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InfluxDB vs StatsD: What are the differences?
Introduction
InfluxDB and StatsD are two popular database technologies used for collecting and analyzing time-series data. While both serve similar purposes, there are several key differences between them.
Purpose: InfluxDB is a time-series database designed for handling high volumes of time-stamped data efficiently. It offers features like data retention policies, data replication, and continuous queries for real-time analytics. On the other hand, StatsD is a network daemon and a protocol for collecting statistics from various sources and sending them to backend services like InfluxDB for further analysis.
Architecture: InfluxDB follows a NoSQL database architecture, which is optimized for fast writes and efficient storage of time-series data. It uses a flexible schema, where measurements are stored in series with predefined tags and fields. StatsD, on the other hand, follows a client-server architecture, where clients send metrics data to a StatsD server that aggregates and forwards it to the backend system.
Data Model: InfluxDB organizes data into series, where each series represents a measurement with a unique combination of tags and fields. Tags are used for indexing and filtering, while fields store the actual data points. In contrast, StatsD collects metrics as key-value pairs, where the key represents the metric name and the value represents the measured value.
Query Language: InfluxDB provides a powerful query language called InfluxQL, which is SQL-like and optimized for time-series data. It supports various operations like aggregation, filtering, grouping, and joining of series. StatsD, being a simple protocol, doesn't have its own query language. It relies on other systems like InfluxDB to perform complex queries.
Integration: InfluxDB offers native integrations with popular data visualization tools like Grafana, making it easy to create real-time dashboards and charts. It also integrates well with other systems in the data pipeline, including data collection agents and stream processing frameworks. Though StatsD doesn't have direct integrations, it can easily send metrics to various backends, including InfluxDB, for further analysis.
Scalability: InfluxDB is designed to scale horizontally, allowing you to add more servers as your data grows. It can handle millions of writes per second and terabytes of data with ease. StatsD, being a lightweight daemon, can handle a moderate amount of metrics data, but it may require additional tools or systems to handle high volumes of metrics.
In summary, InfluxDB is a dedicated time-series database with a powerful query language and native integration support, whereas StatsD is a simple protocol for collecting and forwarding metrics data. While InfluxDB offers advanced features like flexible schema, data retention policies, and real-time analytics, StatsD focuses on lightweight metric collection and integration with backend systems.
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 StatsD
- Open source9
- Single responsibility7
- Efficient wire format5
- Handles aggregation3
- Loads of integrations3
- Many implementations1
- Scales well1
- Simple to use1
- NodeJS1
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Cons of InfluxDB
- Instability4
- Proprietary query language1
- HA or Clustering is only in paid version1
Cons of StatsD
- No authentication; cannot be used over Internet1