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Cassandra vs Google Cloud Bigtable: What are the differences?
Introduction
Cassandra and Google Cloud Bigtable are both NoSQL databases, designed for handling large amounts of data with high scalability and performance. However, they have some key differences that set them apart from each other.
Data Model: Cassandra uses a wide-column data model, which allows for flexible schema and dynamic addition of columns. On the other hand, Google Cloud Bigtable utilizes a sparse, distributed, persistent multidimensional sorted map data model, where data is structured in rows and columns similar to a traditional database table.
Consistency Model: Cassandra offers tunable consistency, where users can choose between strong consistency or eventual consistency based on their requirements. In contrast, Google Cloud Bigtable provides only eventual consistency, which means that data may be inconsistent for a brief period of time before it becomes consistent across all nodes.
Concurrency Control: Cassandra uses a distributed versioning approach known as "last write wins" to handle conflicts during concurrent updates. In contrast, Google Cloud Bigtable relies on optimistic concurrency control, where concurrent requests are allowed and conflicts are detected and resolved based on timestamps.
Storage Architecture: Cassandra employs a distributed, peer-to-peer architecture where data is distributed across a cluster of nodes. Data in Cassandra is stored in memory and disk-based data structures, with built-in support for replication and fault-tolerance. On the other hand, Google Cloud Bigtable utilizes a distributed file system called Colossus, where data is stored in a hierarchical structure of tablets for efficient storage and retrieval.
Query Language: Cassandra uses its own query language called CQL (Cassandra Query Language), which is similar to SQL but with some differences. Google Cloud Bigtable, on the other hand, does not provide a query language out of the box. Instead, it encourages the use of client libraries and frameworks to interact with the database.
Scaling: Both Cassandra and Google Cloud Bigtable are designed for horizontal scalability. However, Cassandra offers automatic data partitioning and distribution across nodes for seamless scalability. Google Cloud Bigtable, on the other hand, requires manual sharding and management of tablets to achieve scalability.
In summary, Cassandra and Google Cloud Bigtable differ in their data model, consistency model, concurrency control, storage architecture, query language, and scaling mechanisms.
The problem I have is - we need to process & change(update/insert) 55M Data every 2 min and this updated data to be available for Rest API for Filtering / Selection. Response time for Rest API should be less than 1 sec.
The most important factors for me are processing and storing time of 2 min. There need to be 2 views of Data One is for Selection & 2. Changed data.
Scylla can handle 1M/s events with a simple data model quite easily. The api to query is CQL, we have REST api but that's for control/monitoring
Cassandra is quite capable of the task, in a highly available way, given appropriate scaling of the system. Remember that updates are only inserts, and that efficient retrieval is only by key (which can be a complex key). Talking of keys, make sure that the keys are well distributed.
i love syclla for pet projects however it's license which is based on server model is an issue. thus i recommend cassandra
By 55M do you mean 55 million entity changes per 2 minutes? It is relatively high, means almost 460k per second. If I had to choose between Scylla or Cassandra, I would opt for Scylla as it is promising better performance for simple operations. However, maybe it would be worth to consider yet another alternative technology. Take into consideration required consistency, reliability and high availability and you may realize that there are more suitable once. Rest API should not be the main driver, because you can always develop the API yourself, if not supported by given technology.
Pros of Cassandra
- Distributed119
- High performance98
- High availability81
- Easy scalability74
- Replication53
- Reliable26
- Multi datacenter deployments26
- Schema optional10
- OLTP9
- Open source8
- Workload separation (via MDC)2
- Fast1
Pros of Google Cloud Bigtable
- High performance11
- Fully managed9
- High scalability5
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Cons of Cassandra
- Reliability of replication3
- Size1
- Updates1
Cons of Google Cloud Bigtable
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