High Performance Hadoop with New Generation AWS Instances

By Mayank AhujaOctober 27, 2014

Welcome New Generation Instance Types

Amazon Web Services (AWS) offers a range of instance types for supporting compute-intensive workloads. The compute optimized instance family has a higher ratio of compute power to memory. The older generation C1 and CC2 instance types have been very useful in batch data processing  frameworks such as Hadoop.

Late last year Amazon Web Services (AWS) introduced a new generation of compute optimized EC2 instances. The table below illustrates the comparison between instance types from older and newer generation with comparable configuration.

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* ECU is EC2 Compute Unit which provides the relative measure of the integer processing power of an Amazon EC2 instance.

The new generation c3.2xlarge has a clear edge over c1.xlarge instance type:

  • 40% better performance in terms of EC2 compute units
  • 19% cheaper
  • 2x the memory
  • SSD storage
  • Supports enhanced networking capabilities in a VPC

Hadoop and New Generation Instance Types

Terasort Performance

To get a better idea about the performance of the new instance types using Hadoop framework, we ran the Hadoop Terasort benchmark on sample data and compared the runtime for sorting 100GB of data with block size set to 512M. We observed 30-50% improvement using c3.2xlarge (new generation) compared to c1.xlarge (old generation). The improvements are in the expected range as suggested by ECU improvement.


The Dark Side – Low Storage Capacity

However, one place where C3 instance family lacks is the low local data storage capacity compared to previous generation. For example, c1.xlarge has 1.6 TB of instance storage compared to only 160 GB available on c3.xlarge, or 640 GB on the high-end expensive c3.8xlarge instance.

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The amount of storage per instance might not be sufficient for running Hadoop clusters with high volumes of data.

Issue with Low Instance Storage

Hadoop extensively uses instance storage for important features such as caching, fault tolerance and storage. Distributed file system component of Hadoop, HDFS, relies on the local storage across participating instances to store replicas of data blocks. MapReduce framework uses local file system for  storing intermediate map outputs, spill files, distributed cache etc. which can result in high volumes of disk usage while working with reasonably sized datasets.

The lack of storage space on C3 instances can make them unusable for jobs which need to process large chunks of data. Addition of more nodes – only for storage – will reduce the cost benefits which this class of instances offer.

Elastic Block Storage (EBS) – Why and Why Not?

AWS offers raw block devices called EBS volumes which can be attached to EC2 instances. It is possible to attach multiple EBS volumes with a size up to 1 TB per volume. This can easily compensate for the low instance storage available on the new generation instances. Also, use of EBS volumes for storage purpose costs much less than adding cheaper instances with more storage capacity.

However, EBS comes with its own set of disadvantages. Magnetic EBS volumes provides bursty performance with around 100 IOPS on an average which is significantly slower than SSD drives. Hadoop uses the available disks in a JBOD configuration and if EBS volumes were to participate in all operations, the performance will be highly degraded since all configured disks are used in a round-robin fashion.

EBS has also been at the centre of AWS cloud outages which makes it less of a preferred candidate for heavy usage (,,,

In addition to the performance impact and availability, there is an additional cost associated with I/O performed on EBS volumes. A million I/O requests to a magnetic  volume costs only few cents but a large number of requests over time in the cluster can result in unnecessary costs.

Introducing Reserved Disks

We have introduced the notion of reserved disks within our stack. The idea is to have a disk hierarchy where faster disks on an instance get priority over slower disks. The reserved disks are used only when the storage requirements cannot be satisfied by existing disks on the instance. In our case the SSD disks will be used for regular functioning and the mounted EBS volumes will act as reserved disks.


Reserved Disks and MapReduce (MR) Requirements

Various contexts within MR framework need to read and write to local disk storage. A common example is the list of directories specified using mapred.local.dir configuration which are used for storing intermediate data files. The configuration value contains directories residing on low latency and high throughput SSD volumes.

On the instances with low local storage, an additional configuration property (local.reserved.dir) can be set to contain list of directories residing on mounted EBS volumes. These directories are accessed only in case the request to read, write or existence check could not be served from the regular volumes. The above arrangement ensures that if there was sufficient space on fast (SSD in our case) volumes, then the high latency EBS volumes are not used.

Additionally, once a job completes, the intermediate data associated with it is cleaned up making the SSD volumes available for other jobs. This reduces access to reserved volumes.

The reserved disks are used in a round-robin scheme for allocation. The last reserved disk on which  a file write operation was performed is tracked. The subsequent write request is catered by the next disk in the list having sufficient capacity. This ensures that tasks on the same instance are distributed across volumes and a single volume does not become a bottleneck. The scheme used is similar to the one used by local directory allocation mechanism within Hadoop (LocalDirAllocator).

Reserved Disks and HDFS

Similar to the above approach, HDFS can be configured to use reserved volumes using configuration parameter dfs.reserved.dir. The volumes so specified are marked as reserved. The target node selected for placing a block replica uses the reserved disk/EBS volume only if there is insufficient space on disks specified using

The reserved volumes for HDFS follow the regular round-robin scheme. In both cases it is ensured that a reserved volume is used only if it is healthy.

Auto-scaling Clusters and Reserved Disks

Qubole Hadoop clusters auto-scale based on workload. Since Hadoop workloads generally have uneven spikes, the clusters witness up-scaling and down-scaling activity. As soon as the instance is added to the cluster, the reserved volumes are automatically mounted to the instance and are ready to use. To save additional costs, we plan to delay this process till the disk utilization on the node crosses a certain threshold. When the cluster down scales, the instances within the cluster are terminated. The termination process automatically removes and terminates the EBS volumes mounted (if any) on the instance. As a result, during low load, the cluster runs with minimum number of instances and hence with a lower number of EBS volumes.

Configurable Reserved Volumes

On Qubole platform, using new generation instances, users have an option to use reserved volumes if data requirement exceeds the local storage available in the cluster .

AWS EBS volumes come in various flavors e.g. magnetic, SSD backed.  Users can select the size and type of EBS volumes based on the data and performance requirements.

Reserved Volume Cost Analysis

For a node in a cluster with 100GB EBS reserved volume, the static cost of using reserved volume will be around 17₵ ($0.17) per day. This amounts to less than 2% of the daily cost of c3.2xlarge instance and will be lower for other instances in the family such as c3.4xlarge (0.8%) and c3.8xlarge (0.4%).

The only additional cost is for the I/O operations performed on these volumes which will be proportional to the amount of data stored in the cluster in addition to local instance storage. The additional requests are reasonably priced at $0.05 per 1 million I/O requests.


Using the reserved volume technique with new generation instances, it is possible to achieve 50% better performance for Hadoop jobs with approximately 17% reduction of costs. Better performance at lower cost!

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