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InfluxDB vs TimescaleDB: What are the differences?
Introduction
This markdown code provides a comparison between InfluxDB and TimescaleDB, highlighting their key differences.
Data Model: InfluxDB is a time-series database designed specifically for handling time-based data, whereas TimescaleDB is an extension of PostgreSQL that adds time-series capabilities. InfluxDB uses a specialized data model optimized for time-series data, with measurements, fields, tags, and timestamps, while TimescaleDB uses the same relational model as PostgreSQL, with tables, columns, and rows.
Scalability: InfluxDB provides horizontal scalability out-of-the-box, allowing you to easily add more nodes to the cluster to handle increases in data and query volume. On the other hand, TimescaleDB leverages sharding techniques for scalability, distributing the data across multiple server nodes, but requires more manual configuration for scaling.
Data Partitioning: InfluxDB automatically partitions data across time intervals called shards, which allows for efficient storage and querying of time-series data. In contrast, TimescaleDB requires manual partitioning based on time intervals using hypertables, which involves specifying partitioning policies and managing partitions yourself.
SQL Support: TimescaleDB is built as an extension to PostgreSQL, and therefore inherits all the SQL capabilities of PostgreSQL. This means you can use the full power of SQL to query your time-series data in TimescaleDB. InfluxDB, on the other hand, provides a simplified query language called InfluxQL, which is specifically designed for time-series data and may be more intuitive for time-series analysis.
Data Retention and Compression: InfluxDB offers built-in data retention policies that allow you to automatically expire old data, while also providing efficient compression techniques to reduce storage requirements. TimescaleDB does not provide native data retention policies but offers the flexibility of using PostgreSQL's mechanisms for data retention. TimescaleDB also supports compression, but it is not as tightly integrated as in InfluxDB.
Ecosystem and Integrations: InfluxDB has a mature ecosystem with a wide range of integrations and libraries, making it easier to integrate with other tools and systems for data collection, monitoring, and analysis. TimescaleDB, being an extension of PostgreSQL, benefits from the vast ecosystem and integrations available for PostgreSQL, making it suitable for use alongside other non-time-series data.
In Summary, InfluxDB and TimescaleDB differ in their data model, scalability, data partitioning, SQL support, data retention and compression, and ecosystem/integrations.
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.
I have a lot of data that's currently sitting in a MariaDB database, a lot of tables that weigh 200gb with indexes. Most of the large tables have a date column which is always filtered, but there are usually 4-6 additional columns that are filtered and used for statistics. I'm trying to figure out the best tool for storing and analyzing large amounts of data. Preferably self-hosted or a cheap solution. The current problem I'm running into is speed. Even with pretty good indexes, if I'm trying to load a large dataset, it's pretty slow.
Druid Could be an amazing solution for your use case, My understanding, and the assumption is you are looking to export your data from MariaDB for Analytical workload. It can be used for time series database as well as a data warehouse and can be scaled horizontally once your data increases. It's pretty easy to set up on any environment (Cloud, Kubernetes, or Self-hosted nix system). Some important features which make it a perfect solution for your use case. 1. It can do streaming ingestion (Kafka, Kinesis) as well as batch ingestion (Files from Local & Cloud Storage or Databases like MySQL, Postgres). In your case MariaDB (which has the same drivers to MySQL) 2. Columnar Database, So you can query just the fields which are required, and that runs your query faster automatically. 3. Druid intelligently partitions data based on time and time-based queries are significantly faster than traditional databases. 4. Scale up or down by just adding or removing servers, and Druid automatically rebalances. Fault-tolerant architecture routes around server failures 5. Gives ana amazing centralized UI to manage data sources, query, tasks.
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 InfluxDB
- Time-series data analysis59
- Easy setup, no dependencies30
- Fast, scalable & open source24
- Open source21
- Real-time analytics20
- Continuous Query support6
- Easy Query Language5
- HTTP API4
- Out-of-the-box, automatic Retention Policy4
- Offers Enterprise version1
- Free Open Source version1
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 InfluxDB
- Instability4
- Proprietary query language1
- HA or Clustering is only in paid version1
Cons of TimescaleDB
- Licensing issues when running on managed databases5
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What is InfluxDB?
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