!!!Chapter 7 Deadlock

本文深入探讨了死锁的概念,包括必要条件、资源分配图和解决方法。通过阐述预防、避免死锁的策略,如互斥、持有等待、无预抢占和循环等待条件的管理,以及资源分配图算法的应用,旨在为系统设计者提供有效防止和检测死锁的指南。

A process requests resources; if the resources are not available at that time, the process enters a waiting state. Sometimes, a waiting process is never again able to change state, because the resources it has requested are held by other waiting processes. This situation is called a deadlock.

7.1 System Model

A process may utilize a resource in following sequence:

1. Request. The process requests the resource. If the request cannot be granted immediately, then the requesting process must wait until it can acquire the resource.

2. Use. The process can operate on the resource.

3. Release. The process releases the resource.

7.2 Deadlock Characterization

7.2.1 Necessary Conditions

1. Mutual exclusion. At least one resource must be held in a nonsharable mode; that is, only one process at a time can use the resource.

2. Hold and wait. A process must be holding at least one resource and waiting to acquire additional resources that are currently being held by other processes.

3. No preemption. Resources cannot be preempted; that is, a resource can be released only voluntarily by the process holding it, after that process has completed task.

4. Circular wait. A set{P0, P1...Pn} of waiting processes must exist such that P0 is waiting for resource hold by P1, P1 is waiting for...

7.2.2 Resource-Allocation Graph

System resource-allocation graph contains a set of vertices V and a set of edges E. Vertices V has two different types of nodes P = {P1, P2...} means active processes; R = {R1, R2...} means all resource types.

A directed edge Pi -> Rj is a request edge; a direct edge Rj -> Pi is called aassignment edge.

If the graph contains no cycles, then no process in the system is deadlocked. If the graph does contain cycle, then a deadlock may exist.

7.3 Methods for Handling Deadlocks

We can deal with deadlock in three ways:

  • User protocol to prevent or avoid deadlocks.
  • We can allow system enter deadlock, detect it, and recover
  • We can ignore the problem, pretend deadlock never occur.  (This is used by most OS)

7.4 Deadlock Prevention

Deadlock prevention provides a set of methods for ensuring that at least one of the necessary conditions cannot hold.

7.4.1 Mutual Exclusion

The mutual-exclusion condition must hold for nonsharable resources.

In general, however, we cannot prevent deadlocks by denying the mutual-exclusion condition, because some resources are intrinsically nonsharable.

7.4.2 Hold and Wait          Method 1

To ensure hold-and-wait never occur, we must guarantee that whenever a process requests a resource, it does not hold any other resources.

One protocol can require each process to request and be allocated all its resources before it begins execution.

Another protocol allows a process to request resources only when it has none.

- resource utilization may be low

- starvation is possible

7.4.3 No Preemption        Method 2

Protocol one: if a process is holding some resources and requests another resource that cannot be immediately allocated to it, then all resources the process is currently holding are preempted.

Protocol two: if a process request some resources, we first check whether they are available. If they are, we allocate them. If they are not, we check whether they are allocated to some other process that is waiting for additional resources. If so, we preempt the desired resources from the waiting process and allocate them to the requesting process.

This protocol is often applied to resources whose state can be easily saved and restored later.

7.4.4 Circular Wait           Method 3

One way to ensure this condition never hold is to impose a total ordering of all resource types and to require that each process requests resources in an increasing order of enumeration.

7.5 Deadlock Avoidance

An alternative method for avoiding deadlocks is to require additional information about how resources are to be requested.

The simplest and most useful model requires that each process declare the maximum number of resources of each type that it may need.

A deadlock-avoidance algorithm dynamically examines the resource-allocation state to ensure that a circular-wait condition can never exist.

7.5.1 Safe State

A system is in a safe state only if there exists a safe sequence.

A sequence of processes <P1, P2, ... Pn> is a safe sequence for the current allocation state if, for each Pi, the resource requests that Pi can still make can be satisfied by the currently available resources plus the resources held by all Pj, with j < i.


We can define avoidance algorithms that ensure the system will always remain in a safe state.

7.5.2 Resource-Allocation-Graph Algorithm

If the resource-allocation system has only one instance of each resource type, we can use resource-allocation-graph algorithm.

In addition to the request and assignment edges, we add a new type of edge calledclaim edge. A claim edge Pi ---> Rj indicates that process Pi may request resource Rj at some time in the future.
Suppose process Pi requests resource Rj. The request can be granted only if converting the request edge Pi ---> Rj to an assignment edge Rj ---> Pi does not result in the information of a cycle in the resource-allocation-graph. The cycle-detection algorithm takes n^2, n is the number of processes. 








<think>我们正在解决Spark任务中更新PostgreSQL时出现的死锁问题。死锁通常发生在多个事务试图以不同顺序锁定资源时。在Spark并行写入PostgreSQL的场景中,多个任务可能同时尝试更新相同的数据行,导致死锁。 解决方案思路: 1. 减少并发写入:通过调整Spark的写入并发度(如降低分区数)来减少同时进行的事务数量。 2. 确保写入顺序:如果多个任务更新相同的行,确保它们以相同的顺序更新,避免循环等待。 3. 重试机制:捕获死锁异常并重试该事务。 4. 批量更新策略:使用批量更新代替逐行更新,减少事务冲突。 5. 调整事务隔离级别:使用更低的隔离级别(如读已提交)以减少锁冲突,但需注意数据一致性。 具体步骤: 1. 降低并发度: 在写入DataFrame时,可以通过`repartition`或`coalesce`减少分区数,从而减少同时写入的任务数。 ```scala val df = ... // 你的DataFrame df.repartition(5) // 将数据重新分区为5个分区 .write .format("jdbc") .option("url", jdbcUrl) .option("dbtable", tableName) .option("user", username) .option("password", password) .mode(SaveMode.Append) // 或Overwrite,根据需求 .save() ``` 2. 使用批量插入/更新: Spark的JDBC写入默认是并行执行的,每个分区一个事务。我们可以设置批量写入参数,但注意PostgreSQL的JDBC驱动中,批量更新需要设置`rewriteBatchedInserts=true`(仅对INSERT有效)。对于UPDATE,可能需要采用其他方式。 注意:Spark的JDBC写入不支持批量更新(UPDATE),通常使用INSERT或覆盖的方式。如果必须更新,可以考虑: a) 使用临时表,然后通过一条UPDATE语句批量更新(在PostgreSQL中执行)。 b) 使用`foreachPartition`自定义更新逻辑,并在每个分区内使用批量更新。 3. 自定义foreachPartition实现批量更新并重试: 我们可以使用`foreachPartition`在每个分区内进行批量更新,并加入重试逻辑。 ```scala import java.sql.{Connection, DriverManager, PreparedStatement} import org.apache.spark.sql.{Row, SparkSession} import scala.util.{Try, Success, Failure} // 假设df是要更新的DataFrame,并且有需要更新的列和条件列 df.foreachPartition { partition: Iterator[Row] => // 每个分区一个连接 val url = "jdbc:postgresql://your_host/your_db" val username = "your_username" val password = "your_password" var connection: Connection = null var preparedStatement: PreparedStatement = null try { connection = DriverManager.getConnection(url, username, password) // 设置自动提交为false,以便批量提交 connection.setAutoCommit(false) // 设置事务隔离级别为读已提交,减少锁持有时间 connection.setTransactionIsolation(Connection.TRANSACTION_READ_COMMITTED) val sql = "UPDATE your_table SET column1 = ?, column2 = ? WHERE id = ?" preparedStatement = connection.prepareStatement(sql) // 设置重试次数 val maxRetries = 3 var currentRetry = 0 var success = false // 将整个分区数据分成小批量(每批100条)处理,避免一个大事务 val batchSize = 100 var count = 0 partition.grouped(batchSize).foreach { batch => var retry = false do { try { batch.foreach { row => preparedStatement.setString(1, row.getString(0)) preparedStatement.setString(2, row.getString(1)) preparedStatement.setInt(3, row.getInt(2)) // 假设id是第三个字段 preparedStatement.addBatch() } preparedStatement.executeBatch() connection.commit() // 提交当前批次 retry = false } catch { case e: Exception if e.getMessage.contains("deadlock detected") => // 死锁异常,回滚并重试 connection.rollback() currentRetry += 1 retry = currentRetry < maxRetries if (retry) { Thread.sleep(100 * currentRetry) // 指数退避? } else { throw e // 重试次数用尽,抛出异常 } } } while (retry) } } finally { if (preparedStatement != null) preparedStatement.close() if (connection != null) connection.close() } } ``` 4. 注意事项: - 在重试时,最好等待一段时间(如指数退避)。 - 确保连接资源正确释放。 - 批量大小(batchSize)不宜过大,避免大事务导致锁长时间持有。 5. 调整PostgreSQL配置: 可以调整PostgreSQL的死锁检测参数(如`deadlock_timeout`),但不推荐,因为可能掩盖问题。 6. 优化索引: 确保更新条件列(如WHERE子句中的列)有索引,减少锁的粒度。 引用: [1] PostgreSQL官方关于死锁的文档:https://www.postgresql.org/docs/current/explicit-locking.html#LOCKING-DEADLOCKS [2] Spark JDBC写入文档:https://spark.apache.org/docs/latest/sql-data-sources-jdbc.html 请注意,使用foreachPartition自定义写入会增加代码复杂度,但可以更灵活地控制事务和重试。 相关问题: 1. 如何调整Spark的分区数以优化写入性能? 2. 在Spark中如何使用自定义函数(如foreachPartition)处理数据? 3. PostgreSQL死锁产生的常见原因有哪些? 4. 如何使用JDBC在Scala中进行批量更新? </think>### PostgreSQL 死锁错误解决方案 在 Scala 编写的 Spark 任务中,使用 DataFrame 更新 PostgreSQL 时出现 `org.postgresql.util.PSQLException: ERROR: deadlock detected` 错误,通常是由于 **并行事务竞争资源** 导致的。以下是系统性的解决方案: --- ### 一、核心原因分析 1. **事务冲突** Spark 任务并行写入时,多个分区同时更新相同行或索引,导致事务相互等待(如:事务A持有行X锁请求行Y,事务B持有行Y锁请求行X)[^1]。 2. **锁粒度问题** PostgreSQL 默认行级锁,但批量更新可能导致锁升级到页级或表级。 3. **索引争用** 频繁更新索引字段(如主键)时,B-tree 索引分裂可能引发死锁[^2]。 --- ### 二、解决方案 #### ✅ 方案1:降低写入并发度 ```scala // 减少分区数(建议4-8个分区) df.repartition(4) .write .format("jdbc") .option("url", "jdbc:postgresql://host/db") .option("dbtable", "target_table") .option("user", "user") .option("password", "pass") // 关键参数:限制并发连接数 .option("numPartitions", 4) .mode("append") .save() ``` **原理**:通过 `numPartitions` 控制同时打开的JDBC连接数,减少锁竞争[^3]。 --- #### ✅ 方案2:批量提交与重试机制 ```scala import org.apache.spark.sql.{Row, SparkSession} import java.sql.{Connection, DriverManager, Statement} df.foreachPartition { partition: Iterator[Row] => Class.forName("org.postgresql.Driver") val conn: Connection = DriverManager.getConnection(...) conn.setAutoCommit(false) // 关闭自动提交 val stmt = conn.createStatement() val maxRetries = 3 partition.grouped(100).foreach { batch => // 每批100条 var retry = 0 var committed = false while (!committed && retry < maxRetries) { try { batch.foreach { row => val id = row.getInt(0) val value = row.getString(1) stmt.addBatch(s"UPDATE table SET col='$value' WHERE id=$id") } stmt.executeBatch() conn.commit() // 显式提交批次 committed = true } catch { case e: PSQLException if e.getMessage.contains("deadlock") => conn.rollback() Thread.sleep((math.pow(2, retry) * 100).toLong) // 指数退避重试 retry += 1 } } } stmt.close() conn.close() } ``` **优势**: - 小批量提交减少锁持有时间 - 指数退避重试应对临时死锁 --- #### ✅ 方案3:优化数据更新策略 1. **避免热点更新** ```scala // 添加随机前缀分散热点 df.withColumn("bucket", (rand() * 100).cast("int")) .repartition($"bucket") ``` 2. **使用 MERGE 语句** 在 PostgreSQL 9.5+ 中使用 `ON CONFLICT` 替代独立 UPDATE: ```sql INSERT INTO table (id, col) VALUES (?, ?) ON CONFLICT (id) DO UPDATE SET col = EXCLUDED.col ``` --- #### ✅ 方案4:PostgreSQL 配置调优 ```conf # postgresql.conf 调整 deadlock_timeout = 500ms # 降低死锁检测阈值(默认1s) max_locks_per_transaction = 128 # 增加锁数量配额 ``` **注意**:需重启 PostgreSQL 生效[^4]。 --- ### 三、预防措施 1. **索引优化** - 为 `WHERE` 条件列创建索引 - 避免冗余索引 ```sql CREATE INDEX CONCURRENTLY idx_id ON table (id); ``` 2. **监控工具** 使用 PostgreSQL 死锁检测: ```sql SELECT * FROM pg_stat_activity WHERE query LIKE '%UPDATE%'; ``` 3. **隔离级别调整** ```scala .option("isolationLevel", "READ_COMMITTED") // 默认REPEATABLE_READ ``` --- ### 总结建议 1. 优先使用 **批量提交+重试机制**(方案2) 2. 结合 **降低并发度**(方案1)和 **数据分桶**(方案3) 3. 高频更新场景考虑改用 **INSERT ON CONFLICT** 4. 生产环境部署前进行 **死锁压力测试** > 死锁是分布式系统的常见问题,解决方法通常需结合应用层逻辑和数据库配置 —— Martin Kleppmann《设计数据密集型应用》[^5] [^1]: PostgreSQL 官方锁文档 [^2]: B-tree 索引并发控制机制 [^3]: Spark JDBC 写入最佳实践 [^4]: PostgreSQL 事务配置指南 [^5]: 《Designing Data-Intensive Applications》Chapter 7 --- ### 相关问题 1. 如何监控 PostgreSQL 中的死锁事件? 2. Spark 写入数据库时有哪些性能优化技巧? 3. 数据库事务隔离级别对并发控制有何影响? 4. 除 PostgreSQL 外,Spark 集成其他数据库(如 MySQL)时如何避免死锁?
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