Rack Awareness in Hadoop HDFS – An Introductory Guide 2


1. Objective

This Hadoop tutorial will help you in understanding Hadoop rack awareness concept, racks in Hadoop environment, why rack awareness is needed, replica placement policy in Hadoop via Rack awareness and advantages of implementing rack awareness in Hadoop HDFS.

Hadoop HDFS Rack Awareness

2. What is Rack Awareness in Hadoop HDFS?

In a large cluster of Hadoop, in order to improve the network traffic while reading/writing HDFS file, namenode chooses the datanode which is closer to the same rack or nearby rack to Read/Write request. Namenode achieves rack information by maintaining the rack id’s of each datanode. This concept that chooses closer datanodes based on the rack information is called Rack Awareness in Hadoop.

Rack awareness is having the knowledge of Cluster topology or more specifically how the different data nodes are distributed across the racks of a Hadoop cluster. Default Hadoop installation assumes that all data nodes belong to the same rack.

3. Why Rack Awareness?

In Big data Hadoop, rack awareness is required for below reasons:

  • To improve data high availability and reliability.
  • Improve the performance of the cluster.
  • To improve network bandwidth.
  • Avoid losing data if entire rack fails though the chance of the rack failure is far less than that of node failure.
  • To keep bulk data in the rack when possible.
  • An assumption that in-rack id’s higher bandwidth, lower latency.

4. Replica Placement via Rack Awareness in Hadoop

Placement of replica is critical for ensuring high reliability and performance of HDFS. Optimizing replica placement via rack awareness distinguishes HDFS from other Distributed File System. Block Replication in multiple racks in HDFS is done using a policy as follows:

“No more than one replica is placed on one node. And no more than two replicas are placed on the same rack. This has a constraint that the number of racks used for block replication should be less than the total number of block replicas”.

For Example:

When a new block is created: The First replica is placed on the local node. The Second one is placed on a different rack and the third one is placed on a different node at the local rack.

When re-replicating a block, if the number of an existing replica is one, place the second one on the different rack. If the number of an existing replica is two and if the two existing replicas are on the same rack, the third replica is placed on a different rack.

A simple but nonoptimal policy is to place replicas on the different racks. This prevents losing data when an entire rack fails and allows us to use bandwidth from multiple racks while reading the data. This policy evenly distributes the data among replicas in the cluster which makes it easy to balance load in case of component failure. But the biggest drawback of this policy is that it will increase the cost of write operation because a writer needs to transfer blocks to multiple racks and communication between the two nodes in different racks has to go through switches.

In most cases, network bandwidth between machines in the same rack is greater than network bandwidth between machines in different racks. That’s why we use replica replacement policy. The chance of the rack failure is far less than that of node failure. It does not impact on data reliability and availability guarantee. However, it does reduce the aggregate network bandwidth used when reading data since a block replica is placed in only two unique racks rather than three.

4.1. What about performance?

  • Faster replication operation: Since the replicas are placed within the same rack it would use higher bandwidth and lower latency hence making it faster.
  • If YARN is unable to create a container in the same data node where the queried data is located it would try to create the container in a data node within the same rack. This would be more performant because of the higher bandwidth and lower latency of the data nodes inside the same rack.

5. Advantages of Implementing Rack Awareness

  • Minimize the writing cost and Maximize read speed – Rack awareness places read/write requests to replicas on the same or nearby rack. Thus minimizing writing cost and maximizing reading speed.
  • Provide maximize network bandwidth and low latency – Rack awareness maximizes network bandwidth by blocks transfer within a rack. Especially with rack awareness, the YARN is able to optimize MapReduce job performance. It assigns tasks to nodes that are ‘closer’ to their data in terms of network topology. This is particularly beneficial in cases where tasks cannot be assigned to nodes where their data is stored locally.
  • Data protection against rack failure – By default, the namenode assigns 2nd & 3rd replicas of a block to nodes in a rack different from the first replica. This provides data protection even against rack failure; however, this is possible only if Hadoop was configured with knowledge of its rack configuration.

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