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World Congress 2026 Europe • Jul 9, 2026 • Session details

Strategies for Efficient Log Management in Large-Scale Kubernetes Clusters

Aliaksandr Valialkin

Are chatty Kubernetes microservices draining your RAM and budget? Stop scaling expensive legacy clusters. Discover how VictoriaLogs shrinks petabytes of log data into gigabytes on a single node.

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#1 about 2 min

Introduction to the speaker and software development philosophy

Building highly optimized programs in the Go programming language provides simple maintenance while ensuring high performance capabilities.

#2 about 4 min

Analyzing log generation scale in Kubernetes microservice architectures

Microservices architectures produce massive daily log volumes that aggregate into enormous petabyte datasets annually.

#3 about 5 min

Challenges with relational databases and traditional log management tools

Conventional solutions like Elasticsearch, Grafana Loki, and ClickHouse struggle with managing petabyte-scale data without significant resource overhead.

#4 about 4 min

Core features and architecture of the VictoriaLogs database system

A zero-configuration logging system uses local storage disks for fast data collection without needing specialized object storage.

#5 about 2 min

Real-world compression and resource usage benchmarks for production environments

Evaluated production metrics demonstrate significant disk space reduction and minimal memory usage on a single node deployment.

#6 about 4 min

Storage formatting techniques that accelerate log metric query performance

Specialized block header architecture and optimal disk allocation enable rapid scanning when parsing aggregate timestamp conversions.

#7 about 3 min

Optimizing full-text search performance using cached massive Bloom filters

Operating system page caches hold specialized Bloom filters for exceedingly fast text identification across huge log datasets.

#8 about 6 min

Leveraging stream filters and columnar storage for rapid querying

Stream filters reduce block header scanning while columnar storage structures quickly isolate specific container metrics efficiently.

#9 about 5 min

Extrapolating single-node limitations and cluster horizontal scaling capacity models

Projecting storage hardware requirements to petabyte proportions allows cluster architectures to utilize multiple nodes for linear performance scaling.

Matching moments

4:11 min

Building a unified stack leveraging core open source tools

Mathias Palmersheim Mathias Palmersheim · Europe 2026 Virtual

3:41 min

Understanding the daily challenges of massive log volumes

Michal Bojko Michal Bojko · WWC 2025

3:05 min

Optimizing log storage and file formats for reduced footprints

Sohan Maheshwar · LIVE

3:38 min

Diving into tiered storage operations and horizontal scaling

Mary Grygleski Mary Grygleski · LIVE

1:32 min

Minimizing infrastructure waste by reducing application log volumes

Pierre-Luc Noel +1 · WWC 2023

3:58 min

Struggling with ungoverned data lakes and massive storage costs

Kateřina Ščavnická Kateřina Ščavnická · WWC 2025

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