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InfluxDB vs TempoDB: What are the differences?
What is 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..
What is TempoDB? Store & analyze time series data from sensors, smart meters, servers & more. TempoDB is the first database service for time series data (ex: measuring thermostat temperatures, network latencies, heart rates). Time series is a unique Big Data problem that breaks traditional databases (MySQL, MongoDB, etc). Today, businesses spend months and millions attempting to build solutions to manage all this data and yet still fail to store as much as they need or analyze it effectively. TempoDB is a purpose-built database service that enables businesses to store and analyze massive streams of time series data, so they can learn from the past, understand the present, and predict the future.
InfluxDB and TempoDB are primarily classified as "Databases" and "SQL Database as a Service" tools respectively.
Some of the features offered by InfluxDB are:
- Time-Centric Functions
- Scalable Metrics
- Events
On the other hand, TempoDB provides the following key features:
- Flexible, powerful API
- Store as much high resolution (1 ms max) time series data as you need for long range historical analysis
- We guarantee data availability and protect against loss by replicating each live datapoint 3x and using geographically distributed backup environments
InfluxDB is an open source tool with 16.7K GitHub stars and 2.39K GitHub forks. Here's a link to InfluxDB's open source repository on GitHub.
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 TempoDB
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Cons of InfluxDB
- Instability4
- Proprietary query language1
- HA or Clustering is only in paid version1
Cons of TempoDB
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What is InfluxDB?
What is TempoDB?
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