Influx Database vs Structured Query Language Database
Which database to use for your application
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As we progress in the Industrial Internet of Things (IIOT), choosing the right database is an important part of managing applications. Two common options are Influx Database (InfluxDB), a time-series database, and Structured Query Language (SQL) database, often called Sequel. Both store and retrieve data, but they are designed with very different priorities in mind.
Main Differences
The most fundamental difference is in the data model. SQL databases use the relational model, where information is stored in tables with rows and columns. Relationships between tables ensure consistency and allow complex queries. This makes SQL databases well suited for structured business data such as orders and invoices.
Influx Database, on the other hand, is built specifically for time series data. Every entry is automatically timestamped and organized into measurements, tags, and fields. This design makes it efficient at handling values that change over time, such as sensor readings, logs, or condition monitoring metrics.
The query languages also differ. SQL databases use Structured Query Language, a long-established standard for joins, filtering, and aggregations. Influx Database introduced Influx Query Language (InfluxQL), which looks like SQL, and later developed Flux, a functional scripting language designed for time-based analysis such as moving averages.
SQL databases are optimized for transactional workloads where frequent updates, multi table relationships, and consistency are required. Influx Database is optimized for very high write throughput, potentially millions of new data points per second, while enabling fast queries across time intervals.
Data retention strategies also separate the two. SQL databases typically hold data indefinitely unless deleted or archived by the administrator. Influx Database includes built in retention policies that can automatically remove or compress older data, which is more relevant when monitoring machine health where recent data matters most.
Overlapping Features
Although different in design, Influx Database and SQL databases share important similarities. Both rely on structured queries to filter and analyze data. Both use indexing strategies to improve performance, with SQL focusing on keys and Influx Database indexing time and tags. Both can integrate with visualization platforms such as Grafana.
When to Use Each
Influx Database is the better choice when data is time stamped and arrives in large volumes. Examples include industrial equipment monitoring, application performance metrics, and event logging. In these cases, automatic retention makes storage efficient while still supporting real-time queries.
SQL databases remain the preferred option for relational and transactional needs. Business systems with customers, orders, and payments rely on the relational model to maintain integrity. Financial and accounting applications also depend on the strict consistency guarantees of SQL. When data must be stored long term and analyzed across many related tables, SQL databases are the better solution.
Conclusion
Influx Database and Structured Query Language databases are not direct replacements for each other but complementary tools. Influx Database excels at handling high volume time series data, while SQL databases are ideal for transactional applications. The best choice depends on the problem. Use Influx Database for monitoring and metrics and use Structured Query Language databases for business operations and transactions. In many cases, organizations benefit by combining both, letting each database type serve its intended purpose.
Keywords
- Industry 4.0
- Internet of Things
Author
Sam Thompson
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