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Cassandra vs RocksDB: What are the differences?
Introduction
Cassandra and RocksDB are both popular database management systems, but they have key differences in their design and functionality. This Markdown code will showcase those differences, providing specific details to understand their distinctions.
Storage Architecture: Cassandra follows a distributed architecture that allows data to be stored across multiple nodes, ensuring high availability and fault tolerance. On the other hand, RocksDB is a local storage engine that operates as a single-node database, providing high performance for read-heavy workloads.
Data Model: Cassandra is a columnar NoSQL database that enables flexible schema design and supports a wide variety of data types. It uses a distributed key-value store model, where data is structured using column families and tables. In contrast, RocksDB is a key-value store optimized for solid-state drives (SSDs), offering faster data retrieval but with a fixed schema and limited data type support.
Consistency Model: Cassandra implements tunable consistency, allowing clients to choose between strong consistency and eventual consistency based on their application requirements. This provides trade-offs between data consistency and availability. Meanwhile, RocksDB guarantees strong consistency since it operates as a single-node database and does not support distributed transactions.
Concurrency Control: Cassandra adopts an optimistic concurrency control mechanism, utilizing conflict resolution to handle concurrent writes and updates. It uses a versioned write model to maintain data consistency. In contrast, RocksDB employs a single-threaded model by default but also supports multi-threading for concurrent read and write operations.
Durability and Write Performance: Cassandra achieves durability and fault tolerance through its distributed architecture and replication factor, ensuring data availability even if a node fails. However, this replication incurs additional write overhead, affecting write performance. On the other hand, RocksDB offers high write performance due to its local storage nature, but it lacks built-in replication for fault tolerance.
Use Cases and Scalability: Cassandra is designed for high scalability and can handle massive amounts of data and concurrent requests across multiple nodes. It is well-suited for applications requiring high availability and scalability, such as large-scale web applications and time-series data storage. In comparison, RocksDB is more suitable for embedded applications, edge devices, and scenarios with limited storage capacities where low-latency data access is vital.
In Summary, Cassandra excels in distributed architectures, flexible data modeling, tunable consistency, high availability, and scalability, making it ideal for large-scale applications. In contrast, RocksDB is optimized for local storage systems, providing high-performance read-heavy workloads, strong consistency, and low-latency data access.
I am researching different querying solutions to handle ~1 trillion records of data (in the realm of a petabyte). The data is mostly textual. I have identified a few options: Milvus, HBase, RocksDB, and Elasticsearch. I was wondering if there is a good way to compare the performance of these options (or if anyone has already done something like this). I want to be able to compare the speed of ingesting and querying textual data from these tools. Does anyone have information on this or know where I can find some? Thanks in advance!
You've probably come to a decision already but for those reading...here are some resources we put together to help people learn more about Milvus and other databases https://zilliz.com/comparison and https://github.com/zilliztech/VectorDBBench. I don't think they include RocksDB or HBase yet (you could could recommend on GitHub) but hopefully they help answer your Elastic Search questions.
Developing a solution that collects Telemetry Data from different devices, nearly 1000 devices minimum and maximum 12000. Each device is sending 2 packets in 1 second. This is time-series data, and this data definition and different reports are saved on PostgreSQL. Like Building information, maintenance records, etc. I want to know about the best solution. This data is required for Math and ML to run different algorithms. Also, data is raw without definitions and information stored in PostgreSQL. Initially, I went with TimescaleDB due to PostgreSQL support, but to increase in sites, I started facing many issues with timescale DB in terms of flexibility of storing data.
My major requirement is also the replication of the database for reporting and different purposes. You may also suggest other options other than Druid and Cassandra. But an open source solution is appreciated.
Hi Umair, Did you try MongoDB. We are using MongoDB on a production environment and collecting data from devices like your scenario. We have a MongoDB cluster with three replicas. Data from devices are being written to the master node and real-time dashboard UI is using the secondary nodes for read operations. With this setup write operations are not affected by read operations too.
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.
Fauna is a serverless database where you store data as JSON. Also, you have build in a HTTP GraphQL interface with a full authentication & authorization layer. That means you can skip your Backend and call it directly from the Frontend. With the power, that you can write data transformation function within Fauna with her own language called FQL, we're getting a blazing fast application.
Also, Fauna takes care about scaling and backups (All data are sharded on three different locations on the globe). That means we can fully focus on writing business logic and don't have to worry anymore about infrastructure.
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 RocksDB
- Very fast5
- Made by Facebook3
- Consistent performance2
- Ability to add logic to the database layer where needed1
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Cons of Cassandra
- Reliability of replication3
- Size1
- Updates1
Cons of RocksDB
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