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InfluxDB enables real-time analytics by serving as a purpose-built database that optimizes processing and scaling

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InfluxDB

InfluxDB enables real-time analytics by serving as a purpose-built database that optimizes processing and scaling

Choose the Right Product See Performance Comparison

In the Cloud

For startups and small teams
Start small, pay for what you use

For enterprises and organizations
Dedicated resources, scale, and advanced security

On-Prem

For enterprises and organizations
Scale, enterprise features, control where you run

Platform Overview Real-Time Analytics Easy Data Collection Integrations

InfluxDB by Use Cases

Network monitoring IoT Monitoring and Analytics SaaS Operational Monitoring

InfluxDB by Industries

Manufacturing Telecommunications Energy and Utilities

InfluxDB Customers

Customers Become an InfluxDB Inventor

Tech papers, deep dives, videos, and more

InfluxDB University

Free trainings

Options for running InfluxDB on AWS

300+ plugins

Explore the latest news and tutorials

Join the largest time series developer community

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InfluxDB OSS & 3.0;
A Performance Comparison

InfluxDB 3.0. Overview Download Benchmark Report

With more than 900k active instances running globally, InfluxDB is the leading database for generating real-time insights from time series data. With the scale, ease, and high availability of the 3.0 product suite, and the flexibility of OSS, there’s an InfluxDB for every use case.

Dive in and compare InfluxDB open source and InfluxDB 3.0 for your workloads.

Storage Footprint
Data Ingest
Query Latency

Testing Approach
Tested Configurations
Tested Dataset
Tested Queries

InfluxDB 3.0 delivers 90% lower storage costs. Using object storage to optimize storage costs delivers lower storage footprint and costs compared with the SSD-based (Solid State Devices) storage used by InfluxDB OSS.

InfluxDB 3.0

InfluxDB OSS

InfluxDB 3.0 ingests data sets faster—without performance degradation as you scale.
Higher rates of ingest are better.

InfluxDB 3.0

InfluxDB OSS

Users expect fast access to data they want. Lower is better.

Show me how this compares with InfluxDB 3.0

InfluxDB 3.0 InfluxDB OSS 1.8

Display Queries were run 10 times in the test. P95 shows the 95th percentile, Min shows the minimum, Max shows the maximum and Avg shows the average of the query results from the 10 runs. Select one

P95 Min Max Avg

Time range Last 5 mins includes results from querying past 5 minutes of data. Last 60 mins includes results from querying past 60 minutes.

Last 5 mins Last 60 mins

Queries Select the query types to show the query latency comparisons for. Select all Select the type of queries to include

Aggregate These are queries that output the aggregate values (e.g. count, sum, min, max etc.) derived from the original rows. Stat / Math These are queries that perform math (e.g. mode, median, etc.) or statistical (e.g. spread, stddev, percentile, etc.) functions on the rows selected. Group / Order by These are queries that return rows grouped by certain columns or rows that are sorted by a certain column. Group by time These queries perform time-based operations on the rows returned. In / Or These queries return rows that match multiple values (e.g. search by hosts or devices by id) as provided by the IN / OR clauses Like / Union These queries return multiple rows by matching the pattern provided by the LIKE clause or return the union of multiple queries Limit / Top / Bottom These queries return rows specified by the LIMIT or TOP or BOTTOM clause. Threshold These queries return rows whose fields meet one or more threshold conditions

InfluxDB 3.0

InfluxDB OSS

Methodology

Testing Approach

We automated the benchmarking scripts to create reproducible benchmark results at any time.
The client workload generator running InfluxDB Telegraf agents produces the data ingestion workload for databases under comparison. We chose Telegraf as the data collection agent of choice for familiarity, ease of use, and ability to scale. Telegraf is representative of the majority of our customer workloads.
To detect maximum ingest capacity, the test increases the number of clients until a degradation is observed in the ingestor performance as reflected by CPU and memory usage on the ingestors.
The test executes queries sequentially for various time ranges and schedules during ingest to simulate actual workload conditions. Future benchmark testing plans include adding parallel execution of query workloads.

Tested Configurations

InfluxDB 3.0 (The tests were performed against Cloud Dedicated.)

We deployed the InfluxDB Cloud Dedicated (v3.0) XS cluster using AWS EKS (Elastic Kubernetes Service) to run as a 3-node (1 ingestor, 1 querier, and 1 compactor) cluster in AWS.
Cost to end user: $60K/yr

InfluxDB OSS 1.8 (The tests were performed against InfluxDB OSS. The tests ran in the same class as Cloud Dedicated, on the biggest available instance.)

We deployed the InfluxDB Open Source v1.8 on m5.24xlarge instance in AWS. Storage is backed by 10 TB General Purpose SSD (gp3).
Total CPU: 96vCPU
Total Memory: 384GiB RAM
Additional AWS Services used: None
AWS Infrastructure cost to end user: ~$60K/yr

Tested Dataset

We designed the data for this benchmark to simulate an application performance monitoring workload using Telegraf input plugins to collect a variety of metrics and measurements. The data schema produced by the Telegraf plugins used in this test is found here.

Total values in dataset: 372 billion
Dataset duration: 24 hours
Measurement interval: 10 seconds

Tested Queries

The benchmark automated test framework used the following set of queries to observe query latencies for the products under observation:

See the science behind the numbers. Download the full report.

Download Benchmarks

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