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Clickhouse vs InfluxDB: What are the differences?
Clickhouse and InfluxDB are both popular time-series databases used for storing and analyzing time-series data. Let's explore the key differences between them.
Data Model: Clickhouse uses a columnar data model, which stores data in columns rather than rows. This allows for efficient compression and faster query performance, especially for analytical workloads. InfluxDB, on the other hand, uses a tag-value data model, which stores data in the form of key-value pairs. This model is optimized for storing and retrieving time-series data, making InfluxDB a preferred choice for real-time monitoring and IoT applications.
Query Language: Clickhouse uses an SQL-like query language called ClickHouse SQL, which provides a wide range of expressive analytical functions and supports complex queries for data analysis. InfluxDB, on the other hand, uses its own query language called InfluxQL, which is specifically designed for time-series data. InfluxQL offers a simplified syntax and focuses on retrieving and manipulating time-series data efficiently.
Architecture: Clickhouse is a distributed columnar database with a shared-nothing architecture, allowing for horizontal scalability and high availability. It can handle massive amounts of data and perform distributed queries across multiple nodes. InfluxDB, on the other hand, is a single-node database by default, but it also offers clustering capabilities for high availability and scalability. The architecture of InfluxDB is optimized for real-time data ingestion and query performance on single nodes or small clusters.
Data Ingestion: Clickhouse provides a variety of ingestion methods, including batch ingestion, streaming ingestion, and data replication. It supports various formats like CSV, JSON, and Apache Kafka for data ingestion. InfluxDB, on the other hand, excels in real-time data ingestion and provides a built-in HTTP API and line protocol for data ingestion. It also has integrations with popular monitoring and IoT platforms, making it easier to ingest data from various sources.
Data Processing: Clickhouse is primarily designed for offline data analytics and supports complex analytical queries like window functions, subqueries, and joins. It also provides support for materialized views and data aggregation. InfluxDB, on the other hand, is focused on real-time data processing and supports continuous queries, downsampling, and data retention policies. It also offers built-in functions for anomaly detection and data downsampling.
Ecosystem and Integrations: Clickhouse has a growing ecosystem of integrations with popular data processing frameworks like Apache Spark and Apache Hadoop. It also provides drivers for different programming languages like Python, Java, and Go. InfluxDB, on the other hand, has a strong ecosystem of integrations with monitoring and visualization tools like Grafana and Prometheus. It also provides libraries and clients for various programming languages, making it easy to integrate with existing workflows.
In summary, Clickhouse excels in offline analytical workloads, while InfluxDB is optimized for real-time monitoring and IoT applications.
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 Clickhouse
- Fast, very very fast21
- Good compression ratio11
- Horizontally scalable7
- Utilizes all CPU resources6
- RESTful5
- Open-source5
- Great CLI5
- Great number of SQL functions4
- Buggy4
- Server crashes its normal :(3
- Highly available3
- Flexible connection options3
- Has no transactions3
- ODBC2
- Flexible compression options2
- In IDEA data import via HTTP interface not working1
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 Clickhouse
- Slow insert operations5
Cons of InfluxDB
- Instability4
- Proprietary query language1
- HA or Clustering is only in paid version1
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