Open-Source Databases for Real-Time Analytics

Real-time analytics break conventional databases. When milliseconds matter and data floods in by the millions, you need purpose-built solutions.

For a deep dive, jump to 👉 The Complete Guide to Time-Series Databases.

Real-Time Analytics Requirements

Real-time analytics systems have several critical requirements:

• Low ingestion latency: Data must be queryable immediately after collection
• High write throughput: Systems must handle thousands to millions of writes per second
• Fast query performance: Analysis queries must return results with minimal delay
• Downsampling capabilities: Real-time and historical views require different resolutions
• Continuous aggregation: Pre-computed views enable faster dashboard refreshes

Specialized Time-Series Databases

InfluxDB

• Real-time capabilities: Sub-second ingestion latency; built for high-throughput writes

• Query performance: Optimized for time-bounded queries

• Aggregation: Tasks (formerly Continuous Queries) for real-time aggregation

🔷 Use case fit: Well-suited for IoT, monitoring, and operational analytics

⚠️ Limitations: Query performance can degrade with high cardinality data

Prometheus

• Real-time capabilities: 10-second default scrape interval; pull-based architecture

• Query performance: Fast range queries with PromQL

• Aggregation: Recording rules for pre-computed metrics

🔷 Use case fit: Excellent for infrastructure and application monitoring

⚠️ Limitations: Not designed for long-term storage; samples limited by memory

VictoriaMetrics

• Real-time capabilities: High ingestion rate with low CPU/memory requirements

• Query performance: Claims 20x better performance than InfluxDB for some queries

• Aggregation: Compatible with Prometheus recording rules

🔷 Use case fit: High-cardinality metrics at scale

⚠️ Limitations: Younger project with evolving feature set

PostgreSQL-Based Solutions

Standard PostgreSQL

• Real-time capabilities: Adequate for moderate data volumes (~10K inserts/sec)

• Query performance: Requires careful indexing and table partitioning

• Aggregation: Materialized views, but manual refresh required

🔷 Use case fit: Applications with mixed workloads beyond just time-series

⚠️ Limitations:

• Performance degrades significantly at scale without extensions
  Lack of native time-series optimizations

• Lacks built-in features designed explicitly for time-series data, such as automatic data retention, downsampling, or time-based partitioning.

To mitigate common challenges, developers can use PostgreSQL extensions, like Timescale, specifically designed for time-series data and real-time analytics.

TimescaleDB

An open-source PostgreSQL extension that transforms PostgreSQL into a highly performant time-series database.

• Real-time capabilities: Chunk-based architecture optimized for time-partitioned inserts

• Query performance: Time-based indexing for fast range scans

• Aggregation: Continuous aggregates for real-time pre-computation

“Continuous aggregates are what well and truly sold me on Timescale. We went from 6.4 s to execute a query to 30 ms. Yes, milliseconds.”
— Caroline Rodewig, Senior Software Engineer

📖 👉 Real-Time Analytics for Time Series: A Dev’s Intro to Continuous Aggregates

🔷 Use case fit:

• IoT applications that combine device metadata with sensor readings

• Financial systems requiring time-series analysis with transactional data

• Application monitoring where relational context enhances metrics

• Industrial systems that analyze equipment performance across multiple dimensions

• Hybrid workloads where time-series and relational queries must coexist

⚠️ Limitations: Requires PostgreSQL as a foundation; built on relational database architecture

Selecting the Right Database

Time-series databases have evolved significantly to meet real-time analytics requirements. The best choice depends on your specific workload characteristics, existing infrastructure, and team expertise.

“I’m using Timescale because it’s the same as PostgreSQL but magically faster."
— Florian Herrengt, Co-founder at Nocodelytics

Why Developers Rely on Timescale

Learn how users leverage key features like Continuous Aggregates, Compression, and Hypertables to successfully:

• Compress data by 90% while keeping raw data accessible.
• Query 50 billion rows in seconds for real-time insights.
• Simplify database management for millions of users.
• Save $12,000/month on database costs with Timescale Cloud.

“In order for predictive maintenance and collision avoidance to provide contextualized and accurate results, we must gather and process 100M+ data points per machine. We use hypertables to handle these large datasets. We've saved lives using Timescale.”
— Jean-Francois Lambert, Lead Data Engineer at Newtrax

Try Timescale Cloud free for 30 days

Or use the open-source TimescaleDB extension

👇
Install from a Docker container:

1. Run the TimescaleDB container:

docker run -d --name timescaledb -p 5432:5432 -e POSTGRES_PASSWORD=password timescale/timescaledb:latest-pg17

1. Connect to a database:

docker exec -it timescaledb psql -d "postgres://postgres:password@localhost/postgres"

💻 𝗙𝗶𝗻𝗱 𝗨𝘀 𝗢𝗻𝗹𝗶𝗻𝗲!

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🔍 GitHub ⇒ https://github.com/timescale
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🔍 Timescale Blog ⇒ https://tsdb.co/blog
🔍 Timescale Documentation ⇒ https://tsdb.co/docs