재해 복구 및 지역 간 복제
Kafka를 활용해 비즈니스 연속성을 보장하는 재해 복구 및 데이터센터 간 복제 전략을 구현합니다.
재해 복구 및 지역 간 복제은(는) CoddyKit의 무료 Apache Kafka & Stream Processing Fundamentals 강의입니다. 이것은 4개 중 2번째 강의입니다. 아래에서 전체 강의를 무료로 읽을 수 있으며, 내장 코드 에디터와 24/7 AI 튜터와 함께 브라우저에서 직접 실습할 수 있습니다. 이 강의는 Apache Kafka & Stream Processing Fundamentals 학습 경로의 일부이며, 진행 상황이 웹과 CoddyKit 앱에 동기화됩니다. Apache Kafka & Stream Processing Fundamentals 강의에는 총 4개의 강의가 포함되어 있습니다.
이 강의의 일부는 아직 번역되지 않았으며 영어로 표시됩니다.
Why Disaster Recovery?
In the world of real-time data, ensuring continuous operation is paramount. What happens if an entire datacenter hosting your Kafka cluster goes offline?
- Disaster Recovery (DR): A plan to recover from major outages and resume critical business functions.
- Geo-Replication: Replicating data across geographically distant locations to protect against regional disasters.
- The goal is to minimize data loss (Recovery Point Objective - RPO) and downtime (Recovery Time Objective - RTO).
Intra-Cluster Replication Isn't Enough
You've learned that Kafka topics are replicated across multiple brokers within a single Kafka cluster. This built-in replication protects against individual broker failures.
However, if an entire datacenter experiences a catastrophic event (e.g., power outage, network failure, natural disaster), this internal replication won't protect your data or services. All replicas in that datacenter would be lost.
The Need for Cross-Cluster Replication
To truly achieve disaster recovery, you need to replicate data beyond the boundaries of a single Kafka cluster. This means having a separate, operational Kafka cluster in a different physical location or region.
This setup allows your applications to seamlessly switch over to the secondary cluster if the primary one becomes unavailable, ensuring continuous data flow and service availability even during major outages.
Introducing Kafka MirrorMaker 2 (MM2)
Kafka MirrorMaker 2 (MM2) is the industry-standard tool for replicating data between distinct Kafka clusters. It's built on Kafka Connect, providing a robust and fault-tolerant replication framework.
MM2 can replicate not just topics, but also consumer groups, ACLs (Access Control Lists), and configurations from a source cluster to a target cluster, making it ideal for geo-replication scenarios.
How MM2 Works: Architecture
MM2 operates as a Kafka Connect cluster itself. It leverages Kafka Connect's framework to manage replication tasks:
- Source Connector: Reads messages from topics in the source Kafka cluster.
- Sink Connector: Writes those messages to topics in the target Kafka cluster.
- MM2 also replicates internal topics (like consumer offsets, heartbeats, and checkpoints) to ensure proper state transfer during failover.
Configuring MirrorMaker 2
MM2 is configured using properties files, much like a regular Kafka Connect worker. You define the source and target Kafka clusters and specify which topics should be replicated.
You can use regular expressions to match topic names, allowing for flexible replication of many topics with minimal configuration. This simplifies management of evolving topic sets.
MM2 Configuration Snippet
Here's a simplified example of a mirror-maker.properties file. This defines two clusters and enables replication from 'primary' to 'secondary' for specific topics:
clusters = primary, secondary
primary.bootstrap.servers = localhost:9092
secondary.bootstrap.servers = remotehost:9092
primary->secondary.enabled = true
primary->secondary.topics = topic-sales, topic-inventory
# You can also use regex for multiple topics:
# primary->secondary.topics = .*_eventsActive-Passive DR Pattern
This is a widely adopted disaster recovery pattern:
- Primary (Active) Cluster: Handles all application reads and writes under normal operation.
- Secondary (Passive) Cluster: Continuously replicates data from the primary and is on standby, not actively serving client requests.
If the primary fails, applications are reconfigured (e.g., via DNS changes) to point to the secondary. This pattern is simpler to manage but requires a clear failover process.
Active-Active DR Pattern
In an active-active setup, both Kafka clusters can simultaneously handle reads and writes. This can be for different applications or even the same applications if designed carefully.
This pattern offers higher availability and potentially lower latency for geographically dispersed users. However, it introduces significant complexity, especially in handling data conflicts and ensuring strict consistency across clusters.
Failover & Failback Considerations
Executing a failover (switching operations to the secondary cluster) and subsequent failback (returning to the primary) requires careful planning and automation:
- DNS Updates: Directing producers and consumers to the new active cluster.
- Consumer Group Management: Ensuring consumers resume processing from correct offsets on the new cluster.
- Data Sync: During failback, ensuring any data written to the secondary during the outage is synced back to the primary before switching.
MM2 Replication Check
You've learned about Kafka MirrorMaker 2's role in geo-replication. Consider its architecture and capabilities:
Recap: DR & Geo-Replication
This lesson covered the critical importance of disaster recovery and geo-replication for Kafka:
- Intra-cluster replication protects against broker failure, but not datacenter failure.
- Kafka MirrorMaker 2 (MM2) is the primary tool for robust cross-cluster replication.
- MM2 runs on Kafka Connect and can replicate topics, consumer groups, and more.
- Common DR patterns include Active-Passive (simpler failover) and Active-Active (higher availability, more complex).
- Successful failover and failback require meticulous planning and automation.
자주 묻는 질문
“재해 복구 및 지역 간 복제” 강의는 무료인가요?
네 — “재해 복구 및 지역 간 복제” 전체 내용을 이 웹사이트에서 무료로 읽을 수 있습니다. 인터랙티브하게 실습하려면(내장 코드 에디터와 24/7 AI 튜터), CoddyKit PRO로 업그레이드하면 Apache Kafka & Stream Processing Fundamentals 강의 전체를 잠금 해제할 수 있습니다. Apache Kafka & Stream Processing Fundamentals 강의에는 총 4개의 강의가 포함되어 있습니다.
“재해 복구 및 지역 간 복제”에서 뭘 배우나요?
Kafka를 활용해 비즈니스 연속성을 보장하는 재해 복구 및 데이터센터 간 복제 전략을 구현합니다. 브라우저에서 직접 실행하는 실습 코드로 Apache Kafka & Stream Processing Fundamentals을(를) 배우며, 24/7 AI 튜터가 강의를 진행하면서 질문에 답변해줍니다.
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사전 경험은 필요하지 않습니다. CoddyKit의 Apache Kafka & Stream Processing Fundamentals은(는) 초급자부터 고급 학습자까지를 위해 구성되어 있으므로, 여기서 시작하거나 처음부터 시작할 수 있으며 자신의 속도대로 진행할 수 있습니다. 이것은 4개 중 2번째 강의입니다.
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이 강의의 모든 강의
- 높은 처리량을 위한 설계
- 재해 복구 및 지역 간 복제
- 스트림 처리의 미래 동향
- 대규모 환경의 백프레셔와 흐름 제어