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5 Elasticsearch Disadvantages You Should Know

Since its initial release in 2010, Elasticsearch has grown into the most popular enterprise search engine with use cases that range from web crawling and website search to application performance monitoring and security log analytics.

But despite its widespread adoption and success, Elasticsearch does have some notable disadvantages that you should consider - especially if you’re envisioning a high-scale deployment with a large amount of daily ingestion.

In this blog, we’re taking a closer look at five common Elasticsearch drawbacks or disadvantages that you should know about before implementing Elasticsearch or choosing ELK stack for your log analytics solution.


Elasticsearch Disadvantages


5 Elasticsearch Disadvantages You Should Know

From slow indexing and query performance at scale to data retention tradeoffs, here are the Elasticsearch disadvantages you’ll want to watch out for in your deployment – and how to avoid them.


1. Steep Learning Curve and High Management Complexity

Elasticsearch is no longer open source, but new users can still download Elastic software - including Elasticsearch, Kibana, Logstash, and Beats - and deploy in a self-managed service model with no up-front costs. A free trial of Elastic Cloud is also available, which includes 8GB of RAM and 240GB of storage across supported cloud providers.

But despite the relatively low barrier to entry, Elasticsearch still presents a steep learning curve and high levels of management complexity for new users. To effectively deploy and manage an Elasticsearch cluster, you’ll need to develop knowledge and skills around:

  • Elasticsearch Configuration - Adjusting parameters like heap size, shard allocation, and threat pools to optimize stability and query performance based on data volume, query complexity, and hardware specifications.
  • Index Management - Understanding Elasticsearch index properties like sharding, replication, and mapping, and determining the number of shards/replicas needed based on data volume, query patterns, and fault tolerance needs.
  • Query DSL - Mastering the Elasticsearch query language known as Query DSL (Domain-Specific-Language).
  • Cluster Monitoring and Management - Managing clusters of Elasticsearch nodes, handling node failures, adding/removing nodes to scale the cluster as needed, configuring cluster monitoring tools to identify performance issues or detect bottlenecks, and executing maintenance tasks like index optimization and node health checks.
  • ETL Process - Configuring log parsing and ingestion, building and configuring data pipelines to get data from multiple sources into Elasticsearch.

Deploying and managing Elasticsearch ultimately requires a range of specialized skills, and you’ll need to invest in a training program, spend time learning, or hire an Elasticsearch professional to get things working.


2. Budget-breaking Total Cost of Ownership (TCO)

When it comes to deploying Elasticsearch, you can either download the software and run it on your own machines in a self-managed service model or subscribe to the Elastic Cloud managed service and deploy in your preferred public cloud (AWS, GCP, or Azure).

If you choose to self-manage, you’ll incur all of the management overhead associated with deploying and operating Elasticsearch. If you choose to deploy Elastic Cloud, you’ll incur additional monthly costs depending on the public cloud you choose, your hardware configuration, and the size of your deployment.

But regardless of where you deploy, you’ll incur high data storage and computing costs that scale exponentially based on the volume of data you ingest each day and how long you retain the data. This includes the cost of scaling your Elasticsearch cluster with additional nodes (which consume computing resources), as well as data transfer and monitoring costs.

Ultimately, the more data you ingest and the longer you retain it for, the higher your data storage and querying costs will climb. To see cost projections for Elasticsearch and how they compare to alternative solutions, download our white paper on the subject (linked below).


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3. Troublesome Application Stability and Uptime Issues

Elasticsearch was never architected to handle the massive volumes of data generated by modern organizations on a daily basis in 2023.

As organizations ingest increasing daily volumes of log data, Elasticsearch indices can become extremely large, often resulting in poor stability and query performance. Organizations can use Elasticsearch sharding to distribute data across nodes for parallel query processing, but running those additional nodes results in higher costs and poorly configured sharding or inadequate replication can lead to uneven data distribution, slow query performance, or unexpected crashes.

Elasticsearch is experimenting with serverless architecture to help address stability issues, but so far it’s proving difficult to overcome challenges that are intrinsically linked to its underlying data representation and solution architecture. Ultimately, precise cluster configuration and resource planning are essential to keeping costs down and avoiding crashes.


4. Painfully Slow Indexing and Query Performance at Scale

Scaling your Elasticsearch deployment involves increasing the capacity and performance of your Elasticsearch cluster with adding additional nodes, sharding, and replicas to handle increased volumes of data.

Elasticsearch was designed for scalability, but achieving optimal performance at scale can be a significant challenge when dealing with such a complex application. Common scalability challenges associated with operating Elasticsearch include:

  • Slow Indexing - Elasticsearch can be prone to sluggish performance when indexing large-scale data. There’s plenty of advice in the Elasticsearch documentation on how to tune Elasticsearch for indexing speed, such as by using bulk requests, disabling replicas for initial loads, or increasing the refresh interval. 
  • Slow Query Performance - Another typical challenge when scaling Elasticsearch is degrading search speed. As the size of an index increases, query time also increases and it may be necessary to simplify the data model or reduce query complexity to speed things up.
  • Sub-optimal Sharding Strategy - Shards allow you to split the contents of an index across multiple nodes to accelerate query performance. However, poor sharding strategy can be a major cause of poor Elasticsearch performance at scale. Too little sharding results in massive indices and slow query performance, while oversharding (dividing an index into too many shards) can lead to poor responsiveness and stability issues.


Check out the Blog! The Business Case for Switching from the ELK Stack. Cost Savings Explained and Quantified!


5. Nerve-wracking Data Retention Tradeoffs

Data retention trade-offs are an unfortunate but common reality of running Elasticsearch at scale for log analytics applications. As daily log volume increases, Elasticsearch users run into issues like ballooning costs, degrading query performance, and increased management overhead.

These are all challenges that can be somewhat mitigated by limiting the retention window for log data stored in Elasticsearch. By retaining data for as little as seven days, Elasticsearch users can reduce their data storage costs, limit the size of Elastic indices to preserve query performance, and reduce the need for complex sharding strategies.

But restricting data retention comes with a cost.

The more you try to reduce Elasticsearch costs by limiting data retention, the less data you have available to support long-term log analytics use cases like root cause and forensic analysis, advanced persistent threat (APT) detection, and long-term application performance or user behavior analysis.

Trading off these capabilities to reduce can prove disastrous to your organization’s security posture. It now takes enterprise IT departments an average of 9-12 months to detect a data breach or cyber attack, so an organization that deletes security logs after 7 days has no hope of detecting a security breach from more than a week ago - nevermind conducting a root cause analysis to understand how it happened.


Sidestep these Elasticsearch Drawbacks by Replacing Your ELK Stack with ChaosSearch

Leave the Elasticsearch disadvantages behind! ChaosSearch is the industry-leading alternative/replacement for Elasticsearch for log analytics. Our new data lake solution, Chaos LakeDB, transforms your AWS or GCP cloud object storage into a live analytics database with support for full-text search, SQL, GenAI workloads, and unlimited data retention.

Chaos LakeDB leverages proprietary technology to aggregate diverse data streams into one data lake database, automate data pipelines and schema management, and enable both real-time and historical insights with lower management overhead, no stability issues, no data retention tradeoffs, and up to 80% cheaper than Elasticsearch.


ChaosSearch LakeDB for Elasticsearch alternative or replacement


Ready to learn more?

Download our free resource Considering the Switch from ELK? and we’ll show you how to calculate the TCO of your current or proposed Elasticsearch deployment and determine how much you’ll save by switching to ChaosSearch.

About the Author, David Bunting

David Bunting is the Director of Demand Generation at ChaosSearch, the cloud data platform simplifying log analysis, cloud-native security, and application insights. Since 2019 David has worked tirelessly to bring ChaosSearch’s revolutionary technology to engineering teams, garnering the company such accolades as the Data Breakthrough Award and Cybersecurity Excellence Award. A veteran of LogMeIn and OutSystems, David has spent 20 years creating revenue growth and developing teams for SaaS and PaaS solutions. More posts by David Bunting