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Cassandra vs TimescaleDB: What are the differences?
Introduction
Cassandra and TimescaleDB are both popular databases used for different purposes. Cassandra is a distributed NoSQL database designed for handling large amounts of data across many commodity servers, while TimescaleDB is a time-series database optimized for handling time-stamped or time-series data. Although they serve different use cases, there are several key differences between Cassandra and TimescaleDB.
Data Model: Cassandra uses a wide-column data model, also known as a column-family model. It allows for flexible schemas and supports denormalized data storage. On the other hand, TimescaleDB uses a relational model with tables and rows, similar to traditional SQL databases like PostgreSQL. This makes it easier to query and analyze time-series data using SQL.
Scalability: Cassandra is known for its ability to scale horizontally across multiple machines, providing high availability and fault tolerance. It achieves this through its decentralized architecture and distributed data storage. On the contrary, TimescaleDB is designed for scaling vertically on a single machine or a cluster using PostgreSQL's built-in replication mechanisms. This makes it more suitable for workloads where vertical scalability is sufficient.
Indexing: Cassandra uses a distributed index structure called "Bloom filters" to enable fast lookups of data based on keys. This allows for quick read performance, especially in large-scale deployments. In contrast, TimescaleDB uses B-tree indexes, which are efficient for range queries and filtering based on time intervals. This makes it well-suited for time-series data analysis and aggregation.
Data Consistency: Cassandra offers tunable consistency, allowing users to choose between high availability or strong consistency for their data. It achieves eventual consistency through its distributed architecture and replication. On the other hand, TimescaleDB provides strong consistency by default, ensuring that queries return the most up-to-date results. This is vital for time-series data, which often requires accurate analysis based on the latest information.
Query Language: Cassandra uses its own query language called CQL (Cassandra Query Language), which is similar to SQL but has some notable differences. It includes additional data types and syntax specific to Cassandra's data model. In contrast, TimescaleDB leverages the power of SQL, allowing users to leverage existing SQL knowledge and tools for querying and manipulating data.
Community and Ecosystem: Cassandra has a large and active community, with extensive documentation, online forums, and support resources available. It is widely adopted by companies for various use cases, ranging from real-time analytics to powering distributed systems. On the other hand, while TimescaleDB has a smaller community compared to Cassandra, it benefits from being built on top of PostgreSQL. This allows users to leverage the existing PostgreSQL ecosystem, including various extensions, libraries, and tools.
In summary, Cassandra and TimescaleDB differ in their data models, scalability options, indexing mechanisms, data consistency models, query languages, and community ecosystems. Each database has its own strengths and is optimized for specific use cases, making it crucial to choose the right database depending on the requirements of your application or workload.
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.
We are building an IOT service with heavy write throughput and fewer reads (we need downsampling records). We prefer to have good reliability when comes to data and prefer to have data retention based on policies.
So, we are looking for what is the best underlying DB for ingesting a lot of data and do queries easily
We had a similar challenge. We started with DynamoDB, Timescale, and even InfluxDB and Mongo - to eventually settle with PostgreSQL. Assuming the inbound data pipeline in queued (for example, Kinesis/Kafka -> S3 -> and some Lambda functions), PostgreSQL gave us a We had a similar challenge. We started with DynamoDB, Timescale and even InfluxDB and Mongo - to eventually settle with PostgreSQL. Assuming the inbound data pipeline in queued (for example, Kinesis/Kafka -> S3 -> and some Lambda functions), PostgreSQL gave us better performance by far.
Druid is amazing for this use case and is a cloud-native solution that can be deployed on any cloud infrastructure or on Kubernetes. - Easy to scale horizontally - Column Oriented Database - SQL to query data - Streaming and Batch Ingestion - Native search indexes It has feature to work as TimeSeriesDB, Datawarehouse, and has Time-optimized partitioning.
if you want to find a serverless solution with capability of a lot of storage and SQL kind of capability then google bigquery is the best solution for that.
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
I chose TimescaleDB because to be the backend system of our production monitoring system. We needed to be able to keep track of multiple high cardinality dimensions.
The drawbacks of this decision are our monitoring system is a bit more ad hoc than it used to (New Relic Insights)
We are combining this with Grafana for display and Telegraf for data collection
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 TimescaleDB
- Open source9
- Easy Query Language8
- Time-series data analysis7
- Established postgresql API and support5
- Reliable4
- Paid support for automatic Retention Policy2
- Chunk-based compression2
- Postgres integration2
- High-performance2
- Fast and scalable2
- Case studies1
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Cons of Cassandra
- Reliability of replication3
- Size1
- Updates1
Cons of TimescaleDB
- Licensing issues when running on managed databases5