When I was learning about the secondary index in Cassandra, I've found the mention of special Cassandra's algorithm used to range and secondary index queries. After some time passed on exploring secondary index mechanism, it's a good moment to discover the algorithm making it work.
Some time ago I tried to create Docker image with Cassandra and some other programs. For the "others", the operation was quite easy but Cassandra caused some problems because of several configuration properties.
Before writing some code in Apache Cassandra, we'll try to explore very interesting dependency - cassandra-driver-mapping.
I/O operations are slower than memory lookups. It's the reason why memory cache helps to improve performances, in Cassandra too.
One of interesting data types used in Apache Cassandra are collections. In our model we can freely use maps, sets or lists.
Because tables in Apache Cassandra are very similar to the tables of relational databases, this article describing them won't focus on basic points. Instead, we'll explore more Cassandra specific subjects, such as configuration or different types.
Disk compaction helps to save space. Since Cassandra is supposed to store a lot of data, it can't miss this useful process.
Since Cassandra is distributed storage system, it holds data in different nodes. But how it determines data should be stored by each node ? It's the role of partitioners.
Keeping old data eternally takes place and makes reads longer. Apache Cassandra is not an exception and has a mechanism to remove data.
Previously we've presented theory of data consistency in Cassandra. Now it's a good moment to show some examples of consistency levels.
Distributed data brings a new problem to historical standalone relational databases - data consistency. Cassandra deals with this problem pretty nice with its different consistency levels.
Until now we're working with Cassandra without looking on what happens. It's a time to be a little bit more curious.
The previous article introduced us to Apache Cassandra by presenting vaguely its main concepts. This article focuses more in details on data topics.
After some articles about data ingestion and serialization in Big Data applications, it's time to start to learn about storage. This part begins with Apache Cassandra.
This article presents basic concepts of Apache Cassandra. In the first part it tries to explain architecture and general concepts of this solution. The second part is focused more on developer topics and it describes some main points about data organization.
