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Cassandra vs HBase: What are the differences?
### Introduction
This Markdown code provides a comparison between Cassandra and HBase, highlighting the key differences between the two popular NoSQL databases.
1. **Database Model**: Cassandra utilizes a wide-column store data model, where data is stored in rows and columns within column families, offering high availability and fault tolerance. On the other hand, HBase uses a column-oriented key-value data store model inspired by Google Bigtable, providing strong consistency and scalability.
2. **Consistency**: In terms of consistency, Cassandra follows the eventual consistency model, which allows for changes to be propagated across the system at different intervals. In contrast, HBase offers strong consistency, ensuring that all read and write operations are processed in a linearizable order.
3. **Partitioning**: Cassandra partitions data across multiple nodes using consistent hashing, enabling efficient distribution and scalability. On the contrary, HBase partitions data based on row keys and relies on region servers to manage data storage and retrieval within regions.
4. **Scalability**: Cassandra is designed for distributed scalability, allowing nodes to be added or removed easily to accommodate growing data needs. HBase also supports horizontal scalability by adding more region servers, but it may require manual configuration for optimal performance.
5. **Query Language**: Cassandra uses CQL (Cassandra Query Language), a SQL-like language for querying data, making it easier for developers familiar with SQL to work with the database. HBase, on the other hand, offers a Java API for data access and retrieval, requiring developers to write custom code for queries.
6. **Write Performance**: In terms of write performance, Cassandra excels in handling high write throughput by utilizing a log-structured storage engine for efficient write operations. HBase, although efficient for random read and write operations, may face performance degradation with high write workloads due to its design.
In Summary, the key differences between Cassandra and HBase lie in their database models, consistency models, partitioning strategies, scalability options, query languages, and write performance characteristics.
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.
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.
i love syclla for pet projects however it's license which is based on server model is an issue. thus i recommend cassandra
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 HBase
- Performance9
- OLTP5
- Fast Point Queries1
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Cons of Cassandra
- Reliability of replication3
- Size1
- Updates1
Cons of HBase
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