cassandra and hadoop - realtime vs batch

Question

As per http://www.dbta.com/Articles/Columns/Notes-on-NoSQL/Cassandra-and-Hadoop---Strange-Bedfellows-or-a-Match-Made-in-Heaven-75890.aspx

Cassandra has pursued somewhat different solutions than has Hadoop. Cassandra excels at high-volume real-time transaction processing, while Hadoop excels at more batch-oriented analytical solutions.

What are the differences in the architecture/implementation of Cassandra and Hadoop which account for this sort of difference in usage. (in lay software professional terms)

Answer 1

I wanted to add, because I think there might be a misleading statement here saying Cassandra might perform good for reads. Cassandra is not very good at random reads either, it's good compared to other solutions out there in how can you read randomly over a huge amount of data, but at some point if the reads are truly random you can't avoid hitting the disk every single time which is expensive, and it may come down to something useless like a few thousand hits/second depending on your cluster, so planning on doing lots of random queries might not be the best, you'll run into a wall if you start thinking like that. I'd say everything in big data works better when you do sequential reads or find a way to sequentially store them. Most cases even when you do real time processing you still want to find a way to batch your queries. This is why you need to think beforehand what you store under a key and try to get the most information possible out of a read. It's also kind of funny that statement says transaction and Cassandra in the same sentence, cause that really doesn't happen. On the other hand hadoop is meant to be batch almost by definition, but hadoop is a distributed map reduce framework, not a db, in fact, I've seen and used lots of hadoop over cassandra, they're not antagonistic technologies. Handling your big data in real time is doable but requires good thinking and care about when and how you hit the database.

Edit: Removed secondary indices example, as last time I checked that used random reads (though I've been away from Cassandra for more than a year now).

Answer 2

The Vanilla hadoop consists of a Distributed File System (DFS) at the core and libraries to support Map Reduce model to write programs to do analysis. DFS is what enables Hadoop to be scalable. It takes care of chunking data into multiple nodes in a multi node cluster so that Map Reduce can work on individual chunks of data available nodes thus enabling parallelism.

The paper for Google File System which was the basis for Hadoop Distributed File System (HDFS) can be found here

The paper for Map Reduce model can be found here

For a detailed explanation on Map Reduce read this post

Cassandra is a highly scalable, eventually consistent, distributed, structured key-value store. It is not a conventional database but is more like Hashtable or HashMap which stores a key/value pair. Cassandra works on top of HDFS and makes use of it to scale. Both Cassandra and HBase are implementations of Google's BigTable. Paper for Google BigTable can be found here .

BigTable makes use of a String Sorted Table (SSTable) to store key/value pairs. SSTable is just a File in HDFS which stores key followed by value. Furthermore BigTable maintains a index which has key and offset in the File for that key which enables reading of value for that key using only a seek to the offset location. SSTable is effectively immutable which means after creating the File there is no modifications can be done to existing key/value pairs. New key/value pairs are appended to the file. Update and Delete of records are appended to the file, update with a newer key/value and deletion with a key and tombstone value. Duplicate keys are allowed in this file for SSTable. The index is also modified with whenever update or delete take place so that offset for that key points to the latest value or tombstone value.

Thus you can see Cassandra's internal allow fast read/write which is crucial for real time data handling. Whereas Vanilla Hadoop with Map Reduce can be used to process batch oriented passive data.

Answer 3

Hadoop consists of two fundamental components: distributed datastore (HDFS) and distributed computation framework (MapReduce). It reads a bunch of input data then writes output from/to the datastore. It needs distributed datastore since it performs parallel computing with the local data on cluster of machines to minimize the data loading time.

While Cassandra is the datastore with linear scalability and fault-tolerance ability. It lacks of the parallel computation ability provided by MapReduce in Hadoop.

The default datastore (HDFS) of Hadoop can be replaced with other storage backend, such as Cassandra, Glusterfs, Ceph, Amazon S3, Microsoft Azure's file system, MapR's FS, and etc. However, each alternatives has its pros and cons, they should be evaluated based on the needs.

There are some resources that help you integrate Hadoop with Cassandra: http://docs.datastax.com/en/cassandra/3.0/cassandra/configuration/configHadoop.html