一样，需要用户自主实现一个 `evaluate` 方法，
);
```
-3. 使用 Java-UDTF, 在Doris 中使用UDTF 需要结合 [Lateral View](../lateral-view.md),
实现行转列的效果 :
+3. 使用 Java-UDTF, 在 Doris 中使用 UDTF 需要结合 [Lateral View](../lateral-view.md),
实现行转列的效果 :
```sql
select id, str, e1 from test_table lateral view java_utdf(str,',') tmp as
e1;
@@ -397,11 +401,11 @@ UDTF 和 UDF 函数一样，需要用户自主实现一个 `evaluate` 方法，
当前在 Doris 中，执行一个 UDF 函数，例如 `select udf(col) from table`, 每一个并发 Instance 会加载一次
udf.jar 包，在该 Instance 结束时卸载掉 udf.jar 包。
所以当 udf.jar 文件中需要加载一个几百 MB 的文件时，会因为并发的原因，使得占据的内存急剧增大，容易 OOM。
-或者想使用一个连接池时，这样无法做到仅在static 区域初始化一次。
+或者想使用一个连接池时，这样无法做到仅在 static 区域初始化一次。
-这里提供两个解决方案，其中方案二需要Doris 版本在branch-3.0 以上才行。
+这里提供两个解决方案，其中方案二需要 Doris 版本在 branch-3.0 以上才行。
-*解决方案1:*
+*解决方案 1:*
是可以将资源加载代码拆分开，单独生成一个 JAR 包文件，然后其他包直接引用该资源 JAR 包。
@@ -453,9 +457,9 @@ public class FunctionUdf {
jar -cvf ./FunctionUdf.jar ./FunctionUdf.class
```
-3. 由于想让资源 JAR 包被所有的并发引用，所以想让它被JVM 直接加载，可以将它放到指定路径 `be/custom_lib` 下面，BE
服务重启之后就可以随着 JVM 的启动加载进来，因此都会随着服务启动而加载，停止而释放。
+3. 由于想让资源 JAR 包被所有的并发引用，所以想让它被 JVM 直接加载，可以将它放到指定路径 `be/custom_lib` 下面，BE
服务重启之后就可以随着 JVM 的启动加载进来，因此都会随着服务启动而加载，停止而释放。
-4. 最后利用 `CREATE FUNCTION` 语句创建一个 UDF 函数， 这样每次卸载仅是FunctionUdf.jar。
+4. 最后利用 `CREATE FUNCTION` 语句创建一个 UDF 函数，这样每次卸载仅是 FunctionUdf.jar。
```sql
CREATE FUNCTION java_udf_dict(string) RETURNS string PROPERTIES (
@@ -466,16 +470,16 @@ public class FunctionUdf {
);
```
-*解决方案2:*
+*解决方案 2:*
-BE 全局缓存 JAR 包，自定义过期淘汰时间，在create function 时增加两个属性字段，其中
-static_load: 用于定义是否使用静态cache 加载的方式。
+BE 全局缓存 JAR 包，自定义过期淘汰时间，在 create function 时增加两个属性字段，其中
+static_load: 用于定义是否使用静态 cache 加载的方式。
expiration_time: 用于定义 JAR 包的过期时间，单位为分钟。
-若使用静态cache 加载方式，则在第一次调用该UDF 函数时，在初始化之后会将该UDF 的实例缓存起来，在下次调用该UDF 时，首先会在cache
中进行查找，如果没有找到，则会进行相关初始化操作。
+若使用静态 cache 加载方式，则在第一次调用该 UDF 函数时，在初始化之后会将该 UDF 的实例缓存起来，在下次调用该 UDF 时，首先会在
cache 中进行查找，如果没有找到，则会进行相关初始化操作。
-并且后台有线程定期检查，如果在配置的过期淘汰时间内，一直没有被调用过，则会从缓存cache 中清理掉。如果被调用时，则会自动更新缓存时间点。
+并且后台有线程定期检查，如果在配置的过期淘汰时间内，一直没有被调用过，则会从缓存 cache 中清理掉。如果被调用时，则会自动更新缓存时间点。
```sql
public class Print extends UDF {
diff --git
a/i18n/zh-CN/docusaurus-plugin-content-docs/version-1.2/install/standard-deployment.md
b/i18n/zh-CN/docusaurus-plugin-content-docs/version-1.2/install/standard-deployment.md
index baf1fce552e..66686ad3973 100644
---
a/i18n/zh-CN/docusaurus-plugin-content-docs/version-1.2/install/standard-deployment.md
+++
b/i18n/zh-CN/docusaurus-plugin-content-docs/version-1.2/install/standard-deployment.md
@@ -113,7 +113,7 @@ Doris 各个实例直接通过网络进行通讯。以下表格展示了所有
| 实例名称 | 端口名称 | 默认端口 | 通讯方向 | 说明 |
|---|---|---|---| ---|
| BE | be_port | 9060 | FE --> BE | BE 上 thrift server 的端口，用于接收来自 FE 的请求 |
-| BE | webserver_port | 8040 | BE <--> BE | BE 上的 http server 的端口 |
+| BE | webserver_port | 8040 | BE <--> BE, Client <--> FE | BE 上的 http server
的端口 |
| BE | heartbeat\_service_port | 9050 | FE --> BE | BE 上心跳服务端口（thrift），用于接收来自
FE 的心跳 |
| BE | brpc\_port | 8060 | FE <--> BE, BE <--> BE | BE 上的 brpc 端口，用于 BE 之间通讯 |
| FE | http_port | 8030 | FE <--> FE，用户 <--> FE |FE 上的 http server 端口 |
diff --git
a/i18n/zh-CN/docusaurus-plugin-content-docs/version-2.1/query-acceleration/high-concurrent-point-query.md
b/i18n/zh-CN/docusaurus-plugin-content-docs/version-2.1/query-acceleration/high-concurrent-point-query.md
index 8299a631b27..49e8ffa7db3 100644
---
a/i18n/zh-CN/docusaurus-plugin-content-docs/version-2.1/query-acceleration/high-concurrent-point-query.md
+++
b/i18n/zh-CN/docusaurus-plugin-content-docs/version-2.1/query-acceleration/high-concurrent-point-query.md
@@ -80,7 +80,7 @@ PROPERTIES (
5. 开启行存会导致空间膨胀，占用更多的磁盘空间，如果只需要查询部分列，在 Doris 2.1
后建议使用`"row_store_columns"="key,v1,v2"` 类似的方式指定部份列作为行存，查询的时候只查询这部份列，例如
```sql
- SELECT key, v1, v2 FROM tbl_point_query WHERE key = 1
+ SELECT `key`, v1, v2 FROM tbl_point_query WHERE key = 1
```
## 使用 PreparedStatement
diff --git
a/i18n/zh-CN/docusaurus-plugin-content-docs/version-3.0/install/cluster-deployment/standard-deployment.md
b/i18n/zh-CN/docusaurus-plugin-content-docs/version-3.0/install/cluster-deployment/standard-deployment.md
index 42d3549283c..026e8f9853b 100644
---
a/i18n/zh-CN/docusaurus-plugin-content-docs/version-3.0/install/cluster-deployment/standard-deployment.md
+++
b/i18n/zh-CN/docusaurus-plugin-content-docs/version-3.0/install/cluster-deployment/standard-deployment.md
@@ -225,7 +225,7 @@ Doris 各个实例直接通过网络进行通讯，其正常运行需要网络
| 实例名称 | 端口名称 | 默认端口 | 通信方向 | 说明
|
| -------- | ---------------------- | -------- |----------------------------|
---------------------------------------------------- |
| BE | be_port | 9060 | FE --> BE |
BE 上 thrift server 的端口，用于接收来自 FE 的请求 |
-| BE | webserver_port | 8040 | BE <--> BE |
BE 上的 http server 的端口 |
+| BE | webserver_port | 8040 | BE <--> BE, Client <--> FE
| BE 上的 http server 的端口 |
| BE | heartbeat_service_port | 9050 | FE --> BE |
BE 上心跳服务端口（thrift），用于接收来自 FE 的心跳 |
| BE | brpc_port | 8060 | FE <--> BE，BE <--> BE |
BE 上的 brpc 端口，用于 BE 之间通讯 |
| FE | http_port | 8030 | FE <--> FE，Client <--> FE |
FE 上的 http server 端口 |
diff --git
a/versioned_docs/version-2.1/install/cluster-deployment/standard-deployment.md
b/versioned_docs/version-2.1/install/cluster-deployment/standard-deployment.md
index 1d3e7b005df..8486ebd30ef 100644
---
a/versioned_docs/version-2.1/install/cluster-deployment/standard-deployment.md
+++
b/versioned_docs/version-2.1/install/cluster-deployment/standard-deployment.md
@@ -211,7 +211,7 @@ Doris instances communicate directly over the network,
requiring the following p
| Instance | Port | Default Port | Communication Direction
| Description |
| -------- | ---------------------- | ------------ |
-------------------------- |
------------------------------------------------------------ |
| BE | be_port | 9060 | FE --> BE
| thrift server port on BE, receiving requests from FE |
-| BE | webserver_port | 8040 | BE <--> BE
| http server port on BE |
+| BE | webserver_port | 8040 | BE <--> BE, Client <-->
FE | http server port on BE
|
| BE | heartbeat_service_port | 9050 | FE --> BE
| heartbeat service port (thrift) on BE, receiving heartbeats from FE |
| BE | brpc_port | 8060 | FE <--> BE，BE <--> BE
| brpc port on BE, used for communication between BEs |
| FE | http_port | 8030 | FE <--> FE，Client <--> FE
| http server port on FE |
diff --git
a/versioned_docs/version-2.1/query-acceleration/high-concurrent-point-query.md
b/versioned_docs/version-2.1/query-acceleration/high-concurrent-point-query.md
index f0e417ffdf1..2d626436f5b 100644
---
a/versioned_docs/version-2.1/query-acceleration/high-concurrent-point-query.md
+++
b/versioned_docs/version-2.1/query-acceleration/high-concurrent-point-query.md
@@ -82,7 +82,7 @@ PROPERTIES (
5. Enabling rowstore may lead to space expansion and occupy more disk space.
For scenarios where querying only specific columns is needed, starting from
Doris 2.1, it is recommended to use `"row_store_columns"="key,v1,v2"` to
specify certain columns for rowstore storage. Queries can then selectively
access these columns, for example:
```sql
- SELECT key, v1, v2 FROM tbl_point_query WHERE key = 1
+ SELECT `key`, v1, v2 FROM tbl_point_query WHERE key = 1
```
## Using `PreparedStatement`
diff --git
a/versioned_docs/version-3.0/install/cluster-deployment/standard-deployment.md
b/versioned_docs/version-3.0/install/cluster-deployment/standard-deployment.md
index 92cb607dba5..9e816c0ed38 100644
---
a/versioned_docs/version-3.0/install/cluster-deployment/standard-deployment.md
+++
b/versioned_docs/version-3.0/install/cluster-deployment/standard-deployment.md
@@ -221,7 +221,7 @@ Doris instances communicate directly over the network,
requiring the following p
| Instance | Port | Default Port | Communication Direction
| Description |
| -------- | ---------------------- | ------------
|-----------------------------|
------------------------------------------------------------ |
| BE | be_port | 9060 | FE --> BE
| thrift server port on BE, receiving requests from FE |
-| BE | webserver_port | 8040 | BE <--> BE
| http server port on BE |
+| BE | webserver_port | 8040 | BE <--> BE, Client <-->
FE | http server port on BE
|
| BE | heartbeat_service_port | 9050 | FE --> BE
| heartbeat service port (thrift) on BE, receiving heartbeats from FE |
| BE | brpc_port | 8060 | FE <--> BE，BE <--> BE
| brpc port on BE, used for communication between BEs |
| FE | http_port | 8030 | FE <--> FE，Client <--> FE
| http server port on FE |
diff --git
a/versioned_docs/version-3.0/query-acceleration/high-concurrent-point-query.md
b/versioned_docs/version-3.0/query-acceleration/high-concurrent-point-query.md
index f0e417ffdf1..2d626436f5b 100644
---
a/versioned_docs/version-3.0/query-acceleration/high-concurrent-point-query.md
+++
b/versioned_docs/version-3.0/query-acceleration/high-concurrent-point-query.md
@@ -82,7 +82,7 @@ PROPERTIES (
5. Enabling rowstore may lead to space expansion and occupy more disk space.
For scenarios where querying only specific columns is needed, starting from
Doris 2.1, it is recommended to use `"row_store_columns"="key,v1,v2"` to
specify certain columns for rowstore storage. Queries can then selectively
access these columns, for example:
```sql
- SELECT key, v1, v2 FROM tbl_point_query WHERE key = 1
+ SELECT `key`, v1, v2 FROM tbl_point_query WHERE key = 1
```
## Using `PreparedStatement`
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