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Cassandra vs TimescaleDB: What are the differences?
What is Cassandra? A partitioned row store. Rows are organized into tables with a required primary key. 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 TimescaleDB? Scalable time-series database optimized for fast ingest and complex queries. Purpose-built as a PostgreSQL extension. TimescaleDB is the only open-source time-series database that natively supports full-SQL at scale, combining the power, reliability, and ease-of-use of a relational database with the scalability typically seen in NoSQL databases.
Cassandra and TimescaleDB can be primarily classified as "Databases" tools.
Cassandra and TimescaleDB are both open source tools. TimescaleDB with 7.21K GitHub stars and 382 forks on GitHub appears to be more popular than Cassandra with 5.23K GitHub stars and 2.33K GitHub forks.
Uber Technologies, Spotify, and Instagram are some of the popular companies that use Cassandra, whereas TimescaleDB is used by ScreenAware, WakaTime, and AgFlow. Cassandra has a broader approval, being mentioned in 337 company stacks & 231 developers stacks; compared to TimescaleDB, which is listed in 15 company stacks and 3 developer stacks.
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.
MongoDBHi 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
PostgreSQLWe 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.
DruidDruid 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.
Google BigQueryif 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.
CassandraCassandra 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.
ScyllaDBBy 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.
ScyllaDBScylla 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
Cassandrai 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
- Distributed116
- High performance97
- High availability81
- Easy scalability74
- Replication52
- Reliable26
- Multi datacenter deployments26
- OLTP9
- Schema optional9
- Open source8
- Workload separation (via MDC)2
- Fast1
Pros of TimescaleDB
- Open source8
- Easy Query Language7
- Time-series data analysis6
- Established postgresql API and support5
- Reliable4
- Chunk-based compression2
- High-performance2
- Paid support for automatic Retention Policy2
- Postgres integration2
- 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
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