Scalability Patterns for ELK

Scalability patterns for ELK to improve data ingestion, logging, and search. Check the Elasticsearch, Logstash, and Kibana and their scalability patterns.

Digital Analytics
5 min
Digital Analytics
Scalability Patterns for ELK

Providing real-time data for business decisions is vital if companies want to remain competitive. Ensuring the availability of this data is not without challenges.

Here, we’ll discuss how monitoring with ELK is beneficial and provide scalability patterns for ELK to keep up with user demand.

What is The ELK Stack

The ELK stack is a powerful open-source platform that collects and processes data from multiple data sources. The data is stored in one centralized data store that can scale as data grows.

The solution is a combination of three open-source projects namely:

  • Elasticsearch - This tool performs text search
  • Logstash - The tool project retrieves data from various sources. It then transforms and enriches the data before pushing it to various destinations
  • Kibana - The visualization layer used to view and analyze the stored data is Kibana

These three components work together to perform a variety of use cases. The most common use case is for monitoring IT environments.

Scalability Patterns for ELK

The ELK stack is ideal for Enterprise use because of its ability to scale. Each layer of the stack can be scaled to get the best performance.


Real-time information analysis requires fast response times. Increased data loads can affect query performance.

Below are best practices for improving search performance as the system scales. Common horizontal scaling patterns include:

Add More Nodes

As the load on indexing increases, performance could be affected. Adding more nodes can help improve performance.

Optimize Shards

The increased number of shards can degrade search performance as users run queries that may span multiple shards.

It is important to determine the best number of shards to support efficient and timely query response times.

Common vertical scalability patterns include:

Optimize Disk Storage

Plan disk space to accommodate the storage needs. Implement separate nodes for each index.

Doing so prevents the system from depleting space on one server.

Add more CPU or Memory

When performance suffers. An alternative to adding more nodes is to add more resources.

Adding more CPU or memory can help improve performance as the system scales.


Users require information from various sources for analysis. However, aggregating data from these disparate sources can slow the ingestion process.

There are a few best practices to implement to minimize these problems when scaling. Horizontal scaling patterns include:

Add More Nodes

Logstash uses adaptive buffering to store data on a disk, thus requiring less memory to process the information.

This approach helps improve performance during periods of peak ingestion. If Logstash becomes a bottleneck during ingestion, consider adding more nodes.

Horizontal scaling is the ideal method for scaling Logstash. However, vertical scaling is possible as follows:

Create additional Logstash instances

Adding more instances can help improve performance as the system grows. However, this approach isn’t an ideal long-term strategy.

Using a horizontal approach is the preferred method for scaling Logstash.


Companies often have a large user base that needs to view analytics.

The best way to scale Kibana is to create multiple Kibana instances that all connect to the same Elasticsearch instance.

Common ELK Scalability Issues

Scaling ELK can be challenging. Because of the number of technologies in the stack, more areas will need to be considered.

Each of these technologies presents a unique set of challenges for scaling.

1. Issues Scaling Ingestion

Elasticsearch stores data in indexes which degrade performance. This happens because the index is updated every second to support real-time data analysis.

Updating so frequently creates bottlenecks.

2. Indexing Challenges

As the number of documents stored grows, the index requires additional memory, processing, and storage.

Another issue arises from the way the system distributes indexes. The platform breaks indexes into shards to distribute them across nodes.

The system also replicates each shard for redundancy.

This creates complexity in the system and increases the number of indexes you will need to manage.

3. Interconnectivity Problems

The coupling between the tools in the stack causes problems during upgrades. An upgrade to any tool in the stack could cause incompatibility problems which affect availability and performance.

4. Network Performance Problems

Running Logstash on the server used for indexing forces ingesting and logging to compete for resources. As a result timeout and disconnection errors may arise.

5. Memory Consumption

The indexing layer runs on JVM and uses significant resources which can affect performance.

6. Throughput Spikes

The growing number of events may prevent Logstash from keeping up with ingestion.

Why Consider the ELK Stack?

Implementing the ELK stack helps companies handle increasingly larger volumes of data.

The ELK stack provides exceptional loading and analytics performance for large data sets.

Additional benefits include:

  • Scalability - ELK can work with any technical infrastructure. It can work for onsite implementations, in the cloud and can be used as a SaaS solution.
  • Centralized Logging - The ELK stack can pull logs generated from any system. This centralized logging gives a central dashboard for issues across systems. It also helps speed time to recovery as companies can identify issues in one spot rather than logging into different systems.
  • User Friendly - The platform does not require a large learning curve to get started. It is easy to set up and simple to use.
  • Cost-Effective - As an open-source platform, ELK is very cost-effective.
  • Robust Security - The platform provides security as index encryption and field-level security.

Leaders need real-time information to help make strategic decisions. Ensuring this information is available and ready to use requires a robust solution to ingestion, logging, and searching.

If you are looking to take the guesswork out of scaling ELK for your analytics initiatives, contact one of our software professionals.

Published on
October 15, 2021

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