HomeData scienceApache Cassandra vs. Hadoop Distributed File System: When Every is Higher

Apache Cassandra vs. Hadoop Distributed File System: When Every is Higher


Apache Cassandra and Apache Hadoop are members of the identical Apache Software program Basis household. We may have contrasted these two frameworks, however that comparability wouldn’t be honest as a result of Apache Hadoop is the ecosystem that encompasses a number of elements. As Cassandra is chargeable for massive information storage, we’ve chosen its equal from the Hadoop’s ecosystem, which is Hadoop Distributed File System (HDFS). Right here, we’ll attempt to discover out if Cassandra and HDFS are like twins who’re equivalent in look and simply bear totally different names, or they’re fairly a brother and a sister who could look related, however nonetheless are very totally different.

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Grasp/slave vs. masterless structure

Earlier than we dwell on the options that distinguish HDFS and Cassandra, we must always perceive the peculiarities of their architectures, as they’re the explanation for a lot of variations in performance. In the event you have a look at the image beneath, you’ll see two contrasting ideas. HDFS’s structure is hierarchical. It accommodates a grasp node, in addition to quite a few slave nodes. Quite the opposite, Cassandra’s structure consists of a number of peer-to-peer nodes and resembles a hoop.

Cassandra vs. Hadoop architecture

5 Key useful variations

1. Coping with large information units

Each HDFS and Cassandra are designed to retailer and course of large information units. Nevertheless, you would want to choose between these two, relying on the information units you must take care of. HDFS is an ideal alternative for writing massive recordsdata to it. HDFS is designed to take one massive file, break up it into a number of smaller recordsdata and distribute them throughout the nodes. The truth is, if you have to learn some recordsdata from HDFS, the operation is reverse: HDFS has to gather a number of recordsdata from totally different nodes and ship some consequence that corresponds to your question. Against this, Cassandra is the right alternative for writing and studying a number of small information. Its masterless structure allows quick writes and reads from any node. This makes IT resolution architects go for Cassandra whether it is required to work with time collection information, which is normally the premise for the Web of Issues.

Whereas in principle HDFS and Cassandra look mutually unique, in actual life they might coexist. If we proceed with the IoT massive information, we are able to give you a situation the place HDFS is used for an information lake. On this case, new readings will likely be added to Hadoop recordsdata (say, there will likely be a separate file per every sensor). On the similar time, an information warehouse could also be constructed on Cassandra.

2. Resisting to failures

Each HDFS and Cassandra are thought of dependable and failure resistant. To make sure this, each apply replication. Merely put, when you have to retailer an information set, HDFS and Cassandra distribute it to some node and create the copies of the information set to retailer on a number of different nodes. So, the precept of failure resistance is easy: if some node fails, the information units that it contained will not be irretrievably misplaced – their copies are nonetheless accessible on different nodes. For instance, by default, HDFS will create three copies, although you might be free to set every other variety of replicas. Simply don’t neglect that extra copies imply extra cupboard space and longer time to carry out the operation. Cassandra additionally permits selecting the required replication parameters.

Nevertheless, with its masterless structure, Cassandra is extra dependable. If HDFS’s grasp node and secondary node fail, all the information units will likely be misplaced with out the potential for restoration. After all, the case shouldn’t be frequent, however nonetheless this will occur.

3. Guaranteeing information consistency

Knowledge consistency stage determines what number of nodes ought to verify that they’ve saved a duplicate in order that the entire write operation is taken into account successful. In case of learn operations, information consistency stage determines what number of nodes ought to reply earlier than the information is returned to a person.

By way of information consistency, HDFS and Cassandra behave fairly in a different way. Let’s say, you ask HDFS to put in writing a file and create two replicas. On this case, the system will confer with Node 5 first, then Node 5 will ask Node 12 to retailer a duplicate and at last Node 12 will ask Node 20 to do the identical. Solely after that, the write operation is acknowledged.

Data consistency scheme for HDFS

Cassandra doesn’t use HDFS’s sequential strategy, so there isn’t a queue. In addition to, Cassandra lets you declare the variety of nodes you wish to verify the success of operation (it might probably vary from any node to all nodes responding). Another benefit of Cassandra is that it permits various information consistency ranges for every write and skim operation. By the way in which, if a learn operation reveals inconsistency amongst replicas, Cassandra initiates a learn restore to replace the inconsistent information.

Data consistency scheme for Cassandra

4. Indexing

As each methods work with monumental information volumes, scanning solely a sure a part of massive information as a substitute of a full scan would enhance the system’s pace. Indexing is precisely the characteristic that permits doing that.

Each Cassandra and HDFS assist indexing, however in numerous methods. Whereas Cassandra has many particular strategies to retrieve information quicker and even permits creating a number of indexes, HDFS’s capabilities go solely to a sure stage – to the recordsdata the preliminary information set was break up into. Nevertheless, record-level indexing may be achieved with Apache Hive.

5. Delivering analytics

Each designed for giant information storage, Cassandra and HDFS nonetheless must do with analytics. Not by themselves, however together with specialised massive information processing frameworks comparable to Hadoop MapReduce or Apache Spark.

Apache Hadoop’s ecosystem already consists of MapReduce and Apache Hive (a question engine) together with HDFS. As we described above, Apache Hive helps overcome the shortage of record-level indexing, which allows to hurry up an intensive evaluation the place the entry to information is required. Nevertheless, for those who want Apache Spark’s performance, you may go for this framework, as additionally it is suitable with HDFS.

Cassandra additionally runs easily along with both Hadoop MapReduce or Apache Spark that may run on prime of this information storage.

HDFS and Cassandra within the framework of CAP theorem

In line with the CAP theorem, a distributed information retailer can solely assist two of the next three options:  

  • Consistency: a assure that the information is all the time up-to-date and synchronized, which implies that at any given second any person will get the identical response to their learn question, regardless of which node returns it.
  • Availability: a assure {that a} person will all the time get a response from the system inside an affordable time.
  • Partition tolerance: a assure that the system will proceed operation even when a few of its elements are down.

If we have a look at HDFS and Cassandra from the attitude of the CAP theorem, the previous will characterize CP and the latter – both AP or CP properties. The presence of consistency in Cassandra’s listing may be fairly puzzling. However, if wanted, your Cassandra specialists could tune the replication issue and information consistency ranges for writes and reads. Consequently, Cassandra will lose the Availability assure, however acquire so much in Consistency. On the similar time, there’s no chance to alter the CAP theorem orientation for HDFS.

In a nutshell

In case you are to choose between Apache Cassandra and HDFS, the very first thing to have in mind is the character of your uncooked information. If you must retailer and course of massive information units, you may take into account HDSF, if a number of small information – Cassandra could also be a greater choice. In addition to, you must type your necessities in direction of information consistency, availability and partition tolerance. To make a last determination, it’s crucial to know the precise use of huge information storage.

Big data storage: Cassandra vs HDFS

Even when Cassandra appears to outperform HDFS normally described, this doesn’t imply that HDFS is weak. Primarily based on your corporation wants, knowledgeable Hadoop consulting staff could recommend a mixture of frameworks and applied sciences with HDFS and Hive or HBase at core that will allow nice and seamless efficiency.

Want skilled recommendation on massive information and devoted applied sciences? Get it from ScienceSoft, massive information experience since 2013. 



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