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Amazon RDS vs InfluxDB: What are the differences?
Introduction
In this Markdown code, we will discuss the key differences between Amazon RDS and InfluxDB. Amazon RDS is a managed relational database service offered by Amazon Web Services (AWS), while InfluxDB is an open-source time series database designed to handle high write and query loads. Below are the key differences between these two databases.
Scalability: Amazon RDS provides horizontal scalability by allowing users to scale database instances vertically or horizontally. It supports multiple database engines like MySQL, PostgreSQL, Oracle DB, etc. On the other hand, InfluxDB is specifically designed for time series data and provides high scalability with built-in support for sharding and replication. It can handle a large volume of writes and queries efficiently.
Data Model: Amazon RDS follows a traditional relational database model where data is organized in tables with rows and columns. It supports complex data relationships, transactions, and ACID properties. In contrast, InfluxDB follows a time series data model, where data is stored as series consisting of points with a timestamp and associated tags and fields. It optimizes storage and query performance for time-based data.
Query Language: Amazon RDS supports SQL as the query language, providing a wide range of capabilities for data retrieval, manipulation, and analysis. Developers familiar with SQL can easily work with Amazon RDS. On the other hand, InfluxDB uses its own query language called InfluxQL, specifically designed for time series data. It provides functions and operations optimized for time-based querying and filtering.
Data Retention: In Amazon RDS, data retention is managed based on the storage capacity of the chosen database engine. Data can be stored for a long duration, but it may have performance and cost implications. In InfluxDB, data retention is configurable at the database and individual measurement level. It supports data expiration policies and downsampling to efficiently manage the retention of time series data.
High Availability: Amazon RDS offers high availability through Multi-AZ deployments, where data is automatically replicated across different availability zones. This ensures that the database remains accessible even in case of infrastructure failures. InfluxDB, as an open-source database, relies on clustering and replication techniques for achieving high availability. Users need to set up and manage a cluster of InfluxDB nodes to ensure data availability.
Ecosystem and Integrations: Amazon RDS benefits from the extensive AWS ecosystem and provides seamless integration with other AWS services like Amazon S3 for data backup, AWS CloudWatch for monitoring, and AWS Identity and Access Management (IAM) for security. InfluxDB also offers integrations with various tools and platforms like Grafana for data visualization, Telegraf for data collection, and Kapacitor for real-time streaming data processing.
In summary, Amazon RDS is a managed relational database service with horizontal scalability, support for various database engines, and comprehensive SQL querying capabilities. InfluxDB, on the other hand, is a specialized time series database designed for high write and query loads, optimized for time-based data modeling, and using InfluxQL as its query language.
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.
Using on-demand read/write capacity while we scale our userbase - means that we're well within the free-tier on AWS while we scale the business and evaluate traffic patterns.
Using single-table design, which is dead simple using Jeremy Daly's dynamodb-toolbox library
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 Amazon RDS
- Reliable failovers165
- Automated backups156
- Backed by amazon130
- Db snapshots92
- Multi-availability87
- Control iops, fast restore to point of time30
- Security28
- Elastic24
- Push-button scaling20
- Automatic software patching20
- Replication4
- Reliable3
- Isolation2
Pros of InfluxDB
- Time-series data analysis58
- 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
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Cons of Amazon RDS
Cons of InfluxDB
- Instability4
- Proprietary query language1
- HA or Clustering is only in paid version1
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