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Citus vs TimescaleDB: What are the differences?
Introduction:
Both Citus and TimescaleDB are popular extensions for PostgreSQL that are designed to handle large volumes of data. While they both have similarities, there are key differences between the two technologies.
Horizontal versus Vertical Scaling: The main difference between Citus and TimescaleDB lies in their approach to scaling. Citus focuses on horizontal scaling by distributing data across multiple nodes, allowing it to handle larger workloads and deliver higher performance. On the other hand, TimescaleDB specializes in vertical scaling, utilizing advanced compression techniques and optimized indexing structures to efficiently store and query time-series data.
Data Distribution: Citus employs a technique known as sharding to distribute data across multiple physical or virtual machines. This allows for parallel processing of queries across the shards, providing faster query response times. In contrast, TimescaleDB utilizes a hypertable concept that abstracts the underlying sharding details. It automatically partitions data into smaller chunks and distributes them across hypertable chunks, simplifying the management of distributed data.
Query Optimization: Citus provides distributed query execution and optimization techniques to enhance query performance. By leveraging distributed joins and aggregations, it can parallelize query execution across multiple nodes for faster processing. TimescaleDB, on the other hand, focuses on optimizing time-series queries. It offers a variety of time-based optimizations, including time-based indexing, chunk-aware query planning, and continuous aggregations, which significantly speed up time-series queries.
Workload Support: Citus is well-suited for multi-tenant applications and scenarios that require real-time analytics on large datasets. It can handle diverse workloads, including OLTP and OLAP workloads, due to its ability to distribute data across multiple nodes. TimescaleDB, on the other hand, is purpose-built for time-series data, making it an excellent choice for applications that generate and analyze large volumes of time-stamped data, such as IoT, monitoring, and financial data.
Managing Data Locality: In terms of data locality, Citus offers more control as it allows users to define placement rules for distributing data across nodes based on specific criteria. This enables users to optimize data placement for specific workloads and customize data placement strategies. TimescaleDB, on the other hand, abstracts data distribution details, allowing users to focus on time-series modeling and query optimization, without having to worry about data placement.
Ecosystem and Community: Both Citus and TimescaleDB have active open-source communities and vibrant ecosystems. However, Citus has been commercially acquired by Microsoft, which may provide additional support and integration options within the Microsoft ecosystem. TimescaleDB is an independent company focused solely on time-series data and has partnerships and integrations with various cloud providers and third-party tools, offering a broader set of options for users.
In Summary, Citus and TimescaleDB differ in their scaling approaches, data distribution methods, query optimization techniques, workload support, data locality management, and the respective ecosystems they are part of. These differences make them suitable for different use cases, with Citus being ideal for distributed workloads and real-time analytics, while TimescaleDB excels at handling 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.
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.
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 Citus
- Multi-core Parallel Processing6
- Drop-in PostgreSQL replacement3
- Distributed with Auto-Sharding2
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 Citus
Cons of TimescaleDB
- Licensing issues when running on managed databases5
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