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InfluxDB vs Google Cloud Spanner: What are the differences?
Developers describe InfluxDB as "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.. On the other hand, Google Cloud Spanner is detailed as "Fully managed, scalable, relational database service for regional and global application data". It is a globally distributed database service that gives developers a production-ready storage solution. It provides key features such as global transactions, strongly consistent reads, and automatic multi-site replication and failover.
InfluxDB and Google Cloud Spanner belong to "Databases" category of the tech stack.
Some of the features offered by InfluxDB are:
- Time-Centric Functions
- Scalable Metrics
- Events
On the other hand, Google Cloud Spanner provides the following key features:
- Global transactions
- Strongly consistent reads
- Automatic multi-site replication
InfluxDB is an open source tool with 17.5K GitHub stars and 2.47K 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 Google Cloud Spanner
- Strongly consistent1
- Horizontal scaling1
- Scalable1
Pros of InfluxDB
- Time-series data analysis57
- 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 Google Cloud Spanner
Cons of InfluxDB
- Instability4
- HA or Clustering is only in paid version1
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What is Google Cloud Spanner?
What is InfluxDB?
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