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  • 来自专栏SmartSi

    Flink Savepoints和Checkpoints的3个不同点

    上面所有关于 Savepoints 的内容听起来与我们在之前的文章中对 Checkpoints 的介绍非常相似。

    4.4K20发布于 2020-12-29
  • 来自专栏爱可生开源社区

    MySQL 核心模块揭秘 | 12 期 | 创建 savepoint

    trx_savepoints:InnoDB 中多个 trx_named_savept_t 对象形成的链表。 用户线程中有一个 m_savepoints 链表,用户创建的多个 savepoint 通过 prev 属性形成链表,m_savepoints 就指向最新创建的 savepoint。 删除同名 savepoint 如果在用户线程的 m_savepoints 链表中找到了和本次创建的 savepoint 同名的 savepoint,需要先删除 m_savepoints 链表中的同名 savepoint InnoDB 从事务对象的 trx_savepoints 链表中删除 trx_named_savept_t 对象之后,server 层接着从用户线程的 m_savepoints 链表中删除 server server 层创建的 SAVEPOINT 对象会放入 m_savepoints 链表的末尾。

    22110编辑于 2024-04-11
  • 来自专栏爱可生开源社区

    MySQL 核心模块揭秘 | 13 期 | 回滚到 savepoint

    查找 savepoint 每个用户线程都有一个 m_savepoints 链表,用户每创建一个 savepoint,它的对象都会追加到链表末尾。 m_savepoints 链表的指针,指向最新加入的 savepoint 对象。 m_savepoints 链表的指针指向最新加入的 savepoint 对象,查找过程自然就是从后往前了。 从后往前遍历 m_savepoints 链表的过程中,如果当前遍历的 savepoint 对象名字等于要回滚的那个 savepoint 对象名字,就找到了,否则,继续往前遍历。 删除 savept3 之后,m_savepoints 链表如下图所示: 6. 总结 回滚到某个 savepoint,首先要从 m_savepoints 链表中找到这个 savepoint。

    54610编辑于 2024-04-11
  • 来自专栏码匠的流水账

    聊聊flink的checkpoint配置

    state.checkpoints.dir: hdfs://namenode-host:port/flink-checkpoints ​ # Default target directory for savepoints , optional. # # state.savepoints.dir: hdfs://namenode-host:port/flink-checkpoints ​ # Flag to enable/ 该目录必须对所有参与的TaskManagers及JobManagers可见 state.checkpoints.num-retained,默认为1,用于指定保留的已完成的checkpoints个数 state.savepoints.dir ,默认为none,用于指定savepoints的默认目录 taskmanager.state.local.root-dirs,默认为none 小结 可以通过使用StreamExecutionEnvironment.enableCheckpointing 的相关配置,主要是state backend的配置,比如state.backend.async、state.backend.incremental、state.checkpoints.dir、state.savepoints.dir

    5.7K32发布于 2018-12-09
  • 来自专栏DataLink数据中台

    用户投稿 | Dinky 从保存点恢复 FlinkSQL 作业

    # Default target directory for savepoints, optional. # # state.savepoints.dir: hdfs://namenode-host:port /flink-savepoints state.savepoints.dir: hdfs://bd171:8020/sp 二、在 Dinky 中恢复 FlinkSQL 作业 创建 Yarn Session

    1.2K40编辑于 2023-10-24
  • 来自专栏后端码事

    mysql 数据库引擎

    PERFORMANCE_SCHEMA Support: YES Comment: Performance Schema Transactions: NO XA: NO Savepoints Comment: Supports transactions, row-level locking, and foreign keys Transactions: YES XA: YES Savepoints Support: YES Comment: Collection of identical MyISAM tables Transactions: NO XA: NO Savepoints /dev/null storage engine (anything you write to it disappears) Transactions: NO XA: NO Savepoints Engine: MyISAM Support: YES Comment: MyISAM storage engine Transactions: NO XA: NO Savepoints

    1.6K20发布于 2020-09-11
  • 来自专栏时悦的学习笔记

    MySQL information_schema详解 ENGINES

    ENGINE 存储引擎的名称 SUPPORT 存储引擎的支持级别,后面做介绍 COMMENT 对于该存储引擎的一个简介 TRANSACTIONS 该存储引擎是否支持事务 XA 该存储引擎是否支持分布式事务 SAVEPOINTS 该存储引擎是否支持保存点(SAVEPOINTS) 接下来对上面的一些栏位做进一步的介绍 1.1 存储引擎的支持级别 该表的SUPPORT栏位有如下可能的值 值 意义 YES 引擎是被支持的且被激活 DEFAULT

    1.1K20发布于 2020-08-18
  • 来自专栏Lansonli技术博客

    大数据必学Java基础(九十七):事务及回滚点

    Connection connection = null; PreparedStatement preparedStatement=null; LinkedList<Savepoint> savepoints // 设置回滚点 Savepoint savepoint = connection.setSavepoint(); savepoints.addLast = connection){ try { //Savepoint sp = savepoints.getLast(); Savepoint sp = savepoints.get(4); if(null !

    66141编辑于 2022-12-15
  • Flink Savepoint深度解析:版本管理、升级部署与实操全指南

    这条命令会为作业ID为a1b2c3d4e5f6g7h8的作业生成Savepoint,存储到/tmp/savepoints目录,并关联YARN应用ID以确保集群模式一致性。 例如,如果Savepoint存储在HDFS上,用户可以运行: hdfs dfs -ls /tmp/savepoints 这会显示所有Savepoint目录,名称通常包含作业ID和时间戳,便于识别。 例如,对于HDFS存储: hdfs dfs -rm -r /tmp/savepoints/savepoint-a1b2c3-202507251030 或者对于本地文件系统: rm -rf /tmp/savepoints /bin/bash JOB_ID=$(bin/flink list | grep RUNNING | awk '{print $4}') SAVEPOINT_DIR="/tmp/savepoints" 查询 Savepoint 状态 端点:/jobs/:jobid/savepoints/:triggerid 方法:GET 功能:根据触发器 ID 查询 Savepoint 操作的执行状态(如进行中、

    34810编辑于 2025-11-28
  • 来自专栏大数据

    Flink容错机制:Checkpoint和Savepoint深入解析

    集群迁移与回滚在跨集群迁移或版本升级失败时,Savepoint提供状态级回滚能力:# 从旧集群导出Savepointflink savepoint job-123 hdfs://old-cluster/savepoints # 在新集群恢复作业flink run -s hdfs://old-cluster/savepoints/savepoint-abc123 MyApp.jar此过程无需重新处理原始数据,大幅缩短迁移时间 解决方案:使用@SavepointMigration注解处理状态迁移通过StateMigration工具手动转换格式保留历史版本的StateDescriptor定义资源优化策略压缩存储:配置state.savepoints.dir 使用Snappy压缩state.savepoints.dir: hdfs:///flink/savepoints? compression=SNAPPY增量清理:设置state.savepoints.cleanup-strategy避免存储膨胀冷热分离:将近期Savepoint存HDFS,历史归档至S3Checkpoint

    65520编辑于 2025-10-22
  • 来自专栏码匠的流水账

    聊聊flink的FsStateBackend

    . */ @Nullable private final Path baseCheckpointPath; ​ /** The path where savepoints will * @param baseSavepointPath The default directory for savepoints, or null, if none is set. * @param baseSavepointPath The default directory for savepoints, or null, if none is set. * * @return The default directory for savepoints, or null, if no default directory has been File State Backend (" + "checkpoints: '" + getCheckpointPath() + "', savepoints

    1.4K10发布于 2018-12-15
  • 来自专栏码匠的流水账

    聊聊flink的FsStateBackend

    . */ @Nullable private final Path baseCheckpointPath; /** The path where savepoints will * @param baseSavepointPath The default directory for savepoints, or null, if none is set. * @param baseSavepointPath The default directory for savepoints, or null, if none is set. * * @return The default directory for savepoints, or null, if no default directory has been File State Backend (" + "checkpoints: '" + getCheckpointPath() + "', savepoints

    86070发布于 2018-12-26
  • 来自专栏chaplinthink的专栏

    Flink checkpoint

    Savepoint目录,有两种方式来指定 需要配置Savepoint的默认路径,需要在Flink的配置文件conf/flink-conf.yaml中,添加如下配置,设置Savepoint存储目录 state.savepoints.dir : hdfs://namenode01.td.com/flink/flink-savepoints 手动执行savepoint命令的时候,指定Savepoint存储目录 bin/flink savepoint Savepoint数据 bin/flink savepoint 40dcc6d2ba90f13930abce295de8d038 hdfs://namenode01.td.com/tmp/flink/savepoints savepointPath [:runArgs] 以上面保存的Savepoint为例,恢复Job运行 bin/flink run -s hdfs://namenode01.td.com/tmp/flink/savepoints

    1K20编辑于 2022-05-19
  • 来自专栏码匠的流水账

    聊聊flink的MemoryStateBackend

    . */ @Nullable private final Path baseCheckpointPath; ​ /** The path where savepoints will MemoryStateBackend, setting optionally the path to persist checkpoint metadata * to, and to persist savepoints * @param savepointPath The path to write savepoints to. Creates a new MemoryStateBackend, setting optionally the paths to persist checkpoint metadata * and savepoints * @param savepointPath The path to write savepoints to.

    1K30发布于 2018-12-10
  • 来自专栏全栈程序员必看

    sql数据库回滚操作_sql回滚语句 rollback

    其实我们可以使用SQL Server中的Savepoints来解决上述问题。 示例如下: 1.先建立测试表: CREATE TABLE [dbo]. values(4,’4′); rollback tran point1 commit 执行结果如下: Id mark 3 3 可见,虽然3,4都在一个事务中,但是由于使用了SavePoints

    4.9K30编辑于 2022-09-27
  • 来自专栏码匠的流水账

    聊聊flink的MemoryStateBackend

    . */ @Nullable private final Path baseCheckpointPath; /** The path where savepoints will MemoryStateBackend, setting optionally the path to persist checkpoint metadata * to, and to persist savepoints * @param savepointPath The path to write savepoints to. Creates a new MemoryStateBackend, setting optionally the paths to persist checkpoint metadata * and savepoints * @param savepointPath The path to write savepoints to.

    54320发布于 2018-12-25
  • 来自专栏SmartSi

    Flink1.4 外部检查点

    但是,你可以配置检查点定期持久化存储在外部系统中,类似于保存点(savepoints)。这些外部持久化的检查点将其元数据写入持久性存储中,即使在作业失败时也不会自动清除。 checkpointMetaDataPath [:runArgs] 备注: Flink版本:1.4 术语翻译: 术语 翻译 Checkpoints 检查点 Externalized Checkpoints 外部检查点 savepoints

    1.6K20发布于 2019-08-07
  • Flink中StateBackend配置不当导致的Checkpoint失败问题排查

    第五步:修改配置并验证效果最终,我们决定显式设置 state.checkpoints.dir 和 state.savepoints.dir,并将它们指向一个稳定的共享存储路径(如 HDFS 或 S3)。 state.backend=rocksdbstate.checkpoints.dir=hdfs:///flink/checkpointsstate.savepoints.dir=hdfs:///flink /savepoints在更新配置后,我们重新部署了作业,并观察了日志,发现 Checkpoint 失败的情况明显减少,作业运行更加稳定。

    38610编辑于 2025-09-15
  • 来自专栏猫头虎博客专区

    《PostgreSQL事务管理深入解析》

    ALTER SYSTEM SET wal_level = 'minimal'; 4.3 Savepoints Savepoints 允许事务在进行部分回滚时定义一个保存点,以便稍后可以回到该点继续执行。 可以使用 SAVEPOINT 和 ROLLBACK TO 语句来操作 Savepoints

    43510编辑于 2024-04-09
  • 来自专栏Spark学习技巧

    Flink流式处理概念简介

    十三,Savepoints 使用Data Stream API编写的程序可以从Savepoints恢复执行。Savepoints允许更新程序和Flink集群,而不会丢失任何状态。 Savepoints 是手动触发的checkpoints,它们记录程序的快照并将其写入状态后端。他们依靠这个常规的检查点机制。执行过程中,定期在工作节点上快照并生成检查点。 Savepoints 与这些定期checkpoints类似,除了它们由用户触发,并且在较新的检查点完成时不会自动过期。可以从命令行创建保存点,也可以通过REST API取消作业。

    2.3K60发布于 2018-01-30
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