HBase and Cassandra are both distributed systems. These systems have similar architectures but differ in how they store and retrieve data. This article will look at the differences between these two systems. This article will also examine the architecture of both systems, including the masterless and shared-nothing features.

HBase's row key is sorted

HBase is an open-source key-value data store. It uses a distributed hash table model. All data elements are associated with a key. The server owns data that lies between an id and its key. Cassandra is similar to HBase in this respect. The two are similar in their architecture and use different mechanisms for sorting and indexing.

HBase's row key is a single column, whereas Cassandra uses a column family, which is more like a table. A column family contains multiple sub-columns and is organized evenly. This method allows storing data in both logs and caches simultaneously.

Cassandra's row key order makes data retrieval patterns efficient

HBase's masterless architecture

Cassandra and HBase share some similarities in the way they store data, and while they may have different names, both have similar writing paths. Cassandra has a more flexible data model for aggregations and specialized workloads like real-time analytics. At the same time, HBase is suited for use in applications that don't require a high amount of reads or writes.

Both databases use clusters to store data and are built around multiple master nodes. However, the difference is the way they communicate. Cassandra uses a protocol called Zookeeper to communicate with the master node. HBase uses a custom-based query language called HQL, while Cassandra has its CQL. HBase has better documentation than Cassandra, but both require installing and configuring clustering.

Cassandra's shared-nothing architecture

Cassandra's shared-nothing architecture allows users to write data anywhere, on any node. This makes it highly scalable and eliminates the risk of unexpected downtime. In addition, Cassandra's architecture enables data replication across many nodes.

To achieve this, each node configures a list of "seeds." The seed node bootstraps any new node which is part of the cluster. A seed node is not a single point of failure, and nodes do not need seeds on subsequent restarts. Two to three seed nodes are recommended for a single Cassandra data centre. Ideally, each seed node should have a uniform list of seed values.

Cassandra has strong high availability guarantees. DSEFS also features shared-nothing architecture. This means that any DSE client can access any DSE cluster node. This is particularly useful for applications that require high insertion rates.

HBase's lack of coprocessor-like functionality

One of the most obvious drawbacks of HBase is its lack of coprocessor-like functionality. Google's BigTable does offer coprocessors, but they run as separate processes co-located with tablet servers. Coprocessors are an important part of BigTable but also increase performance and limit flexibility. Luckily, Apache Phoenix adds extra table attributes to HBase tables, making them faster. And you can remove them at any time.

HBase uses a column-oriented design. Each column is referred to by a qualifier, which can be an arbitrary array of bytes. Unlike traditional databases, HBase does not limit the number of columns or type of columns. It also does not impose limits on the size of the values within a column. Despite its lack of coprocessor-like functionality, it is extremely easy to add new features to HBase without having to modify its core code. To do this, developers can create extension surfaces that implement the Endpoint interface. These extensions are loaded into the regional context. They extend the BaseEndpointCoprocessor class, which hides the internal details.