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InfluxDB vs MonetDB: What are the differences?
Introduction: InfluxDB and MonetDB are both popular database management systems used for different purposes. Understanding the key differences between these two systems is crucial for selecting the best option for specific use cases.
1. Data Model: InfluxDB is a time-series database designed for storing and querying time-stamped data, making it ideal for IoT, monitoring, and analytics applications. On the other hand, MonetDB is a relational column-store database that excels in handling complex analytical queries on large datasets with multiple tables and relationships.
2. Query Language: InfluxDB uses InfluxQL, a SQL-like query language specialized for time-series data manipulation, while MonetDB supports standard SQL queries with additional advanced features like columnar storage optimization and vectorized query processing.
3. Performance: InfluxDB is optimized for high-throughput write and read operations on time-series data, providing excellent performance for real-time monitoring and data analysis. MonetDB, on the other hand, offers high performance for complex analytical queries involving joins, aggregations, and subqueries across large relational datasets.
4. Scalability and Horizontal Partitioning: InfluxDB provides built-in support for horizontal data partitioning and clustering to scale out across multiple nodes and handle growing volumes of time-series data efficiently. MonetDB also supports scalability through horizontal partitioning but focuses more on optimizing query performance through columnar storage and indexing strategies.
5. Use Cases: InfluxDB is commonly used in applications that require real-time data processing, event monitoring, IoT sensor data storage, and DevOps analytics. MonetDB is preferred for data warehousing, business intelligence, ad-hoc query analysis, scientific research, and other analytical workloads that involve complex queries on massive datasets.
6. Ecosystem and Integrations: InfluxDB has a rich ecosystem with support for various integrations, including Grafana, Telegraf, and Kapacitor, making it a popular choice for building monitoring and visualization solutions. MonetDB also offers integrations with tools like R, Python, and Tableau for advanced analytics and reporting capabilities.
In Summary, understanding the key differences between InfluxDB and MonetDB is essential for selecting the right database management system based on the specific requirements of time-series data processing or complex analytical queries.
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 MonetDB
- High Performance2
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Cons of InfluxDB
- Instability4
- Proprietary query language1
- HA or Clustering is only in paid version1
Cons of MonetDB
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