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Cassandra vs KairosDB: What are the differences?
Key Differences Between Cassandra and KairosDB
Cassandra and KairosDB are both open-source NoSQL databases, but they have several key differences that set them apart.
Data Model: Cassandra is designed for handling large amounts of data spread across multiple servers, using a wide column data model that enables efficient storage and retrieval of structured as well as unstructured data. In contrast, KairosDB is specifically optimized for time-series data, providing features like data retention policies, natively supporting metrics and analytics.
Data Consistency and Availability: Cassandra uses a distributed architecture with a peer-to-peer node structure, ensuring high availability and fault tolerance. It supports eventual consistency, allowing for flexible trade-offs between consistency and availability. KairosDB, on the other hand, provides strong consistency guarantees, which can be critical in some use cases such as financial transactions or real-time data processing.
Query Language: Cassandra uses CQL (Cassandra Query Language) for defining and manipulating data, which is similar to SQL. CQL supports complex queries and offers CRUD operations, as well as support for transactions. KairosDB, being a time-series database, offers its own query language specifically tailored for time-series data, allowing for efficient retrieval and analysis of time-based data patterns.
Aggregation and Analytics: Cassandra does not provide in-built support for advanced analytics or aggregation functions, requiring external tools or frameworks for data analysis purposes. In contrast, KairosDB includes built-in support for aggregating time-series data, making it easier to perform operations like sum, average, min, and max on data points within time intervals.
Data Size and Scalability: Cassandra is designed to handle large-scale datasets and can scale horizontally by adding more nodes to the cluster. It provides linear scalability, allowing it to handle massive data growth efficiently. KairosDB, being optimized for time-series data, also supports scaling horizontally as the number of time-series data points increases, ensuring efficient storage and retrieval of data.
Use Cases and Industry Adoption: Cassandra is widely adopted by companies dealing with large-scale data processing, such as Netflix, Apple, and Spotify. It is commonly used for applications requiring real-time and highly available data, like social media platforms, e-commerce, and IoT applications. KairosDB, with its focus on time-series data, is popular in industries dealing with metric monitoring, sensor data, and IoT analytics.
In Summary, Cassandra is a general-purpose database suitable for handling large amounts of structured and unstructured data, while KairosDB is a specialized time-series database optimized for managing and analyzing time-series data.
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.
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 KairosDB
- As fast as your cassandra/scylla cluster go1
- Time-Series data analysis1
- Easy setup1
- Easy Rest API1
- Open source1
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Cons of Cassandra
- Reliability of replication3
- Size1
- Updates1