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Heroku Postgres vs InfluxDB: What are the differences?
Heroku Postgres: Heroku's Database-as-a-Service. Based on the most powerful open-source database, PostgreSQL. Heroku Postgres provides a SQL database-as-a-service that lets you focus on building your application instead of messing around with database management; InfluxDB: An open-source distributed time series database with no external dependencies. InfluxDB is a scalable datastore for metrics, events, and real-time analytics. It has a built-in HTTP API so you don't have to write any server side code to get up and running InfluxDB is designed to be scalable, simple to install and manage, and fast to get data in and out..
Heroku Postgres can be classified as a tool in the "PostgreSQL as a Service" category, while InfluxDB is grouped under "Databases".
Some of the features offered by Heroku Postgres are:
- High Availability
- Rollback
- Dataclips
On the other hand, InfluxDB provides the following key features:
- Time-Centric Functions
- Scalable Metrics
- Events
"Easy to setup" is the top reason why over 27 developers like Heroku Postgres, while over 36 developers mention "Time-series data analysis" as the leading cause for choosing InfluxDB.
InfluxDB is an open source tool with 16.7K GitHub stars and 2.38K GitHub forks. Here's a link to InfluxDB's open source repository on GitHub.
trivago, Redox Engine, and Thumbtack are some of the popular companies that use InfluxDB, whereas Heroku Postgres is used by Luckycycle, FarmLogs, and Watsi. InfluxDB has a broader approval, being mentioned in 119 company stacks & 39 developers stacks; compared to Heroku Postgres, which is listed in 74 company stacks and 39 developer stacks.
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.
Considering moving part of our PostgreSQL database infrastructure to the cloud, however, not quite sure between AWS, Heroku, Azure and Google cloud. Things to consider: The main reason is for backing up and centralize all our data in the cloud. With that in mind the main elements are: -Pricing for storage. -Small team. -No need for high throughput. -Support for docker swarm and Kubernetes.
Good balance between easy to manage, pricing, docs and features.
DigitalOcean's offering is pretty solid. Easy to scale, great UI, automatic daily backups, decent pricing.
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 Heroku Postgres
- Easy to setup29
- Follower databases3
- Dataclips for sharing queries3
- Extremely reliable3
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
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Cons of Heroku Postgres
- Super expensive2
Cons of InfluxDB
- Instability4
- Proprietary query language1
- HA or Clustering is only in paid version1
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