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refactor: move remote WAL under the disaster recovery #1186
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Deploying greptime-docs with Cloudflare Pages
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remote wal is not for DR only, we'd better keep it standalone. |
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"description": "The label for category Disaster Recovery in sidebar docs" | |||
}, | |||
"sidebar.docs.category.Remote WAL": { | |||
"message": "Remote WAL", | |||
"message": "远程 WAL", |
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Remote
is fine; translation isn't needed.
@@ -36,7 +36,7 @@ ORDER BY datanode_id ASC | |||
## 数据管理 | |||
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* [存储位置说明](/user-guide/concepts/storage-location.md)。 | |||
* 通过 [设置Remote WAL](./remote-wal/quick-start.md) 实现 GreptimeDB 的集群容灾。 | |||
* 通过 [设置Remote WAL](/user-guide/operations/disaster-recovery/remote-wal/quick-start.md) 实现 GreptimeDB 的集群容灾。 |
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* 通过 [设置Remote WAL](/user-guide/operations/disaster-recovery/remote-wal/quick-start.md) 实现 GreptimeDB 的集群容灾。 | |
* 通过 [设置 Remote WAL](/user-guide/operations/disaster-recovery/remote-wal/quick-start.md) 实现 GreptimeDB 的集群容灾。 |
@@ -42,7 +42,7 @@ GreptimeDB 基于存储计算分离的云原生架构设计: | |||
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* **Frontend**:数据插入和查询的服务层,将请求转发到 Datanode 并处理和合并 Datanode 的响应。 | |||
* **Datanode**:GreptimeDB 的存储层,是一个 LSM 存储引擎。Region 是在 Datanode 中存储和调度数据的基本单元。Region 是一个表分区,是一组数据行的集合。Region 中的数据保存在对象存储中(例如 AWS S3)。未刷新的 Memtable 数据被写入 WAL,并可以在灾难发生时恢复。 | |||
* **WAL**:持久化内存中未刷新的 Memtable 数据。当 Memtable 被刷新到 SSTable 文件时,WAL 将被截断。它可以是基于本地磁盘的(本地 WAL)或基于 Kafka 集群的(远程 WAL)。 | |||
* **WAL**:持久化内存中未刷新的 Memtable 数据。当 Memtable 被刷新到 SSTable 文件时,WAL 将被截断。它可以是基于本地磁盘的(本地 WAL)或[基于 Kafka 集群的(远程 WAL)](./remote-wal/quick-start.md)。 |
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* **WAL**:持久化内存中未刷新的 Memtable 数据。当 Memtable 被刷新到 SSTable 文件时,WAL 将被截断。它可以是基于本地磁盘的(本地 WAL)或[基于 Kafka 集群的(远程 WAL)](./remote-wal/quick-start.md)。 | |
* **WAL**:持久化内存中未刷新的 Memtable 数据。当 Memtable 被刷新到 SSTable 文件时,WAL 将被截断。它可以是基于本地磁盘的(Local WAL)或[基于 Kafka 集群的(Remote WAL)](./remote-wal/quick-start.md)。 |
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# 快速开始 | |||
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## 什么是 Remote WAL | |||
## 什么是远程 WAL(Remote WAL) |
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## 什么是远程 WAL(Remote WAL) | |
## 什么是 Remote WAL |
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[WAL](/contributor-guide/datanode/wal.md#introduction)(Write-Ahead Logging) 是 GreptimeDB 中的一个关键组件,它持久记录每一次数据修改,以确保不会丢失缓存在内存中的数据。我们在 [Datanode](/user-guide/concepts/why-greptimedb.md) 服务中用持久的嵌入式存储引擎 [raft-engine](https://github.com/tikv/raft-engine) 将 WAL 实现为一个模块。在公共云中部署 GreptimeDB 时,我们可以在云存储(AWS EBS、GCP 持久盘等)中持久存储 WAL 数据,以实现 0 RPO。然而,由于 WAL 与 Datanode 紧密耦合,导致部署过程中的 RTO(Recovery Time Objective)较长。此外,由于 raft-engine 无法支持多日志订阅,这使得实现 region 热备份和 region 迁移变得困难。 | ||
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为了解决上述问题,我们决定设计并实现一个远程 WAL。远程 WAL 将 WAL 从 Datanode 分离到远程服务,我们选择了 Apache Kafka 作为远程服务。Apache Kafka 在流处理中被广泛采用,展现出卓越的分布式容错能力和基于主题的订阅机制。在发布 v0.5.0 版本时,我们引入了 Apache Kafka 作为 WAL 的可选存储引擎。 | ||
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## 运行带有 Remote WAL 的 Standalone GreptimeDB | ||
## 运行带有远程 WAL 的 Standalone GreptimeDB |
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## 运行带有远程 WAL 的 Standalone GreptimeDB | |
## 运行带有 Remote WAL 的 Standalone GreptimeDB |
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What's Changed in this PR
as title
Checklist
sidebars.ts
matches the current document structure when you changed the document structure.