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Apache Kudu vs Cassandra: What are the differences?
Apache Kudu vs. Cassandra
Apache Kudu and Cassandra are both popular distributed database management systems used for handling big data. However, there are several notable differences between the two.
1. Storage Architecture: Apache Kudu utilizes a columnar storage architecture, which provides fast analytical scans and aggregation queries. On the other hand, Cassandra uses a row-based storage architecture, making it better suited for high write throughput and transactional workloads.
2. Data Model: Cassandra follows a wide-column store data model, where data is organized into rows with multiple columns. It supports flexible schema design and allows for the storage of large amounts of structured and semi-structured data. In contrast, Kudu adopts a table-like, structured data model with strong schema enforcement, making it more appropriate for use cases that require strict data consistency.
3. Consistency Model: Cassandra employs an eventual consistency model, allowing data to be written to multiple replicas with a possibility of data inconsistencies that are resolved over time. On the contrary, Kudu offers strong consistency guarantees, ensuring that all read operations are always consistent with the most recent write.
4. Secondary Indexes: Kudu provides native support for secondary indexes, allowing efficient search operations on multiple columns. On the other hand, Cassandra requires the use of external tools or custom solutions for secondary indexing.
5. Data Compression and Compression: Kudu supports efficient data compression algorithms, enabling reduced storage requirements and improved query performance. Additionally, it provides support for automatic data compaction, which ensures optimal disk space utilization. In contrast, Cassandra does not offer built-in data compression or automatic compaction capabilities.
6. Query Language Support: Cassandra uses its proprietary query language, Cassandra Query Language (CQL), which is similar to SQL but with some differences. Kudu, on the other hand, provides an extensive SQL-like query language, making it easier for users familiar with SQL to work with the database.
In summary, Apache Kudu and Cassandra differ in their storage architecture, data model, consistency model, support for secondary indexes, data compression and compaction, and query language support. These distinctions make each database system suitable for specific use cases and scenarios.
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
Pros of Apache Kudu
- Realtime Analytics10
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
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Cons of Apache Kudu
- Restart time1
Cons of Cassandra
- Reliability of replication3
- Size1
- Updates1
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