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The following commit(s) were added to refs/heads/main by this push:
new 6baeb6ba add spark in table model (#724)
6baeb6ba is described below
commit 6baeb6ba62a210ca3315c798b0551e11500bd672
Author: leto-b <[email protected]>
AuthorDate: Thu May 15 10:19:06 2025 +0800
add spark in table model (#724)
* add spark in table model
* fix connector version
* adjust ecosystem integration sidebar
---
src/.vuepress/public/img/table-spark-1.png | Bin 0 -> 299249 bytes
src/.vuepress/public/img/table-spark-2.png | Bin 0 -> 42924 bytes
src/.vuepress/public/img/table-spark-en-1.png | Bin 0 -> 316809 bytes
src/.vuepress/public/img/table-spark-en-2.png | Bin 0 -> 42924 bytes
src/.vuepress/sidebar/V2.0.x/en-Table.ts | 15 +-
src/.vuepress/sidebar/V2.0.x/zh-Table.ts | 17 +-
src/.vuepress/sidebar_timecho/V2.0.x/en-Table.ts | 15 +-
src/.vuepress/sidebar_timecho/V2.0.x/zh-Table.ts | 29 ++-
.../Master/Table/Ecosystem-Integration/DBeaver.md | 2 +-
.../Table/Ecosystem-Integration/Spark-IoTDB.md | 250 +++++++++++++++++++++
.../latest-Table/Ecosystem-Integration/DBeaver.md | 2 +-
.../Ecosystem-Integration/Spark-IoTDB.md | 250 +++++++++++++++++++++
.../Master/Table/Ecosystem-Integration/DBeaver.md | 2 +-
.../Table/Ecosystem-Integration/Spark-IoTDB.md | 241 ++++++++++++++++++++
.../latest-Table/Ecosystem-Integration/DBeaver.md | 2 +-
.../Ecosystem-Integration/Spark-IoTDB.md | 241 ++++++++++++++++++++
16 files changed, 1050 insertions(+), 16 deletions(-)
diff --git a/src/.vuepress/public/img/table-spark-1.png
b/src/.vuepress/public/img/table-spark-1.png
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index 00000000..fac9c701
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diff --git a/src/.vuepress/public/img/table-spark-2.png
b/src/.vuepress/public/img/table-spark-2.png
new file mode 100644
index 00000000..343c71fe
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diff --git a/src/.vuepress/public/img/table-spark-en-1.png
b/src/.vuepress/public/img/table-spark-en-1.png
new file mode 100644
index 00000000..8760de4c
Binary files /dev/null and b/src/.vuepress/public/img/table-spark-en-1.png
differ
diff --git a/src/.vuepress/public/img/table-spark-en-2.png
b/src/.vuepress/public/img/table-spark-en-2.png
new file mode 100644
index 00000000..343c71fe
Binary files /dev/null and b/src/.vuepress/public/img/table-spark-en-2.png
differ
diff --git a/src/.vuepress/sidebar/V2.0.x/en-Table.ts
b/src/.vuepress/sidebar/V2.0.x/en-Table.ts
index 97e3e5a4..fb6d4455 100644
--- a/src/.vuepress/sidebar/V2.0.x/en-Table.ts
+++ b/src/.vuepress/sidebar/V2.0.x/en-Table.ts
@@ -154,7 +154,20 @@ export const enSidebar = {
collapsible: true,
prefix: 'Ecosystem-Integration/',
children: [
- { text: 'DBeaver(IoTDB)', link: 'DBeaver' },
+ {
+ text: 'Computing Engine',
+ collapsible: true,
+ children: [
+ { text: 'Apache Spark', link: 'Spark-IoTDB' },
+ ],
+ },
+ {
+ text: 'SQL Development',
+ collapsible: true,
+ children: [
+ { text: 'DBeaver', link: 'DBeaver' },
+ ],
+ },
],
},
{
diff --git a/src/.vuepress/sidebar/V2.0.x/zh-Table.ts
b/src/.vuepress/sidebar/V2.0.x/zh-Table.ts
index 1a42383d..e7651b35 100644
--- a/src/.vuepress/sidebar/V2.0.x/zh-Table.ts
+++ b/src/.vuepress/sidebar/V2.0.x/zh-Table.ts
@@ -140,11 +140,24 @@ export const zhSidebar = {
],
},
{
- text: '系统集成',
+ text: '生态集成',
collapsible: true,
prefix: 'Ecosystem-Integration/',
children: [
- { text: 'DBeaver(IoTDB)', link: 'DBeaver' },
+ {
+ text: '计算引擎',
+ collapsible: true,
+ children: [
+ { text: 'Apache Spark', link: 'Spark-IoTDB' },
+ ],
+ },
+ {
+ text: 'SQL 开发',
+ collapsible: true,
+ children: [
+ { text: 'DBeaver', link: 'DBeaver' },
+ ],
+ },
],
},
{
diff --git a/src/.vuepress/sidebar_timecho/V2.0.x/en-Table.ts
b/src/.vuepress/sidebar_timecho/V2.0.x/en-Table.ts
index 73704e2b..9f7a19d0 100644
--- a/src/.vuepress/sidebar_timecho/V2.0.x/en-Table.ts
+++ b/src/.vuepress/sidebar_timecho/V2.0.x/en-Table.ts
@@ -159,7 +159,20 @@ export const enSidebar = {
collapsible: true,
prefix: 'Ecosystem-Integration/',
children: [
- { text: 'DBeaver(IoTDB)', link: 'DBeaver' },
+ {
+ text: 'Computing Engine',
+ collapsible: true,
+ children: [
+ { text: 'Apache Spark', link: 'Spark-IoTDB' },
+ ],
+ },
+ {
+ text: 'SQL Development',
+ collapsible: true,
+ children: [
+ { text: 'DBeaver', link: 'DBeaver' },
+ ],
+ },
],
},
{
diff --git a/src/.vuepress/sidebar_timecho/V2.0.x/zh-Table.ts
b/src/.vuepress/sidebar_timecho/V2.0.x/zh-Table.ts
index 739fc076..62e8a524 100644
--- a/src/.vuepress/sidebar_timecho/V2.0.x/zh-Table.ts
+++ b/src/.vuepress/sidebar_timecho/V2.0.x/zh-Table.ts
@@ -143,14 +143,27 @@ export const zhSidebar = {
{ text: 'RESTAPI V1 ', link: 'RestServiceV1' },
],
},
- {
- text: '系统集成',
- collapsible: true,
- prefix: 'Ecosystem-Integration/',
- children: [
- { text: 'DBeaver(IoTDB)', link: 'DBeaver' },
- ],
- },
+ {
+ text: '生态集成',
+ collapsible: true,
+ prefix: 'Ecosystem-Integration/',
+ children: [
+ {
+ text: '计算引擎',
+ collapsible: true,
+ children: [
+ { text: 'Apache Spark', link: 'Spark-IoTDB' },
+ ],
+ },
+ {
+ text: 'SQL 开发',
+ collapsible: true,
+ children: [
+ { text: 'DBeaver', link: 'DBeaver' },
+ ],
+ },
+ ],
+ },
{
text: 'SQL手册',
collapsible: true,
diff --git a/src/UserGuide/Master/Table/Ecosystem-Integration/DBeaver.md
b/src/UserGuide/Master/Table/Ecosystem-Integration/DBeaver.md
index ad56129d..453474c1 100644
--- a/src/UserGuide/Master/Table/Ecosystem-Integration/DBeaver.md
+++ b/src/UserGuide/Master/Table/Ecosystem-Integration/DBeaver.md
@@ -19,7 +19,7 @@
-->
-# DBeaver(IoTDB)
+# DBeaver
DBeaver is an SQL client and database management tool. It can interact with
IoTDB using IoTDB's JDBC driver.
diff --git a/src/UserGuide/Master/Table/Ecosystem-Integration/Spark-IoTDB.md
b/src/UserGuide/Master/Table/Ecosystem-Integration/Spark-IoTDB.md
new file mode 100644
index 00000000..9c3c8ecb
--- /dev/null
+++ b/src/UserGuide/Master/Table/Ecosystem-Integration/Spark-IoTDB.md
@@ -0,0 +1,250 @@
+<!--
+
+ Licensed to the Apache Software Foundation (ASF) under one
+ or more contributor license agreements. See the NOTICE file
+ distributed with this work for additional information
+ regarding copyright ownership. The ASF licenses this file
+ to you under the Apache License, Version 2.0 (the
+ "License"); you may not use this file except in compliance
+ with the License. You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing,
+ software distributed under the License is distributed on an
+ "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ KIND, either express or implied. See the License for the
+ specific language governing permissions and limitations
+ under the License.
+
+-->
+
+# Apache Spark
+
+## 1. Functional Overview
+
+IoTDB provides the `Spark-IoTDB-Table-Connector` to integrate IoTDB's table
model with Spark, enabling data read/write operations in Spark environments
through both DataFrame and Spark SQL interfaces.
+
+### 1.1 DataFrame
+
+DataFrame is a structured data abstraction in Spark, containing schema
metadata (column names, data types, etc.) and serving as the primary data
carrier between Spark operators. DataFrame transformations follow a lazy
execution mechanism, where operations are only physically executed upon
triggering an *action* (e.g., writing results or invoking `collect()`), thereby
optimizing resource utilization by avoiding redundant computations.
+
+The `Spark-IoTDB-Table-Connector` allows:
+
+• Write: Processed DataFrames from upstream tasks can be directly written into
IoTDB tables.
+
+• Read: Data from IoTDB tables can be loaded as DataFrames for downstream
analytical tasks.
+
+
+
+### 1.2 Spark SQL
+
+Spark clusters can be accessed via the `Spark-SQL Shell` for interactive SQL
execution. The `Spark-IoTDB-Table-Connector` maps IoTDB tables to temporary
external views in Spark, enabling direct read/write operations using Spark SQL.
+
+## 2. Compatibility Requirements
+
+| Software | Version |
+| ----------------------------------- |-----------|
+| `Spark-IoTDB-Table-Connector` | `2.0.3` |
+| `Spark` | `3.3-3.5` |
+| `IoTDB` | `2.0.1+` |
+| `Scala` | `2.12` |
+| `JDK` | `8,11` |
+
+## 3. Deployment Methods
+
+### 3.1 DataFrame
+
+Add the following dependency to your project’s `pom.xml`:
+
+```XML
+<dependency>
+ <groupId>org.apache.iotdb</groupId>
+ <artifactId>spark-iotdb-table-connector-3.5</artifactId>
+ <version>2.0.3</version>
+</dependency>
+```
+
+### 3.2 Spark SQL
+
+1. Download the `Spark-IoTDB-Table-Connector` JAR from the official
repository.
+2. Copy the JAR file to the `${SPARK_HOME}/jars` directory.
+
+
+
+## 4. Usage Guide
+
+### 4.1 Reading Data
+
+#### 4.1.1 DataFrame
+
+```Scala
+val df =
spark.read.format("org.apache.iotdb.spark.table.db.IoTDBTableProvider")
+ .option("iotdb.database", "$YOUR_IOTDB_DATABASE_NAME")
+ .option("iotdb.table", "$YOUR_IOTDB_TABLE_NAME")
+ .option("iotdb.username", "$YOUR_IOTDB_USERNAME")
+ .option("iotdb.password", "$YOUR_IOTDB_PASSWORD")
+ .option("iotdb.url", "$YOUR_IOTDB_URL")
+ .load()
+```
+
+#### 4.1.2 Spark SQL
+
+```SQL
+CREATE TEMPORARY VIEW spark_iotdb
+ USING org.apache.iotdb.spark.table.db.IoTDBTableProvider
+ OPTIONS(
+ "iotdb.database"="$YOUR_IOTDB_DATABASE_NAME",
+ "iotdb.table"="$YOUR_IOTDB_TABLE_NAME",
+ "iotdb.username"="$YOUR_IOTDB_USERNAME",
+ "iotdb.password"="$YOUR_IOTDB_PASSWORD",
+ "iotdb.urls"="$YOUR_IOTDB_URL"
+);
+
+SELECT * FROM spark_iotdb;
+```
+
+#### 4.1.3 Parameters
+
+| Parameter | Default | Description
| Mandatory |
+| ---------------------- | ---------------------- |
------------------------------------------------------------------- |
----------- |
+| `iotdb.database` | — | IoTDB database name (must pre-exist
in IoTDB) | Yes |
+| `iotdb.table` | — | IoTDB table name (must pre-exist in
IoTDB) | Yes |
+| `iotdb.username` | `root` | IoTDB username
| No |
+| `iotdb.password` | `root` | IoTDB password
| No |
+| `iotdb.urls` | `127.0.0.1:6667` | IoTDB DataNode RPC endpoints
(comma-separated for multiple nodes) | No |
+
+#### 4.1.4 Key Notes
+
+IoTDB supports several filtering conditions, column pruning, and
`OFFSET`/`LIMIT` pushdown.
+
+* The filtering conditions that can be pushed down include:
+
+| Name | SQL( IoTDB) |
+| -------------------- | ---------------------------------- |
+| `IS_NULL` | `expr IS NULL` |
+| `IS_NOT_NULL` | `expr IS NOT NULL` |
+| `STARTS_WITH` | `starts_with(expr1, expr2)` |
+| `ENDS_WITH` | `ends_with(expr1, expr2)` |
+| `CONTAINS` | `expr1 LIKE '%expr2%'` |
+| `IN` | `expr IN (expr1, expr2,...)` |
+| `=` | `expr1 = expr2` |
+| `<>` | `expr1 <> expr2` |
+| `<` | `expr1 < expr2` |
+| `<=` | `expr1 <= expr2` |
+| `>` | `expr1 > expr2` |
+| `>=` | `expr1 >= expr2` |
+| `AND` | `expr1 AND expr2` |
+| `OR` | `expr1 OR expr2` |
+| `NOT` | `NOT expr` |
+| `ALWAYS_TRUE` | `TRUE` |
+| `ALWAYS_FALSE` | `FASLE` |
+
+> Constraints:
+> * `CONTAINS` requires constant values for `expr2`.
+> * Non-pushdown-capable child expressions invalidate the entire conjunctive
clause.
+
+* Column Pruning:
+
+Supports specifying column names when constructing IoTDB SQL queries to avoid
transferring unnecessary column data.
+
+* Offset/Limit Pushdown:
+
+Supports pushdown of OFFSET and LIMIT clauses, enabling direct integration of
Spark-provided pagination parameters into IoTDB queries.
+
+### 4.2 Writing Data
+
+#### 4.2.1 DataFrame
+
+```Scala
+val df = spark.createDataFrame(List(
+ (1L, "tag1_value1", "tag2_value1", "attribute1_value1", 1, true),
+ (2L, "tag1_value1", "tag2_value2", "attribute1_value1", 2, false)))
+ .toDF("time", "tag1", "tag2", "attribute1", "s1", "s2")
+
+df
+ .write
+ .format("org.apache.iotdb.spark.table.db.IoTDBTableProvider")
+ .option("iotdb.database", "$YOUR_IOTDB_DATABASE_NAME")
+ .option("iotdb.table", "$YOUR_IOTDB_TABLE_NAME")
+ .option("iotdb.username", "$YOUR_IOTDB_USERNAME")
+ .option("iotdb.password", "$YOUR_IOTDB_PASSWORD")
+ .option("iotdb.urls", "$YOUR_IOTDB_URL")
+ .save()
+```
+
+#### 4.2.2 Spark SQL
+
+```SQL
+CREATE TEMPORARY VIEW spark_iotdb
+ USING org.apache.iotdb.spark.table.db.IoTDBTableProvider
+ OPTIONS(
+ "iotdb.database"="$YOUR_IOTDB_DATABASE_NAME",
+ "iotdb.table"="$YOUR_IOTDB_TABLE_NAME",
+ "iotdb.username"="$YOUR_IOTDB_USERNAME",
+ "iotdb.password"="$YOUR_IOTDB_PASSWORD",
+ "iotdb.urls"="$YOUR_IOTDB_URL"
+);
+
+INSERT INTO spark_iotdb VALUES ("VALUE1", "VALUE2", ...);
+INSERT INTO spark_iotdb SELECT * FROM YOUR_TABLE;
+```
+
+#### 4.2.3 Key Notes
+
+* No Auto-Schema Creation: Tables/columns must pre-exist in IoTDB.
+* Order Sensitivity:
+ * `INSERT INTO VALUES`: Values must follow IoTDB table schema order (as
per `DESC TABLE`).
+ * `INSERT INTO SELECT`: Columns must exist in the target table. Mismatched
column counts trigger `IllegalArgumentException`.
+* Column Name Mapping: DataFrame or `INSERT INTO SELECT` with explicit column
names allows schema order flexibility.
+
+### 4.3 Data Type Mapping
+
+1. Read (From IoTDB To Spark)
+
+| IoTDB Type | Spark Type |
+| ---------------------------- | ------------------- |
+| `TsDataType.BOOLEAN` | `BooleanType` |
+| `TsDataType.INT32` | `IntegerType` |
+| `TsDataType.DATE` | `DateType` |
+| `TsDataType.INT64` | `LongType` |
+| `TsDataType.TIMESTAMP` | `LongType` |
+| `TsDataType.FLOAT` | `FloatType` |
+| `TsDataType.DOUBLE` | `DoubleType` |
+| `TsDataType.STRING` | `StringType` |
+| `TsDataType.TEXT` | `StringType` |
+| `TsDataType.BLOB` | `BinaryType` |
+
+2. Write (From Spark To IoTDB)
+
+The mapping primarily converts data into IoTDB Tablet format for writing.
+
+> During the Tablet ingestion process into IoTDB, secondary type conversion
will be automatically performed if data type mismatches occur.
+
+| Spark Type | IoTDB Type |
+| ------------------- | -------------------------- |
+| `BooleanType` | `TsDataType.BOOLEAN` |
+| `ByteType` | `TsDataType.INT32` |
+| `ShortType` | `TsDataType.INT32` |
+| `IntegerType` | `TsDataType.INT32` |
+| `LongType` | `TsDataType.INT64` |
+| `FloatType` | `TsDataType.FLOAT` |
+| `DoubleType` | `TsDataType.DOUBLE` |
+| `StringType` | `TsDataType.STRING` |
+| `BinaryType` | `TsDataType.BLOB` |
+| `DateType` | `TsDataType.DATE` |
+| `Others` | `TsDataType.STRING` |
+
+### 4.4 Security
+
+1. Authentication & Authorization
+
+* Credentials: Username/password are required for IoTDB access.
+* Access Control:
+ * Write: Requires `INSERT` privilege on the target table/database.
+ * Read: Requires `SELECT` privilege on the target table/database.
+
+2. Constraints
+
+* Automatic table/column creation is unsupported.
+* Schema validation is enforced during writing.
\ No newline at end of file
diff --git a/src/UserGuide/latest-Table/Ecosystem-Integration/DBeaver.md
b/src/UserGuide/latest-Table/Ecosystem-Integration/DBeaver.md
index ad56129d..453474c1 100644
--- a/src/UserGuide/latest-Table/Ecosystem-Integration/DBeaver.md
+++ b/src/UserGuide/latest-Table/Ecosystem-Integration/DBeaver.md
@@ -19,7 +19,7 @@
-->
-# DBeaver(IoTDB)
+# DBeaver
DBeaver is an SQL client and database management tool. It can interact with
IoTDB using IoTDB's JDBC driver.
diff --git a/src/UserGuide/latest-Table/Ecosystem-Integration/Spark-IoTDB.md
b/src/UserGuide/latest-Table/Ecosystem-Integration/Spark-IoTDB.md
new file mode 100644
index 00000000..9c3c8ecb
--- /dev/null
+++ b/src/UserGuide/latest-Table/Ecosystem-Integration/Spark-IoTDB.md
@@ -0,0 +1,250 @@
+<!--
+
+ Licensed to the Apache Software Foundation (ASF) under one
+ or more contributor license agreements. See the NOTICE file
+ distributed with this work for additional information
+ regarding copyright ownership. The ASF licenses this file
+ to you under the Apache License, Version 2.0 (the
+ "License"); you may not use this file except in compliance
+ with the License. You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing,
+ software distributed under the License is distributed on an
+ "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ KIND, either express or implied. See the License for the
+ specific language governing permissions and limitations
+ under the License.
+
+-->
+
+# Apache Spark
+
+## 1. Functional Overview
+
+IoTDB provides the `Spark-IoTDB-Table-Connector` to integrate IoTDB's table
model with Spark, enabling data read/write operations in Spark environments
through both DataFrame and Spark SQL interfaces.
+
+### 1.1 DataFrame
+
+DataFrame is a structured data abstraction in Spark, containing schema
metadata (column names, data types, etc.) and serving as the primary data
carrier between Spark operators. DataFrame transformations follow a lazy
execution mechanism, where operations are only physically executed upon
triggering an *action* (e.g., writing results or invoking `collect()`), thereby
optimizing resource utilization by avoiding redundant computations.
+
+The `Spark-IoTDB-Table-Connector` allows:
+
+• Write: Processed DataFrames from upstream tasks can be directly written into
IoTDB tables.
+
+• Read: Data from IoTDB tables can be loaded as DataFrames for downstream
analytical tasks.
+
+
+
+### 1.2 Spark SQL
+
+Spark clusters can be accessed via the `Spark-SQL Shell` for interactive SQL
execution. The `Spark-IoTDB-Table-Connector` maps IoTDB tables to temporary
external views in Spark, enabling direct read/write operations using Spark SQL.
+
+## 2. Compatibility Requirements
+
+| Software | Version |
+| ----------------------------------- |-----------|
+| `Spark-IoTDB-Table-Connector` | `2.0.3` |
+| `Spark` | `3.3-3.5` |
+| `IoTDB` | `2.0.1+` |
+| `Scala` | `2.12` |
+| `JDK` | `8,11` |
+
+## 3. Deployment Methods
+
+### 3.1 DataFrame
+
+Add the following dependency to your project’s `pom.xml`:
+
+```XML
+<dependency>
+ <groupId>org.apache.iotdb</groupId>
+ <artifactId>spark-iotdb-table-connector-3.5</artifactId>
+ <version>2.0.3</version>
+</dependency>
+```
+
+### 3.2 Spark SQL
+
+1. Download the `Spark-IoTDB-Table-Connector` JAR from the official
repository.
+2. Copy the JAR file to the `${SPARK_HOME}/jars` directory.
+
+
+
+## 4. Usage Guide
+
+### 4.1 Reading Data
+
+#### 4.1.1 DataFrame
+
+```Scala
+val df =
spark.read.format("org.apache.iotdb.spark.table.db.IoTDBTableProvider")
+ .option("iotdb.database", "$YOUR_IOTDB_DATABASE_NAME")
+ .option("iotdb.table", "$YOUR_IOTDB_TABLE_NAME")
+ .option("iotdb.username", "$YOUR_IOTDB_USERNAME")
+ .option("iotdb.password", "$YOUR_IOTDB_PASSWORD")
+ .option("iotdb.url", "$YOUR_IOTDB_URL")
+ .load()
+```
+
+#### 4.1.2 Spark SQL
+
+```SQL
+CREATE TEMPORARY VIEW spark_iotdb
+ USING org.apache.iotdb.spark.table.db.IoTDBTableProvider
+ OPTIONS(
+ "iotdb.database"="$YOUR_IOTDB_DATABASE_NAME",
+ "iotdb.table"="$YOUR_IOTDB_TABLE_NAME",
+ "iotdb.username"="$YOUR_IOTDB_USERNAME",
+ "iotdb.password"="$YOUR_IOTDB_PASSWORD",
+ "iotdb.urls"="$YOUR_IOTDB_URL"
+);
+
+SELECT * FROM spark_iotdb;
+```
+
+#### 4.1.3 Parameters
+
+| Parameter | Default | Description
| Mandatory |
+| ---------------------- | ---------------------- |
------------------------------------------------------------------- |
----------- |
+| `iotdb.database` | — | IoTDB database name (must pre-exist
in IoTDB) | Yes |
+| `iotdb.table` | — | IoTDB table name (must pre-exist in
IoTDB) | Yes |
+| `iotdb.username` | `root` | IoTDB username
| No |
+| `iotdb.password` | `root` | IoTDB password
| No |
+| `iotdb.urls` | `127.0.0.1:6667` | IoTDB DataNode RPC endpoints
(comma-separated for multiple nodes) | No |
+
+#### 4.1.4 Key Notes
+
+IoTDB supports several filtering conditions, column pruning, and
`OFFSET`/`LIMIT` pushdown.
+
+* The filtering conditions that can be pushed down include:
+
+| Name | SQL( IoTDB) |
+| -------------------- | ---------------------------------- |
+| `IS_NULL` | `expr IS NULL` |
+| `IS_NOT_NULL` | `expr IS NOT NULL` |
+| `STARTS_WITH` | `starts_with(expr1, expr2)` |
+| `ENDS_WITH` | `ends_with(expr1, expr2)` |
+| `CONTAINS` | `expr1 LIKE '%expr2%'` |
+| `IN` | `expr IN (expr1, expr2,...)` |
+| `=` | `expr1 = expr2` |
+| `<>` | `expr1 <> expr2` |
+| `<` | `expr1 < expr2` |
+| `<=` | `expr1 <= expr2` |
+| `>` | `expr1 > expr2` |
+| `>=` | `expr1 >= expr2` |
+| `AND` | `expr1 AND expr2` |
+| `OR` | `expr1 OR expr2` |
+| `NOT` | `NOT expr` |
+| `ALWAYS_TRUE` | `TRUE` |
+| `ALWAYS_FALSE` | `FASLE` |
+
+> Constraints:
+> * `CONTAINS` requires constant values for `expr2`.
+> * Non-pushdown-capable child expressions invalidate the entire conjunctive
clause.
+
+* Column Pruning:
+
+Supports specifying column names when constructing IoTDB SQL queries to avoid
transferring unnecessary column data.
+
+* Offset/Limit Pushdown:
+
+Supports pushdown of OFFSET and LIMIT clauses, enabling direct integration of
Spark-provided pagination parameters into IoTDB queries.
+
+### 4.2 Writing Data
+
+#### 4.2.1 DataFrame
+
+```Scala
+val df = spark.createDataFrame(List(
+ (1L, "tag1_value1", "tag2_value1", "attribute1_value1", 1, true),
+ (2L, "tag1_value1", "tag2_value2", "attribute1_value1", 2, false)))
+ .toDF("time", "tag1", "tag2", "attribute1", "s1", "s2")
+
+df
+ .write
+ .format("org.apache.iotdb.spark.table.db.IoTDBTableProvider")
+ .option("iotdb.database", "$YOUR_IOTDB_DATABASE_NAME")
+ .option("iotdb.table", "$YOUR_IOTDB_TABLE_NAME")
+ .option("iotdb.username", "$YOUR_IOTDB_USERNAME")
+ .option("iotdb.password", "$YOUR_IOTDB_PASSWORD")
+ .option("iotdb.urls", "$YOUR_IOTDB_URL")
+ .save()
+```
+
+#### 4.2.2 Spark SQL
+
+```SQL
+CREATE TEMPORARY VIEW spark_iotdb
+ USING org.apache.iotdb.spark.table.db.IoTDBTableProvider
+ OPTIONS(
+ "iotdb.database"="$YOUR_IOTDB_DATABASE_NAME",
+ "iotdb.table"="$YOUR_IOTDB_TABLE_NAME",
+ "iotdb.username"="$YOUR_IOTDB_USERNAME",
+ "iotdb.password"="$YOUR_IOTDB_PASSWORD",
+ "iotdb.urls"="$YOUR_IOTDB_URL"
+);
+
+INSERT INTO spark_iotdb VALUES ("VALUE1", "VALUE2", ...);
+INSERT INTO spark_iotdb SELECT * FROM YOUR_TABLE;
+```
+
+#### 4.2.3 Key Notes
+
+* No Auto-Schema Creation: Tables/columns must pre-exist in IoTDB.
+* Order Sensitivity:
+ * `INSERT INTO VALUES`: Values must follow IoTDB table schema order (as
per `DESC TABLE`).
+ * `INSERT INTO SELECT`: Columns must exist in the target table. Mismatched
column counts trigger `IllegalArgumentException`.
+* Column Name Mapping: DataFrame or `INSERT INTO SELECT` with explicit column
names allows schema order flexibility.
+
+### 4.3 Data Type Mapping
+
+1. Read (From IoTDB To Spark)
+
+| IoTDB Type | Spark Type |
+| ---------------------------- | ------------------- |
+| `TsDataType.BOOLEAN` | `BooleanType` |
+| `TsDataType.INT32` | `IntegerType` |
+| `TsDataType.DATE` | `DateType` |
+| `TsDataType.INT64` | `LongType` |
+| `TsDataType.TIMESTAMP` | `LongType` |
+| `TsDataType.FLOAT` | `FloatType` |
+| `TsDataType.DOUBLE` | `DoubleType` |
+| `TsDataType.STRING` | `StringType` |
+| `TsDataType.TEXT` | `StringType` |
+| `TsDataType.BLOB` | `BinaryType` |
+
+2. Write (From Spark To IoTDB)
+
+The mapping primarily converts data into IoTDB Tablet format for writing.
+
+> During the Tablet ingestion process into IoTDB, secondary type conversion
will be automatically performed if data type mismatches occur.
+
+| Spark Type | IoTDB Type |
+| ------------------- | -------------------------- |
+| `BooleanType` | `TsDataType.BOOLEAN` |
+| `ByteType` | `TsDataType.INT32` |
+| `ShortType` | `TsDataType.INT32` |
+| `IntegerType` | `TsDataType.INT32` |
+| `LongType` | `TsDataType.INT64` |
+| `FloatType` | `TsDataType.FLOAT` |
+| `DoubleType` | `TsDataType.DOUBLE` |
+| `StringType` | `TsDataType.STRING` |
+| `BinaryType` | `TsDataType.BLOB` |
+| `DateType` | `TsDataType.DATE` |
+| `Others` | `TsDataType.STRING` |
+
+### 4.4 Security
+
+1. Authentication & Authorization
+
+* Credentials: Username/password are required for IoTDB access.
+* Access Control:
+ * Write: Requires `INSERT` privilege on the target table/database.
+ * Read: Requires `SELECT` privilege on the target table/database.
+
+2. Constraints
+
+* Automatic table/column creation is unsupported.
+* Schema validation is enforced during writing.
\ No newline at end of file
diff --git a/src/zh/UserGuide/Master/Table/Ecosystem-Integration/DBeaver.md
b/src/zh/UserGuide/Master/Table/Ecosystem-Integration/DBeaver.md
index dbe6b688..1d89d831 100644
--- a/src/zh/UserGuide/Master/Table/Ecosystem-Integration/DBeaver.md
+++ b/src/zh/UserGuide/Master/Table/Ecosystem-Integration/DBeaver.md
@@ -19,7 +19,7 @@
-->
-# DBeaver(IoTDB)
+# DBeaver
DBeaver 是一个 SQL 客户端和数据库管理工具。DBeaver 可以使用 IoTDB 的 JDBC 驱动与 IoTDB 进行交互。
diff --git a/src/zh/UserGuide/Master/Table/Ecosystem-Integration/Spark-IoTDB.md
b/src/zh/UserGuide/Master/Table/Ecosystem-Integration/Spark-IoTDB.md
new file mode 100644
index 00000000..5557f4a5
--- /dev/null
+++ b/src/zh/UserGuide/Master/Table/Ecosystem-Integration/Spark-IoTDB.md
@@ -0,0 +1,241 @@
+<!--
+
+ Licensed to the Apache Software Foundation (ASF) under one
+ or more contributor license agreements. See the NOTICE file
+ distributed with this work for additional information
+ regarding copyright ownership. The ASF licenses this file
+ to you under the Apache License, Version 2.0 (the
+ "License"); you may not use this file except in compliance
+ with the License. You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing,
+ software distributed under the License is distributed on an
+ "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ KIND, either express or implied. See the License for the
+ specific language governing permissions and limitations
+ under the License.
+
+-->
+
+# Apache Spark
+
+## 1. 功能概述
+
+IoTDB 提供 `Spark-IoTDB-Table-Connector` 作为实现 IoTDB 表模型的 Spark 连接器，支持在 Spark
环境中，通过 DataFrame 以及 Spark SQL 两种方式对 IoTDB 表模型的数据进行读写。
+
+### 1.1 DataFrame
+
+DataFrame 是 Spark 编程中不同算子之间传递数据的常用数据结构，包含表头等元信息。DateFrame 的转换操作均采用惰性执行（Lazy
Execution）机制，只有在触发动作时（如输出或存储等）才会实际执行，从而避免冗余计算资源消耗。
+
+在使用时，上游任务处理好的 DataFrame 可以通过 `Spark-IoTDB-Table-Connector` 直接写入 IoTDB 的表中，也可以从
IoTDB 的表中直接读取数据成 DataFrame 的形式，供下游任务继续分析。
+
+
+
+### 1.2 Spark SQL
+
+`Spark-IoTDB-Table-Connector` 还支持将 IoTDB 中的表映射成 Spark 中的外表（temporary view），然后在
`Spark-SQL Shell` 中，使用 Spark SQL 直接读写。
+
+## 2. 兼容性要求
+
+| 软件 | 版本 |
+| ----------------------------------- |-----------|
+| `Spark-IoTDB-Table-Connector` | `2.0.3` |
+| `Spark` | `3.3-3.5` |
+| `IoTDB` | `2.0.1+` |
+| `Scala` | `2.12 ` |
+| `JDK` | `8、11` |
+
+## 3. 部署方式
+
+### 3.1 DataFrame
+
+通过 DataFrame 方式时，只需要在项目的 pom 中引入` Spark-IoTDB-Table-Connector` 的依赖。
+
+```xml
+<dependency>
+ <groupId>org.apache.iotdb</groupId>
+ <artifactId>spark-iotdb-table-connector-3.5</artifactId>
+ <version>2.0.3</version>
+</dependency>
+```
+
+### 3.2 Spark SQL
+
+通过 Spark SQL 方式时，需要先在官网下载 `Spark-IoTDB-Table-Connector` 的 Jar 包。然后再将 Jar 包拷贝到
`${SPARK_HOME}/jars` 目录中即可。
+
+
+
+## 4. 使用方式
+
+### 4.1 读取数据
+
+#### 4.1.1 DataFrame
+
+```Scala
+val df =
spark.read.format("org.apache.iotdb.spark.table.db.IoTDBTableProvider")
+ .option("iotdb.database", "$YOUR_IOTDB_DATABASE_NAME")
+ .option("iotdb.table", "$YOUR_IOTDB_TABLE_NAME")
+ .option("iotdb.username", "$YOUR_IOTDB_USERNAME")
+ .option("iotdb.password", "$YOUR_IOTDB_PASSWORD")
+ .option("iotdb.url", "$YOUR_IOTDB_URL")
+ .load()
+```
+
+#### 4.1.2 Spark SQL
+
+```SQL
+CREATE TEMPORARY VIEW spark_iotdb
+ USING org.apache.iotdb.spark.table.db.IoTDBTableProvider
+ OPTIONS(
+ "iotdb.database"="$YOUR_IOTDB_DATABASE_NAME",
+ "iotdb.table"="$YOUR_IOTDB_TABLE_NAME",
+ "iotdb.username"="$YOUR_IOTDB_USERNAME",
+ "iotdb.password"="$YOUR_IOTDB_PASSWORD",
+ "iotdb.urls"="$YOUR_IOTDB_URL"
+);
+
+SELECT * FROM spark_iotdb;
+```
+
+#### 4.1.3 参数介绍
+
+| 参数 | 默认值 | 描述
| 是否必填 |
+| ---------------- | -------------- |
--------------------------------------------------- | ------- |
+| `iotdb.database` | -- | IoTDB 的数据库名，需要是已经在 IoTDB 中存在的数据库 | 是
|
+| `iotdb.table` | -- | IoTDB 中的表名，需要是已经在 IoTDB 中存在的表 |
是 |
+| `iotdb.username` | `root` | 访问 IoTDB 用户名
| 否 |
+| `iotdb.password` | `root` | 访问 IoTDB 密码
| 否 |
+| `iotdb.urls` | `127.0.0.1:6667` | 客户端连接 datanode rpc 的 url，有多个时以 `'，'`
分隔 | 否 |
+
+#### 4.1.4 注意事项
+
+查询 IoTDB 时支持在 IoTDB 侧完成部分查询条件的过滤、列裁剪、offset 和 limit 下推。
+
+* 可下推的查询过滤条件包括：
+
+| Name | SQL( IoTDB) |
+| -------------- | ---------------------------- |
+| `IS_NULL` | `expr IS NULL` |
+| `IS_NOT_NULL` | `expr IS NOT NULL` |
+| `STARTS_WITH` | `starts_with(expr1, expr2)` |
+| `ENDS_WITH` | `ends_with(expr1, expr2)` |
+| `CONTAINS` | `expr1 LIKE '%expr2%'` |
+| `IN` | `expr IN (expr1, expr2,...)` |
+| `=` | `expr1 = expr2` |
+| `<>` | `expr1 <> expr2` |
+| `<` | `expr1 < expr2` |
+| `<=` | `expr1 <= expr2` |
+| `>` | `expr1 > expr2` |
+| `>=` | `expr1 >= expr2` |
+| `AND` | `expr1 AND expr2` |
+| `OR` | `expr1 OR expr2` |
+| `NOT` | `NOT expr` |
+| `ALWAYS_TRUE` | `TRUE` |
+| `ALWAYS_FALSE` | `FASLE` |
+
+
+> 注意：
+> * CONTAINS 的 expr2 只支持常量
+> * 如果出现某个 child 无法下推的情况，对应的整个合取式都不会下推
+
+* 列裁剪：
+
+支持在拼接 IoTDB 的 SQL 时指定列名，避免传输不需要的列的数据
+
+* offset 和 limit 下推：
+
+支持下推 offset 和 limit，支持直接根据 Spark 提供的 offset 和 limit 参数进行拼接
+
+### 4.2 写入数据
+
+#### 4.2.1 DataFrame
+
+```Scala
+val df = spark.createDataFrame(List(
+ (1L, "tag1_value1", "tag2_value1", "attribute1_value1", 1, true),
+ (2L, "tag1_value1", "tag2_value2", "attribute1_value1", 2, false)))
+ .toDF("time", "tag1", "tag2", "attribute1", "s1", "s2")
+
+df
+ .write
+ .format("org.apache.iotdb.spark.table.db.IoTDBTableProvider")
+ .option("iotdb.database", "$YOUR_IOTDB_DATABASE_NAME")
+ .option("iotdb.table", "$YOUR_IOTDB_TABLE_NAME")
+ .option("iotdb.username", "$YOUR_IOTDB_USERNAME")
+ .option("iotdb.password", "$YOUR_IOTDB_PASSWORD")
+ .option("iotdb.urls", "$YOUR_IOTDB_URL")
+ .save()
+```
+
+#### 4.2.2 Spark SQL
+
+```SQL
+CREATE TEMPORARY VIEW spark_iotdb
+ USING org.apache.iotdb.spark.table.db.IoTDBTableProvider
+ OPTIONS(
+ "iotdb.database"="$YOUR_IOTDB_DATABASE_NAME",
+ "iotdb.table"="$YOUR_IOTDB_TABLE_NAME",
+ "iotdb.username"="$YOUR_IOTDB_USERNAME",
+ "iotdb.password"="$YOUR_IOTDB_PASSWORD",
+ "iotdb.urls"="$YOUR_IOTDB_URL"
+);
+
+INSERT INTO spark_iotdb VALUES ("VALUE1", "VALUE2", ...);
+INSERT INTO spark_iotdb SELECT * FROM YOUR_TABLE
+```
+
+#### 4.2.3 注意事项
+
+* 向 IoTDB 写入数据时，不支持自动建表/自动扩展列。
+* 通过`INSERT INTO VALUES`方式写入时，无法指定 VALUES 中对应的列名，VALUES 的顺序必须与表结构（即在 IoTDB 中执行
`Desc Table` 输出的列顺序）一致。否则，会抛出 `SparkException` 异常。
+* 通过`INSERT INTO SELECT`方式写入时，所有列必须在 IoTDB 的表中已经存在，当出现 Schema 中缺少的列时，会像
`INSERT INTO VALUES` 一样尝试按顺序进行写入。
+* 当使用 DataFrame 或 `INSERT INTO SELECT` 方式写入时，如果指定了每一列的列名，则允许与 Table Schema
顺序不一致。
+* 当写入的列数超过表的列数时，会抛出 `IllegalArgumentException`异常。
+
+### 4.3 数据类型映射
+
+1. 读取数据时，从 IoTDB 的数据类型映射到 Spark 的数据类型。
+
+| IoTDB Type | Spark Type |
+| ---------------------------- | ------------------- |
+| `TsDataType.BOOLEAN` | `BooleanType` |
+| `TsDataType.INT32` | `IntegerType` |
+| `TsDataType.DATE` | `DateType` |
+| `TsDataType.INT64` | `LongType` |
+| `TsDataType.TIMESTAMP` | `LongType` |
+| `TsDataType.FLOAT` | `FloatType` |
+| `TsDataType.DOUBLE` | `DoubleType` |
+| `TsDataType.STRING` | `StringType` |
+| `TsDataType.TEXT` | `StringType` |
+| `TsDataType.BLOB` | `BinaryType` |
+
+2. 向 IoTDB 写入数据时，需要从 Spark 的数据类型映射到 IoTDB 的数据类型
+
+> 主要是映射成 Tablet 进行写入，而 Tablet 在写入到 IoTDB 时如果类型不一致可再次进行类型转换。
+
+| Spark Type | IoTDB Type |
+| ------------------- | -------------------------- |
+| `BooleanType` | `TsDataType.BOOLEAN` |
+| `ByteType` | `TsDataType.INT32` |
+| `ShortType` | `TsDataType.INT32` |
+| `IntegerType` | `TsDataType.INT32` |
+| `LongType` | `TsDataType.INT64` |
+| `FloatType` | `TsDataType.FLOAT` |
+| `DoubleType` | `TsDataType.DOUBLE` |
+| `StringType` | `TsDataType.STRING` |
+| `BinaryType` | `TsDataType.BLOB` |
+| `DateType` | `TsDataType.DATE` |
+| `其他` | `TsDataType.STRING` |
+
+### 4.4 权限控制
+
+1. 身份验证和授权
+
+通过 Spark 连接器进行 IoTDB 的读取和写入时，需要提供用户名和密码，确保只有合法用户才能访问系统。
+
+2. 访问控制
+
+* 写入时：与 IoTDB 中对于写入操作的鉴权相同，但因为 Spark 连接器不支持自动建表和自动扩展列，所以需要对应表（或所属 DB 、或 ANY
）上的 INSERT 权限
+* 查询时：与 IoTDB 中对于查询操作的鉴权相同，所以需要对应表（或所属 DB、或 ANY ）上的 SELECT 权限
diff --git a/src/zh/UserGuide/latest-Table/Ecosystem-Integration/DBeaver.md
b/src/zh/UserGuide/latest-Table/Ecosystem-Integration/DBeaver.md
index dbe6b688..1d89d831 100644
--- a/src/zh/UserGuide/latest-Table/Ecosystem-Integration/DBeaver.md
+++ b/src/zh/UserGuide/latest-Table/Ecosystem-Integration/DBeaver.md
@@ -19,7 +19,7 @@
-->
-# DBeaver(IoTDB)
+# DBeaver
DBeaver 是一个 SQL 客户端和数据库管理工具。DBeaver 可以使用 IoTDB 的 JDBC 驱动与 IoTDB 进行交互。
diff --git a/src/zh/UserGuide/latest-Table/Ecosystem-Integration/Spark-IoTDB.md
b/src/zh/UserGuide/latest-Table/Ecosystem-Integration/Spark-IoTDB.md
new file mode 100644
index 00000000..5557f4a5
--- /dev/null
+++ b/src/zh/UserGuide/latest-Table/Ecosystem-Integration/Spark-IoTDB.md
@@ -0,0 +1,241 @@
+<!--
+
+ Licensed to the Apache Software Foundation (ASF) under one
+ or more contributor license agreements. See the NOTICE file
+ distributed with this work for additional information
+ regarding copyright ownership. The ASF licenses this file
+ to you under the Apache License, Version 2.0 (the
+ "License"); you may not use this file except in compliance
+ with the License. You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing,
+ software distributed under the License is distributed on an
+ "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ KIND, either express or implied. See the License for the
+ specific language governing permissions and limitations
+ under the License.
+
+-->
+
+# Apache Spark
+
+## 1. 功能概述
+
+IoTDB 提供 `Spark-IoTDB-Table-Connector` 作为实现 IoTDB 表模型的 Spark 连接器，支持在 Spark
环境中，通过 DataFrame 以及 Spark SQL 两种方式对 IoTDB 表模型的数据进行读写。
+
+### 1.1 DataFrame
+
+DataFrame 是 Spark 编程中不同算子之间传递数据的常用数据结构，包含表头等元信息。DateFrame 的转换操作均采用惰性执行（Lazy
Execution）机制，只有在触发动作时（如输出或存储等）才会实际执行，从而避免冗余计算资源消耗。
+
+在使用时，上游任务处理好的 DataFrame 可以通过 `Spark-IoTDB-Table-Connector` 直接写入 IoTDB 的表中，也可以从
IoTDB 的表中直接读取数据成 DataFrame 的形式，供下游任务继续分析。
+
+
+
+### 1.2 Spark SQL
+
+`Spark-IoTDB-Table-Connector` 还支持将 IoTDB 中的表映射成 Spark 中的外表（temporary view），然后在
`Spark-SQL Shell` 中，使用 Spark SQL 直接读写。
+
+## 2. 兼容性要求
+
+| 软件 | 版本 |
+| ----------------------------------- |-----------|
+| `Spark-IoTDB-Table-Connector` | `2.0.3` |
+| `Spark` | `3.3-3.5` |
+| `IoTDB` | `2.0.1+` |
+| `Scala` | `2.12 ` |
+| `JDK` | `8、11` |
+
+## 3. 部署方式
+
+### 3.1 DataFrame
+
+通过 DataFrame 方式时，只需要在项目的 pom 中引入` Spark-IoTDB-Table-Connector` 的依赖。
+
+```xml
+<dependency>
+ <groupId>org.apache.iotdb</groupId>
+ <artifactId>spark-iotdb-table-connector-3.5</artifactId>
+ <version>2.0.3</version>
+</dependency>
+```
+
+### 3.2 Spark SQL
+
+通过 Spark SQL 方式时，需要先在官网下载 `Spark-IoTDB-Table-Connector` 的 Jar 包。然后再将 Jar 包拷贝到
`${SPARK_HOME}/jars` 目录中即可。
+
+
+
+## 4. 使用方式
+
+### 4.1 读取数据
+
+#### 4.1.1 DataFrame
+
+```Scala
+val df =
spark.read.format("org.apache.iotdb.spark.table.db.IoTDBTableProvider")
+ .option("iotdb.database", "$YOUR_IOTDB_DATABASE_NAME")
+ .option("iotdb.table", "$YOUR_IOTDB_TABLE_NAME")
+ .option("iotdb.username", "$YOUR_IOTDB_USERNAME")
+ .option("iotdb.password", "$YOUR_IOTDB_PASSWORD")
+ .option("iotdb.url", "$YOUR_IOTDB_URL")
+ .load()
+```
+
+#### 4.1.2 Spark SQL
+
+```SQL
+CREATE TEMPORARY VIEW spark_iotdb
+ USING org.apache.iotdb.spark.table.db.IoTDBTableProvider
+ OPTIONS(
+ "iotdb.database"="$YOUR_IOTDB_DATABASE_NAME",
+ "iotdb.table"="$YOUR_IOTDB_TABLE_NAME",
+ "iotdb.username"="$YOUR_IOTDB_USERNAME",
+ "iotdb.password"="$YOUR_IOTDB_PASSWORD",
+ "iotdb.urls"="$YOUR_IOTDB_URL"
+);
+
+SELECT * FROM spark_iotdb;
+```
+
+#### 4.1.3 参数介绍
+
+| 参数 | 默认值 | 描述
| 是否必填 |
+| ---------------- | -------------- |
--------------------------------------------------- | ------- |
+| `iotdb.database` | -- | IoTDB 的数据库名，需要是已经在 IoTDB 中存在的数据库 | 是
|
+| `iotdb.table` | -- | IoTDB 中的表名，需要是已经在 IoTDB 中存在的表 |
是 |
+| `iotdb.username` | `root` | 访问 IoTDB 用户名
| 否 |
+| `iotdb.password` | `root` | 访问 IoTDB 密码
| 否 |
+| `iotdb.urls` | `127.0.0.1:6667` | 客户端连接 datanode rpc 的 url，有多个时以 `'，'`
分隔 | 否 |
+
+#### 4.1.4 注意事项
+
+查询 IoTDB 时支持在 IoTDB 侧完成部分查询条件的过滤、列裁剪、offset 和 limit 下推。
+
+* 可下推的查询过滤条件包括：
+
+| Name | SQL( IoTDB) |
+| -------------- | ---------------------------- |
+| `IS_NULL` | `expr IS NULL` |
+| `IS_NOT_NULL` | `expr IS NOT NULL` |
+| `STARTS_WITH` | `starts_with(expr1, expr2)` |
+| `ENDS_WITH` | `ends_with(expr1, expr2)` |
+| `CONTAINS` | `expr1 LIKE '%expr2%'` |
+| `IN` | `expr IN (expr1, expr2,...)` |
+| `=` | `expr1 = expr2` |
+| `<>` | `expr1 <> expr2` |
+| `<` | `expr1 < expr2` |
+| `<=` | `expr1 <= expr2` |
+| `>` | `expr1 > expr2` |
+| `>=` | `expr1 >= expr2` |
+| `AND` | `expr1 AND expr2` |
+| `OR` | `expr1 OR expr2` |
+| `NOT` | `NOT expr` |
+| `ALWAYS_TRUE` | `TRUE` |
+| `ALWAYS_FALSE` | `FASLE` |
+
+
+> 注意：
+> * CONTAINS 的 expr2 只支持常量
+> * 如果出现某个 child 无法下推的情况，对应的整个合取式都不会下推
+
+* 列裁剪：
+
+支持在拼接 IoTDB 的 SQL 时指定列名，避免传输不需要的列的数据
+
+* offset 和 limit 下推：
+
+支持下推 offset 和 limit，支持直接根据 Spark 提供的 offset 和 limit 参数进行拼接
+
+### 4.2 写入数据
+
+#### 4.2.1 DataFrame
+
+```Scala
+val df = spark.createDataFrame(List(
+ (1L, "tag1_value1", "tag2_value1", "attribute1_value1", 1, true),
+ (2L, "tag1_value1", "tag2_value2", "attribute1_value1", 2, false)))
+ .toDF("time", "tag1", "tag2", "attribute1", "s1", "s2")
+
+df
+ .write
+ .format("org.apache.iotdb.spark.table.db.IoTDBTableProvider")
+ .option("iotdb.database", "$YOUR_IOTDB_DATABASE_NAME")
+ .option("iotdb.table", "$YOUR_IOTDB_TABLE_NAME")
+ .option("iotdb.username", "$YOUR_IOTDB_USERNAME")
+ .option("iotdb.password", "$YOUR_IOTDB_PASSWORD")
+ .option("iotdb.urls", "$YOUR_IOTDB_URL")
+ .save()
+```
+
+#### 4.2.2 Spark SQL
+
+```SQL
+CREATE TEMPORARY VIEW spark_iotdb
+ USING org.apache.iotdb.spark.table.db.IoTDBTableProvider
+ OPTIONS(
+ "iotdb.database"="$YOUR_IOTDB_DATABASE_NAME",
+ "iotdb.table"="$YOUR_IOTDB_TABLE_NAME",
+ "iotdb.username"="$YOUR_IOTDB_USERNAME",
+ "iotdb.password"="$YOUR_IOTDB_PASSWORD",
+ "iotdb.urls"="$YOUR_IOTDB_URL"
+);
+
+INSERT INTO spark_iotdb VALUES ("VALUE1", "VALUE2", ...);
+INSERT INTO spark_iotdb SELECT * FROM YOUR_TABLE
+```
+
+#### 4.2.3 注意事项
+
+* 向 IoTDB 写入数据时，不支持自动建表/自动扩展列。
+* 通过`INSERT INTO VALUES`方式写入时，无法指定 VALUES 中对应的列名，VALUES 的顺序必须与表结构（即在 IoTDB 中执行
`Desc Table` 输出的列顺序）一致。否则，会抛出 `SparkException` 异常。
+* 通过`INSERT INTO SELECT`方式写入时，所有列必须在 IoTDB 的表中已经存在，当出现 Schema 中缺少的列时，会像
`INSERT INTO VALUES` 一样尝试按顺序进行写入。
+* 当使用 DataFrame 或 `INSERT INTO SELECT` 方式写入时，如果指定了每一列的列名，则允许与 Table Schema
顺序不一致。
+* 当写入的列数超过表的列数时，会抛出 `IllegalArgumentException`异常。
+
+### 4.3 数据类型映射
+
+1. 读取数据时，从 IoTDB 的数据类型映射到 Spark 的数据类型。
+
+| IoTDB Type | Spark Type |
+| ---------------------------- | ------------------- |
+| `TsDataType.BOOLEAN` | `BooleanType` |
+| `TsDataType.INT32` | `IntegerType` |
+| `TsDataType.DATE` | `DateType` |
+| `TsDataType.INT64` | `LongType` |
+| `TsDataType.TIMESTAMP` | `LongType` |
+| `TsDataType.FLOAT` | `FloatType` |
+| `TsDataType.DOUBLE` | `DoubleType` |
+| `TsDataType.STRING` | `StringType` |
+| `TsDataType.TEXT` | `StringType` |
+| `TsDataType.BLOB` | `BinaryType` |
+
+2. 向 IoTDB 写入数据时，需要从 Spark 的数据类型映射到 IoTDB 的数据类型
+
+> 主要是映射成 Tablet 进行写入，而 Tablet 在写入到 IoTDB 时如果类型不一致可再次进行类型转换。
+
+| Spark Type | IoTDB Type |
+| ------------------- | -------------------------- |
+| `BooleanType` | `TsDataType.BOOLEAN` |
+| `ByteType` | `TsDataType.INT32` |
+| `ShortType` | `TsDataType.INT32` |
+| `IntegerType` | `TsDataType.INT32` |
+| `LongType` | `TsDataType.INT64` |
+| `FloatType` | `TsDataType.FLOAT` |
+| `DoubleType` | `TsDataType.DOUBLE` |
+| `StringType` | `TsDataType.STRING` |
+| `BinaryType` | `TsDataType.BLOB` |
+| `DateType` | `TsDataType.DATE` |
+| `其他` | `TsDataType.STRING` |
+
+### 4.4 权限控制
+
+1. 身份验证和授权
+
+通过 Spark 连接器进行 IoTDB 的读取和写入时，需要提供用户名和密码，确保只有合法用户才能访问系统。
+
+2. 访问控制
+
+* 写入时：与 IoTDB 中对于写入操作的鉴权相同，但因为 Spark 连接器不支持自动建表和自动扩展列，所以需要对应表（或所属 DB 、或 ANY
）上的 INSERT 权限
+* 查询时：与 IoTDB 中对于查询操作的鉴权相同，所以需要对应表（或所属 DB、或 ANY ）上的 SELECT 权限
