Part Two: InfluxDB 3.0 Under the Hood

By Neha Julka / Developer
Nov 12, 2024

Introduction

In the first blog in this series, Setting Up InfluxDB and Visualizing Data: Part 1, we built a data collection and visualization platform for time series data using InfluxDB Cloud Serverless. Inspired by the CSTR with PID controllers use case, the project showcased how to ingest real-time data and visualize it using InfluxDB and Grafana.

This follow-up post focuses on InfluxDB’s 3.0 architecture, giving an in-depth look at the platform’s inner workings. By understanding InfluxDB’s core components, you’ll gain insights into how the system efficiently processes and stores large-scale time series data, preparing you for more advanced use cases.

Overview of time series databases

Time series databases (TSDBs) are optimal for handling large volumes of time-stamped data. Unlike relational databases that prioritize flexibility in data types and relationships, TSDBs focus on efficiently storing and retrieving data points indexed by time.

This is critical in applications like IoT monitoring, financial systems, and real-time analytics, where data streams come in fast and need quick processing. InfluxDB, the leading time series database platform, offers high write throughput and efficient query handling for this data, making it perfect for real-time insights and long-term storage.

InfluxDB 3.0 architecture

InfluxDB 3.0 introduces key architectural improvements to handle time series data at scale. Here’s a breakdown of its major components:

Core Components

• InfluxDB Engine: Built for high-speed ingestion and query processing, the engine handles large-scale data applications, such as IoT and analytics.
• Apache Arrow: InfluxDB 3.0 uses Apache Arrow for in-memory data processing. Arrow’s columnar memory format allows faster data access and query performance, especially for real-time analytics.
• Storage Engine: The engine uses Parquet files for disk storage, leveraging the columnar format for efficient compression and fast query performance.
• Ingester: The ingester in InfluxDB 3.0 plays a crucial role in handling real-time data ingestion and processing. It manages the following tasks:
  • Real-Time Querying: It makes fresh data queryable by loading it into memory (via Apache Arrow) before it’s written to disk, enabling immediate access to recent data.
  • Data Storage: It processes incoming data and writes it to Parquet files in object storage, ensuring efficient long-term storage.
  • Metadata Management: The ingester updates the system’s metadata catalog with the latest information on ingested data, optimizing query performance and retrieval.

Data Storage

• Parquet Files and Object Storage: InfluxDB 3.0 stores data as Parquet files. These files are highly compressed and optimized for large-scale data analysis. They are kept in object storage, which provides cost-efficient, scalable storage for long-term data retention.
• Data Writing: InfluxDB 3.0 ingests data in real-time. It first loads data into Apache Arrow for in-memory processing, making it immediately queryable. The data is then batched and written to compact Parquet files for long-term object storage. This process optimizes storage efficiency while ensuring quick data retrieval.

Data model

InfluxDB’s data model is specifically tailored for time series data. It organizes information into measurements, tags, and fields, which allows for efficient storage and retrieval.

• Measurements: These are similar to tables in a traditional database, representing the name of the data being collected (e.g., “cpu_usage”, “temperature”).
• Tags: Tags are key-value pairs that add metadata to the data, such as “location=server_room” or “device=sensor_1”. Tags are indexed, which makes querying based on these metadata fields very efficient.
• Fields: Fields represent measured values, such as temperature readings or CPU utilization, and are not indexed like tags. Fields are optimized for high-write performance.

This model stores large datasets compactly, especially those with frequent writes, such as sensor or performance data, allowing for faster query responses even as data scales.

Data retention and optimization

Managing large volumes of time series data requires efficient strategies for storage and retention. InfluxDB 3.0 introduces several techniques to optimize data storage, retrieval, and retention over time. Users can control data volume while maintaining valuable insights by leveraging advanced retention policies, compression techniques, and downsampling. These strategies ensure that storage costs remain manageable while the system handles high-frequency data ingestion at scale.

Let’s explore how InfluxDB handles retention policies and advanced optimization techniques like downsampling and compression:

Retention Policies: InfluxDB’s retention policies let users specify how long to store data before deleting it. This feature ensures that users retain only the most relevant data, helping to balance storage space and long-term analysis. For example, a retention policy might keep high-resolution data for 30 days while discarding older data automatically.

Downsampling: InfluxDB 3.0 uses downsampling techniques to aggregate data (reduce data resolution over time). By aggregating data, InfluxDB 3.0 only retails relevant, summarized data for long-term analysis, keeping storage costs manageable.

Compression Techniques: InfluxDB 3.0 leverages Parquet files, which provide efficient data compression and reduce storage needs. Parquet’s columnar format allows for smaller file sizes while maintaining fast query performance, which is crucial for large-scale deployments where the data volume can grow exponentially.

By implementing retention policies, downsampling, and compression techniques, InfluxDB enables efficient data management at scale while maintaining the integrity and usability of time series data.

Scalability, clustering, & high availability

As data volumes grow and system demands increase, ensuring scalability and reliability is essential for any time series database. InfluxDB 3.0 addresses these needs with features designed for enterprise-level scalability, fault tolerance, and availability. Its architecture supports seamless horizontal scaling, providing high throughput and resilience across distributed environments.

Here’s how InfluxDB achieves scalability and high availability while accommodating multi-tenancy and distributed workloads:

Clustering: InfluxDB 3.0 introduces clustering capabilities, allowing the database to scale horizontally by distributing data across multiple nodes. This feature ensures InfluxDB can handle larger workloads, providing greater throughput and improved fault tolerance.

High Availability: Clustering also enables high availability, where data is replicated across nodes, ensuring continued operation even in the case of hardware failure or network issues.

Multi-tenancy: InfluxDB supports multi-tenancy, allowing multiple users or organizations to securely share the same infrastructure, making it suitable for enterprise-scale deployments.

Distributed Architecture: InfluxDB’s distributed architecture enables easy scaling across multiple regions, improving performance and ensuring efficient workload management across different environments.

InfluxDB offers various deployment options to match different scalability and operational needs:

• InfluxDB Cloud Serverless: This multi-tenant, low-cost option is perfect for projects that require quick setup and minimal infrastructure management. It’s ideal for smaller-scale applications like IoT and home projects, where flexibility and ease of use are crucial.
• InfluxDB Cloud Dedicated and Clustered: Both options are single-tenant and designed for larger applications that demand guaranteed resources and isolation. Cloud Dedicated offers complete control over infrastructure, while Clustered deployments provide horizontal scaling and high availability, distributing data across multiple nodes to handle larger workloads.

These flexible options ensure InfluxDB can scale efficiently, whether you’re handling smaller datasets or large-scale enterprise applications, all while offering robust scalability, performance, and resource optimization tailored to your needs.

Performance optimization

InfluxDB 3.0 can handle massive amounts of time series data, and several performance optimization strategies help ensure it does so efficiently.

Key Strategies

• Indexing: InfluxDB 3.0 optimizes query performance using Apache Arrow for in-memory processing and Parquet for highly compressed, efficient storage. Time-based indexes allow InfluxDB to quickly locate and retrieve data, especially in large datasets, without needing traditional, heavy indexing structures.
• Compression: Apache Parquet for data storage introduces excellent compression without sacrificing read speed. By using columnar storage and compression algorithms, InfluxDB ensures that even large datasets are stored efficiently, minimizing storage costs and retrieval time.
• Parallel Processing: InfluxDB uses parallel query processing, distributing query tasks across multiple CPUs or nodes. This approach speeds up complex queries, especially those spanning large datasets or requiring complex aggregations.
• Custom Partitioning: InfluxDB allows users to define custom partitions to enhance query performance. Adjusting how data is partitioned based on tag values or specific time intervals can significantly reduce the data scanned during queries, particularly for commonly filtered tags. By limiting the data scanned during queries, custom partitions can speed up response times in environments with large datasets.

Time Series Data Handling Trade-Offs

• Data Precision vs. Storage Costs: Higher precision (e.g., nanosecond timestamps) allows for more granular data analysis but significantly increases storage requirements. For use cases that don’t need extreme precision, choosing a lower precision (e.g., seconds or milliseconds) can reduce storage costs while preserving valuable insights.
• Retention Policies: Longer retention periods increase storage costs and may affect query performance as datasets grow. On the other hand, setting shorter retention periods reduces costs and boosts query performance but may limit access to historical data. Finding the right balance is key to optimizing both cost and performance.
• Downsampling: Reducing data resolution via downsampling lowers storage requirements but may result in less precise data over time. This approach works well for long-term trend analysis but can be a trade-off if detailed historical data is required.

These strategies help InfluxDB 3.0 maintain high performance even as data volume scales, but understanding the trade-offs is critical to optimizing the system for your specific use case.

Conclusion

In this post, we explored InfluxDB 3.0’s core architecture, focusing on its key components, such as Ingester, Apache Arrow for in-memory processing, and Parquet for efficient data storage. We also discussed how retention policies and downsampling help optimize scalability and performance.

Whether you’re managing IoT data or handling large-scale real-time analytics, InfluxDB 3.0 offers robust solutions tailored for time series data. Get started with InfluxDB to see how it can support your data needs today.