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.
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:
These three components work together to perform a variety of use cases. The most common use case is for monitoring IT environments.
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:
As the load on indexing increases, performance could be affected. Adding more nodes can help improve performance.
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:
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.
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:
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:
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.
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.
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.
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.
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.
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.
The indexing layer runs on JVM and uses significant resources which can affect performance.
The growing number of events may prevent Logstash from keeping up with ingestion.
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:
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.