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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.

  1. 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.

  2. 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.

  3. 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.

  4. 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.

  5. 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.

  6. 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.

Advice on Cassandra and RocksDB
Needs advice
on
HBaseHBaseMilvusMilvus
and
RocksDBRocksDB

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!

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Replies (1)
Recommends

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.

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Umair Iftikhar
Technical Architect at ERP Studio · | 3 upvotes · 442.3K views
Needs advice
on
CassandraCassandraDruidDruid
and
TimescaleDBTimescaleDB

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.

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Replies (1)
Recommends
on
MongoDBMongoDB

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.

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Vinay Mehta
Needs advice
on
CassandraCassandra
and
ScyllaDBScyllaDB

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.

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Replies (4)
Recommends
on
ScyllaDBScyllaDB

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

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Alex Peake
Recommends
on
CassandraCassandra

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.

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Recommends
on
ScyllaDBScyllaDB

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.

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Pankaj Soni
Chief Technical Officer at Software Joint · | 2 upvotes · 154.7K views
Recommends
on
CassandraCassandra

i love syclla for pet projects however it's license which is based on server model is an issue. thus i recommend cassandra

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Decisions about Cassandra and RocksDB
Micha Mailänder
CEO & Co-Founder at Dechea · | 14 upvotes · 81.8K views

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.

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Pros of Cassandra
Pros of RocksDB
  • 119
    Distributed
  • 98
    High performance
  • 81
    High availability
  • 74
    Easy scalability
  • 53
    Replication
  • 26
    Reliable
  • 26
    Multi datacenter deployments
  • 10
    Schema optional
  • 9
    OLTP
  • 8
    Open source
  • 2
    Workload separation (via MDC)
  • 1
    Fast
  • 5
    Very fast
  • 3
    Made by Facebook
  • 2
    Consistent performance
  • 1
    Ability to add logic to the database layer where needed

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Cons of Cassandra
Cons of RocksDB
  • 3
    Reliability of replication
  • 1
    Size
  • 1
    Updates
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    What is Cassandra?

    Partitioning means that Cassandra can distribute your data across multiple machines in an application-transparent matter. Cassandra will automatically repartition as machines are added and removed from the cluster. Row store means that like relational databases, Cassandra organizes data by rows and columns. The Cassandra Query Language (CQL) is a close relative of SQL.

    What is RocksDB?

    RocksDB is an embeddable persistent key-value store for fast storage. RocksDB can also be the foundation for a client-server database but our current focus is on embedded workloads. RocksDB builds on LevelDB to be scalable to run on servers with many CPU cores, to efficiently use fast storage, to support IO-bound, in-memory and write-once workloads, and to be flexible to allow for innovation.

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    What companies use Cassandra?
    What companies use RocksDB?
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    What tools integrate with RocksDB?

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    Blog Posts

    Jan 26 2022 at 4:34AM

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    What are some alternatives to Cassandra and RocksDB?
    HBase
    Apache HBase is an open-source, distributed, versioned, column-oriented store modeled after Google' Bigtable: A Distributed Storage System for Structured Data by Chang et al. Just as Bigtable leverages the distributed data storage provided by the Google File System, HBase provides Bigtable-like capabilities on top of Apache Hadoop.
    Google Cloud Bigtable
    Google Cloud Bigtable offers you a fast, fully managed, massively scalable NoSQL database service that's ideal for web, mobile, and Internet of Things applications requiring terabytes to petabytes of data. Unlike comparable market offerings, Cloud Bigtable doesn't require you to sacrifice speed, scale, or cost efficiency when your applications grow. Cloud Bigtable has been battle-tested at Google for more than 10 years—it's the database driving major applications such as Google Analytics and Gmail.
    Hadoop
    The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage.
    Redis
    Redis is an open source (BSD licensed), in-memory data structure store, used as a database, cache, and message broker. Redis provides data structures such as strings, hashes, lists, sets, sorted sets with range queries, bitmaps, hyperloglogs, geospatial indexes, and streams.
    Couchbase
    Developed as an alternative to traditionally inflexible SQL databases, the Couchbase NoSQL database is built on an open source foundation and architected to help developers solve real-world problems and meet high scalability demands.
    See all alternatives