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Last Updated: 08-21-2026 11:16 AM (UTC)

InfluxDB

With over 31,600 GitHub stars and thousands of production deployments, InfluxDB 3 Core is the open-source time series database rebuilt in Rust on the FDAP stack — Apache Flight for high-throughput data transfer, DataFusion for vectorized SQL query execution, Arrow for columnar in-memory representation, and Parquet for compressed columnar storage. The engine delivers sub-10ms query response times on recent data and handles millions of writes per second through line protocol ingestion over HTTP, with unlimited tag cardinality eliminating the high-cardinality limitations that plagued earlier InfluxDB versions. The diskless architecture persists data as compressed Parquet files to S3-compatible object storage, Azure Blob, Google Cloud Storage, or local disk with configurable partitioning strategies, while the write-ahead log and in-memory buffer serve real-time queries against recent data before compaction. Native SQL support through DataFusion includes window functions, CTEs, subqueries, and joins, while InfluxQL maintains backward compatibility with existing InfluxDB 1.x and 2.x applications through the same query API. The embedded Python VM enables processing engine plugins and triggers that execute custom logic on write events, perform cross-database queries, and transform data in real time without external tooling. Flight SQL clients provide high-performance query access from Python, Go, Java, and Rust, and the HTTP API supports writes in line protocol format compatible with Telegraf's 300+ input plugins. Running on a dedicated VPS on RepoCloud with guaranteed CPU, RAM, and SSD, full root SSH access, and a browser serial console. MIT/Apache 2.0 dual-licensed.

InfluxDB
InfluxDB
InfluxDB
InfluxDB
InfluxDB

Benefits

  • Sub-10ms Real-Time Query Performance
  • In-memory buffer and write-ahead log serve recent data queries in under 10 milliseconds while compacted Parquet files on object storage handle historical range queries efficiently.
  • Unlimited Tag Cardinality at Scale
  • Columnar Apache Arrow and Parquet storage eliminates high-cardinality limitations, supporting millions of unique tag values without performance degradation or index bloat issues.
  • FDAP Stack Architecture in Rust
  • Built on Apache Flight, DataFusion, Arrow, and Parquet for vectorized columnar query execution, zero-copy memory operations, and efficient compressed time series storage.
  • Diskless Object Storage Architecture
  • Persists compressed Parquet files to S3, Azure Blob, GCS, or local disk with configurable partitioning, reducing storage costs while maintaining fast query performance on recent data.

Features

  • Native SQL Queries
  • Apache DataFusion powers full SQL including window functions, CTEs, subqueries, and joins alongside backward-compatible InfluxQL for existing 1.x and 2.x applications.
  • Processing Engine Plugins
  • Embedded Python VM executes custom plugins and triggers on write events for real-time data transformation, cross-database queries, and automated processing pipelines.
  • Line Protocol Ingestion
  • HTTP write API accepts line protocol format compatible with Telegraf's 300+ input plugins for collecting metrics from databases, systems, IoT sensors, and cloud services.
  • Flight SQL Client Access
  • Apache Arrow Flight SQL provides high-performance columnar query results to Python, Go, Java, and Rust clients with zero-copy data transfer and native Arrow integration.
  • Parquet File Storage
  • Columnar Parquet format with automatic compaction provides efficient compression, predicate pushdown for fast scans, and compatibility with external analytics tools like DuckDB.