Scaling your search with Elasticsearch

Search is no longer a luxury, it’s a necessity. As data volumes continue to grow and users demand instant, relevant results, search engines must be scalable, fast and intelligent. Elasticsearch, the powerful open-source search and analytics engine, offers exactly that. Originally developed for full-text search, Elasticsearch has evolved into a distributed system capable of handling vast amounts of structured and unstructured data, making it ideal for scaling enterprise search environments.

Why scaling search matters

Modern applications generate massive volumes of data, from user activity logs and system events to customer records and product catalogs. If your search engine can’t keep up with this growth, it can directly impact performance, user experience, and ultimately, business outcomes.

According to this IDC report report, the global datasphere is expected to reach 175 zettabytes by the end of the year. Without a scalable solution, companies will struggle to extract value from this data. This is where Elasticsearch becomes critical.

Elasticsearch scalability: the fundamentals

Scaling in Elasticsearch can be approached in two ways:

• Vertical scaling (scale up): Adding more CPU, memory or disk to a single node.

• Horizontal scaling (scale out): Adding more nodes to the cluster to distribute load and increase capacity.

Elasticsearch is designed for horizontal scalability, thanks to its distributed architecture. It splits indices into shards and replicates them across multiple nodes, allowing for parallel processing, failover, and load balancing.

Related article: What is Elasticsearch and how does it work?

Key components supporting scalability:

• Shards and replicas: Every index is split into shards, which can be spread across multiple nodes. Replicas provide redundancy and improve read performance.

• Clusters and nodes: Nodes work together as part of a cluster, automatically balancing data and search queries.

• Routing and allocation: Elasticsearch intelligently routes queries to the relevant shards, minimizing latency and resource consumption.

Best practices to scale Elasticsearch efficiently

1. Optimize index design

Poorly designed indices can lead to performance issues. Some key recommendations:

• Use time-based indices for logs and time series data.

• Avoid too many small indices that increase memory usage.

• Leverage index templates to enforce consistency across indices.

2. Tune shard size and count

A common mistake is using the default shard settings. As a rule of thumb:

• Keep shard sizes between 10–50GB.

• Avoid thousands of shards unless truly necessary.

• Use the _cat/shards API to monitor shard distribution.

3. Monitor and scale infrastructure

Use Elastic’s built-in Kibana monitoring or external tools like Zabbix or Prometheus to monitor:

• Heap memory usage

• Node health

• Indexing/search rates

• Query latencies

When demand increases, scale out by adding nodes and rebalancing shards using cluster settings.

Related article: What is Kibana and how to use it with Elasticsearch?

4. Use tiered storage and data lifecycle management

Not all data needs to live on expensive hot nodes. Elasticsearch supports:

• Hot-warm-cold architecture: Place frequently accessed data on fast nodes and archive older data.

• Index Lifecycle Management (ILM): Automate data transitions between storage tiers to save costs.

5. Apply query optimization techniques

Heavy or unfiltered queries can choke performance. Tips include:

• Use filters instead of full-text queries when possible.

• Avoid wildcard queries unless absolutely necessary.

• Cache frequent queries and aggregations.

Scaling search in real-world scenarios

Case: E-commerce search

In large e-commerce platforms, product catalogues can grow to millions of items. Elasticsearch helps by:

• Delivering near-instant product search.

• Handling autocomplete and fuzzy matching.

• Scaling dynamically during sales events (e.g., Black Friday).

Case: Log analytics at scale

Many organizations use Elasticsearch for log analytics with tools like Elastic Stack (ELK). Logs are indexed in near real-time, enabling:

• Fast incident response

• Real-time anomaly detection

• Scalable dashboarding with Kibana

Related article: OpenSearch vs Elasticsearch: what are the differences and how to choose?

Elasticsearch on the cloud vs. on-premises

Scaling on the cloud offers advantages:

• Elastic Cloud handles node provisioning, scaling, and snapshots automatically.

• Integration with Kubernetes simplifies scaling via orchestration.

• Pay-as-you-grow pricing models avoid upfront hardware investment.

However, on-premises deployments offer more control over data governance, security and compliance, especially important in regulated industries.

Integrating Elasticsearch with other tools

Scalability also depends on how well Elasticsearch integrates into your ecosystem. Some useful integrations:

• Beats and Logstash for ingesting structured/unstructured data.

• Camunda or Red Hat JBoss for workflow and BPM solutions.

• Kibana for visualization and dashboarding.

• Zabbix for monitoring large-scale clusters.

Why Elasticsearch is your best bet for scalable search

Scaling search is no longer an afterthought. It is central to user experience, business agility and digital transformation. Elasticsearch provides a flexible, robust, and cost-effective way to scale search for applications of all sizes, whether you're processing logs, powering site search, or delivering real-time analytics.

As an open-source Competence Center and Elastic partner, Syone helps organizations design, implement and scale their Elasticsearch architecture with confidence.

Discover how Syone can support your Elasticsearch scaling strategy or speak with one of our open-source experts.