You need to define the type of very column upfront. Some schemas are well suited for MongoDB and some are well suited for Cassandra. Consistency requirements - This is a tricky one. This makes it really easy to query based on these secondary indexes. They have very different strengths and value propositions — so any comparison has to be a nuanced one. Cassandra does not have a built-in aggregation framework. The queries are structured as JSON fragments.
At this point, you are probably expecting a performance benchmark comparison of the databases. Load characteristics — The characteristics of the benchmark load are very important. The secondary servers can only be used for reads. The more servers you have in the cluster, the better it will scale. Here are some benchmarks you might want to look at: Cassandra has only cursory support for secondary indexes. However, in read-heavy benchmarks, MongoDB and Cassandra should be similar in performance. While this was the default in prior versions in the newer version you have the option to enforce a schema for your documents. External tools like Hadoop, Spark are used for this. With the adoption of CQL as the primary interface for Cassandra, it has taken this a step further — they have made it very simple for legions of SQL programmers to use Cassandra very easily. Essentially your write scalability is limited by the number of servers you have in the cluster. So essentially if you have three node replica set, only the master is taking writes and the other two nodes are only used for reads. Each document in MongoDB can be a different structure and it is up to your application to interpret the data. This process happens automatically but it takes time, usually seconds. This greatly limits write scalability. In this post, I am not going to discuss specific features but will point out some high-level strategic differences to help you make your choice. Cassandra in the newer versions with CQL as the default language provides static typing. In the last couple of years, however, Cassandra has made great strides in this aspect of the product. Consistency requirements - This is a tricky one. Secondary indexes are also limited to single columns and equality comparisons. I have deliberately not included performance benchmarks in the comparison. This is great for small to medium jobs but as your data processing needs become more complicated the aggregation framework becomes difficult to debug. Both are fairly easy to use and ramp up. So, pay close attention to the consistency settings. So when comparing databases it is important to use a model that works reasonably well for both databases. If you need query language support, Cassandra is the better fit for you. One last thing to keep in mind is that the benchmark load may or may not reflect the performance of your application.
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