Elasticsearch is a powerful and flexible open source search and analytics engine designed to handle large amounts of data, providing advanced features such as real-time search, full-text search, faceted search, and analytics capabilities with its distributed architecture
Logging is a software development practice in which application data about events, warnings and errors is being saved in an organized manner that allows for a better understanding of that system's operations and a quicker incidents response.
Some of the many reasons for using logging tools:
- Receive detailed information about what is happening in your code, making it easier to identify and fix bugs.
- Track changes to your code and understand how it is being used.
- Logging tools can provide information about the performance of your code, helping you identify bottlenecks and optimize performance.
- Logging tools can automatically report errors to you or a team, allowing for quick response to critical issues.
- Logging can help you meet regulatory requirements for auditing and security.
- Handle large amounts of data and easily integrate it into a centralized logging system, making it much more manageable to oversee logs across multiple servers and applications.
- Logging can be done at different levels, such as debug, info, warning, error, and critical, so you can filter out or focus on specific types of events.
Some advantages of using Elasticsearch:
- A highly scalable search engine that can handle large amounts of data, while also utilizing built-in mechanisms to keep that data highly-available
- It has a powerful query language that allows for complex search queries and filtering
- It can be easily integrated with other data storage solutions, such as databases and data lakes
- Offers real-time search and analytics capabilities, making it well-suited for applications that require quick search results
- It supports distributed search, which allows for distributed data processing and improved search performance
- Can be used for full-text search, autocomplete, and other natural language processing tasks
- It has built-in support for machine learning which allows for more advanced search and analytics use cases
- Elasticsearch is an open source tool with a large supporting community, which makes it an attractive option for organizations that want to avoid vendor lock-in and have full control over their search infrastructure
After integrating Elasticsearch into several projects, we have gathered the expertise and knowledge necessary to effectively provide exceptional Elasticsearch setups.
Some of the things we did include:
- Deployed Elasticsearch on Kubernetes using Helm
- Improved performance by optimizing sharding and replication
- Implemented autoscaling for Elasticsearch clusters
- Implemented easy interfaces for developers to automatically ship logs to Elasticsearch
- Implemented best-practices for managing indices in Elasticsearch
- Created dashboards analyzing the system's logs using Kibana
- Monitored Elasticsearch clusters using Prometheus and Grafana
We can provide you with end-to-end help utilizing Elasticsearch for your needs.
Things we can do for your company:
- Creation of log analysis dashboards using Kibana
- Deployment of Elasticsearch on Kubernetes with Helm
- Optimization of sharding and replication to enhance performance
- Adoption of best practices for managing indices in Elasticsearch
- Provision of easy interfaces for developers to ship logs automatically to Elasticsearch
- Monitoring of Elasticsearch clusters via Prometheus and Grafana
- Implementation of autoscaling for Elasticsearch clusters