Design and Optimization of Large‑Scale Log Systems for High‑Volume Data

Log data is one of the most common forms of massive data; during events such as Double‑11 sales, an e‑commerce platform can generate billions of log entries per hour, creating severe challenges for technical teams.

The article first introduces the baseline architecture of a log system, comparing simple master/slave setups with more complex scenarios where logs are collected, transmitted, filtered, transformed, stored, and visualized using the ELK stack (Elasticsearch, Beats, Logstash/Kibana).

It distinguishes three usage dimensions: real‑time (critical user‑facing services that must trigger immediate alerts), near‑real‑time (operations like hourly work‑hour reporting that tolerate short delays), and retrospective analysis (cross‑time‑dimensional comparison for root‑cause tracing).

Key components of the ELK‑based solution are:

Elasticsearch for centralized storage and search.

Beats (Filebeat, Topbeat) for lightweight log collection.

Plugin‑based input, output, and filter modules to support secure transmission.

Four optimization directions are proposed:

Basic optimization : memory allocation, garbage collection, caching, network compression, CPU multithreading, and disk fragmentation reduction.

Platform scaling : vertical scaling (adding memory/disk) and horizontal scaling (distributed clusters), plus adding or removing services based on usage.

Data partitioning : classifying logs by level (error, info, debug), handling hot‑spot periods separately, and applying delayed computation and file splitting.

System degradation : defining fallback strategies to disable non‑essential functions during overload.

Practical improvements include:

Increasing per‑core throughput from ~3 k to 15–18 k logs per second.

Reducing server resource consumption and extending log retention beyond the original 7‑day limit.

Minimizing ES protection triggers by off‑loading data streams.

Additional sections cover log formats (UUID, timestamp, host), various ingestion tools (Rsyslog, Kafka, Fluentd), and deployment patterns ranging from simple file‑based storage to full ELK pipelines with HBase integration.

The author, a seasoned big‑data architect, concludes with best‑practice recommendations for building efficient, low‑overhead log pipelines that balance real‑time monitoring, storage cost, and analytical capability.