10.1 章节概述
本章将深入探讨Apache Kylin的高级特性和扩展能力，包括自定义函数开发、插件机制、存储引擎扩展、高级调优技巧等内容。通过本章的学习，您将掌握如何扩展Kylin的功能，优化系统性能，以及开发自定义组件。
10.1.1 学习目标
- 掌握自定义函数（UDF）的开发和部署
- 了解Kylin的插件机制和扩展点
- 学习存储引擎的扩展和优化
- 掌握高级调优技巧和性能优化
- 了解Kylin的内部架构和扩展原理
10.1.2 技术架构
graph TB
subgraph "Kylin扩展架构"
A["应用层"] --> B["扩展接口层"]
B --> C["核心引擎层"]
C --> D["存储层"]
subgraph "扩展接口层"
B1["UDF接口"]
B2["插件接口"]
B3["存储接口"]
B4["查询接口"]
end
subgraph "核心引擎层"
C1["查询引擎"]
C2["构建引擎"]
C3["元数据引擎"]
C4["缓存引擎"]
end
subgraph "存储层"
D1["HBase"]
D2["Parquet"]
D3["自定义存储"]
end
end
10.2 自定义函数开发
10.2.1 UDF基础概念
用户定义函数（UDF）允许开发者扩展Kylin的SQL功能，实现自定义的数据处理逻辑。
UDF类型
- 标量函数（Scalar UDF）：输入单行数据，返回单个值
- 聚合函数（Aggregate UDF）：输入多行数据，返回聚合结果
- 表值函数（Table UDF）：输入数据，返回表格结果
10.2.2 标量UDF开发
// ScalarUDFExample.java - 标量UDF示例
package com.example.kylin.udf;
import org.apache.calcite.linq4j.function.Parameter;
import org.apache.kylin.metadata.datatype.DataType;
import org.apache.kylin.query.udf.UDFMeta;
import org.apache.kylin.query.udf.ScalarUDF;
/**
* 自定义字符串处理UDF
*/
@UDFMeta(
name = "CUSTOM_CONCAT",
description = "自定义字符串连接函数",
returnType = "string"
)
public class CustomConcatUDF implements ScalarUDF {
/**
* 连接字符串并添加分隔符
* @param str1 第一个字符串
* @param str2 第二个字符串
* @param separator 分隔符
* @return 连接后的字符串
*/
public String eval(
@Parameter(name = "str1") String str1,
@Parameter(name = "str2") String str2,
@Parameter(name = "separator") String separator) {
if (str1 == null && str2 == null) {
return null;
}
if (str1 == null) {
return str2;
}
if (str2 == null) {
return str1;
}
if (separator == null) {
separator = "";
}
return str1 + separator + str2;
}
/**
* 重载方法：使用默认分隔符
*/
public String eval(String str1, String str2) {
return eval(str1, str2, "-");
}
}
/**
* 数学计算UDF
*/
@UDFMeta(
name = "MATH_POWER",
description = "计算幂次方",
returnType = "double"
)
public class MathPowerUDF implements ScalarUDF {
public Double eval(Double base, Double exponent) {
if (base == null || exponent == null) {
return null;
}
return Math.pow(base, exponent);
}
public Double eval(Integer base, Integer exponent) {
if (base == null || exponent == null) {
return null;
}
return Math.pow(base.doubleValue(), exponent.doubleValue());
}
}
/**
* 日期处理UDF
*/
@UDFMeta(
name = "DATE_FORMAT_CUSTOM",
description = "自定义日期格式化",
returnType = "string"
)
public class DateFormatUDF implements ScalarUDF {
private static final Map<String, SimpleDateFormat> formatCache =
new ConcurrentHashMap<>();
public String eval(Date date, String pattern) {
if (date == null || pattern == null) {
return null;
}
try {
SimpleDateFormat formatter = formatCache.computeIfAbsent(
pattern,
p -> new SimpleDateFormat(p)
);
return formatter.format(date);
} catch (Exception e) {
return null;
}
}
public String eval(String dateStr, String inputPattern, String outputPattern) {
if (dateStr == null || inputPattern == null || outputPattern == null) {
return null;
}
try {
SimpleDateFormat inputFormatter = formatCache.computeIfAbsent(
inputPattern,
p -> new SimpleDateFormat(p)
);
SimpleDateFormat outputFormatter = formatCache.computeIfAbsent(
outputPattern,
p -> new SimpleDateFormat(p)
);
Date date = inputFormatter.parse(dateStr);
return outputFormatter.format(date);
} catch (Exception e) {
return null;
}
}
}
10.2.3 聚合UDF开发
// AggregateUDFExample.java - 聚合UDF示例
package com.example.kylin.udf;
import org.apache.kylin.query.udf.AggregateUDF;
import org.apache.kylin.query.udf.UDFMeta;
import java.util.*;
/**
* 自定义中位数聚合函数
*/
@UDFMeta(
name = "MEDIAN",
description = "计算中位数",
returnType = "double"
)
public class MedianUDF implements AggregateUDF<Double, MedianUDF.MedianAccumulator> {
public static class MedianAccumulator {
private List<Double> values = new ArrayList<>();
public void add(Double value) {
if (value != null) {
values.add(value);
}
}
public void merge(MedianAccumulator other) {
if (other != null && other.values != null) {
this.values.addAll(other.values);
}
}
public Double getResult() {
if (values.isEmpty()) {
return null;
}
Collections.sort(values);
int size = values.size();
if (size % 2 == 0) {
// 偶数个元素，取中间两个的平均值
return (values.get(size / 2 - 1) + values.get(size / 2)) / 2.0;
} else {
// 奇数个元素，取中间元素
return values.get(size / 2);
}
}
}
@Override
public MedianAccumulator createAccumulator() {
return new MedianAccumulator();
}
@Override
public void accumulate(MedianAccumulator accumulator, Double value) {
accumulator.add(value);
}
@Override
public void merge(MedianAccumulator accumulator, MedianAccumulator other) {
accumulator.merge(other);
}
@Override
public Double getResult(MedianAccumulator accumulator) {
return accumulator.getResult();
}
}
/**
* 自定义标准差聚合函数
*/
@UDFMeta(
name = "STDDEV_CUSTOM",
description = "计算标准差",
returnType = "double"
)
public class StandardDeviationUDF implements AggregateUDF<Double, StandardDeviationUDF.StdDevAccumulator> {
public static class StdDevAccumulator {
private double sum = 0.0;
private double sumSquares = 0.0;
private long count = 0;
public void add(Double value) {
if (value != null) {
sum += value;
sumSquares += value * value;
count++;
}
}
public void merge(StdDevAccumulator other) {
if (other != null) {
this.sum += other.sum;
this.sumSquares += other.sumSquares;
this.count += other.count;
}
}
public Double getResult() {
if (count == 0) {
return null;
}
if (count == 1) {
return 0.0;
}
double mean = sum / count;
double variance = (sumSquares - sum * mean) / (count - 1);
return Math.sqrt(variance);
}
}
@Override
public StdDevAccumulator createAccumulator() {
return new StdDevAccumulator();
}
@Override
public void accumulate(StdDevAccumulator accumulator, Double value) {
accumulator.add(value);
}
@Override
public void merge(StdDevAccumulator accumulator, StdDevAccumulator other) {
accumulator.merge(other);
}
@Override
public Double getResult(StdDevAccumulator accumulator) {
return accumulator.getResult();
}
}
/**
* 自定义百分位数聚合函数
*/
@UDFMeta(
name = "PERCENTILE_CUSTOM",
description = "计算百分位数",
returnType = "double"
)
public class PercentileUDF implements AggregateUDF<Double, PercentileUDF.PercentileAccumulator> {
private final double percentile;
public PercentileUDF() {
this.percentile = 0.5; // 默认中位数
}
public PercentileUDF(double percentile) {
if (percentile < 0.0 || percentile > 1.0) {
throw new IllegalArgumentException("百分位数必须在0.0到1.0之间");
}
this.percentile = percentile;
}
public static class PercentileAccumulator {
private List<Double> values = new ArrayList<>();
public void add(Double value) {
if (value != null) {
values.add(value);
}
}
public void merge(PercentileAccumulator other) {
if (other != null && other.values != null) {
this.values.addAll(other.values);
}
}
public Double getResult(double percentile) {
if (values.isEmpty()) {
return null;
}
Collections.sort(values);
int size = values.size();
if (percentile == 0.0) {
return values.get(0);
}
if (percentile == 1.0) {
return values.get(size - 1);
}
double index = percentile * (size - 1);
int lowerIndex = (int) Math.floor(index);
int upperIndex = (int) Math.ceil(index);
if (lowerIndex == upperIndex) {
return values.get(lowerIndex);
}
double weight = index - lowerIndex;
return values.get(lowerIndex) * (1 - weight) + values.get(upperIndex) * weight;
}
}
@Override
public PercentileAccumulator createAccumulator() {
return new PercentileAccumulator();
}
@Override
public void accumulate(PercentileAccumulator accumulator, Double value) {
accumulator.add(value);
}
@Override
public void merge(PercentileAccumulator accumulator, PercentileAccumulator other) {
accumulator.merge(other);
}
@Override
public Double getResult(PercentileAccumulator accumulator) {
return accumulator.getResult(percentile);
}
}
10.2.4 UDF部署和注册
<!-- pom.xml - UDF项目依赖 -->
<?xml version="1.0" encoding="UTF-8"?>
<project xmlns="http://maven.apache.org/POM/4.0.0"
xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
xsi:schemaLocation="http://maven.apache.org/POM/4.0.0
http://maven.apache.org/xsd/maven-4.0.0.xsd">
<modelVersion>4.0.0</modelVersion>
<groupId>com.example</groupId>
<artifactId>kylin-custom-udf</artifactId>
<version>1.0.0</version>
<packaging>jar</packaging>
<properties>
<maven.compiler.source>8</maven.compiler.source>
<maven.compiler.target>8</maven.compiler.target>
<kylin.version>4.0.3</kylin.version>
</properties>
<dependencies>
<dependency>
<groupId>org.apache.kylin</groupId>
<artifactId>kylin-query</artifactId>
<version>${kylin.version}</version>
<scope>provided</scope>
</dependency>
<dependency>
<groupId>org.apache.calcite</groupId>
<artifactId>calcite-core</artifactId>
<version>1.26.0</version>
<scope>provided</scope>
</dependency>
<dependency>
<groupId>junit</groupId>
<artifactId>junit</artifactId>
<version>4.13.2</version>
<scope>test</scope>
</dependency>
</dependencies>
<build>
<plugins>
<plugin>
<groupId>org.apache.maven.plugins</groupId>
<artifactId>maven-compiler-plugin</artifactId>
<version>3.8.1</version>
<configuration>
<source>8</source>
<target>8</target>
</configuration>
</plugin>
<plugin>
<groupId>org.apache.maven.plugins</groupId>
<artifactId>maven-shade-plugin</artifactId>
<version>3.2.4</version>
<executions>
<execution>
<phase>package</phase>
<goals>
<goal>shade</goal>
</goals>
<configuration>
<createDependencyReducedPom>false</createDependencyReducedPom>
<filters>
<filter>
<artifact>*:*</artifact>
<excludes>
<exclude>META-INF/*.SF</exclude>
<exclude>META-INF/*.DSA</exclude>
<exclude>META-INF/*.RSA</exclude>
</excludes>
</filter>
</filters>
</configuration>
</execution>
</executions>
</plugin>
</plugins>
</build>
</project>
#!/bin/bash
# deploy_udf.sh - UDF部署脚本
set -e
# 配置
KYLIN_HOME=${KYLIN_HOME:-"/opt/kylin"}
UDF_JAR="kylin-custom-udf-1.0.0.jar"
UDF_LIB_DIR="$KYLIN_HOME/lib"
UDF_CONFIG_FILE="$KYLIN_HOME/conf/kylin_udf.properties"
echo "开始部署自定义UDF..."
# 1. 编译UDF项目
echo "编译UDF项目..."
mvn clean package -DskipTests
if [ ! -f "target/$UDF_JAR" ]; then
echo "错误：UDF JAR文件不存在"
exit 1
fi
# 2. 复制JAR文件到Kylin lib目录
echo "复制JAR文件到Kylin lib目录..."
cp "target/$UDF_JAR" "$UDF_LIB_DIR/"
# 3. 创建UDF配置文件
echo "创建UDF配置文件..."
cat > "$UDF_CONFIG_FILE" << EOF
# 自定义UDF配置
# 格式：function_name=class_full_name
# 标量函数
CUSTOM_CONCAT=com.example.kylin.udf.CustomConcatUDF
MATH_POWER=com.example.kylin.udf.MathPowerUDF
DATE_FORMAT_CUSTOM=com.example.kylin.udf.DateFormatUDF
# 聚合函数
MEDIAN=com.example.kylin.udf.MedianUDF
STDDEV_CUSTOM=com.example.kylin.udf.StandardDeviationUDF
PERCENTILE_CUSTOM=com.example.kylin.udf.PercentileUDF
EOF
# 4. 更新Kylin配置
echo "更新Kylin配置..."
KYLIN_CONF="$KYLIN_HOME/conf/kylin.properties"
# 添加UDF配置
if ! grep -q "kylin.query.udf.config.file" "$KYLIN_CONF"; then
echo "" >> "$KYLIN_CONF"
echo "# UDF配置" >> "$KYLIN_CONF"
echo "kylin.query.udf.config.file=$UDF_CONFIG_FILE" >> "$KYLIN_CONF"
echo "kylin.query.udf.massin=true" >> "$KYLIN_CONF"
fi
# 5. 重启Kylin服务
echo "重启Kylin服务..."
"$KYLIN_HOME/bin/kylin.sh" stop
sleep 5
"$KYLIN_HOME/bin/kylin.sh" start
echo "UDF部署完成！"
echo "可以使用以下函数："
echo "- CUSTOM_CONCAT(str1, str2, separator)"
echo "- MATH_POWER(base, exponent)"
echo "- DATE_FORMAT_CUSTOM(date, pattern)"
echo "- MEDIAN(column)"
echo "- STDDEV_CUSTOM(column)"
echo "- PERCENTILE_CUSTOM(column)"
10.2.5 UDF测试
// UDFTest.java - UDF单元测试
package com.example.kylin.udf;
import org.junit.Test;
import org.junit.Assert;
import java.util.Date;
import java.text.SimpleDateFormat;
public class UDFTest {
@Test
public void testCustomConcatUDF() {
CustomConcatUDF udf = new CustomConcatUDF();
// 测试正常情况
String result = udf.eval("Hello", "World", " ");
Assert.assertEquals("Hello World", result);
// 测试默认分隔符
result = udf.eval("Hello", "World");
Assert.assertEquals("Hello-World", result);
// 测试null值
result = udf.eval(null, "World", " ");
Assert.assertEquals("World", result);
result = udf.eval("Hello", null, " ");
Assert.assertEquals("Hello", result);
result = udf.eval(null, null, " ");
Assert.assertNull(result);
}
@Test
public void testMathPowerUDF() {
MathPowerUDF udf = new MathPowerUDF();
// 测试Double类型
Double result = udf.eval(2.0, 3.0);
Assert.assertEquals(8.0, result, 0.001);
// 测试Integer类型
result = udf.eval(2, 3);
Assert.assertEquals(8.0, result, 0.001);
// 测试null值
result = udf.eval(null, 3.0);
Assert.assertNull(result);
result = udf.eval(2.0, null);
Assert.assertNull(result);
}
@Test
public void testDateFormatUDF() throws Exception {
DateFormatUDF udf = new DateFormatUDF();
// 测试日期格式化
SimpleDateFormat sdf = new SimpleDateFormat("yyyy-MM-dd");
Date date = sdf.parse("2023-12-25");
String result = udf.eval(date, "yyyy/MM/dd");
Assert.assertEquals("2023/12/25", result);
// 测试字符串转换
result = udf.eval("2023-12-25", "yyyy-MM-dd", "MM/dd/yyyy");
Assert.assertEquals("12/25/2023", result);
// 测试null值
result = udf.eval(null, "yyyy-MM-dd");
Assert.assertNull(result);
}
@Test
public void testMedianUDF() {
MedianUDF udf = new MedianUDF();
MedianUDF.MedianAccumulator acc = udf.createAccumulator();
// 测试奇数个元素
udf.accumulate(acc, 1.0);
udf.accumulate(acc, 3.0);
udf.accumulate(acc, 2.0);
Double result = udf.getResult(acc);
Assert.assertEquals(2.0, result, 0.001);
// 测试偶数个元素
udf.accumulate(acc, 4.0);
result = udf.getResult(acc);
Assert.assertEquals(2.5, result, 0.001);
// 测试空集合
MedianUDF.MedianAccumulator emptyAcc = udf.createAccumulator();
result = udf.getResult(emptyAcc);
Assert.assertNull(result);
}
@Test
public void testStandardDeviationUDF() {
StandardDeviationUDF udf = new StandardDeviationUDF();
StandardDeviationUDF.StdDevAccumulator acc = udf.createAccumulator();
// 添加测试数据
udf.accumulate(acc, 1.0);
udf.accumulate(acc, 2.0);
udf.accumulate(acc, 3.0);
udf.accumulate(acc, 4.0);
udf.accumulate(acc, 5.0);
Double result = udf.getResult(acc);
// 标准差应该约为1.58
Assert.assertEquals(1.58, result, 0.01);
// 测试单个元素
StandardDeviationUDF.StdDevAccumulator singleAcc = udf.createAccumulator();
udf.accumulate(singleAcc, 1.0);
result = udf.getResult(singleAcc);
Assert.assertEquals(0.0, result, 0.001);
}
@Test
public void testPercentileUDF() {
PercentileUDF udf = new PercentileUDF(0.5); // 中位数
PercentileUDF.PercentileAccumulator acc = udf.createAccumulator();
// 添加测试数据
for (int i = 1; i <= 10; i++) {
udf.accumulate(acc, (double) i);
}
Double result = udf.getResult(acc);
Assert.assertEquals(5.5, result, 0.001);
// 测试90%分位数
PercentileUDF udf90 = new PercentileUDF(0.9);
PercentileUDF.PercentileAccumulator acc90 = udf90.createAccumulator();
for (int i = 1; i <= 10; i++) {
udf90.accumulate(acc90, (double) i);
}
result = udf90.getResult(acc90);
Assert.assertEquals(9.1, result, 0.001);
}
}
-- udf_examples.sql - UDF使用示例
-- 1. 使用自定义字符串连接函数
SELECT
product_name,
category,
CUSTOM_CONCAT(product_name, category, ' - ') as product_category
FROM product_dim
LIMIT 10;
-- 2. 使用数学幂函数
SELECT
product_id,
price,
MATH_POWER(price, 2) as price_squared,
MATH_POWER(price, 0.5) as price_sqrt
FROM product_dim
WHERE price > 0
LIMIT 10;
-- 3. 使用自定义日期格式化
SELECT
order_date,
DATE_FORMAT_CUSTOM(order_date, 'yyyy年MM月dd日') as formatted_date,
DATE_FORMAT_CUSTOM(order_date, 'E, MMM dd, yyyy') as english_date
FROM sales_fact
LIMIT 10;
-- 4. 使用中位数聚合函数
SELECT
category,
AVG(price) as avg_price,
MEDIAN(price) as median_price,
STDDEV_CUSTOM(price) as stddev_price
FROM product_dim
GROUP BY category;
-- 5. 使用百分位数函数
SELECT
category,
PERCENTILE_CUSTOM(price) as p50_price, -- 50%分位数（中位数）
MIN(price) as min_price,
MAX(price) as max_price
FROM product_dim
GROUP BY category;
-- 6. 复杂查询示例：销售分析
SELECT
DATE_FORMAT_CUSTOM(order_date, 'yyyy-MM') as month,
COUNT(*) as order_count,
SUM(sales_amount) as total_sales,
AVG(sales_amount) as avg_sales,
MEDIAN(sales_amount) as median_sales,
STDDEV_CUSTOM(sales_amount) as sales_stddev,
PERCENTILE_CUSTOM(sales_amount) as p50_sales
FROM sales_fact
WHERE order_date >= '2023-01-01'
GROUP BY DATE_FORMAT_CUSTOM(order_date, 'yyyy-MM')
ORDER BY month;
-- 7. 产品性能分析
SELECT
p.category,
CUSTOM_CONCAT(p.category, 'Top Products', ' - ') as category_label,
COUNT(DISTINCT p.product_id) as product_count,
SUM(s.sales_amount) as total_revenue,
MEDIAN(s.sales_amount) as median_order_value,
MATH_POWER(STDDEV_CUSTOM(s.sales_amount), 2) as sales_variance
FROM product_dim p
JOIN sales_fact s ON p.product_id = s.product_id
GROUP BY p.category
HAVING COUNT(DISTINCT p.product_id) >= 5
ORDER BY total_revenue DESC;
10.3 插件机制与扩展
10.3.1 插件架构概述
Kylin的插件机制允许开发者扩展系统功能，包括存储引擎、查询引擎、认证机制等。
// PluginInterface.java - 插件接口定义
package com.example.kylin.plugin;
import java.util.Map;
import java.util.Properties;
/**
* Kylin插件基础接口
*/
public interface KylinPlugin {
/**
* 插件初始化
* @param config 配置参数
*/
void initialize(Properties config);
/**
* 获取插件名称
* @return 插件名称
*/
String getName();
/**
* 获取插件版本
* @return 插件版本
*/
String getVersion();
/**
* 获取插件描述
* @return 插件描述
*/
String getDescription();
/**
* 插件启动
*/
void start();
/**
* 插件停止
*/
void stop();
/**
* 获取插件状态
* @return 插件状态
*/
PluginStatus getStatus();
/**
* 获取插件配置
* @return 配置信息
*/
Map<String, Object> getConfiguration();
}
/**
* 插件状态枚举
*/
enum PluginStatus {
INITIALIZED,
STARTING,
RUNNING,
STOPPING,
STOPPED,
ERROR
}
/**
* 存储插件接口
*/
public interface StoragePlugin extends KylinPlugin {
/**
* 创建存储实例
* @param config 存储配置
* @return 存储实例
*/
IStorageEngine createStorageEngine(Properties config);
/**
* 获取支持的存储类型
* @return 存储类型列表
*/
String[] getSupportedStorageTypes();
/**
* 验证存储配置
* @param config 配置
* @return 验证结果
*/
boolean validateConfiguration(Properties config);
}
/**
* 查询插件接口
*/
public interface QueryPlugin extends KylinPlugin {
/**
* 创建查询引擎
* @param config 查询配置
* @return 查询引擎实例
*/
IQueryEngine createQueryEngine(Properties config);
/**
* 获取支持的查询类型
* @return 查询类型列表
*/
String[] getSupportedQueryTypes();
/**
* 查询优化
* @param sql 原始SQL
* @return 优化后的SQL
*/
String optimizeQuery(String sql);
}
/**
* 认证插件接口
*/
public interface AuthenticationPlugin extends KylinPlugin {
/**
* 用户认证
* @param username 用户名
* @param password 密码
* @return 认证结果
*/
AuthenticationResult authenticate(String username, String password);
/**
* 获取用户权限
* @param username 用户名
* @return 权限列表
*/
String[] getUserPermissions(String username);
/**
* 验证用户权限
* @param username 用户名
* @param permission 权限
* @return 是否有权限
*/
boolean hasPermission(String username, String permission);
}
/**
* 认证结果
*/
class AuthenticationResult {
private boolean success;
private String message;
private Map<String, Object> userInfo;
public AuthenticationResult(boolean success, String message) {
this.success = success;
this.message = message;
}
// Getters and setters
public boolean isSuccess() { return success; }
public void setSuccess(boolean success) { this.success = success; }
public String getMessage() { return message; }
public void setMessage(String message) { this.message = message; }
public Map<String, Object> getUserInfo() { return userInfo; }
public void setUserInfo(Map<String, Object> userInfo) { this.userInfo = userInfo; }
}
10.3.2 自定义存储插件
// CustomStoragePlugin.java - 自定义存储插件
package com.example.kylin.plugin.storage;
import com.example.kylin.plugin.*;
import org.apache.kylin.storage.IStorageEngine;
import java.util.*;
import java.util.concurrent.ConcurrentHashMap;
/**
* Redis存储插件示例
*/
public class RedisStoragePlugin implements StoragePlugin {
private PluginStatus status = PluginStatus.INITIALIZED;
private Properties config;
private Map<String, Object> configuration = new ConcurrentHashMap<>();
@Override
public void initialize(Properties config) {
this.config = config;
// 解析配置
configuration.put("redis.host", config.getProperty("redis.host", "localhost"));
configuration.put("redis.port", Integer.parseInt(config.getProperty("redis.port", "6379")));
configuration.put("redis.database", Integer.parseInt(config.getProperty("redis.database", "0")));
configuration.put("redis.password", config.getProperty("redis.password"));
configuration.put("redis.timeout", Integer.parseInt(config.getProperty("redis.timeout", "5000")));
status = PluginStatus.INITIALIZED;
}
@Override
public String getName() {
return "Redis Storage Plugin";
}
@Override
public String getVersion() {
return "1.0.0";
}
@Override
public String getDescription() {
return "Redis-based storage engine for Kylin";
}
@Override
public void start() {
status = PluginStatus.STARTING;
try {
// 初始化Redis连接
initializeRedisConnection();
status = PluginStatus.RUNNING;
} catch (Exception e) {
status = PluginStatus.ERROR;
throw new RuntimeException("Failed to start Redis storage plugin", e);
}
}
@Override
public void stop() {
status = PluginStatus.STOPPING;
try {
// 关闭Redis连接
closeRedisConnection();
status = PluginStatus.STOPPED;
} catch (Exception e) {
status = PluginStatus.ERROR;
throw new RuntimeException("Failed to stop Redis storage plugin", e);
}
}
@Override
public PluginStatus getStatus() {
return status;
}
@Override
public Map<String, Object> getConfiguration() {
return new HashMap<>(configuration);
}
@Override
public IStorageEngine createStorageEngine(Properties config) {
return new RedisStorageEngine(config);
}
@Override
public String[] getSupportedStorageTypes() {
return new String[]{"redis", "redis-cluster"};
}
@Override
public boolean validateConfiguration(Properties config) {
// 验证必需的配置项
String host = config.getProperty("redis.host");
String port = config.getProperty("redis.port");
if (host == null || host.trim().isEmpty()) {
return false;
}
try {
Integer.parseInt(port);
} catch (NumberFormatException e) {
return false;
}
return true;
}
private void initializeRedisConnection() {
// Redis连接初始化逻辑
String host = (String) configuration.get("redis.host");
Integer port = (Integer) configuration.get("redis.port");
// 这里实现Redis连接逻辑
System.out.println("Connecting to Redis at " + host +":" + port);
}
private void closeRedisConnection() {
// Redis连接关闭逻辑
System.out.println("Closing Redis connection");
}
}
/**
* Redis存储引擎实现
*/
class RedisStorageEngine implements IStorageEngine {
private Properties config;
private RedisClient redisClient;
public RedisStorageEngine(Properties config) {
this.config = config;
this.redisClient = new RedisClient(config);
}
// 实现IStorageEngine接口方法
// 这里省略具体实现细节
}
/**
* Redis客户端封装
*/
class RedisClient {
private Properties config;
public RedisClient(Properties config) {
this.config = config;
// 初始化Redis客户端
}
public void set(String key, String value) {
// Redis SET操作
}
public String get(String key) {
// Redis GET操作
return null;
}
public void delete(String key) {
// Redis DELETE操作
}
public void close() {
// 关闭连接
}
}
10.3.3 自定义认证插件
// LDAPAuthenticationPlugin.java - LDAP认证插件
package com.example.kylin.plugin.auth;
import com.example.kylin.plugin.*;
import javax.naming.*;
import javax.naming.directory.*;
import java.util.*;
import java.util.concurrent.ConcurrentHashMap;
/**
* LDAP认证插件
*/
public class LDAPAuthenticationPlugin implements AuthenticationPlugin {
private PluginStatus status = PluginStatus.INITIALIZED;
private Properties config;
private Map<String, Object> configuration = new ConcurrentHashMap<>();
private DirContext ldapContext;
@Override
public void initialize(Properties config) {
this.config = config;
// 解析LDAP配置
configuration.put("ldap.url", config.getProperty("ldap.url", "ldap://localhost:389"));
configuration.put("ldap.base.dn", config.getProperty("ldap.base.dn", "dc=example,dc=com"));
configuration.put("ldap.user.dn.pattern", config.getProperty("ldap.user.dn.pattern", "uid={0},ou=users"));
configuration.put("ldap.group.search.base", config.getProperty("ldap.group.search.base", "ou=groups"));
configuration.put("ldap.group.search.filter", config.getProperty("ldap.group.search.filter", "member={0}"));
configuration.put("ldap.admin.user", config.getProperty("ldap.admin.user"));
configuration.put("ldap.admin.password", config.getProperty("ldap.admin.password"));
status = PluginStatus.INITIALIZED;
}
@Override
public String getName() {
return "LDAP Authentication Plugin";
}
@Override
public String getVersion() {
return "1.0.0";
}
@Override
public String getDescription() {
return "LDAP-based authentication for Kylin";
}
@Override
public void start() {
status = PluginStatus.STARTING;
try {
// 初始化LDAP连接
initializeLDAPConnection();
status = PluginStatus.RUNNING;
} catch (Exception e) {
status = PluginStatus.ERROR;
throw new RuntimeException("Failed to start LDAP authentication plugin", e);
}
}
@Override
public void stop() {
status = PluginStatus.STOPPING;
try {
// 关闭LDAP连接
if (ldapContext != null) {
ldapContext.close();
}
status = PluginStatus.STOPPED;
} catch (Exception e) {
status = PluginStatus.ERROR;
throw new RuntimeException("Failed to stop LDAP authentication plugin", e);
}
}
@Override
public PluginStatus getStatus() {
return status;
}
@Override
public Map<String, Object> getConfiguration() {
return new HashMap<>(configuration);
}
@Override
public AuthenticationResult authenticate(String username, String password) {
try {
// 构建用户DN
String userDnPattern = (String) configuration.get("ldap.user.dn.pattern");
String baseDn = (String) configuration.get("ldap.base.dn");
String userDn = userDnPattern.replace("{0}", username) + "," + baseDn;
// 尝试绑定用户
Hashtable<String, String> env = new Hashtable<>();
env.put(Context.INITIAL_CONTEXT_FACTORY, "com.sun.jndi.ldap.LdapCtxFactory");
env.put(Context.PROVIDER_URL, (String) configuration.get("ldap.url"));
env.put(Context.SECURITY_AUTHENTICATION, "simple");
env.put(Context.SECURITY_PRINCIPAL, userDn);
env.put(Context.SECURITY_CREDENTIALS, password);
DirContext userContext = new InitialDirContext(env);
userContext.close();
// 获取用户信息
Map<String, Object> userInfo = getUserInfo(username);
AuthenticationResult result = new AuthenticationResult(true, "Authentication successful");
result.setUserInfo(userInfo);
return result;
} catch (AuthenticationException e) {
return new AuthenticationResult(false, "Invalid username or password");
} catch (Exception e) {
return new AuthenticationResult(false, "Authentication error: " + e.getMessage());
}
}
@Override
public String[] getUserPermissions(String username) {
try {
List<String> permissions = new ArrayList<>();
// 搜索用户所属的组
String groupSearchBase = (String) configuration.get("ldap.group.search.base");
String groupSearchFilter = (String) configuration.get("ldap.group.search.filter");
String baseDn = (String) configuration.get("ldap.base.dn");
String userDnPattern = (String) configuration.get("ldap.user.dn.pattern");
String userDn = userDnPattern.replace("{0}", username) + "," + baseDn;
SearchControls searchControls = new SearchControls();
searchControls.setSearchScope(SearchControls.SUBTREE_SCOPE);
String filter = groupSearchFilter.replace("{0}", userDn);
NamingEnumeration<SearchResult> results = ldapContext.search(
groupSearchBase + "," + baseDn,
filter,
searchControls
);
while (results.hasMore()) {
SearchResult result = results.next();
String groupName = result.getName();
// 根据组名映射权限
permissions.addAll(mapGroupToPermissions(groupName));
}
return permissions.toArray(new String[0]);
} catch (Exception e) {
return new String[0];
}
}
@Override
public boolean hasPermission(String username, String permission) {
String[] permissions = getUserPermissions(username);
return Arrays.asList(permissions).contains(permission);
}
private void initializeLDAPConnection() throws NamingException {
Hashtable<String, String> env = new Hashtable<>();
env.put(Context.INITIAL_CONTEXT_FACTORY, "com.sun.jndi.ldap.LdapCtxFactory");
env.put(Context.PROVIDER_URL, (String) configuration.get("ldap.url"));
String adminUser = (String) configuration.get("ldap.admin.user");
String adminPassword = (String) configuration.get("ldap.admin.password");
if (adminUser != null && adminPassword != null) {
env.put(Context.SECURITY_AUTHENTICATION, "simple");
env.put(Context.SECURITY_PRINCIPAL, adminUser);
env.put(Context.SECURITY_CREDENTIALS, adminPassword);
}
ldapContext = new InitialDirContext(env);
}
private Map<String, Object> getUserInfo(String username) {
Map<String, Object> userInfo = new HashMap<>();
try {
String userDnPattern = (String) configuration.get("ldap.user.dn.pattern");
String baseDn = (String) configuration.get("ldap.base.dn");
String userDn = userDnPattern.replace("{0}", username) + "," + baseDn;
Attributes attributes = ldapContext.getAttributes(userDn);
// 提取用户属性
if (attributes.get("cn") != null) {
userInfo.put("displayName", attributes.get("cn").get());
}
if (attributes.get("mail") != null) {
userInfo.put("email", attributes.get("mail").get());
}
if (attributes.get("telephoneNumber") != null) {
userInfo.put("phone", attributes.get("telephoneNumber").get());
}
userInfo.put("username", username);
} catch (Exception e) {
// 忽略错误，返回基本信息
userInfo.put("username", username);
}
return userInfo;
}
private List<String> mapGroupToPermissions(String groupName) {
List<String> permissions = new ArrayList<>();
// 根据组名映射权限
if (groupName.contains("admin")) {
permissions.add("ADMIN");
permissions.add("READ");
permissions.add("WRITE");
permissions.add("MANAGE_CUBE");
permissions.add("MANAGE_PROJECT");
} else if (groupName.contains("developer")) {
permissions.add("READ");
permissions.add("WRITE");
permissions.add("MANAGE_CUBE");
} else if (groupName.contains("analyst")) {
permissions.add("READ");
}
return permissions;
}
}
10.3.4 插件管理器
// PluginManager.java - 插件管理器
package com.example.kylin.plugin;
import java.io.File;
import java.net.URL;
import java.net.URLClassLoader;
import java.util.*;
import java.util.concurrent.ConcurrentHashMap;
import java.util.jar.JarFile;
import java.util.jar.Manifest;
/**
* Kylin插件管理器
*/
public class PluginManager {
private static final String PLUGIN_MANIFEST_ATTRIBUTE = "Kylin-Plugin-Class";
private static final String PLUGIN_DIR = "plugins";
private Map<String, KylinPlugin> loadedPlugins = new ConcurrentHashMap<>();
private Map<String, ClassLoader> pluginClassLoaders = new ConcurrentHashMap<>();
private String pluginDirectory;
public PluginManager(String kylinHome) {
this.pluginDirectory = kylinHome + File.separator + PLUGIN_DIR;
}
/**
* 加载所有插件
*/
public void loadAllPlugins() {
File pluginDir = new File(pluginDirectory);
if (!pluginDir.exists() || !pluginDir.isDirectory()) {
System.out.println("Plugin directory not found: " + pluginDirectory);
return;
}
File[] jarFiles = pluginDir.listFiles((dir, name) -> name.endsWith(".jar"));
if (jarFiles != null) {
for (File jarFile : jarFiles) {
try {
loadPlugin(jarFile);
} catch (Exception e) {
System.err.println("Failed to load plugin: " + jarFile.getName() + ", " + e.getMessage());
}
}
}
}
/**
* 加载单个插件
*/
public void loadPlugin(File jarFile) throws Exception {
// 读取JAR文件的Manifest
JarFile jar = new JarFile(jarFile);
Manifest manifest = jar.getManifest();
if (manifest == null) {
throw new Exception("No manifest found in " + jarFile.getName());
}
String pluginClassName = manifest.getMainAttributes().getValue(PLUGIN_MANIFEST_ATTRIBUTE);
if (pluginClassName == null) {
throw new Exception("No plugin class specified in manifest of " + jarFile.getName());
}
// 创建插件类加载器
URL[] urls = {jarFile.toURI().toURL()};
URLClassLoader classLoader = new URLClassLoader(urls, this.getClass().getClassLoader());
// 加载插件类
Class<?> pluginClass = classLoader.loadClass(pluginClassName);
if (!KylinPlugin.class.isAssignableFrom(pluginClass)) {
throw new Exception("Plugin class must implement KylinPlugin interface");
}
// 创建插件实例
KylinPlugin plugin = (KylinPlugin) pluginClass.newInstance();
// 加载插件配置
Properties pluginConfig = loadPluginConfiguration(jarFile.getName());
// 初始化插件
plugin.initialize(pluginConfig);
// 注册插件
String pluginName = plugin.getName();
loadedPlugins.put(pluginName, plugin);
pluginClassLoaders.put(pluginName, classLoader);
System.out.println("Loaded plugin: " + pluginName + " v" + plugin.getVersion());
jar.close();
}
/**
* 启动插件
*/
public void startPlugin(String pluginName) {
KylinPlugin plugin = loadedPlugins.get(pluginName);
if (plugin == null) {
throw new IllegalArgumentException("Plugin not found: " + pluginName);
}
if (plugin.getStatus() == PluginStatus.INITIALIZED ||
plugin.getStatus() == PluginStatus.STOPPED) {
plugin.start();
System.out.println("Started plugin: " + pluginName);
}
}
/**
* 停止插件
*/
public void stopPlugin(String pluginName) {
KylinPlugin plugin = loadedPlugins.get(pluginName);
if (plugin == null) {
throw new IllegalArgumentException("Plugin not found: " + pluginName);
}
if (plugin.getStatus() == PluginStatus.RUNNING) {
plugin.stop();
System.out.println("Stopped plugin: " + pluginName);
}
}
/**
* 卸载插件
*/
public void unloadPlugin(String pluginName) {
KylinPlugin plugin = loadedPlugins.get(pluginName);
if (plugin != null) {
// 先停止插件
if (plugin.getStatus() == PluginStatus.RUNNING) {
plugin.stop();
}
// 移除插件
loadedPlugins.remove(pluginName);
// 关闭类加载器
ClassLoader classLoader = pluginClassLoaders.remove(pluginName);
if (classLoader instanceof URLClassLoader) {
try {
((URLClassLoader) classLoader).close();
} catch (Exception e) {
System.err.println("Failed to close class loader for plugin: " + pluginName);
}
}
System.out.println("Unloaded plugin: " + pluginName);
}
}
/**
* 启动所有插件
*/
public void startAllPlugins() {
for (String pluginName : loadedPlugins.keySet()) {
try {
startPlugin(pluginName);
} catch (Exception e) {
System.err.println("Failed to start plugin: " + pluginName + ", " + e.getMessage());
}
}
}
/**
* 停止所有插件
*/
public void stopAllPlugins() {
for (String pluginName : loadedPlugins.keySet()) {
try {
stopPlugin(pluginName);
} catch (Exception e) {
System.err.println("Failed to stop plugin: " + pluginName + ", " + e.getMessage());
}
}
}
/**
* 获取插件列表
*/
public List<PluginInfo> getPluginList() {
List<PluginInfo> pluginList = new ArrayList<>();
for (KylinPlugin plugin : loadedPlugins.values()) {
PluginInfo info = new PluginInfo();
info.setName(plugin.getName());
info.setVersion(plugin.getVersion());
info.setDescription(plugin.getDescription());
info.setStatus(plugin.getStatus());
info.setConfiguration(plugin.getConfiguration());
pluginList.add(info);
}
return pluginList;
}
/**
* 获取指定类型的插件
*/
@SuppressWarnings("unchecked")
public <T extends KylinPlugin> List<T> getPluginsByType(Class<T> pluginType) {
List<T> plugins = new ArrayList<>();
for (KylinPlugin plugin : loadedPlugins.values()) {
if (pluginType.isAssignableFrom(plugin.getClass())) {
plugins.add((T) plugin);
}
}
return plugins;
}
/**
* 获取插件
*/
public KylinPlugin getPlugin(String pluginName) {
return loadedPlugins.get(pluginName);
}
/**
* 重新加载插件
*/
public void reloadPlugin(String pluginName) {
// 先卸载插件
unloadPlugin(pluginName);
// 重新加载插件
File pluginDir = new File(pluginDirectory);
File[] jarFiles = pluginDir.listFiles((dir, name) ->
name.endsWith(".jar") && name.contains(pluginName));
if (jarFiles != null && jarFiles.length > 0) {
try {
loadPlugin(jarFiles[0]);
startPlugin(pluginName);
} catch (Exception e) {
System.err.println("Failed to reload plugin: " + pluginName + ", " + e.getMessage());
}
}
}
/**
* 加载插件配置
*/
private Properties loadPluginConfiguration(String jarFileName) {
Properties config = new Properties();
// 从配置文件加载插件配置
String configFileName = jarFileName.replace(".jar", ".properties");
File configFile = new File(pluginDirectory, configFileName);
if (configFile.exists()) {
try (FileInputStream fis = new FileInputStream(configFile)) {
config.load(fis);
} catch (Exception e) {
System.err.println("Failed to load plugin configuration: " + configFileName);
}
}
return config;
}
/**
* 关闭插件管理器
*/
public void shutdown() {
stopAllPlugins();
for (String pluginName : new ArrayList<>(loadedPlugins.keySet())) {
unloadPlugin(pluginName);
}
}
}
/**
* 插件信息
*/
class PluginInfo {
private String name;
private String version;
private String description;
private PluginStatus status;
private Map<String, Object> configuration;
// Getters and setters
public String getName() { return name; }
public void setName(String name) { this.name = name; }
public String getVersion() { return version; }
public void setVersion(String version) { this.version = version; }
public String getDescription() { return description; }
public void setDescription(String description) { this.description = description; }
public PluginStatus getStatus() { return status; }
public void setStatus(PluginStatus status) { this.status = status; }
public Map<String, Object> getConfiguration() { return configuration; }
public void setConfiguration(Map<String, Object> configuration) { this.configuration = configuration; }
}
10.3.5 插件配置和部署
#!/bin/bash
# deploy_plugin.sh - 插件部署脚本
set -e
# 配置
KYLIN_HOME=${KYLIN_HOME:-"/opt/kylin"}
PLUGIN_DIR="$KYLIN_HOME/plugins"
PLUGIN_JAR="$1"
PLUGIN_CONFIG="$2"
if [ -z "$PLUGIN_JAR" ]; then
echo "用法: $0 <plugin.jar> [plugin.properties]"
exit 1
fi
if [ ! -f "$PLUGIN_JAR" ]; then
echo "错误：插件JAR文件不存在: $PLUGIN_JAR"
exit 1
fi
echo "开始部署插件: $PLUGIN_JAR"
# 1. 创建插件目录
mkdir -p "$PLUGIN_DIR"
# 2. 复制插件JAR文件
cp "$PLUGIN_JAR" "$PLUGIN_DIR/"
echo "已复制插件JAR文件到: $PLUGIN_DIR"
# 3. 复制配置文件（如果提供）
if [ -n "$PLUGIN_CONFIG" ] && [ -f "$PLUGIN_CONFIG" ]; then
cp "$PLUGIN_CONFIG" "$PLUGIN_DIR/"
echo "已复制插件配置文件到: $PLUGIN_DIR"
fi
# 4. 验证插件JAR文件
echo "验证插件JAR文件..."
jar tf "$PLUGIN_JAR" | grep -q "META-INF/MANIFEST.MF" || {
echo "错误：插件JAR文件缺少MANIFEST.MF"
exit 1
}
# 检查Manifest中的插件类
PLUGIN_CLASS=$(jar xf "$PLUGIN_JAR" META-INF/MANIFEST.MF -O | grep "Kylin-Plugin-Class:" | cut -d' ' -f2 | tr -d '\r')
if [ -z "$PLUGIN_CLASS" ]; then
echo "错误：插件JAR文件的MANIFEST.MF中缺少Kylin-Plugin-Class属性"
exit 1
fi
echo "插件类: $PLUGIN_CLASS"
# 5. 重启Kylin服务以加载插件
echo "重启Kylin服务以加载插件..."
"$KYLIN_HOME/bin/kylin.sh" stop
sleep 5
"$KYLIN_HOME/bin/kylin.sh" start
echo "插件部署完成！"
echo "插件将在Kylin启动时自动加载"
# redis-storage-plugin.properties - Redis存储插件配置
# Redis服务器配置
redis.host=localhost
redis.port=6379
redis.database=0
redis.password=
redis.timeout=5000
redis.max.connections=100
redis.min.idle=10
redis.max.idle=50
# 集群配置（可选）
redis.cluster.enabled=false
redis.cluster.nodes=localhost:7000,localhost:7001,localhost:7002
redis.cluster.max.redirects=3
# 缓存配置
redis.cache.ttl=3600
redis.cache.prefix=kylin:
redis.cache.compression=true
# 性能配置
redis.pipeline.size=100
redis.batch.size=1000
redis.async.enabled=true
<!-- MANIFEST.MF示例 -->
Manifest-Version: 1.0
Kylin-Plugin-Class: com.example.kylin.plugin.storage.RedisStoragePlugin
Plugin-Name: Redis Storage Plugin
Plugin-Version: 1.0.0
Plugin-Description: Redis-based storage engine for Apache Kylin
Plugin-Author: Example Corp
Plugin-Website: https://example.com
Main-Class: com.example.kylin.plugin.storage.RedisStoragePlugin
10.4 存储引擎扩展
10.4.1 存储引擎架构
// CustomStorageEngine.java - 自定义存储引擎
package com.example.kylin.storage;
import org.apache.kylin.storage.*;
import org.apache.kylin.metadata.model.*;
import org.apache.kylin.metadata.realization.*;
import java.util.*;
import java.util.concurrent.*;
/**
* 分布式存储引擎实现
*/
public class DistributedStorageEngine implements IStorageEngine {
private StorageConfiguration config;
private ConnectionPool connectionPool;
private QueryOptimizer queryOptimizer;
private CacheManager cacheManager;
private MetricsCollector metricsCollector;
public DistributedStorageEngine(StorageConfiguration config) {
this.config = config;
this.connectionPool = new ConnectionPool(config);
this.queryOptimizer = new QueryOptimizer(config);
this.cacheManager = new CacheManager(config);
this.metricsCollector = new MetricsCollector();
}
@Override
public IStorageQuery createQuery(IRealization realization) {
return new DistributedStorageQuery(realization, this);
}
@Override
public void buildCube(CubeInstance cube, CubeBuildTypeEnum buildType) {
try {
metricsCollector.startTimer("cube.build");
// 获取构建配置
CubeBuildConfig buildConfig = createBuildConfig(cube, buildType);
// 并行构建Cube段
List<CubeSegment> segments = cube.getSegments();
ExecutorService executor = Executors.newFixedThreadPool(
config.getBuildParallelism()
);
List<Future<Void>> futures = new ArrayList<>();
for (CubeSegment segment : segments) {
Future<Void> future = executor.submit(() -> {
buildSegment(segment, buildConfig);
return null;
});
futures.add(future);
}
// 等待所有段构建完成
for (Future<Void> future : futures) {
future.get();
}
executor.shutdown();
// 更新元数据
updateCubeMetadata(cube);
} catch (Exception e) {
throw new RuntimeException("Failed to build cube: " + cube.getName(), e);
} finally {
metricsCollector.stopTimer("cube.build");
}
}
@Override
public void deleteCube(CubeInstance cube) {
try {
metricsCollector.startTimer("cube.delete");
// 删除所有段数据
for (CubeSegment segment : cube.getSegments()) {
deleteSegmentData(segment);
}
// 清理缓存
cacheManager.evictCube(cube.getName());
// 删除元数据
deleteCubeMetadata(cube);
} catch (Exception e) {
throw new RuntimeException("Failed to delete cube: " + cube.getName(), e);
} finally {
metricsCollector.stopTimer("cube.delete");
}
}
@Override
public long getStorageSize(String cubeName) {
try {
return calculateStorageSize(cubeName);
} catch (Exception e) {
return 0L;
}
}
@Override
public void cleanupStorage() {
try {
metricsCollector.startTimer("storage.cleanup");
// 清理过期数据
cleanupExpiredData();
// 压缩存储
compactStorage();
// 清理缓存
cacheManager.cleanup();
} catch (Exception e) {
throw new RuntimeException("Failed to cleanup storage", e);
} finally {
metricsCollector.stopTimer("storage.cleanup");
}
}
/**
* 构建Cube段
*/
private void buildSegment(CubeSegment segment, CubeBuildConfig config) {
try {
// 1. 读取源数据
DataReader dataReader = createDataReader(segment);
// 2. 数据预处理
DataProcessor processor = new DataProcessor(config);
// 3. 聚合计算
AggregationEngine aggregator = new AggregationEngine(config);
// 4. 数据写入
DataWriter dataWriter = createDataWriter(segment);
// 流式处理数据
dataReader.read(data -> {
// 预处理
ProcessedData processed = processor.process(data);
// 聚合
AggregatedData aggregated = aggregator.aggregate(processed);
// 写入
dataWriter.write(aggregated);
});
// 完成写入
dataWriter.flush();
dataWriter.close();
} catch (Exception e) {
throw new RuntimeException("Failed to build segment: " + segment.getName(), e);
}
}
/**
* 创建数据读取器
*/
private DataReader createDataReader(CubeSegment segment) {
String sourceType = config.getSourceType();
switch (sourceType.toLowerCase()) {
case "hive":
return new HiveDataReader(segment, config);
case "kafka":
return new KafkaDataReader(segment, config);
case "hdfs":
return new HDFSDataReader(segment, config);
default:
throw new IllegalArgumentException("Unsupported source type: " + sourceType);
}
}
/**
* 创建数据写入器
*/
private DataWriter createDataWriter(CubeSegment segment) {
String storageType = config.getStorageType();
switch (storageType.toLowerCase()) {
case "hbase":
return new HBaseDataWriter(segment, config, connectionPool);
case "parquet":
return new ParquetDataWriter(segment, config);
case "redis":
return new RedisDataWriter(segment, config, connectionPool);
case "elasticsearch":
return new ElasticsearchDataWriter(segment, config, connectionPool);
default:
throw new IllegalArgumentException("Unsupported storage type: " + storageType);
}
}
/**
* 计算存储大小
*/
private long calculateStorageSize(String cubeName) {
long totalSize = 0L;
try {
// 根据存储类型计算大小
String storageType = config.getStorageType();
switch (storageType.toLowerCase()) {
case "hbase":
totalSize = calculateHBaseSize(cubeName);
break;
case "parquet":
totalSize = calculateParquetSize(cubeName);
break;
case "redis":
totalSize = calculateRedisSize(cubeName);
break;
default:
totalSize = 0L;
}
} catch (Exception e) {
// 记录错误但不抛出异常
System.err.println("Failed to calculate storage size for cube: " + cubeName);
}
return totalSize;
}
private long calculateHBaseSize(String cubeName) {
// HBase大小计算逻辑
return 0L;
}
private long calculateParquetSize(String cubeName) {
// Parquet文件大小计算逻辑
return 0L;
}
private long calculateRedisSize(String cubeName) {
// Redis内存使用计算逻辑
return 0L;
}
/**
* 清理过期数据
*/
private void cleanupExpiredData() {
// 实现过期数据清理逻辑
}
/**
* 压缩存储
*/
private void compactStorage() {
// 实现存储压缩逻辑
}
/**
* 更新Cube元数据
*/
private void updateCubeMetadata(CubeInstance cube) {
// 实现元数据更新逻辑
}
/**
* 删除段数据
*/
private void deleteSegmentData(CubeSegment segment) {
// 实现段数据删除逻辑
}
/**
* 删除Cube元数据
*/
private void deleteCubeMetadata(CubeInstance cube) {
// 实现元数据删除逻辑
}
/**
* 创建构建配置
*/
private CubeBuildConfig createBuildConfig(CubeInstance cube, CubeBuildTypeEnum buildType) {
CubeBuildConfig config = new CubeBuildConfig();
config.setCube(cube);
config.setBuildType(buildType);
config.setParallelism(this.config.getBuildParallelism());
config.setBatchSize(this.config.getBatchSize());
return config;
}
}
/**
* 存储配置类
*/
class StorageConfiguration {
private String storageType;
private String sourceType;
private int buildParallelism = 4;
private int batchSize = 10000;
private Properties properties;
// Getters and setters
public String getStorageType() { return storageType; }
public void setStorageType(String storageType) { this.storageType = storageType; }
public String getSourceType() { return sourceType; }
public void setSourceType(String sourceType) { this.sourceType = sourceType; }
public int getBuildParallelism() { return buildParallelism; }
public void setBuildParallelism(int buildParallelism) { this.buildParallelism = buildParallelism; }
public int getBatchSize() { return batchSize; }
public void setBatchSize(int batchSize) { this.batchSize = batchSize; }
public Properties getProperties() { return properties; }
public void setProperties(Properties properties) { this.properties = properties; }
}
/**
* Cube构建配置
*/
class CubeBuildConfig {
private CubeInstance cube;
private CubeBuildTypeEnum buildType;
private int parallelism;
private int batchSize;
// Getters and setters
public CubeInstance getCube() { return cube; }
public void setCube(CubeInstance cube) { this.cube = cube; }
public CubeBuildTypeEnum getBuildType() { return buildType; }
public void setBuildType(CubeBuildTypeEnum buildType) { this.buildType = buildType; }
public int getParallelism() { return parallelism; }
public void setParallelism(int parallelism) { this.parallelism = parallelism; }
public int getBatchSize() { return batchSize; }
public void setBatchSize(int batchSize) { this.batchSize = batchSize; }
}
10.4.2 数据读写器实现
// DataReaderWriter.java - 数据读写器实现
package com.example.kylin.storage.io;
import java.util.function.Consumer;
import java.util.concurrent.BlockingQueue;
import java.util.concurrent.LinkedBlockingQueue;
/**
* 数据读取器接口
*/
interface DataReader {
void read(Consumer<RawData> dataConsumer);
void close();
}
/**
* 数据写入器接口
*/
interface DataWriter {
void write(AggregatedData data);
void flush();
void close();
}
/**
* Kafka数据读取器
*/
class KafkaDataReader implements DataReader {
private CubeSegment segment;
private StorageConfiguration config;
private KafkaConsumer<String, String> consumer;
public KafkaDataReader(CubeSegment segment, StorageConfiguration config) {
this.segment = segment;
this.config = config;
this.consumer = createKafkaConsumer();
}
@Override
public void read(Consumer<RawData> dataConsumer) {
try {
// 订阅主题
String topic = config.getProperties().getProperty("kafka.topic");
consumer.subscribe(Arrays.asList(topic));
// 持续读取数据
while (true) {
ConsumerRecords<String, String> records = consumer.poll(Duration.ofMillis(1000));
for (ConsumerRecord<String, String> record : records) {
RawData data = parseRecord(record);
if (data != null) {
dataConsumer.accept(data);
}
}
// 检查是否应该停止
if (shouldStop()) {
break;
}
}
} catch (Exception e) {
throw new RuntimeException("Failed to read from Kafka", e);
}
}
@Override
public void close() {
if (consumer != null) {
consumer.close();
}
}
private KafkaConsumer<String, String> createKafkaConsumer() {
Properties props = new Properties();
props.put("bootstrap.servers", config.getProperties().getProperty("kafka.bootstrap.servers"));
props.put("group.id", "kylin-cube-builder");
props.put("key.deserializer", "org.apache.kafka.common.serialization.StringDeserializer");
props.put("value.deserializer", "org.apache.kafka.common.serialization.StringDeserializer");
return new KafkaConsumer<>(props);
}
private RawData parseRecord(ConsumerRecord<String, String> record) {
// 解析Kafka记录为原始数据
return new RawData(record.value());
}
private boolean shouldStop() {
// 检查停止条件
return false;
}
}
/**
* Elasticsearch数据写入器
*/
class ElasticsearchDataWriter implements DataWriter {
private CubeSegment segment;
private StorageConfiguration config;
private ConnectionPool connectionPool;
private ElasticsearchClient client;
private BlockingQueue<AggregatedData> writeQueue;
private Thread writerThread;
private volatile boolean running = true;
public ElasticsearchDataWriter(CubeSegment segment, StorageConfiguration config, ConnectionPool connectionPool) {
this.segment = segment;
this.config = config;
this.connectionPool = connectionPool;
this.client = connectionPool.getElasticsearchClient();
this.writeQueue = new LinkedBlockingQueue<>(config.getBatchSize());
// 启动写入线程
startWriterThread();
}
@Override
public void write(AggregatedData data) {
try {
writeQueue.put(data);
} catch (InterruptedException e) {
Thread.currentThread().interrupt();
throw new RuntimeException("Interrupted while writing data", e);
}
}
@Override
public void flush() {
// 等待队列清空
while (!writeQueue.isEmpty()) {
try {
Thread.sleep(100);
} catch (InterruptedException e) {
Thread.currentThread().interrupt();
break;
}
}
// 刷新Elasticsearch索引
client.indices().refresh(RefreshRequest.of(r -> r.index(getIndexName())));
}
@Override
public void close() {
running = false;
if (writerThread != null) {
try {
writerThread.join(5000);
} catch (InterruptedException e) {
Thread.currentThread().interrupt();
}
}
connectionPool.returnElasticsearchClient(client);
}
private void startWriterThread() {
writerThread = new Thread(() -> {
List<AggregatedData> batch = new ArrayList<>();
while (running || !writeQueue.isEmpty()) {
try {
// 收集批次数据
AggregatedData data = writeQueue.poll(1, TimeUnit.SECONDS);
if (data != null) {
batch.add(data);
}
// 批量写入
if (batch.size() >= config.getBatchSize() || (!running && !batch.isEmpty())) {
writeBatch(batch);
batch.clear();
}
} catch (InterruptedException e) {
Thread.currentThread().interrupt();
break;
} catch (Exception e) {
System.err.println("Error writing batch to Elasticsearch: " + e.getMessage());
}
}
});
writerThread.start();
}
private void writeBatch(List<AggregatedData> batch) {
try {
BulkRequest.Builder bulkBuilder = new BulkRequest.Builder();
for (AggregatedData data : batch) {
IndexRequest<Map<String, Object>> indexRequest = IndexRequest.of(i -> i
.index(getIndexName())
.document(data.toMap())
);
bulkBuilder.operations(op -> op.index(indexRequest));
}
BulkResponse response = client.bulk(bulkBuilder.build());
if (response.errors()) {
System.err.println("Bulk write had errors");
}
} catch (Exception e) {
throw new RuntimeException("Failed to write batch to Elasticsearch", e);
}
}
private String getIndexName() {
return "kylin_" + segment.getCubeInstance().getName().toLowerCase() + "_" + segment.getName();
}
}
/**
* 原始数据类
*/
class RawData {
private String content;
private Map<String, Object> fields;
public RawData(String content) {
this.content = content;
this.fields = parseContent(content);
}
private Map<String, Object> parseContent(String content) {
// 解析内容为字段映射
Map<String, Object> fields = new HashMap<>();
// 实现解析逻辑
return fields;
}
public String getContent() { return content; }
public Map<String, Object> getFields() { return fields; }
}
/**
* 处理后数据类
*/
class ProcessedData {
private Map<String, Object> dimensions;
private Map<String, Object> measures;
public ProcessedData(Map<String, Object> dimensions, Map<String, Object> measures) {
this.dimensions = dimensions;
this.measures = measures;
}
public Map<String, Object> getDimensions() { return dimensions; }
public Map<String, Object> getMeasures() { return measures; }
}
/**
* 聚合数据类
*/
class AggregatedData {
private Map<String, Object> dimensionKey;
private Map<String, Object> measureValues;
public AggregatedData(Map<String, Object> dimensionKey, Map<String, Object> measureValues) {
this.dimensionKey = dimensionKey;
this.measureValues = measureValues;
}
public Map<String, Object> getDimensionKey() { return dimensionKey; }
public Map<String, Object> getMeasureValues() { return measureValues; }
public Map<String, Object> toMap() {
Map<String, Object> result = new HashMap<>();
result.putAll(dimensionKey);
result.putAll(measureValues);
return result;
}
}
10.5 高级调优技巧
10.5.1 内存优化
// MemoryOptimizer.java - 内存优化器
package com.example.kylin.optimization;
import java.lang.management.ManagementFactory;
import java.lang.management.MemoryMXBean;
import java.lang.management.MemoryUsage;
import java.util.concurrent.Executors;
import java.util.concurrent.ScheduledExecutorService;
import java.util.concurrent.TimeUnit;
/**
* Kylin内存优化器
*/
public class MemoryOptimizer {
private static final double MEMORY_THRESHOLD = 0.8; // 80%内存使用阈值
private static final long CLEANUP_INTERVAL = 30; // 30秒清理间隔
private MemoryMXBean memoryBean;
private ScheduledExecutorService scheduler;
private CacheManager cacheManager;
private QueryResultCache queryCache;
private MetadataCache metadataCache;
public MemoryOptimizer(CacheManager cacheManager, QueryResultCache queryCache, MetadataCache metadataCache) {
this.memoryBean = ManagementFactory.getMemoryMXBean();
this.scheduler = Executors.newScheduledThreadPool(1);
this.cacheManager = cacheManager;
this.queryCache = queryCache;
this.metadataCache = metadataCache;
}
/**
* 启动内存监控
*/
public void startMonitoring() {
scheduler.scheduleAtFixedRate(this::checkMemoryUsage, 0, CLEANUP_INTERVAL, TimeUnit.SECONDS);
}
/**
* 停止内存监控
*/
public void stopMonitoring() {
scheduler.shutdown();
}
/**
* 检查内存使用情况
*/
private void checkMemoryUsage() {
MemoryUsage heapUsage = memoryBean.getHeapMemoryUsage();
double usageRatio = (double) heapUsage.getUsed() / heapUsage.getMax();
System.out.println(String.format("Heap memory usage: %.2f%% (%d MB / %d MB)",
usageRatio * 100,
heapUsage.getUsed() / 1024 / 1024,
heapUsage.getMax() / 1024 / 1024));
if (usageRatio > MEMORY_THRESHOLD) {
System.out.println("Memory usage exceeds threshold, triggering cleanup...");
performMemoryCleanup();
}
}
/**
* 执行内存清理
*/
private void performMemoryCleanup() {
try {
// 1. 清理查询结果缓存
int queryEvicted = queryCache.evictLRU(0.3); // 清理30%的LRU条目
System.out.println("Evicted " + queryEvicted + " query cache entries");
// 2. 清理元数据缓存
int metadataEvicted = metadataCache.evictExpired();
System.out.println("Evicted " + metadataEvicted + " expired metadata entries");
// 3. 清理临时对象
cacheManager.clearTemporaryObjects();
// 4. 强制垃圾回收
System.gc();
// 5. 等待GC完成并检查效果
Thread.sleep(1000);
MemoryUsage afterCleanup = memoryBean.getHeapMemoryUsage();
double newUsageRatio = (double) afterCleanup.getUsed() / afterCleanup.getMax();
System.out.println(String.format("Memory cleanup completed. New usage: %.2f%%",
newUsageRatio * 100));
} catch (Exception e) {
System.err.println("Error during memory cleanup: " + e.getMessage());
}
}
/**
* 获取内存使用统计
*/
public MemoryStats getMemoryStats() {
MemoryUsage heapUsage = memoryBean.getHeapMemoryUsage();
MemoryUsage nonHeapUsage = memoryBean.getNonHeapMemoryUsage();
MemoryStats stats = new MemoryStats();
stats.setHeapUsed(heapUsage.getUsed());
stats.setHeapMax(heapUsage.getMax());
stats.setHeapCommitted(heapUsage.getCommitted());
stats.setNonHeapUsed(nonHeapUsage.getUsed());
stats.setNonHeapMax(nonHeapUsage.getMax());
stats.setNonHeapCommitted(nonHeapUsage.getCommitted());
return stats;
}
/**
* 优化JVM参数建议
*/
public List<String> getJVMOptimizationSuggestions() {
List<String> suggestions = new ArrayList<>();
MemoryUsage heapUsage = memoryBean.getHeapMemoryUsage();
long heapMaxMB = heapUsage.getMax() / 1024 / 1024;
if (heapMaxMB < 4096) {
suggestions.add("建议增加堆内存大小到至少4GB: -Xmx4g");
}
suggestions.add("使用G1垃圾收集器: -XX:+UseG1GC");
suggestions.add("设置G1最大暂停时间: -XX:MaxGCPauseMillis=200");
suggestions.add("启用大页面: -XX:+UseLargePages");
suggestions.add("优化新生代大小: -XX:NewRatio=2");
suggestions.add("启用压缩指针: -XX:+UseCompressedOops");
suggestions.add("设置元空间大小: -XX:MetaspaceSize=256m -XX:MaxMetaspaceSize=512m");
return suggestions;
}
}
/**
* 内存统计信息
*/
class MemoryStats {
private long heapUsed;
private long heapMax;
private long heapCommitted;
private long nonHeapUsed;
private long nonHeapMax;
private long nonHeapCommitted;
// Getters and setters
public long getHeapUsed() { return heapUsed; }
public void setHeapUsed(long heapUsed) { this.heapUsed = heapUsed; }
public long getHeapMax() { return heapMax; }
public void setHeapMax(long heapMax) { this.heapMax = heapMax; }
public long getHeapCommitted() { return heapCommitted; }
public void setHeapCommitted(long heapCommitted) { this.heapCommitted = heapCommitted; }
public long getNonHeapUsed() { return nonHeapUsed; }
public void setNonHeapUsed(long nonHeapUsed) { this.nonHeapUsed = nonHeapUsed; }
public long getNonHeapMax() { return nonHeapMax; }
public void setNonHeapMax(long nonHeapMax) { this.nonHeapMax = nonHeapMax; }
public long getNonHeapCommitted() { return nonHeapCommitted; }
public void setNonHeapCommitted(long nonHeapCommitted) { this.nonHeapCommitted = nonHeapCommitted; }
public double getHeapUsageRatio() {
return heapMax > 0 ? (double) heapUsed / heapMax : 0.0;
}
public double getNonHeapUsageRatio() {
return nonHeapMax > 0 ? (double) nonHeapUsed / nonHeapMax : 0.0;
}
}
10.5.2 查询优化
// QueryOptimizer.java - 查询优化器
package com.example.kylin.optimization;
import java.util.concurrent.ConcurrentHashMap;
import java.util.concurrent.atomic.AtomicLong;
/**
* Kylin查询优化器
*/
public class QueryOptimizer {
private Map<String, QueryStats> queryStatsMap = new ConcurrentHashMap<>();
private QueryPlanCache planCache = new QueryPlanCache();
private QueryRewriter queryRewriter = new QueryRewriter();
private CubeSelector cubeSelector = new CubeSelector();
/**
* 优化查询
*/
public OptimizedQuery optimizeQuery(String sql, QueryContext context) {
try {
// 1. 查询重写
String rewrittenSql = queryRewriter.rewrite(sql, context);
// 2. 选择最优Cube
List<CubeInstance> candidateCubes = cubeSelector.selectCandidates(rewrittenSql, context);
CubeInstance bestCube = cubeSelector.selectBest(candidateCubes, context);
// 3. 生成执行计划
QueryPlan plan = generateExecutionPlan(rewrittenSql, bestCube, context);
// 4. 缓存执行计划
String planKey = generatePlanKey(rewrittenSql, bestCube.getName());
planCache.put(planKey, plan);
// 5. 记录查询统计
recordQueryStats(sql, plan);
return new OptimizedQuery(rewrittenSql, plan, bestCube);
} catch (Exception e) {
throw new RuntimeException("Failed to optimize query: " + sql, e);
}
}
/**
* 查询重写器
*/
private static class QueryRewriter {
public String rewrite(String sql, QueryContext context) {
String rewritten = sql;
// 1. 谓词下推
rewritten = pushDownPredicates(rewritten);
// 2. 投影下推
rewritten = pushDownProjections(rewritten);
// 3. 聚合优化
rewritten = optimizeAggregations(rewritten);
// 4. 子查询优化
rewritten = optimizeSubqueries(rewritten);
// 5. JOIN优化
rewritten = optimizeJoins(rewritten);
return rewritten;
}
private String pushDownPredicates(String sql) {
// 实现谓词下推逻辑
// 将WHERE条件尽可能推到数据源层
return sql;
}
private String pushDownProjections(String sql) {
// 实现投影下推逻辑
// 只选择需要的列
return sql;
}
private String optimizeAggregations(String sql) {
// 优化聚合函数
// 使用预计算的聚合结果
return sql;
}
private String optimizeSubqueries(String sql) {
// 子查询优化
// 将相关子查询转换为JOIN
return sql;
}
private String optimizeJoins(String sql) {
// JOIN优化
// 选择最优的JOIN顺序和算法
return sql;
}
}
/**
* Cube选择器
*/
private static class CubeSelector {
public List<CubeInstance> selectCandidates(String sql, QueryContext context) {
List<CubeInstance> candidates = new ArrayList<>();
// 解析SQL获取所需的维度和度量
Set<String> requiredDimensions = extractDimensions(sql);
Set<String> requiredMeasures = extractMeasures(sql);
// 查找包含所需维度和度量的Cube
for (CubeInstance cube : context.getAvailableCubes()) {
if (cubeContainsRequiredFields(cube, requiredDimensions, requiredMeasures)) {
candidates.add(cube);
}
}
return candidates;
}
public CubeInstance selectBest(List<CubeInstance> candidates, QueryContext context) {
if (candidates.isEmpty()) {
throw new RuntimeException("No suitable cube found for the query");
}
// 评分标准：数据新鲜度、查询覆盖度、存储大小
CubeInstance bestCube = null;
double bestScore = -1;
for (CubeInstance cube : candidates) {
double score = calculateCubeScore(cube, context);
if (score > bestScore) {
bestScore = score;
bestCube = cube;
}
}
return bestCube;
}
private Set<String> extractDimensions(String sql) {
// 从SQL中提取维度字段
Set<String> dimensions = new HashSet<>();
// 实现SQL解析逻辑
return dimensions;
}
private Set<String> extractMeasures(String sql) {
// 从SQL中提取度量字段
Set<String> measures = new HashSet<>();
// 实现SQL解析逻辑
return measures;
}
private boolean cubeContainsRequiredFields(CubeInstance cube, Set<String> dimensions, Set<String> measures) {
// 检查Cube是否包含所需的维度和度量
return true; // 简化实现
}
private double calculateCubeScore(CubeInstance cube, QueryContext context) {
double score = 0.0;
// 数据新鲜度权重 (40%)
long dataAge = System.currentTimeMillis() - cube.getLastBuildTime();
double freshnessScore = Math.max(0, 1.0 - (dataAge / (24 * 60 * 60 * 1000.0))); // 1天内为满分
score += freshnessScore * 0.4;
// 查询覆盖度权重 (35%)
double coverageScore = calculateQueryCoverage(cube, context);
score += coverageScore * 0.35;
// 存储效率权重 (25%)
double efficiencyScore = calculateStorageEfficiency(cube);
score += efficiencyScore * 0.25;
return score;
}
private double calculateQueryCoverage(CubeInstance cube, QueryContext context) {
// 计算查询覆盖度
return 0.8; // 简化实现
}
private double calculateStorageEfficiency(CubeInstance cube) {
// 计算存储效率
return 0.7; // 简化实现
}
}
/**
* 生成执行计划
*/
private QueryPlan generateExecutionPlan(String sql, CubeInstance cube, QueryContext context) {
QueryPlan plan = new QueryPlan();
plan.setSql(sql);
plan.setCube(cube);
plan.setEstimatedCost(estimateQueryCost(sql, cube));
plan.setEstimatedRows(estimateResultRows(sql, cube));
plan.setExecutionSteps(generateExecutionSteps(sql, cube));
return plan;
}
private double estimateQueryCost(String sql, CubeInstance cube) {
// 估算查询成本
return 100.0; // 简化实现
}
private long estimateResultRows(String sql, CubeInstance cube) {
// 估算结果行数
return 1000L; // 简化实现
}
private List<ExecutionStep> generateExecutionSteps(String sql, CubeInstance cube) {
// 生成执行步骤
List<ExecutionStep> steps = new ArrayList<>();
steps.add(new ExecutionStep("SCAN", "Scan cube segments"));
steps.add(new ExecutionStep("FILTER", "Apply filters"));
steps.add(new ExecutionStep("AGGREGATE", "Perform aggregation"));
steps.add(new ExecutionStep("SORT", "Sort results"));
return steps;
}
private String generatePlanKey(String sql, String cubeName) {
return DigestUtils.md5Hex(sql + cubeName);
}
private void recordQueryStats(String sql, QueryPlan plan) {
String queryKey = DigestUtils.md5Hex(sql);
QueryStats stats = queryStatsMap.computeIfAbsent(queryKey, k -> new QueryStats());
stats.incrementExecutionCount();
stats.addExecutionTime(plan.getEstimatedCost());
}
/**
* 获取查询统计信息
*/
public Map<String, QueryStats> getQueryStats() {
return new HashMap<>(queryStatsMap);
}
/**
* 清理查询统计
*/
public void clearQueryStats() {
queryStatsMap.clear();
}
}
/**
* 优化后的查询
*/
class OptimizedQuery {
private String sql;
private QueryPlan plan;
private CubeInstance cube;
public OptimizedQuery(String sql, QueryPlan plan, CubeInstance cube) {
this.sql = sql;
this.plan = plan;
this.cube = cube;
}
// Getters
public String getSql() { return sql; }
public QueryPlan getPlan() { return plan; }
public CubeInstance getCube() { return cube; }
}
/**
* 查询计划
*/
class QueryPlan {
private String sql;
private CubeInstance cube;
private double estimatedCost;
private long estimatedRows;
private List<ExecutionStep> executionSteps;
// Getters and setters
public String getSql() { return sql; }
public void setSql(String sql) { this.sql = sql; }
public CubeInstance getCube() { return cube; }
public void setCube(CubeInstance cube) { this.cube = cube; }
public double getEstimatedCost() { return estimatedCost; }
public void setEstimatedCost(double estimatedCost) { this.estimatedCost = estimatedCost; }
public long getEstimatedRows() { return estimatedRows; }
public void setEstimatedRows(long estimatedRows) { this.estimatedRows = estimatedRows; }
public List<ExecutionStep> getExecutionSteps() { return executionSteps; }
public void setExecutionSteps(List<ExecutionStep> executionSteps) { this.executionSteps = executionSteps; }
}
/**
* 执行步骤
*/
class ExecutionStep {
private String operation;
private String description;
public ExecutionStep(String operation, String description) {
this.operation = operation;
this.description = description;
}
public String getOperation() { return operation; }
public String getDescription() { return description; }
}
/**
* 查询统计信息
*/
class QueryStats {
private AtomicLong executionCount = new AtomicLong(0);
private AtomicLong totalExecutionTime = new AtomicLong(0);
private long firstExecutionTime = System.currentTimeMillis();
private long lastExecutionTime = System.currentTimeMillis();
public void incrementExecutionCount() {
executionCount.incrementAndGet();
lastExecutionTime = System.currentTimeMillis();
}
public void addExecutionTime(double time) {
totalExecutionTime.addAndGet((long) time);
}
public long getExecutionCount() { return executionCount.get(); }
public long getTotalExecutionTime() { return totalExecutionTime.get(); }
public double getAverageExecutionTime() {
long count = executionCount.get();
return count > 0 ? (double) totalExecutionTime.get() / count : 0.0;
}
public long getFirstExecutionTime() { return firstExecutionTime; }
public long getLastExecutionTime() { return lastExecutionTime; }
}
/**
* 查询计划缓存
*/
class QueryPlanCache {
private Map<String, QueryPlan> cache = new ConcurrentHashMap<>();
private static final int MAX_CACHE_SIZE = 1000;
public void put(String key, QueryPlan plan) {
if (cache.size() >= MAX_CACHE_SIZE) {
// 简单的LRU清理策略
cache.clear();
}
cache.put(key, plan);
}
public QueryPlan get(String key) {
return cache.get(key);
}
public void clear() {
cache.clear();
}
public int size() {
return cache.size();
}
}
/**
* 查询上下文
*/
class QueryContext {
private List<CubeInstance> availableCubes;
private Map<String, Object> parameters;
private String user;
private long timestamp;
public QueryContext() {
this.timestamp = System.currentTimeMillis();
this.parameters = new HashMap<>();
}
// Getters and setters
public List<CubeInstance> getAvailableCubes() { return availableCubes; }
public void setAvailableCubes(List<CubeInstance> availableCubes) { this.availableCubes = availableCubes; }
public Map<String, Object> getParameters() { return parameters; }
public void setParameters(Map<String, Object> parameters) { this.parameters = parameters; }
public String getUser() { return user; }
public void setUser(String user) { this.user = user; }
public long getTimestamp() { return timestamp; }
}
10.5.3 并发控制
// ConcurrencyController.java - 并发控制器
package com.example.kylin.optimization;
import java.util.concurrent.*;
import java.util.concurrent.atomic.AtomicInteger;
/**
* Kylin并发控制器
*/
public class ConcurrencyController {
private static final int DEFAULT_MAX_CONCURRENT_QUERIES = 10;
private static final int DEFAULT_MAX_CONCURRENT_BUILDS = 3;
private static final long DEFAULT_QUERY_TIMEOUT = 300000; // 5分钟
private Semaphore queryPermits;
private Semaphore buildPermits;
private ExecutorService queryExecutor;
private ExecutorService buildExecutor;
private Map<String, Future<?>> runningQueries;
private Map<String, Future<?>> runningBuilds;
private AtomicInteger queryCounter = new AtomicInteger(0);
private AtomicInteger buildCounter = new AtomicInteger(0);
public ConcurrencyController() {
this(DEFAULT_MAX_CONCURRENT_QUERIES, DEFAULT_MAX_CONCURRENT_BUILDS);
}
public ConcurrencyController(int maxConcurrentQueries, int maxConcurrentBuilds) {
this.queryPermits = new Semaphore(maxConcurrentQueries);
this.buildPermits = new Semaphore(maxConcurrentBuilds);
this.queryExecutor = Executors.newFixedThreadPool(maxConcurrentQueries,
new ThreadFactory() {
@Override
public Thread newThread(Runnable r) {
Thread t = new Thread(r, "kylin-query-" + queryCounter.incrementAndGet());
t.setDaemon(true);
return t;
}
});
this.buildExecutor = Executors.newFixedThreadPool(maxConcurrentBuilds,
new ThreadFactory() {
@Override
public Thread newThread(Runnable r) {
Thread t = new Thread(r, "kylin-build-" + buildCounter.incrementAndGet());
t.setDaemon(true);
return t;
}
});
this.runningQueries = new ConcurrentHashMap<>();
this.runningBuilds = new ConcurrentHashMap<>();
}
/**
* 提交查询任务
*/
public CompletableFuture<QueryResult> submitQuery(String queryId, Callable<QueryResult> queryTask) {
return CompletableFuture.supplyAsync(() -> {
try {
// 获取查询许可
if (!queryPermits.tryAcquire(DEFAULT_QUERY_TIMEOUT, TimeUnit.MILLISECONDS)) {
throw new RuntimeException("Query queue is full, please try again later");
}
try {
// 执行查询
Future<QueryResult> future = queryExecutor.submit(queryTask);
runningQueries.put(queryId, future);
QueryResult result = future.get(DEFAULT_QUERY_TIMEOUT, TimeUnit.MILLISECONDS);
return result;
} catch (TimeoutException e) {
// 查询超时，取消任务
cancelQuery(queryId);
throw new RuntimeException("Query timeout after " + DEFAULT_QUERY_TIMEOUT + "ms", e);
} finally {
runningQueries.remove(queryId);
queryPermits.release();
}
} catch (InterruptedException e) {
Thread.currentThread().interrupt();
throw new RuntimeException("Query was interrupted", e);
} catch (ExecutionException e) {
throw new RuntimeException("Query execution failed", e.getCause());
}
});
}
/**
* 提交构建任务
*/
public CompletableFuture<BuildResult> submitBuild(String buildId, Callable<BuildResult> buildTask) {
return CompletableFuture.supplyAsync(() -> {
try {
// 获取构建许可
buildPermits.acquire();
try {
// 执行构建
Future<BuildResult> future = buildExecutor.submit(buildTask);
runningBuilds.put(buildId, future);
BuildResult result = future.get(); // 构建任务不设置超时
return result;
} finally {
runningBuilds.remove(buildId);
buildPermits.release();
}
} catch (InterruptedException e) {
Thread.currentThread().interrupt();
throw new RuntimeException("Build was interrupted", e);
} catch (ExecutionException e) {
throw new RuntimeException("Build execution failed", e.getCause());
}
});
}
/**
* 取消查询
*/
public boolean cancelQuery(String queryId) {
Future<?> future = runningQueries.get(queryId);
if (future != null) {
boolean cancelled = future.cancel(true);
runningQueries.remove(queryId);
return cancelled;
}
return false;
}
/**
* 取消构建
*/
public boolean cancelBuild(String buildId) {
Future<?> future = runningBuilds.get(buildId);
if (future != null) {
boolean cancelled = future.cancel(true);
runningBuilds.remove(buildId);
return cancelled;
}
return false;
}
/**
* 获取并发状态
*/
public ConcurrencyStatus getStatus() {
ConcurrencyStatus status = new ConcurrencyStatus();
status.setAvailableQueryPermits(queryPermits.availablePermits());
status.setAvailableBuildPermits(buildPermits.availablePermits());
status.setRunningQueriesCount(runningQueries.size());
status.setRunningBuildsCount(runningBuilds.size());
status.setQueuedTasksCount(((ThreadPoolExecutor) queryExecutor).getQueue().size() +
((ThreadPoolExecutor) buildExecutor).getQueue().size());
return status;
}
/**
* 获取运行中的查询列表
*/
public Set<String> getRunningQueries() {
return new HashSet<>(runningQueries.keySet());
}
/**
* 获取运行中的构建列表
*/
public Set<String> getRunningBuilds() {
return new HashSet<>(runningBuilds.keySet());
}
/**
* 关闭并发控制器
*/
public void shutdown() {
queryExecutor.shutdown();
buildExecutor.shutdown();
try {
if (!queryExecutor.awaitTermination(60, TimeUnit.SECONDS)) {
queryExecutor.shutdownNow();
}
if (!buildExecutor.awaitTermination(60, TimeUnit.SECONDS)) {
buildExecutor.shutdownNow();
}
} catch (InterruptedException e) {
queryExecutor.shutdownNow();
buildExecutor.shutdownNow();
Thread.currentThread().interrupt();
}
}
}
/**
* 并发状态
*/
class ConcurrencyStatus {
private int availableQueryPermits;
private int availableBuildPermits;
private int runningQueriesCount;
private int runningBuildsCount;
private int queuedTasksCount;
// Getters and setters
public int getAvailableQueryPermits() { return availableQueryPermits; }
public void setAvailableQueryPermits(int availableQueryPermits) { this.availableQueryPermits = availableQueryPermits; }
public int getAvailableBuildPermits() { return availableBuildPermits; }
public void setAvailableBuildPermits(int availableBuildPermits) { this.availableBuildPermits = availableBuildPermits; }
public int getRunningQueriesCount() { return runningQueriesCount; }
public void setRunningQueriesCount(int runningQueriesCount) { this.runningQueriesCount = runningQueriesCount; }
public int getRunningBuildsCount() { return runningBuildsCount; }
public void setRunningBuildsCount(int runningBuildsCount) { this.runningBuildsCount = runningBuildsCount; }
public int getQueuedTasksCount() { return queuedTasksCount; }
public void setQueuedTasksCount(int queuedTasksCount) { this.queuedTasksCount = queuedTasksCount; }
@Override
public String toString() {
return String.format("ConcurrencyStatus{queryPermits=%d, buildPermits=%d, runningQueries=%d, runningBuilds=%d, queuedTasks=%d}",
availableQueryPermits, availableBuildPermits, runningQueriesCount, runningBuildsCount, queuedTasksCount);
}
}
/**
* 查询结果
*/
class QueryResult {
private List<List<Object>> rows;
private List<String> columnNames;
private long executionTime;
private String queryId;
public QueryResult(String queryId) {
this.queryId = queryId;
this.rows = new ArrayList<>();
this.columnNames = new ArrayList<>();
}
// Getters and setters
public List<List<Object>> getRows() { return rows; }
public void setRows(List<List<Object>> rows) { this.rows = rows; }
public List<String> getColumnNames() { return columnNames; }
public void setColumnNames(List<String> columnNames) { this.columnNames = columnNames; }
public long getExecutionTime() { return executionTime; }
public void setExecutionTime(long executionTime) { this.executionTime = executionTime; }
public String getQueryId() { return queryId; }
}
/**
* 构建结果
*/
class BuildResult {
private String buildId;
private String status;
private long buildTime;
private String errorMessage;
public BuildResult(String buildId) {
this.buildId = buildId;
}
// Getters and setters
public String getBuildId() { return buildId; }
public String getStatus() { return status; }
public void setStatus(String status) { this.status = status; }
public long getBuildTime() { return buildTime; }
public void setBuildTime(long buildTime) { this.buildTime = buildTime; }
public String getErrorMessage() { return errorMessage; }
public void setErrorMessage(String errorMessage) { this.errorMessage = errorMessage; }
}
10.6 监控与告警
10.6.1 系统监控
# kylin_monitor.py - Kylin系统监控
import time
import json
import requests
import psutil
import logging
from datetime import datetime, timedelta
from typing import Dict, List, Any
from dataclasses import dataclass
from concurrent.futures import ThreadPoolExecutor
@dataclass
class MetricData:
"""指标数据类"""
name: str
value: float
timestamp: datetime
tags: Dict[str, str] = None
def to_dict(self) -> Dict[str, Any]:
return {
'name': self.name,
'value': self.value,
'timestamp': self.timestamp.isoformat(),
'tags': self.tags or {}
}
class KylinMonitor:
"""Kylin监控系统"""
def __init__(self, kylin_host: str, kylin_port: int = 7070,
username: str = 'ADMIN', password: str = 'KYLIN'):
self.kylin_host = kylin_host
self.kylin_port = kylin_port
self.username = username
self.password = password
self.base_url = f"http://{kylin_host}:{kylin_port}/kylin/api"
self.session = requests.Session()
self.session.auth = (username, password)
# 配置日志
logging.basicConfig(level=logging.INFO)
self.logger = logging.getLogger(__name__)
# 监控配置
self.monitoring_interval = 60 # 60秒
self.alert_thresholds = {
'cpu_usage': 80.0,
'memory_usage': 85.0,
'disk_usage': 90.0,
'query_response_time': 30000, # 30秒
'build_failure_rate': 0.1, # 10%
'concurrent_queries': 50
}
self.metrics_history = []
self.alerts = []
def start_monitoring(self):
"""启动监控"""
self.logger.info("Starting Kylin monitoring...")
with ThreadPoolExecutor(max_workers=4) as executor:
while True:
try:
# 并行收集各类指标
futures = [
executor.submit(self.collect_system_metrics),
executor.submit(self.collect_kylin_metrics),
executor.submit(self.collect_query_metrics),
executor.submit(self.collect_build_metrics)
]
# 等待所有指标收集完成
for future in futures:
metrics = future.result()
self.metrics_history.extend(metrics)
# 检查告警
self.check_alerts()
# 清理历史数据（保留最近24小时）
self.cleanup_old_metrics()
# 等待下一个监控周期
time.sleep(self.monitoring_interval)
except Exception as e:
self.logger.error(f"Monitoring error: {e}")
time.sleep(self.monitoring_interval)
def collect_system_metrics(self) -> List[MetricData]:
"""收集系统指标"""
metrics = []
now = datetime.now()
try:
# CPU使用率
cpu_percent = psutil.cpu_percent(interval=1)
metrics.append(MetricData('system.cpu.usage', cpu_percent, now, {'unit': 'percent'}))
# 内存使用率
memory = psutil.virtual_memory()
memory_percent = memory.percent
metrics.append(MetricData('system.memory.usage', memory_percent, now, {'unit': 'percent'}))
metrics.append(MetricData('system.memory.available', memory.available / 1024 / 1024 / 1024, now, {'unit': 'GB'}))
# 磁盘使用率
disk = psutil.disk_usage('/')
disk_percent = (disk.used / disk.total) * 100
metrics.append(MetricData('system.disk.usage', disk_percent, now, {'unit': 'percent'}))
metrics.append(MetricData('system.disk.free', disk.free / 1024 / 1024 / 1024, now, {'unit': 'GB'}))
# 网络IO
net_io = psutil.net_io_counters()
metrics.append(MetricData('system.network.bytes_sent', net_io.bytes_sent, now, {'unit': 'bytes'}))
metrics.append(MetricData('system.network.bytes_recv', net_io.bytes_recv, now, {'unit': 'bytes'}))
except Exception as e:
self.logger.error(f"Failed to collect system metrics: {e}")
return metrics
def collect_kylin_metrics(self) -> List[MetricData]:
"""收集Kylin服务指标"""
metrics = []
now = datetime.now()
try:
# 检查Kylin服务状态
response = self.session.get(f"{self.base_url}/admin/config")
if response.status_code == 200:
metrics.append(MetricData('kylin.service.status', 1, now, {'status': 'up'}))
else:
metrics.append(MetricData('kylin.service.status', 0, now, {'status': 'down'}))
# 获取项目数量
projects_response = self.session.get(f"{self.base_url}/projects")
if projects_response.status_code == 200:
projects = projects_response.json()
metrics.append(MetricData('kylin.projects.count', len(projects), now))
# 获取Cube数量和状态
cubes_response = self.session.get(f"{self.base_url}/cubes")
if cubes_response.status_code == 200:
cubes = cubes_response.json()
total_cubes = len(cubes)
ready_cubes = sum(1 for cube in cubes if cube.get('status') == 'READY')
building_cubes = sum(1 for cube in cubes if cube.get('status') == 'BUILDING')
metrics.append(MetricData('kylin.cubes.total', total_cubes, now))
metrics.append(MetricData('kylin.cubes.ready', ready_cubes, now))
metrics.append(MetricData('kylin.cubes.building', building_cubes, now))
except Exception as e:
self.logger.error(f"Failed to collect Kylin metrics: {e}")
return metrics
def collect_query_metrics(self) -> List[MetricData]:
"""收集查询指标"""
metrics = []
now = datetime.now()
try:
# 获取查询历史（最近1小时）
end_time = int(now.timestamp() * 1000)
start_time = int((now - timedelta(hours=1)).timestamp() * 1000)
query_url = f"{self.base_url}/query/history"
params = {
'startTime': start_time,
'endTime': end_time,
'pageSize': 1000
}
response = self.session.get(query_url, params=params)
if response.status_code == 200:
queries = response.json().get('data', [])
if queries:
# 查询数量
metrics.append(MetricData('kylin.queries.count', len(queries), now))
# 平均响应时间
response_times = [q.get('duration', 0) for q in queries if q.get('duration')]
if response_times:
avg_response_time = sum(response_times) / len(response_times)
max_response_time = max(response_times)
metrics.append(MetricData('kylin.queries.avg_response_time', avg_response_time, now, {'unit': 'ms'}))
metrics.append(MetricData('kylin.queries.max_response_time', max_response_time, now, {'unit': 'ms'}))
# 成功率
successful_queries = sum(1 for q in queries if q.get('isSuccess', False))
success_rate = successful_queries / len(queries) if queries else 0
metrics.append(MetricData('kylin.queries.success_rate', success_rate, now, {'unit': 'ratio'}))
# 并发查询数（当前运行中的查询）
running_queries = sum(1 for q in queries if q.get('queryStatus') == 'RUNNING')
metrics.append(MetricData('kylin.queries.concurrent', running_queries, now))
except Exception as e:
self.logger.error(f"Failed to collect query metrics: {e}")
return metrics
def collect_build_metrics(self) -> List[MetricData]:
"""收集构建指标"""
metrics = []
now = datetime.now()
try:
# 获取构建作业历史
jobs_response = self.session.get(f"{self.base_url}/jobs")
if jobs_response.status_code == 200:
jobs = jobs_response.json()
# 最近24小时的作业
recent_jobs = [
job for job in jobs
if job.get('last_modified') and
datetime.fromtimestamp(job['last_modified'] / 1000) > now - timedelta(hours=24)
]
if recent_jobs:
# 构建作业数量
metrics.append(MetricData('kylin.builds.count', len(recent_jobs), now))
# 构建成功率
successful_builds = sum(1 for job in recent_jobs if job.get('job_status') == 'FINISHED')
failed_builds = sum(1 for job in recent_jobs if job.get('job_status') == 'ERROR')
if recent_jobs:
success_rate = successful_builds / len(recent_jobs)
failure_rate = failed_builds / len(recent_jobs)
metrics.append(MetricData('kylin.builds.success_rate', success_rate, now, {'unit': 'ratio'}))
metrics.append(MetricData('kylin.builds.failure_rate', failure_rate, now, {'unit': 'ratio'}))
# 平均构建时间
build_times = [
job.get('duration', 0) for job in recent_jobs
if job.get('duration') and job.get('job_status') == 'FINISHED'
]
if build_times:
avg_build_time = sum(build_times) / len(build_times)
metrics.append(MetricData('kylin.builds.avg_duration', avg_build_time, now, {'unit': 'ms'}))
# 当前运行中的构建
running_builds = sum(1 for job in jobs if job.get('job_status') == 'RUNNING')
metrics.append(MetricData('kylin.builds.running', running_builds, now))
except Exception as e:
self.logger.error(f"Failed to collect build metrics: {e}")
return metrics
def check_alerts(self):
"""检查告警条件"""
if not self.metrics_history:
return
# 获取最新指标
latest_metrics = {}
for metric in reversed(self.metrics_history):
if metric.name not in latest_metrics:
latest_metrics[metric.name] = metric
# 检查各种告警条件
alerts_to_send = []
# CPU使用率告警
cpu_metric = latest_metrics.get('system.cpu.usage')
if cpu_metric and cpu_metric.value > self.alert_thresholds['cpu_usage']:
alerts_to_send.append({
'type': 'HIGH_CPU_USAGE',
'message': f'CPU usage is {cpu_metric.value:.1f}%, exceeds threshold {self.alert_thresholds["cpu_usage"]}%',
'severity': 'WARNING',
'timestamp': datetime.now()
})
# 内存使用率告警
memory_metric = latest_metrics.get('system.memory.usage')
if memory_metric and memory_metric.value > self.alert_thresholds['memory_usage']:
alerts_to_send.append({
'type': 'HIGH_MEMORY_USAGE',
'message': f'Memory usage is {memory_metric.value:.1f}%, exceeds threshold {self.alert_thresholds["memory_usage"]}%',
'severity': 'WARNING',
'timestamp': datetime.now()
})
# 磁盘使用率告警
disk_metric = latest_metrics.get('system.disk.usage')
if disk_metric and disk_metric.value > self.alert_thresholds['disk_usage']:
alerts_to_send.append({
'type': 'HIGH_DISK_USAGE',
'message': f'Disk usage is {disk_metric.value:.1f}%, exceeds threshold {self.alert_thresholds["disk_usage"]}%',
'severity': 'CRITICAL',
'timestamp': datetime.now()
})
# 查询响应时间告警
query_time_metric = latest_metrics.get('kylin.queries.avg_response_time')
if query_time_metric and query_time_metric.value > self.alert_thresholds['query_response_time']:
alerts_to_send.append({
'type': 'SLOW_QUERY_RESPONSE',
'message': f'Average query response time is {query_time_metric.value:.0f}ms, exceeds threshold {self.alert_thresholds["query_response_time"]}ms',
'severity': 'WARNING',
'timestamp': datetime.now()
})
# 构建失败率告警
build_failure_metric = latest_metrics.get('kylin.builds.failure_rate')
if build_failure_metric and build_failure_metric.value > self.alert_thresholds['build_failure_rate']:
alerts_to_send.append({
'type': 'HIGH_BUILD_FAILURE_RATE',
'message': f'Build failure rate is {build_failure_metric.value:.1%}, exceeds threshold {self.alert_thresholds["build_failure_rate"]:.1%}',
'severity': 'CRITICAL',
'timestamp': datetime.now()
})
# 并发查询数告警
concurrent_queries_metric = latest_metrics.get('kylin.queries.concurrent')
if concurrent_queries_metric and concurrent_queries_metric.value > self.alert_thresholds['concurrent_queries']:
alerts_to_send.append({
'type': 'HIGH_CONCURRENT_QUERIES',
'message': f'Concurrent queries count is {concurrent_queries_metric.value}, exceeds threshold {self.alert_thresholds["concurrent_queries"]}',
'severity': 'WARNING',
'timestamp': datetime.now()
})
# Kylin服务状态告警
service_status_metric = latest_metrics.get('kylin.service.status')
if service_status_metric and service_status_metric.value == 0:
alerts_to_send.append({
'type': 'SERVICE_DOWN',
'message': 'Kylin service is down',
'severity': 'CRITICAL',
'timestamp': datetime.now()
})
# 发送告警
for alert in alerts_to_send:
self.send_alert(alert)
self.alerts.append(alert)
def send_alert(self, alert: Dict[str, Any]):
"""发送告警"""
self.logger.warning(f"ALERT [{alert['severity']}] {alert['type']}: {alert['message']}")
# 这里可以集成各种告警渠道
# 例如：邮件、短信、Slack、钉钉等
# 示例：发送到Webhook
# self.send_webhook_alert(alert)
# 示例：发送邮件
# self.send_email_alert(alert)
def send_webhook_alert(self, alert: Dict[str, Any]):
"""发送Webhook告警"""
webhook_url = "https://your-webhook-url.com/alerts"
try:
response = requests.post(webhook_url, json=alert, timeout=10)
if response.status_code == 200:
self.logger.info(f"Alert sent to webhook: {alert['type']}")
else:
self.logger.error(f"Failed to send alert to webhook: {response.status_code}")
except Exception as e:
self.logger.error(f"Error sending webhook alert: {e}")
def cleanup_old_metrics(self):
"""清理旧的指标数据"""
cutoff_time = datetime.now() - timedelta(hours=24)
self.metrics_history = [
metric for metric in self.metrics_history
if metric.timestamp > cutoff_time
]
# 清理旧的告警
cutoff_time_alerts = datetime.now() - timedelta(hours=48)
self.alerts = [
alert for alert in self.alerts
if alert['timestamp'] > cutoff_time_alerts
]
def get_metrics_summary(self) -> Dict[str, Any]:
"""获取指标摘要"""
if not self.metrics_history:
return {}
# 按指标名称分组
metrics_by_name = {}
for metric in self.metrics_history:
if metric.name not in metrics_by_name:
metrics_by_name[metric.name] = []
metrics_by_name[metric.name].append(metric)
# 计算摘要统计
summary = {}
for name, metrics in metrics_by_name.items():
values = [m.value for m in metrics]
summary[name] = {
'current': values[-1] if values else 0,
'average': sum(values) / len(values) if values else 0,
'min': min(values) if values else 0,
'max': max(values) if values else 0,
'count': len(values)
}
return summary
def get_recent_alerts(self, hours: int = 24) -> List[Dict[str, Any]]:
"""获取最近的告警"""
cutoff_time = datetime.now() - timedelta(hours=hours)
return [
alert for alert in self.alerts
if alert['timestamp'] > cutoff_time
]
# 使用示例
if __name__ == "__main__":
# 创建监控实例
monitor = KylinMonitor(
kylin_host="localhost",
kylin_port=7070,
username="ADMIN",
password="KYLIN"
)
# 自定义告警阈值
monitor.alert_thresholds.update({
'cpu_usage': 75.0,
'memory_usage': 80.0,
'query_response_time': 20000
})
# 启动监控
try:
monitor.start_monitoring()
except KeyboardInterrupt:
print("Monitoring stopped by user")
10.6.2 告警配置
# alert_config.yaml - 告警配置文件
alert_config:
# 全局配置
global:
enabled: true
check_interval: 60 # 检查间隔（秒）
notification_cooldown: 300 # 通知冷却时间（秒）
# 阈值配置
thresholds:
system:
cpu_usage:
warning: 75.0
critical: 90.0
memory_usage:
warning: 80.0
critical: 95.0
disk_usage:
warning: 85.0
critical: 95.0
load_average:
warning: 2.0
critical: 4.0
kylin:
query_response_time:
warning: 15000 # 15秒
critical: 30000 # 30秒
build_failure_rate:
warning: 0.05 # 5%
critical: 0.15 # 15%
concurrent_queries:
warning: 30
critical: 50
service_availability:
critical: 0 # 服务不可用
# 通知渠道配置
notifications:
email:
enabled: true
smtp_server: "smtp.company.com"
smtp_port: 587
username: "alerts@company.com"
password: "${EMAIL_PASSWORD}"
recipients:
- "admin@company.com"
- "ops@company.com"
subject_prefix: "[Kylin Alert]"
webhook:
enabled: true
url: "https://hooks.slack.com/services/YOUR/SLACK/WEBHOOK"
timeout: 10
retry_count: 3
sms:
enabled: false
provider: "twilio"
account_sid: "${TWILIO_SID}"
auth_token: "${TWILIO_TOKEN}"
from_number: "+1234567890"
to_numbers:
- "+1987654321"
# 告警规则
rules:
- name: "High CPU Usage"
condition: "system.cpu.usage > thresholds.system.cpu_usage.warning"
severity: "warning"
message: "CPU usage is {{ value }}%, exceeds warning threshold {{ threshold }}%"
notifications: ["email", "webhook"]
- name: "Critical CPU Usage"
condition: "system.cpu.usage > thresholds.system.cpu_usage.critical"
severity: "critical"
message: "CPU usage is {{ value }}%, exceeds critical threshold {{ threshold }}%"
notifications: ["email", "webhook", "sms"]
- name: "Kylin Service Down"
condition: "kylin.service.status == 0"
severity: "critical"
message: "Kylin service is not responding"
notifications: ["email", "webhook", "sms"]
- name: "Slow Query Performance"
condition: "kylin.queries.avg_response_time > thresholds.kylin.query_response_time.warning"
severity: "warning"
message: "Average query response time is {{ value }}ms, exceeds threshold {{ threshold }}ms"
notifications: ["email", "webhook"]
- name: "High Build Failure Rate"
condition: "kylin.builds.failure_rate > thresholds.kylin.build_failure_rate.critical"
severity: "critical"
message: "Build failure rate is {{ value | percentage }}, exceeds critical threshold {{ threshold | percentage }}"
notifications: ["email", "webhook"]
10.7 本章小结
本章深入探讨了Apache Kylin的高级特性与扩展能力，涵盖了以下核心内容：
10.7.1 核心知识点
自定义函数开发
- 标量UDF和聚合UDF的实现
- UDF的部署、注册和测试
- 函数性能优化技巧
插件机制与扩展
- Kylin插件架构设计
- 自定义存储插件开发
- 自定义认证插件实现
- 插件管理和部署
存储引擎扩展
- 分布式存储引擎设计
- 多数据源支持
- 数据读写器实现
- 存储优化策略
高级调优技巧
- 内存优化和监控
- 查询优化器实现
- 并发控制机制
- 性能调优最佳实践
监控与告警
- 系统指标监控
- 告警规则配置
- 多渠道通知机制
- 运维自动化
10.7.2 实用工具
- UDF开发框架：提供了完整的自定义函数开发模板
- 插件管理器：支持插件的动态加载和管理
- 性能优化器：包含内存、查询、并发等多维度优化
- 监控系统：全方位的系统和业务指标监控
- 告警平台：灵活的告警规则和通知机制
10.7.3 最佳实践
扩展开发
- 遵循Kylin插件接口规范
- 注重代码质量和测试覆盖
- 考虑向后兼容性
性能优化
- 定期监控系统资源使用
- 根据业务特点调整参数
- 实施渐进式优化策略
运维管理
- 建立完善的监控体系
- 制定合理的告警策略
- 定期进行性能评估
10.7.4 下一章预告
下一章我们将学习”Kylin运维与故障排除”，包括： - 日常运维操作 - 故障诊断方法 - 性能问题排查 - 数据一致性检查 - 备份恢复策略
10.7.5 练习与思考
理论练习： 1. 设计一个自定义的地理位置距离计算UDF 2. 分析不同存储引擎的适用场景 3. 制定一套完整的Kylin监控指标体系
实践练习： 1. 开发并部署一个自定义聚合函数 2. 实现一个简单的缓存存储插件 3. 搭建Kylin监控告警系统
思考题： 1. 如何设计一个支持多租户的Kylin扩展？ 2. 在什么情况下需要开发自定义存储引擎？ 3. 如何平衡监控的全面性和系统性能开销？
通过本章的学习，您应该能够深入理解Kylin的扩展机制，具备开发高级功能的能力，并能够建立完善的监控运维体系。
