Hadoop #Split-Brain Scenario & #Fencing

Let's #hadoop

📌 𝐖𝐡𝐚𝐭 𝐢𝐬 #𝐒𝐩𝐥𝐢𝐭 𝐁𝐫𝐚𝐢𝐧 𝐒𝐜𝐞𝐧𝐚𝐫𝐢𝐨 𝐚𝐧𝐝 #𝐟𝐞𝐧𝐜𝐢𝐧𝐠 𝐢𝐧 𝐇𝐚𝐝𝐨𝐨𝐩?

✔ 𝘐𝘯 𝘵𝘩𝘦 𝘤𝘰𝘯𝘵𝘦𝘹𝘵 𝘰𝘧 𝘥𝘪𝘴𝘵𝘳𝘪𝘣𝘶𝘵𝘦𝘥 𝘴𝘺𝘴𝘵𝘦𝘮𝘴, 𝘪𝘯𝘤𝘭𝘶𝘥𝘪𝘯𝘨 𝘏𝘢𝘥𝘰𝘰𝘱 𝘤𝘭𝘶𝘴𝘵𝘦𝘳𝘴, 𝘢 "#split_brain 𝘴𝘤𝘦𝘯𝘢𝘳𝘪𝘰" 𝘢𝘯𝘥 "#fencing" 𝘢𝘳𝘦 𝘳𝘦𝘭𝘢𝘵𝘦𝘥 𝘤𝘰𝘯𝘤𝘦𝘱𝘵𝘴 𝘵𝘩𝘢𝘵 𝘥𝘦𝘢𝘭 𝘸𝘪𝘵𝘩 𝘵𝘩𝘦 𝘪𝘴𝘴𝘶𝘦 𝘰𝘧 𝘮𝘢𝘪𝘯𝘵𝘢𝘪𝘯𝘪𝘯𝘨 𝘥𝘢𝘵𝘢 𝘤𝘰𝘯𝘴𝘪𝘴𝘵𝘦𝘯𝘤𝘺 𝘢𝘯𝘥 𝘢𝘷𝘰𝘪𝘥𝘪𝘯𝘨 𝘥𝘢𝘵𝘢 𝘤𝘰𝘳𝘳𝘶𝘱𝘵𝘪𝘰𝘯 𝘪𝘯 𝘤𝘢𝘴𝘦 𝘰𝘧 𝘯𝘦𝘵𝘸𝘰𝘳𝘬 𝘱𝘢𝘳𝘵𝘪𝘵𝘪𝘰𝘯𝘴 𝘰𝘳 𝘤𝘰𝘮𝘮𝘶𝘯𝘪𝘤𝘢𝘵𝘪𝘰𝘯 𝘧𝘢𝘪𝘭𝘶𝘳𝘦𝘴 𝘢𝘮𝘰𝘯𝘨 𝘯𝘰𝘥𝘦𝘴.

✅ 𝐒𝐩𝐥𝐢𝐭 𝐁𝐫𝐚𝐢𝐧 𝐒𝐜𝐞𝐧𝐚𝐫𝐢𝐨:

▪ A split-brain scenario occurs when a cluster of nodes loses communication with each other, leading to the formation of isolated subclusters or partitions.

▪ Each partition continues to operate independently, potentially making decisions and processing data unaware of the other partitions' state.

▪ This can 𝐥𝒆𝒂𝒅 𝒕𝒐 𝒅𝒂𝒕𝒂 𝒊𝒏𝒄𝒐𝒏𝒔𝒊𝒔𝒕𝒆𝒏𝒄𝒊𝒆𝒔, 𝒄𝒐𝒏𝒇𝒍𝒊𝒄𝒕𝒔, 𝒂𝒏𝒅 𝒄𝒐𝒓𝒓𝒖𝒑𝒕𝒊𝒐𝒏 when the partitions rejoin or when communication is restored.

▪ In the context of Hadoop, a split-brain scenario could 𝒄𝒂𝒖𝒔𝒆 𝒅𝒂𝒕𝒂 𝒊𝒏𝒕𝒆𝒈𝒓𝒊𝒕𝒚 𝒊𝒔𝒔𝒖𝒆𝒔, 𝒅𝒂𝒕𝒂 𝒍𝒐𝒔𝒔, 𝒐𝒓 𝒊𝒏𝒄𝒐𝒓𝒓𝒆𝒄𝒕 𝒓𝒆𝒔𝒖𝒍𝒕𝒔 when processing large-scale data.

▪ It is important to prevent or manage such scenarios to maintain the stability and reliability of the Hadoop cluster.

✅ 𝐅𝐞𝐧𝐜𝐢𝐧𝐠:

▪ Fencing is a mechanism used to prevent or 𝒓𝒆𝒔𝒐𝒍𝒗𝒆 𝒔𝒑𝒍𝒊𝒕-𝒃𝒓𝒂𝒊𝒏 𝒔𝒄𝒆𝒏𝒂𝒓𝒊𝒐𝒔 in distributed systems like Hadoop.

▪ The purpose of fencing is to ensure that only one partition of the cluster remains active and continues to function while the other partitions are isolated or shut down.

▪ By fencing off the isolated partitions, you prevent multiple "brains" from making independent decisions, reducing the risk of data corruption and inconsistencies.

▪ In Hadoop, fencing mechanisms can be applied to 𝒆𝒏𝒔𝒖𝒓𝒆 𝒉𝒊𝒈𝒉 𝒂𝒗𝒂𝒊𝒍𝒂𝒃𝒊𝒍𝒊𝒕𝒚 𝒂𝒏𝒅 𝒅𝒂𝒕𝒂 𝒄𝒐𝒏𝒔𝒊𝒔𝒕𝒆𝒏𝒄𝒚. One commonly used fencing approach in Hadoop is the use of ZooKeeper, a distributed coordination service.

▪ 𝐙𝐨𝐨𝐊𝐞𝐞𝐩𝐞𝐫 provides a distributed locking mechanism that allows the active Hadoop NameNode to obtain a lock, while other NameNodes are fenced off in case of a network partition or failure.

▪ By doing so, only the active NameNode continues to serve clients and manage data operations, while the others remain in a passive or standby state.

▪ By employing proper fencing mechanisms, Hadoop can effectively manage network partitions, maintain data consistency, and ensure the reliability of its distributed processing tasks.

▪ Fencing is an essential aspect of designing and configuring a robust Hadoop cluster for high availability and fault tolerance.