Effortlessly collect and analyze MongoDB log data from various sources. Explore millions of log messages with a single click, experiencing search responses in seconds—no indexing required. Ensure rapid issue resolution, identify anomalies, and guarantee a seamless user experience with our real-time MongoDB Logs Monitoring tool, facilitating seamless business operations.
Proactively transform raw data by parsing, extracting, and structuring it to derive valuable insights from previously unstructured logs. Prioritize customer security by meticulously anonymizing PII data, completely excluding sensitive fields. Our approach ensures seamless processing, regardless of the data source, format, or schema, optimizing storage, and enabling nuanced analysis for improved monitoring and issue resolution.
Create customized pipelines using diverse filters and fine-tune your log data through custom parsing rules. Extract valuable insights by breaking down logs into structured fields, ensuring your data is organized and easy to analyze. Filter by log source, severity, timestamp, or any custom field to focus on what matters most. Experience instant updates based on your defined filters as logs flow through your pipeline.
Customize your troubleshooting environment and switch effortlessly between different contexts with just a click. Save time and boost efficiency by automating repetitive troubleshooting tasks through Saved Views. Define filters, facets, and visualizations in advance to automatically load when switching to a specific troubleshooting context. Simplify collaboration by enabling team members to adopt and share predefined views.
Immediate notification of high-priority incidents through advanced configurations based on error logs or custom queries.
Enhance debugging by adding/deleting related streams like host, service, source, severity for focused analysis.
Pinpoint events in distributed logs for detailed issue resolution—critical for understanding specific occurrences across systems.
Save, re-run searches, and manage views easily within the event viewer—modify filters swiftly for efficient log event analysis.
Designed to help developers and managers determine when and where their attention is required and enable teams to make fast.
Don't miss out on your events and error stats. Atatus can send you weekly and monthly summaries directly to your inbox.
MongoDB Logs Monitoring refers to the process of tracking and analyzing logs generated by MongoDB databases. It is crucial for identifying performance issues, debugging queries, and ensuring the security of your database. Monitoring MongoDB logs helps in optimizing database performance and maintaining data integrity.
Monitoring MongoDB logs in Atatus enables you to gain visibility into your database's performance, detect issues early, and ensure optimal operation. Atatus provides a centralized platform for log aggregation, analysis, and visualization, making it easier to troubleshoot problems and optimize database performance.
To start monitoring MongoDB logs in Atatus, you need to install the Atatus Agent, configure MongoDB logs to be written to a file, and then use the Agent's log collection feature to send these logs to Atatus.
Yes, Atatus allows you to set up custom alerts based on specific MongoDB log events. You can define alert conditions, thresholds, and notification preferences to receive timely notifications when predefined events occur.
Log data parsing prioritizes customer security by meticulously anonymizing Personally Identifiable Information (PII) data. Sensitive fields can be completely excluded during the parsing process, ensuring the privacy and compliance of customer information.
In MongoDB Logs Monitoring, log pipelines are customizable workflows that allow users to filter, parse, and fine-tune log data based on specific criteria.
Log pipelines can be customized by using diverse filters and defining custom parsing rules. Users have the flexibility to configure pipelines to meet their specific monitoring requirements, ensuring that log data is processed and organized according to their preferences.
Yes, log pipelines can be configured to filter log data based on various attributes such as log source, severity, timestamp, or any custom field. This customization allows users to focus on specific aspects of log data that are most relevant to their monitoring goals.
Saved Views in MongoDB Logs Monitoring enable you to create and save custom configurations for different troubleshooting scenarios. This feature simplifies collaboration, allows for quick context switching, and automates repetitive tasks, significantly improving overall efficiency.
If you exceed your log ingestion limits, we would contact you to discuss on stopping further processing new log data or upgrade your subscription.
You can choose to store logs in Atatus for a limited time (e.g., 7 days) or export them to external storage solutions like Amazon S3 for long-term retention.
To access historical log data beyond the retention period, you can rely on log data exports from Amazon S3, where you can push the logs into Atatus for further analysis.
Yes, Atatus provides users with the flexibility to customize log retention settings. Users can adjust retention periods based on their specific needs, aligning with compliance standards or internal data management policies.
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Atatus is a great product with great support. Super easy to integrate, it automatically hooks into everything. The support team and dev team were also very helpful in fixing a bug and updating the docs.
Atatus is powerful, flexible, scalable, and has assisted countless times to identify issues in record time. With user identification, insight into XHR requests to name a few it is the monitoring tool we choose for our SPAs.
Atatus continues to deliver useful features based on customer feedback. Atatus support team has been responsive and gave visibility into their timeline of requested features.
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