mirror of
https://github.com/PowerJob/PowerJob.git
synced 2025-07-17 00:00:04 +08:00
feat: [ops] enhance Map/MapReduce's dev ops
This commit is contained in:
tjq
parent
37ef35bd80
commit
07e0e17ec0
@ -16,6 +16,10 @@ public class TaskDetailInfo implements PowerSerializable {
|
||||
|
||||
private String taskId;
|
||||
private String taskName;
|
||||
/**
|
||||
* 任务对象（map 的 subTask）
|
||||
*/
|
||||
private String taskContent;
|
||||
/**
|
||||
* 处理器地址
|
||||
*/
|
||||
|
||||
@ -12,6 +12,15 @@ import java.util.Map;
|
||||
*/
|
||||
public class MapUtils {
|
||||
|
||||
public static <K> Long getLong(Map<? super K, ?> map, K key, Long defaultValue) {
|
||||
Long answer = getLong(map, key);
|
||||
if (answer == null) {
|
||||
answer = defaultValue;
|
||||
}
|
||||
|
||||
return answer;
|
||||
}
|
||||
|
||||
public static <K> long getLongValue(Map<? super K, ?> map, K key) {
|
||||
Long longObject = getLong(map, key);
|
||||
return longObject == null ? 0L : longObject;
|
||||
|
||||
@ -20,6 +20,10 @@ public class TaskDetailInfoVO implements Serializable {
|
||||
|
||||
private String taskId;
|
||||
private String taskName;
|
||||
/**
|
||||
* 任务对象（map 的 subTask）
|
||||
*/
|
||||
private String taskContent;
|
||||
/**
|
||||
* 处理器地址
|
||||
*/
|
||||
|
||||
@ -1,21 +1,25 @@
|
||||
package tech.powerjob.samples.processors;
|
||||
|
||||
import com.alibaba.fastjson.JSON;
|
||||
import com.alibaba.fastjson.JSONObject;
|
||||
import lombok.*;
|
||||
import com.google.common.collect.Lists;
|
||||
import lombok.AllArgsConstructor;
|
||||
import lombok.Data;
|
||||
import lombok.extern.slf4j.Slf4j;
|
||||
import org.apache.commons.lang3.StringUtils;
|
||||
import org.springframework.stereotype.Component;
|
||||
import tech.powerjob.common.serialize.JsonUtils;
|
||||
import tech.powerjob.common.utils.MapUtils;
|
||||
import tech.powerjob.worker.core.processor.ProcessResult;
|
||||
import tech.powerjob.worker.core.processor.TaskContext;
|
||||
import tech.powerjob.worker.core.processor.TaskResult;
|
||||
import tech.powerjob.worker.core.processor.sdk.MapReduceProcessor;
|
||||
import tech.powerjob.worker.log.OmsLogger;
|
||||
import com.google.common.collect.Lists;
|
||||
import lombok.extern.slf4j.Slf4j;
|
||||
import org.springframework.stereotype.Component;
|
||||
|
||||
import java.io.Serializable;
|
||||
import java.util.List;
|
||||
import java.util.Optional;
|
||||
import java.util.concurrent.ThreadLocalRandom;
|
||||
import java.util.concurrent.atomic.AtomicLong;
|
||||
|
||||
/**
|
||||
* MapReduce 处理器示例
|
||||
@ -31,70 +35,156 @@ public class MapReduceProcessorDemo implements MapReduceProcessor {
|
||||
@Override
|
||||
public ProcessResult process(TaskContext context) throws Exception {
|
||||
|
||||
// PowerJob 提供的日志 API，可支持在控制台指定多种日志模式（在线查看 / 本地打印）。最佳实践：全部使用 OmsLogger 打印日志，开发阶段控制台配置为 在线日志方便开发；上线后调整为本地日志，与直接使用 SLF4J 无异
|
||||
OmsLogger omsLogger = context.getOmsLogger();
|
||||
|
||||
log.info("============== TestMapReduceProcessor#process ==============");
|
||||
log.info("isRootTask:{}", isRootTask());
|
||||
log.info("taskContext:{}", JsonUtils.toJSONString(context));
|
||||
// 是否为根任务，一般根任务进行任务的分发
|
||||
boolean isRootTask = isRootTask();
|
||||
// Task 名称，除了 MAP 任务其他 taskName 均由开发者自己创建，某种意义上也可以按参数理解（比如多层 MAP 的情况下，taskName 可以命名为，Map_Level1, Map_Level2，最终按 taskName 判断层级进不同的执行分支）
|
||||
String taskName = context.getTaskName();
|
||||
// 任务参数，控制台任务配置中直接填写的参数
|
||||
String jobParamsStr = context.getJobParams();
|
||||
// 任务示例参数，运行任务时手动填写的参数（等同于 OpenAPI runJob 的携带的参数）
|
||||
String instanceParamsStr = context.getInstanceParams();
|
||||
|
||||
// 根据控制台参数获取MR批次及子任务大小
|
||||
final JSONObject jobParams = Optional.ofNullable(context.getJobParams()).map(JSONObject::parseObject).orElse(new JSONObject());
|
||||
omsLogger.info("[MapReduceDemo] [startExecuteNewTask] jobId:{}, instanceId:{}, taskId:{}, taskName: {}, RetryTimes: {}, isRootTask:{}, jobParams:{}, instanceParams:{}", context.getJobId(), context.getInstanceId(), context.getTaskId(), taskName, context.getCurrentRetryTimes(), isRootTask, jobParamsStr, instanceParamsStr);
|
||||
|
||||
Integer batchSize = (Integer) jobParams.getOrDefault("batchSize", 100);
|
||||
Integer batchNum = (Integer) jobParams.getOrDefault("batchNum", 10);
|
||||
// 常见写法，优先从 InstanceParams 获取参数，取不到再从 JobParams 中获取，灵活性最佳（相当于实现了实例参数重载任务参数）
|
||||
String finalParams = StringUtils.isEmpty(instanceParamsStr) ? jobParamsStr : instanceParamsStr;
|
||||
final JSONObject params = Optional.ofNullable(finalParams).map(JSONObject::parseObject).orElse(new JSONObject());
|
||||
|
||||
if (isRootTask()) {
|
||||
log.info("==== MAP ====");
|
||||
omsLogger.info("[DemoMRProcessor] start root task~");
|
||||
List<TestSubTask> subTasks = Lists.newLinkedList();
|
||||
for (int j = 0; j < batchNum; j++) {
|
||||
for (int i = 0; i < batchSize; i++) {
|
||||
int x = j * batchSize + i;
|
||||
subTasks.add(new TestSubTask("name" + x, x));
|
||||
if (isRootTask) {
|
||||
|
||||
omsLogger.info("[MapReduceDemo] [RootTask] start execute root task~");
|
||||
|
||||
/*
|
||||
* rootTask 内的核心逻辑，即为按自己的业务需求拆分子任务。比如
|
||||
* - 从数据库/数仓拉一批任务出来做计算，那 MAP 任务就可以 stream 读全库，每 N 个 ID 作为一个 SubTask 对外分发
|
||||
* - 需要读取几千万个文件进行解析，那么 MAP 任务就可以将 N 个文件名作为一个 SubTask 对外分发，每个子任务接收到文件名称进行文件处理
|
||||
*
|
||||
* eg. 现在需要从文件中读取100W个ID，并处理数据库中这些ID对应的数据，那么步骤如下：
|
||||
* 1. 根任务（RootTask）读取文件，流式拉取100W个ID，并按100个一批的大小组装成子任务进行派发
|
||||
* 2. 非根任务获取子任务，完成业务逻辑的处理
|
||||
*
|
||||
* 以下 demo 进行该逻辑的模拟
|
||||
*/
|
||||
|
||||
|
||||
// 构造子任务
|
||||
|
||||
// 需要读取的文件总数
|
||||
Long num = MapUtils.getLong(params, "num", 100000L);
|
||||
// 每个子任务携带多少个文件ID（此参数越大，每个子任务就“越大”，如果失败的重试成本就越高。参数越小，每个子任务就越轻，当相应的分片数量会提升，会让 PowerJob 计算开销增大，建议按业务需求合理调配）
|
||||
Long batchSize = MapUtils.getLong(params, "batchSize", 100L);
|
||||
|
||||
// 此处模拟从文件读取 num 个 ID，每个子任务携带 batchSize 个 ID 作为一个分片
|
||||
List<Long> ids = Lists.newArrayList();
|
||||
for (long i = 0; i < num; i++) {
|
||||
ids.add(i);
|
||||
|
||||
if (ids.size() >= batchSize) {
|
||||
|
||||
// 构造自己的子任务，自行传递所有需要的参数
|
||||
SubTask subTask = new SubTask(ThreadLocalRandom.current().nextLong(), Lists.newArrayList(ids), "extra");
|
||||
ids.clear();
|
||||
|
||||
try {
|
||||
/*
|
||||
第一个参数：List<子任务>，map 支持批量操作以减少网络 IO 提升性能，简单起见此处不再示例，开发者可自行优化性能
|
||||
第二个参数：子任务名称，即后续 Task 执行时从 TaskContext#taskName 拿到的值。某种意义上也可以按参数理解（比如多层 MAP 的情况下，taskName 可以命名为，Map_Level1, Map_Level2，最终按 taskName 判断层级进不同的执行分支）
|
||||
*/
|
||||
map(Lists.newArrayList(subTask), "L1_FILE_PROCESS");
|
||||
} catch (Exception e) {
|
||||
// 注意 MAP 操作可能抛出异常，建议进行捕获并按需处理
|
||||
omsLogger.error("[MapReduceDemo] map task failed!", e);
|
||||
throw e;
|
||||
}
|
||||
}
|
||||
map(subTasks, "MAP_TEST_TASK");
|
||||
subTasks.clear();
|
||||
}
|
||||
omsLogger.info("[DemoMRProcessor] map success~");
|
||||
return new ProcessResult(true, "MAP_SUCCESS");
|
||||
|
||||
if (!ids.isEmpty()) {
|
||||
map(Lists.newArrayList(new SubTask()), "L1_FILE_PROCESS");
|
||||
}
|
||||
|
||||
// map 阶段的结果，由于前置逻辑为异常直接抛出，执行到这里一定成功，所以无脑设置为 success。开发者可自行调整逻辑
|
||||
return new ProcessResult(true, "MAP_SUCCESS,totalNum:" + num);
|
||||
|
||||
}
|
||||
|
||||
// 如果是简单的二层结构（ROOT - SubTASK），此处一定是子 Task，无需再次判断。否则可使用 TaskContext#taskName 字符串匹配 或 TaskContext#SubTask 对象内自定义参数匹配，进入目标执行分支
|
||||
|
||||
// 获取前置节点 map 传递过来的参数，进行业务处理
|
||||
SubTask subTask = (SubTask) context.getSubTask();
|
||||
log.info("[MapReduceDemo] [SubTask] taskId:{}, taskName: {}, subTask: {}", context.getTaskId(), taskName, JsonUtils.toJSONString(subTask));
|
||||
Thread.sleep(MapUtils.getLong(params, "bizProcessCost", 233L));
|
||||
|
||||
// 模拟有成功有失败的情况，开发者按真实业务执行情况判断即可
|
||||
long successRate = MapUtils.getLong(params, "successRate", 80L);
|
||||
long randomNum = ThreadLocalRandom.current().nextLong(100);
|
||||
if (successRate > randomNum) {
|
||||
return new ProcessResult(true, "PROCESS_SUCCESS:" + randomNum);
|
||||
} else {
|
||||
log.info("==== NORMAL_PROCESS ====");
|
||||
omsLogger.info("[DemoMRProcessor] process subTask: {}.", JSON.toJSONString(context.getSubTask()));
|
||||
log.info("subTask: {}", JsonUtils.toJSONString(context.getSubTask()));
|
||||
Thread.sleep(1000);
|
||||
if (context.getCurrentRetryTimes() == 0) {
|
||||
return new ProcessResult(false, "FIRST_FAILED");
|
||||
} else {
|
||||
return new ProcessResult(true, "PROCESS_SUCCESS");
|
||||
}
|
||||
return new ProcessResult(false, "PROCESS_FAILED:" + randomNum);
|
||||
}
|
||||
}
|
||||
|
||||
@Override
|
||||
public ProcessResult reduce(TaskContext context, List<TaskResult> taskResults) {
|
||||
log.info("================ MapReduceProcessorDemo#reduce ================");
|
||||
log.info("TaskContext: {}", JSONObject.toJSONString(context));
|
||||
|
||||
// 子任务结果太大，上报在线日志会有 IO 问题，直接使用本地日志打
|
||||
log.info("List<TaskResult>: {}", JSONObject.toJSONString(taskResults));
|
||||
context.getOmsLogger().info("MapReduce job finished, result is {}.", taskResults);
|
||||
|
||||
boolean success = ThreadLocalRandom.current().nextBoolean();
|
||||
return new ProcessResult(success, context + ": " + success);
|
||||
}
|
||||
OmsLogger omsLogger = context.getOmsLogger();
|
||||
omsLogger.info("================ MapReduceProcessorDemo#reduce ================");
|
||||
|
||||
@Getter
|
||||
@Setter
|
||||
@ToString
|
||||
@AllArgsConstructor
|
||||
public static class TestSubTask {
|
||||
// 所有 Task 执行结束后，reduce 将会被执行，taskResults 保存了所有子任务的执行结果。（注意 reduce 由于保存了所有子任务的执行结果，在子任务规模巨大时对内存有极大开销，超大型计算任务慎用或使用流式 reduce（开发中））
|
||||
|
||||
/**
|
||||
* 注意：代表子任务参数的类：一定要有无参构造方法！一定要有无参构造方法！一定要有无参构造方法！
|
||||
* 最好把 GET / SET 方法也加上，减少序列化问题的概率
|
||||
*/
|
||||
public TestSubTask() {
|
||||
// 用法举例：统计执行结果
|
||||
AtomicLong successCnt = new AtomicLong(0);
|
||||
AtomicLong failedCnt = new AtomicLong(0);
|
||||
taskResults.forEach(tr -> {
|
||||
if (tr.isSuccess()) {
|
||||
successCnt.incrementAndGet();
|
||||
} else {
|
||||
failedCnt.incrementAndGet();
|
||||
}
|
||||
});
|
||||
|
||||
|
||||
double successRate = 1.0 * successCnt.get() / (successCnt.get() + failedCnt.get());
|
||||
|
||||
String resultMsg = String.format("succeedTaskNum:%d,failedTaskNum:%d,successRate:%f", successCnt.get(), failedCnt.get(), successRate);
|
||||
omsLogger.info("[MapReduceDemo] [Reduce] {}", resultMsg);
|
||||
|
||||
// reduce 阶段的结果，将作为任务真正执行结果
|
||||
if (successRate > 0.8) {
|
||||
return new ProcessResult(true, resultMsg);
|
||||
} else {
|
||||
return new ProcessResult(false, resultMsg);
|
||||
}
|
||||
|
||||
private String name;
|
||||
private int age;
|
||||
}
|
||||
|
||||
|
||||
/**
|
||||
* 自定义的子任务，按自己的业务需求定义即可
|
||||
* 注意：代表子任务参数的类：一定要有无参构造方法！一定要有无参构造方法！一定要有无参构造方法！
|
||||
* 最好把 GET / SET 方法也加上，减少序列化问题的概率
|
||||
*/
|
||||
@Data
|
||||
@AllArgsConstructor
|
||||
private static class SubTask implements Serializable {
|
||||
|
||||
/**
|
||||
* 再次强调，一定要有无参构造方法
|
||||
*/
|
||||
public SubTask() {
|
||||
}
|
||||
|
||||
private Long siteId;
|
||||
|
||||
private List<Long> idList;
|
||||
|
||||
private String extra;
|
||||
}
|
||||
}
|
||||
|
||||
@ -16,17 +16,18 @@ import java.util.Set;
|
||||
@AllArgsConstructor
|
||||
public enum TaskStatus {
|
||||
|
||||
WAITING_DISPATCH(1, "等待调度器调度"),
|
||||
DISPATCH_SUCCESS_WORKER_UNCHECK(2, "调度成功（但不保证worker收到）"),
|
||||
WORKER_RECEIVED(3, "worker接收成功，但未开始执行"),
|
||||
WORKER_PROCESSING(4, "worker正在执行"),
|
||||
WORKER_PROCESS_FAILED(5, "worker执行失败"),
|
||||
WORKER_PROCESS_SUCCESS(6, "worker执行成功");
|
||||
WAITING_DISPATCH(1, "等待调度器调度", "dispatching"),
|
||||
DISPATCH_SUCCESS_WORKER_UNCHECK(2, "调度成功（但不保证worker收到）", "unreceived"),
|
||||
WORKER_RECEIVED(3, "worker接收成功，但未开始执行", "received"),
|
||||
WORKER_PROCESSING(4, "worker正在执行", "running"),
|
||||
WORKER_PROCESS_FAILED(5, "worker执行失败", "failed"),
|
||||
WORKER_PROCESS_SUCCESS(6, "worker执行成功", "succeed");
|
||||
|
||||
public static final Set<Integer> FINISHED_STATUS = Sets.newHashSet(WORKER_PROCESS_FAILED.value, WORKER_PROCESS_SUCCESS.value);
|
||||
|
||||
private final int value;
|
||||
private final String des;
|
||||
private final String simplyDesc;
|
||||
|
||||
public static TaskStatus of(int v) {
|
||||
for (TaskStatus taskStatus : values()) {
|
||||
|
||||
@ -4,6 +4,7 @@ import com.google.common.collect.Lists;
|
||||
import com.google.common.util.concurrent.ThreadFactoryBuilder;
|
||||
import lombok.ToString;
|
||||
import lombok.extern.slf4j.Slf4j;
|
||||
import org.apache.commons.lang3.StringUtils;
|
||||
import tech.powerjob.common.PowerJobDKey;
|
||||
import tech.powerjob.common.RemoteConstant;
|
||||
import tech.powerjob.common.SystemInstanceResult;
|
||||
@ -113,11 +114,12 @@ public class CommonTaskTracker extends HeavyTaskTracker {
|
||||
detail.setTaskDetail(taskDetail);
|
||||
|
||||
// 填充最近的任务结果
|
||||
String customQuery = Optional.ofNullable(req.getCustomQuery()).orElse(" status in (5, 6) order by last_modified_time ");
|
||||
customQuery = customQuery.concat(" limit 10");
|
||||
List<TaskDO> queriedTaskDos = taskPersistenceService.getTaskByQuery(instanceId, customQuery);
|
||||
List<TaskDetailInfo> taskDetailInfoList = Optional.ofNullable(queriedTaskDos).orElse(Collections.emptyList()).stream().map(TaskConverter::taskDo2TaskDetail).collect(Collectors.toList());
|
||||
detail.setQueriedTaskDetailInfoList(taskDetailInfoList);
|
||||
if (StringUtils.isNotEmpty(req.getCustomQuery())) {
|
||||
String customQuery = req.getCustomQuery().concat(" limit 10");
|
||||
List<TaskDO> queriedTaskDos = taskPersistenceService.getTaskByQuery(instanceId, customQuery);
|
||||
List<TaskDetailInfo> taskDetailInfoList = Optional.ofNullable(queriedTaskDos).orElse(Collections.emptyList()).stream().map(TaskConverter::taskDo2TaskDetail).collect(Collectors.toList());
|
||||
detail.setQueriedTaskDetailInfoList(taskDetailInfoList);
|
||||
}
|
||||
|
||||
return detail;
|
||||
}
|
||||
|
||||
@ -250,6 +250,7 @@ public class DbTaskPersistenceService implements TaskPersistenceService {
|
||||
SimpleTaskQuery simpleTaskQuery = new SimpleTaskQuery();
|
||||
simpleTaskQuery.setInstanceId(instanceId);
|
||||
simpleTaskQuery.setFullCustomQueryCondition(customQuery);
|
||||
simpleTaskQuery.setReadOnly(true);
|
||||
try {
|
||||
return execute(() -> taskDAO.simpleQuery(simpleTaskQuery), cost -> log.warn("[TaskPersistenceService] [Slow] [{}] getTaskByQuery cost {}ms", instanceId, cost));
|
||||
}catch (Exception e) {
|
||||
|
||||
@ -44,6 +44,12 @@ public class SimpleTaskQuery {
|
||||
*/
|
||||
private String fullCustomQueryCondition;
|
||||
|
||||
/**
|
||||
* 是否设置为只读模式
|
||||
* 理论上全部查询均可设置，不过出于最小改动原则，仅针对新功能添加 readOnly
|
||||
*/
|
||||
private boolean readOnly = false;
|
||||
|
||||
public String getQueryCondition() {
|
||||
|
||||
StringBuilder sb = new StringBuilder();
|
||||
|
||||
@ -99,6 +99,9 @@ public class TaskDAOImpl implements TaskDAO {
|
||||
String sql = "select * from task_info where " + query.getQueryCondition();
|
||||
List<TaskDO> result = Lists.newLinkedList();
|
||||
try (Connection conn = connectionFactory.getConnection(); PreparedStatement ps = conn.prepareStatement(sql)) {
|
||||
if (query.isReadOnly()) {
|
||||
conn.setReadOnly(true);
|
||||
}
|
||||
rs = ps.executeQuery();
|
||||
while (rs.next()) {
|
||||
result.add(convert(rs));
|
||||
|
||||
@ -1,6 +1,8 @@
|
||||
package tech.powerjob.worker.pojo.converter;
|
||||
|
||||
import tech.powerjob.common.model.TaskDetailInfo;
|
||||
import tech.powerjob.common.serialize.JsonUtils;
|
||||
import tech.powerjob.common.serialize.SerializerUtils;
|
||||
import tech.powerjob.worker.common.constants.TaskStatus;
|
||||
import tech.powerjob.worker.persistence.TaskDO;
|
||||
|
||||
@ -17,13 +19,19 @@ public class TaskConverter {
|
||||
taskDetailInfo.setTaskId(taskDO.getTaskId())
|
||||
.setTaskName(taskDO.getTaskName())
|
||||
.setStatus(taskDO.getStatus())
|
||||
.setStatusStr(TaskStatus.of(taskDetailInfo.getStatus()).name())
|
||||
.setStatusStr(TaskStatus.of(taskDetailInfo.getStatus()).getSimplyDesc())
|
||||
.setResult(taskDO.getResult())
|
||||
.setFailedCnt(taskDO.getFailedCnt())
|
||||
.setProcessorAddress(taskDO.getAddress())
|
||||
.setCreatedTime(taskDO.getCreatedTime())
|
||||
.setLastModifiedTime(taskDO.getLastModifiedTime())
|
||||
.setLastReportTime(taskDO.getLastReportTime());
|
||||
|
||||
try {
|
||||
taskDetailInfo.setTaskContent(JsonUtils.toJSONString(SerializerUtils.deSerialized(taskDO.getTaskContent())));
|
||||
} catch (Exception ignore) {
|
||||
}
|
||||
|
||||
return taskDetailInfo;
|
||||
}
|
||||
}
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user