Hadoop vs. Spark: 5 Key Differences and Using Them Together

In this article:

• Hadoop vs. Apache Spark: 5 Key Differences
• Hadoop and Apache Spark: Better Together

Hadoop vs. Apache Spark: 5 Key Differences

Architecture

Hadoop and Spark have some key differences in their architecture and design:

• Data processing model: Hadoop uses a batch processing model, where data is processed in large chunks (also known as “jobs”) and the results are produced after the entire job has been completed. Spark, on the other hand, uses a more flexible data processing model that allows for both batch processing and real-time (streaming) processing of data.
• Data storage: Hadoop uses the Hadoop Distributed File System (HDFS) to store data, which is a distributed file system that stores data across a large number of machines. Spark, on the other hand, can use a variety of storage systems, including HDFS, as well as other distributed file systems and data lakes, such as Amazon S3 and Azure Data Lake.
• Resource management: Hadoop uses the YARN (Yet Another Resource Negotiator) resource management platform to manage resources and schedule tasks on a cluster. Spark has its own resource manager called the Spark Resource Manager, which is responsible for scheduling tasks and allocating resources on a Spark cluster.
• Programming model: Hadoop uses the MapReduce programming model, which is a parallel, distributed algorithm for processing and generating large data sets. Spark offers a variety of programming interfaces, including the Spark API (which is similar to MapReduce), as well as APIs for SQL, machine learning, and stream processing.

Performance

Here is how Hadoop and Spark compare in terms of performance:

• Speed: Spark is generally faster than Hadoop for certain types of data processing tasks, due to its in-memory processing and ability to cache data in memory. Its developers say it is up to 100 times faster than Hadoop when processing data in-memory and up to 10 times faster at on-disk processing. This makes it well-suited for interactive and iterative workloads, such as machine learning and stream processing. Hadoop, on the other hand, is optimized for batch processing and is generally slower than Spark.
• Scalability: Both Hadoop and Spark are designed to scale up to large clusters of machines, but Spark has a more flexible and efficient architecture for scaling out computations. It can also make more efficient use of resources in a cluster, due to its ability to cache data in memory and its more efficient scheduling of tasks.

Security

Hadoop and Spark have similar security capabilities, but Hadoop is generally considered to have more robust security which is easier to configure:

• Authentication: Both Hadoop and Spark support various authentication mechanisms, such as Kerberos, LDAP, and SAML, for secure access to the cluster and data. Hadoop also supports user-level authentication through its native authentication framework.
• Authorization: Both Hadoop and Spark provide authorization mechanisms to control access to data and resources on the cluster. In Hadoop, this is done through the Apache Ranger framework, which provides fine-grained access control through policies and permissions. Spark also provides fine-grained access control through its own authorization framework, which supports access control lists (ACLs) and role-based access control (RBAC).
• Encryption: Both Hadoop and Spark support encryption of data in transit and at rest. In Hadoop, data can be encrypted in transit through the use of SSL/TLS and in HDFS through the use of transparent data encryption (TDE). Spark also supports encryption in transit through the use of SSL/TLS and supports encryption of data at rest through the use of various encryption libraries, such as JCE and Bouncy Castle.

Costs

Both Spark and Hadoop are free, open-source Apache projects. However, this does not mean running Spark or Hadoop at your organization is free. These solutions can have a significant cost of ownership, including maintenance costs, administration costs, and hardware purchases. 

Disk and memory requirements

When building a big data infrastructure on-premises, a general rule of thumb is that Hadoop requires more disk memory and Spark requires more RAM. This means that the cost of setting up a Spark cluster may be higher. 

Team and skills

Because Spark is a relatively new system, Spark experts are more difficult to hire and might be more expensive than Hadoop experts. Another consideration is that Hadoop is generally more complex and has more overheads, so it may require more staff to maintain in the long term.

Cloud costs

Several providers offer both Spark and Hadoop as a managed service. For example, Cloudera provides hosted Hadoop in the cloud and DataBricks provides a managed version of Spark in the cloud. Each of these services has its own pricing structure, and a direct comparison is not straightforward.

Another option is to run Spark on Hadoop in an infrastructure as a service (IaaS) model with a provider like Amazon. Amazon EMR is a service that lets you run a pre-configured Hadoop or Spark cluster. Because Hadoop can use regular c4.large or similar instances, and Spark requires memory optimized instances, the cost of Spark per instance hour can be as much as 3X that of Hadoop. However, Spark is also faster than Hadoop, so in the end it may end up less expensive because the same workloads use fewer instance hours.

Machine Learning

Hadoop and Spark both have support for machine learning (ML) tasks. However, they have some key differences in their ML capabilities:

• ML libraries: Both Hadoop and Spark provide libraries for machine learning tasks, such as classification, regression, clustering, and dimensionality reduction. Hadoop has the Apache Mahout library, which provides a variety of algorithms for ML tasks, and Spark has the MLlib library, which provides a more comprehensive set of ML algorithms and tools.
• In-memory processing: Spark has an advantage over Hadoop for ML tasks due to its ability to cache data in memory and perform in-memory processing. This makes it well-suited for interactive and iterative workloads, such as model training and evaluation, which can be very time-consuming when using Hadoop.
• Integration with other tools: Spark has better integration with other ML tools and libraries, such as TensorFlow and scikit-learn, which can be useful for building ML pipelines and using advanced ML techniques. Hadoop can also be used with these tools, but the integration may not be as seamless.

However, Hadoop and Spark are often used together to support machine learning use cases. For example, data science teams can pull data from HDFS into Spark to perform fast data exploration and analysis.

Hadoop and Apache Spark: Better Together 

Hadoop and Apache Spark can be used together as part of a big data processing and analytics solution. In fact, many organizations use both Hadoop and Spark in their big data infrastructure to take advantage of the strengths of each platform.

One common way to use Hadoop and Spark together is to use Hadoop for batch processing and data storage, and use Spark for real-time (streaming) processing and interactive analytics. This allows organizations to use the best tool for each type of workload and take advantage of the complementary capabilities of the two platforms.

It’s also possible to use Hadoop and Spark together in a hybrid architecture, where both platforms are used for different parts of the same data processing pipeline. For example, data could be ingested into Hadoop and then transformed and processed using Spark, before being stored back in Hadoop for further analysis.

Optimizing Apache Spark With Granulate

Granulate optimizes Apache Spark on a number of levels. With Granulate, Spark executor dynamic allocation is optimized based on job patterns and predictive idle heuristics. It also autonomously and continuously optimizes JVM runtimes and tunes the Spark infrastructure itself. Granulate also has the ability to reduce costs on other execution engines, like Kafka, PySpark, Tez and MapReduce.