1. Data Storage and Querying: Elasticsearch is a distributed, real-time search and analytics engine designed for storing, searching, and analyzing large volumes of data quickly. It allows for complex queries, full-text search, and real-time data retrieval. On the other hand, Locust is an open-source load testing tool that allows users to define user behavior in Python code, simulate concurrent user activity, and monitor system performance under load. It focuses on load testing web applications and services to check their performance, scalability, and reliability.

2. Primary Use Case: Elasticsearch is commonly used for log and event data analysis, full-text search, real-time monitoring, metrics analysis, and in application search scenarios. Its versatility makes it suitable for a wide range of use cases including website search, log analysis, security information and event management (SIEM), and more. Locust, on the other hand, is specifically designed for load testing web applications and services. Its primary use case is to simulate a large number of users accessing a system concurrently to identify potential bottlenecks, performance issues, and system weaknesses.

3. Scaling Capabilities: Elasticsearch is built for scalability and can easily scale horizontally by adding more nodes to a cluster, allowing it to handle large amounts of data and traffic efficiently. It can be deployed across multiple nodes to create a distributed system that can handle high loads and big data scenarios. Locust, on the other hand, is more focused on simulating user behavior under load and does not have native scaling capabilities like Elasticsearch. It mainly runs on a single machine or distributed across multiple machines for heavier load testing scenarios.

4. Query Language: Elasticsearch uses a powerful query language called Elasticsearch Query DSL, which allows users to construct complex queries using JSON-based syntax. Users can perform searches based on various parameters, filter criteria, aggregations, and sorting options. Locust, on the other hand, uses Python code to define user behavior and simulate user interactions with the system. Users need to define the behavior of virtual users in Python code, specifying tasks, behaviors, and scenarios to be executed during the load testing process.

5. Monitoring and Analytics: Elasticsearch comes with built-in monitoring and analytics tools that allow users to track cluster health, performance metrics, indexing rates, and other key indicators. It provides insights into system activity, resource usage, query performance, and data distribution across nodes in a cluster. Locust, on the other hand, offers basic monitoring and reporting capabilities to track the number of requests sent, response times, and success/failure rates during load testing. Users can monitor system performance in real-time and generate reports to analyze test results.

In Summary, Elasticsearch and Locust differ in terms of data storage and querying capabilities, primary use cases, scaling capabilities, query languages, and monitoring tools provided.