Disadvantages of Elasticsearch

1. Complexity of Setup and Maintenance

• While Elasticsearch offers powerful features, setting up and maintaining a production-grade cluster can be complex and resource-intensive. Configuration parameters, cluster sizing, and performance tuning require careful consideration to ensure optimal performance and reliability. Additionally, managing data backups, security configurations, and software upgrades adds to the operational overhead.
• Example: Setting up a multi-node Elasticsearch cluster involves configuring network settings, JVM parameters, heap sizes, and shard allocations. Administrators must also monitor cluster health, handle node failures, and perform routine maintenance tasks to keep the cluster running smoothly.

2. Resource Intensive

• Elasticsearch is a resource-intensive application requiring sufficient CPU, memory and storage resources to operate effectively. As the size of our dataset grows so do the hardware requirements for running Elasticsearch.
• Inadequate resource allocation can lead to performance degradation, query timeouts and cluster instability.
• Example: A small-scale deployment of Elasticsearch may run smoothly on a single node with limited resources. However, as the dataset grows or query volume increases, you may need to scale up to larger instances or add more nodes to the cluster to maintain performance.

3. Data Consistency and Durability

• Elasticsearch prioritizes search performance and scalability over strict data consistency and durability. While it provides mechanisms for data replication and high availability, it does not guarantee strong consistency across all nodes in the cluster. In scenarios where data integrity and consistency are paramount, additional measures may be required to ensure data reliability.
• Example: In a distributed Elasticsearch cluster, eventual consistency is the norm, meaning that updates made to documents may not be immediately reflected across all nodes. This can lead to inconsistencies in query results and require careful handling of write operations in distributed environments.

4. Learning Curve and Expertise Required

• Elasticsearch has a steep learning curve, especially for users who are new to search and analytics technologies. Understanding concepts such as indexing, querying, mapping and cluster management requires time and effort.
• Additionally, troubleshooting performance issues, optimizing query performance, and diagnosing cluster problems may require specialized knowledge and expertise.
• Example: Developers and administrators may need to undergo training or consult documentation and community resources to gain proficiency in Elasticsearch. Experimenting with sample datasets, conducting performance tests, and seeking guidance from experienced users can help accelerate the learning process.

Elasticsearch is a distributed search and analytics engine that incorporates Multiversion Concurrency Control (MVCC) to enable concurrent transactions while maintaining data consistency. MVCC allows multiple users to access the database simultaneously, isolating transactions from each other’s changes.

In this article, we will learn about the Elasticsearch and its Advantages and Disadvantages of Elasticsearch in detail.