Cassandra vs Hadoop: Apache Cassandra and Apache Hadoop are members of the same Apache Software Foundation family. We could compare these two frameworks, but this comparison would not be appropriate because Apache Hadoop is the environment that surrounds many components.
Since Cassandra is responsible for large data storage, we have chosen its equivalent from the ecosystem of Hadoop, which is the Hadoop Distributed File System (HDFS). Here, we will try to figure out whether Cassandra and HDFS are like twins who are similar in appearance and only have different names or rather they are brother and a sister who can look identical but still are very different.
A difference in Hadoop and Cassandra through this video:
Master/Slave vs. Masterless Architecture
Prior to paying attention to features that distinguish HDFS and Cassandra, we should understand the characteristics of their architectures, because they are the reason for many differences in functionality.
If the image is given below, you will see two opposite concepts.
The architecture of HDFS is hierarchical. It includes a master node, as well as the number of slave nodes. On the contrary, there are several peer-to-peer nodes in the architecture of Cassandra and looks like a ring.
5 Key functional differences
1. Dealing with massive data sets
HDFS and Cassandra, both of them are designed to store and process huge data sets. However, you would need to choose between these two, depending on the datasets with whom you have to deal with.
HDFS is an ideal choice for writing large files to it.
HDFS is designed to take a large file, split it into diversified smaller files and distribute them in nodes. As a matter of fact, if you need to read some files from HDFS, the operation is reverse:
HDFS needs to collect multiple files from different nodes and give some results that match your query. On the contrary, Cassandra is the ultimate choice to write and read multiple short/small records.
Its masterless architecture allows quick writes and reads from any node. This makes IT solution architects opt for Cassandra if it needs to work with time series data, which is usually the basis of the Internet of Things.
While in theory HDFS and Cassandra seem mutually exclusive, they can be co-existent in real life. If we continue with IoT large data, then we can come up with a scenario where HDFS is used for data lake.
In this case, new readings will be added to Hadoop files (say, each sensor will have a separate file). At the same time, a data warehouse can be made on the Cassandra.
2. Resisting to failures
HDFS and Cassandra, both are considered reliable and failure-resistant. To guarantee this, both apply replication (copy). Simply put, when you need to store a data set, HDFS and Cassandra allocate it to some node and create copies of the data set to store on several other nodes.
So, the theory of failure resistance is simple: if some node fails, the data set that is contained are not irretrievably/irreparably – their copies are still available on other nodes. For example, by default, HDFS will create three copies, however, you are free to set up any other number of replicas.
Don’t forget that more copies mean larger storage space and longer time to perform an operation. Cassandra also allows selecting the necessary replication standards.
With its masterless architecture, Cassandra is more reliable anyhow. If the master nodes and secondary nodes of HDFS fail, all the data sets will be lost without the possibility of recovery. As expected, the case doesn’t happen on a regular basis, but still, it can happen.
3. Ensuring data consistency
Data consistency level determines how many nodes should confirm that they have stored a replica so that the entire writing operation is considered a success. In the case of reading operations, data consistency level determines how many nodes should be answered before the data is returned to a user.
Regarding data consistency, HDFS and Cassandra work quite differently. Let’s say, you want to write a file from HDFS and make two replicas. In this case, the system will refer to Node 5 first, then Node 5 will ask Node 12 to store the replication and finally ask Node 12 to do the same. After that only, the writing operation is accepted.
Cassandra doesn’t use a sequential approach of HDFS, so there’s no queue. Apart from this, Cassandra allows declaring a number of nodes you want to confirm the success of operation (it can range from any node to all nodes responding).
Another advantage of Cassandra is – it allows varying data consistency levels for each writing and reading operation. Relevantly, if a reading operation reveals inconsistency between replicas, Cassandra starts a read repair to update inconsistent data.
Considering both systems work with huge data volumes, instead of scanning only a certain portion of the larger data, a full scan would increase the system’s speed. Indexing is definitely that facility which allows doing this.
Both Cassandra and HDFS support indexing, but in various ways. While Cassandra has many special techniques to quickly recover data and even allows creating multiple indexes, HDFS’s capabilities go to a certain level only – for files that split the initial data set. Still, record-level indexing can be achieved with Apache Hive.
5. Delivering analytics
Cassandra and HDFS are both designed for big data storage but they still have to deal with analytics. They don’t have to do it by themselves, but rather in combination specialized big data processing frameworks such as Hadoop MapReduce or Apache Spark.
The ecosystem of Apache Hadoop already includes MapReduce and Apache Hive (a query engine) along with HDFS. As mentioned above, Apache Hive helps overcome the lack of record-level indexing, enabling to accelerate a thorough analysis where access to records is required.
However, if you need the functionality of Apache Spark, you can choose this framework, because it’s also compatible with HDFS.
Cassandra also runs smoothly together with either Hadoop MapReduce or Apache Spark which can run on top of this data storage.
Cassandra and HDFS in the framework of CAP theorem
As per the CAP theorem, a distributed data store can only support two out of the following three features:
- Consistency: A guarantee that the data is always up-to-date and synchronized, which means that at any point any user will get the same response on their read query, no matter what node returns it.
- Availability: A guarantee that a user will always receive a response from the system within the valid time.
- Partition Tolerance: A guarantee that the system will continue to operate even if its components are low.
If we look at HDFS and Cassandra from the viewpoint of the CAP theorem, the former will represent CP and the latter symbolizes, either AP or CP properties. The presence of consistency in the list of Cassandra can be quite confusing.
But, if necessary and needed in future, your Cassandra specialists may tune the replication factor and data consistency levels for writing and reading.
As a result, Cassandra will lose the Availability guarantee but will gain a lot in Consistency. On the top of it, there’s no possibility of changing the CAP theorem orientation for HDFS.
Have a more detailed view via video:
Also Read: Is There Any Future For Apache Cordova? See!
If you have to choose an option between Apache Cassandra and HDFS, keep in mind that the first thing you have to consider is – the nature of your raw data.
If you have to store and process large data sets, then you can consider HDSF, if multiple small records – Cassandra may be a better option.
Moreover, you should shape up your needs for data consistency, availability, and partition tolerance. To make a final decision, it’s important to understand the precise usage of large data collections.
Even if Cassandra seems to surpass HDFS in most cases described, this doesn’t mean that HDFS is weak.
Based on your professional needs, a professional Hadoop Consultation Team can suggest a combination of frameworks and technologies with HDFS and Hive or HBase at the core which will enable excellent and uninterrupted performance.